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arXiv 2024年

作者： Omar, Aseel Cohen, Alejandro Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Haifa Israel

An integrate-and-fire time-encoding machine (IF-TEM) is an effective asynchronous sampler that translates amplitude information into non-uniform time sequences. In this work, we propose a novel Adaptive IF-TEM (AIF-TEM) approach. This design dynamically adjusts the TEM’s sensitivity to changes in the input signal’s amplitude and frequency in real-time. We provide a comprehensive analysis of AIF-TEM’s oversampling and distortion properties. By the adaptive adjustments, AIF-TEM as we show can achieve significant performance improvements in terms of sampling rate-distortion in a practical finite regime. We demonstrate empirically that in the scenarios tested AIF-TEM outperforms classical IF-TEM and traditional Nyquist (i.e., periodic) sampling methods for band-limited signals. In terms of Mean Square Error (MSE), the reduction reaches at least 12dB (fixing the oversampling rate). Additionally, we investigate the quantization process for AIF-TEM and analyze the quantization MSE bound. Empirical results show that classic quantization for AIF-TEM improves performance by at least 14 dB compared to IF-TEM. We introduce a dynamic quantization technique for AIF-TEM, which further improves performance compared to classic quantization. Empirically, this reduction reaches at least 10 dB compared to classic quantization for AIF-TEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Zero-shot unsupervised and text-based audio editing using DDPM inversion 24

Zero-shot unsupervised and text-based audio editing using DD...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hila Manor Tomer Michaeli Faculty of Electrical and Computer Engineering Technion — Israel Institute of Technology Haifa Israel

Editing signals using large pre-trained models, in a zero-shot manner, has recently seen rapid advancements in the image domain. However, this wave has yet to reach the audio domain. In this paper, we explore two zero-shot editing techniques for audio signals, which use DDPM inversion with pre-trained diffusion models. The first, which we coin ZEro-shot Text-based Audio (ZETA) editing, is adopted from the image domain. The second, named ZEro-shot UnSupervized (ZEUS) editing, is a novel approach for discovering semantically meaningful editing directions without supervision. When applied to music signals, this method exposes a range of musically interesting modifications, from controlling the participation of specific instruments to improvisations on the melody. Samples and code can be found on our examples page.

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The comparison of EEG features in specific Mandarin words' speech

The comparison of EEG features in specific Mandarin words' s...

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2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023

作者： Tsai, Wen-Shan Huang, Syue-Guan Huang, Kuan-Chih Chen, Jung-Chih Lin, Chin-Teng National Yang Ming Chiao Tung University Institute of Biomedical Engineering College of Electrical and Computer Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University College of Electrical and Computer Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Brain Science and Technology Center College of Electrical and Computer Engineering Hsinchu300093 Taiwan Faculty of Engineering and Information Technology UltimoNSW2007 Australia

ISBN: (纸本)9798350305791

Due to advancements in brain signal application technology, there has been a growing focus on the human speech Brain-computer Interface (BCI) in recent years. An essential initial phase in crafting speech recognition systems is comprehending the inherent features of nature speech processing. To ensure the decoding capability of human speech BCI, researchers have to investigate the differences between overt speech and imagined speech, and they also need to find the best paradigm to build a reliable BCI. Thus, this study compared the differences in brain dynamics between overt speech and imagined speech while using the four Mandarin words ""(up), ""(down), ""(left), and ""(right). We first calculated the EEG alpha and beta band power for multiple channels situated in the regions of Broca's and Wernicke's areas (specifically F5, F7, FC3, FC5, and C5), to analyze the brain activity changes while speaking or imagining speech. Second, we used Daubechies 4 (db4) wavelet to do a 7-level decomposition to the EEG signal. The corresponding discrete wavelet transform (DWT) coefficients were further calculated with six statistical features for support vector machine (SVM) classification. We found that the "speak (overt speech)"task and the "imagine (imagined speech)"task resulted in significantly different alpha band power changes, and the beta band shows the same phenomenon. The result of average classification accuracy is 71.97% for "speak"and "imagine"distinction. In conclusion, we analyzed the brain dynamics and find that overt speech and imagined speech could be differentiate through DWT coefficients features. This may pave the way to establish a Mandarin imagined speech BCI. © 2023 IEEE.

