As resident space object populations grow, and satellite propulsion capabilities improve, it will become increasingly challenging for space-reliant nations to maintain space situational awareness using current human-in-the-loop methods. This dissertation develops several real-time adaptive approaches to autonomous sensor network management for tracking multiple maneuvering and non-maneuvering satellites with a diversely populated Space Object Surveillance and Identification network. The proposed methods integrate suboptimal Partially Observed Markov Decision Processes (POMDPs) with covariance inflation or multiple model adaptive estimation techniques to task sensors and maintain viable orbit estimates for all targets. The POMDPs developed in this dissertation use information-based and system-based metrics to determine the rewards and costs associated with tasking a specific sensor to track a particular satellite. Like in real-world situations, the population of target satellites vastly outnumbers the available set of sensors. Robust and adaptable tasking algorithms are needed in this scenario to determine how and when sensors should be tasked. The strategies developed in this dissertation successfully track 207 non-maneuvering and maneuvering spacecraft using only 24 ground and space-based sensors. The results show that multiple model adaptive estimation coupled with a multi-metric, suboptimal POMDP can effectively and efficiently task a diverse network of sensors to track multiple maneuvering spacecraft, while simultaneously monitoring a large number of non-maneuvering objects. Overall, this dissertation demonstrates the potential for autonomous and adaptable sensor network command and control for real-world space situational awareness. / Ph. D. / As the number of spacecraft in orbit increase, and satellite propulsion capabilities improve, it will become increasingly difficult for space-reliant nations to keep track of every object orbiting earth using human-in-the-loop methods. Already, the population of target satellites vastly outnumbers the available set of sensors. At any given time, a given network of sensors cannot observe every satellite in orbit, and must manage the available sensors effectively to keep track of every object of interest. The ability to maintain actionable knowledge of every orbiting object of interest is known as space situational awareness. Conventional tracking processes have generally not changed for decades, and were designed when there were far fewer satellites in orbit with little or no ability to maneuver. These methods involve large numbers of operators and engineers who schedule a network of sensors under the assumption that the satellites will not unexpectedly change their orbits for long periods of time. In the near future, traditional space surveillance approaches will become insufficient at maintaining space situational awareness, particularly if more satellites conduct unanticipated maneuvers. This dissertation develops several real-time approaches for controlling a diverse network of ground and space-based sensors that remove the need for human intervention. These fully computer-based command and control processes adapt to dynamic situations and automatically task sensors to rapidly track multiple maneuvering and non-maneuvering satellites. The decision processes used to determine which sensors should be tasked to observe a particular spacecraft compare the amount of information that can be collected in a single observation and the workload a sensor must execute to collect the observation. The command and control strategies developed in this dissertation successfully track 207 non-maneuvering and maneuvering spacecraft using only 24 ground and space-based sensors. The results show that adaptive, fully autonomous sensor network control processes can effectively and efficiently task a diverse set of sensors to track multiple maneuvering spacecraft, while simultaneously monitoring a large number of non-maneuvering objects. Overall, this dissertation demonstrates the potential for adaptive, computer-based sensor network command and control for real-world space situational awareness. This research was supported by the Virginia Tech New Horizons Graduate Scholar Program, the Ted and Karyn Hume Center for National Security and Technology, the DARPA Hallmark program, and the U.S. Joint Warfare Analysis Center.

