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7th International Conference on Advancements of Medicine and Health Care through Technology, MEDITECH 2020

作者： Pavăl, Mihaela Cenușă, Mihai Milici, Dan Laurențiu Faculty of Electrical Engineering and Computer Science University Ștefan cel Mare of Suceava Suceava Romania Integrated Center for Research Development and Innovation in Advanced Materials Nanotechnologies and Distributed Systems for Fabrication and Control University Ștefan cel Mare of Suceava Suceava Romania

ISBN: (纸本)9783030935634

The paper presents a study on the performance of system of monitoring the activity of a person in the office. The equipment proposed by the authors is based on a continuous evaluation, using data collection and processing systems with wireless transmission, so that the monitored individual is not uncomfortable in the activities they perform and it is performed based on the evaluation of the position of the monitored person during the period in which he carries out his activity sitting on the chair, but also during the rest period. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Monitoring

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Systolic Array Based Convolutional Neural Network Inference on FPGA 15

Systolic Array Based Convolutional Neural Network Inference ...

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15th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022

作者： Chua, Shi Hui Teo, T. Hui Tiruye, Mulat Ayinet Wey, I-Chyn Singapore University of Technology and Design Engineering Product Development Singapore Singapore University of Technology and Design Engineering Product Development Science Mathematics and Technology Singapore Artificial Intelligence Research Center School of Electrical and Computer Engineering College of Engineering Graduate Institute of Electrical Engineering Chang Gung University Chang Gung University Taiwan

ISBN: (纸本)9781665464994

Convolutional Neural Networks (CNNs) possess a particular edge over its predecessor, the Multi-Layer Perceptron (MLP). This is due to its weight sharing features that allows the CNN to use less parameters for the same number of outputs as compared to the MLP. Systolic arrays capitalize on the weight sharing property of CNNs to do data reuse while performing convolutional operations, in order to reduce the power consumption from the memory accesses. A kernel fitting systolic processing element array was designed with only positive multiplication to increase the throughput and power efficiency of the CNN accelerator, while using weight stationary dataflow to achieve data reuse in the systolic array. A cost-optimized lightweight solution is implemented through low-cost FPGA hardware so as to allow for greater accessibility. The CNN accelerator consumes 0.363 W power at 100 MHz operating frequency. A peak throughput of 10.98 GOps/s was achieved with peak performance density of 0.200 GOps/s/DSP and peak power efficiency of 30.26 GOps/s/W. Even with the added support for additional functions, proposed design achieved up to 1.59x better power efficiency compared to other systolic implementations and up to 6.17x better power efficiency compared to non-systolic implementations. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Time Series Classification Using Deep Learning for HAR Based on Smart Wearable Sensors 26

Time Series Classification Using Deep Learning for HAR Based...

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26th International computer science and engineering Conference, ICSEC 2022

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

ISBN: (纸本)9781665491983

In the last decades, time series classification (TSC) has emerged as one of the most challenging issues in data mining, and extensive studies have been done on various methods, including algorithm-based and learning-based techniques. Sensor-based human activity recognition (HAR) is a TSC issue that has become one of the most sought-after fields among business and academia specialists because of the proliferation of smartphone technology and wearable movement sensors. Conventional approaches to feature extraction provide a significant challenge in feature selection. Deep learning is an efficient strategy in the HAR scientific field and has solved the issue of feature selection. Nevertheless, several obstacles remain to study topics, including classifier interpretation. This article integrates well-known deep learning methods, namely convolutional neural networks and RNN-based models. The new approach proved to be more effective than the existing state-of-the-art approach. We assessed our network on the multivariant time-series benchmark (UCI-HAR) and revealed that our model surpasses other models in terms of training time and overall accuracy. © 2022 IEEE.

