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IEEE Conference on Wireless Communications and Networking

作者： Joohong Rheey Hyunggon Park Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In response to the security threats in wireless networks with concurrent device connections, deploying Intrusion Detection Systems (IDS) at the network edge is a promising strategy. However, this approach must take into account the resource constraints incurred by power-limited edge devices, requiring a lightweight solution to IDS. At the same time, the lightweight IDS solution has to minimize performance degradation as higher detection performance is also a key requirement of IDS. In this paper, we design a lightweight autoencoder with explainability, employing the Shapley value to measure unit importance and link importance. This approach can selectively activate only critical components, thereby reducing the complexity for IDS while effectively lowering its performance degradation. We confirm that the proposed algorithm is robust against the harsh sparsity of the autoencoder. Moreover, the sparsity of the proposed lightweight autoencoder can be easily manageable, such that it can be controlled to satisfy the potential constraints of power-limited edge devices. Therefore, the solution is a suitable algorithm for IDS that can be deployed on edge devices in wireless networks.

关键词： Performance evaluation Degradation Measurement units Accuracy Image edge detection Wireless networks Intrusion detection

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Dynamic Volumetric Video Coding with Tensor Decomposition

Dynamic Volumetric Video Coding with Tensor Decomposition

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IEEE Visual Communications and Image Processing (VCIP)

作者： Ju-Yeon Shin Yeoneui Kim Je-Won Kang Gun Bang Dept. of Electronic and Electrical Engineering and Graduate Program in Smart Factory Ewha Womans University Seoul South Korea Electronics and Telecommunications Research Institute Daejeon South Korea

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recently, volumetric video coding based on neural radiance fields has gained significant attention for storing and transmitting three-dimensional (3D) scenes captured from multi-view video. Because the neural networks are trained to produce novel view synthesis of surrounding 3D scenes, compressing the model and then rendering the colors and geometry through the decompressed model can be utilized as a 3D video coding system. However, although this approach provides superior performance compared to conventional 3D video coding standards using dept. video, challenges remain in reducing overall model sizes to improve coding efficiency. In this paper, we propose a novel dynamic volumetric video coding technique that employs a Group of Volume (GoV) to divide multi-view video sequences into smaller chunks, addressing complex temporal dynamics. Our method uses volumetric video features represented with 3D spatial and temporal tensor matrices and vectors and encodes them with the GoVs. The tensors are compressed by existing 2D video codec, allowing for fast rendering and easing deployment. Experimental results validate that our method not only reduces memory footprint but also maintains high-quality rendering as compared to state-of-the-art studies.

关键词： Video coding Solid modeling Three-dimensional displays Image coding Tensors Visual communication Video sequences Rendering (computer graphics) Video codecs Standards

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Mutual authentication method between PUFs

Mutual authentication method between PUFs

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2022 IEEE International Conference on electrical, Computer, and Energy Technologies, ICECET 2022

作者： Choi, Yunyeong Lee, Jungwon Shin, Hyungsoon Sun, Wookyung Ewha Womans University Dept. of Electronic and Electrical Engineering Seoul Korea Republic of Seoul National University Dept. of Electrical and Computer Engineering Seoul Korea Republic of Ewha Womans University Smart Factory Multidisciplinary Program Seoul Korea Republic of

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

This paper proposes a new authentication method that enables mutual verification between two different PUFs without a server. The output signal (response) of one PUF is hidden in the opponent's input signal (challenge) list by interlinking the challenge and response information of the two PUFs. In this way, when sending and receiving authentication information, a challenge list index is used instead of exposing the challenge or response information to the network directly. The verification information for checking the validity of the received index is protected with encryption using a hash function. Also, two novel PUF systems, key-map PUF and cube PUF, are presented that improve the security of the PUF-based system using previous authentication information. The proposed authentication method and PUF systems can be used for IoT devices. © 2022 IEEE.

关键词： Hash functions

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Overview of Versatile Video Coding (H.266/VVC) and Its Coding Performance Analysis

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IEIE Transactions on smart Processing and Computing 2023年第2期12卷 122-154页

作者： Lee, Minhun Song, HyeonJu Park, Jeeyoon Jeon, Byeungwoo Kang, Jungwon Kim, Jae-Gon Lee, Yung-Lyul Kang, Je-Won Sim, Donggyu Department of Computer Engineering Kwangwoon University Seoul Korea Republic of Department of Computer Engineering Sejong University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Kyunggi-do Korea Republic of Communication and Media Research Laboratory Electronics and Telecommunications Research Institute Deajeon Korea Republic of School of Electronics and Information Engineering Korea Aerospace University Kyunggi-do Korea Republic of Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of

