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Progress in self-powered sensors—Moving toward artificial intelligent and neuromorphic system

Nano Research 2023年第9期16卷 11801-11821页

作者： Feng Wen Chan Wang Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Center for Intelligent Sensors and MEMS(CISM) National University of SingaporeSingapore 117608Singapore National University of Singapore Suzhou Research Institute(NUSRI) Suzhou 215123China NUS Graduate School-Integrative Sciences and Engineering Programme(ISEP) National University of SingaporeSingapore 119077Singapore

Wearable and flexible electronics are shaping our life with their unique advantages of light weight,good compliance,and desirable *** marching into the era of Internet of Things(IoT),numerous sensor nodes are distributed throughout networks to capture,process,and transmit diverse sensory information,which gives rise to the demand on self-powered sensors to reduce the power ***,the rapid development of artificial intelligence(AI)and fifth-generation(5G)technologies provides an opportunity to enable smart-decision making and instantaneous data transmission in IoT *** to continuously increased sensor and dataset number,conventional computing based on von Neumann architecture cannot meet the needs of brain-like high-efficient sensing and computing applications *** electronics,drawing inspiration from the human brain,provide an alternative approach for efficient and low-power-consumption information ***,this review presents the general technology roadmap of self-powered sensors with detail discussion on their diversified applications in healthcare,human machine interactions,smart homes,*** leveraging AI and virtual reality/augmented reality(VR/AR)techniques,the development of single sensors to intelligent integrated systems is reviewed in terms of step-by-step system integration and algorithm *** order to realize efficient sensing and computing,brain-inspired neuromorphic electronics are next briefly ***,it concludes and highlights both challenges and opportunities from the aspects of materials,minimization,integration,multimodal information fusion,and artificial sensory system.

关键词： wearable and flexible electronics self-powered sensors virtual reality/augmented reality(VR/AR)and artificial intelligence(AI)-enhanced system neuromorphic electronics

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Computational Investigation of Multiband EMNZ Metamaterial Absorber for Terahertz Applications

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computers, Materials & Continua 2022年第5期71卷 3905-3920页

作者： Ismail Hossain Md Samsuzzaman Mohd Hafiz Baharuddin Norsuzlin Binti Mohd Sahar Mandeep Singh Jit Singh Mohammad Tariqul Islam Space Science Center Universiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia Department of Computer and Communication Engineering Faculty of Computer Science and EngineeringPatuakhali Science and Technology UniversityBangladesh Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaMalaysia

This study presents an Epsilon Mu near-zero(EMNZ)nanostructured metamaterial absorber(NMMA)for visible regime *** resonator and dielectric layers are made of tungsten(W)and quartz(fused),where the working band is expanded by changing the resonator layer’s *** to perfect impedance matching with plasmonic resonance characteristics,the proposed NMMA structure is achieved an excellent absorption of 99.99%at 571 THz,99.50%at 488.26 THz,and 99.32%at 598 THz *** absorption mechanism is demonstrated by the theory of impedance,electric field,and power loss density distributions,*** geometric parameters are explored and analyzed to show the structure’s performance,and a near-field pattern is used to explain the absorption mechanism at the resonance frequency *** numerical analysis method describes that the proposed structure exhibited more than 80%absorbability between 550 and 900 *** computer Simulation Technology(CST Microwave Studio 2019)software is used to design the proposed ***,CSTHFSS interference is validated by the simulation data with the help of the finite element method(FEM).The proposed NMMA structure is also exhibits glucose concentration sensing capability as *** the proposed broadband absorber may have a potential application in THz sensing,imaging(MRI,thermal,color),solar energy harvesting,light modulators,and optoelectronic devices.

关键词： Metamaterial absorber terahertz applications multiband EM near zero nanostructured visible regime applications

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Connectivity in the era of the (I)IoT: about security, features and limiting factors of reconfigurable intelligent surfaces

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Discover Internet of Things 2023年第1期3卷 16页

作者： Lipps, Christoph Herbst, Jan Klingel, Steffen Franke, Lars Wolff, Alexander Rüb, Matthias Reddy, Rekha Rahm, Marco Schotten, Hans Dieter Intelligent Networks Research Group German Research Center for Artificial Intelligence Kaiserslautern67663 Germany Institute for Wireless Communication and Radio Positioning RPTU Kaiserslautern-Landau Kaiserslautern67663 Germany Department of Electrical and Computer Engineering Research Center OPTIMAS RPTU Kaiserslautern-Landau Kaiserslautern67663 Germany