关键词： Wearable sensors

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CADEC: A Combinatorial Auction for Dynamic Distributed DNN Inference Scheduling in Edge-Cloud Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Xu, Xiaolong Hu, Yuhao Cui, Guangming Qi, Lianyong Dou, Wanchun Nanjing University of Information Science and Technology School of Software Nanjing210044 China Nanjing University of Information Science and Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China College of Computer science and technology Qingdao China Georgia State University Department of Computer Science United States

Deep Neural Network (DNN) Inference, as a key enabler of intelligent applications, is often computation-intensive and latency-sensitive. Combining the advantages of cloud computing (abundant computing resources) and edge computing (fast transmission), edge-cloud collaborative DNN inference is a powerful solution to these problems. However, in edge-cloud networks with heterogeneous resources, how to obtain reasonable decisions on server selection, model partition and resource allocation for efficient distributed DNN inference is a hard challenge. Furthermore, it is non-trivial to design suitable resource prices to maximize the social welfare. These challenges even escalate in dynamic edge-cloud networks where decisions should be generated as soon as each user arrives without future information. Therefore, we design a combinatorial auction for dynamic distributed DNN inference scheduling, named CADEC. CADEC first constructs a bid set for each user based on convex optimization theory for optimal solution searching. Next, prices of resources in the edge-cloud network are adjusted according to changes in supply-demand relationship, and whether to admit the request of each user is decided. Finally, the dynamic distributed inference scheduling decisions are generated through the primal-dual algorithm to maximize the social welfare. Theoretical analysis shows the good competitive ratio and polynomial time complexity of CADEC. Results of simulation experiments present that CADEC improves social welfare by up to 46% compared with state-of-the-art distributed DNN inference schemes. © 2002-2012 IEEE.

关键词： Convex optimization

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INFEASIBILITY OF CONSTRUCTING A SPECIAL ORTHOGONAL MATRIX FOR THE DETERMINISTIC REMOTE PREPARATION STATE OF AN ARBITRARY N-QUBIT STATE

arXiv

引用

arXiv 2023年

作者： Liu, Wenjie Li, Zixian Yuan, Gonglin Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China School of Mathematics and Information Science Center for Applied Mathematics of Guangxi Guangxi University Nanning530004 China

In this paper, we present a polynomial-complexity algorithm to construct a special orthogonal matrix for the deterministic remote state preparation (DRSP) of an arbitrary n-qubit state, and prove that if n > 3, such matrices do not exist. Firstly, the construction problem is split into two sub-problems, i.e., finding a solution of a semi-orthogonal matrix and generating all semi-orthogonal matrices. Through giving the definitions and properties of the matching operators, it is proved that the orthogonality of a special matrix is equivalent to the cooperation of multiple matching operators, and then the construction problem is reduced to the problem of solving an XOR linear equation system, which reduces the construction complexity from exponential to polynomial level. Having proved that each semi-orthogonal matrix can be simplified into a unique form, we use the proposed algorithm to confirm that the unique form does not have any solution when n > 3, which means it is infeasible to construct such a special orthogonal matrix for the DRSP of an arbitrary n-qubit state. © 2023, CC BY.

关键词： Matrix algebra

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3D/4D printed bio-piezoelectric smart scaffolds for next-generation bone tissue engineering

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International Journal of Extreme Manufacturing 2023年第3期5卷 236-262页

作者： Annan Chen Jin Su Yinjin Li Haibo Zhang Yusheng Shi Chunze Yan Jian Lu State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074People’s Republic of China Engineering Research Center of Ceramic Materials for Additive Manufacturing Ministry of EducationWuhan430074People’s Republic of China Centre for Advanced Structural Materials Department of Mechanical EngineeringCity University of Hong KongPeople’s Republic of China Centre for Advanced Structural Materials City University of Hong Kong Shenzhen Research InstituteGreater Bay Joint DivisionShenyang National Laboratory for Materials ScienceShenzhen 518057People’s Republic of China CityU-Shenzhen Futian Research Institute Shenzhen 518045People’s Republic of China

