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Energy Efficient Green Routing for UAVs Ad-Hoc Network

Intelligent Automation & Soft Computing 2023年第7期37卷 1111-1127页

作者： M.Muthukumar Rajasekar Rangasamy Irshad Hussain Salman A.AlQahtani Department of Computer Science and Engineering Sree Sowdambika College of EngineeringTamil NaduIndia Department of Computer Science and Engineering-AI&ML School of EngineeringMalla Reddy UniversityHyderabadIndia Faculty of Electrical and Computer Engineering University of Engineering and Technology Peshawar25000PeshawarPakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia

The purpose of this article is to propose Stability-based Energy-Efficient Link-State Hybrid Routing(S-ELHR),a low latency routing proto-col that aims to provide a stable mechanism for routing in unmanned aerial vehicles(UAV).The S-ELHR protocol selects a number of network nodes to create a Connected Dominating Set(CDS)using a parameter known as the Stability Metric(SM).The SM considers the node’s energy usage,connectivity time,and node’s *** the highest SM nodes are chosen to form *** node declares a Willingness to indicate that it is prepared to serve as a relay for its neighbors,by employing its own energy state.S-ELHR is a hybrid protocol that stores only partial topological information and routing tables on CDS *** of relying on the routing information at each intermediary node,it uses source routing,in which a route is generated on-demand,and data packets contain the addresses of the nodes the packet will transit.A route recovery technique is additionally utilized,which first locates a new route to the destination before forwarding packets along *** simulation for various network sizes and mobility speeds,the efficiency of S-ELHR is *** findings demonstrate that S-ELHR performs better than Optimized Link State Routing(OLSR)and Energy Enhanced OLSR(EE-OLSR)in terms of packet delivery ratio,end-to-end delay,and energy consumption.

关键词： Connected dominatingset hybridrouting UAVAdhocnetworks link state stability

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A Highly-Scalable Area-Efficient ADC-Direct Neural Recording Front-End with Proportional-Integral Single-Bit Feedback

A Highly-Scalable Area-Efficient ADC-Direct Neural Recording...

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Sayedi, Mina Kassiri, Hossein York University Department of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350300260

This paper presents the design and implementation of a DC-coupled continuous-time-input ADC-direct neural recording front-end architecture. The architecture incorporates a single-bit variable-step DAC to create a proportional-integral feedback path. This makes the design needless of conventionally-used area-inefficient nonlinear high-resolution multi-bit DACs for realizing 2nd-order noise shaping, significantly improving its area efficiency, linearity, and scalability. Additionally, thanks to employing a differential-difference gm stage, the feedback is subtracted from the inputs without the need for connection to the same node, allowing for the realization of a high-ZIN DC-coupled chopping-friendly input structure. The front-end achieves a maximum SNDR of 58dB and tolerates input differential DC offsets/artifacts up to ±350mV. The design is realized using a 130nm standard CMOS technology, occupies 0.021mm2, and consumes 5.6μW. It is verified through system and circuit-level simulations, and boasts a >800MΩ differential input impedance over frequency band up to 500Hz, and an IRN-PSD of 161nV Hz, all while maintaining a minimum use of passives in its structure. © 2023 IEEE.

关键词： ADC-Direct area efficient artifact tolerant brain-implantable devices DC offset tolerant DC-coupled input low-power Neural Recording Proportional-Integral Feedback scalable Variable Step DAC

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Neural Reactivity to Haptics: Virtual Tasks Versus Physical Tasks

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IEEE Sensors Journal 2024年第7期24卷 11817-11828页

作者： Liu, Yi-Hung Vaitheeshwari, R. Yeh, Shih-Ching Wu, Eric Hsiao-Kuang Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan

This study investigates the effectiveness of haptic feedback in hand rehabilitation exercises, within both virtual reality (VR) and real-world settings, to enhance upper limb functionality in post-stroke recovery. We developed a VR system that incorporates haptic feedback into pinch simulations and compared its efficacy with similar real-world pinch training exercises, primarily focusing on the rehabilitation of the paretic hand. Our main objective was to ascertain the similarity of the simulated haptic feedback in the VR environment to actual tactile feedback in the real world. This was achieved by analyzing electroencephalogram (EEG) features and psychometric data collected from 35 healthy subjects. A key aspect of our approach was the use of machine learning (ML) techniques to classify the EEG signal features as originating from VR or Real tasks. This classification was based on data from the top-n electrode channels, enabling us to assess the degree of similarity in brain responses to tactile stimulation in both environments. The study's results revealed that the brain activity patterns elicited by haptic feedback were comparable in both the VR and real-world setups. Additionally, the VR feedback system was positively received by users, scoring high on experience and satisfaction metrics. This research provides a replicable methodology for assessing tactile feedback in existing rehabilitation systems and lays the groundwork for the development of novel therapeutic applications. Our findings are crucial for designing future rehabilitation interventions that effectively utilize haptic feedback, potentially leading to improved treatment outcomes for stroke patients. © 2001-2012 IEEE.

