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IEEE International Conference on Robotics and Automation (ICRA)

作者： Chengxin Li Yihui Hu Zewen Zheng Xiang Gao Yongqiang Mou Peng Nie Jun Li R&D Center Guangzhou Automobile Group Co Ltd. China South China Academy of Advanced Optoelectronics South China Normal University China School of Computer Science and Technology Guangdong University of Technology China School of Information Engineering Guangdong University of Technology China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Monocular 3D lane detection has recently received increasing research attention in autonomous driving due to its application effectiveness and simplicity. However, depending solely on the limited semantic information from a single image makes current monocular detection methods unable to deal with complex scenarios, such as occluded, blurred, and unaligned scenes. In this study, we introduce an end-to-end framework named Bi 2 Lane which models temporal dependency in a continuous sequence. It recurrently utilizes detected lanes within historical frames as prior information to achieve robust lane detection. Additionally, Bi 2 Lane employs temporal reverse refinement together with temporal forward refinement to achieve bi-directional temporal refinement (BDTR) while maintaining a robust temporal dependency. For the refined features of different frames, we design a bi-level feature aggregation module (BLFA) to fuse them in both point-level and line-level manners, enabling a comprehensive feature representation to deal with complicated road scenes. Extensive experiments conducted on the OpenLane dataset demonstrate the superiority of Bi 2 Lane, achieving a notable F1 score of 63.8% using a simple ResNet50 backbone, surpassing the performance of existing state-of-the-art methods.

关键词： Three-dimensional displays Lane detection Fuses Roads Semantics Bidirectional control Feature extraction

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Urban Damage Detection Using Temporally Stacked Synthetic Aperture Radar Interferometric Coherence

Urban Damage Detection Using Temporally Stacked Synthetic Ap...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ryo Natsuaki Ryu Sugimoto Masanobu Shimada Chiaki Tsutsumi Ryosuke Nakamura Department of Electrical Engineering and Information Systems The University of Tokyo Japan Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Japan Division of Architectural Civil and Environmental Engineering Tokyo Denki University Japan

In this paper, we propose a novel disaster damage detection method using Synthetic Aperture Radar (SAR) interferometric analysis. SAR interferometric coherence analysis is an effective method for disaster monitoring especially in the urban area, where the amplitude of SAR image changes only slightly unless the damage level is high. One drawback of the interferometric coherence-based analysis is the existence of the Cramér-Rao lower bound. That is, a small spatial window for its ensemble averaging leads significant bias while a large window makes its spatial resolution worse. To solve this problem, we propose to extend the ensemble average window towards temporal domain by increasing the number of interferometric pairs. Conventional methods which use multiple interferometric pairs firstly calculate coherence values independently. Instead, the proposed method unifies the interferograms first. We report some preliminary experimental results showing the effectiveness of the proposed method.

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Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection

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IEEE Journal of Translational engineering in Health and Medicine 2024年 12卷 43-55页

作者： Chen, Li-Chin Hung, Kuo-Hsuan Tseng, Yi-Ju Wang, Hsin-Yao Lu, Tse-Min Huang, Wei-Chieh Tsao, Yu Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan Linkou Chang Gung Memorial Hospital Department of Laboratory Medicine Taoyuan City33342 Taiwan Taipei Veterans General Hospital Division of Cardiology Department of Internal Medicine Taipei112201 Taiwan Taipei Veterans General Hospital Department of Health Care Center Taipei112201 Taiwan National Yang Ming Chiao Tung University School of Medicine College of Medicine Department of Internal Medicine Taipei112304 Taiwan National Taiwan University Department of Biomedical Engineering Taipei10617 Taiwan

Objective: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. Nonetheless, the inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity;In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. Methods and procedures: GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for target vessel revascularization (TVR) detection. Results: The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority ( p ©;2023 IEEE.

关键词： Diseases

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Discrimination of Quantum States Under Locality Constraints in the Many-Copy Setting

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Communications in Mathematical Physics 2023年第1期404卷 151-183页

作者： Cheng, Hao-Chung Winter, Andreas Yu, Nengkun Department of Electrical Engineering and Graduate Institute of Communication Engineering National Taiwan University Tapei 106 Taiwan Department of Mathematics National Taiwan University Tapei 106 Taiwan Center for Quantum Science and Engineering National Taiwan University Tapei 106 Taiwan Physics Division National Center for Theoretical Sciences Taipei 10617 Taiwan Hon Hai (Foxconn) Quantum Computing Center New Taipei City 236 Taiwan ICREA-Institució Catalana de Recerca i Estudis Avançats Barcelona 08010 Spain Departament de Física Física Teòrica: Informaciò i Fenòmens Quàntics Universitat Autònoma de Barcelona Barcelona 08193 Spain Computer Science Department Stony Brook University New York NY United States Centre for Quantum Software and Information and Faculty of Engineering and Information Technology University of Technology Sydney Ultimo 2007 NSW Australia

