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2024 Laser Applications to Chemical, Security and Environmental Analysis, LACSEA 2024 - Part of Optica Sensing Congress

作者： Kosan, Nicholas Meyer, Zane Nowicki, Keith Rafkin, Scot Wysocki, Gerard Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Planetary Science Directorate Southwest Research Institute BoulderCO80302 United States

We develop quasi-mode hop free tuning of a midinfrared external cavity quantum cascade laser spectrometer designed for extraterrestrial environments. Tuning is achieved through synchronization of ECQCL parameters and ... 详细信息

关键词： Quantum cascade lasers

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Self-Adaptive Electronics using Artificial Neural Networks 9

Self-Adaptive Electronics using Artificial Neural Networks

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Anita, S. Joon, Rakesh Kumar Devi, S. Rukmani Khandan, K. Lakshmi Howard, Eric Rajendiran, M. R.M.K Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Kavaraipettai601206 India Ganga Institute of Technology And Management Department of Electronics And Communication Engineering Haryana Kablana124104 India Saveetha College of Liberal Arts And Sciences Simats Deemed To Be University Department of Computer Science Tamil Nadu Chennai600124 India Madanapalle Institute of Technology & Science Department of Electrical And Electronics Engineering Andhra Pradesh Madanapalle517325 India Macquarie University Department of Physics And Astronomy Australia Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

Electronic gadgets and systems are growing rapidly, requiring new adaptability methods. This research uses Artificial Neural Networks to self-adapt electronic systems. ANNs, inspired by the brain, can optimize electronic circuits and devices in real ***-adaptive electronics uses ANNs for system control and decision-making. ANNs learn to adapt to changing operational circumstances, environmental factors, and user preferences through supervised and reinforcement learning. Feedback systems let neural networks improve energy usage, system parameters, and performance without operator interaction.A sensor array, artificial neural network-based control unit, and actuators make up the self-adaptive electronics system. The system smoothly integrates sensors and computing. Self-adaptive electronics could be used in loT, wearable, and autonomous systems, the research says. Electronic systems operate in unpredictable and dynamic contexts, thus the architecture is ***, self-adaptive electronics outperform static systems in performance, energy efficiency, and adaptability. Artificial neural networks can enable smarter, more responsive, and autonomous gadgets, according to the findings. Finally, self-adapting artificial neural networks in electronics could lead to intelligent systems. This research advances adaptive electronics, enabling new applications in the ever-changing electronics and technology environment. © 2024 IEEE.

关键词： Neural networks

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Extended and Unscented Kalman Filter Based Estimation of State-of-Charge of Li-Ion Batteries Using 3-RC Equivalent Circuit Model 3

Extended and Unscented Kalman Filter Based Estimation of Sta...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Mehta, Chaitali Sant, Amit V. Sharma, Paawan Pandit Deendayal Energy University Department of Computer Science and Engineering Gandhinagar India Pandit Deendayal Energy University Department of Electrical Engineering Gandhinagar India Pandit Deendayal Energy University Department of Information and Communication Technology Gandhinagar India

ISBN: (纸本)9798331540319

The growing presence of electric vehicles (EVs) in the market has spurred investigations into various algorithms for estimating the state-of-charge (SoC) of the battery. Accurately estimating the SoC is of paramount importance for the battery management systems. This paper conducts an exhaustive literature review on diverse methods for SoC estimation, recognizing the critical importance of an accurate battery model in enhancing prediction efficiency. The study introduces and evaluates three methods, a) Coulomb Counting, b) Extended Kalman Filter and c) Unscented Kalman Filter for SoC estimation. Through simulation, the paper presents and analyzes the results derived from these methods. To simulate a Li-ion battery model, a 3-RC battery model was developed utilizing the Equivalent Circuit Model method. SoC estimation is conducted on a Li-ion battery with a capacity of 31A and voltage of 10.8V. In the simulations, a constant current charging method at 1C rate was employed for charging, while discharging was executed at various rates depending on the SoC of the battery, utilizing a random pulse generator and noise to replicate real-world scenarios. The UKF outperforms the EKF due to its ability to effectively handle highly nonlinear battery dynamics, thus yielding more accurate SoC estimations over time. © 2024 IEEE.