关键词： Electroencephalography

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Classification of Sleep Stages Using Single-Channel ECG Signals: A Comparative Analysis of Machine Learning and Deep Learning Methods 14

Classification of Sleep Stages Using Single-Channel ECG Sign...

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14th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2024

作者： Ullah, Ahmad Khan, Muhammad Salman College of Engineering Qatar University Department of Computer Science and Engineering Doha Qatar College of Engineering Qatar University Department of Electrical Engineering Doha Qatar

ISBN: (纸本)9798350366556

In this paper, we provide an extensive evaluation of machine learning (ML) and deep learning (DL) methods for automatic sleep stage classification using a single-channel electrocardiogram (ECG) signal. To explore ML methods, we extracted 21 heart rate variability (HRV) features using the R-peak detection. These features were used to train and compare the performance of various models such as support vector machine (SVM), decision tree (DT), random forest (RF), k-nearest neighbor (K-NN), and Naïve Bayes (NB). Furthermore, we analyzed the DL methods to overcome the manual feature extraction. This method involved feeding raw time-series ECG signal to a 1-dimensional convolutional neural network (CNN) and short-time Fourier transform (STFT) based spectrogram to a 2D CNN. The overall evaluation was performed using 15-subject data each from two publicly available datasets. To process the data for analysis, resampling, bandpass filtering, baseline correction, 30-second interval extraction from whole-night ECG recordings, and z-score normalization techniques were applied. We conducted experiments on one dataset and then replicated the experiments on the other dataset to assess the robustness and generalizability of the methods. Through a comparative analysis, the spectrogram-based 2D CNN emerged as the effective method with the highest test accuracy and k-score of 87.65% and 0.82 respectively. This study contributes to ECG-based sleep classification research, highlighting the effectiveness of deep learning for effective classification. © 2024 IEEE.

关键词： Convolutional neural networks

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A review on simulation models of cascading failures in power systems

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iEnergy 2023年第4期2卷 284-296页

作者： Zhenping Guo Kai Sun Xiaowen Su Srdjan Simunovic Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTNUSA Department of Electrical and Computer Engineering University of Texas at San AntonioSan AntonioTXUSA Computational Sciences&Engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA

Among various power system disturbances,cascading failures are considered the most serious and extreme threats to grid operations,potentially leading to significant stability issues or even widespread power *** power systems’behaviors during cascading failures is of great importance to comprehend how failures originate and propagate,as well as to develop effective preventive and mitigative control *** intricate mechanism of cascading failures,characterized by multi-timescale dynamics,presents exceptional challenges for their *** paper provides a comprehensive review of simulation models for cascading failures,providing a systematic categorization and a comparison of these *** challenges and potential research directions for the future are also discussed.

关键词： Cascading failures power systems security and reliability simulation models

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Quantifying Uncertainty in Machine Learning Based Soil Moisture Retrieval From GNSS-R Measurements 25

Quantifying Uncertainty in Machine Learning Based Soil Moist...

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25th International Conference on Electromagnetics in Advanced Applications, ICEAA 2024

作者： Tsagkatakis, G. Melebari, A. Campbell, J.D. Hodges, E. Moghaddam, M. Institute of Computer Science FORTH Greece Computer Science Department University of Crete 73100 Greece Department of Electrical and Computer Engineering University of Southern California CA90007 United States

ISBN: (纸本)9798350360974

Spaceborne platforms like the NASA SMAP and the ESA SMOS offer global-scale coarse-resolution observations that can be used to estimate surface soil moisture. However, they acquire observations at a moderate revisit frequency, reducing the impact on applications that require higher temporal resolutions. Global navigation satellite system reflectometry (GNSS-R) is a prime example of a signal-of-opportunity (SoOP) which has been shown to be highly sensitive to variations in soil moisture. In this work, we consider measurements from the NASA Cyclone GNSS (CYGNSS) mission which consists of eight low-orbit observatories, that are equipped with two nadir-looking antennas to receive reflected GPS signals. To extract soil moisture content, the traditional approach involves inverting parameterized forward models which account for aspects like dielectric properties of the soil, topography, and incidence angle information among others. Despite their potential, these models typically involve simplified assumptions and demonstrate increased sensitivity to the values of different parameters. To address this challenge, a new line of research tries to address this challenge by utilizing machine learning models and treating the problem as that of supervised regression. © 2024 IEEE.

关键词： NASA

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AI-Driven Energy Optimization in UAV-Assisted Routing for Enhanced Wireless Sensor Networks Performance

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computers, Materials & Continua 2024年第9期80卷 4085-4110页

作者： Syed Kamran Haider Abbas Ahmed Noman Mujeeb Khan Ali Nauman Sung Won Kim College of Internet of Things(IoT)Engineering Hohai UniversityChangzhou213001China Department of Electrical and Electronics Engineering Beaconhouse International CollegeIslamabad44000Pakistan School of Computer Science and Engineering Yeungnam UniversityGyeongsan38428Republic of Korea

In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network *** research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in *** proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path *** a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication *** comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced ***,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.

