Fog, haze, or smoke are usual atmospheric phenomena that dramatically compromise the overall visibility of any scene, critically affecting features such as illumination, contrast, and contour detection of objects. The decrease in visibility compromises the performance of computer vision algorithms such as pattern recognition and segmentation, some of them very relevant for decision-making for the security or autonomous vehicle industries. Several dehazing methods have been proposed, however, to the best of our knowledge, all proposed metrics compare the defogged image to its ground truth for evaluation of the defogging algorithms, or need to estimate parameters through physical models. This fact hinders progress in the field as obtaining proper ground truth images is costly and time-consuming, and physical parameters greatly depend on the scene conditions. This paper aims to tackle this issue by proposing a contour-based metric for image defogging evaluation that does not need a ground truth image. The proposed metric only requires the original hazy RGB image and the RGB image after the defogging procedure. A comparison of the proposed metric with metrics currently used in the NTIRE 2018 defogging challenge is performed to prove its effectiveness in a general situation, showing comparable results to conventional metrics.

According to the risk investigations of being involved in an accident, alcohol-impaired driving is one of the major causes of motor-vehicles accidents. Preventing highly intoxicated persons from driving would potentially save many lives. This paper proposes a lightweight in-vehicle alcohol detection that processes the data generated from 6-alcohol sensors (MQ-3 Alcohol Sensors) using an optimizable shallow neural network (O-SNN). The experimental evaluation results exhibit a high-performance detection system scoring a 99.8% of detection accuracy with a very short inferencing delay of 2.22 µ seconds. Hence, the proposed model can be efficiently deployed and used to discover in-vehicle alcohol with high accuracy and low inference overhead as a part of the driver alcohol detection system for safety (DADSS) system aiming at massive deployment of alcohol sensing systems that could potentially save thousands of lives annually.

Intrusion Detection Systems (IDS) plays a part in modern cyber security, as a result of the increasing need for cyber security systems in the “real” world due to the increasing number of cyber attacks, more sophisticated systems are required in order to prevent these attacks - an IDS can provide this protection. Due to the sophistication of these systems, they must be properly understood, developed and analyzed - research papers can be used as a tool to improve IDS systems. This paper is composed of two main sections: a survey and a taxonomy, providing information, reviews and interpretations from relevant papers, a timeline of important papers, a discussion on the future of IDS and a classification on IDS and how to apply this.

An electrocardiograph (ECG) reflects the health of the human heart and is used to help diagnose arrhythmia and myocardial infarction(MI) in clinical practice. Early diagnosis of arrhythmia helps implement preventive measures and plays a crucial role in saving a patient's life. With the increasing demand of clinicians for ECG analysis technology, intelligent detection and diagnosis of ECG signals has become a more efficient means to assist physicians in diagnosing cardiovascular diseases. This paper introduces an ECG diagnosis approach based on an ensemble deep learning combination of CNN(convolutional neural network) and SLAP(stacked-long short term memory architecture for prediction) architecture. ECG data is denoised and further divided into single heartbeats to achieve data standardization and sample diversity. Adam optimizer and BCEwithlogitsloss multi-classification loss function were used to enhance the model effect, and the system achieved the classification effect of 99.3% average accuracy, 99.0% F1-value, and 99.2% sensitivity in MIT-BIH standard database classification. It also shows good generalization ability on the Tianchi data set.

Coronavirus Disease-19 (COVID-19) is presently wreaking havoc on public health and socio-economic development. Besides the upper and lower respiratory tract involvement, gastrointestinal symptoms are also reported in COVID-19 patients through gut-lung axis. Finding its way through the feces of infected individuals and other sources, the genetic material of SARS-CoV-2 (ssRNA) is reported widely in wastewater and is being used as a fingerprint for its detection. With millions of cases arriving every day, there is a need to level up the testing speed efficiency. Due to the restricted sampling potential of testing laboratories, clinical testing is unable to track all the symptomatic and asymptomatic cases. Wastewater-based epidemiology (WBE) bestows an auxiliary monitoring tool that will contribute in community level screening. Sample collection, concentration, RNA extraction, quantification and data analysis are the main steps involved in implementation of WBE that can be relied upon as an alarm call for an upcoming wave, emergence of a new variant or any future pandemic. WBE can be a cheaper and more practical alternative to high end and sophisticated clinical testing for community transmission detection. Worldwide, there are more than 300 reports entailing the occurrence of SARS-CoV-2 in wastewater exhibiting unique temporal trends with five of them in India. This review aims to address the present knowledge on surveillance of SARS-CoV-2 in wastewater and its implications.

One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. This study presents a real-world implementation of a convolutional neural network (CNN) based Carebot Covid app to detect COVID-19 from chest X-ray (CXR) images. Our proposed model takes the form of a simple and intuitive application. Used CNN can be deployed as a STOW-RS prediction endpoint for direct implementation into DICOM viewers. The results of this study show that the deep learning model based on DenseNet and ResNet architecture can detect SARS-CoV-2 from CXR images with precision of 0.981, recall of 0.962 and AP of 0.993.

The accurate prediction of heat load profiles with a daily resolution is required for a broad range of applications such as potential studies, design, or predictive operation of heating systems. If the heat demand of a consumer is mainly caused by (production) processes independent of the ambient temperature, existing load profile prediction methods fail. To close this gap, this study develops two ex post machine learning models for the prediction of the heat demand with a daily resolution. The selected input features are commonly available to each consumer connected to public natural gas and electricity grids or operating an energy monitoring system: Ambient temperature, weekday, electricity consumption, and heat consumption of the last seven days directly before the predicted day. The study’s database covers electricity and natural gas consumption load profiles from 82 German consumers over a period of two years. Electricity and heat consumption correlate strongly with individual patterns for many consumers. Both, shallow and deep learning algorithms are evaluated. The highest accuracy is achieved by a Long Short Term Memory (LSTM) model with a median of R² of 0.94. The ex post model architecture makes the model suitable for automated anomaly detection in energy monitoring systems.

Deep learning techniques are increasingly being recognized as effective image classifiers. Aside from their successful performance in past studies, the accuracies have varied in complex environments in comparison with the popularly applied machine learning classifiers. This study seeks to explore the feasibility for using a U-Net deep learning architecture to classify bi-temporal high resolution county scale aerial images to determine the spatial extent and changes of land cover classes that directly or indirectly impact tidal marsh. The image set used in the analysis is a collection of a 1-m resolution collection of National Agriculture Imagery Program (NAIP) tiles from 2009 and 2019 covering Beaufort County, South Carolina. The U-net CNN classification results were compared with two machine learning classifiers, the Random Trees (RT) and the Support Vector Machine (SVM). The results revealed a significant accuracy advantage in using the U-Net classifier (92.4%) as opposed to the SVM (81.6%) and RT (75.7%) classifiers for overall accuracy. From the perspective of a GIS analyst or coastal manager, the U-Net classifier is now an easily accessible nad powerful tool for mapping large areas. Change detection analysis indicated little areal change on marsh extent, though increased land development throughout the county has the potential to negatively impact the health of the marshes. Future work should explore applying the constructed U-Net classifier to coastal environments in large geographic areas, while also implementing other data sources (e.g., LIDAR, multispectral data) to enhance classification accuracy.

African swine fever (ASF) has evolved from an exotic animal disease to a threat to global pig production. An important avenue for wide-spread transmission of animal diseases is the dissemination of viruses through boar semen used for artificial insemination (AI). In this context, we investigated the role of male reproductive organs in ASF. Mature domestic boars and adolescent wild boar inoculated with different ASF virus strains were investigated by means of virological and pathological methods. Additionally, electron microscopy was employed to investigate in vitro inoculated sperm. Viral genome, antigen and infectious virus could be found in all gonadal tissues and accessory sex glands. The viral antigen and viral mRNAs were mainly found in mononuclear cells of the respective tissues. However, some other cell types, including Leydig, endothelial and stromal cells were also found positive. Using RNAScope, p72 mRNA could be found in scattered halo cells of the epididymal duct epithelium which could point to disruption of the barrier. No direct infection of spermatozoa was observed by immunohistochemistry or electron microscopy. Taken together, our results strengthen the assumption that ASFV can be transmitted via boar semen. Future studies are needed to explore excretion dynamics and transmission efficiency.

The goal of background reconstruction is to recover the background image of a scene from a sequence of frames showing this scene cluttered by various moving objects. This task is fundamental in image analysis, and is generally the first step before more advanced processing, but difficult because there is no formal definition of what should be considered as background or foreground and the results may be severely impacted by various challenges such as illumination changes, intermittent object motions, highly cluttered scenes, etc. We propose in this paper a new iterative algorithm for background reconstruction, where the current estimate of the background is used to guess which image pixels are background pixels and a new background estimation is performed using those pixels only. We then show that the proposed algorithm, which uses stochastic gradient descent for improved regularization, is more accurate than the state of the art on the challenging SBMnet dataset, especially for short videos with low frame rates, and is also fast, reaching an average of 52 fps on this dataset when parameterized for maximal accuracy using GPU acceleration and a Python implementation.

In this paper we formally define the hierarchical clustering network problem (HCNP) as the problem to find a good hierarchical partition of a network. This new problem focuses on the dynamic process of the clustering rather than on the final picture of the clustering process. To address it, we introduce a new hierarchical clustering algorithm in networks, based on a new shortest path betweenness measure. To calculate it, the communication between each pair of nodes is weighed by the importance of the nodes that establish this communication. The weights or importance associated to each pair of nodes are calculated as the Shapley value of a game, named as the linear modularity game. This new measure, (the node-game shortest path betweenness measure), is used to obtain a hierarchical partition of the network by eliminating the link with the highest value. To evaluate the performance of our algorithm, we introduce several criteria that allow us to compare different dendrograms of a network from two point of view: modularity and homogeneity. Finally, we propose a faster algorithm based on a simplification of the node-game shortest path betweenness measure, whose order is quadratic on sparse networks. This fast version is competitive from a computational point of view with other hierarchical fast algorithms, and, in general, it provides better results.

Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), estimating the health index (HI), and thereafter, calculating a degradation model for predicting the remaining useful lifetime (RUL) are merely impossible using supervised learning. In this paper, a 3 dof delta robot used for pick and place task is studied. In the proposed method, autoencoders (AEs) are used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data is available. Due to the sequential nature of the data, non-linearity of the system, and correlations between parameter time series, convolutional layers are used for feature extraction. Thereafter, a sigmoid function is used to predict the probability of having an anomaly given CIs acquired from AEs. This function can be manually tuned given the sensitivity of the system or optimized by solving a minimax problem. Moreover, the proposed architecture can be used for fault localization for the specified system. Additionally, the proposed method is capable of calculating RUL using Gaussian process (GP), as a degradation model, given HI values as its input.

Animal welfare is not only an ethically important consideration in good animal husbandry, but can also have a significant effect on an animal’s productivity. The aim of this paper is to show that a reduction in animal welfare, in the form of increased stress, can be identified in pigs from frontal images of the animals. We train a Convolutional Neural Network (CNN) using a leave-one-out design and show that it is able to discriminate between stressed and unstressed pigs with an accuracy of >90% in unseen animals. Grad-CAM is used to identify the animal regions used, and these support those used in manual assessments such as the Pig Grimace Scale. This innovative work paves the way for further work examining both positive and negative welfare states with a view to the development of an automated system that can be used in precision livestock farming to improve animal welfare.

