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A TV-Module for DC-Baseline Compensation in Analog Front-End Design

A TV-Module for DC-Baseline Compensation in Analog Front-End...

2022 International Conference on computing, Networking, Telecommunications and engineering Sciences Applications, CoNTESA 2022

作者： Hertz, Pancha Yannick Kamdem Jerome, Folla Evariste, Wembe Tafo Zobo Bernard, Essimbi Sobhan Bhuiyan, Mohammad Arif Miraz, Mahdi H. University of Yaounde I Department of Physics Yaounde Cameroon Department of Physics University of Douala Douala Cameroon Xiamen University Malaysia Electrical and Electronics Engineering Sepang Malaysia Xiamen University Malaysia School of Computing and Data Science Sepang Malaysia

ISBN: (纸本)9798350398014

In the design of analog front-end electronics (FEE) for particle beam energies, the circuit is prone to several shortcomings to ensure accurate extraction of particle data in the semiconductor device. Among these problems, the frequency variability that leads to baseline shift affecting the amplitude /energy detection of particles is recurrent. It impinges on the effective energy extracted from the semiconductor material system. In this work, we propose a correction circuit to solve the above-mentioned problem while preserving the bandwidth characteristics of the analog filter. The proposed circuit is a time-variant high-pass filter based on complementary switches S1 and S2, which stabilise the output baseline of the front-end circuit. Post-layout simulation analysis has been performed and the results are quite promising. That allows the output swing to be switched within the detected baseline and a given voltage, controlled by the discriminator threshold level. The design has been validated by post-layout simulation results, performed in a 65 nm CMOS process from TSMC, under 1.2 V supply voltage. © 2022 IEEE.

关键词： High pass filters

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Q-CSM: Q-Learning-based Cognitive Service Management in Heterogeneous IoT Networks

Q-CSM: Q-Learning-based Cognitive Service Management in Hete...

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

作者： Kubra Duran Mehmet Ozdem Kerem Gursu Berk Canberk School of Computing Engineering and The Built Environment Edinburgh Napier University UK Turk Telekom Istanbul Türkiye BTS Group Istanbul Türkiye Department of Artificial Intelligence and Data Engineering Istanbul Technical University Türkiye

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The dramatic increase in the number of smart services and their diversity poses a significant challenge in Internet of Things (IoT) networks: heterogeneity. This causes significant quality of service (QoS) degradation in IoT networks. In addition, the constraints of IoT devices in terms of computational capability and energy resources add extra complexity to this. However, the current studies remain insufficient to solve this problem due to the lack of cognitive action recommendations. Therefore, we propose a Q-learning-based Cognitive Service Management framework called Q-CSM. In this framework, we first design an IoT Agent Manager to handle the heterogeneity in data formats. After that, we design a Q-learning-based recommendation engine to optimize the devices’ lifetime according to the predicted QoS behaviour of the changing IoT network scenarios. We apply the proposed cognitive management to a smart city scenario consisting of three specific services: wind turbines, solar panels, and transportation systems. We note that our proposed cognitive method achieves $38.7 \%$ faster response time to the dynamical IoT changes in topology. Furthermore, the proposed framework achieves 19.8% longer lifetime on average for constrained IoT devices thanks to its Q-learning-based cognitive decision capability. In addition, we explore the most successive learning rate value in the Q-learning run through the exploration and exploitation phases.

关键词： Q-learning Smart cities Simulation Transportation Quality of service Wind turbines Topology Internet of Things Time factors Solar panels

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Joint Energy Harvesting, Semantic Transmission Selection, Channel Allocation and Power Control for Resource-Constrained IoT Networks

Joint Energy Harvesting, Semantic Transmission Selection, Ch...

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IEEE Conference on Vehicular Technology (VTC)

作者： Nguyen Huu Sang Nguyen Duc Hai Nguyen Duc Duy Anh Nguyen Cong Luong Shimin Gong Dusit Niyato Faculty of Computer Science PHENIKAA University Hanoi Vietnam School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China College of Computing and Data Sciene Nanyang Technological University Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In this work, we propose the use of energy harvesting and semantic communication for Internet of Things (IoT) systems. The system allows IoT devices to harvest energy from a base station and then uses the harvested energy for extracting and transmitting semantic information, i.e., scene graphs, to the base station. The proposed network thus copes with the energy and network resource constraints of the IoT devices. To maximize the total image data or scene graph transmitted to the base station, we formulate a problem that optimizes the energy harvesting duration, the selection of original image or portions of scene graphs, transmit power, and channel allocation to the IoT devices. Under the high dynamics and uncertainty of the context and size of the collected images as well as the wireless channels and computing resources, we propose two advanced deep reinforcement learning (DRL) algorithms, i.e., advantage Actor-Critic (A2C) and proximal policy optimization (PPO), to solve the problem. Simulation results are implemented on the real dataset clearly showing that the performance achieved by the proposed algorithms is much higher than that achieved by the baseline scheme. This implies that more original images or triplets are transmitted.

