A Reinforcement-Learning Approach to Color Quantization

[[abstract]]Color quantization is a process of sampling three-dimensional color space (e.g. RGB) to reduce the number of colors in a color image. By reducing to a discrete subset of colors known as a color codebook or palette, each pixel in the original image is mapped to an entry according to these palette colors. In this paper, a reinforcement-learning approach to color image quantization is proposed. Fuzzy rules, which are used to select appropriate parameters for the adaptive clustering algorithm applied to color quantization, are built through reinforcement learning. By comparing this new method with the original adaptive clustering algorithm on 30 color images, our method shows an improvement of 3.3% to 5.8% in peak signal to noise ratio (PSNR) values on average and results in savings of about 10% in computation time. Moreover, we demonstrate that reinforcement learning is an efficacious as well as efficient way to provide a solution of the learning problem where there is a lack of knowledge regarding the input-output relationship.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

AustimSpace: A Visualized Scenario Learning Aid on Tablet PC for Chinese Children with High-Functioning Autism

[[abstract]]In this paper, a visualized scenario learning aid on Tablet PC is developed for children with high-functioning autism. The aid assists autistic children in learning how to use daily living equipments or daily living skills with specific scenarios. Caretakers of autistic children can directly design learning targets on the corresponding space, e.g. bathroom or kitchen. When the autistic children select a specific space, this aid shows the possible learning targets. After they click on a learning target, then the corresponding videos or pictures are given. The developed aid links the specific space and its learning objective, thereby enhancing the autistic child’s learning outcome. Furthermore, the developed aid employs a cloud server that enables caretakers to upload and share their self-produced learning scenarios with other caretakers who have similar needs. In addition, the developed aid is available to download cost-free from the iTunes App Store, and the software content is presented in Mandarin Chinese. Users of this aid do not require any cost and a specific level of English ability to use it.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]電子

A Home Rehabilitation System Combined with Somatosensory Games for Stroke Patients

[[abstract]]The rehabilitation is a long and boring process. In order to enhance the entertaining and efficient to rehabilitation, we design a rehabilitation system for stroke patients at home. In this system, two somatosensory games are designed by using kinect. The stroke patients are assigned suitable rehabilitation games targeted at different limbic areas of them, and the system can also document the daily health status and rehabilitation efficiency of patients. Furthermore, by participating in games, patients would be more likely to participate in the tedious rehab process. This result illustrated that the designed system successfully provides a new mode of home rehabilitation for stroke patients.[[notice]]補正完畢[[incitationindex]]EI[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]JP

A New Measure of Cluster Validity Using Line Symmetry

[[abstract]]Many real-world and man-made objects are symmetry, therefore, it is reasonable to assume that some kind of symmetry may exist in data clusters. In this paper a new cluster validity measure which adopts a non-metric distance measure based on the idea of "line symmetry" is presented. The proposed validity measure can be applied in finding the number of clusters of different geometrical structures. Several data sets are used to illustrate the performance of the proposed measure.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[incitationindex]]EI[[ispeerreviewed]]Y[[booktype]]紙本[[booktype]]電子版[[countrycodes]]TW

A Machine Learning Approach to Classify Vigilance States in Rats

[[abstract]]Identifying mammalian vigilance states has recently become an important topic in biological science research. The vigilance states are usually categorized in at least three states, including slow wave sleep (SWS), rapid eye movement sleep (REM), and awakening. To identify different vigilance states, even a well-trained expert must spend a lot of time analyzing a mass of physiological recording data. This study proposes an automatic vigilance stages classification method for analyzing EEG signals in rats. The EEG signals were transferred by fast Fourier transform before extracting features. These extracted features were then used as training patterns to construct the proposed classification system. The proposed classification system contains two functional units. The first unit is principle component analysis (PCA) method, which is used to project the high dimensional features into the lower dimensional subspace. The second unit is the k-nearest neighbor (k-NN) method, which identifies the physiological state in each EEG signal epoch. Based on the results of analyzing 810 epochs of EEG signal, the proposed classification method achieves satisfactory classification accuracy for vigilance states. Based on machine-learning algorithms, the classifier learns to approach the configuration that best fits the categorization task. Therefore, additional training in searching best parameters and thresholds can be avoided. Moreover, the PCA algorithm projects data instances into a 3-D space, making it possible to visualize state-changing dynamics. Experimental results show that the proposed machine-learning based classifier performs better than conventional vigilance state classification algorithms. The results also suggest that it is possible to identify the vigilance states with only EEG signals using the proposed pattern recognition technique.[[incitationindex]]SCI[[booktype]]紙