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Fundamental Limits of multiple-Access Integrated Sensing and Communication Systems

IEEE Transactions on Information Theory 2025年第6期71卷 4317-4341页

作者： Liu, Yao Li, Min Liu, An Ong, Lawrence Yener, Aylin Zhejiang University College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China University of Newcastle School of Engineering CallaghanNSW2308 Australia The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States

A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results. © 1963-2012 IEEE.

关键词： signal distortion

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Smart Infant Monitoring System Using computer Vision and AI

Smart Infant Monitoring System Using Computer Vision and AI

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Singh, Gurpreet Shekhar, Abhishek Raj Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

The new era of technology is being greatly influenced by the field of artificial intelligence. computer vision and deep learning have become increasingly important due to their ability to process vast amounts of data and provide insights and solutions in a variety of fields. computer vision, deep learning and signal analysis have been used in a growing number of applications and services including smart devices, image, and speech recognition, healthcare, etc., one such device is an infant monitoring system. It monitors the daily activities of the infant such as their sleeping patterns, sounds, and movements. In this paper, deep learning and computer vision libraries were used to develop algorithms to detect whether the infant was in any uncomfortable situation such as sleeping on its back, face being covered and whether the infant was awake. The smart infant monitoring system detects the infant's unsafe resting situation in real time and sent immediate alerts to the caretaker's device. This paper presents the design flow of a smart infant monitoring system consisting of a night vision camera, a Jetson Nano, and a Wi-Fi internet connection. The pose estimation and awake detection algorithms were developed and tested successfully for different infant resting/sleeping situations. The smart infant monitoring system provides significant benefits for safety and an improved understanding of infants' sleep patterns and behavior. © 2023 IEEE.

关键词： Face recognition

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Fire Security Systems Analysis and Internet of Things Implications

Fire Security Systems Analysis and Internet of Things Implic...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Fuerte, Susana Rodriguez Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

This paper presents the current use of the Internet of Things (IoT) in fire evacuation and extinction. It examines the different approaches to the problem and technologies like Building Information Modeling (BIM) and mathematical algorithms that can be used to determine the optimal evacuation route. It also evaluates existing fire security solutions such as smoke, flame, motion, and gas sensors, LED lights, buzzers, and SMS modules. Entities that specialize in Residential and Commercial Security Systems and Home Automation are also discussed, along with the services they offer. The main objective of this study is to understand current systems and resources regarding fire evacuation and extinction systems and to analyze different developments in smart buildings to create an efficient system for fire detection and evacuation. © 2023 IEEE.

关键词： Internet of things

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Dielectric Resonator Magnetoelectric Dipole Arrays with Low Cross Polarization,Backward Radiation,and Mutual Coupling for MIMO Base Station Applications

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China Communications 2024年第6期21卷 290-304页

作者： Song Simin Da Yiran Qian Bingyi Huang Xiaoyu Chen Xiaoming Li Yingsong Ahmed A.Kishk School of Information and Communications Engineering Xi’an Jiaotong UniversityXi’an 710049China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui UniversityHefei 230039China Department of Electrical and Computer Engineering Concordia UniversityMontrealQC H3G 1M8Canada

Dielectric resonator magnetoelectric dipole(DRMED)arrays with enhanced isolation,reduced cross-polarization,and backward radiation are proposed for base station(BS)*** proposed antenna comprises an elevated dielectric resonator antenna(DRA)on a small metal plate above a sizeable common ground *** DRA is designed in its T Eδ11 mode,acting like a magnetic *** surface current excited by the differential probes flowing on the small ground plane is equivalent to an electric *** these two equivalent dipoles are orthogonal,they have the magnetoelectric dipole characteristics with reduced backward ***,the small ground planes can be treated as decoupling structures to provide a neutralization path to cancel the original coupling path.A linearly-polarized 4-element prototype array was verified experimentally in previous ***,a dual-polarized DRMED antenna is presented to construct a 2-element and 4×4 array for BS *** investigate its MIMO performance,sophisticated multi-cell scenario simulations are carried *** using the proposed dualpolarized DRMED array,the cellular system capacityis improved by 118.6%compared to a conventional DRA *** significant MIMO system improvement is mainly due to the reduced backward radiation and,therefore,reduced inter-cell *** align well with the simulations.

