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2024 International Design Society Conference, Design 2024

作者： Nanjappan, Vijayakumar Georgiev, Georgi V. Casakin, Hernan Soomro, Sohail Ahmed Center for Ubiquitous Computing University of Oulu Finland School of Computer Science and Information Technology University College Cork Ireland Ariel University Israel Sukkur IBA University Pakistan

This paper examines how students' ideas evolve into physical prototypes within a digital fabrication design course. Examining the materials used, customization approaches, iterations, and team dynamics of 26 student projects reveals interplays between ideas, available tools, materials and constraints. Findings show the predominance of techniques, design preferences, concept refinement, and teamwork challenges. The implications highlight the value of hands-on iteration for alignment with reality and the need to support collaboration skills alongside technical prototype development. © 2024 Proceedings of the Design Society. All rights reserved.

关键词： 3D printing

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Density Peak Semi-Tied Covariance Matrices for Gaussian Mixture Models 9

Density Peak Semi-Tied Covariance Matrices for Gaussian Mixt...

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9th International Conference on Cloud computing and Big Data Analytics, ICCCBDA 2024

作者： Li, Min Wang, Guoyin Yu, Zeng Wang, Hongjun Wan, Jihong Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Chongqing China School of Computer Science and Technology Guangdong University of Technology Guangdong China

ISBN: (纸本)9798350373554

People widely use Gaussian mixture models (GMM) in data mining and knowledge discovery. The flexibly tied GMM can solve the memory, computational load, and data shortage problems caused by increasing the number of Gaussian components. However, sharing a full-rank matrix for all Gaussian components in a flexibly tied GMM cannot express the data structure. For example, when facing complex data, Gaussian components that share several full-rank matrices have stronger fitting capabilities. Motivated by this problem, this paper proposes an adaptive semi-tied GMM (ASemi-tied GMM). (1) Adaptive grouping of Gaussian components based on defining a similarity measure for the covariance matrix of the Gaussian components and realizing the adaptive semi-tied covariance of GMM by sharing the semi-tied transformation of the covariance within the group. (2) For ASemi-tied GMM, the Grouped Fast Newton Minimum Residual (G-FNMR) algorithm is proposed to estimate the parameters of this model. This paper validates the proposed ASemi-tied GMM on synthetic and real datasets, and experimental results show that its performance in log-likelihood and clustering is better than algorithms such as flexibly tied GMM. © 2024 IEEE.

关键词： Covariance matrix

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FKQNet: A biometrie sample quality estimation network using transfer learning 4

FKQNet: A biometrie sample quality estimation network using ...

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4th International Conference on Image Information Processing, ICIIP 2017

作者： Jaswal, Gaurav Nath, Ravinder Aggarwal, Divyansh Nigam, Aditya Department of Electrical Engineering National Institute of Technology Hamirpur177005 India Department of Computer Science Engineering Indian Institute of Technology Jodhpur342037 India School of Electrical and Computing Indian Institute of Technology Mandi175001 India

ISBN: (纸本)9781509067343

It is worth mentioning that the use of image quality assessment can play a significant role to achieve the performance improvement for biometric recognition frameworks. The aforementioned works and other analogue studies in low-resolution finger knuckle images have claimed that the precise detection of true features is difficult from poor quality images and the main reason for matching errors. The quality of finger knuckle images are mainly affected by blurred lines, random skin folds, unclear wrinkles, de-focus, poor contrast and varying reflections produced by the camera flash. Due to the lack of well-structured patterns as in the case of face or fingerprint biometrics, the quality assessment of knuckle images becomes challenging. In this paper, we have proposed a novel quality Network (FKQNet), that classifies FKP Images based on six different quality parameters. To the best of our knowledge this is the first attempt, a trained Deep Learning Neural Network has been employed to identify, estimate and quantify the quality attributes of knuckle images. Following this, an image has been classified into three classes viz. good, bad and average and on that basis, the best samples are selected or low weights are assigned to poor quality samples for further level recognition. The objective of the proposed system is to enhance the matching performance of biometric recognition frameworks through the use of quality assessment of image samples. The experimental results obtained from the publicly available finger knuckle database reveals that the proposed method is highly competitive compared with other state-of-the-art approaches. © 2017 IEEE.

