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Optical Aggregation/De-Aggregation Between QPSK and OOK Channels Enabled by Phase-Sensitive Amplifier-based Bi-Directional Vector Moving

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Optical Aggregation/De-Aggregation Between QPSK and OOK Chan...

Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Jiabin Cui Huashun Wen Yanxia Tan Guo-Wei Lu Zhaoyang Liu State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Beijing China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Research Institute of China United Network Communications Co. Ltd Beijing China Division of Computer Engineering The University of Aizu Fukushima Japan

An optical aggregation/de-aggregation scheme between QPSK and OOK channels is proposed. The employed constellation manipulations are mainly based on nondegenerate phase-sensitive amplifier-based bi-directional vector moving. 10G Baud $2\times \text{OOK}-\text{to}-\text{QPSK}-\text{to}-2\times$ OOK aggregation and de-aggregation are achieved and analyzed.

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BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer’s Disease

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arXiv 2023年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung The Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China The Department of Systems and Enterprises Stevens Institute of Technology United States School of Science and Technology Hong Kong Metropolitan University Hong Kong College of Engineering and Computer Science VinUni-Illinois Smart Health Center VinUniversity Viet Nam Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Interdisciplinary Research Center for Biosystems and Machines King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Game Design Faculty of Arts Uppsala University Sweden The School of Software Tiangong University Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer’s disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. Copyright © 2023, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

Reconfigurable MIMO towards Electro-magnetic information Theory: Capacity Maximization Pattern Design

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arXiv 2022年

作者： Wang, Haonan Li, Ang Liu, Ya-Feng Qin, Qibo Song, Lingyang Li, Yonghui School of Information and Communications Engineering Xi’an Jiaotong University Xi’an Shaanxi China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Wireless Network RAN Research Department Shanghai Huawei Technologies Co. Ltd. Shanghai China School of Electrical Engineering and Computer Science Peking University Beijing China School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia

multiple-input multiple-output (PR-MIMO), a technique that has the potential to bridge the gap between electro-magnetics and communications towards the emerging Electro-magnetic information Theory (EIT). Specifically, we focus on the pattern design problem aimed at maximizing the channel capacity for reconfigurable MIMO communication systems, where we firstly introduce the matrix representation of PR-MIMO and further formulate a pattern design problem. We decompose the pattern design into two steps, i.e., the correlation modification process to optimize the correlation structure of the channel, followed by the power allocation process to improve the channel quality based on the optimized channel structure. For the correlation modification process, we propose a sequential optimization framework with eigenvalue decomposition to obtain near-optimal solutions. For the power allocation process, we provide a closed-form power allocation scheme to redistribute the transmission power among the modified subchannels. Numerical results show that the proposed pattern design scheme offers significant improvements over legacy MIMO systems, which motivates the application of PR-MIMO in wireless communication systems. Copyright © 2022, The Authors. All rights reserved.

关键词： MIMO systems

Real-time image carrier generation based on generative adversarial network and fast object detection

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Real-time image carrier generation based on generative adver...

作者： Li, Chuanlong Sun, Xingming Zhou, Zhili Yang, Yimin School of Computer and Software Nanjing University of Information Science and Technology Ning Liu Road No. 219 Nanjing210044 China Jiangsu Engineering Centre of Network Monitoring Ning Liu Road No. 219 Nanjing210044 China Department of Computer Science Lakehead University Thunder Bay Canada

Image steganography aims to conceal the secret information inside another carrier image. And by embedding the information into the carrier image, the carrier image may suffer certain image distortion. Thus, not only the hiding algorithm should be carefully designed, but also the carrier image should be meticulously selected during the hiding process. This paper follows the idea of creating suitable cover images instead of selecting the ones by presenting a unified architecture which combines real-time object detection based on convolutional neural network, local style transfer using generative adversarial network and steganography together to realize real-time carrier image generation. The object in the carrier image is first detected using a fast object detector and then the detected area is reconstructed through a local generative network. The secret message is embedded into the intermediate generated images during the training process in order to generate an image which is suitable as an image carrier. The experimental results show that the reconstructed stego images are nearly indistinguishable to both human eyes and steganalysis tools. Furthermore, the whole carrier image generation process with GPU implementation can achieve around 5 times faster than the regular CPU implementation which meets the requirement of real-time image processing. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Steganography

From compressive sampling to compressive tasking:retrieving semantics in compressed domain with low bandwidth

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PhotoniX 2022年第1期3卷 177-198页

作者： Zhihong Zhang Bo Zhang Xin Yuan Siming Zheng Xiongfei Su Jinli Suo David J.Brady Qionghai Dai Department of Automation Tsinghua University100084 BeijingChina Research Center for Industries of the Future and School of Engineering Westlake University310024 HangzhouChina Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province Westlake University310024 HangzhouChina Computer Network Information Center Chinese Academy of Sciences100190 BeijingChina University of Chinese Academy of Sciences 100049 BeijingChina Wyant College of Optical Sciences University of ArizonaAZ 85721 TucsonUSA

