Neural network method for direction of arrival estimation with uniform cylindrical microstrip patch array

In this study, a new neural network algorithm is proposed for real-time multiple source tracking problem with cylindrical patch antenna array based on a previously reported Modified Neural Multiple Source Tracking (MN-MUST) algorithm. The proposed algorithm, namely cylindrical microstrip patch array modified neural multiple source tracking (CMN-MUST) algorithm implements MN-MUST algorithm on a cylindrical microstrip patch array structure. CMN-MUST algorithm uses the advantage of directive pattern of microstrip patch elements by considering only a part of array elements for a chosen sector. This reduces neural network sizes and also improves the spatial filtering performance. The proposed algorithm improves MN-MUST algorithm in the sense of accuracy and speed while covering the full azimuth range

An automated differential blood count system

While the early diagnosis of hematopoietic system disorders is very important in hematolgy, it is a highly complex and time consuming task. The early diagnosis requires a lot of patients to be followed-up by experts which, in general is infeasible because of the required number of experts. The differential blood counter (DBC) system that we have developed is an attempt to automate the task performed manually by experts in routine. In our system, the cells are segmented using active contour models (snakes and ballons), which are initialized using morphological operators. Shape based and texture based features are utilized for the classification task. Different classifiers such as k-nearest neighbors, learning vector quantization, multi-layer perceptron and support vector machine are employed

An automated blood cell analysis and classification system

Direct analysis of blood and bone marrow smear images obtained from microscope are not common in current trends of hematology. Current blood smear analysis methods heavily depend on flow cytometry based techniques in which the blood cells are flew through microtubes thus classified according to their flow characteristics and cell volumes. This method is an indirect way of measuring features hence is accurate at a certain level. In this work an automated blood cell classification system is presented including cell segmentation, cell partitioning and classification

Local decision making and decision fusion in hierarchical levels

Decision-making, Decision-fusion, Neural networks, Classification, Machine learning, 68T01, 68T05, 68T30, 68T37, 68T40,