A cultural change to enable improved decision-making in forensic science: A six phased approach

There has been an increased engagement by researchers in understanding the decision-making processes that occur within forensic science. There is a rapidly growing evidence base underpinning our understanding of decision-making and human factors and this body of work is the foundation for achieving truly improved decision-making in forensic science. Such an endeavour is necessary to minimise the misinterpretation of scientific evidence and maximize the effectiveness of crime reconstruction approaches and their application within the criminal justice system. This paper proposes and outlines a novel six phased approach for how a broadening and deepening knowledge of decision-making in forensic science can be articulated and incorporated into the spheres of research, practice, education, and policy making within forensic science specifically, and the criminal justice system more generally. Phases 1 and 2 set out the importance of systematic examination of the decisions which play a role throughout forensic reconstruction and legal processes. Phase 3 focuses on how these decisions can, and should, be studied to understand the underlying mechanisms and contribute to reducing the occurrence of misleading decisions. Phase 4 highlights the ways in which the results and implications of this research should be communicated to the forensic community and wider criminal justice system. Lastly, the way in which the forensic science domain can move forwards in managing the challenges of human decision-making and create and embed a culture of acceptance and transparency in research, practice and education (learning and training) are presented in phases 5 and 6. A consideration of all 6 connected phases offers a pathway for a holistic approach to improving the transparency and reproducibility of decision making within forensic science

A cultural change to enable improved decision-making in forensic science: a six phased approach

There has been an increased engagement by researchers in understanding the decision-making processes that occur within forensic science. There is a rapidly growing evidence base underpinning our understanding of decision-making and human factors and this body of work is the foundation for achieving truly improved decision-making in forensic science. Such an endeavour is necessary to minimise the misinterpretation of scientific evidence and maximize the effectiveness of crime reconstruction approaches and their application within the criminal justice system. This paper proposes and outlines a novel six phased approach for how a broadening and deepening knowledge of decision-making in forensic science can be articulated and incorporated into the spheres of research, practice, education, and policy making within forensic science specifically, and the criminal justice system more generally. Phases 1 and 2 set out the importance of systematic examination of the decisions which play a role throughout forensic reconstruction and legal processes. Phase 3 focuses on how these decisions can, and should, be studied to understand the underlying mechanisms and contribute to reducing the occurrence of misleading decisions. Phase 4 highlights the ways in which the results and implications of this research should be communicated to the forensic community and wider criminal justice system. Lastly, the way in which the forensic science domain can move forwards in managing the challenges of human decision-making and create and embed a culture of acceptance and transparency in research, practice and education (learning and training) are presented in phases 5 and 6. A consideration of all 6 connected phases offers a pathway for a holistic approach to improving the transparency and reproducibility of decision making within forensic science

An overview of lidar imaging systems for autonomous vehicles

Lidar imaging systems are one of the hottest topics in the optronics industry. The need to sense the surroundings of every autonomous vehicle has pushed forward a race dedicated to deciding the final solution to be implemented. However, the diversity of state-of-the-art approaches to the solution brings a large uncertainty on the decision of the dominant final solution. Furthermore, the performance data of each approach often arise from different manufacturers and developers, which usually have some interest in the dispute. Within this paper, we intend to overcome the situation by providing an introductory, neutral overview of the technology linked to lidar imaging systems for autonomous vehicles, and its current state of development. We start with the main single-point measurement principles utilized, which then are combined with different imaging strategies, also described in the paper. An overview of the features of the light sources and photodetectors specific to lidar imaging systems most frequently used in practice is also presented. Finally, a brief section on pending issues for lidar development in autonomous vehicles has been included, in order to present some of the problems which still need to be solved before implementation may be considered as final. The reader is provided with a detailed bibliography containing both relevant books and state-of-the-art papers for further progress in the subject.Peer ReviewedPostprint (published version