The goal of this study was to analyze the forest degradation in the Reserve for San Rafael National Park, Paraguay, during the period 2005-2019. This Reserve is one of the most important forest remnants of the Upper Paraná Atlantic Forest Ecoregion. A multitemporal analysis of degradation was carried out due to the occurrence of three disturbances: forest fires, a twister and illicit crops, using the Continuous Degradation Detection (CODED) algorithm, for which 3 factors were considered: variations due to pixel in the NDFI index values before, during and after every disturbance registered. In this context, the phenomenon with the greatest impact in terms of magnitude of degradation were the forest fires of 2005, being that year at the same time, the one that reported the highest degradation values. Secondly, there are the illicit crops established until the first semester of 2019, and lastly, the twister that occurred in 2017. Our findings demonstrate that CODED algorithm can detect multi-temporal degradation events in a Subtropical Broadleaf Forest, and the post-disturbance regeneration process after every disturbance tends to occur immediately. The response in terms of degradation-regeneration is highly variable, depending of the nature and severity of each disturbance and the vegetation recovery dynamics.

An evaluation of image contrast in a controlled foggy environment has been made by comparison between intensity imaging and different modes of polarimetric imaging. A small-scale fog chamber has been designed and constructed to create the necessary controlled foggy environment. A linear polarimetric camera of division of focal plane and a linearly polarized light source has been used for performing the experiments with polarized light. In order to evaluate the image contrast of the different imaging modes, the Michelson’s contrast of samples of different materials relative to their background has been calculated. The higher the image contrast, the easier is to detect and segment the targets of interest that are surrounded by fog. It has been proved quantitatively that in the studied situations polarimetric images present an improvement in contrast when compared to conventional intensity images.

Natural circulation loop (NCL) systems are buoyancy-driven heat exchangers that are used in various industrial applications. The concept of passive heat exchange in NCL systems is attractive, because there is no need for an externally driven equipment (e.g., a pump) to maintain the fluid circulation. However, relying on buoyancy as the sole driving force may lead to several potential difficulties, one of which is generation of (possibly) time-varying nonlinearities in the dynamical system, where a difference in the time scales of heat transfer and fluid flow causes the flow to change from a steady-state regime to either an oscillatory regime or a flow-reversal regime, both of which are undesirable. In this paper, tools of symbolic time series analysis (e.g., probabilistic finite state automata (PFSA)) are proposed to identify selected regimes of operation in NCL systems, where the underlying principle is built upon the concept of pattern classification from measurements of fluid-flow dynamics. The proposed method is shown to be capable of identifying the current regime of operation from the initial time response under a given set of operational parameters. The efficacy of regime classification is demonstrated by testing on two data sets, generated from numerical simulation of a MATLAB SimuLink model that has previously been validated with experimental data. The results of the proposed PFSA-based classification are compared with those of a hidden Markov model (HMM) that serves as the baseline.

This paper provides a concise review of remote sensing of ocean fronts and its applications in marine ecology and fisheries, with a particular focus on the most popular front detection algorithms/techniques: Canny (1986), Cayula and Cornillon (1990, 1992, 1995), Miller (2004, 2009), Shimada et al. (2005), Belkin and O’Reilly (2009), and Nieto et al. (2012). A case is made for feature-based approach in marine ecology and fisheries that emphasizes fronts as major structural and circulation features of the ocean realm that play key roles in various aspects of marine ecology.

Pipeline integrity is an important area of concern for the oil and gas, refining, chemical, hydrogen, carbon sequestration, and electric-power industries, due to the safety risks associated with pipeline failures. Regular monitoring, inspection, and maintenance of these facilities is therefore required for safe operation. Large standoff magnetometry (LSM) is a non-intrusive, passive magnetometer-based mea- surement technology that has shown promise in detecting defects (anomalies) in regions of elevated mechanical stresses. However, analyzing the noisy multi-sensor LSM data to clearly identify regions of anomalies is a significant challenge. This is mainly due to the high frequency of the data collection, mis-alignment between consecutive inspections and sensors, as well as the number of sensor measurements recorded. In this paper we present LSM defect identification approach based on ma- chine learning (ML). We show that this ML approach is able to successfully detect anomalous readings using a series of methods with increasing model complexity and capacity. The methods start from unsupervised learning with "point" methods and eventually increase complexity to supervised learning with sequence methods and multi-output predictions. We observe data leakage issues for some methods with randomized train/test splitting and resolve them by specific non-randomized splitting of training and validation data. We also achieve a 200x acceleration of support-vector classifier (SVC) method by porting computations from CPU to GPU leveraging the cuML RAPIDS AI library. For sequence methods, we develop a customized Convolutional Neural Network (CNN) architecture based on 1D convolutional filters to identify and characterize multiple properties of these defects. In the end, we report the scalability of the best-performing methods and compare them, for viability in field trials.

For the Remotely Piloted Aircraft Systems (RPAS) market to continue its current growth rate, cost-effective "Detect and Avoid" systems that enable safe beyond visual line of sight (BVLOS) operations are critical. We propose an audio-based "Detect and Avoid" system, composed of microphones and an embedded computer, which performs real-time inferences using a sound event detection (SED) deep learning model. Two state-of-the-art SED models, YAMNet and VGGish, are fine-tuned using our dataset of aircraft sounds and their performances are compared for a wide range of configurations. YAMNet, whose MobileNet architecture is designed for embedded applications, outperformed VGGish both in terms of aircraft detection and computational performance. YAMNet's optimal configuration, with > 70% true positive rate and precision, results from combining data augmentation and undersampling with the highest available inference frequency (i.e. 10 Hz). While our proposed "Detect and Avoid" system already allows the detection of small aircraft from sound in real time, additional testing using multiple aircraft types is required. Finally, a larger training dataset, sensor fusion, or remote computations on cloud-based services could further improve system performance.

The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a vaccine, early detection and containment is the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers' interest. This survey marks a detailed inspection of the deep-learning-based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets, and others, along with probable solutions with different pre-processing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, Normal, and Pneumonia from x-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

There is a major clinical need for accurate biomarkers for prostate cancer prognosis, to better inform treatment strategies and disease monitoring. Current clinically recognised prognostic factors, including prostate-specific antigen (PSA) levels, lack sensitivity and specificity in distinguishing aggressive from indolent disease, particularly in patients with localised intermediate grade prostate cancer. There has therefore been a major focus on identifying molecular biomarkers that can add prognostic value to existing markers, including investigation of DNA methylation, which has a known role in tumorigenesis. In this review, we will provide a comprehensive overview of the current state of DNA methylation biomarker studies in prostate cancer prognosis, and highlight the advances that have been made in this field. We cover the numerous studies into well-established candidate genes, and explore the technological transition that has enabled hypothesis-free genome-wide studies and the subsequent discovery of novel prognostic genes.

Accurate and timely testing has become an essential measure in combatting the COVID-19 global pandemic. Currently, polymerase chain reaction (PCR) based assays are the most relied on methods for SARS-CoV-2 detection. This traditional workflow involves a viral RNA extraction from the viral transport media storing nasopharyngeal swabs collected from patients, followed by PCR based detection. While accurate, this methodology is time consuming and resource heavy, causing for delays in receiving results or limited access to testing. Herein, we demonstrate a validated method for SARS-CoV-2 detection from viral transport media using a two-step, direct-to-PCR workflow revolving around shaker-mill homogenization. This method completely bypasses the extraction steps of the traditional workflow, replacing it with 30 seconds of mechanical disruption sufficient to allow for COVID-19 detection with a 96.43% sensitivity and 100% specificity when compared to traditional extraction to PCR based methods.

An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we represent a concrete crack detection framework to aid the process of automated inspection. The proposed approach employs a deep convolutional neural network architecture for crack segmentation from concrete image. The proposed network alleviates the effect of gradient vanishing problem present in deep neural network architectures. A feature silencing module is incorporated in the crack detection framework, for eliminating unnecessary feature maps from the network. The overall performance of the network significantly improves as a result. Experimental results support the benefit of incorporating feature silencing within a convolutional neural network architecture for improving the network’s robustness, sensitivity, and specificity. An added benefit of the proposed architecture is its ability to accommodate for the trade-off between specificity (positive class detection accuracy) and sensitivity (negative class detection accuracy) with respect to the target application. Furthermore, the proposed framework achieves a high precision rate and processing time than crack detection architectures present in literature.

An electrochemical sensing platinum nanoparticle in the tantalum electrode is provided by means of an Ion Beam Sputtering Deposition (IBSD). The electrode was made with a Pt solution, sputtered simultaneously with hydrochloric acid corrosion on tantalum substrate. In the study of heavy metal ions, for example, the platinum nanoparticle electrodes as prepared were used Square wavelength voltammetry (OSWV) Hg²⁺, Cu²⁺ and Ag²⁺. The porous electrodes were observed in a broader range by the Pt nanostructure electrode for heavy metal ions. Furthermore, the susceptibility to detection has been shown to be saturated as the thickness of the layer electrode exceeded 50 nm. For Hg²⁺ 0,003-1 M, for Cu²⁺ 0,005-3 M and for Ag²⁺ the linear detection scale is 0,009-4 M. There has also been good reusability and repeatability. In addition, a scan electron microscope (SEM) used to study platinum electrode forming process and nanostructure. This electrode will have interesting applications in sensing systems.

Today, contamination from heavy metals in the atmosphere is a global concern. Efficient detection techniques are therefore necessary if heavy metal exposure levels in different media are to be determined. The voltammetry method for in situ detection of heavy metal ions is a very sensitive electrochemical method. This thesis explores emerging developments in electrode alteration, materials production and experimental optimization. An electrochemical sensing platinum nanoparticle in the tantalum electrode is provided by means of an Ion Beam Sputtering Deposition (IBSD). The electrode was made with a Pt solution, sputtered simultaneously with hydrochloric acid corrosion on tantalum substrate. In the study of heavy metal ions, for example, the platinum nanoparticle electrodes as prepared were used Square wavelength voltammetry (OSWV) Hg²⁺, Cu²⁺ and Ag²⁺. The porous electrodes were observed in a broader range by the Pt nanostructure electrode for heavy metal ions. Furthermore, the susceptibility to detection has been shown to be saturated as the thickness of the layer electrode exceeded 50 nm. For Hg²⁺ 0,003-1 M, for Cu²⁺ 0,005-3 M and for Ag²⁺ the linear detection scale is 0,009-4 M. There has also been good reusability and repeatability. In addition, a scan electron microscope (SEM) used to study platinum electrode forming process and nanostructure. This electrode will have interesting applications in sensing systems.

