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

作者： Hongbo Wu Yukuan Jia Jintao Yan Sheng Zhou Zhisheng Niu Zheng Chang Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing P.R. China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu P.R. China

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

In wireless body area networks (WBANs), the deep channel fading between the nodes and the hub significantly impairs the reliability of end-to-end signal transmission. However, some nodes in WBANs necessitate high-priority data transmission with stringent latency and accuracy requirements. Retransmission is ineffective against channel fading and can result in increased communication overhead and extended transmission delays. This paper proposes an opportunistic relay strategy tailored to the characteristics of WBAN nodes with varying priorities. This strategy converts suitable low-priority nodes as relays during high-priority time slots to forward high-priority data to the hub when deep fading occurs. The relay can be opportunistically selected based on the channel condition and the power usage. With Lyanupov optimization, we maximize the transmission reliability while ensuring the extra power consumption of low-priority nodes are acceptable. Subsequently, simulations are conducted in the Network Simulator 3 (NS3) to validate the proposed relay selection strategy, showing that the proposed strategy effectively improves the reliability from 90.2 % to 99.1 % with an additional power consumption of 30%.

关键词： Fading channels Wireless communication Power demand Simulation Body area networks Real-time systems Reliability Resource management Relays Optimization

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Broad Learning with Uniform Local Binary Pattern for Fingerprint Liveness Detection 3rd

Broad Learning with Uniform Local Binary Pattern for Finge...

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3rd International Conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Chen, Mingyu Yuan, Chengsheng Li, Xinting Zhou, Zhili Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China School of International Relations National University of Defense Technology Nanjing210044 China

ISBN: (纸本)9789811961410

Recently, with the widespread application of mobile communication devices, fingerprint identification is the most prevalent in all types of mobile computing. While they bring a huge convenience to our lives, the resulting security and privacy issues have caused widespread concern. Fraudulent attack using forged fingerprint is one of the typical attacks to realize illegal intrusion. Thus, fingerprint liveness detection (FLD) for True or Fake fingerprints is very essential. This paper proposes a novel fingerprint liveness detection method based on broad learning with uniform local binary pattern (ULBP). Compared to convolutional neural networks (CNN), training time is drastically reduced. Firstly, the region of interest of the fingerprint image is extracted to remove redundant information. Secondly, texture features in fingerprint images are extracted via ULBP descriptors as the input to the broad learning system (BLS). ULBP reduces the variety of binary patterns of fingerprint features without losing any key information. Finally, the extracted features are fed into the BLS for training. The BLS is a flat network, which transfers and places the original input as a mapped feature in feature nodes, generalizing the structure in augmentation nodes. Experiments show that in Livdet 2011 and Livdet 2013 datasets, the average training time is about 1 s and the performance of identifying real and fake fingerprints is effect. Compared to other advanced models, our method is faster and more miniature. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning systems

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A Scalable, Efficient, and Robust Dynamic Memory Management Library for HLS-based FPGAs

A Scalable, Efficient, and Robust Dynamic Memory Management ...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Qinggang Wang Long Zheng Zhaozeng An Shuyi Xiong Runze Wang Yu Huang Pengcheng Yao 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 Zhejiang Lab Hangzhou China School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Nowadays, high-level synthesis (HLS) has gained prominence for FPGA-based architecture prototyping, enhancing productivity significantly. Despite this advancement, HLS tools are impeded by a critical drawback: they lack support for dynamic memory management (DMM), leading to static mem-ory allocation and suboptimal use of memory resources. In response, numerous efforts have been made to develop DMM solutions compatible with HLS. However, our analysis indicates that existing solutions fail to concurrently meet the desired trifecta of scalability (efficient management of memory of any size), efficiency (minimal latency in memory (de-)allocation), and robustness (low allocation failure rates). This limitation hampers their applicability in real-world scenarios. In this paper, we introduce GraDMM, a “three-birds-one- stone” solution that comprehensively enhances the scalability, efficiency, and robustness of DMM. The key insight is to formulate memory (de-)allocation as graph analytics and lever-age sophisticated FPGA-based graph processing techniques. To achieve scalability, GraDMM specializes a simplified pipeline that significantly suppresses resource utilization expansion caused by managed memory scaling. This is crucial for managing arbitrarily sized memory on resource-limited FPGA platforms. For efficiency, GraDMM implements a data-centric concurrent traversal scheme and a shortcut-assisted fast traversal policy to accelerate (de-)allocation-guided graph traversal, reducing mem-ory (de-)allocation latency. To enhance robustness, GraDMM incorporates an adaptive memory defragmenter that defragments managed memory to minimize fragmentation-induced allocation failures. GraDMM is encapsulated as a library, providing high- level interfaces for users and ensuring synthesizability with Vi- vado HLS. Experimental results demonstrate that GraDMM out-performs three state-of-the-art HLS-compatible DMM solutions by significant margins: 56.71 %-85.59% in resource consumption sa

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FDAM: Filter-Dedicated Approximate Multiplier Design for Real-Time CNN Acceleration

