Wireless multimedia sensor network technology: a survey

Wireless Multimedia Sensor Networks (WMSNs) is comprised of small embedded video motes capable of extracting the surrounding environmental information, locally processing it and then wirelessly transmitting it to parent node or sink. It is comprised of video sensor, digital signal processing unit and digital radio interface. In this paper we have surveyed existing WMSN hardware and communicationprotocol layer technologies for achieving or fulfilling the objectives of WMSN. We have also listed the various technical challenges posed by this technology while discussing the communication protocol layer technologies. Sensor networking capabilities are urgently required for some of our most important scientific and societal problems like understanding the international carbon budget, monitoring water resources, monitoring vehicle emissions and safeguarding public health. This is a daunting research challenge requiring distributed sensor systems operating in complex environments while providing assurance of reliable and accurate sensing

Light field image compression

Light field imaging based on a single-tier camera equipped with a micro-lens array has currently risen up as a practical and prospective approach for future visual applications and services. However, successfully deploying actual light field imaging applications and services will require identifying adequate coding solutions to efficiently handle the massive amount of data involved in these systems. In this context, this chapter presents some of the most recent light field image coding solutions that have been investigated. After a brief review of the current state of the art in image coding formats for light field photography, an experimental study of the rate-distortion performance for different coding formats and architectures is presented. Then, aiming at enabling faster deployment of light field applications and services in the consumer market, a scalable light field coding solution that provides backward compatibility with legacy display devices (e.g., 2D, 3D stereo, and 3D multiview) is also presented. Furthermore, a light field coding scheme based on a sparse set of microimages and the associated blockwise disparity is also presented. This coding scheme is scalable with three layers such that the rendering can be performed with the sparse micro-image set, the reconstructed light field image, and the decoded light field image.info:eu-repo/semantics/acceptedVersio

Flexi-WVSNP-DASH: A Wireless Video Sensor Network Platform for the Internet of Things

abstract: Video capture, storage, and distribution in wireless video sensor networks (WVSNs) critically depends on the resources of the nodes forming the sensor networks. In the era of big data, Internet of Things (IoT), and distributed demand and solutions, there is a need for multi-dimensional data to be part of the Sensor Network data that is easily accessible and consumable by humanity as well as machinery. Images and video are expected to become as ubiquitous as is the scalar data in traditional sensor networks. The inception of video-streaming over the Internet, heralded a relentless research for effective ways of distributing video in a scalable and cost effective way. There has been novel implementation attempts across several network layers. Due to the inherent complications of backward compatibility and need for standardization across network layers, there has been a refocused attention to address most of the video distribution over the application layer. As a result, a few video streaming solutions over the Hypertext Transfer Protocol (HTTP) have been proposed. Most notable are Apple’s HTTP Live Streaming (HLS) and the Motion Picture Experts Groups Dynamic Adaptive Streaming over HTTP (MPEG-DASH). These frameworks, do not address the typical and future WVSN use cases. A highly flexible Wireless Video Sensor Network Platform and compatible DASH (WVSNP-DASH) are introduced. The platform's goal is to usher video as a data element that can be integrated into traditional and non-Internet networks. A low cost, scalable node is built from the ground up to be fully compatible with the Internet of Things Machine to Machine (M2M) concept, as well as the ability to be easily re-targeted to new applications in a short time. Flexi-WVSNP design includes a multi-radio node, a middle-ware for sensor operation and communication, a cross platform client facing data retriever/player framework, scalable security as well as a cohesive but decoupled hardware and software design.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

News Session-Based Recommendations using Deep Neural Networks

News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift. Some promising results have been recently achieved by the usage of Deep Learning techniques on Recommender Systems, specially for item's feature extraction and for session-based recommendations with Recurrent Neural Networks. In this paper, it is proposed an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News Recommender Systems. This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks. The recommendation task addressed in this work is next-item prediction for users sessions: "what is the next most likely article a user might read in a session?" Users sessions context is leveraged by the architecture to provide additional information in such extreme cold-start scenario of news recommendation. Users' behavior and item features are both merged in an hybrid recommendation approach. A temporal offline evaluation method is also proposed as a complementary contribution, for a more realistic evaluation of such task, considering dynamic factors that affect global readership interests like popularity, recency, and seasonality. Experiments with an extensive number of session-based recommendation methods were performed and the proposed instantiation of CHAMELEON meta-architecture obtained a significant relative improvement in top-n accuracy and ranking metrics (10% on Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada. https://recsys.acm.org/recsys18/dlrs

MIJ2K: Enhanced video transmission based on conditional replenishment of JPEG2000 tiles with motion compensation

A video compressed as a sequence of JPEG2000 images can achieve the scalability, flexibility, and accessibility that is lacking in current predictive motion-compensated video coding standards. However, streaming JPEG2000-based sequences would consume considerably more bandwidth. With the aim of solving this problem, this paper describes a new patent pending method, called MIJ2K. MIJ2K reduces the inter-frame redundancy present in common JPEG2000 sequences (also called MJP2). We apply a real-time motion detection system to perform conditional tile replenishment. This will significantly reduce the bit rate necessary to transmit JPEG2000 video sequences, also improving their quality. The MIJ2K technique can be used both to improve JPEG2000-based real-time video streaming services or as a new codec for video storage. MIJ2K relies on a fast motion compensation technique, especially designed for real-time video streaming purposes. In particular, we propose transmitting only the tiles that change in each JPEG2000 frame. This paper describes and evaluates the method proposed for real-time tile change detection, as well as the overall MIJ2K architecture. We compare MIJ2K against other intra-frame codecs, like standard Motion JPEG2000, Motion JPEG, and the latest H.264-Intra, comparing performance in terms of compression ratio and video quality, measured by standard peak signal-to-noise ratio, structural similarity and visual quality metric metrics.This work was supported in part by Projects CICYT TIN2008– 06742-C02–02/TSI, CICYT TEC2008–06732-C02–02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008–07029-C02–02.Publicad