Accurate and efficient traffic incident detection methods can effectively alleviate traffic congestion caused by traffic incidents, prevent secondary accidents and improve the safety of urban road traffic.Aiming at the problems that the traditional machine learning event detection method cannot fully extract the parameter characteristics of traffic flow and is not suitable formulti-dimensional and non-linear massive data, we propose a new traffic event detection method(CNN-XGBoost).This method combines the respective advantages of Convolution Neural Network(CNN) and Extreme Gradation Boosting (XGBoost). Firstly, we preprocessed the original freeway traffic incident detection data set by constructing initial variable set, data normalization, data balance processing and dimension reorganization. Secondly,we use CNN network to automatically extract the deep features of event detection data, and use XGBoost as a classifier to classify the extracted features for expressway traffic event detection.Finally, we use the data set of Hangzhou expressway microwave detector in China to carry out simulation experiments on CNN-XGBoost. The experimental results show that compared with XGBoost, CNN, Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT) and other methods, CNN-XGBoost method can effectively improve the accuracy of expressway traffic event detection and has better generalization ability.

Based on detection of elliptic particle tracks, $\simeq 137^2 n^2$ bigger than Bohr-Sommerfeld electron orbits, indicating the possible detection of superluminal electrons masquerading as magnetic monopoles, a new structure emerges leading to: (i) a new set of seven elementary lengths; (ii) replacement of the usual motion (Lorentz) transformations by scale transformations between $v^2<c^2$ and $v^2>c^2$ frames; (iii) equivalence of charge between $v^2<c^2$ and $v^2>c^2$ frames based on the Dirac-Schwinger quantization condition; (iv) a relativistic foundation for the Dirac-Schwinger quantization condition; (v) a possible cause of charge quantization; and (vi) the prospect of symmetry in Maxwell's equations. If the elliptic particle tracks are viewed as a magnification of electron orbits, the effect is suggestive of a spacetime distortion such as those predicted in general relativistic theories.

Our main objective in this work was to examine the possibility of non-intrusive, label-free, detection of whole Zika viruses using terahertz signals with or without a targeting/binding oligonucleotide (aptamers). We report for the first time the use of terahertz electromagnetic waves (0.75 THz – 1.1 THz) to detect Zika viruses. The Zika/aptamer complexes showed a reproducible terahertz reflection coefficient minimum at 1.064 THz while the Zika virus’s reflection minimum was at 1.073 THz. Of different substrates we examined, the polyester petri dish provided a very low loss and excellent terahertz transmission. To increase the interaction between the terahertz signal and the sample we also used polyester microbeads coated with aptamers. We then measured the terahertz reflection from the microbeads as a function of Zika concentration. The resulting terahertz Zika sensor had sensitivity of 63 Hz/Zika and minimum detectable signal of ~ 16x10³ Zika. Other substrates such as Graphene on polyethylene terephthalate (PET), 50 nm-thick gold film on polycarbonate, thin (30 um-thick) glass slide and Teflon were also examined. Graphene substrate enabled direct detection of the Zika without any aptamers.

In recent years, the field of deep learning achieved considerable success in pattern recognition, image segmentation and may other classification fields. There are a lot of studies and practical applications of deep learning on images, video or text classification. In this study, we suggest a method for changing the architecture of the most performing CNN models with the aim of designing new models to be used as stand-alone networks or as a component of an ensemble. We propose to replace each activation layer of a CNN (usually a ReLu layer) by a different activation function stochastically drawn from a set of activation functions: in this way the resulting CNN has a different set of activation function layers.

The purpose of the work is an underwater positioning safety study that used the GNSS-like underwater navigation systems. In the process of research, we used the methods of software modeling of underwater spoofing processes. The spoofing problem consists of three stages: design of spoofers, design of spoofing detection systems, and design of anti-spoofing systems. This article discusses some methods of spoofing detection. We briefly describe the known methods of underwater positioning systems. Unlike GNSS, currently only LNSS (Local Navigation Satellite System) can be considered in this case. Spoofing detection systems with one hydrophone are of great practical importance, as they allow for use of standard hydroacoustic equipment. However, detection of spoofing is not possible in static mode, which is with underwater vehicle at rest. In case of two hydrophones the detection of spoofing in static mode is possible. We discuss the navigation based on the use of an acoustically passive receiver. The receiver “listens” to the buoys and solves the problem of finding its own position using the coordinates of the buoys (such systems are called GNSS-like Underwater Positioning Systems or GNSS-like UPS). Depending on the scale of system service area, GNSS-like UPS-es are divided into global, regional, zonal and local systems. In this article, we take into account only the local class of GNSS-like UPS. The acoustic signal generator transmits a simulation of several buoy signals. If the level of the simulated signal exceeds the signal strength of actual buoys, the UPS receiver will “lock onto” the fake signal and then calculate a false position basing on it. The development of further research should be focused on the creation of hardware and software systems for conducting physical experiments at depths up to 400 m.

The success of a software system is highly dependent upon its quality which is a very critical aspect and is the ultimate goal to be achieved. Defect Management is the most important process in ensuring software quality, it involves defect detection, defect categorization, defect prioritization, defect removal and defect verification. The main purpose of this process is to develop and produce defect free or least defected software systems of high quality. It plays a vital role in gaining customer confidence and satisfaction which is essential for the reputation of the organization developing the system. The central objective of this study is to explain the importance of defect management and its impact on the quality of the software along with various defect management techniques to support the objective.

Microfluidics is facing critical challenges in the quest of miniaturizing, integrating, and automating in vitro diagnostics, including the increasing complexity of assays, the gap between the macroscale world and the microscale devices, and the diverse throughput demands in various clinical settings. Here a “3D extensible” microfluidic design paradigm that consists of a set of basic structures and unit operations was developed for constructing any application-specific assay. Four basic structures- check valve (in), check valve (out), double-check valve (in and out), and on-off valve, were designed to mimic basic acts in biochemical assays. By combining these structures linearly, a series of unit operations can be readily formed. We then proposed a “3D extensible” architecture to fulfill the needs of the function integration, the adaptive “world-to-chip” interface, and the adjustable throughput in the X, Y, and Z directions, respectively. To verify this design paradigm, we developed a fully integrated loop-mediated isothermal amplification microsystem that can directly accept swab samples and detect Chlamydia trachomatis automatically with a sensitivity one order higher than that of the conventional kit. This demonstration validated the feasibility of using this paradigm to develop integrated and automated microsystems in a less risky and more consistent manner.

The formal transfer of GNSS under water is not possible. It probably makes sense to talk only about the transformation of GPS into LPS, that is, in the Local Position System. However, the basic methods that are used to solve the problem of Spoofing Detection above water can be used under water. It should be understood that engineering problems are significantly different, since the nature of the propagation of acoustic waves in water and electromagnetic waves in the atmosphere are fundamentally different. In this article, we will limit the navigation with acoustically passive receiver. The receiver “listens” to the buoys and solves the problem of finding its own position based on the coordinates of the buoys (such systems are called GNSS-like Underwater Positioning Systems or GNSS-like UPS). Depending on the scale of system service areas, GNSS-like UPS are divided into global, regional, zonal and local system. In this article, we will limit ourselves to considering only local GNSS-like UPS. The acoustic signal generator transmits a signal simulation of several satellites. If the level of the simulated signal exceeds the signal strength of real satellites, the receiver of UPS will “capture” the fake signal and calculate the false position based on it.

The problem of real-time estimation of occupancy of buildings (number of people in various zones at every time instant) is relevant to a number of emerging applications that achieve high energy efficiency through feedback control. The measurement of CO2 concentration can be considered an important indicator that allows to define the occupation of closed and crowded spaces. Interesting cases can be school buildings and other buildings used in civil and residential (shopping centres, hospitals, etc.). This paper, starting from an experimental analysis in different classrooms of a University campus in real operating conditions, in different period of the year, proposes a possible correlation between CO2 concentration and the occupancy profile of the spaces. The acquired data are used to present some graphical correlations and to understand the most important variables or combination of them. Starting from an accurate analysis of the data, attempts are made to define a preliminary estimation method through the development of a mathematical models of occupancy dynamics in a building, which show interesting results.

Bioimpedance measurements are used increasingly in health applications because bioelectric parameters have been associated with anatomical and physiological properties, thus enabling to distinguish medical conditions. For bone fracture diagnostics, nevertheless, there is no established non-invasive method. Ex vivo studies and In vivo bioimpedance procedures, both invasive and non-invasive, on mammalians long bones are associated with promising results. In this work, out of a total of 568 papers, we reviewd 59 articles that mention long bone integrity by electric properties, be it Bioimpedance Analysis, Electrical Impedance Spectroscopy or Electrical Impedance Tomography. The papers are described in three sections, “Ex vivo measurements”, “In vivo invasive measurements” and “In vivo non-invasive measurements”. This review allows to establish the basics to planning the development of new technology to detect bone fracture via bioimpedance measurements.

The recent growing attention to energy saving and environmental protection issues has brought to attention the possibility of exploiting syngas from gasification of biomass and coal for the firing of industrial plants included the so called Integrated Gasification Combined Cycle ones. In order to improve the knowledge about the employ of syngas in lean premixed turbulent flames, a large scale swirl stabilized gas-turbine burner has been operated with a simplified model of H₂ enriched syngas from coal gasification. The experimental campaign has been performed at atmospheric pressure with operating conditions derived from scaling the real gas turbines ones. The results are here reported and consist of OH-PLIF measurements carried out at decreasing air equivalence ratio conditions and are analysed together with the mean aerodynamic characterisation of the burner flowfield in isothermal condition. The OH concentration distributions have been analysed statistically in order to obtain information about the location of the most reactive zones and an algorithm has been applied to the data in order to identify the flame fronts. In addition, the flame front locations have been successively interpreted statistically in order to obtain information about their main features and about their dependence on the air to fuel ratio behaviour.

Abstract: The recent growing attention to energy saving and environmental protection issues has brought to attention the possibility of exploiting syngas from gasification of biomass and coal for the firing of industrial plants included the so called Integrated Gasification Combined Cycle ones. In order to acquire a detailed knowledge about the behaviour of lean turbulent premixed flames, the present work resent the results of an experimental characterisation of a prototypical gas turbine burner operated at atmospheric pressure at condition scaled from real gas turbines ones. The results here presented derive from OH-PLIF measurements carried out at decreasing air equivalence ratio conditions and are analysed together with a mean aerodynamic characterisation of the burner operating in isothermal condition. The OH concentration distributions have been analysed statistically in order to obtain information about the location of the most reactive zones and an algorithm has been applied to the data sets in order to identify the flame fronts. In addition, the flame front locations have been successively interpreted statistically in order to obtain information about their main features and about their dependence on the air to fuel ratio behaviour.

Fire accidents are causing a huge human death as well as economic losses throughout the world every year. But a novel fire detection and extinguishing system could reduce these losses to a great extent. We are proposing a cost effective and yet very efficient system which has been developed in our laboratory and evaluated while in operation. Our system consists of two units: detection unit and extinguishing unit. For detection purposes we used smoke, IR and temperature sensors. While for extinguishing purposes we used both foam and water. We used a PC to process and control our system through LabVIEW software of National Instrument, USA. The detection unit will send signal through a wireless module to PC if fire is detected. Then PC (LabVIEW) will process the signal and using relay module first of all the electric power and gas lines will be turned off. The extinguishing unit will then release foam or water to extinguish the fire. The system will send SMS to the predefined numbers using GSM module. Our system is location independent as it uses GSM and can be operated from remote location using wireless module. After performance evaluation it was found to be fast and reliable.

