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A Literature Survey on Various Streaming Protocol and Compression Techniques in multimedia Transmission with Internet of Things

A Literature Survey on Various Streaming Protocol and Compre...

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11th International conference of Computer Communication and Informatics (ICCCI)

作者： Vijaykumar, Sathya Prakash, Shiva T. Vijaya Vittala Inst Technol Dept Comp Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9781728158754

In recent times, with wide spread of Internet, streaming media is getting more prevalent. Various streaming protocols have been primarily established to make transference of multimedia content easier to end user. Recent streaming protocols are frequently established with networking development and computing prospects. In the past few years, Real Time Communications, comprising numerous protocols and technologies have been contributory to the creation of media sessions through switching systems. Familiar legacy procedures such as Real Time Control Protocol (RTCP) and Real Time Transfer Protocol (RTP) are the ideal instruments to achieve the transfer process;although, in Internet of Things (IoT) environment, convergence properties are inadequate and their usage turn out to be a major challenge. In recent years, multimedia application comprises mobile networks, incorporated sensors and IoT devices. Only some of the research works are obtainable to assist protected multimedia communications achieved in IoT. In this paper, we analyze numerous streaming protocols employed in IoT organizations and explain appropriate taxonomies with classification. The integration of multimedia Services and IoT still pose challenge due to the limited capabilities of IoT devices and unique features of multimedia applications. The main intention of this paper is to support the future investigations to further enhance streaming protocols and various compression techniques used for transmission of multimedia data in IoT.

关键词： multimedia Streaming Protocol Real Time Transport Protocol Real Time Control Protocol Real Time Streaming Protocol Resource Reservation Protocol Iot - Internet of Things

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Semantic Communication-Aware End-to-End Routing in Large-Scale LEO Satellite Networks

Semantic Communication-Aware End-to-End Routing in Large-Sca...

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Metaverse computing, networking and Applications (MetaCom), IEEE International conference on

作者： Binquan Guo Zehui Xiong Bo Wang Tony Q. S. Quek Zhu Han State Key Laboratory of Integrated Service Networks Xidian University Xi’an P. R. China Tianjin Artificial Intelligence Innovation Center (TAIIC) Tianjin P. R. China Pillar of ISTD Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9798331515997

ISBN: (纸本)9798331516000

Enhanced by inter-satellite links, large-scale satellite networks (SNs) hold promise to deliver low-latency services globally. However, given the scarcity of available spectrum and the capacity limitations of Shannon’s classic information theory, supporting the ever-growing multimedia communication services poses a challenge for SNs. A promising direction is to employ semantic communication (SC), which utilizes artificial intelligence (AI) to extract and transmit the "meaning" of raw data, thereby reducing bandwidth consumption. We identify that SC requires the raw data to be transmitted to a satellite equipped with the AI encoder supporting the same knowledge base for successful decoding at the destination. This advanced functionality renders traditional routing schemes such as contact graph routing (CGR) not seamlessly suitable. Hence, we develop the SC-aware routing (SCR) strategy aimed at optimizing SC’s delay and bandwidth consumption. Firstly, we define the problem using integer linear programming (ILP) and observe its intractability in practice. Then, we use the temporal graph to represent diverse knowledge bases, semantic encoders and transmission resources of SNs, and design SCR based on it. Simulations conducted on the Starlink constellation show that SCR runs much faster than the ILP solver. Additionally, we verify that SC can bring significant performance gains in terms of higher data throughput and lower average end-to-end delay when compared to CGR.

关键词： Satellites Spectral efficiency Knowledge based systems Bandwidth Performance gain Routing Throughput Polynomials Delays multimedia communication

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Mobile-based Cloud computing with Autonomous Assets Supply

Mobile-based Cloud Computing with Autonomous Assets Supply

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Electronics and Sustainable Communication Systems (ICESC), 2020 International conference on

作者： B. Shadaksharappa B. Maruthu Kannan S. Yuvaraj S.P. Maniraj Department of Computer Science and Engineering Sri Sairam College of Engineering Bengaluru Karnataka India Department of Computer Science and Engineering Sphoorthy Engineering College Nadergul Hyderabad Telangana India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

The overwhelming rise in computing and networking skills makes Mobile platforms as predominant means of Internet access. Specific portable apps are unusable due to the inadequate computing capacities of mobile platforms. The constrained conne ctivity and reaction times involved in offloading data into multiple servers, such as a cloud data center, make permitting applications needing real-time information collection and processing on mobile stages are very challenging. The hybrid statics/mobile computing network is the latest supply system which has the heterogeneous ability for sensing, computing, and networking with static/mobile devices nearby. This robust computing network can allow creative mobile information and calculation-intensive applications, including omnipresent context-aware health and wellbeing checks of older adults, remote precipitation and flood risk evaluation, shared object detection and monitoring, and shared multimedia content searching and sharing. A detailed experimental study is provided to validate and demonstrate the advantages of the autonomous capability of the proposed system.

