Video Streaming Research Articles

Design and Control of Ultrasound Image-guided Prostatic Brachytherapy Robot

Background:: With the continuous development of medical robots, they have become a powerful tool for surgery and treatment. A prostate brachytherapy robot is a kind of robot widely used in prostate cancer treatment; its main function is to accurately implant radioactive seeds into prostate tissue to kill cancer cells. Using prostate brachytherapy robots for surgery can reduce damage to surrounding tissue during surgery, thereby reducing the risk of external electrolysis and metastasis of tumor cells. Objective:: This study aimed to design a prostate brachytherapy robot that is more in line with practical usage needs, based on the method of manually performing prostate brachytherapy surgery in clinical practice. Finally, its feasibility has been demonstrated through brachytherapy experiments. Methods:: Firstly, the actual requirements for prostate particle implantation surgery have been analyzed, which has been followed by reviewing previous literature and patents to determine the design scheme of the robot. Finally, SOLIDWORKS software has been used for mechanical design of the robot, and the control system of the robot has been designed based on Arduino. Results:: A robotic arm type prostate particle implantation robot has been successfully designed. We have installed a visual module on the prostate particle implantation robot, which has enabled the robot system to have image transmission monitoring function to assist in the calibration of the puncture position before surgery. Finally, we have employed other sensors to complete the particle implantation experiment, and successfully confirmed its superiority through comparing errors. Conclusion:: This work has analyzed and studied the clinical process of prostate particle implantation surgery, and designed a prostate particle implantation robot, including the mechanical design and control system design of the robot. We have also simulated the scene of prostate particle implantation surgery in the human body through particle implantation experiments, and successfully and accurately implanted radioactive particles to the target. The experimental results have proven the feasibility of the robot. We have made improvements in execution efficiency by using a parallelogram-based robotic arm as the representation of the prostate particle implantation robot and equipped it with a visual module. The visual module collects a video stream of the puncture needle's perspective and provides feedback to the control end to assist in the calibration of the puncture position before surgery, greatly reducing the probability of secondary injury to patients.

P‐9.3: A Typical Interconnect Model for High Bandwidth Video Streaming Transmission in Mixed‐Reality Devices

P‐9.3: A Typical Interconnect Model for High Bandwidth Video Streaming Transmission in Mixed‐Reality Devices

Interactive technology of high voltage tower acceptance UAV Based on AR and YOLOv7

To address many shortcomings of the current unmanned aerial vehicle (UAV) inspection mode of the transmission line high-voltage towers and improve the inspection efficiency, the UAV tower foreign matter detection technology based on Augmented Reality (AR) and You Only Look Once: Unified, Real-Time Object Detection (YOLOv7) has been developed. Firstly, the system architecture, including UAV, ground station, and AR glasses is established. Then, the AR scene is built by using the Unity engine, and the interactive control mode between AR glasses and UAV is designed. Subsequently, the mapping mode of head action and UAV command is devised to synchronize the UAV action and head action. Finally, the tower is detected for foreign objects by using YOLOv7 vision detection technology. The experimental results validate the efficacy of the built AR scene, demonstrating seamless functionality in completing the video streaming and attitude data transmission functions. The use of AR glasses can achieve interactive control of the UAV attitude. In addition, the YOLOv7 visual detection algorithm can accurately detect the attachment of foreign objects to the high-voltage tower.

Control of Video Buffering for Video Streams Decoded from Cyclic Structures

Control of Video Buffering for Video Streams Decoded from Cyclic Structures

SDSR: Optimizing Metaverse Video Streaming via Saliency-Driven Dynamic Super-Resolution

SDSR: Optimizing Metaverse Video Streaming via Saliency-Driven Dynamic Super-Resolution

Human Face Emotion in 3D Using Machine Learning

Abstract: Emotion detection, which is an effortless task for humans, is complex to perform on machines. The interaction between human beings and computers are very natural if computers can be able to perceive and respond to human nonverbal communication such as emotions. This project targets to develop a multi-modal emotion detection web app that utilizes advanced computer vision, speech, and natural language processing (NLP) techniques to accurately identify and track human emotions in images, and video streams. Automatic emotions recognition is based on face expressions is a engaging discipline, which has several areas such as safety, health and in human interaction interfaces. Utilizing neural networks in conjunction with feature e traction techniques enables the recognition of various facial emotions such s happiness, sadness, anger, fear, surprise, and neutrality through analysis of facial expressions.. So it is very important to detect these emotions on the face. Human beings are can capable to produce thousands of face reactions during communication that can be vary in Depth, Strength, and Significance. We are using Convolutional Neural Network(CNN), to extract features from image to detect emotions and RNN and classify facial emotions. CNN is very effective for emotions recognition task. They can extract features from input image, and a real time video and then use these features to train a classifier. Facial expression analysis software like Face Reader is ideal for collecting this emotion data.

