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Classic Vocal Performance Training Through C-VaC Method: Study of Core Muscle Stability Warm-Up Techniques via computer-Aided Analysis

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Journal of Voice 2024年

作者： Hou, Zhenyi Zhang, Tianhao Zou, Yan Tao, Zhirong Luo, Daijun Jiang, Shanggerile Ye, Kejie Klaushofer, Margit Schnack, Michael Fan, Guangyu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China Department of Musicology Shanghai Conservatory of Music Shanghai China Department of Vocal Studies and Music Theatre University of Music and Performing Arts Vienna Vienna Austria Department of Music and Movement Education University of Music and Performing Arts Vienna Vienna Austria

Vocal warm-up is essential for achieving optimal vocal performance;however, traditional methods often neglect the role of core muscle stability. This study presents the Combined Vocal and Core Muscle Stability Warm-Up Method (C-VaC), which integrates core stability training with vocal exercises to improve vocal performance. The effectiveness of the C-VaC method was assessed among soprano majors at various stages using computer-assisted audio analysis for objective measurement. Comparative analyses of pitch accuracy, jitter, shimmer, and singing power ratio were conducted across different groups of singers undergoing both traditional and C-VaC warm-ups. The results indicate that the C-VaC method significantly enhances pitch accuracy and reduces acoustic perturbations. These findings provide valuable insights into optimizing warm-up protocols and propose a novel approach for improving vocal performance. © 2024 The Voice Foundation

关键词： Classic acoustic analysis computer-assisted audio analysis Vocal warm-up

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Autonomous Voltage Stabilization in Hybrid Energy Grids with Reinforcement Learning

Autonomous Voltage Stabilization in Hybrid Energy Grids with...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Saranya R K.R. Jansi M. Gowthami Priya N T. R. GaneshBabu S Murugan Department of Computer Science and Engineering Annapoorana Engineering College (Autonomous) Salem Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Application Vinayaka Mission's Kirupananda Variyar Arts and Science College Vinayaka Mission’s Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The growing incorporation of renewable energy sources into hybrid energy networks requires creative ways to maintain voltage stability. This work investigates using reinforcement learning (RL) algorithms to autonomously regulate voltage levels in hybrid energy systems that include solar, wind, and traditional power sources. It presents an innovative RL-based controller that dynamically modulates the output of distributed energy resources (DERs) in response to variable load demands and generation fluctuations. This research addresses voltage fluctuations in hybrid energy networks using RL. The proposed RL-based system improves voltage stability, flexibility, and efficiency, outperforming traditional techniques in both simulations and practical applications. The RL agent uses real-time data to learn optimum control techniques to reduce voltage variances successfully. The efficacy of the proposed system is assessed by simulations on a benchmark microgrid model, revealing substantial improvements in voltage regulation and system resilience relative to traditional control approaches. The results demonstrate that the RL-based method improves voltage stability and optimizes overall energy utilization efficiency within the grid. It emphasizes the possibility of incorporating AI-driven solutions in managing hybrid energy systems, facilitating the development of more sustainable and dependable energy infrastructures.

关键词： Adaptation models Wind Fluctuations Computational modeling Reinforcement learning Microgrids Power system stability Stability analysis Real-time systems Voltage control

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STAR-RIS Enhanced Covert Communication with Delay Constraint

STAR-RIS Enhanced Covert Communication with Delay Constraint

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IEEE International Conference on Communications in China (ICCC)

作者： Beixi Cheng Jinsong Hu Youjia Chen Feng Shu Zhizhang David Chen College of Physics and Information Engineering Fuzhou University Fuzhou Fujian Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China School of Information and Communication Engineering Hainan University Haikou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

This work confirms the performance gain achieved by deploying a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in covert communication. To maximize the system covert rate, we jointly optimize the power allocation coefficients at the transmitter and the transmission and reflection coefficients at the STAR-RIS, subject to the delay-constrained and power-constrained. Furthermore, we propose a two-stage alternating optimization algorithm based on the semidefinite relaxation (SDR) method to derive the optimal solutions to the above non-convex optimization problem. The effectiveness and superiority of the proposed algorithm and STAR-RIS-assisted covert scheme are demonstrated by the numerical results.

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A Deep Learning Approach for Locating Microalgae in Image Captured by Smartphone and Digital Microscope Camera

A Deep Learning Approach for Locating Microalgae in Image Ca...

