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IEEE International Conference on Image Processing

作者： A. Aboudessouki Kh. M. Ali M. Elsharkawy A. Alksas A. Mahmoud F. Khalifa M. Ghazal J. Yousaf H. Abu Khalifeh A. El-Baz Bioengineering Department BioImaging Laboratory University of Louisville USA Pathology Department Faculty of Medicine Mansoura University Egypt Electrical and Computer Engineering Department Morgan State University USA Electrical Computer and Biomedical Engineering Department Abu Dhabi University UAE

Breast cancer is a prevalent and diverse type of cancer that exhibits unique clinicopathologic characteristics, making the correct identification of its subtype critical to providing targeted treatment and increasing survival rates. This identification process involves testing for the presence of four key molecular biomarkers, namely estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and antigen Ki67. For accurate diagnosis ,the expertise of a pathologist and immunohistochemistry is required. To overcome this diagnostic challenge, we present a novel approach based on a deep learning pipeline for automated classification. Our approach can detect tumor and non-tumoral regions of the HER2 biomarker. Our deep learning framework comprises a Dense Convolutional Network (DenseNet), which process whole slide images (WSIs) of breast tissues, dividing them into patches for input into the network. Moreover, our approach provides both patchwise and pixelwise classification and analyzes ten WSIs of breast cancer histology. Our proposed approach generates an image map that classifies slide images on the pixel-level, detecting the status of hormone HER2 receptor as either positive or negative. The obtained results show that our deep learning-based approach has the potential to enhance the pathologist’s capabilities in diagnosing histopathological images with automated classification.

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Line Spectral Estimation with Unlimited Sensing

arXiv

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arXiv 2024年

作者： Wang, Hongwei Fang, Jun Li, Hongbin Leus, Geert The National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China The Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States The Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2826 CD Netherlands

In the paper, we consider the line spectral estimation problem in an unlimited sensing framework (USF), where a modulo analog-to-digital converter (ADC) is employed to fold the input signal back into a bounded interval before quantization. Such an operation is mathematically equivalent to taking the modulo of the input signal with respect to the interval. To overcome the noise sensitivity of higher-order difference-based methods, we explore the properties of the first-order difference of modulo samples, and develop two line spectral estimation algorithms based on first-order difference, which are robust against noise. Specifically, we show that, with a high probability, the first-order difference of the original samples is equivalent to that of the modulo samples. By utilizing this property, line spectral estimation is solved via a robust sparse signal recovery approach. The second algorithms is built on our finding that, with a sufficiently high sampling rate, the first-order difference of the original samples can be decomposed as a sum of the first-order difference of the modulo samples and a sequence whose elements are confined to be three possible values. This decomposition enables us to formulate the line spectral estimation problem as a mixed integer linear program that can be efficiently solved. Simulation results show that both proposed methods are robust against noise and achieve a significant performance improvement over the higher-order difference-based method. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantization (signal)

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Enhancing Multi-Label ECG Classification via Task-Guided Lead Correlations in Internet of Medical Things

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IEEE Internet of Things Journal 2025年第12期12卷 20544-20555页

作者： Yuan, Xiaoyan Wang, Wei Chen, Junxin Fang, Kai Bashir, Ali Kashif Mondal, Tapas Hu, Xiping Deen, M. Jamal Beijing Institute of Technology School of Medical Technology Beijing100081 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Guangdong Shenzhen518172 China Dalian University of Technology School of Software Dalian116621 China Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Chitkara University Institute of Engineering and Technology Centre for Research Impact and Outcome Chitkara University Punjab Rajpura140401 India McMaster University Faculty of Health Science and the Department of Pediatrics Hamilton Canada McMaster University McMaster School of Biomedical Engineering Hamilton Canada McMaster University Electrical and Computer Engineering Department Hamilton Canada

With the rise of the Internet of Things (IoT), wearable devices have enabled real-time health monitoring, particularly through physiological signals like electrocardiograms (ECG). The standard 12-lead ECG records the electrical activity of the heart from multiple perspectives, providing valuable insights into cardiac health. However, existing 12-lead ECG analysis methods often treat leads as channel-level arrangements or rely on spatial adjacency to predefine lead connections, limiting their ability to capture the complex spatial and functional relationships between leads fully. To address this limitation, we propose TGLLNet, a task-driven model that automatically learns inter-lead relationships to improve multi-label ECG classification. TGLLNet adaptively learns lead connectivity patterns and relational strengths, enhancing ECG representation and improving model generalizability across tasks. Specifically, TGLLNet employs a Temporal Graph Construction (ETGC) module to convert ECG signals into temporal graphs and uses a Residual Pyramid Graph Convolution (RPG) module for multi-level graph embeddings, utilizing a Graph Convolutional Network (GCN) with independently learnable adjacency matrices. Combined with a Temporal Context Convolution (TCC) module, TGLLNet captures spatio-temporal dependencies, significantly improving ECG representation. Experimental results on 7 tasks from PTB-XL and CPSC2018 datasets demonstrate that TGLLNet outperforms existing methods, showing superior generalizability across different tasks. Our code is available at https://***/rosemary333/TGLLnet. © 2014 IEEE.