关键词： Electroencephalography

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion

arXiv

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arXiv 2024年

作者： Manor, Hila Michaeli, Tomer Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa Israel

关键词： Zero-shot learning

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Single-shot single-beam coherent Raman scattering thermometry based on optically induced air lasing

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Light(Science & Applications) 2024年第12期13卷 3253-3260页

作者： Xu Lu Yewei Chen Francesco Mazza Siyi He Zihan Li Shunlin Huang Quanjun Wang Ning Zhang Bo Shen Yuzhu Wu Jinping Yao Ya Cheng State Key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Faculty of Aerospace Engineering Delft University of TechnologyKluyverweg 12629 HS DelftThe Netherlands Institute of Modern Optics Nankai UniversityTianjin 300350China School of Microelectronics Shanghai UniversityShanghai 200444China

Thermometric techniques with high accuracy,fast response and ease of implementation are desirable for the study of dynamic combustion environments,transient reacting flows,and non-equilibrium ***,single-shot singlebeam coherent Raman scattering(SS-CRS)thermometry is developed,for the first time to our knowledge,by using air lasing as a *** show that the air-lasing-assisted CRS signal has a high signal-to-noise ratio enabling single-shot measurements at a 1 kHz repetition *** SS-CRS thermometry consistently exhibits precision of<2.3%at different temperatures,but the inaccuracy grows with the increase in *** high measurement repeatability,1 kHz acquisition rate and easy-to-implement single-beam scheme are achieved thanks to the unique temporal,spectral and spatial characteristics of air *** work opens a novel avenue for high-speed CRS thermometry,holding tremendous potential for fast diagnostics of transient reacting flows and plasmas.

关键词： scattering lasing beam

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An Empirical Investigation in Applying Reliable Industry 4.0 Based Machine Learning (ML) Approaches in Analysing and Monitoring Smart Meters using Multivariate Analysis of Variance (Manova) 2

An Empirical Investigation in Applying Reliable Industry 4.0...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Rana, Md. Sohel Dixit, Anil Kumar Rajan, M.Sundar Malhotra, Sumit Radhika, S. Pant, Bhasker Nanjing University of Information and Science Technology Computer Science and Technology India Uttaranchal University Dehradun Department of Lcd India Arbaminch University Faculty of Electrical and Computer Engineering Ethiopia Chandigarh University Computer Science and Engineering Gharuan Mohali India Raghu Institute of Technology Department of CSE AndhraPradesh Visakhapatnam India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

Conventional power systems have been evolving towards prospective smart grids in recent decades as market needs have changed and independent innovations, such as the 'Internet of Things' (IoT), have developed. The further automation of traditional development of industrial operations with a variety of current advanced techniques is termed as Industry 4.0. The most efficient strategy to boost the productivity of the enterprise environment, such as smart grids, is to use contemporary control technology that regulates and supervises interaction between smart devices. Furthermore, the advancement of loT -enabled smart meters is aided by a separate data connection, such as 'Bluetooth' or the 'Global System for Mobiles' (GSM), for remotely inspecting and operating smart meters. In this context, SPSS software is used for analysing the role of Industry 4.0 based Machine Learning or ML in analysing Smart Meters and MANOV A (and correlation) approach is considered to get proper result. This study proposes a machine learning-based architecture for analysing and monitoring smart meter data. The advantages of loT integration into Industry 4.0 include the ability to exchange actual information across a wide range of smart meters for various applications, such as factories, hospitals and residences, which is critical for smart grids. Results suggested that Smart Grid is beneficial to the extent of reducing electricity cost;however, maintenance cost and security issues are the major challenging factors in the new technology. © 2022 IEEE.

