Content-aware packet scheduling strategy for medical ultrasound videos over LTE wireless networks

In parallel to the advancements in communication technologies, telemedicine research has continually adapted to develop various healthcare applications. The latest wireless technology Long-Term Evolution(LTE) is being increasingly deployed across developed countries and rapidly adopted by developing countries. In this paper, a content-aware packet scheduling approach for medical ultrasound videos is proposed. The contribution of this work is introducing a utility function based on the temporal complexity of the video frames. The utility function is used with four schedulers to prioritise the video packets based on their temporal complexity and type of frame (e.g. I frame). The results show that the utility function improves the packet delay performance obtained in our simulation when compared with content-unaware approach. Further, gain in average PSNR and SSIM are also observed in the received video quality. Research on content-aware packet scheduling for telemedicine applications over advanced wireless networks is limited and our work contributes towards addressing this research gap

QoE-aware optimization of video stream downlink scheduling over LTE networks using RNNs and genetic algorithm:The 11th International Conference on Future Networks and Communications (FNC 2016)

AbstractLong Term Evolution (LTE) is the initial version of fourth-generation (4G) networks which provides ubiquitous broadband access. LTE supports multimedia Quality of Service (QoS) traffic with high data transfer speed, fast communication connectivity, and high security. Multimedia traffic over LTE networks is one of the highest percentages of mobile traffic and it has been growing rapidly in recent years. Our approach focuses on the development of Quality of Experience (QoE) aware optimization downlink scheduling video traffic flow. QoE is the overall acceptability of a service or application, as perceived subjectively by end users. In this work we aim to maximise QoE of video traffic streaming over LTE networks. This work introduces a novel integration framework between genetic algorithm (GA) and random neural networks (RNN) applied to QoE-aware optimization of video stream downlink scheduling. The proposed framework has been applied and evaluated using an open source simulation tool for LTE networks (LTE-Sim). A comparison between our framework and state-of-the-art LTE downlink scheduling algorithms (FLS, EXP-rule, and LOG-rule) has been done under different network conditions. Simulation results have shown that our scheduler can achieve better performance in terms of QoE (∼10% increase), throughput and fairness

Offline Writer Identification using Deep Convolution Neural Network

Deep convolutional neural networks (DCNN) are efficient in solving different pattern recognition problems and have been applied to extract image features (IFs). This paper investigates using deep learning (DL) techniques to improve the performance of the writer identification (WI) process. This work presents a novel approach for WI tasks by combining a DL technique with machine learning (ML). A convolutional neural network (CNN) is employed as a feature extractor along with a ML algorithm to classify those features. The standard Alex-Net model is utilized to extract IFs that located in the fully connected layers (FCLs). The support vector machine (SVM) model is selected as the classifier due to its efficient capabilities to improve identification performance (IP). The proposed model is tested using various types of the datasets, namely the Islamic Heritage Project (IHP) and Clusius. Furthermore, IAM and ICFHR-2012 datasets have been employed for benchmarking the proposed model. The results demonstrate the model achieves superior performance