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PGG:An Online Pattern Based Approach for Stream Variation Management

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Journal of Computer science & Technology 2008年第4期23卷 497-515页

作者：唐绿岸崔斌李红燕苗高杉杨冬青周新彪 department of Computer science University of Illinois at Urbana-Champaign School of electronics engineering and Computer science Peking University

Many database applications require efficient processing of data streams with value variations and fluctuant sampling frequency. The variations typically imply fundamental features of the stream and important domain knowledge of underlying objects. In some data streams, successive events seem to recur in a certain time interval, but the data indeed evolves with tiny differences as time elapses. This feature, so called pseudo periodicity, poses a new challenge to stream variation management. This study focuses on the online management for variations over such streams. The idea can be applied to many scenarios such as patient vital signal monitoring in medical applications. This paper proposes a new method named Pattern Growth Graph （PGG） to detect and manage variations over evolving streams with following features： 1） adopts the wave-pattern to capture the major information of data evolution and represent them compactly; 2） detects the variations in a single pass over the stream with the help of wave-pattern matching algorithm; 3） only stores different segments of the pattern for incoming stream, and hence substantially compresses the data without losing important information; 4） distinguishes meaningful data changes from noise and reconstructs the stream with acceptable accuracy. Extensive experiments on real datasets containing millions of data items, as well as a prototype system, are carried out to demonstrate the feasibility and effectiveness of the proposed scheme.

关键词： data stream noise reorganization pattern representation variation management

Enhancing the performance of future wireless networks with software-defined networking

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Frontiers of Information Technology & Electronic engineering 2016年第7期17卷 606-619页

作者： Mingjie FENG Shiwen MAO Tao JIANG department of Electrical & Computer engineering Auburn University School of electronics & Information engineering Huazhong University of Science & Technology

To provide ubiquitous Internet access under the explosive increase of applications and data traffic,the current network architecture has become highly heterogeneous and complex,making network management a challenging *** this end,software-defined networking(SDN) has been proposed as a promising *** the SDN architecture,the control plane and the data plane are decoupled,and the network infrastructures are abstracted and managed by a centralized *** SDN,efficient and flexible network control can be achieved,which potentially enhances network *** harvest the benefits of SDN in wireless networks,the software-defined wireless network(SDWN) architecture has been recently *** this paper,we first analyze the applications of SDN to different types of wireless *** then discuss several important technical aspects of performance enhancement in SDN-based wireless ***,we present possible future research directions of SDWN.

关键词： Software-defined networking(SDN) Software-defined wireless networks(SDWN) Open Flow Performance enhancement

Robust sparse representation based face recognition in an adaptive weighted spatial pyramid structure

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science China(Information sciences) 2018年第1期61卷 86-98页

作者： Xiao MA Fandong ZHANG Yuelong LI Jufu FENG School of electronics engineering and Computer science Peking University School of Computer science and Software engineering Tianjin Polytechnic University department of Computer science University of York

The sparse representation based classification methods has achieved significant performance in recent years. To fully exploit both the holistic and locality information of face samples, a series of sparse representation based methods in spatial pyramid structure have been proposed. However, there are still some limitations for these sparse representation methods in spatial pyramid structure. Firstly, all the spatial patches in these methods are directly aggregated with same weights, ignoring the differences of patches’ reliability. Secondly, all these methods are not quite robust to poses, expression and misalignment variations, especially in under-sampled cases. In this paper, a novel method named robust sparse representation based classification in an adaptive weighted spatial pyramid structure(RSRC-ASP) is proposed. RSRC-ASP builds a spatial pyramid structure for sparse representation based classification with a self-adaptive weighting strategy for residuals’ aggregation. In addition, three strategies, local-neighbourhood representation, local intra-class Bayesian residual criterion, and local auxiliary dictionary, are exploited to enhance the robustness of RSRC-ASP. Experiments on various data sets show that RSRC-ASP outperforms the classical sparse representation based classification methods especially for under-sampled face recognition problems.