关键词： State of charge

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Phonetically-Anchored Domain Adaptation for Cross-Lingual Speech Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Upadhyay, Shreya G. Martinez-Lucas, Luz Katz, William Busso, Carlos Lee, Chi-Chun Department of Electrical Engineering National Tsing Hua University Taiwan Department of Electrical and Computer Engineering University of Texas at Dallas United States Department of Speech Language and Hearing Department of Electrical and Computer Engineering University of Texas at Dallas United States Language Technologies Institute School of Computer Science Carnegie Mellon University United States

The prevalence of cross-lingual speech emotion recognition (SER) modeling has significantly increased due to its wide range of applications. Previous studies have primarily focused on technical strategies to adapt features, domains, and labels across languages, often overlooking the underlying commonalities between the languages. In this study, we address the language adaptation challenge in cross-lingual scenarios by incorporating vowel-phonetic constraints. Our approach is structured in two main parts. Firstly, we investigate the vowel-phonetic commonalities associated with specific emotions across languages, particularly focusing on common vowels that prove to be valuable for SER modeling. Secondly, we utilize these identified common vowels as anchors to facilitate cross-lingual SER. To demonstrate the effectiveness of our approach, we conduct case studies using American English and Taiwanese Mandarin with two naturalistic emotional speech corpora: the MSP-Podcast and BIIC-Podcast corpora. The approach leverages evidence that certain vowels, including monophthongs and diphthongs, exhibit emotion-specific commonality across languages, serving as phonetic anchors to enhance unsupervised cross-lingual SER learning. The proposed model surpasses baseline performance, highlighting the importance of phonetic similarities for effective language adaptation in cross-lingual SER scenarios. © 2010-2012 IEEE.

关键词： Emotion Recognition

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Ultra-High Rate-Reliability Fairness in Grant-Free Massive URLLC NOMA System: Joint Power and Channel Allocation Using Meta-Heuristic Search

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IEEE Transactions on Vehicular Technology 2023年第12期72卷 15899-15915页

作者： Katwe, Mayur Singh, Keshav Li, Chih-Peng Ding, Zhiguo National Sun Yat-Sen University Department of Electrical Engineering Kaohsiung804 Taiwan Nanyang Technological University 639798 Singapore National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates University of Manchester Department of Electrical and Electronic Engineering ManchesterM13 9PL United Kingdom

Next-generation wireless applications are envisaged to cater extended ultra-reliability low-latency communication (URLLC) services involving high data rates and improved user fairness for massive users by synergizing multiple driving technologies. Nevertheless, effective resource management with this heterogeneous quality of services (QoS) requirements under limited spectral resources becomes extremely imminent and challenging which is beyond the capabilities of existing architectures. In this article, we investigate a grant-free non-orthogonal multiple access-based uplink transmission to prompt a new service called massive URLLC (mURLLC) for high rate-reliability fairness for massive users under strict latency constraints. To enable spectral-efficient and reliable resource allocation policy, we formulate distinct resource optimization problems for the considered GF-NOMA scheme which involves worst-case rate and reliability maximization and latency minimization under joint power and channel allocation. To tackle the NP-hardness and mixed integer non-linear programming of formulated problems, we propose a general low-cost differential evolution-based meta-heuristic framework which jointly allocates the available resources among multiple URLLC users. Simulations demonstrate the effectiveness of the considered GF-NOMA-based mURLLC system over state-of-art approaches. Moreover, it is shown that the proposed DE algorithm exhibits efficient channel utilization and a high degree of user fairness when compared to random channel contention. © 1967-2012 IEEE.

关键词： Reliability

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Analyzing energy efficiency of flying objects in Wireless Sensor and Mobile Ad Hoc Networks

Analyzing energy efficiency of flying objects in Wireless Se...

