Continuous-time Markov decision processes under the risk-sensitive average cost criterion

This paper studies continuous-time Markov decision processes under the risk-sensitive average cost criterion. The state space is a finite set, the action space is a Borel space, the cost and transition rates are bounded, and the risk-sensitivity coefficient can take arbitrary positive real numbers. Under the mild conditions, we develop a new approach to establish the existence of a solution to the risk-sensitive average cost optimality equation and obtain the existence of an optimal deterministic stationary policy.Comment: 14 page

Breast metastasis from rectal carcinoma: A case report and review of the literature

BackgroundMetastasis from extramammary primary tumor to breast is extremely rare. Case SummaryA 59-year-old woman with 1-year history of rectal cancer presented with asymptomatic breast mass. At 16 months after the diagnosis of rectal mucinous adenocarcinoma, a breast mass was confirmed by ultrasonography and identified by pathology and immunohistochemistry as a metastasis from the rectal cancer. Treatments included chemotherapy (6 cycles: 300 mg irinotecan on day 1, 4.5 mg raltitrexed on day 2, 450 mg bevacizumab on day 3), radiotherapy, and surgical resection. Two years of follow-up examinations (6-months intervals) showed no evidence of recurrence or novel distant metastasis. ConclusionBreast metastasis from rectal carcinoma is a rare secondary malignancy. Final diagnosis can be established by histopathology and immunohistochemistry

Sensorless stator field orientation controlled induction motor drive with a fuzzy speed controller

AbstractA sensorless stator-field oriented control induction motor drive with a fuzzy logic speed controller is presented. First, a current-and-voltage parallel-model stator-flux estimator is established using measured phase currents and voltages of the induction motor. Then the estimated rotor shaft position is obtained from the magnitude and position of the estimated stator flux. The speed controller is developed by utilizing fuzzy logic control techniques. The control algorithms are realized by a DSP 6713 and, using a DSP F2812 to generate PWM signals to the power stage, drive the motor to experimentally validate the proposed approach

Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms

We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010

Alignment-Free and High-Frequency Compensation in Face Hallucination

Face hallucination is one of learning-based super resolution techniques, which is focused on resolution enhancement of facial images. Though face hallucination is a powerful and useful technique, some detailed high-frequency components cannot be recovered. It also needs accurate alignment between training samples. In this paper, we propose a high-frequency compensation framework based on residual images for face hallucination method in order to improve the reconstruction performance. The basic idea of proposed framework is to reconstruct or estimate a residual image, which can be used to compensate the high-frequency components of the reconstructed high-resolution image. Three approaches based on our proposed framework are proposed. We also propose a patch-based alignment-free face hallucination. In the patch-based face hallucination, we first segment facial images into overlapping patches and construct training patch pairs. For an input low-resolution (LR) image, the overlapping patches are also used to obtain the corresponding high-resolution (HR) patches by face hallucination. The whole HR image can then be reconstructed by combining all of the HR patches. Experimental results show that the high-resolution images obtained using our proposed approaches can improve the quality of those obtained by conventional face hallucination method even if the training data set is unaligned