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Sensor Array and Multichannel Signal Processing Workshop

作者： Mirza A. Haider Yimin D. Zhang Yanwu Ding Dan Shen Khanh Pham Genshe Chen Department of Electrical and Computer Engineering Temple University Philadelphia PA USA Department of Electrical and Computer Engineering Wichita State University Wichita KS USA Intelligent Fusion Technology Inc. Germantown MD USA Air Force Research Laboratory Kirtland Air Force Base NM USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

The popularity of mmWave in 5G and future communications is hindered by challenging propagation environments, such as line-of-sight obstruction. Reconfigurable intelligent surfaces (RIS) address this issue by dynamically modifying wireless channels, thereby enhancing data rates, reducing latency, and improving reliability in non-line-of-sight scenarios. For high data-rate communication and precise mobile user localization, a large RIS is required, resulting in a high pilot overhead for channel estimation. To address this issue, we exploit a semi-passive RIS with sparsely distributed active RIS elements in lieu of fully passive RIS. This approach efficiently enables channel estimation both between the base station and the RIS as well as between the RIS and the mobile users. Structured covariance matrix interpolation optimally utilizes the array aperture from the sparsely placed active RIS elements. Recognizing the need for frequent channel estimation, we introduce a recurrent neural network-based model for sequential channel prediction, resulting in a significant reduction of the required training pilot signals. Simulation results affirm the capability and effectiveness of the proposed approach to enhance data transmission.

关键词： Wireless communication Training Wireless sensor networks Machine learning algorithms Array signal processing Simulation Channel estimation

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Cooperative Multiple-Access Channels with Distributed State Information

Cooperative Multiple-Access Channels with Distributed State ...

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作者： Miretti, Lorenzo Kobayashi, Mari Gesbert, David De Kerret, Paul Department of Communication Systems EURECOM Sophia Antipolis France Department of Electrical and Computer Engineering Technical University of Munich Munich Germany

This paper studies a memoryless state-dependent multiple access channel (MAC) where two transmitters wish to convey a message to a receiver under the assumption of causal and imperfect channel state information at transmitters (CSIT) and imperfect channel state information at receiver (CSIR). In order to emphasize the limitation of transmitter cooperation between physically distributed nodes, we focus on the so-called distributed CSIT assumption, i.e., where each transmitter has its individual channel knowledge, while the message can be assumed to be partially or entirely shared a priori between transmitters by exploiting some on-board memory. Under this setup, the first part of the paper characterizes the common message capacity of the channel at hand for arbitrary CSIT and CSIR structure. The optimal scheme builds on Shannon strategies, i.e., optimal codes are constructed by letting the channel inputs be a function of current CSIT only. For a special case when CSIT is a deterministic function of CSIR, the considered scheme also achieves the capacity region of a common message and two private messages. The second part addresses an important instance of the previous general result in a context of a cooperative multi-antenna Gaussian channel under i.i.d. fading operating in frequency-division duplex mode, such that CSIT is acquired via an explicit feedback of perfect CSIR. The capacity of the channel at hand is achieved by distributed linear precoding applied to Gaussian codes. Surprisingly, we demonstrate that it is suboptimal to send a number of data streams bounded by the number of transmit antennas as typically considered in a centralized CSIT setup. Finally, numerical examples are provided to evaluate the sum capacity of the binary MAC with binary states as well as the Gaussian MAC with i.i.d. fading. © 1963-2012 IEEE.

关键词： Transmitters

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CLIP-AE: A Multi-modal Unsupervised Images Enhancement Method Based on High-order Adaptive Curve for Visual Disbalance Defects

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IEEE Transactions on Multimedia 2025年

作者： Wu, Jiaqi Zhang, Shihao Hou, Mingshuo Wang, Zehua Chen, Wei Tian, Zijian Yu, F. Richard Leung, Victor C. M. School of Artificial Intelligence Beijing100083 China University of British Columbia Department of Electrical and Computer Engineering 2332 Main Mall Vancouver VancouverBCV6T 1Z4 Canada The University of British Columbia Department of Electrical and Computer Engineering 2332 Main Mall Vancouver VancouverBCV6T 1Z4 Canada China University of Mining and Technology School of Computer Science & Technology Jiangsu Xuzhou221116 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