ANALISIS SIMULASI PROTOKOL ROUTING DINAMIS OSPF UNTUK PENINGKATAN EFISIENSI JARINGAN HYBRID KAMPUS UNUD DENPASAR

UNUD GDLN building located in UNUD Sudirman campus acts as an information and communication technology service center as an internet access service provider, radio broadcast and online learning. The UNUD Campus network currently uses a static hybrid routing protocol, where the static routing protocol has the disadvantage that the network administrator must always reconfigure the network if there is a topology change. The Open Shortest Path First (OSPF) dynamic routing protocol can be used as a solution to overcome the weaknesses of static routing protocols because it is more suitable for larger networks and has the advantage of automatically determining the best route. This research aims to analyze the application of OSPF routing protocol in UNUD Denpasar Campus with GNS3 simulation. This research uses Quality of Service (QoS) parameters to analyze network performance, and test scenarios by PING with large data transmission that imitates YouTube video streaming. The results showed that OSPF dynamic routing protocol has better performance and has met the TIPHON standard with very good results for packet loss, throughput, and delay parameters and good results for jitter parameters. Tests on static routing protocol simulations and OSPF routing protocols are carried out by comparing the transmission process based on the resolution on YouTube video streaming imitation, starting with 360p, 480p, 720p and 1080p resolutions. Based on the results of the study, it can be concluded that OSPF routing is more suitable and produces better parameter values when transmitting streaming video with a resolution of 480p, 720p and 1080p, while the transmission of streaming video with a resolution of 360p is more suitable for static routing which is more suitable for transmission with lower resolutions.

Social Distancing Detector using Deep Learning

Abstract: Social Distancing is the best possible way to detain the spread of Covid-19. Even though vaccine has been found and working effectively in saving the lives of people, social distancing is necessary to reduce the spread of virus to maximum extent which not only saves people from being infected but also reduces the impact of spreading of the disease. In our proposed system, we use Deep Learning with python to monitor social distancing in public places. This is a software tool that monitor if people are maintaining proper social distancing norms or not by analyzing real time video streams from CC camera. We use YOLO Model which is trained by COCO dataset

Data Pre-processing of Website Browsing Records: To Prepare Quality Dataset for Web Page Classification

The increased usage of the internet worldwide has led to an abundance of web pages designed to supply information to internet users. The use of web page classification is becoming increasingly necessary to organize the growing number of web pages. This classification model serves as a tool to restrict internet usage to specific categories of web pages. To develop the classification model, it’s crucial to check the quality of the dataset, as it determines the performance of the web page classification model. Raw datasets are typically unreliable and subject to noise, which complicates data analysis. This is why data pre-processing is necessary to prepare the dataset properly. In this study, website browsing records serve as the dataset. The primary goal of this paper is to investigate data pre-processing techniques for website browsing records, focusing on Game and Online Video Streaming web pages. Data pre-processing involves two main steps: data cleaning and web content pre-processing. After completing the data cleaning process, the datasets are reduced from the original. This demonstrates that many datasets can be eliminated due to their inactivity or unsuitability as the datasets for Game and Online Video Streaming web pages. Meanwhile, web content pre-processing removes noise from an HTML document, retaining only relevant words that can represent the web page by creating a word cloud image. Convolutional Neural Networks (CNN) will be used to construct a model for categorizing web pages to determine whether they fall under Game or Online Video Streaming. The pre-processed data will be used as the input for this model.

