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Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
A fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system that achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopy beats.
Rate adaptation for adaptive HTTP streaming
A receiver-driven rate adaptation method for HTTP/TCP streaming that deploys a step-wise increase/ aggressive decrease method to switch up/down between the different representations of the content that are encoded at different bitrates is presented.
A Generic and Robust System for Automated Patient-Specific Classification of ECG Signals
This paper presents a generic and patient-specific classification system designed for robust and accurate detection of ECG heartbeat patterns that can adapt to significant interpatient variations in ECG patterns by training the optimal network structure, and achieves higher accuracy over larger datasets.
The Emerging MVC Standard for 3D Video Services
Multiview applications and solutions to support generic multiview as well as 3D services are introduced and cover a wide range of requirements for 3D video related to interface, transport of the MVC bitstreams, and MVC decoder resource management.
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
A fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body is proposed.
The error concealment feature in the H.26L test model
The specific concealment strategy and some special methods, including handling of B-pictures, multiple reference frames and entire frame losses, are described and both subjective and objective results are given based on simulations under Internet conditions.
Voice Conversion Using Dynamic Kernel Partial Least Squares Regression
A drawback of many voice conversion algorithms is that they rely on linear models and/or require a lot of tuning. In addition, many of them ignore the inherent time-dependency between speech
Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks
This work proposed a deep learning methodology, based on Convolutional Neural Networks (CNNs), that predicts the price movements of stocks, using as input large-scale, high-frequency time-series derived from the order book of financial exchanges.
The 2nd competition on counter measures to 2D face spoofing attacks
The objective of the 2nd Competition on Counter Measures to 2D Face Spoofing Attacks is to challenge researchers to create counter measures effectively detecting a variety of attacks.