We study quantum hypothesis testing between orthogonal states under restricted local measurements in the many-copy scenario. For testing arbitrary multipartite entangled pure state against its orthogonal complement state via the local operation and classical communication (LOCC) operation, we prove that the optimal average error probability always decays exponentially in the number of copies. Second, we provide a sufficient condition for the LOCC operations to achieve the same performance as the positive-partial-transpose (PPT) operations. We further show that testing a maximally entangled state against its orthogonal complement and testing extremal Werner states both fulfill the above-mentioned condition. Hence, we determine the explicit expressions for the optimal average error probability, the optimal trade-off between the type-I and type-II errors, and the associated Chernoff, Stein, Hoeffding, and strong converse exponents. Then, we show an infinite asymptotic separation between the separable (SEP) and PPT operations by providing a pair of states constructed from an unextendible product basis (UPB). The quantum states can be distinguished perfectly by PPT operations, while the optimal error probability, with SEP operations, admits an exponential lower bound. On the technical side, we prove this result by providing a quantitative version of the well-known statement that the tensor product of UPBs is a UPB. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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A Lightweight Deep Learning Network for Sensor-based Human Activity Recognition using IMU sensors of a Low-Power Wearable Device 25

A Lightweight Deep Learning Network for Sensor-based Human A...

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25th International computer science and engineering Conference, ICSEC 2021

作者： Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665411974

Human Activity Recognition (HAR) is an intriguing approach to healthcare monitoring that necessitates the ongoing utilization of wearable sensors to capture everyday activities. The most advanced studies using wearable devices have used a cloud computing paradigm that categorized data at remote systems. This method has drawbacks in terms of energy consumption, latency, and privacy. As a result, we adhere to a low-power computing structure in which wearable device solutions achieve an acceptable level of performance while being energy and memory-efficient. This study proposes a lightweight deep learning model termed a gate recurrent unit (GRU) network for energy-efficient HAR appropriate for low-power wearable devices. The introduced GRU network is evaluated against a benchmark dataset named the w-HAR dataset, including essential deep learning networks. The proposed GRU surpasses baseline deep learning networks in terms of overall accuracy, with a score of 95.160%. © 2021 IEEE.

关键词： Energy efficiency

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Bidirectional Gate Recurrent Unit Neural Network for Recognizing Face Touching Activities using Smartwatch Sensors 25

Bidirectional Gate Recurrent Unit Neural Network for Recogni...

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25th International computer science and engineering Conference, ICSEC 2021

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Mahidol University Faculty of Engineering Image Information and Intelligence Laboratory Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665411974

Globally, the COVID-19 pandemic has caused dev-Astation and continues to do so even a year after its first outbreak. Behavioral modifications could help to mitigate a mechanism for acquiring and spreading illnesses. Using wearable devices such as smartwatches to recognize face contact has the opportunity to decrease face touching and, therefore, the spread of respiratory disease through fomite transmission. The purpose of this paper is to demonstrate how we can utilize accelerometer data from wristwatch sensors to identify face touching actions using deep learning techniques. We proposed the BiGRU deep learning model for the high-performance recognition of hand-To-face actions. The Face Touching dataset is used as a benchmark for evaluating the recognition accuracy of deep learning networks, including our network model. The experimental findings indicate that the BiGRU surpasses other baseline deep learning models regarding accuracy (98.56%) and F1-score (98.56%). © 2021 IEEE.

关键词： Accelerometers

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Robust Generative Steganography for image Hiding Using Concatenated Mappings

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IEEE Transactions on information Forensics and Security 2025年 20卷 5952-5965页

作者： Liyan Chen Bingwen Feng Zhihua Xia Wei Lu Jian Weng College of Cyber Security Jinan University Guangzhou China College of Cyber Security Engineering Research Center of Trustworthy AI Ministry of Education Jinan University Guangzhou China School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou China

Generative steganography stands as a promising technique for information hiding, primarily due to its remarkable resistance to steganalysis detection. Despite its potential, hiding a secret image using existing generative steganographic models remains a challenge, especially in lossy or noisy communication channels. This paper proposes a robust generative steganography model for hiding full-size image. It lies on three reversible concatenated mappings proposed. The first mapping uses VQGAN with an order-preserving codebook to compress an image into a more concise representation. The second mapping incorporates error correction to further convert the representation into a robust binary representation. The third mapping devises a distribution-preserving sampling mapping that transforms the binary representation into the latent representation. This latent representation is then used as input for a text-to-image Diffusion model, which generates the final stego image. Experimental results show that our proposed scheme can freely customize the stego image content. Moreover, it simultaneously attains high stego and recovery image quality, high robustness, and provable security.

关键词： Steganography Robustness Security image coding Diffusion models Training Transforms Neural networks Gaussian noise Electronic mail

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Traffic Pattern Sharing for Federated Traffic Flow Prediction with Personalization

Traffic Pattern Sharing for Federated Traffic Flow Predictio...