What are the key drivers of the (Industrial) Internet of Things ((I)IoT)? Alongside the interconnection of the various devices, sensors, and actuators, it is above all the data collected and transmitted that is of particular significance, the (I)IoT itself is the source and recipient of various types of data. Within this, there are two main aspects that need to be considered: (i) the transmission of data with appropriate latency and reliability requirements, and (ii) the safeguarding of this data. Both requirements are taken into account during the development of the Sixth Generation (6G) Wireless systems. This is conceived to deliver higher data rates, reduced cost deployment, and enhanced reliability while providing universal connectivity. Hence, to assist such a vast network domain, (new) 6G-enabling technologies are developed, which include Reconfigurable intelligent Surfaces (RISs), that are capable of modifying and reconfiguring the wireless propagation channels. This work describes concepts on how security, in particular Physical Layer Security (PhySec) methods, can be supported by RISs. Furthermore, an effective impedance surface model is presented, allowing efficient simulation of beamsplitting and shaping properties of RISs. Finally, the applicability of the use cases is mentioned, with a particular focus on Visible Light Communication (VLC). © The Author(s) 2023.

关键词： Internet of things

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Performance of AI-Empowered Anti-Jamming Communications under Hardware Impairments

Performance of AI-Empowered Anti-Jamming Communications unde...

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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Arif, Muhammad Shahzad Muhaidat, Sami Argyriou, Antonios Sofotasios, Paschalis C. Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Khalifa University KU 6G Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering Ottawa Canada University of Thessaly Department of Electrical and Computer Engineering Volos Greece Khalifa University KU C2PS and 6G Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Tampere University Department of Electrical Engineering Tampere Finland

ISBN: (纸本)9798350376715

The present contribution quantifies the effect of Hardware Imperfections (HWI) on Artificial Intelligence (AI)empowered anti-jamming wireless communication scenarios. To that end, we consider an AI-empowered wireless link that employs reinforcement learning (RL) to counteract reactive jamming attacks. It is shown that contrary to the common belief that HWI always degrade the performance of wireless systems, our findings reveal that imperfections in the jammer's spectrum sensing capability can actually enhance its effectiveness against AI-empowered anti-jamming wireless communications. Extensive simulations demonstrate that a non-ideal reactive jammer with detection errors (e.g., false alarms and miss-detections) can perform significantly better than an ideal reactive jammer while using the same jamming resources. This improvement is attributed to the randomness induced in jamming patterns due to sensing errors, which distorts the learning process of the antijamming AI agent resulting in a considerable reduction in the anti-jamming capability of the AI-empowered wireless link. © 2024 IEEE.

关键词： Jamming

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Automated Access Control via License Plate Recognition using Neocognitron Neural Network 36

Automated Access Control via License Plate Recognition using...

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36th International Conference on Information Technologies, InfoTech 2022

作者： Rothkrantz, Leon Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Department of Intelligent Systems Delft2628 CD Netherlands

ISBN: (数字)9781665468701

ISBN: (纸本)9781665468701

In 1979 Fukushima developed a hierarchical, multilayered neural network called Neocognitron and used it for the automatic recognition of handwritten Japanese symbols. We combined the Neocognitron classifier with a special image and segment processor and applied the system in 2001 for automatic recognition of license plates in laboratory experiments. In this paper we report about a special image acquisition module and a postprocessor. We tested the system in real life conditions in an application of automated access control. © 2022 IEEE.

关键词： Image segmentation

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Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 1014-1030页

作者： Zaipeng Xie Cheng Ji Chentai Qiao WenZhan Song Zewen Li Yufeng Zhang Yujing Zhang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjingChina College of Computer and Information Hohai UniversityNanjingChina Center for Cyber‐Physical Systems University of GeorgiaAthensGeorgiaUSA Information Networking Institute Carnegie Mellon UniversityPittsburghPennsylvaniaUSA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPennsylvaniaUSA

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual ***,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent *** tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration *** rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment *** addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent *** was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty *** experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

关键词： artificial intelligence techniques decision making intelligent multi‐agent systems

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Enhancing Pothole Detection and Characterization: Integrated Segmentation and Depth Estimation in Road Anomaly systems

arXiv

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arXiv 2025年

作者： Baroudi, Uthman BaHamid, Alala Elalfy, Yasser Alami, Ziad Al Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia

Road anomaly detection plays a crucial role in road maintenance and in enhancing the safety of both drivers and vehicles. Recent machine learning approaches for road anomaly detection have overcome the tedious and time-consuming process of manual analysis and anomaly counting;however, they often fall short in providing a complete characterization of road potholes. In this paper, we leverage transfer learning by adopting a pre-trained YOLOv8-seg model for the automatic characterization of potholes using digital images captured from a dashboard-mounted camera. Our work includes the creation of a novel dataset, comprising both images and their corresponding depth maps, collected from diverse road environments in Al-Khobar city and the KFUPM campus in Saudi Arabia. Our approach performs pothole detection and segmentation to precisely localize potholes and calculate their area. Subsequently, the segmented image is merged with its depth map to extract detailed depth information about the potholes. This integration of segmentation and depth data offers a more comprehensive characterization compared to previous deep learning-based road anomaly detection systems. Overall, this method not only has the potential to significantly enhance autonomous vehicle navigation by improving the detection and characterization of road hazards but also assists road maintenance authorities in responding more effectively to road damage. © 2025, CC BY.