For visual disbalance defects (VDDs) in low-light images, such as brightness unevenness and color imbalance, existing enhancement methods struggle to extract defect features from local regions and apply adaptive enhancement based on varying degrees of these defects. To address these challenges, we propose an unsupervised multi-modal enhancement method based on a high-order adaptive curve, named CLIP-AE. Specifically, we introduce a multi-modal recurrent optimization approach utilizing contrastive language-image pre-training (CLIP). This method iteratively optimizes variable embedded prompts and an Adaptive Enhancement Module (AEM) to establish dependencies between the prompts and detailed style features in the images, guiding the AEM to perform adaptive image enhancement. Additionally, we implement a progressive feature alignment strategy to enhance the model's ability to perceive style features and improve optimization efficiency by using multiple enhanced images with identical content features and incremental style features. In the AEM, the optimized Hyperparameters Generative Network (HGN) generates the optimal hyperparameters, which drive a High-Dimensional Nested Gamma correction (HDN-Gamma) to perform pixel-wise adaptive enhancement for VDDs. HDN-Gamma further maps pixel values using specific enhancement curves to avoid artifacts. Extensive experiments demonstrate that our method effectively improves visual disbalance defects and reduces artifacts. Compared to seven state-of-the-art algorithms, our method shows significant improvements (PSNR: 16.46%, 16.89%, and 15.14%;SSIM: 9.26%, 8.02%, and 9.85%;MUSIQ: 6.37%, 6.54%, and 7.45%) on the LOL, SICE, and MIT-Adobe FiveK datasets. Our approach offers a novel solution for applying multimedia technology in low-light image enhancement tasks. © 1999-2012 IEEE.

关键词： Image enhancement

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Extracting Biomedical Entities from Noisy Audio Transcripts 30

Extracting Biomedical Entities from Noisy Audio Transcripts

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ebadi, Nima Morgan, Kellen Tan, Adrian Linares, Billy Osborn, Sheri Majors, Emma Davis, Jeremy Rios, Anthony Department of Electrical and Computer Engineering The University of Texas San Antonio United States Department of Management Science and Statistics The University of Texas San Antonio United States Data Analytics The University of Texas San Antonio United States Department of Information Systems and Cyber Security The University of Texas San Antonio United States Department of Neurology Division of Neuropsychology UT Health San Antonio United States

ISBN: (纸本)9782493814104

Automatic Speech Recognition (ASR) technology is fundamental in transcribing spoken language into text, with considerable applications in the clinical realm, including streamlining medical transcription and integrating with Electronic Health Record (EHR) systems. Nevertheless, challenges persist, especially when transcriptions contain noise, leading to significant drops in performance when Natural Language Processing (NLP) models are applied. Named Entity Recognition (NER), an essential clinical task, is particularly affected by such noise, often termed the ASR-NLP gap. Prior works have primarily studied ASR's efficiency in clean recordings, leaving a research gap concerning the performance in noisy environments. This paper introduces a novel dataset, BioASR-NER, designed to bridge the ASR-NLP gap in the biomedical domain, focusing on extracting adverse drug reactions and mentions of entities from the Brief Test of Adult Cognition by Telephone (BTACT) exam. Our dataset offers a comprehensive collection of almost 2,000 clean and noisy recordings. In addressing the noise challenge, we present an innovative transcript-cleaning method using GPT4, investigating both zero-shot and few-shot methodologies. Our study further delves into an error analysis, shedding light on the types of errors in transcription software, corrections by GPT4, and the challenges GPT4 faces. This paper aims to foster improved understanding and potential solutions for the ASR-NLP gap, ultimately supporting enhanced healthcare documentation practices. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Error analysis

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AM-Align: Globally Optimal Estimation of Accelerometer-Magnetometer Misalignment

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Hu, Xiangcheng Wu, Jin Xue, Bohuan Zhu, Yilong Jia, Mingkai Qi, Yuhua Jiang, Yi Tan, Ping Zhang, Wei Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong Sun Yat-Sen University School of Systems Science and Engineering Guangzhou China City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong

Misalignment estimation of the accelerometer-magnetometer (AM) combination is crucial for accurate attitude and position estimation in various applications. Despite significant research efforts over the past decades, existing algorithms rely on sufficient and outlier-free data to guarantee a satisfactory initial guess for optimization. This paper presents AM-Align, a novel globally optimal method that addresses this limitation. The key innovation of AM-Align is its ability to find all global optima by identifying the solution set with the least loss function values using the polynomial eigenvalue method, making it independent of initial guesses and robust to insufficient data and outliers. Simulation studies demonstrate the complete solution set for the minimal case of the AM calibration problem, providing valuable insights. Experimental results showcase the superiority of AMAlign compared to state-of-the-art methods, requiring fewer measurements while achieving higher accuracy and robustness. Notably, AM-Align outperforms representative algorithms by a significant margin in terms of calibration precision and resilience to data scarcity and outliers. This work presents a significant advancement in AM calibration, enabling more reliable attitude and position estimation in real-world scenarios. We will make the AM-Align codebase1 publicly available to the community, further advancing the development of this field. © 2025 IEEE.