关键词： Wireless communication Base stations Uncertainty Heuristic algorithms Simulation Semantics Channel allocation

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Image Denoising Method Based on Channel Compression and Split-Attention Mechanism

Image Denoising Method Based on Channel Compression and Spli...

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Computer engineering and Intelligent Control (ICCEIC) International Conference on

作者： Hao Sun Xiaoyan Qiao Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion School of Computer Science and Technology Shandong Technology and Business University Technology and Evaluation Shandong Engineering Research Center Yantai Shandong

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

In the image restoration task, how to make full use of spatial and channel feature information to improve the reconstruction quality of the model without significantly increasing the computational complexity is an important research issue. To address this challenge, this paper proposes a CS module (Channel Compression and Split-Attention) specifically for optimizing image denoising tasks. The module first compresses the features through a preliminary convolution operation, then introduces the split attention convolution mechanism to split the features by channel and dynamically weight them, thereby enhancing the model's adaptive attention to different feature channels and improving feature expression capabilities. Through experiments on multiple image denoising datasets, the results show that the CS module significantly improves the image reconstruction quality, especially in terms of peak signal-to-noise ratio (PSNR). At the same time, the introduction of the CS module does not lead to a significant increase in computational complexity, maintaining a high computational efficiency, which verifies the practicality and superiority of the module in image denoising tasks.

关键词： Image coding PSNR Convolution Computational modeling Refining Image restoration Computational efficiency Computational complexity Image reconstruction Image denoising

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One-shot Human Motion Transfer via Occlusion-Robust Flow Prediction and Neural Texturing

arXiv

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arXiv 2024年

作者： Ji, Yuzhu Zheng, Chuanxia Cham, Tat-Jen The School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China The Department of Engineering Science University of Oxford United Kingdom The College of Computing and Data Science Nanyang Technological University Singapore

Human motion transfer aims at animating a static source image with a driving video. While recent advances in one-shot human motion transfer have led to significant improvement in results, it remains challenging for methods with 2D body landmarks, skeleton and semantic mask to accurately capture correspondences between source and driving poses due to the large variation in motion and articulation complexity. In addition, the accuracy and precision of DensePose degrade the image quality for neural-rendering-based methods. To address the limitations and by both considering the importance of appearance and geometry for motion transfer, in this work, we proposed a unified framework that combines multi-scale feature warping and neural texture mapping to recover better 2D appearance and 2.5D geometry, partly by exploiting the information from DensePose, yet adapting to its inherent limited accuracy. Our model takes advantage of multiple modalities by jointly training and fusing them, which allows it to robust neural texture features that cope with geometric errors as well as multi-scale dense motion flow that better preserves appearance. Experimental results with full and half-view body video datasets demonstrate that our model can generalize well and achieve competitive results, and that it is particularly effective in handling challenging cases such as those with substantial self-occlusions. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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A data-Centric Accelerator for High-Performance Hypergraph Processing 22

A Data-Centric Accelerator for High-Performance Hypergraph P...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Qinggang Wang Long Zheng Ao Hu Yu Huang Pengcheng Yao Chuangyi Gui Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China and Zhejiang Lab Hangzhou China National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (纸本)9781665462723

Hypergraph processing has emerged as a powerful approach for analyzing complex multilateral relationships among multiple entities. Past research on building hypergraph systems suggests that changing the scheduling order of bipartite edge tasks can improve the overlap-induced data locality in hypergraph processing. However, due to the complex intertwined connections between vertices and hyperedges, it is almost impossible to find a locality-optimal scheduling order. Thus, these task-centric hypergraph systems often suffer from substantial off-chip *** this paper, we first propose a novel data-centric Load-Trigger-Reduce (LTR) execution model to exploit fully the locality in hypergraph processing. Unlike a task-centric model that loads the required data along with a task, our LTR model invokes tasks as per the data used. Specifically, once the hypergraph data is loaded into the on-chip memory, all of its relevant computation tasks will be triggered simultaneously to output intermediate results, which are finally reduced to update the final results. Our LTR model enables all hypergraph data to be accessed once in each iteration. To fully exploit the LTR performance potential, we further architect an LTR-driven hypergraph accelerator, XuLin, which features with an adaptive data loading mechanism to minimize the loading cost via chunk merging at runtime. XuLin is also equipped with a priority-based differential data reduction scheme to reduce the impact of conflicting updates on performance. We have implemented XuLin both on a Xilinx Alveo U250 FPGA card and using a cycle-accurate simulator. The results show that XuLin outperforms the state-of-the-art hypergraph processing solutions Hygra and ChGraph by 20.47× and 8.77× on average, respectively.