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Nguyen, Kihwan Kim Xuan Truong Jeon, Dongsuk Lee, Hyuk-Jae Korea Republic of program Department of Electrical Engineering Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Graduate School of Convergence Science and Technology Research Institute for Convergence Science Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

Video super-resolution (VSR) is widely used in various high-definition applications, such as HDTVs and smartphones, requiring a dedicated upscaling technique for realtime full-HD generation. To reduce on-chip buffers for largesize output feature maps, a streaming VSR accelerator may employ an output stationary dataflow, leading to a large energy consumption caused by frequent filter switching. To mitigate this, we introduce a new filter-dedicated multiplier design for realtime VSR acceleration. We replace costly multipliers with adders, shifters, and multiplexers (MUXes), referred to as unified multiple constant multiplications (UMCM). The conventional UMCM, however, may incur considerable area/power overhead due to the unified topology constraint among different filter sets. To address this problem, we propose a new approximated MCM (AMCM) problem to relax the constraint and an approximate compatible graph synthesis (A-CGS) framework to efficiently solve AMCM by jointly searching for approximated filters and constructing a unified graph. Additionally, we suggest a lightweight finetuning method by freezing approximated filters and only finetuning biases, which can recover the original model’s accuracy within a few epochs. Experimental results with synthetic data demonstrate that AMCM reduces the area by up to 49.8%, 44.8%, and 40.3% when considering constant sets of 2, 4, and 8, respectively. Our designs with SR application achieve up to a 73.3% reduction in energy consumption. Experiments with the Set5 and Set14 datasets show that our model with bias correction achieves similar restoration performance compared to the eightbit models. © 1982-2012 IEEE.

关键词： Acceleration

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RELNet-An Efficient and Lightweight Classification Network 9

RELNet-An Efficient and Lightweight Classification Network

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9th International Conference on Digital Home, ICDH 2022

作者： Chen, Cheng Li, Ji Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

In the present era of information explosion, the ability of the computer to replace artificial recognition images are widely used in our life, such as face recognition, intelligent robot, information filtering and so on. Driven by the rapid development of deep learning technology and led by Convolutional Neural Network (CNN), these applications are maturer and maturer. In this paper, we designed a network for image classification on the CIFARtO, CIFARtOO and MNIST which is based on the convolutional neural network. we independently set the hyper-parameters and design the model structure to generate a convolutional neural network model with a small number of convolutional layers. Based on understanding the ResNet network structure, we got our network named RELNet. This model has certain advantages over other models with a similar number of convolutional layers, and it is an improved model that can achieve certain improvements in various evaluation indicators. In addition, this model has a faster convergence speed and fewer training parameters, which is considerable progress compared with other similar models. © 2022 IEEE.

关键词： Neural network models

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Life Jacket Wearing Recognition Based on Semantic Segmentation 9

Life Jacket Wearing Recognition Based on Semantic Segmentati...

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9th International Conference on Digital Home, ICDH 2022

作者： Ye, Cheng Li, Ji Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

In order to automatically identify whether a person wear life jacket, we propose an automatic detection model based on semantic segmentation. To gain a better result, we made improvements and optimizations on the basis of U-net, which is the most classic network in semantic segmentation. We replaced the max-pooling layer with a special convolutional layer, such a way can extract more features from feature map and can achieve the same effect of max-pooling. We added an extra dropout layer to prevent the overfitting in our model. We also used reflection padding in it. Reflection padding is used to make the convolution kernel to extract more features. We used binary cross entropy as loss function and Leaky Relu as activation function. Compared with the traditional semantic segmentation networks such as U-net and FCN, the precision of our improved model is about 2% higher than U-net, and the F1-measure is 2.6% higher than it. Our precision is 9% higher than FCN-32S and F1-measure is about 10% higher. The final accuracy of our model can achieve 93% and the IoU value is 4% higher than the traditional U-net. The last result can reach the level of human eyes, such an accuracy is sufficient to practical applications. © 2022 IEEE.

关键词： Semantic Segmentation

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Aircraft Detection in Airport Remote Sensing Images based on YOLOv5 9

Aircraft Detection in Airport Remote Sensing Images based on...

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9th International Conference on Digital Home, ICDH 2022

作者： Ji, Chengyun Zhang, Li Li, Ji Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

With the development of remote sensing, aerospace, and other related technologies, remote sensing image application scenarios are more and more extensive. In this paper, the training of remote sensing data in different airports is improved based on the YOLOv5 (You Only Look Once) model. Based on the analysis of existing deep learning frameworks and models, a remote sensing image segmentation program is designed to solve the problems of large-scale, large variation, and multiple data channels of remote sensing images. To improve the performance of small object detection, the original image is segmented on the detector. When the size of the original image is large, it will be reduced in the detector input, and the number of pixel representatives of small targets will be seriously reduced, leading to the degradation of detector detection performance. So, we propose the purpose of image segmentation and separation detection is to reduce the loss of small target pixels during image reduction. It shows great advantages in solving the problem of precision and speed of aircraft identification, and it is feasible to identify aircraft with a computer quickly and efficiently. © 2022 IEEE.

