Pattern Recognition System Program Advancement to Compensate Strip Expansion on 1-Map Strips

Package singulation process is where the whole strip was sheared to produce individual units. Existing singulation program for 1-map strip have only six (6) pattern recognition system (PRS) points along the whole strip with 240 mm length. Considering the length of the strip, it is prone with misalignment especially when the unit pitching is small. Due to a big gap of PRS points, the compensation of unit pitch has a significant variable value due to strip expansion that results to misalignment when not monitored. PRS works to calculate and compensate the appropriate alignment of the strip including the unit pitching. Distance between adjacent PRS points divided by the number of cut lines it covers results to the unit pitch. The lesser accumulation of strip expansion, the more it compensates and align with the actual unit pitch. Modification of PRS Program to add PRS points along the strip in order to lessen the distance between adjacent PRS points were made and results were promising compared with the existing with only 6 PRS points. It has been found out that unit pitch varies by adding PRS points that will compensate the expansion of the whole strip.

Positive Pattern Recognition System Using Alanine Aminotransferase, TypeIV Collagen 7S and E Value (Liver Stiffness) for the Diagnosis of Nonalcoholic Steatohepatitis Based on Natural History

Background: A simple diagnostic system for nonalcoholic fatty liver disease (NAFLD) instead of a biopsy is expected. We investigated the possibility of a positive pattern recognition system for evaluation of nonalcoholic fatty liver (NAFL) and the stages of nonalcoholic steatohepatitis (NASH). Methods: 68 Japanese patients with biopsy-proven NAFLD were enrolled. Serological biomarkers and markers obtained by medical imaging were investigated to explore candidates for diagnostic system. After selected markers were statistically evaluated, every patient was distributed in pattern combinations.Results: We selected three markers based on natural history and decided the critical values: alanine aminotransferase/ALT (persistent ≧ 45 IU/L) as hepatitis marker, type Ⅳ collagen 7S (≧ 5.1 ng/ml) as fibrosis one and E value (≧5.5 kPa) as stiffness one. After we statistically evaluated their accuracies, every patient was classified into their combination patterns. Major patterns were limited to four. Comparing relationships between histological classifications and positive patterns , the patients with NAFL were mainly distributed in pattern (ALT, type Ⅳ collagen , E value : -, -, -), those with NASH stage 0-1 in (+, -, +), those with NASH stage 2-3 in (+, +, +), and those with NASH stage 4 in (-, +, +).Conclusion: The positive patters changed with NAFL and NASH conditions. Our results showed a correlation between the positive patterns using three markers and histological results. Positive pattern recognition system based on natural history is useful in a differential diagnosis of NAFLD and for evaluation of the severity of fibrosis in patients with NASH.

Analysis of Digital Forensic Tools

This paper aims to analyze different tools for Forensic Data Analysis comes under the branch of Digital Forensics. Forensic data analysis is done with digital techniques. Digital forensics becomes more important in law enforcement, due to the large use of computers and mobile devices. The pattern recognition system most appropriately fits into the Analysis Phase of the Digital Forensics. Pattern Recognition involves two processes. One Process is an analysis and the second process is recognition. The result of the analysis is taken out of the attributes from the patterns to be recognized i.e., a pattern of different faces and fingerprints. These attributes are then utilized for the further process in the analysis phase which provides attention on various techniques of pattern recognition that are applied to digital forensic examinations and is proposed to develop different forensic tools to collect evidence that would be helpful to solve specific types of crimes. This evidence further helps the examiner in the analysis phase of the digital forensic process by identifying the applicable data.

ON THE METHOD OF DETERMINING LEARNING DESCRIPTIONS TO FORECAST NATURAL DISASTERS WITH THE PATTERN RECOGNITION SYSTEM

To forecast natural disasters (floods, mud-slides) in the fixed region and in period T0 with SPRL – the System of Pattern Recognition with Learning (elaborated by us) it is necessary to have the data of the previous 12 months of period T0 and learning descriptions (LDs). To identify this latter, the fact of occurrence or non-occurrence of disasters in the same region and the period T0 should be known in other years and also, the above mentioned 12- month date for each year. Determining LDs based on them is the aim of the article. For this purpose, the method which will be included in the first model of the SPRL is elaborated. The SPRL comprises: 1) preliminary elaboration of the initial information, 2) learning and 3) recognition models. This system is implemented on a PC. It is verified on the basis of the real data to recognize objects of different classis. Primary, additional and formal additional parameters are determined in the method given in the article. On the basis of their values in correlation with the aforementioned 12 months two matrices are determined. The first of them corresponds to the fact of occurrence of disasters and the second one – of non-occurrence. By using these parameter values given in these matrices LDs will be determined. The best LDs will be given to the learning model of the SPRL for transformation and increasing of informativity. Based on the LDs obtained after the transformation, the learning model will make knowledge and data bases.