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international conference on Electrical, Electronics, and Optimization techniques (ICEEOT)

作者： Suryaprasad, J. Sandesh, D. S. Priyanka, I Pravalika, G. N. Kumar, Aman PES Inst Technol Bangalore South Campus Bangalore Karnataka India

ISBN: (纸本)9781467399395

Critical to any automated surveillance system is the recognition component. Since the inception of Pattern Recognition & Image processing, researchers across the globe continue to propose newer facial recognition algorithms. Each of these facial algorithms has its own pros and cons. A typical facial recognition algorithm consists of three steps namely: facial detection, feature extraction and recognition. In this paper we discuss serial and parallel implementation of four standard facial recognition algorithms namely, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA) and FISHER Face. Work presented in this paper consists of two sections. In section 1, we discuss serial and parallel implementation of the algorithms. Section 2, presents a comparative performance analysis of the above 4 algorithms. A major challenge in this effort is the ability to explore and exploit concurrency in existing facial recognition algorithms to improve performance. Based on the algorithms performance, we have made few suggestions regarding the suitability of each of these algorithms for different domains/applications. All algorithms are implemented using open source OPENCV and OPENMP libraries.

关键词： Facial Recognition algorithms Performance analysis parallel algorithms Open Source technologies

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A formal specification of the CORBA event service

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IFIP TC6/WG6 1 4th international conference on Formal Methods for Open Object-Based distributed Systems (FMOODS 2000)

作者： Bastide, R Sy, O Navarre, D Palanque, P Univ Toulouse 1 LIHS F-31042 Toulouse France

ISBN: (纸本)0792379233

CORBA is a standard proposed by the Object Management Group (OMG) that promotes interoperability between distributed object systems. Following the standardization of this object-oriented middleware, the OMG has specified a set of Common Object Services (COS) that are meant to serve as the building blocks of distributed CORBA applications. The COSes are specified using CORBA Interface Definition Language (IDL), that describes the syntactic aspects of services supported by remote objects. However, CORBA-IDL does not support specification of the behaviour of objects in an abstract and formal way, and behavioural specification is mostly provided in plain English. To overcome this problem, we have proposed a formal description technique (Cooperative Objects) based on high-level Petri nets, and developed a software support environment. The goal of this paper is to demonstrate that our approach is suited to the formal specification of typical CORBA COS, and presents a Cooperative Object model of the CORBA event service, a COS that provides asynchronous, one-to-many communication between objects. The advantages of dealing with a tool-supported, executable formal notation are detailed, as well as the results that can be obtained through Petri net analysis techniques.

关键词： distributed systems behavioural specification CORBA high-level Petri nets

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An XML gateway to patient data for medical research applications

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techniques IN BIOINFORMATICS AND MEDICAL INFORMATICS 2002年第1期980卷 236-246页

作者： Bui, AAT Weinger, GS Barretta, SJ Dionisio, JDN Kangarloo, H Univ Calif Los Angeles Telemed Div Dept Radiol Los Angeles CA 90024 USA

As the medical environment becomes increasingly electronic, clinical databases are continually growing, accruing masses of patient information. This wealth of data is an invaluable source of information to researchers, serving as a testbed for the development of new information technologies and as a repository of real-world data for data mining and population-based studies. However, the true utility of this information is not fulfilled, in part because of issues pertaining to security and patient confidentiality, but also due to the lack of an effective infrastructure to access the data. This paper describes a system, DataServer, that permits researchers to query and retrieve data from multiple clinical data sources, automatically deidentifying patient data so that it can be used for research purposes. DataServer functions as an application framework, enabling extensible markup language (XML)-based querying of existing medical databases. Key aspects of DataServer include ready inclusion of new information resources, minimal processing impact on existing clinical systems via a distributed cache, and flexible output representation via XSL (eXtensible Style Language) transforms.

