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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Phan, Quoc Bao Nguyen, Linh Nguyen, Tuy Tan Nguyen, Dinh C. Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States The University of Alabama in Huntsville Department of Electrical and Computer Engineering HuntsvilleAL35899 United States

ISBN: (纸本)9798350324136

This paper presents a novel two-stage approach to enhance the quality and privacy of X-ray medical images. The first stage leverages generative adversarial networks (GANs) for effective denoising, eliminating noise and artifacts from X-ray images while improving the visibility of critical anatomical structures. Subsequently, number-Theoretic transform (NTT) polynomial multiplication is integrated with Kyber to accelerate the encryption and decryption of the denoised X-ray images. This encryption safeguards the privacy of sensitive patient data and provides resilience against potential quantum computing attacks, ensuring long-Term data security. Implementing Kyber-based encryption and decryption on a graphics processing unit (GPU) architecture significantly reduces latency, enabling real-Time and secure access to critical healthcare information. © 2024 IEEE.

关键词： GANs Images denoising Kyber number theoretic transform

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Hybrid Optimization Based Long Short-Term Memory for Android Malware Detection

Hybrid Optimization Based Long Short-Term Memory for Android...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Lande, Jayapal Maheswari, M. Kamali, S.M. Dineshkumar, R. Priyanka, M.S. Sr University School of Computer Science & Artificial Intelligence Warangal India Srm Institue of Science and Technology School of Computing Department of Computational Intelligence Chennai India Annapoorana Engineering College Department of Electrical and Electronics Engineering Salem India Saveetha School of Engineering Department of Ece Sriperumbudur Thandalam India Nitte Meenakshi Institute of Technology Department of Ai and Ml Yelahanka Bengaluru India

ISBN: (纸本)9798350313413

Malware detection is process of detecting the suspicious files and activities, that can classify as benign and malicious depends on different characters like capability of infection, structure of malware and speed rate. In this paper, proposed a Particle Swarm Optimization and Ant Colony Optimization (HPSALO) based Long Short-Term Memory (LSTM) model for malware detection and classification. The combination of these two algorithms that leads to the balance between exploration and exploitation. The dataset utilized for the research is DREBIN dataset and process of data cleaning is performed by NumPy library. The feature selection is performed by combination of hybrid PSALO algorithms and selected features are given to Long Short-Term Memory (LSTM) classifier that classifies as benign and malware files. The performance of proposed method is estimated by performance metrics of accuracy, precision, recall and f1-score. The proposed method attained accuracy of 99.38%, precision of 99.30%, recall of 99.08% and f1-score of 99.42% which is comparatively higher than other existing methods like Whale Optimization Algorithm-LSTM (WOA-LSTM), Cat Swarm Optimization-LSTM (CSO-LSTM), Particle Swarm Optimization-LSTM and Ant Lion Optimization-LSTM. © 2023 IEEE.

关键词： Ant Lion Optimization Long Short-Term Memory Malware Detection NumPy and Particle Swarm Optimization

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Empowering Supply Chain Transparency and Risk Management through Hybrid CNN and BiGRU Approach 1

Empowering Supply Chain Transparency and Risk Management thr...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Guhan, K. Lakshmi, S. Verma, Virendra Kumar Sai, M K Satya Sharma, Amit Kumar, B. Suresh Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India Bharati Vidyapeeth Management Studies and Research Department of Management Business Administration Navi Mumbai India Symbiosis Institute of Business Management Bengaluru India Vignana Bharathi Institute of Technology Department of Mechanical Engineering Hyderabad India Lovely Professional University School of Computer Application Phagwara India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronic Engineering Telangana Hyderabad India

ISBN: (纸本)9798350352931

Everything from needs to luxuries has been impacted by the COVID-19 pandemic in the manufacturing sector, and that includes the automotive industry. Thus, there is an immediate need for research into the automotive supply chain's sustainability, transparency, and competency development practices. For the automotive supply chains to successfully apply the Transparency for Sustainability (TfS) architecture in the long run, it also specifies a set of skills that are necessary. The three steps that comprise this proposed method are preprocessing, feature selection, and model training. The data had to be preprocessed in order for it to be ready for analysis. The feature selection procedure is comprised of the LDA topic model, term frequency, and correlation analysis. A GRU-DT was utilized for the model's training. The suggested method outperforms GRU and DT with an average accuracy of 96.29%. © 2024 IEEE.

