Bloodstain Pattern Analysis

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Bloodstain Pattern Analysis   An Introduction to Crime Scene Reconstruction pdf

Bloodstain Pattern Analysis An Introduction to Crime Scene Reconstruction pdf

The text provides specific details on crime scene analysis and reconstruc- tion in explaining the scientific methodology involved in the process. The authors provide an excellent historical perspective to acquaint the reader with the significant chronology of the application of this technique. The authors also provide excellent information on distinguishing crime scene analysis from behavioral analysis and discuss the considerations involved in the recon- struction of the crime. There is an entire chapter dedicated to the important subject of proper terminology to be utilized in these analyses. Bloodstain Pattern Analysis explains the complex mechanics of blood spatter analysis with chapters that address the dynamics of reconstruction, such as determin- ing motion and directionality, convergence and the point of origin, evaluating impact spatter bloodstains, the characteristic patterns of blood which aid in analysis and the proper documentation of blood stains and the reconstruc- tion of a crime. The authors have included two chapters on software appli- cations and demonstrative evidence for court presentation. Most significant in the Second Edition is the addition of over 100 photographs, which graph- ically illustrate the dynamics of bloodstain patterns, practical applications, case histories and excellent appendices.
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BASIC BLOODSTAIN PATTERN ANALYSIS

BASIC BLOODSTAIN PATTERN ANALYSIS

Bloodstain pattern analysis involves the scientific study of the static consequences resulting from dynamic blood shedding events. A detailed study of bloodstain patterns at crime scenes often develops invaluable evidence. The distribution, size and shape of bloodstains on a victim, on a suspect, or on the walls, floors, ceilings, or on objects at the scene can help reconstruct these blood shedding events. Bloodstain pattern analysis can also help one evaluate the credibility of statements provided by a witness, a victim, or a suspect.

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Bloodstain Pattern Analysis: implementation of a fluid dynamic model for position determination of victims

Bloodstain Pattern Analysis: implementation of a fluid dynamic model for position determination of victims

Bloodstain Pattern Analysis is a forensic discipline in which, among others, the position of victims can be determined at crime scenes on which blood has been shed. To determine where the blood source was investigators use a straight-line approximation for the trajectory, ignoring effects of gravity and drag and thus overestimating the height of the source. We determined how accurately the location of the origin can be estimated when including gravity and drag into the trajectory reconstruction. We created eight bloodstain patterns at one meter distance from the wall. The origin’s location was determined for each pattern with: the straight-line approximation, our method including gravity, and our method including both gravity and drag. The latter two methods require the volume and impact velocity of each bloodstain, which we are able to determine with a 3D scanner and advanced fluid dynamics, respectively. We conclude that by including gravity and drag in the trajectory calculation, the origin’s location can be determined roughly four times more accurately than with the straight-line approximation. Our study enables investigators to determine if the victim was sitting or standing, or it might be possible to connect wounds on the body to specific patterns, which is important for crime scene reconstruction.
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Bloodstain Pattern Analysis On Fabric

Bloodstain Pattern Analysis On Fabric

Identificationof a murder weapon can be done by analyzing weapon transfer stains(if any) together with othercircumstantial evidence at a crime scene .Hand transfer stains, shoe transfer stains as also weapontransfer stains left at a crime scene play an integral role in the reconstruction of the crime scene, henceproper documentation of such stains is mandatory. The structure of textile has a profound effect on the formation of the bloodstain pattern. So in coherence with the work of Slemko and White, the authors present in this paper that fabric stain patterns should in all probability be carefully studied and analyzed.
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Bloodstain Patterns on Textile Surfaces- A Fundamental Analysis.

Bloodstain Patterns on Textile Surfaces- A Fundamental Analysis.

Analysis of bloodstains from textile surfaces could not be carried out as that of regular stains due to the anisotropic nature of textiles and the plethora of options available for choosing a textile substrate. For this reason, bloodstain pattern analysis on two of the most common fabrics, such as plain woven and knitted fabrics, was carried out. Preliminary experiments suggested that the blood mixtures did not yield reliable results. It was concluded that formulating a synthetic mixture that is very close to real blood was possible by conducting thermocouple, viscosity and surface tension experiments. Several parameters including designing and building a Drip-Tower Apparatus, finite volume of blood drip with no secondary drops and development of test protocols that accurately account for parameters that affect or alter the pattern were investigated and met.
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Dripped Bloodstain Patterns on Textile Surfaces.

Dripped Bloodstain Patterns on Textile Surfaces.