Rule based complex event processing forIoTapplications: Review, classification and challenges

AbstractIn recent decades, data aggregation and correlation have emerged as a significant and challenging area of research for finding useful relationships between different features of streaming data. Similarly, complex event processing (CEP) has emerged as a vital tool for aggregating and finding patterns from streaming data such as video streaming, real‐time stock trades, sensor data and more. In this context, rule‐based classifier algorithms are widely employed to discover valuable patterns from streaming data and cause‐and‐effect relationships between events. However, streaming data is dynamic and complex in nature, and it is not feasible to update the rules manually by domain experts continuously. On the contrary, dynamic data sometimes fails to adopt rules, which leads to sub optimal CEP implementation. Rule‐based CEP systems come up with their own set of challenges, which primarily includes rules adaptability for streaming IoT data. In dynamic environment, shifting patterns in data streams can impact the performance of the CEP system, as well as the correlation between events, and finding useful patterns is very much challenging. In this article, our objective is to provide a comprehensive survey for CEP using rule‐based algorithms applied across various domain and applications. We present a broad literature review using a sequential expansion of methods used for CEP, which primarily include event producers, preprocessing of events, robust rule implementation, and decision support. Additionally, we present a detailed classification of approaches used for different applications. Finally, after applying the rigorous review, it was feasible to present the state‐of‐the‐art research, which focuses on designing robust rules, application specific insights, scalable event‐driven architectures and finding the domain's trends with significant challenges and future scope.

Enhancing and Securing a Real-Time Embedded Face Recognition System using Raspberry Pi

The modern world is full of data of all kinds; however, the vast amount of video and image data available provides the data set needed for facial recognition. Facial recognition is crucial in safety and surveillance systems that analyze visual data and millions of images. Using facial recognition software, a person's identity can be verified through a variety of media images. For facial recognition, there is a variety of algorithms available. The article presents an approach to a face recognition framework using Haar Cascade, a biometric technology in safety and surveillance systems. It investigates combining standard machine learning techniques for face detection and identification with Raspberry Pi face detection, a cost-effective and easy-to-use embedded system. The system detects faces from indirect and direct images, achieving high speed using the latest Raspberry Pi 4 and Python libraries. The work demonstrates a machine-learning-based design method and a complete embedded system. The face detection accuracy is 92%, and the average time is 0.35 compared to the local binary model (LBP). Many facial recognition algorithms on the web and in literature reviews are vulnerable to image attacks. These methods are very effective in identifying faces in webcams, video streams, images, and videos. This system's use of the Raspberry Pi 4 and advanced Python libraries results in fast and accurate real-time face detection. This paper extends the work first presented at the 12th Iranian/Second International Conference on Machine Vision and Image Processing (MVIP).

DEVELOPMENT OF A VISUAL ODOMETRY MODEL BASED ON SENSORS AND VIDEO STREAM ANALYSIS

The article is devoted to the development of a visual odometry model based on sensors of an inertial measuring device and the analysis of a video stream arriving in real time. Modeling is based on the analysis and evaluation of methods for measuring the correct coordinates of a moving object, systems for estimating the movement of an object in three-dimensional space, algorithms at intermediate stages of image processing, principles for selecting special points on the frame and optical flow for selected points.

Enhancing Crowd-Sourced Video Sharing through P2P-Assisted HTTP Video Streaming

This paper introduces a decentralized architecture designed for the sharing and distribution of user-generated video streams. The proposed system employs HTTP Live Streaming (HLS) as the delivery method for these video streams. In the architecture, a creator who captures a video stream using a smartphone camera subsequently transcodes it into a sequence of video chunks called HLS segments. These chunks are then stored in a distributed manner across the worker network, forming the core of the proposed architecture. Despite the presence of a coordinator for bootstrapping within the worker network, the selection of worker nodes for storing generated video chunks and autonomous load balancing among worker nodes are conducted in a decentralized fashion, eliminating the need for central servers. The worker network is implemented using the Golang-based IPFS (InterPlanetary File System) client, called kubo, leveraging essential IPFS functionalities such as node identification through Kademlia-DHT and message exchange using Bitswap. Beyond merely delivering stored video streams, the worker network can also amalgamate multiple streams to create a new composite stream. This bundling of multiple video streams into a unified video stream is executed on the worker nodes, making effective use of the FFmpeg library. To enhance download efficiency, parallel downloading with multiple threads is employed for retrieving the video stream from the worker network to the requester, thereby reducing download time. The result of the experiments conducted on the prototype system indicates that those concerned with the transmission time of the requested video streams compared with a server-based system using AWS exhibit a significant advantage, particularly evident in the case of low-resolution video streams, and this advantage becomes more pronounced as the stream length increases. Furthermore, it demonstrates a clear advantage in scenarios characterized by a substantial volume of viewing requests.