关键词： backward radiation dielectric resonator antennas(DRAs) isolation magnetoelectric-dipole(ME-dipole)antennas

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High Precision Analog Gauge Reader Using Optical Flow and computer Vision

High Precision Analog Gauge Reader Using Optical Flow and Co...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Chavan, Shruti Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

This paper aims at developing algorithms to automate the process of reading analog gauges at different operational industries, healthcare sector and automobiles using Artificial Intelligence (AI) techniques. Proposed algorithms start with video stabilization using optical flow followed by image processing which performs segmentation using HSV color space and morphological operations. Next, Hough transform is applied to determine the dial region and pointer. Using the coordinates locating the pointer in the frame, angle made with minimum valued scale mark is calculated using trigonometry and evaluated reading is displayed in the form of text and a progress bar. The proposed algorithms for Analog Gauge Reader (AGR) is executed using open-source libraries such as OpenCV, NumPy, PIL with Python programming and it is developed in the PyCharm environment. The experimental results show significant decrease in the error with the introduction of image preprocessing and the generalizability of the algorithm to different types of analog gauges captured in different conditions of illuminance. The accuracy and precision of the algorithms is 99% to 100% in the experimental studies. © 2022 IEEE.

关键词： Hough transforms

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Artificial Intelligence System for Emotion Recognition and Text Analytics

Artificial Intelligence System for Emotion Recognition and T...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Chaudhari, Namrata Agarwal, Reshu Narwade, Meghna Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

Harnessing the power of emotional intelligence by analyzing a person's behavioral and linguistic skills can help humans improve their approach to social interactions. In this paper, we propose an artificial intelligence-based stand-alone system that will allow us to classify and analyze facial expressions in real-time and perform sentiment analysis by examining the body of the text (extracted from audio) to understand the opinion expressed by it. This helps us provide a deeper understanding of how humans really feel at a given time. The proposed system uses a deep neural network (DNN) for classifying eight basic emotions based on features extracted from facial expressions and uses pretrained sentiment analysis tools to quantify text (extracted from audio) based on polarity. The system is implemented by extracting the audio and visual cues from real-time scenarios and using these extracted cues to perform facial expression recognition and text sentiment analysis. The integrated system extracts video and audio simultaneously with a frame rate of 4-5 fps. The facial emotion detection system successfully detects facial expressions of faces detected in real-time video with an accuracy of about 86.75%. The speech extracted is converted to text, cleaned, and processed to determine if the attitude of the speaker in a given situation is positive, negative, or neutral. © 2022 IEEE.

关键词： Deep neural networks

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Soccer Player Recognition using Artificial Intelligence and computer Vision

Soccer Player Recognition using Artificial Intelligence and ...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Diop, Charles-Alexandre Pelloux, Baptiste Yu, Xinrui Yi, Won-Jae Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665480093

Groundbreaking features and functionalities are available in sports broadcasting programs, specifically in soccer games, such as post-game analysis, tracking of players, tracking of the ball, and associated teams' and players' statistical information. However, viewers don't have control to choose which features to view on-demand and aren't able to interact with any of these functionalities. Our system aims for the fundamental system of an on-demand viewer-driven application that would be able to track players on the field, identify formation changes, follow team strategy changes, and gather all statistical information of the player and the team on a single screen. In order to realize such an application, we focus on developing a method to track players on the field using multiple Artificial Intelligence (AI) and computer vision techniques where our application employs facial recognition and jersey number recognition algorithms. As a live soccer game broadcast would have various camera views of the players, our custom-made database contains captured images from different scenarios of camera views such as different angles and zooms of the player and is used to train the model using Convolutional Neural Network (CNN). Our system is scalable to different types of sports, such as basketball, baseball, volleyball, and more where players have their numbers on their jerseys and would be feasible to apply our system since soccer involves more players on the field than other sports games. © 2022 IEEE.