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International ECTI Northern Section Conference on electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Nitiphong Kaewman Phasit Charoenkwan Jinnapat Yana Phon-Ubon Suanoi Kritsana Duangjan Jeeraporn Pekkoh Chayakorn Pumas Department of Biology Faculty of Science Chiang Mai University Chaing Mai Thailand College of Arts Media and Technology Chaing Mai University Chaing Mai Thailand Department of Computer Engineering Faculty of Engineering Chulalongkorn University Bankok Thailand Science and Technology Research Institute Chiang Mai University Chaing Mai Thailand Department of Biology Faculty of Science Chiang Mai University Chiang Mai Thailand

The utilization of microalgae as a bioindicator for water quality assessment has traditionally relied on the expertise of morphological identification. With the advent of technological advancements, particularly the smartphone camera, there has been increasing interest in using it as a tool for various fields of microbiological and medical diagnostics, including image analysis of microalgae. Despite the growing popularity of smartphone-based image analysis, the image quality of smartphone cameras for detecting microalgae has yet to be thoroughly evaluated. Therefore, the purpose of this study was to investigate the suitability of smartphone-based camera for collecting microalgae images and compare it to that of a digital microscope camera. Additionally, the effect of different microscopic magnifications at 100x and 400x on model performance was thoroughly studied. Four models, YOLOv5, RetinaNet, EfficientDet, and Faster-RCNN, were trained on microalgae image datasets taken by both a smartphone camera (SC) and a digital microscope camera (DMC) under 100x and 400x magnification. A total of 11,608 images were divided into three parts: train (70%), validation (10%), and test (20 % ), with the test dataset separated into four parts for each device and magnification. The results of the study indicated that Faster-RCNN was the best model for microalgae detection, with the highest average precision $(\mathbf{AP}^{0.5})$ values of 0.60, 0.84, 0.91, and 0.96 at 100x magnification of SC and DMC and 400x magnification of SC and DMC, respectively. The comparison between the image quality of SC and DMC at each magnification revealed that the SC was less suitable for microalgae identification, particularly at 100x magnification. Conversely, the 400x magnification of SC had the potential to identify microalgae through the use of smartphone-based photography, as well as digital microscope camera.

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The Influence of Audio Sensory Input on the Anxiety Level of the VR Users

The Influence of Audio Sensory Input on the Anxiety Level of...

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2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022

作者： Moosavi, Mahdiyeh Sadat Chong, Marcus Yung Nazri, Ainul Khairiyah Mohd Merienne, Frederic Guillet, Christophe Rambli, Dayang Rohaya Awang Sulaiman, Suziah Arts et Metiers Institute of Technology LISPEN HESAM Université Chalon-Sur-SaoneF-71100 France Universiti Teknologi Petronas Electrical and Electronics Engineering Department Seri Iskandar Perak Malaysia Université de Bourgogne LISPEN UBFC Chalon-Sur-SaoneF-71100 France Universiti Teknologi PETRONAS Computer Information Sciences Department Seri Iskandar Perak Malaysia

ISBN: (纸本)9798350334548

This study investigates subjects' behavior in visual audio conflict conditions in stressful and stressless environments. To increase subjects' stress and anxiety in subjects we placed subjects in a chaotic city and to decrease subjects' stress we benefit from a green virtual environment. On the other hand to study the effect of sound on physiological data we designed confilict condition in which in the City environment participants heared the sound of Forest and in the Forest nvironment they heared the sound of City. To evaluate our findings we benefit from the heart rate of the participants which is directly related to subjects' emotions and participants can not involve their opinion to change this feature autonomously. © 2022 IEEE.

关键词： User interfaces

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Internet of Things based Smart Flood forecasting and Early Warning System 5

Internet of Things based Smart Flood forecasting and Early W...

