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Advancements in electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： M. Karthik G. Hemalatha M. Mathankumar L. Sreevidya R. Deepalakshmi V. Kandasamy Department of Electrical and Electronics Engineering SRM Madurai College for Engineering and Technology Pottapalayam Tamil Nadu India Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Trichy Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science Engineering SRM Madurai College for Engineering and Technology Pottapalayam Tamil Nadu India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The dual active bridge (DAB) has become a crucial interface element in today's sophisticated systems. Renewable energy sources produce DC power, which is then stored in batteries, ultra-capacitors, and other energy storage devices. DAB is a crucial part of power exchange between two different buses because of its galvanic separation and bidirectional power transfer capabilities. The operation of the DAB is compatible with a broad range of input voltages and output loads. The creation of DAB is covered in most works. A well-known semiconductor integrated circuit with programmable control parameters is used in this straightforward, inexpensive method.

关键词： Time-frequency analysis Renewable energy sources Costs Bridge circuits Switched reluctance motors Stability analysis Batteries Telecommunication computing Time-domain analysis Stress

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Dynamic characteristics of terahertz hot-electron graphene FET bolometers: effect of electron cooling in channel and at side contacts

arXiv

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arXiv 2024年

作者： Ryzhii, V. Tang, C. Otsuji, T. Ryzhii, M. Mitin, V. Shur, M.S. Research Institute of Electrical Communication Tohoku University Sendai980-8577 Japan Frontier Research Institute for Interdisciplinary Sciences Tohoku University Sendai980-8578 Japan Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu965-8580 Japan Department of Electrical Engineering University at Buffalo SUNY BuffaloNY14260 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We analyze the operation of the hot-electron FET bolometers with the graphene channels (GCs) and the gate barrier layers (BLs). Such bolometers use the thermionic emission of the hot electrons heated by incident modulated THz radiation. The hot electron transfer from the GC into the metal gate. As the THz detectors, these bolometers can operate at room temperature. We show that the response and ultimate modulation frequency of the GC-FET bolometers are determined by the efficiency of the hot-electron energy transfer to the lattice and the GC side contacts due to the 2DEG lateral thermal conductance. The dependences of these mechanisms on the band structure and geometrical parameters open the way for the GC-FET bolometers optimization, in particular, for the enhancement of the maximum modulation frequency. © 2024, CC BY.

关键词： Graphene

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anjaneya L. H K. Maheswari N. Revathi Siva. K. P Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Electrical and Electronics Engineering Bapuji Institute of Engineering and Technology Davangere Karnataka India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score.

关键词： Training Image segmentation Sensitivity Artificial neural networks Aneurysm Feature extraction Reliability

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PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory systems

PeRP: Personalized Residual Policies For Congestion Mitigati...

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International Conference on Intelligent Transportation

作者： Aamir Hasan Neeloy Chakraborty Haonan Chen Jung-Hoon Cho Cathy Wu Katherine Driggs-Campbell Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Department of Civil and Environmental Engineering (CEE) MIT Laboratory for Information & Decision Systems (LIDS) Massachusetts Institute of Technology Department of Civil and Environmental Engineering (CEE) MIT Laboratory for Information & Decision Systems (LIDS) Institute for Data Systems & Society (IDSS) at the Massachusetts Institute of Technology

Intelligent driving systems can be used to mitigate congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these systems assume precise control over autonomous vehicle fleets, and are hence limited in practice as they fail to account for uncertainty in human behavior. Piecewise Constant (PC) Policies address these issues by structurally modeling the likeness of human driving to reduce traffic congestion in dense scenarios to provide action advice to be followed by human drivers. However, PC policies assume that all drivers behave similarly. To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP. PeRP advises drivers to behave in ways that mitigate traffic congestion. We first infer the driver's intrinsic traits on how they follow instructions in an unsupervised manner with a variational autoencoder. Then, a policy conditioned on the inferred trait adapts the action of the PC policy to provide the driver with a personalized recommendation. Our system is trained in simulation with novel driver modeling of instruction adherence. We show that our approach successfully mitigates congestion while adapting to different driver behaviors, with 4 to 22% improvement in average speed over baselines. 1

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Automatic Test Case Generation for Prime Field Elliptic Curve Cryptographic Circuits 17

Automatic Test Case Generation for Prime Field Elliptic Curv...