Drinking water contamination of lead from various environmental sources, leaching consumer products and intrinsic water-pipe infrastructure is still today a matter of great concern. Therefore, new highly sensitive and convenient Pb²⁺ measurement schemes are necessary, especially for in-situ measurements at a low-cost. Within this work dye/ionophore/Pb²⁺ co-extraction and effective water phase de-colorization was utilized for highly sensitive lead measurements and sub-ppb naked-eye detection. A low-cost ionophore Benzo-18-Crown-6-ether was used, and a simple test-tube mix and separate procedure was developed. Instrumental detection limits were in the low ppt region (LOD=3, LOQ=10), and naked-eye detection was 500 ppt. Note, however, that this sensing scheme still has improvement potential as concentrations of fluorophore and ionophore were not optimized. Artificial tap-water samples, leached by a standardized method, demonstrated drinking water application. Implications for this method are convenient in-situ lead ion measurements.

The use of machine learning techniques in predictive health care is on the rise with minimal data used for training machine-learning models to derive high accuracy predictions. In this paper, we propose such a system, which utilizes Heart Rate Variability (HRV) as features for training machine learning models. This paper further benchmarks the usefulness of HRV as features calculated from basic heart-rate data using a window shifting method. The benchmarking has been conducted using different machine-learning classifiers such as artificial neural network, decision tree, k-nearest neighbour and naive bays classifier. Empirical results using MIT-BIH Arrhythmia database shows that the proposed system can be used for highly efficient predictability of abnormality in heartbeat data series.

Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information based change detection methods have been proposed during past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. While the whole bi-temporal images are scanned pixel-by-pixel, the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by two land cover change cases with Landsat bi-temporal remote sensing images. In comparison to several widely used change detection methods, the proposed approach can achieve a land cover change inventory map with a competitive accuracy.

The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people use to stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches were not tested with the target population, or are not feasible to be implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We test our approach with the SisFall dataset achieving 99.4% of accuracy. Then, we validate it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected.

This paper considers the problem of quantifying the quality of a model selection problem for a graphical model. The model selection problem often uses a distance measure such as the Kulback-Leibler (KL) distance to quantify the quality of the approximation between the original distribution and the model distribution. We extend this work by formulating the problem as a detection problem between the original distribution and the model distribution. In particular, we focus on the covariance selection problem by Dempster, [1], and consider the cases where the distributions are Gaussian distributions. Previous work showed that if the approximation model is a tree, that the optimal tree that minimizes the KL divergence can be found by using the Chow-Liu algorithm [2]. While the algorithm minimizes the KL divergence it does not minimize other measures such as other divergences and the area under the curve (AUC). These measures all depend on the eigenvalues of the correlation approximation measure (CAM). We find expressions for KL divergence, log-likelihood ratio, and AUC as a function of the CAM. Easily computable upper and lower bounds are also found for the AUC. The paper concludes by computing these measures for real and synthetic simulation data.

This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf) and noise less vessels. Generally, in passive sonar the targets are detected by sonar equation (with constant threshold) which increase the detection error in shallow water. Purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound) is processed in time and frequency domain. For classifying, Bayesian classification is used and prior distribution is estimated by Maximum Likelihood algorithm. Finally, target was detected by combining the detection points in both domains using LMS adaptive filter. Results of this paper has showed that proposed method has improved true detection rate about 27% compare other the best detection method.

Traffic shaping is a critical task in software-defined -IoT networks (SDN-IoTs) to efficiently manage network resources and ensure Quality of Service (QoS) for end-users. However, traditional traffic shaping approaches based on queuing theory or static policies may not be effective due to the dynamic and unpredictable nature of network traffic. In this paper, we propose a novel approach that leverages Graph Neural Networks (GNNs) and Multi-arm Bandit algorithms to dynamically optimize traffic shaping policies based on real-time network traffic patterns. Specifically, our approach uses a GNN model to learn and predict network traffic patterns and a Multi-arm Bandit algorithm to optimize traffic shaping policies based on these predictions. We evaluate the proposed approach on three different datasets, including a simulated corporate network (KDD Cup 1999), a collection of network traffic traces (CAIDA), and a simulated network environment with both normal and malicious traffic (NSL-KDD). The results demonstrate that our approach outperforms other state-of-the-art traffic shaping methods, achieving higher throughput, lower packet loss, and lower delay, while effectively detecting anomalous traffic patterns. The proposed approach offers a promising solution to traffic shaping in SDNs, enabling efficient resource management and QoS assurance

Mapping pavement types, especially in sidewalks, is essential for urban planning and mobility studies. Identifying pavement materials is a key factor in assessing mobility, such as walkability and wheelchair usability. However, satellite imagery in this scenario is limited, and in-situ mapping can be costly. A promising solution is to extract such geospatial features from street-level imagery. This study explores using open vocabulary classification algorithms to segment and identify pavement types and surface materials in this scenario. Our approach uses Large Language Models (LLMs) to improve the accuracy of classifying different pavement types. The methodology involves two experiments: the first uses free prompting with random street-view images, employing Grounding Dino and SAM algorithms to assess performance across categories. The second experiment evaluates standardized pavement classification using the Deep-Pavements Dataset and a fine-tuned CLIP algorithm optimized for detecting OSM-compliant pavement categories. The study presents open resources, such as the Deep Pavements Dataset and a fine-tuned CLIP-based model, demonstrating a significant improvement in the True Positive Rate (TPR) from 56.04% to 93.5%. Our findings highlight both the potential and limitations of current open vocabulary algorithms and emphasize the importance of diverse training datasets. This study advances urban feature mapping by offering a more intuitive and accurate approach to geospatial data extraction, enhancing urban accessibility and mobility mapping.

In this research, we investigate the integration of an Intrusion Detection System (IDS) with a Digital Twin (DT) to enhance the cybersecurity of physical devices in cyber-physical systems. Using Eclipse Ditto as the DT platform and Snort as the IDS, we developed a near-realistic test environment that included a Raspberry Pi as the physical device and a Kali Linux virtual machine to perform common cyberattacks such as Hping3 flood attacks and NMAP reconnaissance scans. The results demonstrated that the IDS effectively detected Hping3-based flood attacks but showed limitations in identifying NMAP scans, suggesting areas for IDS configuration improvements. Furthermore, the study uncovered significant system resource impacts, including high CPU usage during SYN and ACK flood attacks and persistent memory usage after NMAP scans, highlighting the need for enhanced recovery mechanisms. This research presents a novel approach by coupling a Digital Twin with an IDS, enabling real-time monitoring and providing a dual perspective on both system performance and security. The integration offers a holistic method for identifying vulnerabilities and understanding resource impacts during cyberattacks. The work contributes new insights into the use of Digital Twins for cybersecurity and paves the way for further research into automated defense mechanisms, real-world validation of the proposed model, and the incorporation of additional attack scenarios. The results suggest that this combined approach holds significant promise for enhancing the security and resilience of IoT devices and other cyber-physical systems.

Brain tumor being one of the major health hazards always needs a prompt diagnosis for the early treatment options to improve the chances of the survival of the patients. Traditional manual assessment of MRI (magnetic resonance imaging) to identify these conditions is a common practice. Hence, automation of the diagnosis practices can considerably improve the quality of the treatment procedures. In recent years almost every method for the automated diagnosis of brain tumor detection uses a deep learning technique. Despite decent works current techniques need a significant improvement to produce better results. Along with failure to produce decent results, the current deep learning models present very complex architectures which sometimes require huge computational resources. Therefore, in this paper we present a novel method that uses a lightweight dual-stream model with dual-input to detect brain tumors on MRI images. Both the inputs use different pre-processing mechanisms based on Contrast Limited Adaptive Histogram Equalization (CLAHE) and White Patch Retinex algorithm in order to enhance the feature learning capabilities of the proposed model. In addition, the proposed method uses a two-fold margin loss to boost training process for better feature learning. The proposed method produces state-of-the-art results for the detection of Glioma, Meningioma and Pituitary tumors. The model presents accuracy of 99%, 98% and 99% respectively for Glioma, Meningioma and Pituitary tumor detection.

Information dissemination and preservation are crucial for societal progress, especially in the technological age. While technology fosters knowledge sharing, it also risks spreading misinformation. Audio deep fakes, convincingly fabricated audio using Artificial intelligence (AI), exacerbate this issue. We present Sonic Sleuth, an AI model for detecting audio deepfakes. Our approach leverages Deep Learning (DL) for a robust detection model. Meticulous data preprocessing and rigorous experimentation with various models led to the implementation of the most effective solution with a custom CNN model. Our comprehensive testing resulted in a highly accurate model (98.27% accuracy, 0.016 EER) trained on a substantial dataset of real and synthetic audio. In addition to an 84.92% accuracy and 0.085 EER on an external dataset, these results demonstrate Sonic Sleuth’s potential as a powerful tool against audio misinformation.

This article discusses advances in molecular technologies for detecting foodborne pathogens, specifically Listeria monocytogenes and Salmonella. Although traditional detection methods are reliable, they are time- and labor-intensive. On the other hand, molecular methods include polymerase chain reaction (PCR), quantitative real-time PCR (qPCR), and next-generation sequencing (NGS), which enable rapid, specific, and sensitive detection of pathogens. The introduction of such procedures has significantly improved food safety testing and monitoring. Although challenges such as accessibility and cost remain, future developments in molecular diagnostics will lead to greater implementation in improving pathogen management throughout the food supply chain.

Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impacts from over-height vehicles. Impacts can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts to railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data is transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events like impact detection that require rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications.

Alpha emitters like plutonium pose severe health risks when ingested, damaging DNA and potentially causing cancer. Traditional detection methods require proximity within millimeters of the contamination source, presenting safety risks and operational inefficiencies. Long-range detection through alpha radioluminescence (RL) offers a promising alternative. However, most of the previous experiments have been carried out under controlled conditions that preclude the overwhelming effect of ambient light. This study demonstrates successful detection of a 3 MBq alpha emitter in an open environment using a newly developed, lightweight compact alpha camera. This camera incorporates a deep-cooled CCD and a specially designed lens system with a low f-number to maximum the signal intensity. The lens was also designed to minimize blue shift effects of filters. Nighttime imaging was achieved with a dual-filter system using a sandwich filters assembly centered at 337 nm and 343 nm for capturing alpha RL and subtracting background light, respectively. At night, the alpha source was detected from 1 meter away within one minute. The system was also evaluated under simulated urban lighting conditions. For daytime imaging, a stack of tilted 276 nm short pass filters minimized sunlight interference, enabling clear detection of the alpha source at 70 cm within 10 minutes under indirect sunlight. This research not only underscores the viability of long-range optical detection of alpha emitters for environmental monitoring and scientific research but also highlights its ability to enhance nuclear safety and public health in real-world settings.