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Recent Trends in computer science and Technology (ICRTCST), International Conference on

作者： Shams Tabrez Siddiqui Adeel Ahmad Jorair Ahmad Mohammad Haseebuddin Alighazi Siddiqui Abu Salim Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Electrical and Electronics Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

The convergence of flying objects, Mobile Ad Hoc Networks (MANETs), and Wireless Sensor Networks (WSNs) has been made feasible by the widespread proliferation of wireless communication technology. This study delves into the historical development and significant achievements of WSNs and MANETs, with a focus on the incorporation of drones and UAVs to enhance network capabilities. Our main goals involve conducting thorough assessments of communication protocols, routing algorithms, and network architectures in the presence of flying objects. Our particular emphasis is on maximizing energy efficiency. Examining the dynamics of flying objects in WSNs and MANETs, this analysis delves into the various challenges and opportunities that arise. A special emphasis is placed on the management of mobility, the optimization of energy usage, and the effectiveness of routing protocols. The study explores existing protocols such as LEACH, AODV and OLSR, highlighting their benefits and challenges in flying objects. A comparative analysis evaluates adapted protocols based on metrics such as energy efficiency, latency, and reliability.

关键词： Wireless communication Wireless sensor networks Scalability Machine learning Standardization Energy efficiency Routing protocols

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Sequential Datum-Wise Joint Feature Selection and Classification in the Presence of External Classifier 48

Sequential Datum-Wise Joint Feature Selection and Classifica...

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

作者： Ekanayake, Sachini Piyoni Zois, Daphneynstavroula Chelmis, Charalampos University at Albany State University of New York Department of Electrical and Computer Engineering AlbanyNY United States University at Albany State University of New York Department of Computer Science AlbanyNY United States

ISBN: (纸本)9781728163277

We introduce a supervised machine learning framework for sequential datum-wise joint feature selection and classification. Our proposed approach sequentially acquires features one at a time during testing until it decides that acquiring more features will not improve label assignment. At that point, and in contrast to prior art, it assigns a label to the example under consideration by selecting between a simple internal and a more powerful external classifier. Easy-to-classify examples are handled by the internal classifier, which assigns labels based on the lowest expected misclassification cost. On the other hand, difficult-to-classify examples are forwarded to the external classifier to be assigned a label based on the acquired features. We demonstrate the performance of the proposed approach compared to existing methods using six publicly available datasets. Experiments indicate that the proposed approach improves accuracy up to 50% with respect to existing sequential methods, while acquiring up to 85% less number of features on average. © 2023 IEEE.

关键词： Dynamic programming

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How Lasers Exploit Photoacoustic and Photoelectric Phenomena to Inject Signals into MEMS Microphones

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Journal of Hardware and Systems Security 2025年 1-17页

作者： Cyr, Benjamin Sumaria, Vedant Long, Yan Tadigadapa, Srinivas Sugawara, Takeshi Fu, Kevin Computer Science and Engineering University of Michigan Ann Arbor USA Electrical & Computer Engineering Northeastern University Boston USA Department of Informatics The University of Electro-Communications Tokyo Japan

An amplitude-modulated laser can be used to generate false, yet coherent acoustic signals on the outputs of MEMS microphones. While this vulnerability has ramifications on the security of cyber-physical systems that trust these microphones, the physical explanation of this effect remained a mystery. Without an understanding of the physical phenomena contributing to this signal injection, it is difficult to design effective and reliable defenses. In this work, we show the degree to which the mechanisms of thermoelastic bending, thermal diffusion, and photocurrent generation are used to inject signals into MEMS microphones. We provide models for each of these mechanisms, develop a procedure to empirically determine their relative contributions, and highlight the effects on eight commercial MEMS microphones. We accomplish this with a precise setup to isolate each mechanism using several laser wavelengths and a vacuum chamber. The results indicate that the injected signal on the microphone is dependent on the wavelength of the incoming light. Shorter wavelengths (such as a 450 nm blue laser) exploit photoacoustic effects, and the periodic heating and expansion of air is the dominant factor in seven of eight sample microphones. Longer wavelengths (such as a 904 nm infrared laser) exploit photoelectric effects on the sensitive ASIC, generating signals that are between 2x and 100x stronger than photoacoustic signals in six of eight sample microphones. This understanding of the physical causality of laser signal injection leads to recommendations for future laser-resistant microphone designs. These include adding light-blocking structures at the system or device level, improving to glob top application, and adding simple light or temperature sensors for injection detection. Based on the fundamental causality, we also suggest potential vulnerabilities within other sensors with similar characteristics to MEMS microphones, such as conventional microphones, ultrasonic