关键词： XML distributed databases patient deidentification

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parallel I/O: Getting ready for prime time

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IEEE parallel & distributed Technology: Systems & applications 1995年第2期3卷 64-71页

作者： Dan Reed Charles Catlett Alok Choudhary David Kotz Marc Snir University of Illinois National Center for Supercomputing Applications Syracuse University Dartmouth College IBM T j. Watson Research Center

During the international conference on parallel processing, held August 15-19, 1994, we convened a panel to discuss the state of the art in parallel I/0, tools and techniques to address current problems, and challenge...

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SELF-MANAGEMENT IN A CONTROL ARCHITECTURE FOR parallel KINEMATIC ROBOTS

SELF-MANAGEMENT IN A CONTROL ARCHITECTURE FOR PARALLEL KINEM...

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ASME international Design Engineering Technical conferences/Computers and Information in Engineering conference

作者： Maass, Jochen Steiner, Jens Amado, Ana Huhn, Michaela Raatz, Annika Hesselbach, Juergen Tech Univ Carolo Wilhelmina Braunschweig Dept Mech Engn Inst Machine Tools & Prod Technol D-38106 Braunschweig Germany

ISBN: (纸本)9780791843277

Maintainability, extendibility and reusability of components in the design of robot control architectures is a major challenge. parallel kinematic robots feature a wide variety of structures and applications. They are subject to easy reconfiguration because of the passive structure limbs. This class of robots requires more extensive calculations in their control laws than serial manipulators. During complex motion tasks, such as the ones required in assembly sequences, the algorithmic load may also vary over time. However, no generic control approach exists in order to reduce the complexity of control design for these kind of robots. In this paper the authors introduce an architecture for handling and assembly applications featuring self-management techniques as an approach to tackle these problems. The existing architecture features a modular and layered design. Concepts of self-management and self-optimization applied to this architecture are outlined. These properties are realized by the integration of self-managers within crucial system components. The mechanisms are extended for a future distributed version of the architecture. Real-time properties are guaranteed by an online formal analysis that verifies planned adaptations before realizing them.

关键词： Reusability

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Leveraging Neural Networks to Enhance Large-Scale Data processing Performance 3

Leveraging Neural Networks to Enhance Large-Scale Data Proce...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Pandey, Megha Vinay Kumar, S.B. Khurana, Jatin Kalpana, B. Pathak, Kishor R. Kumar, Mukesh Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India 1 Electronics and Communication Engineering Faculty of Engineering and Technology Karnataka Bangalore India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350319125

Recent advances in neural networks have enabled them to become powerful tools in data processing for tasks such as pattern recognition and classification. This has enabled neural networks to be applied to large-scale data processing problems, where they have been shown to perform very well. However, due to the large amount of data involved in such tasks, it is essential that the processing performance is also improved. This paper presents several methods to leverage available resources such as distributed computing and improved algorithmic techniques to improve the performance of large-scale data processing tasks using neural networks. Examples of such techniques are given for both supervised and unsupervised learning tasks, which can significantly enhance the performance of large-scale data processing applications. The results of several experiments are presented, demonstrating the effectiveness of the proposed techniques. The authors also provide insights for future work that could further enhance the capabilities of neural networks when applied to large-scale data processing applications. © 2023 IEEE.

关键词： applications Data Networks presented processing

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Contrastive Learning-Based Generic Audio-to-Lyrics Alignment 21

Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

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21st IEEE international Symposium on parallel and distributed processing with applications, 13th IEEE international conference on Big Data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Huang, Shuai Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business UIniversity Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on BigData Security Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350329223

In this research, we tackled the complex task of aligning audio and lyrics content automatically. This task entails the precise matching of lyrics with the corresponding audio segments in songs, necessitating the coordination of two distinct modalities: text and audio. To surmount this challenge, we employed a contrastive learning approach that generates cross-modal embeddings capable of establishing connections between the audio and text domains. Furthermore, we adopted a hierarchical and cascading design. Initially, the system predicts synchronization at the sentence level and subsequently refines the alignment at the word level. This design equips the system to handle extended sequences effectively, as cross-correlations leverage secondary memory that corresponds to sequence length. Through this innovative approach, we developed a trainable end-to-end system that can harness weakly annotated training data, concurrently learning a robust text model optimized for alignment tasks. Our experiments demonstrated substantial enhancements in average alignment accuracy when utilizing our proposed system, underscoring its robustness compared to previous state-of-the-art models. © 2023 IEEE.