It is estimated that as many as 2.7 million crashes occur each year at intersections or are intersection related; resulting in over 8500 fatalities each year. These statistics have prompted government and corporate sponsored research into collision countermeasure systems that can enhance safety at intersections. Researchers are investigating technologies to provide an infrastructure-based or infrastructure-cooperative Intersection Decision Support (IDS) systems. Such systems would use pre-specified algorithms to identify drivers that have a high likelihood of violating the traffic signal and thus increase the risk of a collision. The system would subsequently warn the violating driver to stop though an in-vehicle or infrastructure-mounted interface. An IDS algorithm must be designed to provide adequate time for the driver to perceive, react, and stop the vehicle, while simultaneously avoiding a high false alarm rate. Prior to developing these algorithms, scientists must understand how drivers respond to traffic signals. Little research has focused on the influence of driver state on red-light running behavior or methods for distinguishing red light violators from non-violators. The objective of the present study was to define trends associated with intersection crossings under different driver states and to explore the point detection method of predicting red light running upstream of the intersection. This was accomplished through a test-track mixed-factor experiment with 28 participants. Each participant experienced a baseline (complete a full stop at the red light), distracted (misses signal phase change due to inattention), and willful (driver knowingly makes a late crossing in an attempt to 'beat the light') driver state conditions. To provide the opportunity for red-light running behavior from participants, the amber change interval began at five different distances from the intersection. These distances were located near and within the dilemma zone, a region in which drivers have a difficult time deciding whether to go or to stop. Data collected from in-vehicle sensors was statistically analyzed to determine significant effects between driver states, and to investigate point detection algorithms. / Master of Science

[ES] Esta tesis doctoral propone el desarrollo de una arquitectura para sistema de detección de actividades criminales en vídeo aplicado a sistemas de mando y control para seguridad ciudadana. Este sistema está basado en la técnica de Deep Learning Faster R-CNN y tiene el novedoso enfoque de tratar las acciones criminales como los hurtos callejeros, en donde pueden ser identificados objetos como evidencia en una escena de vídeo. Esta tesis muestra el desarrollo de dicha aplicación, que demuestra ser efectiva, identificando la manera de reducir el costo computacional del análisis de vídeo cuadro a cuadro obteniendo rendimientos congruentes con las tasas de cuadros por segundo generados por cámaras de sistema de vídeo vigilancia ciudadana. También es objeto de estudio una posible implementación en el sistema de seguridad ciudadana de la Policía Nacional de Colombia. / [EN] This doctoral thesis proposes the development of a system to detect criminal activities in video applied to command and control systems for citizen security. This system is based on the Deep Learning technique called Faster R-CNN and has the novel approach of treating criminal actions like street thefts as objects that can be identified in a video scene. This thesis shows the development of this application and the way to reduce the computational cost of the video analysis frame by frame, obtaining performances congruent with the frame rate generated by citizen video surveillance system cameras. There is also a possible implementation in the citizen security system of the National Police of Colombia is being studied. / [CA] Esta tesi doctoral proposa el desenrotllament d'una arquitectura per a sistema de detecció d'activitats criminals en vídeo aplicat a sistemes de comandament i control per a seguretat ciutadana. Este sistema està basat en la tècnica de Deep Learning Faster R-CNN i té el nou enfocament de tractar les accions criminals com les afanades guies de carrers com a objectes que poden ser identificats en una escena de vídeo. Esta tesi mostra el desenrotllament de la dita aplicació, que demostra ser efectiva, identificant la manera de reduir el cost computacional de l'anàlisi de vídeo quadro a quadro obtenint rendiments congruents amb les taxes de cuados per segon generats per cambres de sistema de vídeo vigilància ciutadana. També s'estudia una possible implementació en el sistema de seguretat ciutadana de la Policia Nacional de Colòmbia. / Suárez Páez, JE. (2020). Arquitectura de detección de actividades criminales basada en análisis de vídeo en tiempo real [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/153162

Software is the crux of many commercial, industrial and military systems. The software systems need to be very reliable especially in case of safety critical systems. Unmanned Aerial Vehicle (UAV) and manned aircraft are safety critical systems and hence failures related to software or software-hardware interaction leads to huge problems. The software systems need to be certified before they are deployed. Even after being certified several accidents and incidents have occurred and are occurring. The software errors can occur during any phase of software development. The reliability of the software is enhanced using the verification process. Runtime monitoring has various advantages over testing and model checking. Hence this thesis work explores runtime monitoring of UAV. The runtime monitoring shall verify the run of the current system state. The runtime monitoring shall monitor the health of the UAV and shall report to the operator about its status. The software faults and errors if not prevented shall lead to software failure. UAV lacks the situational awareness due to absence of pilot onboard. This motivated to use runtime monitor to enhance the situation awareness. The runtime monitor shall detect the software errors and avoid failures. This monitor shall also enhance the situational awareness of the remote operator. The runtime monitor that enhance situation awareness shall not only be applicable to specific UAV but this shall be applicable to all the UAV’s. Hence this work provides an independent Generic Runtime Monitor (GRM) to enhance the situation awareness. The runtime monitor has various methods but using formal specifications in specific using Linear Temporal Logic(LTL) to generate monitor is considered in this work. Runtime monitoring makes UAV more safe and at the same time reduces the costs as it verifies only the current run of the system state by providing a detection of critical errors. The situation awareness includes functional and environmental states that remote pilot shall not be aware of. The architecture plays vital role for the system design. GRM architecture is one such architecture which chalks out the overall independent system design for the runtime monitoring of the UAV system. This architecture is an extensible one. The generic requirements were elicited from different sources such as Aircraft Incidents and Accidents, Boeing Aero Magazine, Autonomous Rotorcraft Testbed for Intelligent Systems (ARTIS) requirements, generic Autonomy Levels for Unmanned Rotorcraft Systems (ALFURS) framework etc. The situation awareness can be categorized into three levels namely perception, comprehension and projection. The requirements were elicited for all the three levels of situation awareness. These requirements further formalized using temporal logics. The formalized requirements further translated into state automaton automatically.