关键词： Graph embeddings

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Review of Different Current Control Strategies for LC-coupling Hybrid Active Power Filter 48

Review of Different Current Control Strategies for LC-coupli...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Hong, Qian-Rong Sou, Wai-Kit Chan, Pak-Ian Gong, Cheng Lam, Chi-Seng State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau China Institute of Microelectronics University of Macau Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Compared with traditional active power filter (APF), the dc-link voltage of LC-coupling hybrid active power filter (LC-HAPF) is lower, thus reducing the system cost and power loss. In order to improve the compensation performance of the LC-HAPF, many current controllers have been proposed in recent decade, which are hysteresis current control (HCC), quasi-proportional-resonant control (QPRC), deadbeat current control (DBCC), second-order sliding-mode control (SOSMC), vector proportional-integral current control (VPIC) and improved model predictive control (IMPC). In this paper, the model of these six controllers will be first reviewed and introduced. After that, their corresponding parameters design methodology are given. Finally, simulation results will be given to compare their compensation performance and summarize their characteristics. © 2022 IEEE.

关键词： Electric current control

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High-Performance Multistage Constant Current Charging for Wireless Power Transfer Systems 48

High-Performance Multistage Constant Current Charging for Wi...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Ieong, Chi-Fong Iam, Io-Wa Ding, Zhaoyi Lam, Chi-Seng State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau China Institute of Microelectronics University of Macau Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In lithium-ion (li-ion) battery charging process, multistage constant-current (MSCC) charging can effectively increase the charging capacity with less charging time compared with the conventional constant current-to-constant voltage (CC-to-CV) charging in wired charging. This paper proposes and designs a MSCC charging strategy for the wireless power transfer system. By using the LCC-S compensation topology for the inductive power transfer (IPT) converter, the current gain is designed with mathematical derivation to achieve load-independent current output with zero phase angle (ZPA) according to the MSCC charging pattern. Phase-shift modulation (PSM) is adopted to control the input DC-link of the WPT system to achieve different current outputs according to different CC stages. Finally, the charging system is simulated to verify the correctness of the design and the feasibility of the proposed WPT system with MSCC charging. The maximum efficiency of the system is 92.4% and the time-averaged efficiency is 91.3%. © 2022 IEEE.

关键词： Lithium-ion batteries

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Wave-front Behaviour of the Pulsed EM Field – Complexity and Implications

Wave-front Behaviour of the Pulsed EM Field – Complexity an...

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European Conference on Antennas and Propagation, EuCAP

作者： Junhong Gu Andrea Neto Ioan E. Lager Martin Štumpf Terahertz Sensing Group Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands Lerch Laboratory of EM Research Dept. Radioelectronics Faculty of Electrical Engineering and Communication Brno University of Technology Brno The Czech Republic

The pulsed electromagnetic (EM) field radiated by a gap-fed, long slot in a perfectly conducting thin sheet located in between dielectric and free-space subdomains is examined. A phenomenological interpretation of the so-called head wave (HW) constituent is proposed, this fostering the understanding of the complex EM behaviour at, and immediately behind, the HW wave-front. The EM field is also examined numerically for identifying features that may lead the way towards inferring a causal counterpart of the leaky-wave propagation.

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A Bivariate Control Strategy on Inductive Power Transfer Converter for Multi-Stage Constant Current Charging 48

A Bivariate Control Strategy on Inductive Power Transfer Con...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Ding, Zhaoyi Iam, Io-Wa Ieong, Chi-Fong Lam, Chi-Seng State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau China Institute of Microelectronics University of Macau Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Compared with the conventional constant current-constant voltage (CC-CV) charging profile of Li-ion battery, multi-stage constant current (MSCC) charging profile that composed of different constant current stages can improve the charging rate. This paper proposes a MSCC charging profile for an inductive power transfer (IPT) converter with LCC-S compensation topology. However, the wide load range variation during charging process impacts the transfer efficiency of the IPT converter. Thus, aiming to improve the transfer efficiency throughout the whole MSCC charging process, a bivariate operation approach is proposed with a variable inductance to achieve different constant current stages, while the voltage-controlled semi-active rectifier (VCSAR) is controlled to ensure the converter's efficiency optimization. Finally, simulation study is conducted based on PSIM software simulation to verify the validity and effectiveness of the bivariate control strategy and MSCC charging for the LCC-S compensated IPT converter. © 2022 IEEE.