关键词： Automotive industry

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Microfluidic-organic thin film transistor coupled platform for simple solution exposure

RSC Applied Interfaces

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RSC Applied Interfaces 2024年第4期1卷 771-778页

作者： Boileau, Nicholas T. King, Benjamin Kapar, Sparsh Sohi, Ali Najafi Manion, Joseph G. Godin, Michel Lessard, Benoît H. Department of Chemical and Biological Engineering University of Ottawa 161 Louis Pasteur Pvt Ottawa ON Canada Department of Physics University of Ottawa 150 Louis Pasteur Pvt Ottawa ON Canada Ottawa-Carleton Institute for Biomedical Engineering University of Ottawa 161 Louis Pasteur Pvt Ottawa ON Canada School of Electrical Engineering and Computer Science University of Ottawa 800 King Edward Ave. Ottawa ON Canada

Efforts to combine organic thin film transistors (OTFTs) within microfluidic networks to create sensitive, versatile, and low-cost sensors for rapid chemical analysis have been limited by the need for complex equipment and by the sensitivity of OTFTs to common processing techniques used in traditional microfluidic fabrication. We designed and validated a robust, easy to use, and simple to manufacture prototype microfluidic-OTFT pressure coupled system. Our design enables multiple OTFT architectures to be combined with microfluidic analyte delivery, eliminates common processing steps that can alter OTFT performance, and only requires easily-accessible equipment. As a proof of concept, we demonstrate the exposure of aluminium chloride phthalocyanine (AlClPc) based OTFTs to different solutions of sodium fluoride (NaF). This work could accelerate the design of more versatile, rapid, and reliable OTFT based liquid chemical sensors. © 2024 RSC.

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Optimal Privacy-Aware State Estimation

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IEEE Transactions on Automatic Control 2025年

作者： Weng, Chuanghong Nekouei, Ehsan Johansson, Karl H. City University of Hong Kong Department of Electrical Engineering Hong Kong KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Sweden Digital Futures United Kingdom

In this paper, we study the optimal privacy-aware estimation problem for a (nonlinear or non-Gaussian) system where a private process derives the system's states. In our setup, the private process is modeled as a first-order Markov chain, and the state estimates are shared with an untrusted party, called the "adversary", who might attempt to infer the private process based on the state estimates. We cast the optimal design of a privacy-aware estimator as an optimization problem that minimizes a linear combination of the estimation error and the leakage of private information via the estimator, captured by the mutual information between the private process and the state estimates. We first derive the Bellman optimality principle for the optimal privacy-aware estimation problem, which is used to study the structural properties of the optimal estimator. Different from classical estimation, our results indicate that the privacy-aware estimation is a closed-loop control problem wherein the estimator controls the belief of the adversary about the private process, i.e., the conditional distribution of the private process given the estimator's outputs. We next develop a policy gradient algorithm for computing an optimal estimation policy. To this end, we derive a closed-form expression for the gradient of the objective function of the privacy-aware estimation with respect to the parameter of the policy. We also develop a novel variational formulation of the mutual information, which allows us to compute the mutual information numerically with a low computational complexity. We finally study the performance of the optimal estimator in a building automation application, and compare our results with the additive perturbation approach to privacy. © 1963-2012 IEEE.

关键词： Markov processes

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Fast Detection and Tracking of Worn Lane Markings 6

Fast Detection and Tracking of Worn Lane Markings

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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Xu, Zezhong Qian, Cheng You, Qingxiang Wu, Feng Changzhou Institute of Technology Department of Computer and Information Engineering Changzhou China School of Electrical and Information Engineering Changzhou Institute of Technology Changzhou China

ISBN: (纸本)9781665453110

A fast method for lanes detection is proposed to deal with worn lane markings. Lanes are detected based on regional pixels rather than edge points in this paper by concerning that the worn or faded lane markings lead to disruption to edge extraction. After pixels have voted, the local maximum is searched and a peak region is defined in the Hough space. The voting of a column in the peak region is considered as a stochastic variable, and the statistical characteristics are computed. The statistical variances are used to fit a quadratic function. The direction parameter of the lane marking is determined by the minimization of the fitted quadratic function. The statistical means are used to fit a linear function. The position parameter of the lane marking is computed using the interpolation technique. Lane tracking is implemented with lower computation cost by defining the peak region based on the lane parameters detected in previous frame. The experimental results show that the proposed method can detect effectively the lane markings even in presence of seriously worn roads. The computation time is less than 2ms for a road image. © 2022 IEEE.

关键词： Hough transforms

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Ripple Sparse Self-Attention for Monaural Speech Enhancement 48

Ripple Sparse Self-Attention for Monaural Speech Enhancement

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Qiquan Zhu, Hongxu Song, Qi Qian, Xinyuan Ni, Zhaoheng Li, Haizhou University of New South Wales School of Electrical Engineering and Telecommunications Australia National University of Singapore Department of Electrical and Computer Engineering Singapore Alibaba Group China Meta United States The Chinese University of HongKong Shenzhen Research Institute of Big Data School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

The use of Transformer represents a recent success in speech enhancement. However, as its core component, self-attention suffers from quadratic complexity, which is computationally prohibited for long speech recordings. Moreover, it allows each time frame to attend to all time frames, neglecting the strong local correlations of speech signals. This study presents a simple yet effective sparse self-attention for speech enhancement, called ripple attention, which simultaneously performs fine- and coarse-grained modeling for local and global dependencies, respectively. Specifically, we employ local band attention to enable each frame to attend to its closest neighbor frames in a window at fine granularity, while employing dilated attention outside the window to model the global dependencies at a coarse granularity. We evaluate the efficacy of our ripple attention for speech enhancement on two commonly used training objectives. Extensive experimental results consistently confirm the superior performance of the ripple attention design over standard full self-attention, blockwise attention, and dual-path attention (Sep-Former) in terms of speech quality and intelligibility. © 2023 IEEE.

