A novel underdetermined source recovery algorithm based on k-sparse component analysis

Sparse component analysis (SCA) is a popular method for addressing underdetermined blind source separation in array signal processing applications. We are motivated by problems that arise in the applications where the sources are densely sparse (i.e. the number of active sources is high and very close to the number of sensors). The separation performance of current underdetermined source recovery (USR) solutions, including the relaxation and greedy families, reduces with decreasing the mixing system dimension and increasing the sparsity level (k). In this paper, we present a k-SCA-based algorithm that is suitable for USR in low-dimensional mixing systems. Assuming the sources is at most (m−1) sparse where m is the number of mixtures; the proposed method is capable of recovering the sources from the mixtures given the mixing matrix using a subspace detection framework. Simulation results show that the proposed algorithm achieves better separation performance in k-SCA conditions compared to state-of-the-art USR algorithms such as basis pursuit, minimizing norm-L1, smoothed L0, focal underdetermined system solver and orthogonal matching pursuit

Fuzzy Expert System for Diagnosis of Bacterial Meningitis from Other Types of Meningitis in Children

Introduction: Bacterial meningitis requires timely diagnosis and treatment; otherwise it will have relatively high complications and mortality and morbidity. In the early stages of the disease distinguishing between bacterial meningitis that it is most dangerous type and other type is so complicated and inaccurate. Hence in this study a fuzzy expert system for distinguish bacterial meningitis from other kind of meningitis in children is presented. Method: In the proposed fuzzy system, two fuzzy inference engines (The diagnosis of bacterial meningitis and the proposed new LP) were used. Mamdani model was used in both fuzzy inference engines using Max-Min as AND-OR operators and Centroid method was used as defuzzification technique. Results: The first fuzzy inference engine was evaluated using data obtained from 106 patients’ records admitted with meningitis. Accuracy, sensitivity, and precision of the system in terms of bacterial meningitis diagnosis were 91%, 100% and 89% respectively. The ROC curve was used to show system performance graphically and the area under the ROC curve was 0.947. To measure agreement of system results with the physician diagnosis, Kappa statistics was employed and showed a high relation (K=0.79, P<0.001). Extracted data from 75 cases with non-bacterial meningitis were used to evaluate the second inference engine and accuracy, sensitivity, and precision of this system were 96%, 100%, and 95% respectively, and the area under the ROC curve was 0.96 and Kappa statistic showed a very high agreement between the system output with physician diagnosis (K=0.87,P<0/001). Conclusion: According to the complexity and importance of early diagnosis of bacterial meningitis, and favorable results of the implementation and evaluation of the suggested expert system, therefore this system can be useful for detecting and differentiating acute bacterial meningitis of other meningitis, but more studies must be performed for better assessment and verification of system

A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis

Introduction Bacterial meningitis is a known infectious disease which occurs at early ages and should be promptly diagnosed and treated. Bacterial and aseptic meningitis are hard to be distinguished. Therefore, physicians should be highly informed and experienced in this area. The main aim of this study was to suggest a system for distinguishing between bacterial and aseptic meningitis, using fuzzy logic.    Materials and Methods In the first step, proper attributes were selected using Weka 3.6.7 software. Six attributes were selected using Attribute Evaluator, InfoGainAttributeEval, and Ranker search method items. Then, a fuzzy inference engine was designed using MATLAB software, based on Mamdani’s fuzzy logic method with max-min composition, prod-probor, and centroid defuzzification. The rule base consisted of eight rules, based on the experience of three specialists and information extracted from textbooks. Results Data were extracted from 106 records of patients with meningitis (42 cases with bacterial meningitis) in order to evaluate the proposed system. The system accuracy, specificity, and sensitivity were 89%, 92 %, and 97%, respectively. The area under the ROC curve was 0.93, and Kappa test revealed a good level of agreement (k=0.84,

Detection and segmentation of erythrocytes in blood smear images using a line operator and watershed algorithm

Most of the erythrocyte related diseases are detectable by hematology images analysis. At the first step of this analysis, segmentation and detection of blood cells are inevitable. In this study, a novel method using a line operator and watershed algorithm is rendered for erythrocyte detection and segmentation in blood smear images, as well as reducing over-segmentation in watershed algorithm that is useful for segmentation of different types of blood cells having partial overlap. This method uses gray scale structure of blood cell, which is obtained by exertion of Euclidian distance transform on binary images. Applying this transform, the gray intensity of cell images gradually reduces from the center of cells to their margins. For detecting this intensity variation structure, a line operator measuring gray level variations along several directional line segments is applied. Line segments have maximum and minimum gray level variations has a special pattern that is applicable for detections of the central regions of cells. Intersection of these regions with the signs which are obtained by calculating of local maxima in the watershed algorithm was applied for cells′ centers detection, as well as a reduction in over-segmentation of watershed algorithm. This method creates 1300 sign in segmentation of 1274 erythrocytes available in 25 blood smear images. Accuracy and sensitivity of the proposed method are equal to 95.9% and 97.99%, respectively. The results show the proposed method′s capability in detection of erythrocytes in blood smear images