关键词： Image quality

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

A Comprehensive Review of Leap Motion Controller-Based Hand ...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

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

ISBN: (纸本)9798350319088

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 IEEE.

关键词： Gesture recognition

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System for measuring teacher–student communication in the classroom using smartphone accelerometer sensors 19th

System for measuring teacher–student communication in the c...

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19th International Conference on Human–computer Interaction, HCI International 2017

作者： Harada, Naoyoshi Kimura, Masatoshi Yamamoto, Tomohito Miyake, Yoshihiro Department of Information and Computer Science College of Engineering Kanazawa Institute of Technology 7-1 Oogigaoka NonoichiIshikawa921-8501 Japan Department of Computer Science School of Computing Tokyo Institute of Technology 4259 Nagatsuta-cho Midori-ku YokohamaKanagawa226-8503 Japan

ISBN: (纸本)9783319580760

The quality of communication between a teacher and students is deeply related to the cultivation of students’ motivation, autonomy, and creativity in a university education. It is important to evaluate such communication and improve it to enhance faculty development. In this study, a system for measuring this communication has been developed. To implement the system, an application for measuring students’ body movements using the acceleration sensor of a smartphone was developed. At the same time, a server-side web system that visualizes the measured data was developed. Using this measurement system, the communication in a seminar of a university laboratory was measured. The results show that the activities of a presenter and audience can be clearly detected by the raw and frequency-analyzed accelerometer data. Moreover, the correlation between the sonograms of the presenter and of the audience members became stronger when they had constructive discussion. These results suggest that the synchronization between a presenter and the audience is related to their level of rapport. © Springer International Publishing AG 2017.

关键词： Smartphones

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computer Vision-Control-Based CNN-PID for Mobile Robot

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computers, Materials & Continua 2021年第7期68卷 1065-1079页

作者： Rihem Farkh Mohammad Tabrez Quasim Khaled Al jaloud Saad Alhuwaimel Shams Tabrez Siddiqui College of Engineering Muzahimiyah BranchKing Saud UniversityP.O.Box 2454Riyadh11451Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazanSaudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar UniversityTunisTunisia

With the development of artificial intelligence technology,various sectors of industry have *** them,the autonomous vehicle industry has developed considerably,and research on self-driving control systems using artificial intelligence has been extensively *** on the use of image-based deep learning to monitor autonomous driving systems have recently been *** this paper,we propose an advanced control for a serving robot.A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified *** robot should be able to follow the trajectory with speed *** controllers were used simultaneously to achieve *** neural networks(CNNs)are used for target tracking and trajectory prediction,and a proportional-integral-derivative controller is designed for automatic steering and speed *** study makes use of a Raspberry PI,which is responsible for controlling the robot car and performing inference using CNN,based on its current image input.

关键词： Autonomous car pid control deep learning convolutional neural network differential drive system raspberry pi

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Asymmetric multiparty-controlled teleportation of arbitrary n-qudit states using different quantum channels

Asymmetric multiparty-controlled teleportation of arbitrary ...

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2011 2nd International Conference on Theoretical and Mathematical Foundations of computer science, ICTMF 2011

作者： Shi, Run-Hua Zhong, Hong Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9783642249983

We present two schemes for asymmetric multiparty-controlled teleportation of an arbitrary n-qudit state using different quantum channels between the sender Alice and the controllers, where the first scheme utilizes the generalized Bell states in d-dimensional Hilbert space as the quantum channels and the second scheme takes the generalized multi-particle GHZ maximally entangled states as quantum resources. In addition, Alice shares n generalized Bell states with the receiver Bob in two schemes. In order to avoid performing three-particle or multi-particle joint measurements and consuming more qudits of the multi-particle maximally entangled states, Alice introduces the generalized CNOT gate operations and then obtains higher communication efficiency than the previous schemes based on entanglement swapping. © 2011 Springer-Verlag.

