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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Ali H. Al-Timemy Javier Escudero Guido Bugmann Nicholas Outram Centre for Robotics and Neural Systems (CRNS) School of Computing and Mathematics Plymouth University Signal Processing and Multimedia Communication (SPMC) research group School of Computing and Mathematics Plymouth University Centre for Robotics and Neural Systems School of Computing and Mathematics Plymouth University

ISBN: (纸本)9781457702150

Although there have been many advances in electromyography (EMG) signal processing and pattern recognition (PR) for the control of multi-functional upper-limb prostheses, some the outstanding problems need to be solved before practical PR-based prostheses can be put into service. Some of these are the lack of training and deployment protocols and the provision of the tools required. Therefore, we present a preliminary procedure to personalize the prosthesis deployment. In the first step, we record the demographic information of each individual amputee person and their background. In the second step of the protocol, the EMG signals are acquired. PR algorithms and parameters will be chosen in the 3rd step of the protocol. In the 4th step, the best number of EMG sensors to achieve the maximal performance with a full set of gestures is identified. The final step involves finding the best set of movements that the amputee person can produce with an accuracy > 95% as well as identifying the movements with the worst performance, which would require further training. This proposed approach is validated with 2 transradial amputees.

关键词： Electromyography Protocols Accuracy Prosthetics Electrodes Muscles Training

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Single Channel-Based Myoelectric Control of Hand Movements with Empirical Mode Decomposition

Single Channel-Based Myoelectric Control of Hand Movements w...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Ali H. Al-Timemy Guido Bugmann Nicholas Outram Javier Escudero Centre for Robotics and Neural Systems (CRNS) School of Computing and Mathematics University of Plymouth Signal Processing and Multimedia Communication (SPMC) research group School of Computing and Mathematics University of Plymouth

ISBN: (纸本)9781424441211

Myoelectric control has been an important area of research for the past 40 years for prosthetic control, since it targets amputees who lost their body limbs. Advances were achieved concerning the number of movements to be classified with high accuracy. Hence, not much research was done to extract information from single channel Electromyogram (EMG). This paper presents Empirical Mode Decomposition (EMD) for Feature Extraction (FE) from single-channel EMG for ten class wrist movements and handgrips. Two classification schemes were applied based on Time Domain-Auto Regression (TDAR) features (a commonly used approach in the Literature) and EMD, with Principle Component Analysis (PCA) for dimensionality reduction, and Support Vector Machine (SVM) for classification. With the use of only one single-channel EMG, the EMD achieved an improvement in the classification rate for a single flexor and extensor EMG channel of 11.2% (from 83.7% to 94.4%) and 13% (from 80.16% to 93.16%), respectively. The results suggested that EMD remarkably improves the classification performance for a single-channel EMG over the traditional time domain FE technique. This will reduce the computational cost of applying only one channel EMG and facilitates the acquisition of the EMG. The main drawback of using EMD technique is that it is not suitable for real time processing of prosthetic control.

关键词： myoelectric control Electromyogram single-channel Movements hand movement real-time process Empirical mode decomposition Amputees

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Impact of end devices on subjective video quality assessment for QCIF video sequences

Impact of end devices on subjective video quality assessment...

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International Workshop on Quality of multimedia Experience, QoMEx

作者： Asiya Khan Lingfen Sun Jose-Oscar Fajardo Ianire Taboada Fidel Liberal Emmanuel Ifeachor Centre for Signal Processing and Multimedia Communication School of Computing and Mathematics University of Plymouth Plymouth UK Department of Electronics and Telecommunications University of the Basque County (UPV/EHU) Bilbao Spain

The aim of this paper is to assess the impact of devices on Quality of Experience (QoE) of the user. As the types of video content viewed over handheld devices increases e.g. streaming video, it is important to evaluate the impact of the end device on video quality assessment. In this paper subjective tests were conducted with QCIF (176×144) videos over two devices - Personal Computer (PC) and mobile handset with 90 test conditions. The dataset was generated with a combination of parameters associated with the access network (block error rates and mean burst lengths), H.264 codec related parameter (sender bitrates) and content types. Subjects favoured PC test over handset test in almost all test scenarios. These studies should help in context-aware services in deciding the appropriate encoding parameters based on type of device being used to receive content.

关键词： multimedia communication Conferences

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An ANFIS-based hybrid quality prediction model for H.264 video over UMTS networks

An ANFIS-based hybrid quality prediction model for H.264 vid...