Piezoelectricity in native bones has been well recognized as the key factor in bone ***,bio-piezoelectric materials have gained substantial attention in repairing damaged bone by mimicking the tissue’s electrical microenvironment(EM).However,traditional manufacturing strategies still encounter limitations in creating personalized bio-piezoelectric scaffolds,hindering their clinical ***-dimensional(3D)/four-dimensional(4D)printing technology based on the principle of layer-by-layer forming and stacking of discrete materials has demonstrated outstanding advantages in fabricating bio-piezoelectric scaffolds in a more complex-shaped ***,4D printing functionality-shifting bio-piezoelectric scaffolds can provide a time-dependent programmable tissue EM in response to external stimuli for bone *** this review,we first summarize the physicochemical properties of commonly used bio-piezoelectric materials(including polymers,ceramics,and their composites)and representative biological findings for bone ***,we discuss the latest research advances in the 3D printing of bio-piezoelectric scaffolds in terms of feedstock selection,printing process,induction strategies,and potential ***,some related challenges such as feedstock scalability,printing resolution,stress-to-polarization conversion efficiency,and non-invasive induction ability after implantation have been put ***,we highlight the potential of shape/property/functionality-shifting smart 4D bio-piezoelectric scaffolds in bone tissue engineering(BTE).Taken together,this review emphasizes the appealing utility of 3D/4D printed biological piezoelectric scaffolds as next-generation BTE implants.

关键词： 3D/4D printing bio-piezoelectric materials biomimetic scaffolds electrical microenvironment bone regeneration

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A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking

A Novel Deep Learning Approach Featuring Graph-Based Algorit...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Keliang Niyogisubizo, Jovial Xiao, Linxia Pan, Yi Wei, Dongqing Rosiyadi, Didi Wei, Yanjie Chinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Shenzhen518055 China Shanghai Jiao Tong University State Key Laboratory of Microbial Metabolism School of Life Sciences and Biotechnology Shanghai200240 China National Research and Innovation Agency Research Center for Artificial Intelligence and Cybersecurity Bandung Indonesia

ISBN: (纸本)9798350337488

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs. © 2023 IEEE.

关键词： cell segmentation cell tracking deep learning Deeplabv3+ instance segmentation iPS cell reprogramming

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Heterogeneous Integration of Lithium Niobate and Silicon Photonics for Nonlinear Optics

Heterogeneous Integration of Lithium Niobate and Silicon Pho...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Palici, Alexandra Maria Lu, Xiyuan Srinivasan, Kartik Lu, Xinda Labbe, Fabien Ding, Yunhong Laroque, Hugo Hamerly, Ryan Errando-Herranz, Carlos Pu, Minhao Englund, Dirk Heuck, Mikkel Department of Electrical and Photonics Engineering Technical University of Denmark Lyngby2800 Denmark Microsystems and Nanotechnology Division Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

We demonstrate a hybrid device consisting of a thin film lithium niobate membrane transfer-printed onto a silicon nitride ring resonator. We measure quality factors in the 105 range at telecom wavelengths. CLEO 2024 &... 详细信息

ISBN: (纸本)9781957171395

关键词： Silicon nitride

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An Adaptive Tensor-Train Decomposition Approach for Efficient Deep Neural Network Compression

arXiv

引用

arXiv 2024年

作者： Luo, Shiyi Liu, Mingshuo Sun, Pu Yu, Yifeng Ren, Shangping Bai, Yu Computational Science Research Center San Diego State University San Diego United States Computational Science Research Center University of California Irvine Irvine United States Department of Electrical and Computer Engineering University of California Davis Davis United States Department of Mathematics University of California Irvine Irvine United States Department of Computer Science San Diego State University United States Department of Electrical and Computer Engineering California State University Fullerton Fullerton United States