SLAM research for port AGV based on 2D LIDAR

With the increase in international trade, the transshipment of goods at international container ports is very busy. The AGV (Automated Guided Vehicle) has been used as a new generation of automated container horizontal transport equipment. The AGV is an automated unmanned vehicle that can work 24 hours a day, increasing productivity and reducing labor costs compared to using container trucks. The ability to obtain information about the surrounding environment is a prerequisite for the AGV to automatically complete tasks in the port area. At present, the method of AGV based on RFID tag positioning and navigation has a problem of excessive cost. This dissertation has carried out a research on applying light detection and ranging (LIDAR) simultaneous localization and mapping (SLAM) technology to port AGV. In this master's thesis, a mobile test platform based on a laser range finder is developed to scan 360-degree environmental information (distance and angle) centered on the LIDAR and upload the information to a real-time database to generate surrounding environmental maps, and the obstacle avoidance strategy was developed based on the acquired information. The effectiveness of the platform was verified by the experiments from multiple scenarios. Then based on the first platform, another experimental platform with encoder and IMU sensor was developed. In this platform, the functionality of SLAM is enabled by the GMapping algorithm and the installation of the encoder and IMU sensor. Based on the established environment SLAM map, the path planning and obstacle avoidance functions of the platform were realized.Com o aumento do comércio internacional, o transbordo de mercadorias em portos internacionais de contentores é muito movimentado. O AGV (“Automated Guided Vehicle”) foi usado como uma nova geração de equipamentos para transporte horizontal de contentores de forma automatizada. O AGV é um veículo não tripulado automatizado que pode funcionar 24 horas por dia, aumentando a produtividade e reduzindo os custos de mão-de-obra em comparação com o uso de camiões porta-contentores. A capacidade de obter informações sobre o ambiente circundante é um pré-requisito para o AGV concluir automaticamente tarefas na área portuária. Atualmente, o método de AGV baseado no posicionamento e navegação de etiquetas RFID apresenta um problema de custo excessivo. Nesta dissertação foi realizada uma pesquisa sobre a aplicação da tecnologia LIDAR de localização e mapeamento simultâneo (SLAM) num AGV. Uma plataforma de teste móvel baseada num telémetro a laser é desenvolvida para examinar o ambiente em redor em 360 graus (distância e ângulo), centrado no LIDAR, e fazer upload da informação para uma base de dados em tempo real para gerar um mapa do ambiente em redor. Uma estratégia de prevenção de obstáculos foi também desenvolvida com base nas informações adquiridas. A eficácia da plataforma foi verificada através da realização de testes com vários cenários e obstáculos. Por fim, com base na primeira plataforma, uma outra plataforma experimental com codificador e sensor IMU foi também desenvolvida. Nesta plataforma, a funcionalidade do SLAM é ativada pelo algoritmo GMapping e pela instalação do codificador e do sensor IMU. Com base no estabelecimento do ambiente circundante SLAM, foram realizadas as funções de planeamento de trajetória e prevenção de obstáculos pela plataforma

RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing

In this work we present RAPID, a joint communication and radar (JCR) system based on next-generation IEEE 802.11ay WiFi networks operating in the 60 GHz band. In contrast to most existing approaches for human sensing at millimeter-waves, which employ special-purpose radars to retrieve the small-scale Doppler effect (micro-Doppler) caused by human motion, RAPID achieves radar-level sensing accuracy by retrofitting IEEE 802.11ay access points. For this, it leverages the IEEE 802.11ay beam training mechanism to accurately localize and track multiple individuals, while the in-packet beam tracking fields are exploited to extract the desired micro-Doppler signatures from the time-varying phase of the channel impulse response (CIR). The proposed approach enables activity recognition and person identification with IEEE 802.11ay wireless networks without requiring modifications to the packet structure specified by the standard. RAPID is implemented on an IEEE 802.11ay-compatible FPGA platform with phased antenna arrays, which estimates the CIR from the reflections of transmitted packets. The proposed system is evaluated on a large dataset of CIR measurements, proving robustness across different environments and subjects, and outperforming state-of-the-art sub-6 GHz WiFi sensing techniques. Using two access points, RAPID reliably tracks multiple subjects, reaching activity recognition and person identification accuracies of 94% and 90%, respectively.Comment: 16 pages, 18 figures, 4 table