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IEEE International Conference on Data Mining (ICDM)

作者： Hang Zhou Wentao Yu Sheng Wan Yongxin Tong Tianlong Gu Chen Gong School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China SKLSDE Beihang University China Engineering Research Center of Trustworthy AI (Ministry of Education) Jinan University China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Accurate Traffic Flow Prediction (TFP) is crucial for enhancing the efficiency and safety of transportation systems, so it has attracted intensive researches by exploiting spatial-temporal dependencies within road networks. However, existing works only consider the case of centralized data collection with all traffic data observed, which may raise privacy concerns as each region of a city may have its own traffic administration department and the traffic data is not allowed to distribute. Therefore, this paper proposes to use Federated Learning (FL) to address this issue by allowing all clients (i.e., traffic administration departments in all regions in our problem) to collaboratively train TFP models without exchanging raw data, thereby offering a solution in maintaining data privacy. Nevertheless, most existing FL methods aim to learn a global model that performs well universally, so they cannot well handle the non-Independent and Identically Distributed (non-IID) traffic data naturally over different regions. To cope with this problem, this paper develops a new FL framework termed “personalized Federated learning with Traffic Pattern Sharing” (FedTPS) to solve federated TFP problem. Our FedTPS critically exploits the underlying common traffic patterns (e.g., morning and evening rush hours) shared across different city regions and meanwhile maintaining the region-specific data characteristics in a personalized FL manner. Specifically, to extract the common traffic patterns, we decompose the traffic data in each client via using discrete wavelet transform, where the low-frequency components uncover the stable traffic dynamics of different regions and thus can be considered as the common traffic patterns. These common patterns are then shared among different clients through traffic pattern repositories on the server side to aid the global collaborative traffic flow modeling. Moreover, the model components capturing spatial-temporal dependencies in traffic data are r

关键词： Training Data privacy Federated learning Urban areas Transportation Transforms Traffic control Data models Safety Servers

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Incremental Learning with Pseudo Labels for Enhanced Glomeruli Segmentation Using SAM

Incremental Learning with Pseudo Labels for Enhanced Glomeru...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Xinkai Liu Boyuan Peng Xin Zhu Tsuyoshi Iwasaki Atsuya Sato Junichiro James Kazama Jie Huang Graduate Department of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Fukushima Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Tokyo Japan Department of Nephrology and Hypertension Fukushima Medical University Fukushima Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Aizu-Wakamatsu Fukushima Japan

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

The advent of the Segment Anything Model (SAM) marks a significant milestone in deep learning semantic segmentation tasks. In this study, an incremental learning model based on pseudo labels is proposed. The model generates bounding box prompts for SAM, enhancing the accuracy of glomeruli segmentation in renal pathology images. The model uses DenseNet-121 along with contrastive learning to enable self-supervised pre-training using mouse glomeruli images. Transfer learning is then conducted using labeled human renal pathology images to develop a binary classification model for glomeruli. Unlabeled images of human renal pathology are subsequently introduced for incremental learning. Class activation maps (CAMs) generated by the incremental learning model are post-processed to extract bounding rectangles for connected regions. These rectangles are then used as prompts for SAM segmentation. The proposed approach resulted in an average Dice similarity coefficient (DSC) of 0.825 in the glomeruli segmentation task. This result demonstrates the effectiveness of the incremental learning model and post-processing algorithms in improving SAM's segmentation accuracy for renal pathology.

关键词： Deep learning Pathology Analytical models Incremental learning Accuracy Semantic segmentation Transfer learning Contrastive learning Clinical trials Cams

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Multiuser computational imaging encryption and authentication with OFDM-assisted key management

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advanced Photonics Nexus 2024年第5期3卷 162-173页

作者： Hongran Zeng Ping Lu Xiaowei Li Lingling Huang Chaoyun Song Dahai Li In-kwon Lee Seok-Tae Kim Qiong-Hua Wang Yiguang Liu Sichuan University School of Electronics and Information EngineeringChengduChina Beijing Institute of Technology School of Optics and PhotonicsBeijing Engineering Research Center of Mixed Reality and Advanced DisplayBeijingChina King’s College London Department of EngineeringLondonUnited Kingdom Yonsei University Department of Computer ScienceSeoulRepublic of Korea Pukyong National University Department of Information and CommunicationsBusanRepublic of Korea Beihang University School of Instrumentation and Optoelectronic EngineeringBeijingChina Sichuan University College of Computer ScienceChengduChina

Single-pixel imaging(SPI)enables an invisible target to be imaged onto a photosensitive surface without a lens,emerging as a promising way for indirect optical ***,due to its linear and broadcast imaging principles,SPI encryption has been confined to a single-user framework for the long *** propose a multi-image SPI encryption method and combine it with orthogonal frequency division multiplexing-assisted key management,to achieve a multiuser SPI encryption and authentication *** images are first encrypted as a composite intensity sequence containing the plaintexts and authentication information,simultaneously generating different sets of keys for ***,the SPI keys for encryption and authentication are asymmetrically isolated into independent frequency carriers and encapsulated into a Malus metasurface,so as to establish an individually private and content-independent channel for each *** can receive different plaintexts privately and verify the authenticity,eliminating the broadcast transparency of SPI *** improved linear security is also verified by simulating *** the combination of direct key management and indirect image encryption,our work achieves the encryption and authentication functionality under a multiuser computational imaging framework,facilitating its application in optical communication,imaging,and security.

关键词： computational imaging optical encryption optical authentication key management

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