关键词： Calibration

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Hyperbolic Delaunay Geometric Alignment

arXiv

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arXiv 2024年

作者： Medbouhi, Aniss Aiman Marchetti, Giovanni Luca Polianskii, Vladislav Kravberg, Alexander Poklukar, Petra Varava, Anastasia Kragic, Danica Division of Robotics Perception and Learning Department of Intelligent Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

Hyperbolic machine learning is an emerging field aimed at representing data with a hierarchical structure. However, there is a lack of tools for evaluation and analysis of the resulting hyperbolic data representations. To this end, we propose Hyperbolic Delaunay Geometric Alignment (HyperDGA) – a similarity score for comparing datasets in a hyperbolic space. The core idea is counting the edges of the hyperbolic Delaunay graph connecting datapoints across the given sets. We provide an empirical investigation on synthetic and real-life biological data and demonstrate that HyperDGA outperforms the hyperbolic version of classical distances between sets. Furthermore, we showcase the potential of HyperDGA for evaluating latent representations inferred by a Hyperbolic Variational Auto-Encoder. © 2024, CC BY.

关键词： Geometry

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Creating Single Color Centers in Nanodiamonds with Ion Implantation

Creating Single Color Centers in Nanodiamonds with Ion Impla...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Xu, Xiaohui Martin, Zachariah O. Titze, Michael Wang, Yongqiang Sychev, Demid Henshaw, Jacob Lagutchev, Alexei S. Htoon, Han Bielejec, Ed Bogdanov, Simeon Shalaev, Vladimir M. Boltasseva, Alexandra School ofMaterials Engineering Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States SandiaNationalLaboratories AlbuquerqueNM87123 United States Los Alamos National Laboratory Los AlamosNM87545 United States Department of Electrical and Computer Engineering Nick Holonyak Jr. Micro and Nanotechnology Laboratory Illinois Quantum Information Science and Technology Center University of Illinois at Urbana-Champaign UrbanaIL60801 United States

We report the first demonstration of single silicon vacancy center creation in 20 nm nanodiamonds using silicon ion implantation combined with thermal annealing. Room-temperature single photon emission with linewidth ... 详细信息

ISBN: (纸本)9781957171258

关键词： Nanodiamonds

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Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 402-411页

作者： Xin Luo Wen Qin Ani Dong Khaled Sedraoui MengChu Zhou the School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808 Hengrui(Chongqing)Artificial Intelligence Research Center Department of Big Data Analyses TechniquesCloudwalkChongqing 401331China the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the Department of Computer and Information Science City College of Dongguan University of TechnologyDongguan 523419China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia

A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning ***,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial *** at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training *** it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality *** results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.

关键词： Big data industrial application industrial data latent factor analysis machine learning parallel algorithm recommender system(RS) stochastic gradient descent(SGD)

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Real-Time Testing of Microgrid Control Algorithms: Development of Experimental Setup

Real-Time Testing of Microgrid Control Algorithms: Developme...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Miodrag Forcan Goran Dobrić Amir Abed Mileta Žarković Marko Ikić Jovana Forcan Manuel Barragán-Villarejo José Maria Maza-Ortega Francisco de Paula García-López Faculty of Electrical Engineering University of East Sarajevo East Sarajevo Bosnia and Herzegovina School of Electrical Engineering University of Belgrade Belgrade Serbia Department of Computer Science and Systems Faculty of Philosophy University of East Sarajevo East Sarajevo Bosnia and Herzegovina Department of Electrical Engineering University of Sevilla Seville Spain

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

This paper provides an overview of microgrid control strategies, examining differences between centralized and decentralized approaches, and focusing on classical hierarchical control schemes as well as the structure of three-layer microgrid control systems. It also explores Hardware-in-the-Loop modeling, a crucial approach for microgrid simulation, with particular emphasis on devices supporting microgrid modeling. The paper primarily focuses on the design and implementation requirements of an experimental setup for real-time testing of microgrid control algorithms. The setup incorporates Hardware-in-the-Loop device, a power amplifier, a power converter, a battery energy storage system, and a digital controller. It is specifically designed to handle both power and information (control) signals, facilitating a comprehensive approach to evaluating and validating microgrid control strategies in real-world conditions. A detailed analysis of the setup elements and their interactions is also provided.

关键词： Battery energy storage system Power amplifiers Focusing Microgrids Control systems Real-time systems Information technology Testing

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Emotion Classification Through Speech Data Analysis

Emotion Classification Through Speech Data Analysis

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Simulation Winter Conference

作者： Luzalen Marcos Kristiina Valter Mai Abdolreza Abhari Resilience Engineering Lab Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Ontario Canada Department of Computer Science Distributed Systems and Multimedia Processing (DSMP) Lab Toronto Metropolitan University Ontario Canada

Good quality healthcare services require effective communication between the patient and the healthcare provider. This work will help improve the areas of healthcare systems automation and optimization by applying Speech Emotion Recognition (SER) in health consultations to prevent miscommunication between patients and healthcare providers. Crowd-Sourced Emotional Multimodal Actors Dataset (CREMA-D) was used to compare the performances of different machine learning models in classifying emotions. Before feeding the raw dataset to the models, exploratory data analysis was done to determine features that should be considered for future analysis. Our results showed that depending on the emotion, there are some syllables in the text that were emphasized or took time to be pronounced by the speaker. After data analysis, the dataset was fed into different models and determined that the Support Vector Machine (SVM) is a machine-learning model for SER.

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