关键词： Efficiency

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Integrated Sensing and Full-Duplex Communication: Joint Transceiver Beamforming and Power Allocation 48

Integrated Sensing and Full-Duplex Communication: Joint Tran...

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

作者： He, Zhenyao Xu, Wei Shen, Hong Kwan Ng, Derrick Wing Eldar, Yonina C. You, Xiaohu Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel

ISBN: (纸本)9781728163277

In this paper, we investigate the beamforming design for an integrated sensing and communication (ISAC) system involved full-duplex (FD) communications. Specifically, an FD ISAC base station (BS) performs target detection and communicates with multiple downlink users and uplink users reusing the same time and frequency resources. We jointly optimize the downlink dual-functional transmit signal and the uplink receive beamformers at the BS and the transmit power at the uplink users. The problem is formulated to minimize the total transmit power of the system while ensuring the communication and sensing requirements. The downlink and uplink transmissions are tightly coupled, making the joint optimization challenging. To solve this intractable problem, we first determine the receive beamformers in closed forms with respect to the BS transmit beamforming and the user transmit power and then suggest an iterative solution to the remaining problem. We demonstrate via numerical results that the optimized FD communication-based ISAC leads to power efficiency improvement compared to conventional ISAC with HD communication. © 2023 IEEE.

关键词： Beamforming

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Dual-Polarized 3-D Microwave Absorbers With Synthesizable Operating Bandwidth, Absorptive Resistance, and Absorption Ratio

Dual-Polarized 3-D Microwave Absorbers With Synthesizable Op...

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IEEE International Wireless Symposium (IWS)

作者： Tian-Xi Feng Lei Zhu Hui Li School of Information and Communication Engineering Dalian University of Technology Dalian China Department of Electrical and Computer Engineering Faculty of Science and Technology State Key Laboratory of Internet of Things for Smart City University of Macau Macau China

ISBN: (数字)9798350389999

ISBN: (纸本)9798350390001

This paper proposes dual-polarized 3-D microwave absorbers. The working strategy of single-polarized design is firstly analyzed with the equivalent transmission line model. In this way, the absorptive performance, including operating bandwidth, absorptive resistance, and absorption ratio, can be easily synthesized. Characteristic impedances for different absorptive performance are given to provide more design selection. Besides, only one absorptive resistor is employed in the proposed absorber element, avoiding complex structure with multiple resistors. Two absorbers are successfully implemented with different operating bandwidths and absorptive resistances to verify the selectable performance. Finally, the single-polarized designs are extended to dual-polarized versions. Good agreement can be seen for two perpendicular polarizations with expected performance, which further prove the proposed design concept.

关键词： Resistance Resistors Microwave measurement Wireless communication Analytical models Solid modeling Power transmission lines Absorption Bandwidth Transmission line measurements

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Near-Field Mobile Tracking: A Framework of Using XL-RIS Information

arXiv

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arXiv 2024年

作者： Wu, Tuo Pan, Cunhua Zhi, Kangda Yao, Junteng Ren, Hong Elkashlan, Maged Yuen, Chau The School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The School of Electrical Engineering and Computer Science Technical University of Berlin Berlin10623 Germany The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

This paper introduces a novel mobile tracking framework leveraging the high-dimensional signal received from extremely large-scale (XL) reconfigurable intelligent surfaces (RIS). This received signal, named XL-RIS information, has a much larger data dimension and therefore offers a richer feature set compared to the traditional base station (BS) received signal, i.e., BS information, enabling more accurate tracking of mobile users (MUs). As the first step, we present an XL-RIS information reconstruction (XL-RIS-IR) algorithm to reconstruct the high-dimensional XL-RIS information from the low-dimensional BS information. Building on this, this paper proposes a comprehensive framework for mobile tracking, consisting of a Feature Extraction Module and a Mobile Tracking Module. The Feature Extraction Module incorporates a convolutional neural network (CNN) extractor for spatial features, a time and frequency (T&F) extractor for domain features, and a near-field angles of arrival (AoAs) extractor for capturing AoA features within the XL-RIS. These features are combined into a comprehensive feature vector, forming a time-varying sequence fed into the Mobile Tracking Module, which employs an Auto-encoder (AE) with a stacked bidirectional long short-term memory (Bi-LSTM) encoder and a standard LSTM decoder to predict MUs’ positions in the upcoming time slot. Simulation results confirm that the tracking accuracy of our proposed framework is significantly enhanced by using reconstructed XL-RIS information and exhibits substantial robustness to signal-to-noise ratio (SNR) variations. © 2024, CC BY.

关键词： Convolutional neural networks

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