A Multimodal Fusion Approach for Bullet Identification Systems.

In the field of forensic science, bullet identification is based on the fact that firing the cartridge from a barrel leaves exclusive microscopic striation on the fired bullets as the fingerprint of the firearm. The bullet identification methods are categorized in 2-D and 3-D based on their image acquisition techniques. In this study, we focus on 2-D optical images using a multimodal technique and propose several distinct methods as its modalities. The proposed method uses a multimodal rule-based linear weighted fusion approach which combines the semantic level decisions from different modalities with a linear technique that its optimized modalities weights have been identified by the genetic algorithm. The proposed approach was applied on a dataset, which includes 180 2-D bullet images fired from 90 different AK-47 barrels. The experimentations showed that our approach attained better results compared to common methods in the field of bullet identification.

A correlation based bullet identification method using empirical mode decomposition.

The striations on bullet surface are 3D micro structures formed when a bullet is forcing its way out of barrel. Each barrel leaves individual striation patterns on bullets. Hence, the striation information of bullets is helpful for firearm identification. Common automatic identification methods process these images using linear time invariant (LTI) filters based on correlation. These methods do not consider the sensitivity of correlation based comparisons to nonlinear baseline drifts. The striations are undeniably random unique micro structures caused by random non-model-based imperfections in the tools used in rifling process, therefore any characteristic profile that is extracted from a bullet image is statistically non-stationary. Due to limitations of LTI filters, using them in smoothing bullet images and profiles may cause information loss and impact the process of identification. To address these problems, in this article, we consider bullet images as nonlinear non-stationary processes and propose a novel method which uses ensemble empirical mode decomposition (EEMD) as a preprocessing algorithm for smoothing and feature extraction. The features extracted by EEMD algorithm not only contain less noise, but also have no nonlinear baseline drifts. These improvements help the correlation based comparison methods to perform more robustly and efficiently. The experiments showed that our proposed method attained better results compared with two common methods in the field of automatic bullet identification.

Identifying relationships among acute phase proteins (haptoglobin, serum amyloid A, fibrinogen, ceruloplasmin) and clinical findings in dairy calf diarrhea.

The acute phase response is a nonspecific inflammatory reaction of the host that occurs shortly after any tissue injury. The response includes changes in the concentration of plasma proteins called acute phase proteins (APPs). Calf diarrhea is an important disease that occurs in association with the interaction of various infectious agents and calf susceptibility. The economic losses is associated with death loss and treatment costs, reduction of live weight gain, and reduction of productive life span, which may be considerable. The aim of the present study was to identify relationships among APPs in calves with diarrhea in the different clinical features. Holstein calves (50) within 1 day to 4 months old with signs of diarrhea and healthy calves (40) with similar age and sex were selected. Standard clinical examinations and also dehydration degree were carried out on each calf and were recorded. Calves with clinical signs of diarrhea were divided in different groups based on the severity of the clinical findings, fever and degree of dehydration. Blood samples were taken from the jugular vein from all calves into vacutainers containing ethylenediaminetetraacetic acid (EDTA) for separating plasma and without EDTA for serum biochemical analysis. APP [haptoglobin (Hp), serum amyloid A (SAA), fibrinogen (Fib), and ceruloplasmin (Cp)] concentrations were measured using validated standard methods. The results indicated a significant increases in APPs in diarrheic calves which was most obvious in Hp and SAA (P < 0.001). Calves with severe clinical signs of diarrhea had a significant increases in their Hp and SAA (P < 0.001) compared to calves with moderate or without systemic clinical signs. Diarrheic calves with fever compared to diarrheic calves without fever had a significant increases in their Hp and SAA (P < 0.01). Also, diarrheic calves with severe dehydration compared to diarrheic calves with mild and moderate dehydration had significant increases in Hp and SAA (P < 0.05), and these parameters (Hp, SAA, Fib, and Cp) among calves with mild and moderate dehydration had no significant changes. Our results indicated that monitoring the APP responses in diarrheic calves with different clinical signs could be useful as prognostic tools and facilitate treatment decisions.