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Closed-form solution for scaling a wireless acoustic sensor network

IET Wireless Sensor Systems 2023年第5期13卷 190-200页

作者： Patel, Kashyap Kovalyov, Anton Panahi, Issa Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX United States

A closed-form solution for localising and synchronising an acoustic sensor node with respect to a Wireless Acoustic Sensor Network (WASN) is presented. The aim is to allow efficient scaling of a WASN by individually calibrating newly joined sensor nodes instead of recalibrating the entire array. A key contribution is that the sensor to be calibrated does not need to include a built-in emitter. The proposed method uses signals emitted from spatially distributed sources to compute time difference of arrival (TDOA) measurements between the existing WASN and a new sensor. The problem is then modelled as a set of multivariate non-linear TDOA equations. Through a simple transformation, the non-linear TDOA equations are converted into a system of linear equations. Then, weighted least squares is applied to find an accurate estimate of the calibration parameters. Signal sources can either be known emitters within the existing WASN or arbitrary sources in the environment, thus allowing for flexible applicability in both active and passive calibration scenarios. Simulation results under various conditions show high joint localisation and synchronisation performance, often compared to the Cramér-Rao lower bound. © 2023 The Authors. IET Wireless Sensor Systems published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Calibration

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Multi-Agent Double Deep Q-Learning for Fairness in Multiple-Access Underlay Cognitive Radio Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 580-595页

作者： Ali, Zain Rezki, Zouheir Sadjadpour, Hamid Electrical and Computer Engineering Department Baskin School of Engineering University of California at Santa Cruz Santa CruzCA95064 United States

Underlay Cognitive Radio (CR) systems were introduced to resolve the issue of spectrum scarcity in wireless communication. In CR systems, an unlicensed Secondary Transmitter (ST) shares the channel with a licensed Primary Transmitter (PT). Spectral efficiency of the CR systems can be further increased if multiple STs share the same channel. In underlay CR systems, the STs are required to keep interference at a low level to avoid outage at the primary system. The restriction on interference in underlay CR prevents some STs from transmitting while other STs may achieve high data rates, thus making the underlay CR network unfair. In this work, we consider the problem of achieving fairness in the rates of the STs. The considered optimization problem is non-convex in nature. The conventional iteration-based optimizers are time-consuming and may not converge when the considered problem is non-convex. To deal with the problem, we propose a deep-Q reinforcement learning (DQ-RL) framework that employs two separate deep neural networks for the computation and estimation of the Q-values which provides a fast solution and is robust to channel dynamic. The proposed technique achieves near optimal values of fairness while offering primary outage probability of less than 4%. Further, increasing the number of STs results in a linear increase in the computational complexity of the proposed framework. A comparison of several variants of the proposed scheme with the optimal solution is also presented. Finally, we present a novel cumulative reward framework and discuss how the combined-reward approach improves the performance of the communication system. ©2024 The Authors.

关键词： Cognitive radio

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Enhancing cyber threat detection with an improved artificial neural network model

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Data Science and Management 2025年第1期8卷 107-115页

作者： Oyinloye, Toluwase Sunday Arowolo, Micheal Olaolu Prasad, Rajesh Department of Computer Science African University of Science and Technology Abuja900107 Nigeria Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO65201 United States

Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems (IDS). Data labeling difficulties, incorrect conclusions, and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity. To overcome these obstacles, researchers have created several network IDS models, such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques. This study provides an updated learning strategy for artificial neural network (ANN) to address data categorization problems caused by unbalanced data. Compared to traditional approaches, the augmented ANN's 92% accuracy is a significant improvement owing to the network's increased resilience to disturbances and computational complexity, brought about by the addition of a random weight and standard scaler. Considering the ever-evolving nature of cybersecurity threats, this study introduces a revolutionary intrusion detection method. © 2024 Xi'an Jiaotong University

关键词： Network intrusion

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Classification of X-Ray Images for the Automated Severity Grading of Knee Osteoarthritis by Ensemble Learning Through EfficientNet Architectures with Grad-CAM Visualization 2

Classification of X-Ray Images for the Automated Severity Gr...