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Unsupervised Domain Adaptation Semantic Segmentation on Thyroid Ultrasound Images Based on Task-Oriented Feature Disentanglement

Unsupervised Domain Adaptation Semantic Segmentation on Thyr...

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IEEE International conference on multimedia and Expo (ICME)

作者： Zijian Zhang Ruiguo Yu Xi Wei Jie Gao Mei Yu Xuewei Li Zhiqiang Liu College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China School of Feature Technology Tianjin University Tianjin China Tianjin Medical University Cancer Institute and Hospital Tianjin China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Unsupervised Domain Adaptation (UDA) methods have become essential for computer-aided diagnostic analysis on medical images due to the advantage of improving the model generalization ability with fewer annotations. The disentanglement-based UDA methods improve the segmentation performance significantly by disentangling the features into domain-specific and domain-invariant components. However, such methods neglect detailed texture in ultrasound images, resulting in inaccurate segmentations of nodules. To address the above problem, we propose a Task-Oriented Feature Disentanglement (TOFD) method to achieve better UDA segmentation on images from different ultrasound machines. TOFD achieves finer-grained disentanglement by the Feature Constraint Module based on maximum entropy. Furthermore, the Domain Label Encoding Module can improve the stability of features by integrating the latent feature of domain bias. Extensive experiments demonstrate that TOFD can effectively enhance the model’s generalization capability and confirm the performance of TOFD on ultrasound images exceeds that of state-of-the-art UDA semantic segmentation.

关键词： Degradation Adaptation models Ultrasonic imaging Semantic segmentation Stability analysis Entropy Encoding

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multimedia Data Transfer using NOMA

Multimedia Data Transfer using NOMA

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Smart Generation computing, Communication and networking (SMART GENCON), International conference on

作者： Peruru Laxmana Aravind Sharma Pidugu Priyanka Kanchumarthy Venkata Sreeram Priya B K Department of Electronics and Communication Engineering Amrita school of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

5G innovation is an impending upheaval, which vows to benefit each part of living souls. 5G advances can give network qualities that are fundamental for various areas. This innovation will take into account expanded adaptability and lower costs. NOMA is perchance the preponderance encouraging radio access algorithm in the cutting-edge remote correspondence. Contrasted with Orthogonal Multiple Access (OMA) strategy, NOMA offers advantages like upgraded range proficiency, low idleness and different network. The possibility of NOMA is to serve different clients involving similar asset with regards to time, recurrence and space. This project proposes an idea to transfer the multimedia data using Power Domain NOMA. Transferring the data with low latency. In this work Quadrature Amplitude Modulation and Superposition Encoding scheme is used. Study about various noise channels, channel models is made and compared them. The noise that is being added is AWGN. Developed a code for multimedia data transfer in ideal situation considering the generalized flow chart of the wireless communication.

关键词： Wireless communication NOMA Technological innovation Interference cancellation 5G mobile communication Quadrature amplitude modulation multimedia computing

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Image Processing using Cloud for Surveillance 5

Image Processing using Cloud for Surveillance

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5th International conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Tiwari, Shivam Phukan, Aarhee Vedhavathy, T.R. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798400709418

In the era of digitization and big data, the world is inundated with an ever-growing volume of visual content, be it images or videos. As organizations strive to harness the potential of these multimedia data sources, there is an increasing need for advanced image processing techniques that can automate the analysis and extraction of valuable information. Amazon Web Services (AWS) Rekognition emerges as a powerful solution in this landscape, offering a comprehensive system for image and video analysis through the lens of machine learning and computer vision. This paper delves into the realm of image processing using AWS Rekognition, unveiling the transformative capabilities of this cloud-based service and its applications in various domains. As we embark on this journey, we will explore the principles, methodologies, and real-world implications of leveraging AWS Rekognition for image analysis. © 2023 ACM.

关键词： Computer vision

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Comparative analysis of Start-up Success Rate Prediction Using Machine Learning Techniques

Comparative analysis of Start-up Success Rate Prediction Usi...