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IEEE International Workshop Technical Committee on communications Quality and Reliability

作者： Khan, Asiya Sun, Lingfen Ifeachor, Emmanuel Fajardo, Jose Oscar Liberal, Fidel Centre for Signal Processing and Multimedia Communication School of Computing and Mathematics University of Plymouth Plymouth PL4 8AA United Kingdom Bilbao Spain

ISBN: (纸本)9781424477951

The Quality of Service (QoS) of Universal Mobile Telecommunication System (UMTS) is severely affected by the losses occurring in Radio Link Control (RLC) due to high error probability. Therefore, for any video quality prediction model, it is important to model the radio-link loss behaviour appropriately. In addition, video content has an impact on video quality under same network conditions. The aim of this paper is to present video quality prediction models for objective, nonintrusive prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over UMTS networks. In order to characterize the QoS level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). ANFIS is well suited for video quality prediction over error prone and bandwidth restricted UMTS as it combines the advantages of neural networks and fuzzy systems. The loss models considered are 2-state Markov models with variable Mean Burst Lengths (MBLs) depicting the various UMTS scenarios. The proposed model is trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from the model. The work should help in the development of a reference-free video prediction model and Quality of Service (QoS) control methods for video over UMTS networks. ©2010 IEEE.

关键词： Quality of service

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Impact of RLC losses on quality prediction for H.264 video over UMTS networks

Impact of RLC losses on quality prediction for H.264 video o...

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IEEE International Conference on multimedia and Expo (ICME)

作者： Asiya Khan Lingfen Sun Emmanuel Ifeachor Jose Oscar Fajardo Fidel Liberal Centre of Signal Processing and Multimedia Communication School of Computing and Mathematics University of Plymouth Plymouth UK Department of Electronics and Telecommunications University of Basque Country (UPV-EHU) Bilbao Spain

In Universal Mobile Telecommunication System (UMTS) Radio Link Control (RLC) losses severely affect the Quality of Service (QoS) due to high error probability. Therefore, for any video quality prediction model, it is important to model the radio-link loss behaviour. In this paper we evaluate the impact of the radio access network on the end-to-end QoS for H.264 encoded video. In order to characterize the QoS level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) is proposed that takes into account the RLC loss models to predict the video quality in terms of the Mean Opinion Score (MOS). The RLC loss models considered are 2-state Markov models with variable mean burst lengths. The aim of the paper is two-fold. First, to find the impact of QoS parameters in both the physical and application layer on end-to-end video quality. Second, to propose a prediction model based on ANFIS to predict video quality over UMTS networks. ANFIS is well suited for video quality prediction over error prone and bandwidth restricted UMTS as it combines the advantages of neural networks and fuzzy systems. The ANFIS model is trained with a combination of application and physical layer parameters. The performance of the proposed model is validated with unseen dataset. These studies should help in the understanding of the impact of both the application and physical layer parameters on end-to-end video quality and in QoS control methods and adaptation.

关键词： 3G mobile communication Predictive models Streaming media Physical layer Training Markov processes Quality of service

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An ANFIS-based hybrid quality prediction model for H.264 video over UMTS networks

An ANFIS-based hybrid quality prediction model for H.264 vid...

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IEEE International Workshop Technical Committee on communications Quality and Reliability, CQR

作者： Asiya Khan Lingfen Sun Emmanuel Ifeachor Jose Oscar Fajardo Fidel Liberal Centre for Signal Processing and Multimedia Communication School of Computing and Mathematics University of Plymouth Plymouth UK Department of Electronics and Telecommunications University of Basque Country (UPV-EHU) Bilbao Spain

The Quality of Service (QoS) of Universal Mobile Telecommunication System (UMTS) is severely affected by the losses occurring in Radio Link Control (RLC) due to high error probability. Therefore, for any video quality prediction model, it is important to model the radio-link loss behaviour appropriately. In addition, video content has an impact on video quality under same network conditions. The aim of this paper is to present video quality prediction models for objective, non-intrusive prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over UMTS networks. In order to characterize the QoS level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). ANFIS is well suited for video quality prediction over error prone and bandwidth restricted UMTS as it combines the advantages of neural networks and fuzzy systems. The loss models considered are 2-state Markov models with variable Mean Burst Lengths (MBLs) depicting the various UMTS scenarios. The proposed model is trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from the model. The work should help in the development of a reference-free video prediction model and Quality of Service (QoS) control methods for video over UMTS networks.

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Content classification-based and QoE-driven video send bitrate adaptation scheme 5

Content classification-based and QoE-driven video send bitra...