Identifying punch types as part of punch kinematic analysis is a key component for both coaches and players, providing critical insights into the variety and effectiveness of punches, which are essential for refining overall performance. Existing research indicates that punch type classification typically relies on data from either wearables or video data. Both approaches have their merits and demerits but require a significant amount of labeled data for modeling the classification algorithm. In this work, we propose a novel approach that significantly reduces the labeling effort by one-sixth by leveraging video data to enhance the training information of sensor data. This enriched training phase enables more accurate punch classification with reduced labeled data. Subsequently, the real-time punch identification and classification use only the wearable data. On a real dataset, with 15% of the labeling efforts, our approach achieved an accuracy rate of 91.41% for rear hand punch recognition and 91.91% for lead hand punch recognition, along with 92.33% and 94.56% for punch classification, respectively. Through the development of our Smart Boxer system, we aim to revolutionize punch analytics in boxing, providing comprehensive insights while enhancing the overall training and competitive experience for athletes and enriching fan engagement with the sport.

This paper introduces the improvements in the natural frequency based Structural Health Monitoring (SHM) by applying bio-inspired optimization methods and the vision-based monitoring system for the effective damage detection. This paper proposes a natural frequency extraction method using the motion magnification based vision monitoring system with bio-inspired optimization techniques to estimate the damage location and depth in a cantilever beam. The proposed optimization techniques are inspired by natural processes and biological evolution including Genetic Algorithms, Particle Swarm Optimization, Sea Lion Optimization, and Coral Reefs Optimization. To verify the performances of each bio-inspired optimization methods, the eigenvalues of a two-bay truss structure are used for estimating the damaged elements. Then, using the proposed video motion magnification method, the natural frequency for each of undamaged and damaged cantilever beam have been extracted and compared with the LDV sensor to verify the proposed vision-based monitoring system. The performance of each bio-inspired optimizer for the damage detection has been compared. As a result, Coral Reefs Optimization has showed the lowest average error, around 1%, in the damage detection using natural frequency.

The rapid growth of the global population is causing a significant increase in waste production, leading to serious environmental and public health challenges. To address these issues, waste management systems are incorporating advanced technologies. Machine learning and computer vision are used to predict waste patterns, optimize collection schedules, and improve sorting accuracy. Deep learning automates the sorting process, provides predictive analytics, and enhances recycling rates. Robotics, combined with AI and computer vision, improves sorting efficiency, while the Internet of Things (IoT) monitors waste levels and optimizes collection routes. Despite these benefits, challenges such as data scarcity, high computational demands, and the need for substantial infrastructure investments must be addressed. This research explores the integration of advanced technologies into waste management and evaluates their effectiveness using waste datasets. It highlights the potential to tackle environmental challenges and lay the groundwork for more intelligent waste management solutions.

This paper investigates the integration of SHM within the frame of Industry 4.0 technologies, highlighting the potential for intelligent infrastructure management through the utilization of Big Data analytics, Machine Learning, and the Internet of Things (IoT). The study also presents a success case focused on a novel SHM methodology for detecting and locating damages in metallic aircraft structures, employing dimensional reduction techniques such as Principal Component Analysis (PCA). By analyzing strain data collected from a network of sensors and comparing it to a baseline pristine condition, the methodology aims to identify subtle changes in local strain distribution indicative of damage. Through extensive Finite Element Analysis (FEA) simulations and a PCA contribution analysis, the research explores the influence of various factors on damage detection, including sensor placement, noise levels, and damage size and type. The findings demonstrate the effectiveness of the proposed methodology in detecting cracks and holes as small as 2mm in length, showcasing the potential for early damage identification and targeted interventions in diverse sectors such as aerospace, civil engineering, and manufacturing. Ultimately, this paper underscores the synergistic relationship between SHM and Industry 4.0, paving the way for a future of intelligent, resilient, and sustainable infrastructure.

The number of connected IoT devices is increasing significantly due to their many benefits, including automation, improved efficiency and quality of life, and waste reducing. However, these devices have several vulnerabilities that have led to the rapid growth in the number of attacks. Therefore, several machine learning-based Intrusion Detection Systems (IDS) tools have been developed to detect intrusions and suspicious activity to and from a host (HIDS - Host IDS) or, in general, within the traffic of a network (NIDS - Network IDS). The proposed work performs a comparative analysis and an ablative study among recent machine learning-based NIDS to develop a benchmark of the different proposed strategies. The proposed work compares both shallow learning algorithms, such as Decision Trees, Random Forest, Naïve Bayes, Logistic Regression, XGBoost, and Support Vector Machine, and deep learning algorithms, such as DNN, CNN, and LSTM, whose approach is relatively new in the literature. The algorithms are evaluated on the KDD-99, NSL-KDD, UNSW-NB15, and IoT-23 datasets. The results show that the NIDS tools based on deep learning approaches achieve better performance in detecting network anomalies than shallow learning approaches.

Technological developments within the maritime sector are resulting in rapid progress that will see the commercial use of autonomous vessels, known as Maritime Autonomous Surface Ships (MASS). Such ships are equipped with a range of advanced technologies, such as IoT devices, artificial intelligence (AI) systems, machine learning (ML) based algorithms and augmented reality (AR) tools. Through such technologies, the autonomous vessels can be remotely controlled from Shore Control Centres (SCC) by using real-time data to optimise their operations, enhance safety and reduce the possibility of human error. Apart from the regulatory aspects, which are under definition by the International Maritime Organisation (IMO), cybersecurity aspects must be considered and properly addressed to prevent such complex systems from being tampered with. In this paper, we propose an approach to secure communication channels between the SCCs and the vessels using SECOM standard and the underlying digital infrastructure in charge of data collection, storage and processing by relying on a set of machine learning (ML) algorithms for anomaly detection. The proposed approach is validated against a real implementation of the SCC deployed at Livorno seaport premises. Finally, the experimental results and the performance evaluation are provided to assess its effectiveness accordingly.

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small targets in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small target objects. Finally, we propose the Smooth Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conduct comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the mAP@0.5 metric, and 72.03%, 65.37%, and 38.91% in the more stringent mAP@0.5:0.95 metric, respectively.

Focusing on the problem of identifying and classifying aero-engine models, this paper measures the infrared spectrum data of aero-engine hot jets using a telemetry Fourier transform infrared spectrometer. Simultaneously, infrared spectral data sets with the six different types of aero-engines are created. For the purpose of classifying and identifying infrared spectral data, a CNN architecture based on the continuous wavelet transform peak seeking attention mechanism (CWT-AM-CNN) is suggested. This method calculates the peak value of middle wave band by continuous wavelet transform, and the peak data is extracted by the statistics of the wave number locations with high frequency. Attention mechanism is used for the peak data, and the attention mechanism is weighted to the feature map of the feature extraction block. The training set, vali-dation set and prediction set are divided in the ratio of 8:1:1 for the infrared spectral data sets. For three different data sets, CWT-AM-CNN proposed in this paper is compared with the classical classifier algorithm based on CO2 feature vector and the popular AE, RNN and LSTM spectral processing networks. The prediction accuracy of the proposed algorithm in the three data sets is as high as 97%, and the lightweight network structure design not only guarantees high precision, but also has a fast running speed, which can realize the rapid and high-precision classification of the infrared spectral data of the aero-engine hot jets.

3D reality capturing has demonstrated increased efficiency and consistently accurate outcomes in architectural digitisation. Nevertheless, despite advancements in data collecting, 3D reality capturing still lacks full automation, especially in the post-processing and modelling phase. Artificial intelligence (AI) has been a significant focus, especially in computer vision, and tasks such as image classification and object recognition might be beneficial to the digitisation process and its subsequent utilisation. This article aims to examine the potential outcomes of integrating AI technology into the field of 3D reality-capturing, with a particular focus on its use in architectural scenarios. The main methods used for data collection are laser scanning (static or mobile) and photogrammetry. As a result, image data including RGB-D data (files containing both RGB colours and Depth information) and point clouds have become the most common raw datasets available for object mapping. The study comprehensively analyses the current use of 2D and 3D deep learning techniques in documentation tasks, particularly downstream applications. It also highlights the ongoing research efforts in developing real-time applications with the ultimate objective of achieving generalisation and improved accuracy.

This work presents a methodology for the generation and classification of phase resolved partial discharges (PRPD) patterns based on the use of a printed UHF monopole antenna and signal con-ditioning circuit to reduce hardware requirements. For this purpose, envelope detection technique was applied. In addition, test objects such as a hydrogenerator bar, dielectric disks with internal cavities in an oil cell, potential transformer, and tip-tip electrodes immersed in oil were used to generate partial discharge (PD) signals. To detect and classify partial discharges, the standard IEC 60270 (2000) method was used as reference. After acquisition of conditioned UHF signals, digital signal filtering threshold technique was used, and peaks of partial discharges envelopes pulses were extracted. Feature selection techniques were used to classify the discharges and choose the best features to train machine learning algorithms, such as multilayer perceptron, support vector ma-chine and decision tree. Accuracies greater than 84% were met, revealing the classification potential of the methodology proposed in this work.

In response to the burgeoning global demand for premium agricultural products, particularly within the competitive nut market, this paper introduces an innovative methodology aimed at enhancing the grading process for almonds and their shells. Leveraging state-of-the-art Deep Convolutional Neural Networks (CNNs), specifically the AlmondNet-20 architecture, our study achieves exceptional accuracy exceeding 99%, facilitated by the utilization of a 20-layer CNN model. To bolster robustness in differentiating between almonds and shells, data augmentation techniques are employed, ensuring the reliability and accuracy of our classification system. Our model, meticulously trained over 1000 epochs, demonstrates remarkable performance, boasting an accuracy rate of 99% alongside a minimal loss function of 0.0567. Rigorous evaluation through test datasets further validates the efficacy of our approach, revealing impeccable precision, recall, and F1-score metrics for almond detection. Beyond its technical prowess, this advanced classification system offers tangible benefits to both industry experts and non-specialists alike, ensuring globally reliable almond classification. The application of deep learning algorithms, as showcased in our study, not only enhances grading accuracy but also presents opportunities for product patents, thereby contributing to the economic value of our nation. Through the adoption of cutting-edge technologies such as the AlmondNet-20 model, we pave the way for future advancements in agricultural product classification, ultimately enriching global trade and economic prosperity.

The vast network of interconnected devices of the Internet of Things poses significant security challenges, necessitating the upgrade of current wireless networks to the 6G standard and an improved intrusion detection system for host networks. Thus this article therefore underscores a game theory concept to analyze the security impediments in wireless communication systems, where a host network bandwidth is jeopardized by the intruder while acting as a legitimate user. While game theory can help with the impending security concerns, network slicing is saliently proposed for large and complicated networks, precipitating a layer wise fragmentation of the concerned network and effectively addressing the security issues in different layers of the IoT framework. However, with increasing network complexity, network security issues incorporating traditional measures become increasingly cumbersome. Therefore, this paper also briefly mentions some of the ML techniques that can be used to classify and segregate valid users from the compromised nodes in the host network. The paper discusses the potential of incorporating intelligence into wireless communication systems to enhance the host network security. The paper further aids in analyzing existing security issues in different layers of wireless communication systems, underscoring network slicing for further classification of intruders from user nodes and training the host network using ML/AI per the user application specification.

