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Segmentation of multiple sclerosis lesions in MR images: a review
This paper reviews and compares various MS lesion segmentation methods proposed in recent years and suggests that integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy.
A Review of Vision-Based Gait Recognition Methods for Human Identification
A comprehensive survey of recent developments on gait recognition approaches, focusing on three major issues involved in a general gait Recognition system, namely gait image representation, feature dimensionality reduction and gait classification.
Bag-of-words representation for biomedical time series classification
A simple yet effective bag-of-words representation that is originally developed for text document analysis is extended for biomedical time series representation and is able to capture high-level structural information because both local and global structural information are well utilized.
Detection and classification of road signs in natural environments
The issues associated with automatic road sign recognition are described, the existing methods developed to tackle the road sign Recognition problem are reviewed, and a comparison of the features of these methods is given.
Human Identification From ECG Signals Via Sparse Representation of Local Segments
This work proposes a novel framework to extract compact and discriminative features from Electrocardiogram (ECG) signals for human identification based on sparse representation of local segments that achieves an 99.48% accuracy on a 100 subjects dataset constructed from a publicly available database.
Coordinated energy management of vehicle air conditioning system
Whilst air conditioning systems increase thermal comfortableness in vehicles, they also raise the energy consumption of vehicles. Achieving thermal comfort in an energy-efficient way is a difficult
Lab-on-a-chip: a component view
Miniaturization is being increasingly applied to biological and chemical analysis processes. Lab-on-a-chip systems are direct creation of the advancement in the miniaturization of these processes.
Clustering based multi-label classification for image annotation and retrieval
Empirical results suggest that the proposed approach can improve the performance and reduce the training time of standard multi-label classification algorithms, particularly in the case of large number of labels.
Development of a Compact Rectenna for Wireless Powering of a Head-Mountable Deep Brain Stimulation Device
A rectangular spiral planar inverted-F antenna (PIFA) at 915 MHz for wireless power transmission applications is proposed, which can drive a deep brain stimulation pulse generator at a distance of 30 cm from a radio frequency energy transmitter, which transmits power of 26.77 dBm.
A Triple-Random Ensemble Classification Method for Mining Multi-label Data
The experimental results reveal that the proposed method outperforms the examined counterparts in most occasions when tested on six small to larger multi-label datasets from different domains, demonstrating that the developed method possesses general applicability for various multi- label classification problems.