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5th International Conference on Mobile multimedia communications, MobiMedia 2009

作者： Khan, Asiya Sun, Lingfen Jammeh, Emmanuel Ifeachor, Emmanuel Centre of Signal Processing and Multimedia Communication School of Computing Communications and Electronics University of Plymouth Drake Circus PlymouthPL4 8AA United Kingdom

ISBN: (纸本)9789639799622

Initial video quality requirement is not well understood and content providers usually send video at the highest send bitrate resulting in over-provisioning. The main aim of this paper is to present a new scheme that can adapt video send bitrate according to the dynamics of the content and the user’s Quality of Experience (QoE) requirement at the pre-encoding stage. Contents are classified based on their spatio-temporal feature extraction. Video quality is predicted in terms of the Peak-signal-to-Noise-Ratio (PSNR). Statistical analysis of the experimental results confirms that the proposed adaptation scheme performs well for all content types and hence, improves the perceived end-to-end video quality. The proposed scheme makes it possible for content providers to achieve an optimum streaming scheme (with an appropriate send bitrate) suitable for the content type for a requested QoE. Copyright 2009 ICST.

关键词： signal to noise ratio

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Open IMS Core with VoIP Quality Adaptation

Open IMS Core with VoIP Quality Adaptation

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International Conference on Autonomic and Autonomous Systems (ICAS)

作者： Is-Haka Mkwawa Emmanuel Jammeh Lingfen Sun Asiya Khan Emmanuel Ifeachor Centre of Signal Processing and Multimedia Communication School of Computing Communication an Electronics University of Plymouth Plymouth UK

As IMS becomes more available to academia and industry, the requirements for IMS clients are growing faster than ever. From all IMS components, IMS clients are vital components for the success of overall services provided by the IMS. Most of ongoing research is focusing on adding more services on clients side, none has been done for voice over Internet protocol (VoIP) quality adaptation so far. Since the end user is the only one to perceive the quality of services provided by the IMS network, this paper reports a testbed that demonstrates the concept of adaptation of VoIP system based on an open IMS core and open source mobile terminal.

关键词： IMS 3GPP SIP QoS PQoS AMR

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Content Clustering Based Video Quality Prediction Model for MPEG4 Video Streaming over Wireless Networks

Content Clustering Based Video Quality Prediction Model for ...

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IEEE International Conference on communications (ICC)

作者： A. Khan L. Sun E. Ifeachor Centre for Signal Processing and Multimedia Communication School of Computing Communications and Electronics University of Plymouth Plymouth UK

The aim of this paper is quality prediction for streaming MPEG4 video sequences over wireless networks for all video content types. Video content has an impact on video quality under same network conditions. This feature has not been widely explored when developing reference-free video quality prediction model for streaming video over wireless or mobile communications. In this paper, we present a two step approach to video quality prediction. First, video sequences are classified into groups representing different content types using cluster analysis. The classification of contents is based on the temporal (movement) and spatial (edges, brightness) feature extraction. Second, based on the content type, video quality (in terms of Mean Opinion Score) is predicted from network level parameter (packet error rate) and application level (i.e. send bitrate, frame rate) parameters using Principal Component Analysis (PCA). The performance of the developed model is evaluated with unseen datasets and good prediction accuracy is obtained for all content types. The work can help in the development of reference-free video prediction model and priority control for content delivery networks.

关键词： Predictive models MPEG 4 Standard Streaming media Wireless networks Video sequences Principal component analysis Wireless communication Mobile communication Brightness Feature extraction

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Content-based video quality prediction for MPEG4 video streaming over wireless networks

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Journal of multimedia 2009年第4期4卷 228-239页

作者： Khan, Asiya Sun, Lingfen Ifeachor, Emmanuel Centre for Signal Processing and Multimedia Communication School of Computing Communications and Electronics University of Plymouth Plymouth PL4 8AA United Kingdom

There are many parameters that affect video quality but their combined effect is not well identified and understood when video is transmitted over mobile/ wireless networks. In addition, video content has an impact on video quality under same network conditions. The main aim of this paper is the prediction of video quality combining the application and network level parameters for all content types. Firstly, video sequences are classified into groups representing different content types using cluster analysis. The classification of contents is based on the temporal (movement) and spatial (edges, brightness) feature extraction. Second, to study and analyze the behaviour of video quality for wide range variations of a set of selected parameters. Finally, to develop two learning models based on - (1) ANFIS to estimate the visual perceptual quality in terms of the Mean Opinion Score (MOS) and decodable frame rate (Q value) and (2) regression modeling to estimate the visual perceptual quality in terms of the MOS. We trained three ANFIS-based ANNs and regression based- models for the three distinct content types using a combination of network and application level parameters and tested the two models using unseen dataset. We confirmed that the video quality is more sensitive to network level compared to application level parameters. Preliminary results show that a good prediction accuracy was obtained from both models. However, the regression based model performed better in terms of the correlation coefficient and the root mean squared error. The work should help in the development of a reference-free video prediction model and Quality of Service (QoS) control methods for video over wireless/mobile networks. © 2009 ACADEMY PUBLISHER.

关键词： Video streaming

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