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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Kh'ng, Xin Yi Ooi, Boon Yaik Teoh, Sheng Kang Ooi, Boon Sheng Chen, Yen-Lin Universiti Tunku Abdul Rahman Department of Computer Science Kampar Malaysia Xilnex Web Bytes Sdn. Bhd. Penang Malaysia National Taipei University of Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

A comprehensive visual traits-based recommendation system is designed for proactive retailing in a physical store environment. The proposed system utilizes computer vision algorithms to analyze various visual traits of customers, including facial features, clothing and accessories, to provide targeted product recommendations. The system does not require customers to provide any personal information, making it a less intrusive and more hassle-free approach compared to the conventional membership approach. By providing tailored product suggestions, the system offers retailers the opportunity to enhance their customers' shopping experience and increase sales. This paper provides a detailed description of the system's design and setup. © 2023 IEEE.

关键词： Recommender systems

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Electromagnetic Near-Field Scanning with a Spatially Sparse Sampling Strategy Utilizing Kriging-DMD

Electromagnetic Near-Field Scanning with a Spatially Sparse ...

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IEEE Workshop on Signal Propagation on Interconnects (SPI)

作者： Yanming Zhang Steven Gao Lijun Jiang Dept. of Electronic Engineering Chinese University of Hong Kong Hong Kong Dept. of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

ISBN: (数字)9798350382938

ISBN: (纸本)9798350382945

This paper proposes a hybrid method for time-resolved electromagnetic near-field scanning, merging model-based (Gaussian processes regression model, a.k.a. Kriging method) and data-driven (dynamic mode decomposition) techniques. Specifically, Latin hypercube sampling enables spatially sparse measurements, followed by dynamic mode decomposition to analyze resulting sparse spatial-temporal data, extracting frequency information and sparse dynamic modes. The Kriging method is then employed for full-state reconstruction. The proposed approach is evaluated using crossed dipole antennas. Results indicate that, even with a spatial subsampling factor of 130, achieving a fully reconstructed field distribution suitable for engineering applications with frequency information extraction is feasible. This hybrid framework presents a promising avenue to enhance efficiency in electromagnetic near-field measurements, potentially finding applications across diverse electromagnetic measurement scenarios.

关键词： Antenna measurements Electromagnetic measurements Dipole antennas Merging Gaussian processes Information retrieval Hypercubes

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Performance Evaluation of Coherent MIMO Radar Assisted with Space-Time Coding 9

Performance Evaluation of Coherent MIMO Radar Assisted with ...

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9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Barakat, Mohammed Elmogtaba Saeed, Rashid A. Barakat, Mohammed Edam, Salaheldin Khalifa, Othman O. Sudan University of Science and Technology School of Electronics Engineering Khartoum Sudan Taif University College of Computers and Information Technology Department of Computer Engineering P.O. Box 11099 Taif21944 Saudi Arabia International Islamic University Malaysia Dept. of Electrical and Computer Engineering Malaysia Libyan Center for Engineering Research and Information Technology Bani Waleed Libya

ISBN: (纸本)9798350325218

Using multiple-input multiple-output (MIMO) technology has revolutionized the radar field by providing higher resolution, improved target detection, and reduced interference. However, interrupting signals can significantly degrade the Performance of MIMO radar systems to transmit and receive signals simultaneously;MIMO radar systems employ multiple antennas at the transmitter and receiver. This allows for improved spatial resolution and increased target detection capabilities. However, MIMO radar systems are susceptible to fading and interference, degrading performance. STC is a technique to mitigate these effects by transmitting multiple signals simultaneously from different antennas. This article evaluates the performance of space-time coding (STC) with coherent multiple-input multiple-output (MIMO) radar. The paper investigates the impact of STC on the radar system's detection and estimation capabilities in various scenarios, including target detection in cluttered environments and target tracking in the presence of interference. The simulation results demonstrate that STC can significantly improve the radar system's performance by reducing interference effects and increasing the signal-to-noise ratio. Furthermore, the study shows that different STC schemes have varying degrees of effectiveness depending on the specific scenario. Overall, this research provides valuable insights into the potential benefits of using STC with coherent MIMO radar for various applications. © 2023 IEEE.

关键词： Target tracking

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Optimization Of Fuzzy Logic-Based Clustering For Wireless Sensor Networks 2

Optimization Of Fuzzy Logic-Based Clustering For Wireless Se...