关键词： hypergraph data-centric accelerator

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Transfer metric learning: algorithms, applications and outlooks

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Vicinagearth 2024年第1期1卷 1-20页

作者： Luo, Yong Wen, Yonggang Hu, Han Du, Bo Duan, Ling-Yu Tao, Dacheng School of Computer Science Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore School of Information and Electronics Beijing Institute of Technology Beijing China School of Computer Science Peking University Beijing China College of Computing & Data Science Nanyang Technological University Singapore Singapore

Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data relationship. It is critical in many machine learning, pattern recognition and data mining algorithms, and usually require large amount of label information (such as class labels or pair/triplet constraints) to achieve satisfactory performance. However, the label information may be insufficient in real-world applications due to the high-labeling cost, and DML may fail in this case. Transfer metric learning (TML) is able to mitigate this issue for DML in the domain of interest (target domain) by leveraging knowledge/information from other related domains (source domains). Although achieved a certain level of development, TML has limited success in various aspects such as selective transfer, theoretical understanding, handling complex data, big data and extreme cases. In this survey, we present a systematic review of the TML literature. In particular, we group TML into different categories according to different settings and metric transfer strategies, such as direct metric approximation, subspace approximation, distance approximation, and distribution approximation. A summarization and insightful discussion of the various TML approaches and their applications will be presented. Finally, we indicate some challenges and provide possible future directions.

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Diffuse3D: Wide-Angle 3D Photography via Bilateral Diffusion

Diffuse3D: Wide-Angle 3D Photography via Bilateral Diffusion

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International Conference on Computer Vision (ICCV)

作者： Yutao Jiang Yang Zhou Yuan Liang Wenxi Liu Jianbo Jiao Yuhui Quan Shengfeng He School of Computer Science and Engineering South China University of Technology School of Computing and Information Systems Singapore Management University College of Computer and Data Science Fuzhou University School of Computer Science University of Birmingham

This paper aims to resolve the challenging problem of wide-angle novel view synthesis from a single image, a.k.a. wide-angle 3D photography. Existing approaches rely on local context and treat them equally to inpaint occluded RGB and depth regions, which fail to deal with large-region occlusion (i.e., observing from an extreme angle) and foreground layers might blend into background inpainting. To address the above issues, we propose Diffuse3D which employs a pre-trained diffusion model for global synthesis, while amending the model to activate depth-aware inference. Our key insight is to alter the convolution mechanism in the denoising process. We inject depth information into the denoising convolution operation with bilateral kernels, i.e., a depth kernel and a spatial kernel, to consider layered correlations among pixels. In this way, foreground regions are overlooked in background inpainting and only pixels close in depth are leveraged. On the other hand, we propose a global-local balancing approach to maximize both contextual understandings. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in novel view synthesis, especially in wide-angle scenarios. More importantly, our method does not require any training and is a plug-and-play module that can be integrated with any diffusion model. Our code can be found at https://***/yutaojiang1/Diffuse3D.

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MedCare4Home: A Mobile Medical System for Families

MedCare4Home: A Mobile Medical System for Families

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

作者： Mengting Wang Yen Le Chen-Hsiang Yu School of Computing and Data Science Wentworth Institute of Technology Boston MA U.S.A Multidisciplinary Graduate Engineering College of Engineering Northeastern University Boston MA U.S.A

Managing healthcare data is essential for everyone, but it can be challenging and time-consuming. Although several Web and mobile applications existed for personal medical record management, there were few home-based, family-oriented solutions. In this research, we proposed to design an easy-to-use solution for a family. We designed and implemented an innovative Home Medical Care System, MedCare4Home, by deploying a MERN stack Web application to a minicomputer, Raspberry Pi 4, and enabling a local network to connect different devices at home. The users can access the Web application using either a desktop, tablet, mobile phone, or simply a 7-inch LCD touchscreen. MedCare4Home not only allows users to keep track of appointments and organize healthcare documents, but it can also help set up medication reminders and provide a self-report interface for symptom collection for family members easily and securely in one place. The final developed system demonstrated that we achieved the original goal and beyond. We expect to extend our current hardware design and software development to support physiological data collection with external sensors in the future.

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ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment

arXiv

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arXiv 2024年

作者： Luo, Yangyifei Chen, Zhuo Guo, Lingbing Li, Qian Zeng, Wenxuan Cai, Zhixin Li, Jianxin School of Computer Science and Engineering Beihang University China College of Computer Science and Technology Zhejiang University China Bejing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

Entity alignment (EA) aims to identify entities across different knowledge graphs that represent the same real-world objects. Recent embedding-based EA methods have achieved state-of-the-art performance in EA yet faced interpretability challenges as they purely rely on the embedding distance and neglect the logic rules behind a pair of aligned entities. In this paper, we propose the Align-Subgraph Entity Alignment (ASGEA) framework to exploit logic rules from Align-Subgraphs. ASGEA uses anchor links as bridges to construct Align-Subgraphs and spreads along the paths across KGs, which distinguishes it from the embedding-based methods. Furthermore, we design an interpretable Path-based Graph Neural Network, ASGNN, to effectively identify and integrate the logic rules across KGs. We also introduce a node-level multi-modal attention mechanism coupled with multi-modal enriched anchors to augment the Align-Subgraph. Our experimental results demonstrate the superior performance of ASGEA over the existing embedding-based methods in both EA and Multi-Modal EA (MMEA) tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Computer circuits

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