Bloodstain pattern analysis (BPA) is the examination of the shapes, and the categorization and distribution of bloodstain patterns in order to provide an interpretation of the physical events of a crime that gave rise to their origin. These stains occur in a large proportion of homicide cases. They offer extensive information and are an important part of a functional, medically and scientifically based reconstruction of a crime. Many BPA studies have been published, however, most of them dealt with hard, non-absorbent surfaces. Although textiles are present at most crime scenes, BPA on textiles has not been developed to the same extent as on non-porous materials.
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Impact Bloodstain Patterns on Textiles.

Impact Bloodstain Patterns on Textiles.

Bloodstain pattern analysis(BPA) plays an important role in forensic science, which intends to provide useful evidence through interpreting the size, shape, number, and distribution of bloodstains left in a crime scene. BPA has quite a rich history. The first methodical study in BPA area was introduced in 1895 by Dr. Eduard Piotrowski, an assistant at the institute for Forensic Medicine in Poland. He published the work titled “Concerning the origin, shape, Direction and Distribution of the Bloodstains Following Head Wounds Caused by Blows.” Piotrowski used live rabbit to mimic victims and battered them with different weapons, which included hammers, rocks, and hatchets, and to see how the bloodstains were changed with the change of hitting angles and positions[1]. In his paper, he stated his purpose of the research: “It is
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Analysis of bloodstain patterns at the crime scene due to arterial bleeding

Analysis of bloodstain patterns at the crime scene due to arterial bleeding

The detailed study of bloodstain patterns obtained from a crime scene could prove to be invaluable evidence for part/full crime scene reconstruction, in testing the credibility of the statements of the victim, suspect, bystander/eyewitness (if any). As per the International Association of Bloodstain Pattern Analysts (IABPA), a bloodstain pattern is defined as ‘a grouping or distribution of bloodstains that indicate through regular or repetitive form, order, or arrangement the manner in which the pattern was deposited’(Scientific Working Group on Bloodstain Pattern Analysis, 2009). Based on the different case studies presented at the IABPA conference (http:// www.iabpa.org/journal) the authors are of the view that of the different types of bloodstain patterns, the most common stain patterns visible at the crime scene, particularly in the case where the victim was found to suffer blunt force injuries, are saturation, impact , cast off and transfer stain patterns. In the IABPA Conference held in Tucson, Arizona, 2004, Peter Lamb presented the investigation report of the late night assault of a young man who was intoxicated at the time of attack and could only recollect part of the savagery that he had been subjected to (Peter Lamb, 2004). Due to rain drop that had soaked the garment at the time of the assault it was difficult to examine the bloodstains on the soaked garment (Scott Lamont, 2004). However there was evidence of kicking and stomping (Peter Lamb, 2004). Based on the evidence the case finally proceeded for trial and the accused was proved guilty and hence imprisoned (Peter Lamb, 2004). In his review of the Windsor city homicide case Scott Lamont pointed out that barefoot transfer impressions and footwear transfer
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Interpretation of Bloodstain Pattern for Re construction  of Crime Scene

Interpretation of Bloodstain Pattern for Re construction  of Crime Scene

Locard believed that no matter what a perpetrator does or where he goes, by coming in contact with things at or around a crime scene he can leave all sorts of evidence, including DNA , fingerprints, footprints, hair, skin cells, blood, body fluids, pieces of clothing fibers and more (Welding, S. 2012). While the criminal leaves something at the crime scene he is also expected to take something away from the scene with him (Welding, S. 2012). On a very loose connect it m might be said that when killing an individual with a hammer hit the criminal might take away the murder weapon with him but at the same time he might end up leaving behind bloody stains of the blood bearing hammer at the crime scene. ‘A bloodstain resulting from contact between a blood- bearing surface and another surface’ has been termed as ‘Transfer Stain’ by the International Association of Bloodstain Pattern Analysts (IABPA) (SWGBPA , 2009). Thus this work is particu larly directed at studying different transfer stain patterns at a crime scene. The scientific study/interpretation of bloodstain patterns at a crime scene, provide invaluable evidence for sequencing, reconstruction of events that might have occurred at the crime scene. As per the Federal Bureau of Investigation(FBI) each year a greater number of people die as a consequence of blunt force trauma compared to the number of people who are killed w ith a rifle or shot gun. From documentation phase to final interpretation, bloodstain pattern analysis is deeply rooted in the principles of physics, fluid mechanics, medical science, computer science, mathematics etc. Bloodstains are classified into three basic types: passive stains, transfer stains and projected or impact stains. Passive stains include drops, flow and pools, and typically result from gravity acting on an injured body. Transfer stains result from objects coming into contact with existing bloodstains and leaving wipes, swipes or pattern transfers behind such as a bloody shoe print or a smear from a body being dragged. Impact stains result from blood projecting through the air and are usually seen as spatter, but may also include gushes, splashes and arterial spurts. The authors conducting the study are particularly interested in recording, analysis and interpretation of the Transfer Stain and Saturation Stain patterns one can expect to see in the event of head hit by a blunt ended object (such as hammer, golf stick, candle stand etc.)[1-2].
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AN ANALYSIS OF FREQUENT PATTERN MINING METHODS