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Smart Technologies and Systems for Next Generation computing (ICSTSN), International conference on

作者： Mohammad Riyaz Belgaum Ediga Kasi Viswanath Pavukolla Chakradhar Ediga Chenna Kesava Goud Kuruva Chandu Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

Software startups create innovative products or services using programming, which can lead to a need for products from potential customers. Researchers suggest that trial and error methodology can increase the success rate of these organizations by evaluating assumptions about client needs before fostering a product. However, programming new businesses typically do not require trial and error. The study aims to predict the success of startup companies in their early development stages by analyzing their success based on initial funding. A study of 106 unsuccessful software start-ups was conducted to predict the success rate. The experimentation progression model (X Pro) was developed to show that effective experimentation requires understanding trial and error, setting objectives, conducting tests, analyzing results, and acting based on new knowledge. The findings provide insights into supporting procedures and approaches that enhance trial- and-error reception in developing new business models. Researchers can become aware of potential factors leading to startup disappointment and take precautions to avoid them. The research focuses on using web-based open sources, including social networking services and the web, to model the task. The study uses machine learning techniques like Random Forest, Gradient Boost Classifier, XG Boost, and Ada Boost Classifier. The analysis provides insights into the types of useful signals discovered on the web and market mechanisms underlying the funding process. This approach offers a more comprehensive approach to predicting startup success.

关键词： Analytical models Accuracy Social networking (online) Machine learning Companies Predictive models Programming

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Discovering Influencers and Communities Through the Use of Social Network and Topic Modelling: Insights from Traveloka

Discovering Influencers and Communities Through the Use of S...

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Artificial Intelligence, Blockchain, Cloud computing, and Data Analytics (ICoABCD), International conference on

作者： Zainnary Dwiwani Maria Sugiat Indrawati School of Economics and Business Telkom University Bandung Indonesia

ISBN: (数字)9798350368451

ISBN: (纸本)9798350368468

This study examines the Traveloka social network to address questions about community formation, influential actors, discussion topics, and marketing strategy recommendations. A combination of social network analysis (SNA) and topic modeling was used, analyzing data from Traveloka reviews on Google Play Store, Instagram, and Twitter from June 2022 to May 2024. The results identified 87,479 nodes and 159,156 edges within the network, with five distinct communities and influential individuals like anthoni.023 and amie020516 based on eigenvector centrality. Key sentiments included ease of use, efficiency, service quality, and pricing. Marketing strategy recommendations include team training, AI technology integration, streamlining refund processes, regular app updates, intuitive interface design, and adding secure payment methods.

关键词： Training Analytical models Social networking (online) Blogs User-generated content Pricing Data models Web sites multimedia communication Artificial intelligence

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Translation-Based Fusion for Multimodal Sentiment Analysis: Short Comprehensive Review

Translation-Based Fusion for Multimodal Sentiment Analysis: ...

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International conference on Intelligent computing in Data Sciences (ICDS)

作者： Takfaoui Nada Kharrou Saad Yassir Oubenaalla Youness Nfaoui El Habib LISAC Laboratory Faculty of Sciences Dhar EI Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

As social media continues to grow in popularity and multimedia content becomes increasingly prevalent, traditional text-based sentiment analysis methods may not capture the complete picture. Multimodal sentiment analysis (MSA) aims to overcome this limitation by incorporating multiple modes of data, such as text, images, and audio, to analyze and understand the sentiment expressed by individuals. This paper offers an extensive review of approaches employing translation-based fusion techniques to tackle challenges in Multimodal Sentiment Analysis. Firstly, it briefly outlines the fundamentals of MSA, including the MSA process, the importance of modalities, and the most widely used datasets. It also explains the existing categories of fusion methods in MSA. Subsequently, it details the architectures and key contributions of existing translation-based MSA techniques, and discusses the performance of these translation-based models on the two benchmark datasets, CMU-MOSI and CMU-MOSEI. It concludes by discussing challenges faced by multimodal sentiment analysis.

关键词： Sentiment analysis Visualization Reviews Social networking (online) Soft sensors Psychology Feature extraction Robustness Synchronization Risk management

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Topic Modeling for Identifying Emerging Trends on Instagram Using Latent Dirichlet Allocation and Non-Negative Matrix Factorization

Topic Modeling for Identifying Emerging Trends on Instagram ...

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International conference on Advanced computing and Communication Systems (ICACCS)

作者： Ishmeen Kaur Garewal Shruti Jha Chaitanya V Mahamuni Department of Computer Engineering Fr. C Rodrigues Institute of Technology Navi Mumbai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

This research tackles the challenge of tracking emerging trends on Instagram through advanced topic modeling techniques, utilizing Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) to unveil latent topics in dynamic social media content. Our implementation uses LDA and NMF algorithms to extract and analyze content themes, demonstrating effectiveness through experimental results. These models capture latent topics, adapt to changing trends, reduce dimensionality, and provide interpretable results, essential for understanding the evolving discussions online. The paper details algorithm implementation in Python, presenting accuracy and error measure results, while outlining identified trends. This work contributes a reliable approach for social media trend identification, enhancing our understanding of user interests and enabling proactive decision-making across diverse fields.

关键词： Analytical models Social networking (online) Heuristic algorithms Computational modeling Decision making Market research Data models Web sites multimedia communication Resource management

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