关键词： Quantum entanglement

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Diffusion Model with Self-supervised Training for Subject-aware Photoplethysmography Recovery

Diffusion Model with Self-supervised Training for Subject-aw...

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Wang, Daomiao Hu, Qihan Feng, Xujian Yang, Cuiwei Fudan University Center for Biomedical Engineering School of Information Science and Technology Shanghai China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

ISBN: (纸本)9798350300260

Photoplethysmography (PPG) has emerged as a promising technology for wearable personal healthcare due to its non-invasive and cost-effective nature. However, owing to the vulnerability to various noise, the useful PPG signals are few and the usability of PPG-based systems are limited. In this paper, we address the challenge of recovering extremely corrupted PPG waveforms with severe distortions, which lead to missing data. Specifically, we propose a novel diffusion-based framework that incorporates remaining values and subject-aware embedding, along with a self-supervised generative training strategy. Extensive experiments were performed on two datasets under three missing rates and two missing patterns. Our proposed model achieves significant improvements of nearly 100% over existing imputation models on quantitative metrics, and together with the visualization, demonstrating its effectiveness for data distribution generation and corrupted PPG restoration. Our proposed method also provides a new perspective for PPG denoising. © 2023 IEEE.

关键词： Deep generative model Diffusion model Motion artifact Photoplethysmography Self-supervised training

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Clustering-Based Analysis on 5G Networks: A Comparison 4

Clustering-Based Analysis on 5G Networks: A Comparison

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Annam, Sangeetha Mithillesh Kumar, P. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engg India

ISBN: (纸本)9798350300093

To satisfy the growing capacity demand and provide consumers with a higher level of service, it is anticipated that future-generation wireless networks will run entirely automatically. Utilizing spatiotemporal data on users and network health, mobile wireless networks may infuse the system with end-to-end visibility and intelligence. To handle the shifting conditions and the delivery of complex requirements for complicated services in a digital world with a highly engaged machine ecology, the future cellular system must be a neural carrier. By enabling the network to train and act, artificial intelligence enabled by '5G technology' can improve the network's interactive and decision-making capabilities. This study offers a thorough analysis of current clustering models in 5G networks. © 2023 IEEE.

关键词： 5G mobile communication systems

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Predicting Heart Disease Using Gaussian Confidence Distance Algorithm with Extra Tree Classifier 2nd

Predicting Heart Disease Using Gaussian Confidence Distance ...

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2nd International Conference on Deep sciences for computing and Communications, IconDeepCom 2023

作者： Amudha, S. Satheesh Kumar, M. Amutha Devi, C. Sankar, Sasi Rekha Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India

ISBN: (纸本)9783031689079

To prevent fatal heart failure early detection of Heart attack is essential steps. Heart Disease prediction happened well in advance using Machine learning, Artificial algorithms in Information Communication technology, and the use of a combination of multiple algorithms can further improve accuracy of predictions. However, it is important to note that the accuracy of algorithms in predicting heart disease depends on the quality and quantity factors. Important privacy concerns and potential biases are also very much essential in data. Reason for heart disease includes improper food habits and an increase in fat content due to less physical work. The diagnosis of heart illness is mainly related to symptoms, and clinical examination of patients. In this study, we try to classify and predict heart diseases at early stages on specifical features by fusing Various Machine Learning Algorithms. Machine learning when being used in health-care is able to detect disease earlier and accurately. This work yielded an accuracy of 88.5% in predicting heart disease using Random Forest Algorithm. To increase the prediction, many algorithms were combined with more tree classifiers to select features with domain-knowledge adding and physician expertise to the prediction models. This can lead to more accurate and reliable predictions, that will ultimately improve patient diagnosis and reduce health-care costs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

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