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5th International Conference on Computing Methodologies and Communication, ICCMC 2021

作者： Shankar, B Maruthi John, Tegil J Karthick, S. Pattanaik, Balachandra Pattnaik, Manjula Karthikeyan, S. Sri Krishna College of Engineering and Technology Department of Ece Coimbatore India Kaamadhenu Arts and Science College Department of Computer Science Erode India Erode Sengunthar Engineering College Department of Ece Erode India Bule Hora University Department of Electrical and Computer Engineering Ethiopia Princess Noura Bint Abdul Rahman University Department of Accounting Riyadh Saudi Arabia Chaitanya Bharathi Institute of Technology Department of Ece Proddatur India

ISBN: (纸本)9781665403603

In general. Flood is considered as an inescapable natural event. Also, flood creates a huge economic damage and cause a significant loss in human life. Using different sensors, researchers made a flood level detecting device with the help of Arduino UNO. Proposed model is also useful for us in anticipating the upcoming calamities and to take essential activities by emergency and recover experts to spare the life of thousands of individuals before this basic condition happens. The Flood Detector System using Arduino is created to be one of the quickest strategies to monitor flood and Internet of Things (IoT) is one of the most important technical trends, which is utilized to monitor flood and human made resource to help in predicting and detecting essential events like flood, fire, gas and water leak that can position an threat to human life. © 2021 IEEE.

关键词： Floods

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Air Purification Robotics using Cloud and Deep Q Networks for Autonomous Systems

Air Purification Robotics using Cloud and Deep Q Networks fo...

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Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS), International Conference on

作者： Elangovan Guruva Reddy T. S. Balaji Damodhar Yuvaraj S B. Santha Vathani S. Sivakumar N. Malathi Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Management Studies Mahalashmi Women's College of Arts and Science Chennai Tamil Nadu India Department of Computer Science Thanthai Hans Roever College (Autonomous) Perambalur Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

Rising urban air pollution poses serious health dangers. Through robots, cloud computing, and deep reinforcement learning, this research proposes a new air purification method. The suggested autonomous system analyzes real-time air quality parameters and optimizes air purification tactics using cloud-based data processing. Advanced sensors and purification processes allow robotic agents to intelligently adapt to shifting pollution patterns in dynamic settings. Robots use Deep Q Networks (DQN) to learn and change their purifying tactics based on past data and environmental input. Cloud computing and deep learning improve air purification efficiency and enable real-time pollution response decision-making. The autonomous system is scalable and adaptable to varied urban environments since it requires little human involvement. The cloud-based design lets autonomous agents communicate and coordinate to improve air quality. Experimental findings show that the suggested strategy improves air purification over previous approaches. This system advances environmental robotics and lays the groundwork for intelligent, autonomous systems that handle urban air quality and pollution issues.

关键词： Cloud computing Technological innovation Purification Autonomous systems Robot kinematics Decision making Urban areas

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SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Vibrations in the Upper Limb

SkinSource: A Data-Driven Toolbox for Predicting Touch-Elici...

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International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (HAPTICS)

作者： Neeli Tummala Gregory Reardon Simone Fani Dustin Goetz Matteo Bianchi Yon Visell the Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara CA USA the Media Arts and Technology Program University of California Santa Barbara Santa Barbara CA USA the School of Biological and Health Systems Engineering Arizona State University Tempe AZ USA the Soft Robotics for Human Cooperation and Rehabilitation Lab Instituto Italiano di Tecnologia Geneva GE Italy the Department of Mechanical Engineering University of California Santa Barbara Santa Barbara CA USA the Department of Information Engineering University of Pisa PI Italy the Department of Biological Engineering University of California Santa Barbara Santa Barbara CA USA

Vibrations transmitted throughout the hand and arm during touch contact play a central role in haptic science and engineering but are challenging to model or experimentally characterize. Here, we present SkinSource, a data-driven toolbox for predicting skin vibrations across the upper limb in response to user-specified input forces. The toolbox leverages impulse response measurements that encode the physics of vibration transmission across the hands and arms of four participants and provides software tools for analyzing the predicted skin responses. We show that the SkinSource predictions closely match experimental measurements and confirm the underlying assumption of linear vibration transmission in the skin. We also demonstrate through several usage examples how SkinSource can act as a versatile computational platform for haptic research applications, such as characterizing vibrotactile transmission in the skin, engineering haptic interfaces, and investigating touch perception.