关键词： face recognition sparse representation self-adaptive weighted aggregating spatial pyramid structure local robust strategies

A comprehensive analysis of DAC-SDC FPGA low power object detection challenge

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science China(Information sciences) 2024年第8期67卷 300-320页

作者： Jingwei ZHANG Guoqing LI Meng ZHANG Xinye CAO Yu ZHANG Xiang LI Ziyang CHEN Jun YANG School of electronics science and engineering Southeast University

The lower power object detection challenge(LPODC) at the IEEE/ACM Design Automation Conference is a premier contest in low-power object detection and algorithm(software)-hardware co-design for edge artificial intelligence, which has been a success in the past five years. LPODC focused on designing and implementing novel algorithms on the edge platform for object detection in images taken from unmanned aerial vehicles(UAVs), which attracted hundreds of teams from dozens of countries to participate. Our team SEUer has been participating in this competition for three consecutive years from 2020 to 2022 and obtained sixth place respectively in 2020 and 2021. Recently, we achieved the championship in 2022. In this paper, we presented the LPODC for UAV object detection from 2018 to 2022, including the dataset, hardware platform,and evaluation method. In addition, we also introduced and discussed the details of methods proposed by each year's top three teams from 2018 to 2022 in terms of network, accuracy, quantization method, hardware performance, and total score. Additionally, we conducted an in-depth analysis of the selected entries and results, along with summarizing representative methodologies. This analysis serves as a valuable practical resource for researchers and engineers in deploying the UAV application on edge platforms and enhancing its feasibility and reliability. According to the analysis and discussion, it becomes evident that the adoption of a hardware-algorithm co-design approach is paramount in the context of tiny machine learning(TinyML).This approach surpasses the mere optimization of software and hardware as separate entities, proving to be essential for achieving optimal performance and efficiency in TinyML applications.

关键词： tiny machine learning object detection convolutional neural networks algorithm-hardware co-design low power field programmable gate array

A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

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Computer Modeling in engineering & sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer engineering Ronin InstituteMontclairNJ 07043USA department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&department of Computer science and Information engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

Characteristic Verification and Parameter Optimization of Airbags Cushion System for Airborne Vehicle

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Chinese Journal of Mechanical engineering 2014年第1期27卷 50-57页

作者： WANG Hongyan HONG Huangjie HAO Guixiang DENG Huaxia RUI Qiang LI Jianyang department of Mechanical engineering Academy of Armored Force Engineering School of Instrument science and Opto-electronics engineering Hefei University of Technology

Abstract： The major methods to investigate the airbags cushion system are experimental method, thermodynamic method and finite element method （FEM）. Airbags cushion systems are very complicated and very difficult to be investigated thoroughly by such methods For experimental method, it is nearly impossible to completely analyze and optimize the cushion characteristics of airbags of airborne vehicle because of charge issue, safety concern and time constraint. Thermodynamic method fails to take the non-linear effects of large airbag deformation and varied contact conditions into consideration. For finite element method, the FE model is usually complicated and the calculation takes tens of hours of CPU time. As a result, the optimization of the design based on a nonlinear model is very difficult by traditional iterative approach method. In this paper, a model based on FEM and control volume method is proposed to simulate landing cushion process of airborne vehicle with airbags cushion system in order to analyze and optimize the parameters in airbags cushion system. At first, the performance of airbags cushion system model is verified experimentally. In airdrop test, accelerometers are fixed in 4 test points distributed over engine mount, top, bottom and side armor plate of hull to obtain acceleration curves with time. The simulation results are obtained under the same conditions of the airdrop test and the simulation results agree very well with the experimental results, which indicate the established model is valid for further optimization. To optimize the parameters of airbags, equivalent response model based on Latin Hypercube DOE and radial basis function is employed instead of the complex finite element model. Then the optimal results based on equivalent response model are obtained using simulated annealing algorithm. After optimization, the maximal acceleration of airborne vehicle landing reduces 19.83%, while the energy absorption by airbags increases 7.85%. The

关键词： airborne vehicle airbag nonlinear finite element method verification equivalent response model

