Performance Analysis of Video On-demand and Live Video Streaming using Cloud based Services

The advent of Cyber-Physical Systems (CPS) has brought a revolutionary change coined as a mixture of information, communication, computation, and control. With applications in smart grid, health monitoring, automatic avionics, distributed robotics, etc., CPS is currently an area of attention among the academia and industry. The advancement of mobile communications and embedded technology has made it possible to build large scale CPS consisting of the interconnection of mobile phones. These devices collect information about the surrounding environment at any time anywhere basis through real-time video capture. Video streaming has proven to be a massive industry that is growing rapidly playing an important role in everyday life. Customer-driven approach wanting best experience with quality has to be the core offering of contemporary scenario. Video streaming is categorized into Video-On-Demand Streaming (VoDS) and Live Video Streaming (LVS) showing the current state-of-art opportunities. Many diverse applications of video streaming are military video surveillance using drones, live sports match player face recognition, on-demand video characters recognition, movie summarization like identifying parts of the movie which are viewed many times by different users, movie and series recognition, motion detection, gesture recognition, image segmentation, etc. This paper introduces an approach to develop video analysis on VoD and LVS using cloud-based services and analyzes the impact of Quality of Experience (QoE), cost, and bandwidth on the cloud. To achieve the best user experience for video streaming and video analysis, Content Delivery Network (CDN) offers the best QoE at various analyzed locations using various cloud providers like Amazon Web Services (AWS) CloudFront, Google Cloud CDN, Azure CDN, Akamai CDN, etc.

The multicellular incoherent feedforward loop motif generates spatial patterns

The multicellular incoherent feedforward loop (mIFFL) is an extension of the traditional intracellular IFFL gene motif where the interacting nodes no longer need to be genes inside the same cell but can be spatially distributed in different cells. We studied for the first time the spatial computing abilities of these mIFFL through in silico simulations done with individual-based models (run in Morpheus and GRO software). We observed that: 1) a genetic circuit working as a mIFFL can behaves as an edge detector of the border of an infection, and 2) a mIFFL can be the inner mechanism generating the complex 7 stripe pattern of eve in D. melanogaster embryogenesis. So, in this work, we show that multicellular IFFL architectures can produce spatial patterns and are a promising spatial computing motif that deserves to be incorporated into the toolbox of pattern generation and multicellular coordination mechanisms. This study opens several future lines of research: multi-agent IFFL applied in ecology as a tool to predict spatial position of interacting animals or in distributed robotics.

Synchronization of distributed robotics control system

The paper presents the novel project that has been proposed and realized together with the implementation of the process of heating thermosetting components based on advanced industrial equipment and software tools. In the fundamental task of optimization, the phenomenon of process synchronization based on a centralized algorithm adapted to the specific requirements of production was taken into account. The proposed research station was optimized and located in the laboratory of the Interdisciplinary Computer Modelling of the University of Rzeszow, that specializes in raising the efficiency of industrial processes