关键词： Image segmentation

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Fuzzy Controller-Assisted Obstacle Avoidance System for Visual Impairment

Fuzzy Controller-Assisted Obstacle Avoidance System for Visu...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Bougheloum, Loubna Salah, Mounir Bousbia Bettayeb, Maamar BADJI Mokhtar Annaba University LASA Laboratory Annaba23000 Algeria University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates King Abdulaziz University Center of Excellence in Intelligent Engineering Systems Jeddah21589 Saudi Arabia

ISBN: (纸本)9798350369106

Navigating safely and independently through dynamic environments is a crucial concern for the visually impaired community. This paper presents an innovative approach for addressing the obstacle avoidance challenges faced by visually impaired individuals using fuzzy logic-based navigation integrated with audio feedback. This system combines fuzzy logic control with dual of distance laser sensors to enable real-time obstacle detection. The dual sensors setup enhance accuracy in obstacle localization, while fuzzy logic provides adaptive decision-making capabilities. The system's audio response mechanism conveys obstacle proximity information audibly, ensuring effective communication with the user. The system's efficacy is validated through simulations and several testing, affirming its potential to enhance obstacle detection and foster confident navigation for the visually impaired. This work contributes to advancing assistive technologies for visually impaired individuals, improving their mobility, and fostering their autonomy. © 2024 IEEE.

关键词： Obstacle detectors

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Assessing adversarial replay and deep learning-driven attacks on specific emitter identification-based security approaches

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Discover Internet of Things 2024年第1期4卷 25页

作者： Tyler, Joshua H. Fadul, Mohamed K. M. Hilling, Matthew R. Reising, Donald R. Loveless, T. Daniel Electrical Engineering Department The University of Tennessee at Chattanooga 735 Vine Street ChattanoogaTN37403 United States Department of Intelligent Systems Engineering Indiana University 700 N. Woodlawn Avenue BloomingtonIN47408 United States Department of Computer Science Tennessee Technological University 1 Williams L. Jones Drive CookevilleTN38505 United States

Specific Emitter Identification (SEI) has been put forward as an Internet of Things (IoT) Physical Layer Security (PLS) approach for its abilities to detect, characterize, and identify wireless emitters by exploiting distinct, inherent, and unintentional features in their transmitted signals. Since its introduction, a significant amount of work has been conducted;however, most assume the emitters are passive and their identifying signal features are immutable and challenging to mimic. This suggests the emitters are reluctant and incapable of developing and implementing effective SEI countermeasures;however, Deep Learning (DL) has been shown capable of learning emitter-specific features directly from their raw in-phase and quadrature signal samples, while Software-Defined Radios (SDRs) are capable of manipulating these signal samples. Based on these capabilities, it is fair to question the ease at which an emitter can effectively mimic the SEI features of another or manipulate its own to hinder or defeat SEI. This work considers SEI mimicry using three signal features mimicking countermeasures;"off-the-self" DL;two SDRs of different sizes, weights, power, and cost (SWaP-C);handcrafted and DL-based SEI processes, and assessment of an SEI mimicry countermeasure in a "coffee shop" deployment. Our results show "off-the-shelf" DL algorithms, and SDR enables SEI mimicry;however, adversary success is hindered by: (i) the use of decoy emitter preambles, (ii) the use of a denoising autoencoder and (iii) SDR SWaP-C constraints. © The Author(s) 2024.

关键词： Deep learning

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Learning to see speckle in the weak laser field through multimode fiber

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Optoelectronics Letters 2025年

作者： JI Yunqi SONG Binbin LI Xueqing LI Yonghui The Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)， the Key Laboratory of Computer Vision and Systems (Ministry of Education)， and the School of Computer Science and Engineering， Tianjin University of Technology

Multimode fibers (MMFs) have great potential for endoscopic imaging due to the high number of modes and a small core diameter. Deep learning based on neural networks has received increasing attention in the field of scattering image reconstruction. However， most studies focus on designing complex network architectures to improve reconstruction， but these network models struggle to reconstruct images in a weak laser field. In the paper， a lightweight generative adversarial network model combined with a histogram specification algorithm is designed to reconstruct speckles in the weak laser field through MMF. Experimental results show that the reconstruction results of our algorithm have better metrics. Moreover， the model demonstrates excellent cross-domain generalization ability with regards to the Fashion-MNIST dataset. It is worth mentioning that we found that the speckles after inactivation still retain the ability to be reconstructed， which enhances the robustness of the model

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