引用

17th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2021

作者： Gupt, Krishn Kumar Kshirsagar, Meghana Sullivan, Joseph P. Ryan, Conor Limerick Institute of Technology Department of Electrical and Electronic Engineering Limerick Ireland University of Limerick Department of Computer Science and Information Systems Limerick Ireland

ISBN: (纸本)9780738143972

Elliptic curve is a major area of research due to its application in elliptic curve cryptography. Due to their small key sizes, they offer the twofold advantage of reduced storage and transmission requirements. This also results in faster execution times. The authors propose an architecture to automatically generate test cases, for verification of elliptic curve operational circuits, based on user-defined prime field and the parameters used in the circuit to be tested. The ECC test case generations are based on the Galois field arithmetic operations which were the subject of previous work by the authors. One of the strengths of elliptic curve mathematics is its simplicity, which involves just three points (P, Q, and R), which pass through a line on the curve. The test cases generate points for a user-defined prime field which sequentially selects the input vector points (P and/or Q), to calculate the resultant output vector (R) easily. The testbench proposed here targets field programmable gate array (FPGAs) platforms and experimental results for ECC test case generation on different prime fields are presented, while ModelSim is used to validate the correctness of the ECC operations. © 2021 IEEE.

关键词： Timing circuits

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A Wideband WiFi/NB-IoT On-Glass Antenna for Smart Building Applications 19

A Wideband WiFi/NB-IoT On-Glass Antenna for Smart Building A...

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19th European Conference on Antennas and Propagation, EuCAP 2025

作者： Uzair, Muhammad Acharjee, Juin Boonpoonga, Akkarat Heberling, Dirk Chalermwisutkul, Suramate King Mongkut's University of Technology North Bangkok High Freqeuncy Systems Laboratory Bangkok Thailand St. Thomas' College of Engineering and Technology Department of Electronics and Communication Engineering Kolkata India King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand Institute of High Frequency Technology Rwth Aachen University Aachen Germany Fraunhofer Institute for High Frequency Physics and Radar Techniques Fhr Wachberg Germany

ISBN: (纸本)9788831299107

In this work, an on-glass antenna is proposed for IoT and smart building applications. The dielectric properties of five common glass types used in architecture were investigated. Clear glass, 3 mm thick, was selected as the substrate. The antenna is a rectangular monopole with a coplanar waveguide feed and an asymmetric ground. It was designed and simulated using Ansys HFSS, then fabricated using clear glass and copper tape. Measurements of the reflection coefficient and radiation pattern showed good agreement with simulations. The antenna exhibits a bidirectional radiation pattern, with 2.8 dBi gain and 94.82% radiation efficiency at 2.1 GHz and 3.9 dBi gain and 95.72% radiation efficiency at 2.4 GHz, respectively. A -10 dB bandwidth of 650 MHz (29.3%) centered at 2.215 GHz was achieved. The proposed antenna shows promise for integration with architectural glass elements in smart building applications, such as smart glass doors and windows. © 2025 European Association on Antennas and Propagation.

关键词： Glass Substrate IoT Antenna Material Characterization Smart Buildings

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Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear systems ⁎

引用

IFAC-PapersOnLine 2024年第15期58卷 265-270页

作者： Patrick J.W. Koelewijn Rajiv Singh Peter Seiler Roland Tóth Sioux Technologies B.V. Eindhoven The Netherlands Control Systems Group Eindhoven University of Technology Eindhoven The Netherlands The MathWorks Inc. Natick USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor USA Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data. This is achieved by a two-step procedure. In the first step, we learn a projection to a lower dimensional state-space. In step two, an LPV model is learned on the reduced-order state-space using a novel, efficient parameterization in terms of neural networks. The improved modeling accuracy of the method compared to an existing method is demonstrated by simulation examples.

关键词： Reduced-order modeling Neural Networks Linear Parameter-Varying Models

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Outlier-Robust Training of Machine Learning Models

arXiv

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arXiv 2024年

作者： Talak, Rajat Georgiou, Charis Shi, Jingnan Carlone, Luca Laboratory of Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

Robust training of machine learning models in the presence of outliers has garnered attention across various domains. The use of robust losses is a popular approach and is known to mitigate the impact of outliers. We bring to light two literatures that have diverged in their ways of designing robust losses: one using M-estimation, which is popular in robotics and computer vision, and another using a risk-minimization framework, which is popular in deep learning. We first show that a simple modification of the Black-Rangarajan duality provides a unifying view. The modified duality brings out a definition of a robust loss kernel σ that is satisfied by robust losses in both the literatures. Secondly, using the modified duality, we propose an Adaptive Alternation Algorithm (AAA) for training machine learning models with outliers. The algorithm iteratively trains the model by using a weighted version of the non-robust loss, while updating the weights at each iteration. The algorithm is augmented with a novel parameter update rule by interpreting the weights as inlier probabilities, and obviates the need for complex parameter tuning. Thirdly, we investigate convergence of the adaptive alternation algorithm to outlier-free optima. Considering arbitrary outliers (i.e., with no distributional assumption on the outliers), we show that the use of robust loss kernels σ increases the region of convergence. We experimentally show the efficacy of our algorithm on regression, classification, and neural scene reconstruction problems. © 2024, CC BY.

