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Recently, the research on Brain-Computer Interface (BCI) technology has achieved great progress, and the BCI system based on Motor Imagery (MI) has been intensively studied in many labs. The essential part of signal processing in BCI is how to extract the MI features in electroencephalographic (EEG) and recognize the MI task accurately. One challenge lies in that EEG signals are non-stationary, whose...
In BCI research community, support vector machine (SVM) is an effective method for motor imagery (MI)-based electroencephalographic (EEG) classification. However, the computation of decision function during SVM classification stage for a new EEG trial is time-consuming due to the large number of support vectors (SV). This paper proposes a new method to reduce the number of support vectors so that...
In this paper, we propose a new feature evaluation method that forms the basis for feature ranking and selection. The method starts by generating a number of feature subsets in a random fashion and evaluates features based on the derived subsets. It then proceeds in a number of stages. In each stage, it inputs the features whose ranks in the previous stage were above the median rank and re-evaluates...
New application traffic occurring on Internet frequently challenges the traditional traffic classifiers based on machine learning. These classifiers always identify it inaccurately and assign it into one of their known classes forcibly, even though the extra class is labeled as 'other' when training. In this case, the precision of identifying known classes is reduced. In this paper, a robust traffic...
The well-built dataset is a pre-requisite for object categorization. The processes of collecting and labeling the images are laborious and monotonous. In order to label images efficiently, we propose an incremental learning model to label images automatically with a bounding box for each visual object category. Our approach combines image classification and object detection. Given a query image, our...
This study investigates military aircrews' ability to adapt to human-use centrifuge and determine what training items cognitively promote trainees' G tolerance to lower the G-induced loss of consciousness (G-LOC) incidence for the crews of high-performance combat aircraft. Questionnaires are used to assess the stressor for crews in taking centrifuge training and measure trainees' ability to adapt...
This study is to analyze the upper and lower extremity's reaction while riding bicycle in 3 different speed and 3 different gradients on both fixed and non-fixed training platform, We used 6 physically healthy male as our subjects, who are aged 23.46±3.21 with an average height 171.26±6.39cm, average weight 64.43±7.48kg, average time of working out per week 13 hours, and no physical obstacles and...
Surgical patients are usually at high risk of developing pressure ulcers after their operation. Usually, the pressure ulcers data sets are imbalanced. Therefore, this study aims to examine the real medical case of pressure ulcers with the use of support vector machines (SVMs). SVMs are used for forecasting and are a type of classification techniques. We utilize the measurement of sensitivity and specificity...
This paper presents a novel classified method that is called extension genetic algorithm (EGA). The new method is a combination of extension theory and genetic algorithm (GA). In the past, we used the extension method in some clustered problems. With the method, we had to rely on experiences to set rules on classical domain and weight, which caused to increase two tedious and complicated steps in...
Many types of shape descriptors have been proposed for 2D shape analysis, but most of them consist of component features that are not adapted to specific problems. This has two drawbacks. First, computation is wasted on the irrelevant components; second, the accuracy is impaired. This paper proposes an effective method that generates compact descriptors adapted to specific problems in hand, where...
We propose a hybrid body representation that represents each typical pose by both template-like view information and part-based structural information. Specifically, each body part as well as the whole body are represented by an off-line learned shape model where both region-based and edge-based priors are combined in a coupled shape representation. Part-based spatial priors are represented by a ldquostarrdquo...
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