A Rapid Adaptation Approach for Dynamic Air‑Writing Recognition Using Wearable Wristbands with Self‑Supervised Contrastive Learning

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Nano-Micro Letters 2025年第2期17卷 417-431页

作者： Yunjian Guo Kunpeng Li Wei Yue Nam‑Young Kim Yang Li Guozhen Shen Jong‑Chul Lee department of Electronic Convergence engineering Kwangwoon UniversitySeoul 01897South Korea Radio Frequency Integrated Circuit(RFIC)Bio Centre Kwangwoon UniversitySeoul 01897South Korea department of Electronic engineering Kwangwoon UniversitySeoul 01897South Korea School of Microelectronics Shandong UniversityJinan 250101People’s Republic of China State Key Laboratory of Integrated Chips and Systems Fudan UniversityShanghai 200433People’s Republic of China School of Integrated Circuits and electronics Beijing Institute of TechnologyBeijing 100081People’s Republic of China

Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily *** existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple *** features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance *** wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist ***,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various *** system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and *** proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific *** utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.

关键词： Wearable wristband Self-supervised contrastive learning Dynamic gesture Air-writing Human-machine interaction

Lumped parameter analytic modeling and behavioral simulation of a 3-DOF MEMS gyro-accelerometer

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Acta Mechanica Sinica 2015年第6期31卷 910-919页

作者： Payal Verma Sandeep K.Arya Ram Gopal MEMS & Micro-sensors Group CSIR – Central Electronics Engineering Research Institute department of electronics & Communication engineering Guru Jambheshwar University of Science & Technology

A new analytical model of a 3-degree-of-freedom （3-DOF） gyro-accelerometer system consisting of a 1-DOF drive and 2-DOF sense modes is presented. The model constructs lumped differential equations associated with each DOFof the system by vector analysis. The coupled differential equations thus established are solved analytically for their responses in both the time and frequency domains. Considering these frequency response equations, novel device design concepts are derived by forcing the sense phase to zero, which leads to a certain relationship between the structural frequencies, thereby causing minimization of the damping effect on the performance of the system. Furthermore, the feasibility of the present gyro-accelerometer structure is studied using a unique discriminatory scheme for the detection of both gyro action and linear acceleration at their events. This scheme combines the formulated settled transient solution of the gyro-accelerometer with the processes of synchronous demodulation and filtration, which leads to the in-phase and quadrature components of the system＇s output signal. These two components can be utilized in the detection of angular motion and linear acceleration. The obtained analytical results are validated by simulation in a MATLAB/Simulink environment, and it is found that the results are in excellent agreement with each other.

关键词： Microelectromechanical systems (MEMS) ·Multimeasurement system. Gyro-accelerometer Gyro-accelerometer model - Transient model

On the measurement complexity of differentially private query answering

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science China(Information sciences) 2015年第9期58卷 150-160页

作者： WANG LiWei ZHANG JiaPeng Key Laboratory of Machine Perception (MOE) School of Electronics Engineering and Computer SciencePeking University Computer science and engineering department University of California

Differentially private query answering is an important problem in machine learning and data analysis. As an example, the users may want to know the empirical errors of some classifiers on a training data set which contains sensitive information. In this case, the answers returned by the database holder must preserve privacy. Previous results show that answering large number of queries with differential privacy is computationally expensive. However, almost all the hardness results are conditional, which means that they rely on unproved conjectures. One exception is the hardness for stateless mechanisms, in which the sanitizer is assumed to have very limited memory and can only answer the queries separately. In this work, we explore the hardness for differentially private query answering mechanisms beyond the stateless restriction. Specifically, we investigate the measurement complexity of a natural class of stateful sanitizer. Here, measurement refers to the evaluation of a query(a Boolean function) on a data point. Measurement complexity is a lower bound for the running *** prove unconditional subexponential lower bound for the measurement complexity of the class of sanitizer with ?-differential privacy. Possible generalization of the results is also discussed.

关键词： privacy differential privacy query measurement complexity learning