Purpose: To simplify the application of optical coherence tomography (OCT) for examination of subsurface morphology in the oral cavity and reduce barriers towards adoption of OCT as a biopsy guidance device. The aim of this work was to develop automated software tools for simplified analysis of the large volume of data collected during OCT. Methods: Imaging and corresponding histopathology were acquired in-clinic using a wide-field endoscopic OCT system. An annotated dataset (n = 294 images) from 60 patients (34 male, 26 female) was assembled to train four unique neural networks. A deep learning pipeline was built using convolutional and modified u-net models to detect the imaging field of view (network 1), detect artifacts (network 2), identify the tissue surface (network 3), and identify the presence and location of the basement membrane (network 4). Results: The area under the curve of the image and artifact detection networks was 1.00 and 0.94 respectively. The Dice similarity score for the surface and basement membrane segmentation networks was 0.98 and 0.83 respectively. Conclusion: Deep Learning (DL) techniques can identify the location and variations in the epithelial surface and basement membrane in OCT images of the oral mucosa. Segmentation results can be synthesized into accessible en face maps to allow easier visualization of changes.

Environmental factors, such as drought-stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought-stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought-stress in maize plants using an existing, custom-developed, low cost, high throughput phenotyping platform. We propose a pipeline for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. We also explored suitable regions on the plant that are more sensitive to drought-stress and show that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Our results show good separation between well-watered and drought-stressed trials for two out of the three imaging trials both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide-disaster management. Existing Remote Sensing approaches mostly use either optical or Synthetic Aperture Radar (SAR) sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data, along with relevant spectral indices and suitable bands using machine-learning-based classification for coseismic landslide detection implemented in Google Earth Engine (GEE). The approach includes a robust and reproducible training and validation strategy. Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide iden-tification and localization, providing a detection accuracy of 87-92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proofed that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of co-seismic landslides, highlighting its potential to contribute towards more effective disaster management.

The paper delves into the critical issue of damage detection within the guy cable of a truss steel mast, a pivotal component in structural integrity. It introduces a model wherein damage manifests as a localized cross-section reduction in a single element of the cable. Employing Discrete Wavelet Transform (DWT), a pioneering methodology, the study scrutinizes the behavior of the affected elements through static structural analyses. Signal decomposition via the Mallat pyramid algorithm facilitates comprehensive examination. Static displacements at the connection point between the cable and the truss mast serve as the measured variables. Through systematic investigation, the paper evaluates the impact of damage size and location, external loading force, and cable tension force on the efficacy of the proposed approach. Utilizing data derived from Finite Element Method (FEM) computations for wavelet analysis the authors substantiate the findings with numerical examples, thus offering valuable insights into damage detection strategies for structural health monitoring and engineering applications.

This study demonstrates the efficacy of structured pruning and architectural fine-tuning on the YOLOv7-tiny model for eye state detection, emphasizing optimization for real-time applications. Structured pruning significantly reduced the model's complexity and storage size while maintaining high detection accuracy, as evidenced by stable precision, recall, and mAP@.5 metrics across iterations. Further fine-tuning adjusted the model's width and depth, optimizing efficiency and processing speed without compromising performance. These optimizations yielded YOLOv7-tiny variants that are both computationally efficient and accurate, suitable for resource-constrained environments. The findings highlight the critical role of model optimization in deploying effective neural networks for specific detection tasks in real-time scenarios.

Recently, there has been considerable research on deepfake detection. However, most existing methods face challenges in adapting to the advancements in new generative models within unknown domains. In addition, the emergence of new generative models capable of producing and editing high-quality images, such as Diffusion, Consistency, and LCM, poses a challenge for traditional deepfake training models. These advancements highlight the need for adapting and evolving existing deepfake detection techniques to effectively counter the threats posed by sophisticated image manipulation technologies. In this paper, our objective is to detect deepfake videos in unknown domains using unlabeled data. Specifically, our proposed approach employs Meta Pseudo Labels (MPL) with Supervised Contrastive Learning, so-called SupCon-MPL, allowing the model to be trained on unlabeled images. MPL involves the simultaneous training of both a Teacher model and a Student model, where the Teacher model generates Pseudo Labels utilized to train the Student model. This method aims to enhance the adaptability and robustness of deepfake detection systems against emerging unknown domains. Supervised Contrastive Learning utilizes labels to compare samples within similar classes more intensively, while encouraging greater distinction from samples in dissimilar classes. This facilitates the learning of features in a diverse set of deepfake images by the model, consequently contributing to the performance of deepfake detection in unknown domains. When utilizing the ResNet50 model as the backbone, SupCon-MPL exhibited an improvement of 1.58% in accuracy compared to traditional MPL in known domain detection. Moreover, in the same generation of unknown domain detection, there was a 1.32% accuracy enhancement, while in the detection of post-generation unknown domains, there was an 8.74% increase in accuracy.

An original approach has been proposed for designing a nanofibrous (NFs) layer using UV-cured polyvinylpyrrolidone (PVP) as a matrix, incorporating mesoporous graphene carbon (MGC) nanopowder both inside and outside the fibres, creating a sandwich-like structure. This architecture is intended to selectively adsorb and detect acetic acid vapours, which are known to cause health issues in exposed workers. The nanocomposite MGC-PVP-NFs layer was fabricated through electrospinning deposition onto interdigitated microelectrodes (IDEs) and stabilised under UV-light irradiation. To enhance the adhesion of MGC onto the surface of the nanocomposite polymeric fibres, the layer was dipped in a suspension of polyethylenimine (PEI) and MGC. The resulting structure demonstrated promising electrical and sensing properties, including rapid responses, high sensitivity, good linearity, reversibility, repeatability, and selectivity towards acetic acid vapours. Initial testing was conducted in a laboratory using a bench electrometer, followed by validation in a portable sensing device based on consumer electronic components (by ARDUINO®). This portable system was designed to provide a compact, cost-effective solution with high sensing capabilities. Under room temperature and ambient air conditions, both laboratory and portable tests exhibited favourable linear responses, with detection limits of 0.16 and 1 ppm, respectively.

Infrared small target detection (ISTD) plays a crucial role in both civilian and military applications. The detection of small targets against dense cluttered backgrounds remains a challenging task, requiring the collaboration of false alarm source elimination and target detection. Existing approaches primarily focus on modeling targets while often neglecting false alarm sources. To address this limitation, we propose a Target and False Alarm Collaborative Detection Network to leverage the information provided by false alarm sources and the background. Firstly, we introduce a False Alarm Source Estimation Block (FEB) that estimates potential interferences present in the background by extracting features on multiple scales and employing stepwise upsampling for feature fusion. Subsequently, we propose a framework that employs multiple FEBs to eliminate false alarm sources across multiple scales. Finally, a Target Segmentation Block (TSB) is introduced to accurately segment the targets to produce the final detection result. Experiments on public datasets demonstrate that, compared to other methods, our model achieves high accuracy in segmenting targets while being able to extract false alarm sources which can be used for further studies.

The necessity for effective intrusion detection systems (IDS) in big data environments has grown critical due to the rising prevalence of big data systems and the rising amount of security threats. Applying conventional intrusion detection methods to the enormous and intricate data produced by big data ecosystems presents difficulties. Deep learning, has proven to be exceptionally adept at deciphering complex, large-scale data. This study proposes a Deep Learning-based Intrusion Detection System (IDS) created specifically for the Big Data environment. Traditional intrusion detection systems struggle to efficiently identify and prevent cyber threats due to the ever-increasing volume and complexity of data in current networks. We suggest an IDS that makes use of Deep Learning (CNN, LSTM, GAN, etc.), to address this problem. These cutting-edge neural network topologies give the system the ability to process and analyze massive amounts of data for precise intrusion detection. Data collection, data preprocessing, feature engineering, DL model training, intrusion detection, warning generation, and reaction are some of the crucial parts of the suggested IDS. The data collection module mines the Big Data environment for network traffic, system logs, and security event information. To make sure the data is suitable for analysis, it is preprocessed. To improve the detection abilities of the DL models, important features are extracted from the data using feature engineering approaches.

Considering the spectrum shortage problem of IoT devices, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources, so that IoT devices can access it and meanwhile avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of the PU’s signal detection in IoT devices, the issue about the stopping time and decision cost arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the stopping time and cost function for IoT devices, and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal stopping time problem as a finite horizon problem, and solve the threshold of the optimal decision rule by dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various cost of each observation taken and thresholds.

In this paper, a relatively new hyper convolution spectra for characterization liquid, viscoelastic and solid samples is presented. Special attention is paid to the biophysical characterization of epithelial tissues and early detection of cancer of the skin, cervix, colon, and oral cavity. The method is based on the use of two types of white light, diffuse and polarized, from the same LED light sources. Based on the subtraction of the intensity values of these two lights and using the hyper convolution algorithm, the ratio of paired and unpaired electrons of tissue is obtained. This ratio correlates well with the state of the tissue: normal, irritating, precancerous, and cancerous. First, a proof-of-concept studies were performed, and later clinical studies on samples from one thousand two hundred fifteen people were performed. It showed that hyper Opto-Magnetic Imaging Spectroscopy (OMIS) can detect epithelial tissue cancers with an accuracy of 85-96%. Bearing in mind that the OMIS method also acquires the biophysical characteristics of the deeper skin layers (epidermis, basement membrane, dermis) by analysing convolution spectra of RGB channels of both diffuse and polarized white light, it is possible to apply this method in cosmetics (skin hydration, collagen, and elastin states, etc.). During OMIS applications, we noticed places where improvements could be made to increase method’s accuracy using advanced machine learning methodologies.

Object detection is important in many applications, such as autonomous driving. While 2D images are lack of depth information and are sensitive to environmental conditions, 3D point cloud can provide accurate depth information and a more descriptive environment. However, sparsity is always a challenge in single-frame point cloud object detection. This paper introduces a two-stage proposal-based feature fusion method for object detection using multiple frames. The proposed method, called proposal features fusion (PFF), utilizes a cosine-similarity approach to associate proposals from multiple frames and employs an attention weighted fusion module to merge features from these proposals. It allows for feature fusion specific to individual objects and offers lower computational complexity while achieving higher precision. The experimental results on the nuScenes dataset demonstrate the effectiveness of our approach, achieving a mAP of 46.7%, which is 1.3% higher than the state-of-the-art 3D object detection method.

Abstract: Botnets pose a grave cybersecurity threat, enabling widescale malicious activities through networks of compromised devices. Detecting botnets is challenging given their frequent use of evasion techniques like encryption. Traditional signature-based methods fail against modern botnets capable of zero-day attacks. This paper surveys recent advances applying machine learning for botnet detection based on analysis of network traffic payloads, flows, DNS data, and hybrid feature fusion. Core machine learning models include support vector machines, neural networks, random forests, and deep learning architectures, which extract patterns to separate benign and botnet behaviors automatically. Results demonstrate machine learning's capabilities in identifying heterogeneous botnets using artefacts in network streams. However, challenges remain around limited labeled data, real-time streaming, adversarial evasion, and model interpretability. Promising directions involve semi-supervised learning, adversarial training, scalable analytics, and explainable AI to address these gaps. Beyond the technical aspects, responsible development and deployment of botnet detection systems raise ethical considerations around privacy, transparency, and accountability. With diligent cross-disciplinary collaboration, machine learning promises enhanced, generalizable, and trustworthy techniques to combat the serious threat posed by continuously evolving botnets across the digital ecosystem.

