AN ENHANCED SMOOTHING SCHEME FOR MPEG VIDEO STREAMS TRANSMISSION

Joint Transmission and Coding Scheme for High-Resolution Video Streams over Multiuser MIMO-OFDM Systems

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e100.a.2304 ◽

2017 ◽

Vol E100.A (11) ◽

pp. 2304-2313 ◽

Cited By ~ 4

Author(s):

Koji TASHIRO ◽

Leonardo LANANTE ◽

Masayuki KUROSAKI ◽

Hiroshi OCHI

Keyword(s):

High Resolution ◽

Ofdm Systems ◽

Video Streams ◽

Multiuser Mimo ◽

Coding Scheme ◽

Mimo Ofdm ◽

Joint Transmission

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Augmented Reality Maintenance Assistant Using YOLOv5

Applied Sciences ◽

10.3390/app11114758 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4758

Author(s):

Ana Malta ◽

Mateus Mendes ◽

Torres Farinha

Keyword(s):

Neural Network ◽

Deep Learning ◽

Object Recognition ◽

Augmented Reality ◽

Real Time ◽

Recognition System ◽

High Accuracy ◽

Video Streams ◽

The Neural Network ◽

Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

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Control-Based Resource Management for Storage of Video Streams

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.1564 ◽

2020 ◽

Vol 53 (2) ◽

pp. 5542-5549

Author(s):

Alexandre Martins ◽

Mikael Lindberg ◽

Martina Maggio ◽

Karl-Erik Årzén

Keyword(s):

Resource Management ◽

Video Streams

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Leveraging Edge Intelligence for Video Analytics in Smart City Applications

Information ◽

10.3390/info12010014 ◽

2020 ◽

Vol 12 (1) ◽

pp. 14

Author(s):

Aluizio Rocha Neto ◽

Thiago P. Silva ◽

Thais Batista ◽

Flávia C. Delicato ◽

Paulo F. Pires ◽

...

Keyword(s):

Distributed System ◽

Smart City ◽

Large Scale ◽

Facial Recognition ◽

Public Spaces ◽

Hardware Accelerators ◽

Video Streams ◽

Video Analytics ◽

Learning Tasks ◽

Naive Approach

In smart city scenarios, the huge proliferation of monitoring cameras scattered in public spaces has posed many challenges to network and processing infrastructure. A few dozen cameras are enough to saturate the city’s backbone. In addition, most smart city applications require a real-time response from the system in charge of processing such large-scale video streams. Finding a missing person using facial recognition technology is one of these applications that require immediate action on the place where that person is. In this paper, we tackle these challenges presenting a distributed system for video analytics designed to leverage edge computing capabilities. Our approach encompasses architecture, methods, and algorithms for: (i) dividing the burdensome processing of large-scale video streams into various machine learning tasks; and (ii) deploying these tasks as a workflow of data processing in edge devices equipped with hardware accelerators for neural networks. We also propose the reuse of nodes running tasks shared by multiple applications, e.g., facial recognition, thus improving the system’s processing throughput. Simulations showed that, with our algorithm to distribute the workload, the time to process a workflow is about 33% faster than a naive approach.

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Surveillance System in Smart Cities: A Dependability Evaluation Based on Stochastic Models

Electronics ◽

10.3390/electronics10080876 ◽

2021 ◽

Vol 10 (8) ◽

pp. 876

Author(s):

Igor Gonçalves ◽

Laécio Rodrigues ◽

Francisco Airton Silva ◽

Tuan Anh Nguyen ◽

Dugki Min ◽

...

Keyword(s):

Surveillance System ◽

Smart Cities ◽

Heterogeneous Data ◽

Time To Failure ◽

Video Streams ◽

Dependability Evaluation ◽

The Mean ◽

Mean Time ◽

The Internet Of Things

Surveillance monitoring systems are highly necessary, aiming to prevent many social problems in smart cities. The internet of things (IoT) nowadays offers a variety of technologies to capture and process massive and heterogeneous data. Due to the fact that (i) advanced analyses of video streams are performed on powerful recording devices; while (ii) surveillance monitoring services require high availability levels in the way that the service must remain connected, for example, to a connection network that offers higher speed than conventional connections; and that (iii) the trust-worthy dependability of a surveillance system depends on various factors, it is not easy to identify which components/devices in a system architecture have the most impact on the dependability for a specific surveillance system in smart cities. In this paper, we developed stochastic Petri net models for a surveillance monitoring system with regard to varying several parameters to obtain the highest dependability. Two main metrics of interest in the dependability of a surveillance system including reliability and availability were analyzed in a comprehensive manner. The analysis results show that the variation in the number of long-term evolution (LTE)-based stations contributes to a number of nines (#9s) increase in availability. The obtained results show that the variation of the mean time to failure (MTTF) of surveillance cameras exposes a high impact on the reliability of the system. The findings of this work have the potential of assisting system architects in planning more optimized systems in this field based on the proposed models.

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