Thomas Pliakas

Mobile healthcare information management utilizing Cloud Computing and Android OS

Cloud Computing provides functionality for managing information data in a distributed, ubiquitous and pervasive manner supporting several platforms, systems and applications. This work presents the implementation of a mobile system that enables electronic healthcare data storage, update and retrieval using Cloud Computing. The mobile application is developed using Googles Android operating system and provides management of patient health records and medical images (supporting DICOM format and JPEG2000 coding). The developed system has been evaluated using the Amazons S3 cloud service. This article summarizes the implementation details and presents initial results of the system in practice.

Wavelet-Based Compression With ROI Coding Support for Mobile Access to DICOM Images Over Heterogeneous Radio Networks

Most of the commercial medical image viewers do not provide scalability in image compression and/or region of interest (ROI) encoding/decoding. Furthermore, these viewers do not take into consideration the special requirements and needs of a heterogeneous radio setting that is constituted by different access technologies [e.g., general packet radio services (GPRS)/ universal mobile telecommunications system (UMTS), wireless local area network (WLAN), and digital video broadcasting (DVB-H)]. This paper discusses a medical application that contains a viewer for digital imaging and communications in medicine (DICOM) images as a core module. The proposed application enables scalable wavelet-based compression, retrieval, and decompression of DICOM medical images and also supports ROI coding/decoding. Furthermore, the presented application is appropriate for use by mobile devices activating in heterogeneous radio settings. In this context, performance issues regarding the usage of the proposed application in the case of a prototype heterogeneous system setup are also discussed.

Advanced Medical Video Services through Context-Aware Medical Networks

The aim of this paper is to present a framework for advanced medical video delivery services, through network and patient-state awareness. Under this scope a context-aware medical networking platform is described. The developed platform enables proper medical video data coding and transmission according to both a) network availability and/or quality and b) patient status, optimizing thus network performance and telediagnosis. An evaluation platform has been developed based on scalable H.264 coding of medical videos. Corresponding results of video transmission over a WiMax network have proved the effectiveness and efficiency of the platform providing proper video content delivery.

Scalable Video Streaming Traffic Delivery in IP/UMTS Networking Environments

The paper discusses the end-to-end QoS provisioning for scalable video streaming traffic delivery over heterogeneous IP/UMTS networks. A prototype architecture is proposed, and is further validated, that explores the joint use of packet prioritization and scalable video coding (SVC) together with the appropriate mapping of UMTS traffic classes to the DiffServ traffic classes. A complete set of simulation scenarios, involving eight different video sequences and using two different scalable encoders, demonstrates the quality gains of both scalable video coding and prioritized packetization. Index Terms— DiffServ, End-to-End QoS, H.264/MPEG-4 AVC, MPEG-4 FGS, Packet Prioritization, SVC, UMTS.

End-to-end QoS issues of MPEG-4 FGS video streaming traffic delivery in an IP/DVB/UMTS network

The paper addresses the end-to-end QoS problem of MPEG-4 FGS video streaming traffic delivery over a heterogeneous IP/DVB/UMTS network. It proposes and validates an architecture that explores the joint use of packet prioritization and scalable video coding together with the appropriate mapping of UMTS traffic classes to the DiffServ traffic classes. A set of experimental scenarios, involving eight different video sequences, demonstrates the quality gains of both scalable video coding and prioritized packetization.

A Novel Method for Pre-Encoding Video Quality Prediction

This paper presents a novel method for fast and quantified estimation of the video quality for H.264 video content. Taking into account the instant video quality variation due to the video dynamics within a given H.264 encoded content, this paper introduces the Mean PQoS as a function of the video encoding rate and the picture resolution, and exploits it as a metric for objective video quality assessment. Based on the proposed metric, the paper presents a novel method for pre-encoding video quality prediction.

End-to-end qos issues of MPEG4-FGS video streaming traffic delivery in an IP/UMTS network

The paper addresses the end-to-end QoS problem of MPEG4-FGS video streaming traffic delivery over a heterogeneous IP/UMTS network. It proposes and validates an architecture that explores the joint use of packet prioritization and scalable video coding together with the appropriate mapping of UMTS traffic classes to the DiffServ traffic classes. A set of simulation scenarios, involving eight different video sequences, demonstrates the quality gains of both scalable video coding and prioritized packetization.

Joint scalable video coding and packet prioritization for video streaming over IP/802.11e heterogeneous networks

The paper discusses scalable video streaming traffic delivery over heterogeneous DiffServ/WLAN networks. A prototype architecture is proposed and further validated that explores the joint use of packet prioritization and scalable video coding (SVC) together with the appropriate mapping of 802.11e access categories to the DiffServ traffic classes. A complete set of simulation scenarios, involving four different video sequences using the scalable extension of H.264/MPEG-4 AVC, demonstrates the quality gains of both scalable video codingand prioritized packetization.

Distortion optimized scheduling and QoS driven prioritization of video streams over WLAN

This paper presents a distortion optimization scheme. It involves a packet scheduling algorithm, based on a novel distortion prediction model, and a prioritization mechanism that categorizes the transmitted packets according to their video distortion importance to different priority classes. In the proposed scheme the video streams have been encoded based on the H.264/AVC codec with its multireference frame capability. A distortion prediction model, designed to capture the multi-referenced frame coding characteristic, is used to predetermine the distortion importance of each video frame in the stream. This information is prestored in each packet header. A scheduling algorithm will select which individual packet or combinations of packets will be drop based on the available transmission rate of the communication network, which in this case is a 802.11e WLAN. The network has been configured to differentiate the transmitted packets according to their distortion impact. Significant video packets are appointed to high priority access categories while least significant packets to lower categories. Simulation results show the improvement in the received video quality between the proposed scheduling scheme and an additive-distortion scheduling approach that omits the correlations among neighboring video frames. Moreover the simulation results show the improvement on the received video quality when the prioritization mechanism is implemented in addition to the packet scheduling algorithm.

Distributed Management of Pervasive Healthcare Data through Cloud Computing

This paper presents a distributed platform based on Cloud Computing for management of pervasive healthcare data. Pervasive applications through continuous monitoring of patients and their context generate a vast amount of data that need to be managed and stored for processing and future usage. Cloud computing and service-oriented applications are the new trends for efficient managing and processing data online. The Cumulocity cloud platform utilized in this work is especially developed for the support of sensors and machine-to-machine (M2M) communication infrastructures. This paper presents an integrated system for managing sensor data related to the detection of disabled or elderly citizens falls. Wearable sensors collect the fall related data, which are then handled by the Cumulocity Cloud Platform.