Accurate pathogenic detection in wastewater is critical for safeguarding public health and the environment. However, the presence of free nucleic acids in wastewater samples poses significant challenges to molecular detection accuracy. This comprehensive review explores the current status and future potential of pretreatment methods to remove free nucleic acids from wastewater samples. The study contributes a comprehensive analysis of the mechanisms, strengths, and limitations of various pretreatment approaches, including physical, chemical, and enzymatic processes. The effect of various factors on the removal efficiency of these pretreatment methods is also discussed. This review enhances our comprehension of pretreatment techniques and their vital role in achieving precise pathogenic detection in complex wastewater matrices. Furthermore, it outlines future perspectives and developments for improving the speed and effectiveness of pathogenic detection, contributing significantly to disease surveillance, early warning systems, and environmental protection.

Radiator reliability is crucial in environments characterized by high temperatures and friction, where prompt interventions are often required to prevent system failures. This study introduces a proactive approach to radiator fault diagnosis, leveraging the integration of Gaussian Mixture Model (GMM) and Long-Short Term Memory (LSTM) autoencoders. Vibration signals from radiators were systematically collected through randomized durability vibration bench tests, resulting in four operating states—two normal, one unknown, and one faulty. Time-domain statistical features of these signals were extracted and subjected to Principal Component Analysis (PCA) to facilitate efficient data interpretation. Subsequently, this study discusses the comparative effectiveness of GMM and LSTM in fault detection. GMMs are deployed for initial fault classification, leveraging their clustering capabilities, while LSTM autoencoders excel in capturing time-dependent sequences, facilitating advanced anomaly detection for previously unencountered faults. This alignment offers a potent and adaptable solution for radiator fault diagnosis, particularly in challenging high-temperature or high-friction environments. Consequently, the proposed methodology not only provides a robust framework for early-stage fault diagnosis but also effectively balances diagnostic capabilities during operation. Additionally, this study presents the foundation for advancing reliability life assessment in accelerated life testing, achieved through dynamic threshold adjustments using both the absolute log-likelihood distribution of the GMM and the reconstruction error distribution of the LSTM autoencoder model.

Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship targets in large-scene SAR images are due to the occlusion of objects or mutual occlusion among targets, especially for small ship targets. To solve this problem, this study proposes a target detection model (TAC_CSAC_Net) that incorporates a multi-attention mechanism for detecting marine vessels in large-scene SAR images. Experiments were conducted on two public datasets, the SAR-Ship-Dataset and high-resolution SAR image dataset (HRSID), with multi-scene and multi-size, and the results showed that the proposed TAC_CSAC_Net model achieves good performance for both small and occluded target detection. Experiments were conducted on a real large-scene dataset, LS-SSDD, to obtain the detection results of subgraphs of the same scene. Quantitative comparisons were made with classical and recently developed deep learning models, and the experiments demonstrated that the proposed model outperformed other models for large-scene SAR image target detection.

In several Indonesian tropical cities, such as Makassar City, frequent flooding during the peak rainy season poses a significant challenge. This study seeks to address this issue by devising effective policies to reduce flood exposure. The main obstacle lies in accurately determining the flood inundation area. To overcome this, the study utilizes multiple satellite data sources on a cloud platform, integrating them with land use data, and DEM data, aligning these with the local government's urban land use plan and existing drainage networks. The research aims to quantify the affected area, identify the predominant inundated land cover, and assess the efficiency of Makassar's drainage system and urban land use plan. The study reveals that an uncoordinated drainage system in the Tamalanrea, Biringkanaya, and Mangala sub-districts results in severe flooding, encompassing a total area of 35.28 km². The most affected land use type is cultivation, constituting approximately 43.5% of the flooded area. Furthermore, 82.26% of the urban land use plan, covering 29.02 km², is submerged. It is imperative for the local government and stakeholders to prioritize the enhancement of drainage systems and urban land use plans, particularly in low-lying and densely populated regions

Parkinson’s Disease (PD) is a neurodegenerative disorder characterized by motor and non-motor impairment with various implications on patients’ quality of life. Since currently available therapies are only symptomatic, identifying individuals with prodromal, preclinical or early-stage PD is crucial, as they would be ideal candidates for future disease-modifying therapies. Our analysis aims to develop a robust model for accurate PD detection using accelerometer data collected from PD and non-PD individuals with mild or no tremor during phone conversations. An open-access dataset comprising accelerometer recordings from 22 PD patients and 11 healthy controls (HC) was utilized. The data were pre-processed to extract relevant time, frequency and energy-related features, and a Bidirectional Long Short-Term Memory (Bi-LSTM) model with attention mechanism was employed for classification. The performance of the model was evaluated using 5-fold cross-validation, and metrics of accuracy, precision, recall, specificity, and f1-score were computed. The proposed model demonstrated high accuracy (98%), precision (99%), recall (98%), specificity (96%), and f1-score (98%) in accurately distinguishing PD patients from HC. Our findings indicate that the proposed model outperforms existing approaches and holds promise for detection of PD with subtle symptoms, like tremor, in the wild. Such symptoms can present in the early or even prodromal stage of the disease, and appropriate mobile-based applications may be a practical tool in real-life settings to alert individuals at risk to seek medical assistance or give patients feedback in monitoring their symptoms.

This article presents a study on brain tumor detection using the VGG-16 model, a convolutional neural network known for its performance in computer vision tasks. The aim of the study is to classify magnetic resonance imaging (MRI) images and accurately identify the presence of brain tumors. The dataset used consists of brain tumor MRI images, categorized into two classes: "NO" (no tumor) and "YES" (tumor). The methodology involves setting up the environment, importing and preprocessing the data, building the VGG-16 model, and evaluating its performance using metrics such as accuracy, precision, and recall. The results demonstrate an accuracy of approximately 88% on the validation set and 80% on the test set, indicating the potential of the VGG-16 model in supporting healthcare professionals in diagnosing brain tumors. The study contributes to the field of medical image analysis and offers insights into the application of deep learning for brain tumor detection.

Fault detection is an important step for subsurface interpretation and reservoir characterization from 3D seismic images. Due to the numerous and complicated faulting structures of seismic images, manual seismic interpretation is time taking and need intensive work. To overcome this problem, geoscientists are coming up with productive computer-aided techniques for assisting in interpreter science for many years. However, in this paper, we used a pre-trained CNN model to predict faults from the 3D seismic volume of the Poseidon field of Browse Basin, Australia. Basically, this field is highly structured with complex normal faulting throughout the targeted Plover Formations. So, our motivation for this work in this field is to compare machine learning-based fault prediction to user interpreted faults identification with supported by seismic variance attribute. We found very satisfying result using DL with having an improved fault probability volume that outperform variance technology. Therefore, we propose that this workflow could reduce time and able to predict fault quite accurately in any field area.

The Internet of Things technology opens the horizon for a broader scope of intelligent applications in smart cities. However, the massive amount of traffic exchanged among devices may cause security risks, significantly when devices are compromised or vulnerable to cyber-attack. An intrusion detection system is the most powerful tool to detect unauthorized attempts to access smart systems. It identifies malicious and benign traffic by analyzing network traffic. In most cases, only a fraction of network traffic can be considered malicious. As a result, it is difficult for an intrusion detection system to detect attacks at high detection rates while maintaining a low false alarm rate. This work proposes an integrated framework to detect suspicious traffic to address secure data communication in smart cities. This paper presents an approach to developing an intrusion detection system to detect various attack types. It can be done by implementing a Principal Component Analysis method that eliminates redundancy and reduces system dimensionality. Furthermore, the proposed model shows how to improve intrusion detection system performance by implementing an ensemble model.

The G-NUMEN array is the future gamma spectrometer of the NUMEN experiment (Nuclear Matrix Element for the Neutrinoless double beta decay), to be installed around the object point of the MAGNEX magnetic spectrometer at the INFN-LNS laboratory.. This project aims at exploring Double Charge Exchange (DCE) reactions in order to obtain crucial information about the neutrinoless double beta decay (0νββ). The primary objective of the G-NUMEN array is to detect the gamma rays emitted from the de-excitation of the excited states populated via DCE reactions with good energy resolution and detection efficiency, amidst a background composed of transitions from competingreaction channels with far higher cross-sections. To achieve this, the G-NUMEN signalswill be processed in coincidence with those generated by the detection of the reaction ejectiles in the MAGNEX Focal Plane Detector(FPD). Under the expected experimental conditions, G-NUMEN detectors will operate at high counting rates, of the order of hundreds of kHz per detector, while maintaining excellent energy and timing resolutions. The complete array will consist of over 100 LaBr3(Ce) scintillators. Initial tests have been conducted on the first detectors of the array, allowing for the determination of their performance at high rates.

Even if the field of medicine has made great strides in recent years, infectious diseases caused by novel viruses that damage the respiratory system continue to plague people all over the world. This type of virus is very dangerous, especially for people who deal with serious long-term breathing problems, like triggering asthma, pneumonia, or bronchitis infections. Thus, this paper demonstrates a new Secure Machine Learning Monitoring System for a model for virus detection. Our proposed model makes use of 4 basic emerging technologies, Internet of Things (IoT), Wireless Sensor Networks (WSN), Cloud Computing (CC), and Machine Learning (ML), to detect dangerous types of viruses that infect people or animals causing panic worldwide and deregulating human daily life. The proposed system is a robust system that could be established in various buildings, like hospitals, entertainment halls, universities, etc., and will provide accuracy, speed, and privacy for data collected in the detection of viruses.

Silicon-based photodetectors, as low-cost and environmentally friendly optical sensors, are attractive. Also, their compatibility with complementary metal-oxide-semiconductor (CMOS) technology is advantageous for the development of silicon photonics systems. However, extending optical responsivity of silicon-based photodetectors to the mid-infrared (mid-IR) wavelength range remains challenging. In developing mid-IR infrared Schottky detectors, nanoscale metals are critical. Nonetheless, one key factor is the Fermi level pinning effect at the metal/silicon interface and the presence of metal-induced gap states (MIGS). Here, we demonstrate the utilization of the passivated surface layer on semiconductor materials as an insulating material in metal-insulator-semiconductor (MIS) contacts to mitigate the Fermi level pinning effect. The removal of Fermi level pinning effectively reduces the Schottky barrier height by 12.5% to 16%. The demonstrated devices exhibit a high responsivity of up to 234 μA/W at a wavelength of 2 μm, 48.2 μA/W at 3 μm, and 1.75 μA/W at 6 μm. The corresponding detectivities at 2 and 3 μm are 1.17×10^8 cm Hz^1/2 W^-1 and 2.41×10^7 cm Hz^1/2 W^-1, respectively. The expanded sensing wavelength range contributes to the application development of future silicon photonics integration platforms.