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2nd International Conference on Power, electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Dipto, Sajid Fardin Goni, Md. Omaer Faruq Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331517984

Knee osteoarthritis (KOA) is a degenerative form of arthritis in the knee joints commonly occurring in old-aged individuals which can cause extreme pain, deterioration of bone joints, joint membranes or ligaments, and irregularity in the knee joints. Being affected by KOA, individuals experience limited social interaction and diminished quality of life. The KOA can be assessed using the Kellgren-Lawrence (KL) grading method. In this study, an ensemble model was developed to predict a KL grading reliably and precisely. Several EfficientNet architectures were implemented for the ensembling of this study. Among them, an ensemble of EfficientNetB0 and EfficientNetB4 pre-trained models gave satisfactory results compared to other deep learning or ensembling models. Here, the proposed model underwent evaluation utilizing the Osteoarthritis Initiative (OAI) dataset which contains X-ray radiographs. The dataset was split into training, testing, and validation sets with percentages of 75%, 17%, and 8% respectively. The overall multiclass classification accuracy was 96.03% with an F1-score of 93%, specificity of 98.56%, and recall of 93%. The proposed model achieved an optimistic result compared to the transfer learning models and state-of-the-art models. Gradient-Weighted Class Activation Mapping was also applied for the radiographs to implement Explainable AI for generating visual explanations for the predictions made by the deep learning networks which revealed the model's potential for grading the severity of KOA. © 2024 IEEE.

关键词： X ray radiography

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An Ant-colony Based Model for Load Balancing in Fog Environments

Supercomputing Frontiers and Innovations

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Supercomputing Frontiers and Innovations 2023年第1期10卷 4-20页

作者： Mirtaheri, Seyedeh Leili Azari, Mahya Greco, Sergio Arianian, Ehsan Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Faculty of Mathematics and Computer Science Kharazmi University Tehran Iran Department of Computer Engineering Modeling Electronics and Systems University of Calabria Italy Tehran Iran

Delay-sensitive applications are becoming more and more in demand as a result of the development of information systems and the expansion of communication in cloud computing technologies. Some of these requests will be overlooked in cloud environments due to the communication delay between the processing center and the client’s request. The ‘fog-based computing paradigm’, a novel processing model, can be added to cloud computing to help with the aforementioned issues. The performance of computing systems is always influenced by latency. In this paper, we focus on balancing the load on the fog nodes to lower the latency. It is also a crucial component of fog computing devices. It necessitates the use of load-balancing algorithms to select the optimal hosts, resulting in an even distribution of the load on the available *** provide a load-balancing approach based on the Ant-colony optimization algorithm’s latency rate for responding to tasks. A random data set evaluation of this model reveals shorter response times than those of earlier strategies suggested in this field. © The Authors 2023. This paper is published with open access at ***

关键词： Fog

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AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference with Transformers

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第11期42卷 4038-4051页

作者： Tuli, Shikhar Jha, Niraj K. Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

Self-attention-based transformer models have achieved tremendous success in the domain of natural language processing. Despite their efficacy, accelerating the transformer is challenging due to its quadratic computational complexity and large activation sizes. Existing transformer accelerators attempt to prune its tokens to reduce memory access, albeit with high compute overheads. Moreover, previous works directly operate on large matrices involved in the attention operation, which limits hardware utilization. In order to address these challenges, this work proposes a novel dynamic inference scheme, DynaTran, which prunes activations at runtime with low overhead, substantially reducing the number of ineffectual operations. This improves the throughput of transformer inference. We further propose tiling the matrices in transformer operations along with diverse dataflows to improve data reuse, thus, enabling higher energy efficiency. To effectively implement these methods, we propose AccelTran, a novel accelerator architecture for transformers. Extensive experiments with different models and benchmarks demonstrate that DynaTran achieves higher accuracy than the state-of-the-art top- k hardware-aware pruning strategy while attaining up to 1.2× higher sparsity. One of our proposed accelerators, AccelTran-Edge, achieves 330K × higher throughput with 93K × lower energy requirement when compared to a Raspberry Pi device. On the other hand, AccelTran-Server achieves 5.73× higher throughput and 3.69× lower energy consumption compared to the state-of-the-art transformer co-processor, Energon. The simulation source code is available at https://***/jha-lab/acceltran. © 1982-2012 IEEE.

关键词： Neural networks

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Nanofabricated high turn-density spiral coils for on-chip electromagneto-optical conversion

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Microsystems & Nanoengineering 2024年第2期10卷 181-192页

作者： Ilhan Bok Alireza Ashtiani Yash Gokhale Jack Phillips Tianxiang Zhu Aviad Hai Department of Biomedical Engineering University of Wisconsin–MadisonMadisonWIUSA Department of Electrical and Computer Engineering University of Wisconsin–MadisonMadisonWIUSA Wisconsin Institute for Translational Neuroengineering(WITNe) University of Wisconsin–MadisonMadisonWIUSA