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2nd International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Hussein, Ahmed Mohammed Kurnaz, Sefer Saad, Mohammed Ayad Ibrahim, Raed Khalid Ali, Adnan Hussein Taher, Anmar Yahya Altinbas University Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept. Turkey Al-Kitab University Dept. of Medical Instrumentations Technique Engineering Kirkuk36001 Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad Iraq Institute of Technology Middle Technical University Electrical Techniques Department Baghdad Iraq

ISBN: (纸本)9798350373363

A strong framework for managing complicated and uncertain data patterns is provided by fuzzy C-Means (FCM) clustering, a potent and frequently used data analysis technique that is adaptable for data clustering in a variety of applications, including pattern recognition, picture segmentation, and data mining. Assigning data points to various clusters with different degrees of membership, in contrast to typical hard clustering approaches, makes FCM especially well-suited for situations where data show ambiguity and overlap. An overview of FCM is given in this abstract, emphasizing its fundamental ideas, iterative optimization procedure, and capacity to manage intricate data patterns. It also goes over the benefits and uses of FCM in practical situations, highlighting how it can be used to provide cluster assignments that are both flexible and nuanced. For researchers and practitioners looking for efficient ways to cluster data even in the face of ambiguity and incomplete memberships, FCM remains a useful tool. © 2023 IEEE.

关键词： Pattern recognition

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Person Re-Identification using Deep Learning 4

Person Re-Identification using Deep Learning

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Saini, Taruna Gandhi, Ridhima Roy, Sanu Diwakar, Manoj Singh, Prabhishek Mishra, Amit Kumar Graphic Era Deemed to Be University Department of Cse Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India Graphic Era Hill University Dept. of Cse Dehradun India

ISBN: (纸本)9798350364729

Re-identification of person (re-ID) is a computer vision task, which is trying to find out the same individual's pictures through a single camera scenario. For addressing Re-ID, we have proposed a deep learning architecture i.e. ResNet, and open Visual Inference and Neural Optimization (Open VINO) toolkit from intel. In this paper, we have utilized the probable feature of learning in the ResNet model that is instructed on enormous datasets and fine-turned on strenuous market-1501 re-ID standard dataset. This process implicates data preprocessing, model selection, training, and optimization steps that are important for acceptable results. We have used data augmentation and triplet loss function to construct implanting more selective as a final step in this pipeline and our developed ResNet model is supported by OpenVINO for hardware-specific optimizations and model conversion. Our Solution on ResNet delivers real-time processing speeds using GPU. This approach shows that is possible and easy to deploy precise person Re-Id for surveillance, forensic identification, and security monitoring. © 2024 IEEE.

关键词： Deep learning

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3D human skeleton keypoint detection using RGB and dept. image

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Transactions of the Korean Institute of electrical Engineers 2021年第9期70卷 1354-1361页

作者： Jeong, Jeongseok Park, Byeongjun Yoon, Kyoungro Dept. of Smart ICT Convergence Engineering Konkuk University Korea Republic of Dept. of Computer Science and Engineering Konkuk University Korea Republic of

As computing technology advances, tasks those are used to judge human behavior with the eyes are turning into tasks those computers try to judge human behavior through keypoint detection. Accordingly, in this paper, we propose a 3D human skeleton keypoint detection system using RGB and dept. images acquired by Azure Kinect's RGB camera and dept. camera, respectively. The 3D human skeleton keypoint detection system proposed in this paper detects 2D human skeleton keypoints from RGB images, and uses dept. value acquired to project the detected 2D human skeleton keypoints onto the dept. image. However, when detecting 3D human skeleton keypoints in such method, the human skeleton keypoints are projected onto the surface of human body. To solve this problem, the skeleton keypoints provided by Azure Kinect is used to calibrate the dept. value of the extracted keypoints. Copyright © The Korean Institute of electrical Engineers.

关键词： Cameras

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VLC Transceiver SoC Design for Real-Time Indoor-Tracking in Smart Hospitals

VLC Transceiver SoC Design for Real-Time Indoor-Tracking in ...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Zijin Pan Tian Lang Gang Chen Albert Wang Dept. of Electrical and Computer Engineering University of California Riverside Riverside CA USA

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

Visible light communication (VLC) technique is well developed and proved to be a practical solution for real-time indoor tracking application, especially in hospitals where radiofrequency (RF) wireless technologies are forbidden due to potential interference hazards. As a continuous work for the LED-based VLP/PLC hybrid indoor tracking system, a fully integrated system-on-chip (SoC) design for the optical transceiver is proposed and discussed in this paper. The SoC integrated with transmitter, receiver and power supply circuits was designed on a commercial 45nm CMOS integrated circuit technology enabling the modulation and demodulation process for multiple frequencies. This work validated the feasibility of developing an efficient and low-cost tracking system using LED VLC technology for future smart hospitals.

