Ultrasonic imaging & NDE applications can greatly improve their signal-to-noise ratios (SNR) by driving each transducer (composing piezoelectric arrays) with a spike giving pulsed power of k-Watts, repetitively at a PRF = 5000 spikes/s, by using a HV capacitive-discharge generator. However very-high levels, of pulsed intensities (3-10 A) and voltages (300-700 V) must be considered for a rigorous spike modeling. Even though the consumed "average" power will be small, the intensity through each transducer achieves several amperes, so the pulsed powers delivered by each HV generator can attain levels higher than in CW high-power ultrasonic applications: e.g., up to 5 kW / spike. This is concluded here from a transient modeling of the loaded generator. Then, unforeseen phenomena rise: intense brief pulses of driving power & emitted force in transducers, and non-linearities in driver semiconductors, because their characteristic curves only include linear ranges. But fortunately, piezoelectric devices working in this intense regime do not show serious heating problems, because the average power remains being moderate. Intensity, power and voltage, driving a broadband transducer from a HV capacitive pulser, are calculated to drastically improve (in ≅ 40 dB) the ultrasonic net dynamic range available, with emitted forces ≅ 250 Newtons pp and E/R received pulses of 70 V pp.

Microbiocontrol of Mycogone perniciosa is focused on casing soil antagonists use. Since no industrial producer strains of polyfunctional biologics were used in previous studies, research goal was to characterize Bacillus subtilis B-10 and M-22 effect on mycopathogen and reveal its control possibilities. Interactions between producer strains and M. perniciosa were evaluated using well method and spraying. Mycoparasite colony area and its lysis zone were determined. Biological efficacy was characterized by lysis zone proportion in total area occupied by the micromycete mycelium. Statistical processing included W-test, ANOVA, means (M) and standard errors (±SEM) calculation. The significance was assessed by t-test. M. perniciosa development suppressing by producer strains was established, indicates a prolonged B-10 and M-22 effect on mycopathogen. High biological efficacy of both strains at the early stages of mycopathogen development by introducing into the wells and spraying was shown: B-10 – 50.9–99.6 % and M-22 – 57.5–99.2 % respectively (p ≤ 0.05). Significant differences between producer strains were not revealed, although during the first exposure day to developed M. perniciosa colonies M-22 has shown greater activity. Preventive treatment high efficiency when producer strains completely suppressed mycoparasite development, permit recommending them both to introduce when preparing casing for M. perniciosa control.

This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.

The next-generation "Industrial Internet of Things" (IIoT) will support "Machine-to-Machine" (M2M) communications for smart Cyber-Physical-Systems and Industry 4.0, and require guaranteed cyber-security. This paper explores hardware-enforced cyber-security for critical infrastructures. It examines a Quantum-Safe "Software-Defined Deterministic IIoT" (SDD-IIoT), with a new forwarding-plane (sub-layer-3a) for deterministic M2M traffic flows. A "Software-Defined-Networking" (SDN) control-plane controls many "SDD Wide Area Networks" (SDD-WANs), realized with FPGAs. The SDN control-plane provides an "Admission-Control/Access-Control" system for network-bandwidth, using collaborating Artificial Intelligence (AI) rule-based "Zero Trust Architectures" (ZTAs). Hardware-enforced access-control eliminates all congestion, BufferBloat, and DoS/DDoS attacks in the forwarding-plane, reduces buffer-sizes by 100,000+ times, and supports ultra-reliable and uItra-low-latency communications in the SDD-WANs. The SDD-WANs can: (i) Encrypt/Authenticate M2M flows using Quantum-Safe ciphers, to withstand attacks by Quantum Computers; (ii) Implement "Guaranteed Intrusion Detection Systems" in FPGAs, to detect cyber-attacks embedded within billions of IIoT packets/second; (iii) Provide guaranteed immunity to external cyber-attacks against critical infrastructure, and exceptionally-strong immunity to internal cyber-attacks; (iv) Save $US100s of billions annually by exploiting FPGAs; and (v) Enable "Quantum Key Distribution" (QKD) Networks by providing a programmable forwarding-plane with "authenticated classical channels" and full-immunity to DoS/DDoS attacks. Extensive experimental results for an SDD-WAN over the European Union are reported.

This paper presents a comprehensive comparative analysis of four different computational vision algorithms for implementing an unpalleting system in a factory environment, under typical edge computing constraints. The primary objective is to automate the process of detecting and locating specific variable-shaped objects on a pallet, enabling a robotic system to accurately unstack them. The four algorithms evaluated are Pattern Matching, Scale-Invariant Feature Transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifiers. Each technique is described in detail in this paper, and their implementations are outlined. Experimental results are thoroughly analyzed, assessing the algorithms’ performance based on accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The findings reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of the unpalleting system. This research contributes to the advancement of automated object detection and localization in industrial environments, paving the way for more efficient and reliable unpalleting systems.

This study explores the integration of Artificial Intelligence (AI) and collaborative robotics in industrial surface finishing, focusing on feature detection on object reflective surfaces using neural networks in computationally constrained environments. Central to this investigation is the utilization of cameras considered as sensors that can capture detailed images of reflective surfaces. These images are then analyzed by a custom neural network, demonstrating the feasibility of feature detection with non-powerful computers. The public dataset for recognizing light reflection defects, available in the SPADD repository, supports this approach. Employing open-source tools like Darknet YOLOv3 and Yolo\_mark, this method proves particularly advantageous for small and medium-sized enterprises (SMEs) due to its low cost and maintenance requirements. This research highlights the challenges of AI and collaborative robotics in industrial applications, especially in adapting to varying conditions and the need for continuous training with new data coming from several sensors. Looking ahead, plans are in place to integrate this system with cobot-assisted polishing tasks, using cameras to enhance manufacturing efficiency and quality. This work contributes to the broader understanding of practical applications of AI in industrial automation, surface finishing processes, and the effective use of sensor technology.

The globe and more particularly the economically developed regions of the world are currently in the era of the fourth Industrial revolution (4IR). Conversely; the economically developing regions in the world and more particularly the African continent have not yet even fully passed through the Third Industrial Revolution (3IR) wave and its economy is still heavily dependent on the agricultural field. On the other hand, the state of global food insecurity is worsening on an annual basis thanks to the exponential growth of the global human population which continuously heightens the food demand in both quantity and quality. This justifies the significance of the focus on digitizing agricultural practices to improve the farm yield to meet up with the steep food demand and stabilize the economy of the African continent and countries like India whose economy is mainly dependent on Agriculture. The tools we have at our disposal to utilize in the digitization of farming practices include space technology and Global Navigation and Satellite System (GNSS) in particular, Machine learning (ML), precision agriculture and communication systems such as the Internet of Things (IoT) and Information And Communication Technologies (ICT). The most pressing challenges in the farming field include the monitoring of diseases, pests, weeds and nutrient deficiencies in the crops as early detection translates to swift and timely correction actions and hence more yield at the end of a farming cycle. Vast opportunities in the field of precision agriculture still exist that can amount to further research studies such as the lack of real-time monitoring and real-time corrective action focus.