Versatile Video Coding (H.266/VVC) is the newest video coding standard jointly developed by the Joint Video Experts Team (JVET), which is organized by the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG). H.266/VVC provides about 40% bitrate reduction compared with High Efficiency Video Coding (H.265/HEVC) for the same visual quality. This paper introduces in detail the core structure of H.266/VVC by highlighting its features within block partitioning structure, intra/inter prediction, transform, quantization, and in-loop filtering, compared to its predecessor (H.265/HEVC). H.266/VVC yields significantly improved the coding performance, but it increased the computational complexity, particularly for the encoder side, which remains a problem to be tackled for successful commercialization in the future. This paper examined the statistical performance of H.266/VVC coding tools from the bitstreams encoded by the VVC test model (VTM12.0) encoder through rate-distortion optimization under the JVET common test conditions. In addition, the complexity and performance analyses are conducted on the block partitioning structure and group of picture structure. It is expected that an optimized H.266/VVC encoder can be designed and developed by minimizing the coding loss based on the analysis data. © 2023 Institute of electronics and Information Engineers. All rights reserved.

关键词： Image compression

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PA-2SBF: Pattern-Adaptive Two-Stage Bloom Filter for Run-Time Memory Diagnostic Data Compression in Automotive SoCs

PA-2SBF: Pattern-Adaptive Two-Stage Bloom Filter for Run-Tim...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Sunyoung Park Hyunji Kim Hana Kim Ji-Hoon Kim Department of Electronic and Electrical Engineering Ewha Womans University Seoul Republic of Korea Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

In the realm of safety-critical Automotive System-on-Chip (SoC) design, memory functionality plays a crucial role in determining overall die yield due to its sizable footprint on the chip. Therefore, efficient methodologies for both post-manufacturing offline testing and real-time monitoring are essential to provide timely diagnostic feedback. This paper proses a real-time memory diagnostic data compression technique Pattern-Adaptive Two-Stage Bloom Filter (PA-2SBF) for automotive System-on-Chip (SoC) applications. PA-2SBF is designed to address the challenge of false positives incorporating frequently encountered failure patterns. Bloom filters, a probabilistic data structure renowned for their space-efficiency and quick approximate membership queries, are employed to expedite fault memory diagnosis information lookup and compression. Furthermore, failure patterns are considered to mitigate the false positive rate inherent in Bloom filters. The paper also presents a strategy for leveraging the compressed diagnostic information during run-time. Specifically, it exploits the quick lookup feature of Bloom filter to prohibit CPU access to defective memory regions, enhancing overall system reliability.

关键词： Memory management Data compression Information filters Probabilistic logic Data structures Real-time systems System-on-chip

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Label Space-Induced Pseudo Label Refinement for Multi-Source Black-Box Domain Adaptation

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IEEE Transactions on Image Processing 2025年 34卷 3181-3193页

作者： Yoo, Chaehwa Liu, Xiaofeng Xing, Fangxu Woo, Jonghye Kang, Je-Won Chungbuk National University School of Electrical Engineering Cheongju Korea Republic of Massachusetts General Hospital Harvard Medical School Gordon Center for Medical Imaging Dept. of Radiology BostonMA United States Yale University New Haven United States Ewha W. University Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Seoul Korea Republic of

Conventional unsupervised domain adaptation (UDA) requires access to source data and/or source model parameters, prohibiting its practical application in terms of privacy, security, and intellectual property. Recent black-box UDA (BDA) reduces such constraints by defining a pseudo label from a single encapsulated source application programming interface (API) prediction, which allows for self-training of the target model. Nonetheless, existing methods have limited consideration for multi-source settings, in which multiple source domain APIs are available to generate pseudo labels. In this work, we introduce a novel training framework for multi-source BDA (MSBDA), dubbed Label Space-Induced Pseudo Label Refinement (LPR). Specifically, LPR incorporates a Pseudo label Refinery Network (PRN) that learns the relationship among source domains conditioned by the target domain only utilizing source API’s prediction. The target model is adapted by our dual phases PRN. First, a warm-up phase targets to avoid failure due to noisy samples and provide an initial pseudo-label, which is followed by a label refinement phase with domain relationship exploration. We provide theoretical support for the mechanism of the LPR. Experimental results on four benchmark datasets demonstrate that MSBDA using LPR achieves competitive performance compared to state-of-the-art approaches with different DA settings. © 1992-2012 IEEE.

