Spectroscopic (analytical) approach to gunshot residue analysis for shooting distance estimation: a systematic review

Background The determination of the shooting distance using gunshot residue (GSR) analysis is crucial in the investigation and reconstruction of firearm-related crimes. However, the conventional chemographic method for GSR analysis is destructive and has limited sensitivity and selectivity. While the spectroscopic method has potential in GSR analysis for crime investigation, there is a current lack of consistency in the spectroscopic results obtained for shooting distance estimation via GSR analysis. Addressing such limitations will enhance the forensic capabilities of law enforcement and provide an added advantage to crime laboratories during an investigation. It will also reinforce the use of such spectroscopic data in a criminal investigation. Main text We obtained all peer-reviewed articles relevant to shooting distance estimation from searching Scopus, Web of Science, PubMed, and Google Scholar databases. We specifically searched the databases using the keywords “shooting distance,” “range of fire,” “gunshot residue,” “firearm discharge residue,” and “firearm-related crime” and obtained 3811 records. We further filtered these records using a combination of two basic keywords “gunshot residue” and “shooting distance estimations” yielding 108 papers. Following a careful evaluation of the titles, abstracts, and full texts, 40 original peer-reviewed articles on shooting distance estimation via GSR analysis were included in the study. The forgoing included additional sources (n = 5) we obtained from looking through the reference lists of the forensic articles we found. Short conclusion This paper discusses the current scope of research concerning the chemographic and spectroscopic analysis of GSR for shooting distance estimation. It also examines the challenges of these techniques and provides recommendations for future research.

Haar Cascade dan Algoritma Eignface Untuk Sistem Pembuka Pintu Otomatis

Teknologi diciptakan untuk mempermudah manusia dalam melakukan segala pekerjaan dan aktifitasnya, termasuk dalam hal mengakses pintu. Menggunakan teknologi pengolahan citra, wajah merupakan salah satu alternatif yang bisa digunakan untuk mengakses pintu dan mengamankannya dari orang yang tidak bertanggung jawab. Hal ini dikarenakan wajah setiap manusia memiliki pola yang berbeda-beda yang bisa ditransformasikan menjadi citra digital dan diolah mengunakan algoritma pengolahan citra. Dalam penelitian ini, mengkombinasikan haar cascade dan algoritma eigenface untuk mengolah citra wajah. Hasil dari pengolahan citra tersebut digunakan untuk menentukan hak akses dalam mengakses pintu, untuk kemudian diintegrasikan ke mikrokontroller, sehingga pintu dapat terbuka otomatis. Penelitian ini menghasilkan prototype system pembuka pintu otomatis dengan pengenalan wajah sebagai penentu hak aksesnya. Dari hasil penelitian, algoritma eigenface tidak dapat bekerja pada pencahayaan 0 lux hingga 8 lux dalam jarak 20 cm hinga 60 cm yaitu menghasilkan akurasi 0%, sedangkan pada pencahayaan 36 lux sampai 44 lux dan 160 lux sampai172 lux algoritma eigenface bekerja dengan baik dengan jarak pengambilan gambar 20-60 cm dengan akurasi 80%. Technology was created to make it easier for humans to do all their work and activities, including accessing doors. Using image processing technology, faces are an alternative that can be used to access doors and secure them from irresponsible people. This is because the face of every human being has a different pattern that can be transformed into a digital image and processed using an image processing algorithm. In this research, combining haar cascade and eigenface algorithm to processing face images. The results of the image processing are used to determine access rights in accessing the door, and then integrated into the microcontroller, so that the door can be opened automatically. This research produces a prototype automatic door opening system with face recognition as a determinant of access rights. From the results of the study, the eigenface algorithm cannot work at 0 lux to 8 lux lighting within a distance of 20 cm to 60 cm which produces 0% accuracy, while at 36 lux to 44 lux and 160 lux to 172 lux lighting the eigenface algorithm works well with a shooting distance of 20 cm to 60 cm with 80% accuracy.

The effect of type of firearm and shooting distance on pattern distribution, particle dispersion and amount of gunshot residue

Background Evidence can be defined as the object’s availability and/or information that indicates whether a belief or proposition is true or valid. Gunshot residue (GSR) is an important evidence that can serve many roles in ballistic investigation such as shooting distance, type of firearm and ammunition used in shooting related to GSR. However, due to minimal amount of GSR that can be found in crime scene, suitable methods and technique are required in order to obtain the information from the evidence. This action is also known as evidence recovery. When a firearm is fired, soot or particles are discharged from any opening of the firearm and deposited at the vicinity of point of shooting. Results This study emphasized on the examination of the soot/particles produced and pattern distribution of GSR deposited on white cotton cloth target at varying shooting distances (from 3 to 50 cm) using a video spectral comparator. Pattern distribution and GSR particle density are the main factors in determining the shooting distances in clothing. Principle component analysis (PCA) and hierarchical clustering analysis (HCA) were used to classify firearms; the differences in the GSR pattern distribution are highly recognizable. This study showed that the relationship between the GSR particle dispersion and shooting distance was proportionally linear. The results obtained from the shooting test showed that the diameter of GSR distribution and the amount of residues being deposited from shots fired decreased at distances greater than 21 cm. Conclusion This study will help the investigators in determining the shooting distances and evaluating the firearms used. There is a promising method for examination of GSR pattern on the target material which is also important for firing distance estimation.

