This study presents a novel solution for ambient assisted living (AAL) applications that utilizes spiking neural networks (SNNs) and reconfigurable neuromorphic processors. As demographic shifts result in an increased need for eldercare, due to a large elderly population that favors independence, there is a pressing need for efficient solutions. Traditional deep neural networks (DNNs) are typically energy-intensive and computationally demanding. In contrast, this study turns to SNNs, which are more energy-efficient and mimic biological neural processes, offering a viable alternative to DNNs. We propose asynchronous cellular automaton-based neurons (ACANs), which stand out for their hardware-efficient design and ability to reproduce complex neural behaviors. By utilizing the remote supervised method (ReSuMe), this study improves spike train learning efficiency in SNNs. We apply this to movement recognition in an elderly population, using motion capture data. Our results highlight a high classification accuracy of 83.4%, demonstrating the approach’s efficacy in precise movement activity classification. This method’s significant advantage lies in its potential for real-time, energy-efficient processing in AAL environments. Our findings not only demonstrate SNNs’ superiority over conventional DNNs in computational efficiency but also pave the way for practical neuromorphic computing applications in eldercare.

In the evolving landscape of precision agriculture, the integration of remote pest traps with deep learning technologies marks a critical step forward in remote pest detection, with the potential to substantially improve traditional pest monitoring methods. This article provides a comprehensive review of the developments, challenges, and innovative solutions in creating sensor-based electronic traps and applying deep learning for efficient and autonomous pest identification. By addressing the complexities of sensor integration, data collection, and the need for adaptive algorithms capable of classifying a wide range of insect pests, this review highlights the effective combination of electronic trap advancements with the precision offered by convolutional neural networks. An in-depth analysis of the technological advancements in electronic pest trap development is presented, highlighting improvements in design, efficiency, and sustainability while referring to ongoing and future challenges. Moreover, this article explores deep learning techniques, emphasizing on dataset enhancement and model optimization to overcome traditional challenges such as data scarcity and to improve the robustness of pest detection models. A thorough evaluation of various trap types against 85 unique pests is conducted, with the delta trap emerging as the most versatile, showcasing compatibility with multiple sensors and effectiveness against various pests. This review equips researchers, practitioners, and agricultural developers with critical insights and methodologies that can significantly enhance pest monitoring efficiency, reduce pesticide usage, and support sustainable agricultural practices.

Due to the rise in data-intensive applications, the von Neumann bottleneck is increasingly restricting modern computer architectures, resulting to latency and energy consumption. Addressing this challenge necessitates a CMOS-compatible solution with high energy efficiency and significant parallelism. Utilizing resistive switching components within a 1T1R crossbar array and the application of Stanford RRAM model, this paper suggests an original method for in-memory computing. Moreover, this work shows a new way to advance the popular RISC-V architecture by including memristive crossbar array. It does this by adding a custom instruction set, special hardware blocks, and the Scouting Logic Scheme. These modifications serve both as a comprehensive testbed for the memory system and a proof of concept for the future integration of memristors in computing architectures. The proposed design undergoes extensive testing and power analysis to validate its functionality and performance under various conditions. The results demonstrate significant improvements in computational efficiency and energy savings, highlighting the potential of memristor-based in-memory computing systems to overcome current architectural limitations.

Modern computer architectures currently face a memory bottleneck, causing higher latency and power consumption due to the rapid increase of data in applications. Despite this, widespread adoption makes replacing them challenging. Thus, a crucial need arises for a CMOS-compatible solution with high energy efficiency and substantial parallelism to address the von Neumann bottleneck. Herein, a promising solution is delivered through the concept of in-memory computing enabled by the utilisation of resistive switching devices using the Stanford RRAM Model, constituting a 1T1R crossbar array that meets all of the specified criteria while featuring optimal design density. This paper introduces a novel modification of the widely-adopted RISC-V architecture designed to operate the aforementioned memristive crossbar array using a custom instruction set and specialised hardware blocks, serving as both a comprehensive test bed for the memory system and a compelling proof of concept for the future integration of memristors in computer architectures. This novel design undergoes thorough testing to evaluate its robustness, incorporating an alternating AND operation to simulate the worst-case scenario.

Effective insect pest remote monitoring is critical for precision agriculture as it facilitates timely pest detection and management strategies. This study conducts a comparative anal-ysis of camera-based and sensor-based traps by evaluating their suitability for real-time monitoring and data collection in agricul-ture fields. By examining the strengths and limitations of each approach, this research highlights the potential of integrating camera-based systems with sensors and wireless communication technologies. These integrated traps, leveraging image processing, AI algorithms, and IoT technologies, contribute to sustainable agricultural practices by enabling targeted interventions and reducing the need for indiscriminate pesticide use. The study discusses the potential benefits of trap network development, providing cost-effectiveness and statistical efficacy, highlights the importance of advancing detection algorithms, and integrating data analytics for proactive and precise pest management in the context of precision agriculture.