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Insights into Non-uniform Aging Effects on Broadband Dielectric Behaviors for High-voltage XLPE Cable Insulation: A Novel Fractional-order Circuit Modeling Approach

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IEEE Transactions on Dielectrics and electrical Insulation 2024年第3期32卷 1793-1801页

作者： Dai, Xize Hao, Jian Jian, Zheng Hosani, Khalifa Hassan Al Liao, Ruijin Chongqing University State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing400044 China Aalborg University Department of Energy Aalborg9220 Denmark State Grid Chongqing Electric Power Company Shinan Power Supply Branch Chongqing401336 China Khalifa University APEC Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates

A non-uniform aging pattern inside insulation systems is a significant contributor to the degradation of high-voltage (HV) power equipment. Therefore, this article presents a comprehensive investigation into the effects of non-uniform aging patterns on broadband dielectric properties of polymer insulation, with a focus on XLPE power cables, aiming to provide a deeper understanding of these effects. By examining two uniformly aged and five non-uniformly aged XLPE insulation cases, the study comparatively analyzes the influence of diverse aging patterns on broadband dielectric properties. Furthermore, a novel fractional-order circuit model is proposed to characterize broadband dielectric behaviors influenced by insulation structure heterogeneity caused by non-uniform aging patterns. The results indicate that non-uniform aging effects on real part of capacitance in XLPE insulation are highly sensitive, though the analysis remains qualitative. Subsequently, the proposed fractional-order circuit model following power-law behaviors successfully reconstructs broadband dielectric properties of non-uniformly aged XLPE insulation systems with average relative errors of less than 10%, making it a potential quantification tool to unveil underlying dynamic behaviors. This model offers a rational physical explanation and characterization of XLPE dielectric properties subject to diverse aging patterns, providing theoretical support for the aging analysis of HV cable equipment. © 1994-2012 IEEE.

关键词： Capacitance

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Learning from the Best: Active Learning for Wireless Communications

arXiv

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arXiv 2024年

作者： Soltani, Nasim Zhang, Jifan Salehi, Batool Roy, Debashri Nowak, Robert Chowdhury, Kaushik Electrical and Computer Engineering Department Northeastern University United States Computer Science Department University of Wisconsin-Madison United States Computer Science and Engineering Department University of Texas Arlington United States Electrical and Computer Engineering Department University of Wisconsin-Madison United States

Collecting an over-the-air wireless communications training dataset for deep learning-based communication tasks is relatively simple. However, labeling the dataset requires expert involvement and domain knowledge, may involve private intellectual properties, and is often computationally and financially expensive. Active learning is an emerging area of research in machine learning that aims to reduce the labeling overhead without accuracy degradation. Active learning algorithms identify the most critical and informative samples in an unlabeled dataset and label only those samples, instead of the complete set. In this paper, we introduce active learning for deep learning applications in wireless communications, and present its different categories. We present a case study of deep learning-based mmWave beam selection, where labeling is performed by a compute-intensive algorithm based on exhaustive search. We evaluate the performance of different active learning algorithms on a publicly available multi-modal dataset with different modalities including image and LiDAR. Our results show that using an active learning algorithm for class-imbalanced datasets can reduce labeling overhead by up to 50% for this dataset while maintaining the same accuracy as classical training. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost reduction

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