Deep learning in forensic gunshot wound interpretation—a proof-of-concept study

While the applications of deep learning are considered revolutionary within several medical specialties, forensic applications have been scarce despite the visual nature of the field. For example, a forensic pathologist may benefit from deep learning-based tools in gunshot wound interpretation. This proof-of-concept study aimed to test the hypothesis that trained neural network architectures have potential to predict shooting distance class on the basis of a simple photograph of the gunshot wound. A dataset of 204 gunshot wound images (60 negative controls, 50 contact shots, 49 close-range shots, and 45 distant shots) was constructed on the basis of nineteen piglet carcasses fired with a .22 Long Rifle pistol. The dataset was used to train, validate, and test the ability of neural net architectures to correctly classify images on the basis of shooting distance. Deep learning was performed using the AIDeveloper open-source software. Of the explored neural network architectures, a trained multilayer perceptron based model (MLP_24_16_24) reached the highest testing accuracy of 98%. Of the testing set, the trained model was able to correctly classify all negative controls, contact shots, and close-range shots, whereas one distant shot was misclassified. Our study clearly demonstrated that in the future, forensic pathologists may benefit from deep learning-based tools in gunshot wound interpretation. With these data, we seek to provide an initial impetus for larger-scale research on deep learning approaches in forensic wound interpretation.

DIC-Based Operational Modal Analysis of Bridges

A new method has been proposed to identify the natural frequencies and mode shapes of a bridge model, in which the digital image correlation (DIC) technique is used to track the dynamic displacement. A key issue in vibration-based damage detection for a bridge is to determine its modal parameters. It is difficult to use traditional acceleration sensors to obtain the accurate mode shapes of bridges as the sensors are only deployed on a few measurement points of the bridges. In this article, the DIC technique is used to capture the movement of the entire experimental bridge model. A steel truss is used as a bridge model and stimulated by a hammer; its dynamic displacement is recorded by using a digital video camera. The correlation analysis is used to track the displacement of the points of interest, and their displacement time histories are inputted into a modal analysis system; the natural frequencies and mode shapes of the bridge model were obtained by both operational modal analysis (OMA) and traditional experimental modal analysis (EMA) methods. (1) The DIC results are compared with those obtained by a traditional acceleration sensor-based method; the natural frequencies obtained by the two measurement methods are very close. (2) The DIC results are sensitive to the amplitude of the measured displacement and the shooting distance; small displacement amplitudes and long shooting distance may result in the low quality of the measured time-history curves, and low-frequency noise signals might be observed in their power spectral density (PSD) curves, while they can be easily solved by the filtering method in this article. (3) In addition, the first frequencies obtained by EMA and OMA are very close, which validates the applicability of the DIC measurement under ambient excitation. The research has illustrated the feasibility of the DIC method for obtaining the modal parameters of the bridges.

Improved FCM algorithm for fisheye image cluster analysis for tree height calculation

<abstract> <p>The height of standing trees is an important index in forestry research. This index is not only hard to measure directly but also the environmental factors increase the measurement difficulty. Therefore, the measurement of the height of standing trees is always a problem that experts and scholars are trying to improve. In this study, improve fuzzy c-means algorithm to reduce the calculation time and improve the clustering effect, used on this image segmentation technology, a highly robust non-contact measuring method for the height of standing trees was proposed which is based on a smartphone with a fisheye lens. While ensuring the measurement accuracy, the measurement stability is improved. This method is simple to operate, just need to take a picture of the standing tree and determine the shooting distance to complete the measurement. The purpose of the fisheye lens is to ensure that the tree remains intact in the photograph and to reduce the shooting distance. The results of different stability experiments show that the measurement error ranged from -0.196m to 0.195m, and the highest relative error of tree measurement was 3.05%, and the average relative error was 1.45%. Analysis shows that compared with previous research, this method performs better at all stages. The proposed approach can provide a new way to obtain tree height, which can be used to analyze growing status and change in contrast height because of high accuracy and permanent preservation of images.</p> </abstract>