关键词： Contrastive Learning

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Deep Reinforcement Learning based Spectral Efficiency Maximization in STAR-RIS-Assisted Indoor Outdoor Communication

Deep Reinforcement Learning based Spectral Efficiency Maximi...

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IEEE Symposium on Network Operations and Management

作者： Pyae Sone Aung Loc X. Nguyen Yan Kyaw Tun Zhu Han Choong Seon Hong Department of Computer Science and Engineering Kyung Hee University Yongin-si Republic of Korea Division of Network and Systems Engineering School of Electrical Engineering and Computer Science (EECS) KTH Royal Institute of Technology Brinellvägen 8 Stockholm Sweden Department of Electrical and Computer Engineering University of Houston Houston TX USA

The significant growth in data consumption among mobile users necessitates the development of new architecture to meet the increasing demand. On the other hand, reconfigurable intelligent surface (RIS) has grown in popularity in 6G due to its improved spectral efficiency, simplicity of deployment, and low cost. However, with the constrained limitation of the coverage by conventional RIS, the research direction has turned towards simultaneously transmitting and reflecting RIS (STAR-RIS) to provide 360° coverage alongside the benefits of RIS. In this paper, a STAR-RIS-assisted downlink communication system for both indoor and outdoor users is investigated. Then, the optimization problem to maximize the spectral efficiency while jointly controlling the beamforming power for each user and phase shift values of the STAR-RIS is formulated. Since the formulated problem is NP-hard and challenging to solve in polynomial time, a policy gradient method for reinforcement learning named proximal policy optimization (PPO) is implemented to solve the problem. To demonstrate the effectiveness of our proposed algorithm, extensive simulation results are executed. Numerical results prove that our proposed algorithm outperforms several benchmark schemes in the literature.

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A Survey on Reconfigurable Intelligent Surface-Assisted Orthogonal Time Frequency Space systems

IEEE Open Journal of Vehicular Technology

引用

IEEE Open Journal of Vehicular Technology 2025年

作者： Tao, Qin Li, Zhongjie Zhi, Kangda Li, Shuangyang Yuan, Weijie Zaniboni, Lorenzo Stanczak, Slawomir Viterbo, Emanuele Wang, Xianbin Hangzhou Normal University School of Information Science and Technology Hangzhou311121 China School of Automation and Intelligent Manufacturing Southern University of Science and Techonology Shenzhen518055 China Technical University of Berlin School of Electrical Engineering and Computer Science Berlin10623 Germany Technical University of Munich Institute for Communications Engineering Munich80333 Germany Fraunhofer Heinrich Hertz Institute Wireless Communications and Networks Department Berlin10587 Germany Monash University Department of Electrical and Computer Systems Engineering ClaytonVIC3800 Australia Western University Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada

The vision for 6th-generation (6G) wireless communication systems emphasizes the need for robust and reliable communication in extremely high-mobility scenarios, while also addressing critical demands for energy and spectral efficiency. Under such scenarios, doubly time-frequency selective fading channels often significantly degrade the performance of orthogonal frequency-division multiplexing (OFDM) based systems due to the impact of large delay and Doppler shifts. Recently, orthogonal time frequency space (OTFS) modulation has emerged as a promising alternative. By processing signals in the delay-Doppler (DD) domain, OTFS offers several advantages, including quasi-static channel characteristics, full-time-frequency diversity, and low peak-to-average power ratio (PAPR), making it a promising candidate for high-mobility communications. Reconfigurable intelligent surfaces (RIS) are being further integrated to enhance the performance of OTFS systems cost-effectively. With their ability to dynamically reconfigure the wireless environment, the integration of RIS can offer significant performance improvements for OTFS systems. This survey offers a comprehensive review of RIS-assisted OTFS systems, including the fundamental principles, recent advances, and future research directions. Specifically, we first introduce the background of RIS-assisted OTFS systems, outlining the opportunities and challenges of their integration. To ensure the survey is self-contained, we provide a brief overview of the fundamental principles of OTFS and RIS technologies. Building on these foundations, we present a general input-output relationship and capacity characterization for RIS-assited MIMO-OTFS systems. Then, this survey further explores cutting-edge research in areas such as input-output analysis, RIS phase shift design, channel estimation, detection techniques, RIS-assisted integrated sensing and communication (ISAC), and other novel technologies. Finally, we outline some future rese

关键词： Orthogonal frequency division multiplexing

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