AN ANALYSIS OF FREQUENT PATTERN MINING METHODS

Finding of association rules is a crucial problem in data mining. Two sub-problems of mining association rules. First find out frequent itemsets from dataset and then develop association rules based on frequent itemsets. The important factor is time required for finding frequent itemsets. All the previous algorithms are not efficient and scalable for mining frequent itemsets in transaction datasets. In this paper, we provide an unifying feature for generating frequent itemset algorithms. The performance analysis with Wine, Hepatitis, Heart datasets. The algorithms analysis using different minimum support, number of rows and columns.
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Crime Pattern Definitions for Tactical Analysis

Crime Pattern Definitions for Tactical Analysis

Through the Standards, Methods, and Technology Committee (SMT), the International Association of Crime Analysts (IACA) is committed to a continuing process of professionalization through standards and knowledge development. In 2011, the IACA chartered the SMT Committee for the purpose of defining “analytical methodologies, technologies, and core concepts relevant to the profession of crime analysis.” 1 This document represents the first in a series of white papers produced by the SMT

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“Clustering of Pattern by using Discrimination analysis”

“Clustering of Pattern by using Discrimination analysis”

Discriminant function analysis or DA is used to classify cases into the values of a categorical dependent, usually a dichotomy. If discriminant function analysis is effective for a set of data, the classification table of correct and incorrect estimates will yield a high percentage correct. Multiple discriminant function analysis is used when the dependent has three or more categories.

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Pattern Recognition Analysis of MR Spectra

Pattern Recognition Analysis of MR Spectra

The need for multivariate analysis of magnetic resonance spectroscopy (MRS) data was recognized about 20 years ago, when it became evident that spectral patterns were characteristic of some diseases. Despite this, there is no generally accepted methodology for performing pattern recognition (PR) analysis of MRS data sets. Here, the data acquisition and processing requirements for performing successful PR as applied to human MRS studies are introduced, and the main techniques for feature selection, extraction, and classification are described. These include methods of dimensionality reduction such as principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF), and feature selection. Supervised methods such as linear discriminant analysis (LDA), logistic regression (LogR), and nonlinear classification are discussed separately from unsupervised and semisupervised classification techniques, including k -means clustering. Methods for testing and metrics for gauging the performance of PR models (sensitivity and specificity, the ‘Confusion Matrix’, ‘k -fold cross-validation’, ‘Leave One Out’, ‘Bootstrapping’, the ‘Receiver Operating Characteristic curve’, and balanced error and accuracy rates) are briefly described. This article ends with a summary of the main lessons learned from PR applied to MRS to date.
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Pattern Recognition with Slow Feature Analysis

Pattern Recognition with Slow Feature Analysis

Slow feature analysis (SFA) is a new unsupervised algorithm to learn nonlinear functions that extract slowly varying signals from time series (Wiskott and Sejnowski, 2002). SFA was originally conceived as a way to learn salient features of time series in a way invariant to frequent transformations (see also Wiskott, 1998). Such a representation would of course be ideal to perform classification in pattern recognition problems. Most such problems, however, do not have a temporal structure, and it is thus necessary to reformulate the algorithm. The basic idea is to construct a large set of small time series with only two elements chosen from patterns that belong to the same class (Fig. 1a). In order to be slowly varying, the functions learned by SFA will need to respond similarly to both elements of the time series (Fig. 1b), and therefore ignore the transformation between the individual patterns. As a consequence, patterns corresponding to the same class will cluster in the feature space formed by the output signals of the slowest functions, making it suitable to perform classification with simple techniques such as Gaussian classifiers. It is possible to show that in the ideal case the output of the functions is constant for all patterns of a given class, and that the number of relevant functions is small (Sect. 3). Notice that this approach does not use any a priori knowledge of the problem. SFA simply extracts the information about relevant features and common transformations by comparing pairs of patterns.
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Student Performance and Academic Pattern Analysis

Student Performance and Academic Pattern Analysis

By integrating and applying data analysis over the student academic data, we are getting the analysed reports of the academic progress. With the help of these reports we can tract down the progress curve of the student and then can take the necessary steps required to improve the curve. This analysis also helps us to get an exact figure of the student’s involvements in the academic programs.

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Perturbation analysis: a complex systems pattern

Perturbation analysis: a complex systems pattern

One purpose for which the Perturbation Analysis pattern may be applied is estimating the largest Lyapunov exponent in order to determine the sta- bility of an attractor. The Lyapunov exponents of a system measure the exponential rate of convergence or divergence of two nearby trajectories. If the largest Lyapunov is negative, the attractor is stable. If the largest Lyapunov is positive, the attractor is chaotic, and the magnitude of the ex- ponent gives an indication of the time scale on which the future behaviour of the system becomes unpredictable.