关键词： Speech enhancement

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Enhancing Cognitive Radio Networks with Multiple Antenna Techniques: Innovations and Performance Optimization 4

Enhancing Cognitive Radio Networks with Multiple Antenna Tec...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Kukreti, Sanjeev Al-Attabi, Kassem Chandrashekar, Rakesh Rani, K. Pushpa Badhoutiya, Arti Boob, Nandini Shirish Graphic Era Deemed to be University Department of Computer Science & Engg Dehradun India The Islamic University College of Technical Engineering Department of Computers Techniques Eng Najaf Iraq New Horizon College of Engineering Department of Mechanical Engg Bangalore India Mlr Institute of Technology Department of Computer Science & Eng. Telangana Hyderabad India Gla University Department of Electrical Engg Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331518981

A revolutionary approach to effectively using spectrum, Cognitive Radio Networks (CRNs) have surfaced to meet the increasing need for wireless communication. Performance gains of up to 30% may be achieved by integrating multiple antenna methods like beamforming and Multiple Input Multiple Output (MIMO) into CRNs. These gains include higher data speeds, better interference resistance, and greater spectral efficiency. This study examines new developments in the use of multiple antenna systems in CRNs, emphasising how these methods maximise spectrum availability and detection, improve communication quality, and increase network capacity overall. Adaptive beamforming techniques, spatial variety, spectrum sensing, and the difficulties of integrating numerous antennas into dynamic CRN settings are important research topics. Simulations and case studies are used to assess the performance advantages of these improvements, emphasising the effects they have on main and secondary users. Furthermore, this study explores how machine learning and sophisticated signal processing methods might be used to optimise antenna layout for real-time adaptation. This work intends to contribute to the continued development of high-performance CRNs, opening the door for more effective and adaptable wireless communication systems, by addressing these advancements and problems. © 2024 IEEE.

关键词： Adaptive algorithms

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Improving Smart Grid Cybersecurity by Addressing the Risk of Incorrect Data Injection

Improving Smart Grid Cybersecurity by Addressing the Risk of...

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2023 International Conference on Data Science and Network Security, ICDSNS 2023

作者： Yadav, Muskan Vyas, Bhavesh Pathak, Paresh Bhatt, Rahul Pareek, Vaidehi Dhabliya, Ritika Invertis University Department of Law Uttar Pradesh Bareilly India School of Engineering and Technology K. R. Mangalam University Department of Electrical and Electronics Engineering Haryana Gurugram India School of Computer Science and Applications Iimt University Meerut India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India Pune India Yashika Journal Publications Private Limited India

ISBN: (纸本)9798350301595

Rapid change is being seen in the electrical grid. Microgrids, renewable energy sources, and the increased automation of grid-level decision-making made possible by sensors have all contributed to the grid's growing complexity. Studies in the field of smart grids have shown the grid's susceptibility to hacking. In example, new research shows how the insertion of erroneous data may cause a wide range of issues in the functioning of smart grids. A sophisticated assault might compromise the power grid's functioning and control without being detected by bad data detection equipment performing state estimation. In this study, we expand upon previous works in the field to detail how safeguarding selected vital sensor in the power system from fake data injection attacks might help mitigate the problem. This article discusses the IEEE 14 bus evaluation system and explains how a fake data injection attack may be carried out given just little information about the network. Different case studies are used to verify the assumptions used to determine the attacking zone. In this study, we see why protecting the electric grid from cyberattacks is so crucial. © 2023 IEEE.

关键词： Smart power grids

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Improving Channel Resilience for Task-Oriented Semantic Communications: A Unified Information Bottleneck Approach

arXiv

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arXiv 2024年

作者： Lyu, Shuai Sun, Yao Guo, Linke Yuan, Xiaoyong Fang, Fang Zhang, Lan Wang, Xianbin Department of Electrical and Computer Engineering Clem-son University SC United States James Watt School of Engineering University of Glasgow Glasgow United Kingdom Department of Electrical and Computer Engineering Department of Computer Science Western University LondonON Canada

Task-oriented semantic communications (TSC) enhance radio resource efficiency by transmitting task-relevant semantic information. However, current research often overlooks the inherent semantic distinctions among encoded features. Due to unavoidable channel variations from time and frequency-selective fading, semantically sensitive feature units could be more susceptible to erroneous inference if corrupted by dynamic channels. Therefore, this letter introduces a unified channel-resilient TSC framework via information bottleneck. This framework complements existing TSC approaches by controlling information flow to capture fine-grained feature-level semantic robustness. Experiments on a case study for real-time subchannel allocation validate the framework’s effectiveness. © 2024, CC BY.

关键词： Semantics

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