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Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond

SSRN

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SSRN 2024年

作者： Wang, Zhiyuan Duan, Jinhao Yuan, Chenxi Chen, Qingyu Chen, Tianlong Yao, Huaxiu Zhang, Yue Wang, Ren Xu, Kaidi Shi, Xiaoshuang Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Computer Science Drexel University PhiladelphiaPA19104 United States Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Section of Biomedical Informatics & Data Science Yale School of Medicine Yale University New HavenCT06510 United States Department of Computer Science University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence interaction systems, particularly in the healthcare engineering domain. However, a general method for quantifying the uncertainty of free-form answers has yet to be established in open-ended medical question-answering (QA) tasks, where irrelevant words and sequences with limited semantic information can be the primary source of uncertainty due to the presence of generative inequality. In this paper, we propose the Word-Sequence Entropy (WSE ), which calibrates the uncertainty proportion at both the word and sequence levels according to the semantic relevance, with greater emphasis placed on keywords and more relevant sequences when performing uncertainty quantification. We compare WSE with 6 baseline methods on 5 free-form medical QA datasets, utilizing 7 "off-the-shelf" large language models (LLMs), and show that WSE exhibits superior performance on accurate uncertainty measurement under two standard criteria for correctness evaluation. Additionally, in terms of the potential for real-world medical QA applications, we achieve a significant enhancement (e.g., +6.36% improvement of model accuracy on the COVID-QA dataset) in the performance of LLMs when employing sequences with lower uncertainty that are identified by WSE as final answers, without requiring additional task-specific fine-tuning or architectural modifications. © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

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A systematic review and meta-data analysis of clinical data repositories in Africa and beyond: recent development, challenges, and future directions

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Discover Data 2024年第1期2卷 1-39页

作者： Adewole, Kayode S. Alozie, Emmanuel Olagunju, Hawau Faruk, Nasir Aliyu, Ruqayyah Yusuf Imoize, Agbotiname Lucky Abdulkarim, Abubakar Imam-Fulani, Yusuf Olayinka Garba, Salisu Baba, Bashir Abdullahi Hussaini, Mustapha Oloyede, Abdulkarim A. Abdullahi, Aminu Kanya, Rislan Abdulazeez Usman, Dahiru Jafaru Department of Computer Science University of Ilorin Ilorin Nigeria Department of Information Technology Sule Lamido University Kafin Hausa Nigeria Department Information and Media Studies Faculty of Communication Bayero University Kano Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Lagos Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Nigeria Department of Electrical and Telecommunications Engineering Kampala International University Kampala Uganda Department of Telecommunication Science University of Ilorin Ilorin Nigeria Department of Software Engineering Sule Lamido University Kafin Hausa Kafin Hausa Nigeria Department of Cyber Security Sule Lamido University Kafin Hausa Kafin Hausa Nigeria Department of Economics Faculty of Social and Management Sciences Kafin Hausa Nigeria Department: University Medical Services Sule Lamido University Kafin Hausa Kafin Hausa Nigeria Department of Computer Science and Information Technology Baze University Abuja Nigeria Department: International Law and Jurisprudence Faculty of Law Bayero University Kano Nigeria

A Clinical Data Repository (CDR) is a dynamic database capable of real-time updates with patients' data, organized to facilitate rapid and easy retrieval. CDRs offer numerous benefits, ranging from preserving patients' medical records for follow-up care and prescriptions to enabling the development of intelligent models that can predict, and potentially mitigate serious health conditions. While several research works have attempted to provide state-of-the-art reviews on CDR design and implementation, reviews from 2013 to 2023 cover CDR regulations, guidelines, standards, and challenges in CDR implementation without providing a holistic overview of CDRs. Additionally, these reviews need to adequately address critical aspects of CDR; development and utilization, CDR architecture and metadata, CDR management tools, CDR security, use cases, and artificial intelligence (AI) in CDR design and implementation. The collective knowledge gaps in these works underscore the imperative for a comprehensive overview of the diverse spectrum of CDR as presented in the current study. Existing reviews conducted over the past decade, from 2013 to 2023 have yet to comprehensively cover the critical aspects of CDR development, which are essential for uncovering trends and potential future research directions in Africa and beyond. These aspects include architecture and metadata, security and privacy concerns, tools employed, and more. To bridge this gap, in particular, this study conducts a comprehensive systematic review of CDR, considering critical facets such as architecture and metadata, security and privacy issues, regulations guiding development, practical use cases, tools employed, the role of AI and machine learning (ML) in CDR development, existing CDRs, and challenges faced during CDR development and deployment in Africa and beyond. Specifically, the study extracts valuable discussions and analyses of the different aspects of CDR. Key findings revealed that most architectural mod

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