In the field of model compression, choosing an appropriate rank for tensor decomposition is pivotal for balancing model compression rate and efficiency. However, this selection, whether done manually or through optimization-based automatic methods, often increases computational complexity. Manual rank selection lacks efficiency and scalability, often requiring extensive trial-and-error, while optimization-based automatic methods significantly increase the computational burden. To address this, we introduce a novel, automatic, and budget-aware rank selection method for efficient model compression, which employs Layer-Wise Imprinting Quantitation (LWIQ). LWIQ quantifies each layer's significance within a neural network by integrating a proxy classifier. This classifier assesses the layer's impact on overall model performance, allowing for a more informed adjustment of tensor rank. Furthermore, our approach includes a scaling factor to cater to varying computational budget constraints. This budget awareness eliminates the need for repetitive rank recalculations for different budget scenarios. Experimental results on the CIFAR-10 dataset show that our LWIQ improved by 63.2% in rank search efficiency, and the accuracy only dropped by 0.86% with 3.2x less model size on the ResNet-56 model as compared to the state-of-the-art proxy-based automatic tensor rank selection *** Codes 68T10, 65K10 Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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Plant regeneration in the new era:from molecular mechanisms to biotechnology applications

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science China(Life sciences) 2024年第7期67卷 1338-1367页

作者： Chunli Chen Yuxin Hu Momoko Ikeuchi Yuling Jiao Kalika Prasad Ying Hua Su Jun Xiao Lin Xu Weibing Yang Zhong Zhao Wenkun Zhou Yun Zhou Jian Gao Jia-Wei Wang National Key Laboratory for Germplasm Innovation&Utilization of Horticultural Crops Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhan 430070China College of Life Science and Technology Huazhong Agricultural UniversityWuhan 430070China Key Laboratory of Plant Molecular Physiology Institute of BotanyChinese Academy of Sciences(CAS)China National Botanical GardenBeijing 100093China Division of Biological Sciences Graduate School of Science and TechnologyNara Institute of Science and TechnologyNara 630-0192Japan State Key Laboratory of Protein and Plant Gene Research School of Life SciencesPeking UniversityBeijing 100871China Peking-Tsinghua Center for Life Sciences Center for Quantitative BiologyAcademy for Advanced Interdisciplinary StudiesPeking UniversityBeijing 100871China Indian Institute of Science Education and Research Pune 411008IndiaThiruvananthapuram 695551India State Key Laboratory of Wheat Improvement College of Life SciencesShandong Agricultural UniversityTai’an 271018China Sino-German Joint Research Center on Agricultural Biology Shandong Agricultural UniversityTai’an 271018China Key Laboratory of Plant Cell and Chromosome Engineering Institute of Genetics and Developmental Biology(IGDB)CASBeijing 100101China CAS-JIC Centre of Excellence for Plant and Microbial Science(CEPAMS) IGDBCASBeijing 100101China National Key Laboratory of Plant Molecular Genetics CEMPSInstitute of Plant Physiology and Ecology(SIPPE)CASShanghai 200032China CEPAMS SIPPECASShanghai 200032China Hefei National Laboratory for Physical Sciences at the Microscale CEMPSSchool of Life SciencesDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei 230027China State Key Laboratory of Plant Environmental Resilience College of Biological SciencesChina Agricultural UniversityBeijing 100193China Department of Botany and Plant Pathology and Center for Plant Biology Purdue UniversityWest Lafayette 47907USA School of Life Science and Technology Shang

Plants or tissues can be regenerated through various *** animal regeneration,cell totipotency and pluripotency are the molecular basis of plant *** systematic studies on Arabidopsis thaliana gradually unravel the fundamental mechanisms and principles underlying plant ***,plant hormones,cell division,epigenetic remodeling,and transcription factors play crucial roles in reprogramming somatic cells and reestablishing meristematic *** research on basal non-vascular plants and monocot crops has revealed that plant regeneration differs among species,with various plant species using distinct mechanisms and displaying significant differences in regenerative *** multi-omics studies at the single-cell level,tracking plant regeneration processes in real-time,and deciphering the natural variation in regenerative capacity will ultimately help understand the essence of plant regeneration,improve crop regeneration efficiency,and contribute to future crop design.

关键词： apical meristem model species regeneration phytohormone plant cell reprogramming somatic embryogenesis

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