Circuit-integrated electromagnets are fundamental building blocks for on-chip signal transduction,modulation,and tunability,with specific applications in environmental and biomedical micromagnetometry.A primary challenge for improving performance is pushing quality limitations while minimizing size and fabrication complexity and retaining spatial *** efforts have exploited highly involved three-dimensional synthesis,advanced insulation,and exotic material ***,we present a rapid nanofabrication process that employs electron beam dose control for high-turn-density diamond-embedded flat spiral coils;these coils achieve efficient on-chip electromagnetic-to-optical signal *** fabrication process relies on fast 12.3 s direct writing on standard poly(methyl methacrylate)as a basis for the metal lift-off *** with 70 micrometer overall diameters and 49–470 nm interturn spacings with corresponding inductances of 12.3–12.8 nH are *** utilize optical micromagnetometry to demonstrate that magnetic field generation at the center of the structure effectively correlates with finite element modeling *** designs based on our process can be integrated with photolithography to broadly enable optical magnetic sensing and spin-based computation.

关键词： utilize retaining high

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Impact of the Effect Term on Convergence Speed in Multi-Agent Systems 10

Impact of the Effect Term on Convergence Speed in Multi-Agen...

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10th International Conference on Control, Instrumentation and Automation, ICCIA 2024

作者： Mehra, Amir Hossein Amiri Shafieirad, Mohsen Zamani, Iman Abbasi, Zohreh University of Kashan Department of Electrical and Computer Engineering Kashan Iran Shahed University Electrical and Electronic Engineering Department Tehran Iran

ISBN: (纸本)9798331509972

The primary objective of this study is to analyze the speed at which the system reaches consensus in multi-Agent systems (MASs) when the effect term (ET) is either present or absent. By examining this, the research seeks to understand how the ET impacts the overall effectiveness and efficiency of the system in achieving a unified agreement or goal among the agents. This involves evaluating whether the ET accelerates or decelerates the consensus process and determining its role in the performance dynamics of the MAS. The insights gained from this analysis could provide valuable information on optimizing MAS configurations for better coordination and control in various applications. © 2024 IEEE.

关键词： Convergence Speed Effect-Term leader-follower Consensus Lyapunov Function Multi-Agent Systems

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Effectiveness of Almost Sub Frame Approach as Inter Cell Interference Management in LTE-A Macro - Pico Scenario 9

Effectiveness of Almost Sub Frame Approach as Inter Cell Int...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Jember, Kehali A. Abose, Tadele A. Kassahun, Ermias Madhav, T. Venu Adama Science And Technology University Department Electronics And Communication Engineering Adama Ethiopia Mettu University Department Electrical And Computer Engineering Ethiopia Gondar University Department Electrical And Computer Engineering Gondar Ethiopia

ISBN: (纸本)9798350394474

Heterogeneous networks are promising solutions for enhancing network performance of LTE-A mobile networks by deploying small cells within the area of the serving macro cells. The goal of deploying such networks is to offload data from the congested macro cells towards the small cells. However, cross tier interference is a serious issue, which is introduced due to the transmitted power difference between Macro cell node and small cell node. Hence, cell age users are mainly affected by high power interference originated from Macro node. The paper develops enhanced inter-cell interference coordination using Almost Blank Sub-Frame (ABS) to improve user throughput. This can be achieved by stopping data transmission on in certain sub frame of macro cell while Pico users are scheduled at the same frame. computer simulation results depict that the performance of Pico cell users is highly degraded, especially for cell edge users. The ABS technique improves cell edge user throughput of Pico cell by 95% and center cell user throughput by 75.25%. © 2024 IEEE.

关键词： Heterogeneous networks

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Arithmetic Calculus Modeling for Approximate Circuits

Arithmetic Calculus Modeling for Approximate Circuits

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2024 International Conference on Microelectronics, ICM 2024

作者： Aoun, Alain Masadeh, Mahmoud Hasan, Osman Tahar, Sofiene Concordia University Department of Electrical and Computer Engineering MontrealQC Canada Yarmouk University Computer Engineering Department Irbid Jordan National University of Sciences & Technology Department of Electrical Engineering Islamabad Pakistan

ISBN: (纸本)9798350379396

Approximate computing (AC) is a paradigm that introduces errors for reduced design metrics. AC has been recommended for implementation in error-resilient applications. Previously proposed AC implementations can be modified to generate new configurations and thus result in a large design space of AC. In this paper, we propose a mathematical modeling technique that allows a fast analysis of hardware designs with the aim of studying the quality of large design spaces. Mathematically modeling the AC circuits facilitates the quality to be assessed using various error metrics while reducing the assessment time. The proposed approach has been applied to various arithmetic units, including an array multiplier, a multiply accumulate unit and a divider. The experimental results showed a quality assessment equivalent to the one obtained by the Monte-Carlo simulation, while offering a remarkable reduction in CPU time. © 2024 IEEE.

关键词： Monte Carlo methods

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