关键词： Internet of things

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Research on the quantum photonic convolutional neural network for artificial intelligence-based healthcare system security

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Optical and Quantum Electronics 2024年第2期56卷 1-18页

作者： Sita Kumari, K. Shivaprakash, G. Arslan, Farrukh Alsafarini, Maram Y. Ziyadullayevich, Avlokulov Anvar Haleem, Sulaima Lebbe Abdul Arumugam, Mahendran Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Electronics and Instrumentation Engineering Ramaiah Institute of Technology Bengaluru India School of Electrical and Computer Engineering Purdue University West Lafayette United States Department of Electrical Engineering University of Engineering and Technology Lahore Lahore Pakistan Computer Science Department Zarqa University Zarqa Jordan The Department of Audit Tashkent Institute of Finance Tashkent Uzbekistan Department of Information and Communication Technology Faculty of Technology South Eastern University of Sri Lanka Oluvil Sri Lanka Center for Transdisciplinary Research Saveetha Dental College Saveetha Institute of Medical and Technical Science Chennai India

Artificial intelligence (AI) has quickly integrated into contemporary healthcare organizations, providing a variety of purposes, including identifying diseases, individualized therapy suggestions, and healthcare picture analytics. However, substantial security concerns are raised by the extensive use of AI in Healthcare, particularly regarding the reliability and confidentiality of private patient information. In this paper, we offer quantum photonic convolutional neural network (QP-CNN) to strengthen AI-based healthcare systems' security to overcome this crucial issue. To create an innovative convolutional neural network (CNN) architecture capable of handling the quantum computations that can endanger the safety of patient data, we analyze the special aspects of quantum photonics in this study. We explore the basic ideas behind quantum photonic computation and show how they can be used to develop strong cryptography methods for securing healthcare information during transport and preservation. Initially, we gather the dataset and preprocess the collected data using min–max normalization to remove duplicates and guarantee homogeneity. The subsequent stage involves utilizing principal component analysis (PCA) to extract pertinent features from the preprocessed data. We simulate trials with Python 3.11 software to assess the efficiency of the suggested algorithm. In terms of accuracy (82.67%), precision (82%), recall (75.84%), and F1-score (85%), specificity (95.67%), AUC (93.15%). Our results show that the QP-CNN technique outperforms other methods in effectiveness. Our suggested QP-CNN technique provides exciting outcomes for the security of an artificial intelligence-based healthcare system. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Healthcare Patient evaluation Prediction Quantum photonic convolutional neural network (QP-CNN) Security

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Design and Implementation of a 5G/4G/Wi-Fi Enabled Video Stream Analysis System for Unicast and Multicast Streaming

Design and Implementation of a 5G/4G/Wi-Fi Enabled Video Str...

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Communications and Networking (MECOM), IEEE Middle East Conference on

作者： Rajesh Kumar Mahato Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa Israel

ISBN: (数字)9798350376715

ISBN: (纸本)9798350376722

The surge in video streaming has created a pressing need for efficient delivery mechanisms and robust real-time analysis capabilities. This paper presents a comprehensive video stream analysis system designed to operate seamlessly across 5G, 4G, and Wi-Fi networks, handling both unicast and multicast streaming. By leveraging the versatility of FFmpeg and MistServer, the system offers extensive real-time analysis capabilities, regardless of the transmission protocol, media container format, or video/audio coding standard. This adaptability makes the system well-suited for the diverse and dynamic nature of internet media *** paper outlines the system’s architecture, details the implementation of its FFmpeg-based decoding and analysis processes, and discusses the integration of $5 \mathrm{G}, 4 \mathrm{G}$, and $\mathrm{Wi}-\mathrm{Fi}$ networks to enhance performance. Through rigorous evaluations, the system’s scalability, reliability, and ability to maintain high-quality streaming across different network environments are demonstrated. These findings highlight the system’s potential to significantly improve multimedia delivery and provide a foundational framework for future advancements in video stream analysis.

关键词： Protocols Unicast Scalability Streaming media Media Real-time systems Reliability Surges Wireless fidelity Standards

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