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.

Nowadays we witness a miniaturisation trend in the semiconductor industry backed up by groundbreaking discoveries and designs in nanoscale characterisation and fabrication. To facilitate the trend and produce ever smaller, faster and cheaper computing devices, the size of nanoelectronic devices is now reaching the scale of atoms or molecules - a technical goal undoubtedly demanding for novel devices. Following the trend, we explore an unconventional route of implementing reservoir computing on a single protein molecule and introduce neuromorphic connectivity with a small-world networking property. We have chosen Izhikevich spiking neurons as elementary processors, corresponding to the atoms of verotoxin protein, and its molecule as a `hardware' architecture of the communication networks connecting the processors. We apply on a single readout layer, various training methods in a supervised fashion to investigate whether the molecular structured Reservoir Computing (RC) system is capable to deal with machine learning benchmarks. We start with the Remote Supervised Method, based on Spike-Timing-Dependent-Plasticity, and carry on with linear regression and scaled conjugate gradient back-propagation training methods. The RC network is evaluated as a proof-of-concept on the handwritten digit images from the standard MNIST and the extended MNIST datasets and demonstrates acceptable classification accuracies in comparison with other similar approaches.

Synchronization transition in neuromorphic networks has attracted much attention recently as a fundamental property of biological neural networks, which relies on network connectivity along with different synaptic features. In this work, an area-optimized FPGA implementation of an Asynchronous Cellular Automata Neuron model that exhibits discrete-state neuron dynamics is introduced. The proposed neuron model is capable of reproducing various neuromorphic oscillations observed in biological neurons using less hardware resources than previous implementations. We investigate synchronization transitions with a focus on the emergence of chimera states in a ring-based network consisting of hardware-based neurons with electrical synaptic coupling. In particular, we study the effects on the network’s phase synchronization through changing two control parameters: the coupling range and the coupling strength. We indicate that via proper configuration of the coupling parameters, we influence the synchronization transition and reveal chimera states which have been associated with neurological disorders.

Computing demands are growing rapidly as bigdata and artificial intelligence applications become increasingly tasking. Bio-inspired and quantum-based techniques are proving to be quite promising for the development of novel circuits and systems. These systems can contribute to the resolution of a wider variety of problems while also providing improvements to existing techniques. As the von Neumann architecture’s expected performance, which has been dominant for the past several decades, is now hindered by physical limitations, novel computing architectures, assisted by novel materials and circuit devices, are starting to emerge and provide promising results. The topic of this work is to examine the memory and computing capabilities of emergent memristor-based nanocircuits and demonstrate their advantages compared to their classical counterparts.

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.

One of the most enticing candidates for next-generation computing systems is the memristor. Memristor-based novel architectures have demonstrated considerable promise in replacing or augmenting traditional computing platforms based on the Von Neumann architecture, which faces many issues in the big-data era, as well as in newly developed neuromorphic tasks. Although the current classical computing architecture is unlikely to be abandoned in the foreseeable future, the growing trend of neuromorphic, quantum, and bio-inspired computing schemes calls for more specialized beyond Von Neumann platforms. Memristors showcase multiple advantages in terms of small area footprint, energy efficiency, high endurance, bio-compatibility, and their inherent synaptic and neuromorphic behavior. The topic of this work is to present the memristive devices that meet the requirements for the implementation of the novel beyond Von Neumann applications and examine their switching mechanism and material selection, as well as to conduct a performance comparison between the fabricated devices paving the way for future computing applications.