关键词： Aircraft detection

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Learning-driven Physically-aware Large-scale Circuit Gate Sizing

arXiv

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arXiv 2024年

作者： Ye, Yuyang Xu, Peng Ren, Lizheng Chen, Tinghuan Yan, Hao Yu, Bei Shi, Longxing National ASIC Research Center Southeast University Nanjing210096 China National Center of Technology Innovation for EDA Nanjing China Department of Computer Science and Engineering The Chinese University of Hong Kong NT Hong Kong School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

Gate sizing plays an important role in timing optimization after physical design. Existing machine learning-based gate sizing works cannot optimize timing on multiple timing paths simultaneously and neglect the physical constraint on layouts. They cause sub-optimal sizing solutions and low-efficiency issues when compared with commercial gate sizing tools. In this work, we propose a learning-driven physically-aware gate sizing framework to optimize timing performance on large-scale circuits efficiently. In our gradient descent optimization-based work, for obtaining accurate gradients, a multi-modal gate sizing-aware timing model is achieved via learning timing information on multiple timing paths and physical information on multiple-scaled layouts jointly. Then, gradient generation based on the sizing-oriented estimator and adaptive back-propagation are developed to update gate sizes. Our results demonstrate that our work achieves higher timing performance improvements in a faster way compared with the commercial gate sizing tool. Copyright © 2024, The Authors. All rights reserved.

关键词： Timing circuits

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Inorganic nanoparticles for photothermal treatment of cancer

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Journal of Materials Chemistry B 2024年第15期12卷 3569-3593页

作者： Thirumurugan, Senthilkumar Ramanathan, Susaritha Muthiah, Kayalvizhi Samuvel Lin, Yu-Chien Hsiao, Michael Dhawan, Udesh Wang, An-Ni Liu, Wai-Ching Liu, Xinke Liao, Mei-Yi Chung, Ren-Jei No. 1 Sec. 3 Zhongxiao E. Rd Taipei10608 Taiwan School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Genomics Research Center Academia Sinica Taipei115 Taiwan Department and Graduate Institute of Veterinary Medicine School of Veterinary Medicine National Taiwan University Taipei10617 Taiwan Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow GlasgowG116EW United Kingdom Scrona AG Grubenstrasse 9 Zürich8045 Switzerland Faculty of Science and Technology Technological and Higher Education Institute of Hong Kong New Territories Hong Kong999077 Hong Kong College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Applied Chemistry National Pingtung University Pingtung90003 Taiwan Taipei106 Taiwan

In recent years, inorganic nanoparticles (NPs) have attracted increasing attention as potential theranostic agents in the field of oncology. Photothermal therapy (PTT) is a minimally invasive technique that uses nanoparticles to produce heat from light to kill cancer cells. PTT requires two essential elements: a photothermal agent (PTA) and near-infrared (NIR) radiation. The role of PTAs is to absorb NIR, which subsequently triggers hyperthermia within cancer cells. By raising the temperature in the tumor microenvironment (TME), PTT causes damage to the cancer cells. Nanoparticles (NPs) are instrumental in PTT given that they facilitate the passive and active targeting of the PTA to the TME, making them crucial for the effectiveness of the treatment. In addition, specific targeting can be achieved through their enhanced permeation and retention effect. Thus, owing to their significant advantages, such as altering the morphology and surface characteristics of nanocarriers comprised of PTA, NPs have been exploited to facilitate tumor regression significantly. This review highlights the properties of PTAs, the mechanism of PTT, and the results obtained from the improved curative efficacy of PTT by utilizing NPs platforms. © 2024 The Royal Society of Chemistry

关键词： Cancer cells

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Strategic Realization of the Highest Possible Pixel Density for Quantum Dot Color Converter with Optimized Performance

Strategic Realization of the Highest Possible Pixel Density ...

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SID International Symposium Digest of Technical Papers, 2023

作者： Chen, Chih-Jung Chen, Kuan-An Kuo, Wei-Hung Wu, Chun-I Shen, Jia-Wun Kuo, Hao-Chung Chiang, Ray-Kuang Taiwan Nanocrystals Corp. Ltd Tainan Taiwan SynthEdge Advanced Materials Corp. Ltd Taoyuan Taiwan Industrial Technology Research Institute Hsinchu Taiwan Hon Hai Precision Industry Ltd Taipei Taiwan Department of Photonics and Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Chiao Tung University Hsinchu30010 Taiwan Semiconductor Research Center Taipei Taiwan

The light extraction efficiency of QDCC (quantum dot color converter) is low when its aspect ratio (width to thickness ratio) of a subpixel is less than two, which causes the problem in their high-resolution applications. Using QDPR (quantum dot photoresist) of high OD (optical density) values and PenTile subpixel arrangement, an apparent high-resolution application is feasible, in which the QDCC film thickness is less than 2 μm. Further incorporation of yellow color filter film or color purified layer (CPL) on the QDCC film can optimize its optical performance. Theoretically, QDCC with pixel density over 3000 ppi is achievable when it is directly applied to the CMOS substrate. In a trial test QDCC is applied on a glass substrate, which results in pixel density of 2116 ppi and color gamut of NTSC 109.64%. © 2023, John Wiley and Sons Inc. All rights reserved.

关键词： Color

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