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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Jing-Xiu Zeng Chih-Yang Lin Wei-Yang Lin National Chung Cheng University Minhsiung TW Dept. of Medical Research China Medical University Taichung Taiwan Dept. of Computer Science & Information Engineering Asia University Taichung Taiwan Department of Computer Science & Information Engineering National Chung Cheng University Chiayi Taiwan

ISBN: (纸本)9781479987467

Currently, most of the human detection methods are based on low-level features. In this paper, we proposed a middle-level feature generation method based on non-negative matrix factorization (NMF) for human detection. We also proposed an improvement scheme to guarantee that a better middle-level feature can be achieved. The proposed scheme can be applied to a complex background and the experimental results are better than those when only the low-level feature is involved.

关键词： Feature extraction Training Testing Conferences computer vision Pattern recognition Biomedical imaging

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Indian Sign Language Recognition Using Scale-Invariant Feature Transform by depth Sensor

Indian Sign Language Recognition Using Scale-Invariant Featu...

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Jayesh Gangrade Shweta Gangrade Ashish Mishra Gautam Kumar Vinit Kumar Gunjan Dept. of Artificial Intelligence & Machine Learning Manipal University Jaipur Jaipur India Dept of Information Technology Manipal University Jaipur Jaipur India Dept of Computer Science & Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering CMR Institute of Technology Hyderabad India

Deaf and hard of hearing people in the Indian community now have a way to communicate with society because to Indian Sign Language (ISL). The hand is accorded significant importance in the framework of the ISL. ISL is composed of both static and dynamic sign hand gestures. Both static and dynamic signs are made up of three components: hand movement, hand orientation, and hand position. The proposed method can recognize static ISL signs. The technique that was suggested makes use of the Microsoft Kinect sensor to locate and separate a hand from its background in a complex scene. SIFT, which stands for “scale-invariant feature transform,” was used to extract the feature. The SIFT algorithm is invariant under changes in perspective, orientation, and lighting. Recognize the 10 ISL digit movements with an accuracy of 94.9% using a multi support vector machine (MSVM).

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Multiple-video-based E-learning Platform for Physical Education

Multiple-video-based E-learning Platform for Physical Educat...

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Joint Conferences on Pervasive Computing (JCPC)

作者： Chun-Hong Huang T. L. Won Chun-Yeh Liu Yen-Da Chen Yung-Hui Chen Dept. of Computer Information and Network Engineering Lunghwa University of Science and Technology Taiwan Dept. of Physical Education National Taipei University of Education Taiwan Physical Education Office Fu Jen Catholic University Taiwan Dept. of Computer Information and Network Engineering Lunghwa University of Science and Technology

ISBN: (纸本)9781424452279;9781424452286

This paper aims to develop a multiple-video-based e-learning platform for physical education. The sports actions of teaching material can be shot and described by editing video into some desired clips for teaching and learning, and they would be stored into the action database. In this paper, we utilize multiple videos to display the sports action clips with three angle views which include in the front, the right front side and the left front side. The design concept is based on instruction system design (ISD). As a result, users can learn various sports skill within the shortest period of time by looking at the actions from our system. The system is expected to become a computerized education aid to sports action teaching and training, and to be the scientific and un-stereotyped system in physical education.

关键词： Electronic learning

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Comparative Analysis of Machine Learning Approaches in Smart Agriculture

Comparative Analysis of Machine Learning Approaches in Smart...

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Machine Learning, computer Systems and Security (MLCSS), International Conference on

作者： Niva Tripathy Subhranshu Sekhar Tripathy Mamata Rath Binod Kumar Pattanayak Kaushik Mishra Dept. of Computer Science & Engg. DRIEMS Autonomous College Cuttack Odisha India Department of Computer Science and Engineering Faculty of Engineering and Technology ITER Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Dept. of Computer Science & Engineering Sambalpur University Institute of Information Technology Burla India

In India's economy, agriculture is crucially significant. Agriculture automation is a major source of concern and a hot topic all around the world. The world's population is constantly growing, and with it comes increased demand for food as well as occupation. The farmers' old methods were inadequate to satisfy these requirements. As a result, new automated approaches were proposed. These new methods met food demands while simultaneously providing work opportunities for billions of people. Several methods are used for efficient farm management like IoT, cloud computing, AI, machine learning, deep learning, big data, etc. Among these big data is an emerging research area for crop yield prediction. Traditionally, these forecasts were reliant on the farmers' expertise, but now they can roughly anticipate the crop output on their farm by employing a variety of methods. In this paper, we have proposed a recommendation model along with an algorithm to evaluate the future year's crop production. We have compared the random forest machine learning algorithm with the big data algorithm. A brief comparison has been made with these algorithms. The proposed model has been implemented using python.

