D-Net

{"id": "id0", "title": "Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks", "abstract": "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 Universal Mobile Telecommunication Systems (UMTS) networks. In order to characterize the Quality of Service (QoS) level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) and a second model based on non-linear regression analysis is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). The objective of the paper is two-fold. First, to find the impact of QoS parameters on end-to-end video quality for H.264 encoded video. Second, to develop learning models based on ANFIS and nonlinear regression analysis to predict video quality over UMTS networks by considering the impact of radio link loss models. The loss models considered are 2-state Markov models. Both the models are 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 both the models. The work should help in the development of a reference-free video prediction model and QoS control methods for video over UMTS networks.", "language": null, "keywords": null, "externalIdentifiers": {"hindawi-id": "608138", "doi": "10.1155\/2010\/608138"}, "journal": "International Journal of Digital Multimedia Broadcasting", "year": 2010, "publisher": "Hindawi Publishing Corporation", "references": [{"basicMetadata": {"title": null, "authors": ["3GPP, TS"], "pages": null, "source": "Third Generation Partnership Project: Technical Specification Group Access network; Radio Link Control,\u201D RLC Specification (Release 5).", "volume": null, "year": null, "edition": null, "publisher": null, "location": null, "series": null, "issue": "322", "url": null}, "position": 1, "text": "[1] 3GPP TS 25.322, \u201CThird Generation Partnership Project: Technical Specification Group Access network; Radio Link Control,\u201D RLC Specification (Release 5)."}, {"basicMetadata": {"title": "\u201CMethods for subjective determination of transmission quality", "authors": ["Rec, ITU-T.", "P"], "pages": null, "source": null, "volume": "800", "year": "1996", "edition": null, "publisher": null, "location": null, "series": null, "issue": null, "url": null}, "position": 2, "text": "[2] ITU-T. 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{"id": "id1", "title": "Robot Learning from Demonstration of Force-based Tasks with Multiple Solution Trajectories", "abstract": "- A learning framework with a bidirectional communication channel is proposed, where a human performs several demonstrations of a task using a haptic device (providing him/her with force-torque feedback) while a robot captures these executions using only its force-based perceptive system. Our work departs from the usual approaches to learning by demonstration in that the robot has to execute the task blindly, relying only on force-torque perceptions, and, more essential, we address goal-driven manipulation tasks with multiple solution trajectories, whereas most works tackle tasks that can be learned by just finding a generalization at the trajectory level. To cope with these multiple-solution tasks, in our framework demonstrations are represented by means of a Hidden Markov Model (HMM) and the robot reproduction of the task is performed using a modified version of Gaussian Mixture Regression that incorporates temporal information (GMRa) through the forward variable of the HMM. Also, we exploit the haptic device as a teaching and communication tool in a human-robot interaction context, as an alternative to kinesthetic-based teaching systems. Results show that the robot is able to learn a container-emptying task relying only on force-based perceptions and to achieve the goal from several non-trained initial conditions.", "language": null, "keywords": null, "externalIdentifiers": null, "journal": null, "year": null, "publisher": null, "references": [{"basicMetadata": {"title": null, "authors": ["Billard, A.", "Calinon, S.", "Dillmann, R.", "Schaal, S."], "pages": {"start": "1371", "end": "1394"}, "source": "Robot Programming by Demonstration", "volume": null, "year": "2008", "edition": null, "publisher": null, "location": null, "series": null, "issue": null, "url": null}, "position": 1, "text": "[1] A. Billard, S. Calinon, R. Dillmann, and S. Schaal, Springer Handbook of Robotics, 2008, ch. 59. 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