High-throughput imaging is highly desirable in intelligent analysis of computer vision *** conventional design,throughput is limited by the separation between physical image capture and digital post *** imaging increases throughput by mixing analog and digital processing through the image capture ***,recent advances of computational imaging focus on the“compressive sampling”,this precludes the wide applications in practical *** paper presents a systematic analysis of the next step for computational imaging built on snapshot compressive imaging(SCI)and semantic computer vision(SCV)tasks,which have independently emerged over the past decade as basic computational imaging *** is a physical layer process that maximizes information capacity per sample while minimizing system size,power and *** is an abstraction layer process that analyzes image data as objects and features,rather than simple pixel *** current practice,SCI and SCV are independent and *** concatenated pipeline results in the following problems:i)a large amount of resources are spent on task-irrelevant computation and transmission,ii)the sampling and design efficiency of SCI is attenuated,and iii)the final performance of SCV is limited by the reconstruction errors of *** these concerns in mind,this paper takes one step further aiming to bridge the gap between SCI and SCV to take full advantage of both *** reviewing the current status of SCI,we propose a novel joint framework by conducting SCV on raw measurements captured by SCI to select the region of interest,and then perform reconstruction on these regions to speed up processing *** use our recently built SCI prototype to verify the *** results are presented and the prospects for a joint SCI and SCV regime are *** conducting computer vision tasks in the compressed domain,we envision that a new era of snapshot compressive imaging wit

关键词： Snapshot compressive imaging Semantic computer vision Computational imaging Deep learning

Photonic Terahertz Phased Array

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arXiv 2024年

作者： Niu, Li Feng, Xi Zhang, Xueqian Lu, Yongchang Wang, Qingwei Xu, Quan Chen, Xieyu Ma, Jiajun Qiu, Haidi Sha, Wei E.I. Zhang, Shuang Alù, Andrea Zhang, Weili Han, Jiaguang Center for Terahertz Waves College of Precision Instrument and Optoelectronics Engineering The Key Laboratory of Optoelectronics Information and Technology Tianjin University Tianjin300072 China Key Laboratory of Micro-nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical & Electronic Engineering University of Hong Kong 999077 Hong Kong Photonics Initiative Advanced Science Research Center City University of New York New York10031 United States Physics Program Graduate Center City University of New York New York10016 United States School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States Guangxi Key Laboratory of Optoelectronic Information Processing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin541004 China

Phased arrays are crucial in various technologies, such as radar and wireless communications, due to their ability to precisely control and steer electromagnetic waves. This precise control improves signal processing and enhances imaging performance. However, extending phased arrays to the terahertz (THz) frequency range has proven challenging, especially for high-frequency operation, broadband performance, two-dimensional (2D) phase control with large antenna arrays, and strong phase modulation. Here, we introduce a photonic platform to realize a THz phased array that bypasses the above challenges. Our method employs 2D phase coding with 2-bit across a broad THz frequency range from 0.8 to 1.4 THz. The core of our design is a pixelated nonlinear Pancharatnam-Berry metasurface driven by a spatially modulated femtosecond laser, allowing precise phase control of THz signals. We showcase the effectiveness of our method through four proof-of-concept applications: single beamforming, dual beamforming, imaging and vortex beam generation. The realized photonic platform provides a promising pathway for developing broadband phased arrays in the THz regime. Copyright © 2024, The Authors. All rights reserved.

关键词： Terahertz waves

Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles

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arXiv 2022年

作者： Liang, Siyuan Wu, Hao Zhen, Li Hua, Qiaozhi Garg, Sahil Kaddoum, Georges Hassan, Mohammad Mehedi Yu, Keping The Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi’an710122 China The Computer School Hubei University of Arts and Science Xiangyang441000 China The Electrical Engineering Department École de Technologie Supérieure MontréalQCH3C 1K3 Canada The Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia The Global Information Telecommunication Institute Waseda University Tokyo169-8050 Japan

Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems. However, it is difficult for the existing DL-OD schemes to realize the responsible, cost-saving, and energy-efficient autonomous vehicle systems due to low their inherent defects of low timeliness and high energy consumption. In this paper, we propose an object detection (OD) system based on edge-cloud cooperation and reconstructive convolutional neural networks, which is called Edge YOLO. This system can effectively avoid the excessive dependence on computing power and uneven distribution of cloud computing resources. Specifically, it is a lightweight OD framework realized by combining pruning feature extraction network and compression feature fusion network to enhance the efficiency of multi-scale prediction to the largest extent. In addition, we developed an autonomous driving platform equipped with NVIDIA Jetson for system-level verification. We experimentally demonstrate the reliability and efficiency of Edge YOLO on COCO2017 and KITTI data sets, respectively. According to COCO2017 standard datasets with a speed of 26.6 frames per second (FPS), the results show that the number of parameters in the entire network is only 25.67 MB, while the accuracy (mAP) is up to 47.3%. © 2022, CC BY.