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Kernel Methods for General Pattern Analysis

Kernel Methods for General Pattern Analysis

• Correlation, factor, cluster and discriminant analysis are just some of the types of pattern analysis tasks that can be performed on data as diverse as sequences, text, images, graph[r]

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Intelligent Pattern Mining and Data Clustering for Pattern Cluster Analysis using Cancer Data

Intelligent Pattern Mining and Data Clustering for Pattern Cluster Analysis using Cancer Data

The system uses three metrics to analyze the performance of the simultaneous clustering scheme. They are memory, process time and fitness point. The simultaneous clustering scheme requires 40% more memory than the data-clustering scheme. The process time reduced 25% than the data clustering process. The fitness values show that the simultaneous clustering scheme produces more accurate clustering than data clustering scheme. The simultaneous pattern extraction and clustering system is implemented to perform the clustering process in minimum time period. The association rule mining techniques are used to fetch frequent items. The pattern extraction process uses the frequent items. The data-clustering scheme is integrated with the pattern clustering mechanism. The system uses the patient diagnosis report for the testing process. The system can be enhanced with the following features: The integrated pattern extraction and clustering scheme is implemented with the K-Means clustering algorithm. The genetic algorithm can be used in the future development. The system can be enhanced to perform the pattern extraction and clustering process on image data collections. The current system is implemented as a stand-alone application. The simultaneous clustering algorithm can be updated for distributed environment.
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Drastic reorganization of the bioconvection pattern of Chlamydomonas: quantitative analysis of the pattern transition response

Drastic reorganization of the bioconvection pattern of Chlamydomonas: quantitative analysis of the pattern transition response

Motile aquatic microorganisms are known to self-organize into bioconvection patterns. The swimming activity of a population of microorganisms leads to the emergence of macroscopic patterns of density under the influence of gravity. Although long-term development of the bioconvection pattern is important in order to elucidate the possible integration of physiological functions of individuals through bioconvection pattern formation, little quantitative investigation has been carried out. In the present paper, we present the first quantitative description of long-term behavior of bioconvection of Chlamydomonas reinhardtii, particularly focusing on the ‘pattern transition response’. The pattern transition response is a sudden breakdown of the steady bioconvection pattern followed by re-formation of the pattern with a decreased wavelength. We found three phases in the pattern formation of the bioconvection of C. reinhardtii: onset, steady-state 1 (before the transition) and steady-state 2 (after the transition). In onset, the wavelength of the bioconvection pattern increases with increasing depth, but not in steady-states 1 or 2. By means of the newly developed two-axis view method, we revealed that the population of C. reinhardtii moves toward the bottom of the experimental chamber just before the pattern transition. This indicates that the pattern transition response could be caused by enhancement of the gyrotaxis of C. reinhardtii as a result of the changes in the balance between the gravitactic and gyrotactic torques. We also found that the bioconvection pattern changes in response to the intensity of red-light illumination, to which C. reinhardtii is phototactically insensitive. These facts suggest that the bioconvection pattern has a potential to drastically reorganize its convection structure in response to the physiological processes under the influence of environmental cues.
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An analysis of cropping pattern in north-western india

An analysis of cropping pattern in north-western india

Analysis of cropping pattern reveals that the food grain crops specially cereals were dominant crops in the study area. As the cultivation of any crop in a region is mainly the outcome of various physical and socio-economic factors that reflects in the cropping pattern of that region. Different crops are grown in the study region, among all the crops, wheat, rice and Bajra were most important from areal point of view. Wheat crop ranks first in overall cropping pattern in Punjab and Haryana; while Bajra ranks first in Rajasthan state. In 1980-81, wheat occupied 41.58 percent and 27.08 percent area in Punjab and Haryana respectively while these percents were 44.57 and 38.5 in 2010-11. In case of Rajasthan percents of Bajra were 29 and 20.9 in 1980-81 and 2010-11 respectively. Rice had second rank in Punjab and Haryana. In 1980-81, rice occupied 17.48 percent area which increased to 35.88 percent in 2010-11 in Punjab while these percents were 8.86 and 19.1 in Haryana. In case of Rajasthan wheat (9.42%) had second rank in 1980-81 whereas rapeseed-mustard (10%) had second rank in 2010-11. Other important crops were cotton (6.13%) in Punjab; rapeseed-mustard (7.8%) and cotton (7.6%) in Haryana; wheat (9.53%), gram (4.75%) and maize (4.40%) in Rajasthan.
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