NERVE: Real-Time Neural Video Recovery and Enhancement on Mobile Devices

As mobile devices become increasingly popular for video streaming, it is crucial to optimize the streaming experience for these devices. Although deep learning-based video enhancement techniques are gaining attention, most of them cannot support real-time enhancement on mobile devices. Additionally, many of these techniques are focused solely on super-resolution and cannot handle partial or complete loss or corruption of video frames, which is common in the Internet and wireless networks. To overcome these challenges, we present NERVE, a novel approach in this paper. NERVE consists of (i) a novel video frame recovery scheme, (ii) a new super-resolution algorithm, and (iii) an enhancement-aware video bit rate adaptation algorithm. We implement NERVE on an iPhone 12, and it can support 30 frames per second (FPS). We evaluate NERVE in various networks such as 3G, 4G, 5G, and WiFi networks. Our evaluation shows that NERVE enables real-time video recovery and enhancement, and results in 24% - 83% increase in video Quality of Experience (QoE) in our video streaming system.

OTT Media Consumption Habits of the Students and Its Impact on Their Behavioral Change: A Study in Jamshedpur City

When the world was going through COVID-19, It was then the internet managed that the mode of entertainment drastically shifted to the small screens known as Over the top streaming platforms. where the user can customize their preferences. OTT is popular for its latest content and good storyline. It focuses on the quality of work rather than depending on commercial success. The objective of this research is to find out whether over-the-top channels have created an impact on the youth of Jamshedpur City. OTT has expanded its market, resulting in increased competition in the market, which can be considered a threat to large platforms such as YouTube. A sample of 600 internet users from institutes in Jamshedpur, the survey research design was used to collect primary data to check the response of the youth and a bar graph was used for anises of data. These technological advancements have paved the way for the recent trend of OTT video streaming services. Therefore, this research is an attempt to understand the impact of OTT media on youngsters and understand the watching preferences of OTT platforms, etc.

Twist: A Multi-site Transmission Solution for On-demand Video Streaming

Twist: A Multi-site Transmission Solution for On-demand Video Streaming

Utilizing Advanced Technologies for eLearning in Clerkship

The clerkship phase in medical school is pivotal for clinical training. In response to the COVID-19 pandemic, the adoption and implementation of educational technology in medical education became inevitable, leading most medical schools to shift toward distance learning. While technologies cannot entirely replace hands-on clinical teaching, faculty members have devised effective ways to utilize technology, such as virtual patients, virtual reality, first-person video streaming, simulators, gamification, and mobile apps. This article provides a summary of the technologies employed, the challenges associated with them, and the advantages and instructional outcomes resulting from their use.

High-speed laser centerline extraction and heterogeneous measurement architectures for low-cost hardware

Due to its real-time efficiency, stability, and reliability, structured light 3D measurement is widely used in measuring surface quality and manufacturing accuracy of industrial products. With the structured light measurement system's slow acquisition speed and low efficiency, it is difficult to meet the needs of online real-time detection, resulting in sparse 3D contour data and loss of details. To improve the algorithm's running speed and balance hardware resource consumption, a structured light extraction algorithm adapted to hardware acceleration is proposed, which executes the three low-coupling computational modules at eight-pixel parallel speed and in a pipelined manner. In order to further reduce the overall computational efficiency and deployment cost of high-speed structured light measurement tasks, an FPGA camera architecture with parallel acquisition and processing is designed, and the FPGA camera is cooperated with the CPU to form a more efficient heterogeneous measurement system. For samples of different materials and shapes, the standard deviation of sub-pixel coordinates extracted is less than 0.65. With this architecture, it is possible to process video stream with one million pixels at 500FPS, and the power consumption is 2.566w. Experimental results of measuring blocks and steel pipes show that the measurement error is below 0.15 mm. The proposed algorithm and heterogeneous processing measurement architecture are applicable in engineering, especially for high-speed real-time 3D measurement.

Comprehensive Model of Facial Detection for Gender-Based Targeted Advertising Using Deep Learning

Facial Detection for Gender-Based Targeted Advertising focuses on the development and implementation of an AI-based system for facial detection and gender classification. Leveraging machine learning techniques, a model is trained to detect faces and determine gender from captured images or video streams. The primary goal is to integrate this model into an advertising system, enabling real-time targeted ad display based on the identified gender of individuals. The project aims to explore the effectiveness of personalized advertising through facial recognition technology while considering ethical implications and potential advancements in marketing strategies.

The role of cognitive factors in consumers’ perceived value and subscription intention of video streaming platforms: a systematic literature review

The role of cognitive factors in consumers’ perceived value and subscription intention of video streaming platforms: a systematic literature review