关键词： UAV trajectory clustering next-generation wireless sensor network(NGWSN) energy efficiency mobile sink

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An Efficient Incremental Learning Bioinspired Model for Improving QoS of Blockchain-Based Large-Scale Wireless Networks 3

An Efficient Incremental Learning Bioinspired Model for Impr...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Kadu, Renuka Kadu, Akshay Yeshwantrao Chavan College of Engineering Department of Computer Science and Engineering Nagpur India Yeshwantrao Chavan College of Engineering Department of Electrical Engineering Nagpur India

ISBN: (纸本)9798350381931

Providing Quality of Service (QoS) under a variety of network conditions and security threats has become more difficult as the demand for large-scale wireless networks based on blockchain technology has increased. We address these issues and significantly raise the overall QoS of blockchain-based networks in this paper by presenting an effective incremental learning bioinspired model This work is necessary because large-scale wireless networks must significantly improve their energy efficiency, throughput, delay reduction, and packet delivery ratio. Existing models frequently have difficulty meeting these demands and reducing the effects of different attacks, including Finney, Distributed Denial of Service (DDoS), Man-in-the-Middle (MITM), Sybil, and Masquerading scenarios. We suggest a novel strategy that combines two optimization techniques to get around these restrictions. In order to create sidechains, we first use Grey Wolf Optimization (GWO), which improves network partitioning and scalability in blockchain-based networks. Our model efficiently distributes the computational load and boosts system performance by dynamically adjusting the sidechain formation. In order to choose the best miner nodes for data mining between network nodes, we integrate Q Learning in the second step. The Q Learning algorithm makes intelligent decisions about the best miner nodes by taking into consideration parameters like throughput, latency, and energy efficiency. This deft choice of miner nodes improves network performance and QoS overall while optimizing data mining process. Through a thorough examination of spatial and temporal parameters, consensus is attained, allowing the system to assess the accuracy and dependability of mined blocks. This guarantees the blockchain network's integrity and security. Results from experiments show how effective our suggested model is. Our model improves energy efficiency by 8.5 percent, delays are cut by 10.4 percent, throughput is increased b

关键词： Blockchain

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Design of electric powertrains to achieve NVH performance using autoencoders and a physical meaningful latent space

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Neural Computing and Applications 2025年第16期37卷 10287-10308页

作者： Ricardo Souza, Marcos Offner, Guenter Soltoggio, Andrea Mohammadpour, Mahdi Theodossiades, Stephanos Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough United Kingdom AVL List GmbH Graz Austria Computer Science Department School of Science Loughborough University Loughborough United Kingdom

One of the fundamental differences in the perception of electric (e-) vehicles is how their radiated noise is perceived with respect to classic internal combustion engines. Even though e-vehicles are usually quieter, the tonal content of the radiated noise can be more annoying. This paper proposes a novel approach that starts from the assumed radiated noise spectrum profile as input to a neural network that can return powertrain design parameters that would lead to generation of that specific noise profile. The proposed network acts as an autoencoder where the latent space is forced to have a physical meaning. As diverse combinations of powertrain parameters can result in similar noise profiles, a variational autoencoder is used to learn a structured latent representation, ensuring continuity and smooth transitions between possible solutions. The network predictions are validated against results of a three-dimensional CAE e-powertrain model. Overall, the mean absolute error is around 5 dBA for this feasibility study, which aims to demonstrate the concept. This work takes an inverse approach to the optimisation problem by starting from the user-perceived noise to predict the parameters required to achieve that. Although this study focuses solely on gear teeth microgeometry changes and bearing preloads, additional powertrain parameters could be incorporated as needed. © The Author(s) 2025.

关键词： Internal combustion engines

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A Unified IoT-based Smart Home System: Integration of Security and Convenience Features for Consumer 3

A Unified IoT-based Smart Home System: Integration of Securi...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Oeishee, Moslema Hoque Ahammad Muyeed, Munyem Alvi, Mishkat Mehedi, Rejoan Shariar Emon Shaikat, M. Molla, Md Nayan Brac University Electrical and Electronic Engineering Department Dhaka Bangladesh United International University Computer Science and Engineering Department Dhaka Bangladesh Brac University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798331523893

Home automation is growing rapidly in the Fourth Industrial Revolution (4IR), providing users with unwavering convenience and enhanced security. This paper presents a comprehensive Internet of Things (IoT) smart home automation solution with a focus on energy management, user-friendliness, and security. Our system includes real-time integrated dashboards accessible via web and mobile interfaces, smart irrigation for indoor gardening, passkeys for door security, voice commands, fingerprint access, night security lighting, and morning alarms. The hardware consists of an Arduino MEGA microcontroller, various sensors (smoke, temperature, humidity, and soil), an ESP Wi-Fi module for connectivity, and additional components. The system demonstrated a satisfactory effectiveness level with an accuracy of 78% based on various tests. The voice assistant bot and accessible dashboard significantly improve user experience. Despite the complexity of home automation processes, increased affordability and accessibility are the aims of this paper. Future work will focus on enhancing real-time capabilities, integrated circuits, data security, and web-based functionalities to improve system performance, scalability, and user accessibility. © 2025 IEEE.

关键词： Smart homes

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