关键词： Alignment

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Research on Optimization of Communication Technology Based on Deep Learning 3

Research on Optimization of Communication Technology Based o...

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3rd international conference on Electronics and Information Technology, EIT 2024

作者： Zhang, Zhaoxiang China Mobile Group Device Co. Ltd BeiJing100053 China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369151

distributed deep learning systems commonly use synchronous data parallelism to train models. However, communication overhead can be costly in distributed environments with limited communication bandwidth. To reduce communication overhead, an intuitive approach is to increase the communication interval. However, increasing the communication interval often affects the convergence speed of the model, requiring more epochs to train the model to the target accuracy, thereby reducing the statistical efficiency of the training algorithm. To address these issues in distributed synchronous data parallel training algorithms and communication interval selection methods, this paper proposes a training algorithm that combines skip communication strategies and correction techniques to ensure low communication overhead and high statistical efficiency. The training algorithm maintains a small batch size through local updates in each training process and employs correction techniques to reduce the divergence among individual local models, thereby ensuring high statistical efficiency. At the same time, the training algorithm utilizes a skip communication strategy to update the global model, meaning that the global model is not updated with every iteration, but rather updated once every few iterations. This reduces the frequency of communication, ensuring low communication overhead. Experimental results show that this training algorithm reduces total training time by 88.9%, 74.9%, 34.1%, and 82.4% compared to SSGD, SkipSSGD, LocalSGD, and SMA, respectively. © 2024 IEEE.

关键词： Training Deep learning distributed databases Bandwidth parallel processing Data models Communications technology Information technology Optimization Convergence

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Towards Network-accelerated ML-based distributed Computer Vision Systems 27

Towards Network-accelerated ML-based Distributed Computer Vi...

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27th IEEE international conference on parallel and distributed Systems, ICPADS 2021

作者： Siddique, Hisham Neves, Miguel Kuzniar, Carson Haque, Israat Dalhousie University Faculty of Computer Science HalifaxNS Canada

ISBN: (纸本)9781665408783

Computer vision is a crucial component in many modern applications (e.g., medical image analysis, environmental monitoring and self-driving cars). However, their stringent computational, latency and bandwidth requirements still pose a huge challenge to system architects, which must seek for alternatives to both the limited resources (e.g., low-end CPU) on client devices and the hurdles of moving data from clients to cloud/edge servers for analysis. In this work, we advocate for the usage of emerging programmable network devices to speed up ML-based computer vision tasks, particularly image classification, on resource constrained environments. To take the first step towards this new paradigm, we propose NetPixel, a framework that enables P4-programmable switches to classify images in realtime, accurately and at scale. We implemented a prototype of NetPixel in a software switch to show its feasibility and conducted a preliminary evaluation on widely adopted datasets. Our results show that NetPixel can classify images with an accuracy within 8% that of a server-based implementation even for shallow classifiers and low-resolution images. © 2021 IEEE.

关键词： Network protocols

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A Case of parallel EEG Data processing upon a Beowulf Cluster

A Case of Parallel EEG Data Processing upon a Beowulf Cluste...

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international conference on parallel and distributed Systems (ICPADS)

作者： Yufeng Yao Jinyi Chang Kaijian Xia School of Computer Science and Engineering Changshu Institute of Technology Changsha Jiangsu China

Electroencephalogram (EEG) data processing applications have become routine tasks in both bioscience and neuroscience research, which are usually highly compute and data intensive. In this paper, we present a parallel method to analyze the huge EEG data with a Beowulf cluster. Through an example of the synchronization measurement of multiple neuronal populations, the procedure of exploiting the parallelism of EEG data processing applications to achieve speed-up has been detailed. The experimental results indicate that the execution efficiency of EEG data processing can be improved dramatically using parallel and distributed computing techniques even with inexpensive computing platform.

关键词： Electroencephalography Data processing distributed computing Application software Concurrent computing Neuroscience Bioinformatics parallel processing Local area networks Message passing

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