关键词： Smart agriculture Deep learning Machine learning algorithms Sociology Crops Production Big Data

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A Comprehensive Review of Leap Motion Controller-based Hand Gesture Datasets

arXiv

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arXiv 2023年

作者： Chakravarthi, Bharatesh Prasad, Prabhu B.M. Kumar, Pavan B.N. School of Computing and Augmented Intelligence Arizona State University TempeAZ United States Dept. of Computer Science and Engineering Indian Institute of Information Technology Dharwad India Dept. of Computer Science and Engineering Indian Institute of Information Technology Andhra Pradesh Sri City India

This paper comprehensively reviews hand gesture datasets based on Ultraleap’s leap motion controller, a popular device for capturing and tracking hand gestures in real-time. The aim is to offer researchers and practitioners a valuable resource for developing and evaluating gesture recognition algorithms. The review compares various datasets found in the literature, considering factors such as target domain, dataset size, gesture diversity, subject numbers, and data modality. The strengths and limitations of each dataset are discussed, along with the applications and research areas in which they have been utilized. An experimental evaluation of the leap motion controller 2 device is conducted to assess its capabilities in generating gesture data for various applications, specifically focusing on touchless interactive systems and virtual reality. This review serves as a roadmap for researchers, aiding them in selecting appropriate datasets for their specific gesture recognition tasks and advancing the field of hand gesture recognition using leap motion controller technology. © 2023, CC BY.

关键词： Palmprint recognition

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X-Clock Tree Construction for Antenna Avoidance

X-Clock Tree Construction for Antenna Avoidance

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2008 9th International Conference on Solid-State and Integrated-Circuit Technology

作者： Chia-Chun Tsai Feng-Tzu Hsu Chung-Chieh Kuo Jan-Ou Wu Trong-Yen Lee Dept. of Computer Science and Information Engineering Nanhua University Graduate Institute of Computer and Communication National Taipei University of Technology Department of Electronic Engineering De Lin Institute of Technology

The antenna effect is a phenomenon in the plasma-based nanometer processes that many charges are accumulated on metal wires which cause the degradation of gate-oxide. It also influences the chip reliability and manufacturing yield. Different with other methods based on Manhattan-architecture for the antenna avoidance, we propose the algorithm that combines jumper insertion and layer assignment (JILA) to eliminate antenna effects on X-architecture clock tree. Experimental results on benchmarks show that our algorithm can reduce all the antenna effects effectively by requiring just extra 20.7% in total vias on average, but the penalties in clock delay, skew, and power dissipation are controlled under the increments of 0.02%, 3.1%, and 0.02%, respectively.

关键词： X-Clock Tree Construction for Antenna Avoidance

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A genetic algorithm based multi-filter method for traffic sign extraction

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Journal of information and Computational science 2015年第15期12卷 5745-5752页

作者： Peng, Gang Chen, Yun Maezono, Masaki Kashima, Masayuki Nakayama, Shigeru Dept. of Computer Science and Technology Huizhou University Huizhou China Educational Technology Center Huizhou University Huizhou China Dept. of Information and Computer Science Faculty of Engineering Kagoshima University 1-21-40 Korimako Kagoshima Japan

In this paper, a novel multi-filter method is proposed for extracting traffic signs. 27 basic image filters including circular Hough transform are provided. The combination of selected filters is called a serial filter. Genetic algorithm is used to find the optimal serial filter. Color and shape information of traffic signs is utilized for pattern recognition and genetic coding. The experimental results show that the proposed method extracts traffic signs successfully. © 2015 by Binary information Press

关键词： Extraction

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Graph neural network based service function chaining for automatic network control

arXiv

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arXiv 2020年

作者： Heo, Dong Nyeong Lange, Stanislav Kim, Hee-Gon Choi, Heeyoul Dept. of Information and Communication Engineering Handong Global University Pohang Korea Republic of Dept. of Information Security and Communication Technology Norwegian University of Science and Technology Trondheim Norway Dept. of Computer Science and Engineering Pohang University of Science and Technology Pohang Korea Republic of