This article presents a comparative analysis of three Machine Learning models, namely Logistic Regression, Decision Tree Classifier, and Random Forest Classifier, for prostate cancer detection. The models were trained and evaluated using clinical data, and their performance was assessed using various evaluation metrics. The results show that Logistic Regression achieved the highest accuracy (90%) among the three models, followed by Random Forest Classifier (76.67%) and Decision Tree Classifier (73.33%). Similarly, Logistic Regression demonstrated superior precision (95.65%) and F1 Score (93.62%), indicating its effectiveness in identifying true positive cases. However, the Decision Tree Classifier exhibited higher recall for the negative class (83.33%) compared to the positive class (70.83%), while Random Forest Classifier showed balanced recall for both classes (66.67% for negative and 79.17% for positive). These findings suggest that Logistic Regression outperforms the other models in terms of accuracy and precision, while the Decision Tree Classifier and Random Forest Classifier provide better recall for certain classes. The results highlight the potential of Machine Learning in prostate cancer detection and provide insights for further research and improvement of the models.

In this work, an IoT system with edge computing capability is proposed facilitating postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises are executed correctly, which is critical since, it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages Internet of Things (IoT) paradigm, in combination with Deep Learning and Edge Computing to classify the extend-flex movement of one’s knee via embedded Machine Learning (ML) classification algorithms. The contribution of the proposed work is multilayered. Furthermore as an outcome of this work a dataset of labeled knee movements is freely available on https://www.kaggle.com/datasets/billskarm/knee-range-of-motion to the research community. It also provides real time movement detection with an accuracy reaching 100%, which is achieved with a ML model trained to fit a low cost, off the shelf, Bluetooth Low Energy platform. The proposed Edge Computing approach allows predictions to be performed on-device rather than solely relying on a cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy, while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99,9% wireless data transfer reduction, a critical 39% prediction delay reduction and a valuable 17% increase of prediction event. Finally, enhanced privacy comprises another significant benefit from the implemented Edge Computing ML model as sensitive data can be processed on-site and only events or predictions are shared with the medical personnel.

Urinary tract infections are among the most frequent infectious diseases and require screening of a great amount of urine samples from patients. However, a high percentage of samples results as negative after urine culture plate test (CPT), demanding a simple and fast preliminary technique to screen out the negative samples. We propose a digital holographic microscopy (DHM) method to inspect fresh urine samples flowing in a glass capillary for 3 minutes, recording holograms at 2 frames per second. After digital reconstruction, bacteria, white and red blood cells, epithelial cells and crystals were identified and counted and the samples were classified as negative or positive according to clinical cutoff values. Taking CPT as reference, we processed 180 urine samples and compared the results with those of urine flow cytometry (UFC). Using standard evaluation metrics for our screening test, we found similar performance for DHM and UFC, indicating DHM as a suitable and fast screening technique retaining several advantages. As a benefit of DHM, the technique is label-free and does not require sample preparation. Moreover, the phase and amplitude images of the cells and other particles present in urine are digitally recorded and can serve for further investigation afterwards.

Forensic document examiners can determine the authenticity of questioned documents by analyzing the ink used to create them. If an ink mismatch is found, it could be a sign of scam, backdating, or forgery. In this research a Hyperspectral Images of iVision HHID dataset is used to detect number of possible inks used in document. By using Hyperspectral Images, it’s possible to detect ink mismatch in a given document. In this research unsupervised learning method K-means is used to detect number of inks. Approximate number of clusters are determined by Elbow and Silhouette method before implementation of K-means.

Tick-borne diseases are a growing concern worldwide, affecting both human and animal populations. Ticks are known to harbor a wide range of pathogens and are considered one of the most important vectors of disease. Lyme disease, caused by Borrelia burgdorferi sensu lato, is the most common tick-borne disease in the US and Europe. However, accurate diagnosis of Lyme disease can be challenging due to the complex immune evasion strategies employed by Borrelia species and the limitations of existing diagnostic tests. To address this issue, researchers are exploring novel approaches, including the use of bacteriophages as diagnostic tools. Bacteriophages are highly specific and offer advantages over traditional methods for detecting bacteria, including Borrelia. In particular, the use of multicopy bacteriophages as molecular markers for Borrelia detection is a promising approach that may provide greater sensitivity than targeting single-copy bacterial genes. Nonetheless, the task of identifying trace amounts of bacteriophages in blood samples necessitates attention, and scientists are devising innovative techniques to surmount this hurdle. In summary, employing bacteriophages as a diagnostic tool for tick-borne diseases, such as Lyme disease, by specifically targeting free circulating bacteriophages in blood samples, offers significant potential for enhancing patient outcomes and public health.

Change detection with heterogeneous remote sensing images (Hete-CD) plays an important role in practical applications, especially when homogenous remote sensing images are unavailable. However, bitemporal heterogeneous remote sensing images (HRSIs) cannot compare directly to measure change magnitude, and many deep learning methods require large amounts of samples to train the module. Moreover, labeling many samples for land cover change detection with HRSIs is time-consuming and labor-intensive. Therefore, acquiring satisfactory performance of Hete-CD remains a challenge for deep learning networks with very small training samples. In this study, we promote a novel deep-learning framework for Hete-CD to obtain satisfactory performance while the initial samples are very small. We initially design a multiscale network with select kernel-attention module to focus on capturing different change targets with various sizes and shapes. Then, a simple yet effective non-parametric sample-enhanced algorithm based on the Pearson correlation coeffi-cient is promoted to explore potential samples around each initial sample. Finally, the proposed network and sample-enhanced algorithm are fused into one iterative framework to improve the change detection performance with very small samples. Experimental results conducted on four pairs of actual HRSIs indicated that the proposed framework can achieve competitive accuracies with very small samples for initialization when compared with some state-of-the-art methods. For example, the improvement is approximately 3.38% and 1.99% when compared with the selected traditional methods and deep learning methods, respectively.

A high performance Driver State Monitoring (DSM) application for the detection of driver drowsiness is presented in this paper. It relies on the usage of an Ensemble of Regression Trees (ERTs) machine learning method that aligns 68 facial landmarks. Special focus is given on the acceleration of the frame processing using reconfigurable hardware. Reducing the frame processing latency saves time that can be used to apply frame-to-frame facial shape coherency rules. False face detection and false shape estimations can be ignored for higher robustness and accuracy in the operation of the DSM application without reducing the frame processing rate that can reach 65 frames per second. The sensitivity and precision in yawning recognition can reach 93% and 97%, respectively. The implementation of the employed DSM algorithm in reconfigurable hardware is challenging since the kernel arguments require large data transfers and the degree of data reuse in the computational kernel is low. Due to this, unconventional hardware acceleration techniques have been employed that can also be useful for the acceleration of several other applications.

Capsule endoscopy (CE) has been a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. But the CE captures a huge number of image frames that are time-consuming and tedious tasks for medical experts to diagnose manually. To address this issue, researchers focused on the computer-aided bleeding detection system to identify bleeding automatically in real-time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories: Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect for all original publications on computer-aided bleeding detection published between 2001 and 2021. The PRISMA methodology was used to perform the review, and 112 full-texts of scientific papers were reviewed. The contributions of this paper are I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified, II) the available state-of-the-art computer-aided bleeding detection algorithms including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers are discussed, and III) identify the most effective algorithm for practical use cases. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.

This paper presents the development of an underwater crack detection system for structural integrity assessment of submerged structures, like offshore oil and gas installations, underwater pipelines, underwater foundations for bridges, dams etc. Focus is on use of machine learning based approaches. First a detailed literature review of state of the current methods for underwater surface crack detection is presented highlighting challenges and opportunities. An overview of image augmentation approach for creation of underwater optical effects is also presented. Experimental results using standard network based machine learning approach, used for surface crack detection in onshore environment, is presented. Series of Test cases are presented where existing networks performance are improved using augmented images for underwater conditions. The experimental results demonstrate the effectiveness and accuracy of the proposed system in detecting cracks in underwater structures. The system has the potential to improve the safety and reliability of underwater structures and prevent catastrophic failures.

Nonconventional luminescent materials (NLMs) that do not contain traditional aromatic chromophores are of great interest due to their unique chemical structures, optical properties and their potential applications in various areas such as cellular imaging and chemical sensing. However, most reported NLMs show weak or no emission in dilute solutions, severely limiting their applications. In this work, the dynamic hydrogen bonds were utilized to design NLMs with efficient emission in dilute solutions. To further validate the results, polymers P1 and P2 were successfully prepared and investigated. It was found that luminescence quantum efficiency of P1 and P2 at concentration of 0.1 mg/mL in water solution is 8.9 and 0.6%, respectively. The high efficiency can be attributed to the fact that polymer P1 has more intra- or intermolecular dynamic hydrogen bonds and other short interactions than P2 in dilute solutions, allowing P1 to achieve the through-space conjugation effect to increase the degree of system conjugation, restrict molecular motion, and decrease non-radiative transitions, which can effectively improve luminescence. In addition, polymer P2 exhibits the characteristics of clustering-triggered emission, excitation wavelength-dependent and concentration-dependent fluorescence properties, excellent photobleaching resistance, low cytotoxicity and selective recognition of Fe3+. This work is a preliminary study of the luminescence properties of NLMs in dilute solutions via modulating dynamic hydrogen bonds, which can be used as a semi-empirical method to design and construct novel NLMs in future.

Infrared dim and small target detection is a key technology of various detection tasks. However, due to the lack of shape, texture, and other information, it is a challenging task to detect dim and small targets. Recently, since many traditional algorithms ingore global information of infrared images, they generate some false alarms in complicated environments. To address this problem, in this paper, a coarse-to-fine deep learning-based method is proposed to detect dim and small targets. Firstly, a coarse-to-fine detection framework integrating deep learning and background prediction is applied for detecting targets. The framework contains of coarse detection module and fine detection module. In coarse detection stage, Region Proposal Network (RPN) is employed to generate masks in target candidate regions. Then, for further optimize the result, inpainting is utilized to predict the background by the global semnantic of images. In this paper, inpainting algorithm with a mask aware dynamic filtering module is incorporated into the fine detection stage to estimate background in candidate target areas. Finally, compared with existing algorithms, the experimental results indicate that the proposed framework have effective detection capability and robustness for complex surroundings.

Electrical impedance tomography (EIT) can monitor the real-time hemodynamic state of a conscious and spontaneously breathing patient noninvasively. However, cardiac volume signal (CVS) extracted from EIT images has a small amplitude and is sensitive to motion artifacts (MAs). This study aimed to develop a new algorithm to reduce MAs from the CVS for more accurate heart rate (HR) and cardiac output (CO) monitoring in patients undergoing hemodialysis based on the source consistency between the electrocardiogram (ECG) and the CVS of heartbeats. Two signals were measured at different locations on the body through independent instruments and electrodes, but the frequency and phase were matched when no MAs. As the number of motions per hour (MI) increased over 30, the proposed algorithm had a correlation of 0.83 and a precision of 1.65 beats per minute (BPM) compared to the conventional statical algorithm of a correlation of 0.56 and a precision of 4.04 BPM. For CO monitoring, the precision and upper limit of the mean ∆CO were 3.41 and 2.82 liters per minute (LPM), respectively, compared to 4.05 and 3.82 LPM of the statistical algorithm. The developed algorithm could reduce MAs and improve HR/CO monitoring accuracy and reliability, particularly in high-motion environments.