关键词： Radio frequency Hospitals Wireless networks Light emitting diodes Transceivers Real-time systems System-on-chip Optical transmitters Visible light communication Demodulation

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Learning Bayes-Optimal Channel Estimation for Holographic MIMO in Unknown EM Environments

Learning Bayes-Optimal Channel Estimation for Holographic MI...

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IEEE International Conference on Communications (ICC)

作者： Wentao Yu Hengtao He Xianghao Yu Shenghui Song Jun Zhang Ross D. Murch Khaled B. Letaief Dept. of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Dept. of Electrical Engineering City University of Hong Kong Hong Kong

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Holographic MIMO (HMIMO) has recently been recognized as a promising enabler for future 6G systems through the use of an ultra-massive number of antennas in a compact space to exploit the propagation characteristics of the electromagnetic (EM) channel. Nevertheless, the promised gain of HMIMO could not be fully unleashed without an efficient means to estimate the high-dimensional channel. Bayes-optimal estimators typically necessitate either a large volume of supervised training samples or a priori knowledge of the true channel distribution, which could hardly be available in practice due to the enormous system scale and the complicated EM environments. It is thus important to design a Bayes-optimal estimator for the HMIMO channels in arbitrary and unknown EM environments, free of any supervision or priors. This work proposes a self-supervised minimum mean-square-error (MMSE) channel estimation algorithm based on powerful machine learning tools, i.e., score matching and principal component analysis. The training stage requires only the pilot signals, without knowing the spatial correlation, the ground-truth channels, or the received signal-to-noise-ratio. Simulation results will show that, even being totally self-supervised, the proposed algorithm can still approach the performance of the oracle MMSE method with an extremely low complexity, making it a competitive candidate in practice.

关键词： Training 6G mobile communication Machine learning algorithms Simulation Channel estimation Estimation Machine learning

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Predicting the Price Direction of Bitcoin Using Twitter Data and Machine Learning 2

Predicting the Price Direction of Bitcoin Using Twitter Data...

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2nd IEEE International Conference on Data science and computer Application, ICDSCA 2022

作者： Kanji, Abdul Mannan Chaudhary, Ishita Shankar, Rithika Lakshmi Srinivasa, Gowri Pes University Pes Center for Pattern Recognition Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

Bitcoin is a decentralized digital currency that was intro- duced in 2009 and since then, has become increasingly popular as one of the most known and highly valued currencies. Contributing factors to its rise include crypto Twitter influencers. An engaged audience on Twitter seems to have an influence on the cryptocurrency market. In this paper, we analyze the impact that tweets have on the price of Bitcoin. Using word-clouds and candlestick plots, we gain insight into the factors that affect Bitcoin prices. We also use various machine learning techniques to automatically classify the sentiment in Tweets related to cryptocurrencies. We incorporate these and other relevant features to build and compare the performance of multiple machine learning models to predict the direction (increase or decrease) of the price of Bitcoin. © 2022 IEEE.

关键词： Forecasting

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Towards Optimal Association of Coexisting RF, THz and mmWave Users in 6G Networks

Towards Optimal Association of Coexisting RF, THz and mmWave...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Noha Hassan Xavier Fernando Isaac Woungang Alagan Anpalagan Dept. of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada Dept. of Computer Science Toronto Metropolitan University Toronto Canada

The sixth generation (6G) mobile communication system is expected to utilize millimeter wave and THz frequency bands, in addition to RF bands. The propagation characteristics and ranges of these bands vary vastly while the multi-band users supposedly experience seamless coverage, high throughput and consistent Quality of Service. Heterogeneous base stations (BSs) equipped with these multiple technologies shall be fairly loaded for this. SINR based approaches tend to assign more users to RF channels while starving other bandwidth rich mediums. In this paper, we propose an algorithm to improve the performance of multi-band 6G networks by optimizing the user association to heterogeneous BS to maximize the cumulative data rate while ensuring an acceptable transmission power and fair load balancing among the BSs. The optimization problem is solved using the Lagrangian method. Simulation results show an improved cumulative throughput and fairness.

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