Software-defined networking (SDN) and the network function virtualization (NFV) led to great developments in software based control technology by decreasing expenditures. Service function chaining (SFC) is an important technology to find efficient paths in network servers to process all of the requested virtualized network functions (VNF). However, SFC is challenging since it has to maintain high Quality of Service (QoS) even for complicated situations. Although some works have been conducted for such tasks with high-level intelligent models like deep neural networks (DNNs), those approaches are not efficient in utilizing the topology information of networks and cannot be applied to networks with dynamically changing topology since their models assume that the topology is fixed. In this paper, we propose a new neural network architecture for SFC, which is based on graph neural network (GNN) considering the graph-structured properties of network topology. The proposed SFC model consists of an encoder and a decoder, where the encoder finds the representation of the network topology, and then the decoder estimates probabilities of neighborhood nodes and their probabilities to process a VNF. In the experiments, our proposed architecture outperformed previous performances of DNN based baseline model. Moreover, the GNN based model can be applied to a new network topology without re-designing and re-training. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Genetic Assessment Agent for High-School Student and Machine Co-Learning Model Construction on Computational Intelligence Experience

Genetic Assessment Agent for High-School Student and Machine...

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Congress on Evolutionary Computation

作者： Chang-Shing Lee Mei-Hui Wang Chih-Yu Chen Fu-Jie Yang Alexander Dockhorn Dept. of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Institute for Information Processing (TNT) Leibniz University Hannover Hannover Germany

This paper presents a genetic assessment agent and a student and machine co-learning model for high-school students' computational intelligence (CI) experience. We invited the IEEE CIS High School Outreach (HSO) subcommittee members of the years 2021–2022 to provide lectures at CIS activities and conferences and constructed a basic CI conceptual knowledge structure for high-school student learning. From 2021 to 2022 in Taiwan, we collected high-school students' learning data, including labels, attitudes, environment, and effort, from the CI&AI-FML platform using robots and learning tools, then processed the data using natural language processing (NLP) techniques to efficiently evaluate high-school students' learning state. We then applied three evolutionary computation techniques: genetic algorithm (GA), particle swarm optimization (PSO), and genetic algorithm neural network (GANN) in the proposed genetic assessment agent for the co-learning model, with learning performance regression analysis. In this paper, a CI&AI-FML human and machine co-learning Metaverse model is presented as a solution, which provides hands-on learning and experience while also supporting student-centered online learning during the COVID-19 pandemic. Students participated in the course during the 2022 Spring semester to learn basic CI concepts and experience CI applications through interaction with machines using the developed CI&AI- FML learning tools. The experimental results indicate that the genetic assessment agent with the GANN method has better performance in the student and machine co-learning model as compared to the other two methods, and it is effective for student and machine co-learning model construction.

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Symmetric and folded tori connected torus network

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Journal of Networks 2011年第1期6卷 26-35页

作者： Rahman, M.M.Hafizur Inoguchi, Yasushi Faisal, Faiz Al Kundu, Monoz Kumar JSPS Postdoctoral Fellow Japan Center for Information Science JAIST Nomi-Shi Ishikawa 923-1292 Japan Dept. of Computer Science and Engineering KUET Khulna-9203 Bangladesh

Hierarchical interconnection networks provide high performance at low cost by exploring the locality that exists in the communication patterns of massively parallel computers. A Symmetric Tori connected Torus Network (STTN) is a 2D-torus network of multiple basic modules, in which the basic modules are 2D-torus networks that are hierarchically interconnected for higher-level networks. We also fold the STTN to minimize the length of the longest wire of STTN. The folded network is called Folded Tori connected Torus Network (FTTN). In this paper, we present the architecture of the STTN and FTTN, addressing of node, routing of message, and evaluate the static network performance of STTN, FTTN, TTN, TESH, mesh, and torus networks. It is shown that both the STTN and FTTN possess several attractive features, including constant node degree, small diameter, low cost, small average distance, moderate bisection width, and high fault tolerant performance than that of other conventional and hierarchical interconnection networks. We further evaluate the longest wire length of FTTN and compare it with other networks. We found that the longest wire length of the FTTN is far lower than that of other networks considered in this paper while keeping good static network performance. It is just about (1/2mth) of its rival STTN. © 2011 ACADEMY PUBLISHER.

关键词： Costs

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