关键词： Object detection

Sentiment Analysis of Amazon Customer Reviews Using Bi-Lstm, Bi-Gru, and Distilbert for the Purpose of Opinion Miningsentiment Analysis of Amazon Customer Reviews Using Bi-Lstm, Bi-Gru, and Distilbert for the Purpose of Opinion Mining

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SSRN

SSRN 2023年

作者： Tebyani, Sahand Jazayeri, Kian Ahadi, Seyedeh Aridis Çelebi, Erbuğ Jahandar, Farzad Department of Management Information Systems School of Applied Sciences Cyprus International University Haspolat Turkish Republic of Northern Cyprus Cyprus Department of Engineering Artificial Intelligence Application and Research Center Cyprus International University Haspolat Turkish Republic of Northern Cyprus Cyprus Khoury College of Computer Science Northeastern University United States Cyprus International University Cyprus

Recurrent Neural networks (RNNs) have long had a dominant position in the fields of sentiment analysis and Natural Language Processing (NLP). This research gives a thorough comparative analysis of two unique models for opinion mining: Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Units (Bi-GRU). Notably, these models have a GloVe-based tokenizer installed to improve their verbal comprehension. We use the Amazon reviews dataset, concentrating on mobile devices and accessories, a rich source of varied user opinions, to assess their effectiveness. We compare their performance to that of the powerful large language model DistilBERT in addition to bespoke models. Our study goes into the complex technique of sentiment analysis to unearth subtle insights. Performance assessments cover a wide range of measures and factors and provide a comprehensive understanding of the models' capabilities. We also contrast how well these models extract highly relevant terms, demonstrating how well they can extract insightful information from user-generated content. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

Reliability of predicting low-burden (≤ 2) positive axillary lymph nodes indicating sentinel lymph node biopsy in primary operable breast cancer — a retrospective comparative study with PET/CT and breast MRI

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World Journal of Surgical Oncology 2024年第1期22卷 12页

作者： Sae-lim, Chayanee Wu, Wen-Pei Chang, Ming-Che Lai, Hung-Wen Chen, Shu-Tian Chou, Chen-Te Liao, Chiung-Ying Huang, Hsin-I Chen, Shou-Tung Chen, Dar-Ren Hung, Che-Lun Department of Surgery Faculty of Medicine Chulalongkorn University Bangkok Thailand School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Biomedical Imaging and Radiological Sciences National Yang Ming Chiao Tung University Taipei Taiwan Division of General Surgery Changhua Christian Hospital Changhua Taiwan Comprehensive Breast Cancer Center Changhua Christian Hospital Changhua Taiwan Endoscopic and Oncoplastic Breast Surgery Center Changhua Christian Hospital 135 Nanxiao Street Changhua 500 Taiwan Minimally Invasive Surgery Research Center Changhua Christian Hospital Changhua Taiwan Department of Radiology Changhua Christian Hospital Changhua Taiwan Department of Nuclear Medicine Changhua Christian Hospital Changhua Taiwan Division of Breast Surgery Yuanlin Christian Hospital Yuanlin Taiwan Kaohsiung Medical University Kaohsiung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Department of Diagnostic Radiology Chang Gung Memorial Hospital Chiayi Branch Chiayi Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 11221 Taiwan Department of Information Management National Sun Yat-Sen University Kaohsiung Taiwan Department of Computer Science and Communication Engineering Providence University Taichung Taiwan Department of Surgery National Yang Ming Chiao Tung University Taipei Taiwan

Background: Sentinel lymph node biopsy (SLNB) is the standard of care for axillary staging in early breast cancer patients with low-burden axillary metastasis (≤ 2 positive nodes). This study aimed to determine the diagnostic performances of 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and breast magnetic resonance imaging in detecting axillary lymph node (ALN) metastases and the reliability to predict ALN burden. Methods: A total of 275 patients with primary operable breast cancer receiving preoperative PET/CT and upfront surgery from January 2001 to December 2022 in a single institution were enrolled. A total of 244 (88.7%) of them also received breast MRI. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of PET/CT and breast MRI were assessed. The predictive values to determine ALN burden were evaluated using radio-histopathological concordance. Results: PET/CT demonstrated a sensitivity of 53.4%, specificity of 82.1%, PPV of 65.5%, NPV of 73.5%, and accuracy of 70.9% for detecting ALN metastasis, and the corresponding values for MRI were 71.8%, 67.8%, 56%, 80.8%, and 69.2%, respectively. Combining PET/CT and MRI showed a significantly higher PPV than MRI (72.7% vs 56% for MRI alone, p = 0.037) and a significantly higher NPV than PET/CT (84% vs 73.5% for PET/CT alone, p = 0.041). For predicting low-burden axillary metastasis (1–2 positive nodes), the PPVs were 35.9% for PET/CT, 36.7% for MRI, and 55% for combined PET/CT and MRI. Regarding patients with 0–2 positive ALNs in imaging, who were indicated for SLNB, the predictive correctness was 96.1% for combined PET/CT and MRI, 95.7% for MRI alone, and 88.6% for PET/CT alone. Conclusions: PET/CT and breast MRI exhibit high predictive values for identifying low-burden axillary metastasis in patients with operable breast cancer with ≦ 2 positive ALNs on imaging. © 2024, The Author(s).

关键词： Axillary lymph node Axillary lymph node disease burden Axillary staging Breast cancer Magnetic resonance imaging Positron emission tomography/computed tomography Sentinel lymph node biopsy (SLNB)