关键词： Adaptation models Labeling Data models Predictive models Training Closed box Application programming interfaces Upper bound Sports Smoothing methods

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Neural Volumetric Video Coding With Hierarchical Coded Representation of Dynamic Volume

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IEEE Transactions on Multimedia 2025年

作者： Shin, Ju-Yeon Lee, Jung-Kyung Bang, Gun Kim, Jun-Sik Kang, Je-Won Ewha W. University Department of Electronic and Electrical Engineering and Graduate Program in Smart Factory Seoul03760 Korea Republic of Ewha W. University Department of Electronic and Electrical Engineering Korea Republic of Ofinno United States Media Coding Research Lab. Daejeon34129 Korea Republic of

This paper proposes a novel multi-view (MV) video coding technique that leverages a four-dimensional (4D) voxel-grid representation to enhance coding efficiency, particularly in novel view synthesis. Although the voxel grid approximation provides a continuous representation for dynamic scenes, its volumetric nature requires substantial storage. The compression of MV videos can be interpreted as the compression of dense features. However, the substantial size of these features poses a significant problem relative to the generation of dynamic scenes at arbitrary viewpoints. To address this challenge, this study introduces a hierarchical coded representation of dynamic volumes based on low-rank tensor decomposition of volumetric features and develops effective coding techniques based on this representation. The proposed method employs a two-level coding strategy to capture the temporal characteristics of the decomposed features. At a higher level, spatial features are encoded, representing 3D structural information, with time-invariant components over short intervals of an MV video sequence. At a lower level, temporal features are encoded to capture the dynamics of current scenes. The spatial features are shared in a group, and temporal features are encoded at each time step. The experimental results demonstrate that the proposed technique outperforms existing MV video coding standards and current state-of-the-art methods, providing superior rate-distortion performance in the novel view synthesis of MV video compression. © 1999-2012 IEEE.

关键词： Electric distortion

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Implicit Neural Representation for Video Coding Through Progressive Feature Extraction

Implicit Neural Representation for Video Coding Through Prog...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Jihoo Lee Je-Won Kang Department of Electronic and Electrical Engineering Ewha W. University Seoul Korea Graduate Program in Smart Factory Ewha W. University Seoul Korea

Implicit neural representation for video (INRV) utilizes a neural network to represent a video without relying on an explicit pixel-wise representation of RGB values, and, therefore an INRV-based video coding method aims to compress a neural network and transmit its parameters through a bit-stream. In this paper, we propose a novel rate-distortion (R-D) optimized INRV-based video coding method through a progressive feature extraction module (PFEM). The PFEM consists of a series of residual blocks (RBs) that can progressively control the quality of the reconstruction, by searching the optimal network architecture. By increasing the number of RBs, the quality of a reconstruction frame is improved. However, the quality comes at the expense of overheads in transmission. In the proposed method, the number of the RBs are decided to provide the best trade-off between the frame quality and bits. Our decision model is based upon the R-D theory. Experimental results demonstrated that the proposed method provides a superior coding gain to the conventional video coding standards.

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Personal Mobility Safe Driving System with Knowledge Distillation

Personal Mobility Safe Driving System with Knowledge Distill...

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International Conference on Ubiquitous Robots (UR)

作者： Damin Yeom Heejun Yoon Soyeon Lee Kahyun Lee Department of Electronic and Electrical Engineering Ewha Womans University Seoul South Korea Graduate Program in Smart Factory Ewha Womans University Seoul South Korea

The global personal mobility market has been rapidly expanding due to its convenience. However, the increasing number of accidents involving personal mobility devices has become a growing concern, including falls, collisions with objects, and riders being struck by moving vehicles or objects. In this paper, we propose a deep learning-based safe driving system that considers both user and road images to address this issue. Our system employs CNN-based models to detect whether the user is 1) wearing a helmet and 2) looking ahead in user-side images. At the same time, the roadside image recognizes whether the user is 3) driving on the sidewalk and 4) near the intersection. These tasks are simultaneously performed in parallel to identify the overall situation, which is done by determining the final speed as the minimum speed of speed values extracted from all tasks. Additionally, we employ knowledge distillation techniques to compress the models and enable real-time inference on edge devices, resulting in a fast and accurate system that is well-suited to the characteristics of personal mobility.

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A Deep Learning Model of Amyloid-β Diffusion Simulation

A Deep Learning Model of Amyloid-β Diffusion Simulation

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2024 International Conference on electronics, Information, and Communication, ICEIC 2024

作者： Jeong, ByeongChang Kim, Daegyeom Jeong, Hyun-Ghang Han, Cheol E. Korea University Alzheimer's Disease Neuroimaging Initiative Dept. of Electronic and Information Engineering Sejong Korea Republic of Korea University Alzheimer's Disease Neuroimaging Initiative Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Sejong Korea Republic of Korea University College of Medicine Alzheimer's Disease Neuroimaging Initiative Dept. of Psychiatry Seoul Korea Republic of

ISBN: (纸本)9798350371888

Alzheimer's disease (AD) is a severe neurodegenerative disease characterized by ongoing brain tissue decline. Since amyloid-β accumulation is supposed as a primary cause of AD progression, mathematical models have been recently employed to simulate amyloid-β diffusion in our brain. These models require parameter estimation through observed data and an iterative process to update parameters, although increased complexity can hinder their optimization. To overcome these difficulties, we converted the mathematical model of amyloid-β diffusion into a deep learning model, combining multi-layer perceptron (MLP) and graph convolutional neural network (GCN). The proposed model well predicted the change of accumulation level of amyloid-β with a high accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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