关键词： Cameras

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multimedia Classification via Tensor Linear Discriminant Analysis

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IEEE Transactions on Broadcasting 2024年第4期70卷 1139-1152页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Yan, Kun Chih-Hao Huang, Scott Wu, Yiyan San Jose State University Department Of Applied Data Science San JoseCA95192 United States Louisiana State University School Of Electrical Engineering And Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center For Ai Applications Taoyuan32003 Taiwan Guilin University Of Electronic Technology Guangxi Key Laboratory Of Wireless Wideband Communication And Signal Processing Department Of Information And Telecommunication Guilin541004 China National Tsing Hua University Department Of Electrical Engineering Hsinchu300 Taiwan Western University Department Of Electrical And Computer Engineering LondonONN6A 3K7 Canada

Linear discriminant analysis (LDA) is a well-known feature-extraction technique for data analytic and pattern classification. As the dimensionality of multimedia data has increased in this big era, it is often to characterize data by tensors. Over the past two decades, researchers have thus explored to extend LDA to the general tensor space, especially in two common ways: LDA of tensors using tensor decomposition methods (by conversion of tensors to matrices) and LDA of tensors built upon the T-product. However, both of the aforementioned approaches have restrictions thereby. A critical problem about how to carry out LDA of arbitrary scatter tensors based on the Einstein product still remains unsolved by the existing methods. Therefore, we propose a novel tensor LDA (a.k.a. TLDA) approach, which can carry out the LDA of arbitrary-dimensional scatter-tensors without any need of tensor decomposition. Besides, for reducing the computation time, we also design a parallel paradigm to execute our proposed TLDA in this work. Numerical experiments conducted over real multimedia data demonstrate the efficacy of our proposed new TLDA in terms of classification accuracy. Moreover, the comparison of the classification accuracies, computational-complexities, and memory-complexities of our proposed novel TLDA scheme and other existing tensor-based LDA methods is made. By leveraging TLDA for high-dimensional feature extraction, segmentation, and user-item interaction data processing, future multimedia recommendation systems can facilitate more accurate, engaging, and satisfactory user experience over the Internet. © 1963-12012 IEEE.

关键词： Tensors

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Automatic Detection and Characterization of Human Veins Using Infra-Red Image processing

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Journal of computer and Communications 2024年第9期12卷 141-159页

作者： Jean Ndoumbe Brice Ekobo Akoa Gaelle Patricia Talotsing Frederic Franck Kounga Samuel Kaissassou Bertin Chouanmo Njo Laboratory of Computer Engineering Data Science and Artificial Intelligence Department of Computer & Telecommunications Engineering National Higher Polytechnic School Douala Cameroon Laboratory of Electrical Engineering Mechatronics and Signal Processing Department of Electrical & Telecommunications Engineering National Advanced School of Engineering Yaound Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada DigiPlus SARL Bonamoussadi Douala Cameroon

The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.

关键词： Vein Detection Blood Radiation Infrared Image CLAHE Algorithm Raspberry Pi

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InfaSafe: A Comprehensive, Non-invasive Infant Monitoring System

InfaSafe: A Comprehensive, Non-invasive Infant Monitoring Sy...

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IEEE International Conference on Electro-Information Technology

作者： Lukas Klicker Alex Maliwat Joanna Findura Xinrui Yu Mikhail Gromov Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL USA

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

InfaSafe emerges as a novel approach to infant health monitoring, uniquely positioned at the convergence of advanced artificial intelligence and edge computing. This system is designed not as a definitive solution but as an advanced platform for comprehensive data archiving, offering valuable insights into the complex and elusive nature of Sudden Unexpected Infant Death (SUID). InfaSafe utilizes AI algorithms for real-time pose estimation, breathing surveillance, and cry analysis, all within an edge computing framework that facilitates prompt and efficient data handling. This paper explores the development and capabilities of InfaSafe, underscoring its role in providing crucial, real-time insights and alerts for caregivers and its potential to contribute significantly to our understanding of neonatal health and SUID. The focus is on leveraging technological advancements to gather comprehensive data, which can be instrumental in shaping future research and interventions in neonatal care.

关键词： Temperature sensors Temperature measurement Pediatrics Temperature distribution Prototypes Real-time systems Thermal analysis

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