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2017-05-23T16:15:28Z No. of bitstreams: 1 Leandro Ferreira D’Avila_.pdf: 2516496 bytes, checksum: ce577684d579d6f920b919aadf28bdb7 (MD5) / Made available in DSpace on 2017-05-23T16:15:28Z (GMT). No. of bitstreams: 1 Leandro Ferreira D’Avila_.pdf: 2516496 bytes, checksum: ce577684d579d6f920b919aadf28bdb7 (MD5) Previous issue date: 2017-02-22 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / PROSUP - Programa de Suporte à Pós-Gradução de Instituições de Ensino Particulares / Diariamente, desenvolvedores de software precisam se envolver com atividades de manutenção para adaptar aplicações existentes a novos cenários e necessidades, como por exemplo, novas funcionalidades, correções de defeitos e requerimentos legais. Entretanto, algumas questões organizacionais podem interferir nas atividades dos desenvolvedores impactando na qualidade e manutenabilidade do software produzido. Grande volume de documentação obsoleta, dificuldades na utilização desta documentação, dependências entre módulos de software e especialistas que deixam as empresas levando o conhecimento de determinados módulos e/ou sistemas são fatores determinantes para o sucesso dos projetos. Uma das formas de mitigar o impacto destas questões seria a disponibilização de informações úteis referentes aos módulos ou artefatos de software de forma qualitativa. A disponibilização destas informações propicia um melhor entendimento do desenvolvedor em relação aos aspectos que cercam o software e o seu ambiente. De acordo com a natureza das informações disponibilizadas, os desenvolvedores podem adquirir informações relevantes sobre o softwareem questão. Essa dissertação apresenta o SW-Context, um modelo que permite a combinação de diferentes informações relacionadas a artefatos de software, a fim de aprimorar a consciência situacional dos desenvolvedores nas atividades de desenvolvimento e manutenção. Desta forma, os principais desafios do modelo são: a definição de quais informações devem compor o contexto para software, o armazenamento estruturado destas informações em históricos de contextos e, finalmente, a análise e disponibilização destas informações de contexto, de forma que possam auxiliar a atividade de desenvolvimento e manutenção de software, utilizando o conceito SoftwareAnalytics. Foi implementado um protótipo contendo os principais conceitos do modelo proposto. Este protótipo utilizou as informações contextuais de aplicações reais de uma empresa de desenvolvimento de software e foi avaliado através de um estudo de caso, onde 12 desenvolvedores o utilizaram pelo período de um mês em suas atividades diárias. Ao final deste período, os desenvolvedores responderam um questionário que abordou a utilidade da ferramenta e a facilidade de uso percebida. A avaliação do modelo obteve respostas com percentuais satisfatórios tanto em relação à facilidade de uso percebida quanto à utilidade do sistema. Pode-se avaliar que a consolidação das informações contextuais em um local único e a disponibilização qualitativa das informações correlacionadas, através de dashboard, atingiu o objetivo de melhorar a consciência situacional dos desenvolvedores nas atividades de manutenção. / Developers need to deal recurrently with the maintenance activities on existing applications in order to adapt them to new scenarios and needs, for example, new features, bug fixing and legal changes. Besides that, developers often deal with organization factors with a potential impact on the success or failure of software development projects. Some of these organization factors are: large amount of old poorly documented software, many interdependencies between software modules and expert developers who left the company. A way to mitigate the impact of these factors on software correctness and maintainability can be providing useful information regarding the context of code or application under development using the analytics approach. The availability of this information provides a better understanding of the developer in relation to issues surrounding the software and its environment. SW-Context aims to allow a combination of different information related to software artifacts in order to improve the situational awareness of developers on development and maintenance activities. On this way, the main challenges of the model are: a definition of what information must compose software context, structured storage of the contextual information and, finally, the analysis and availability of this context information in a way to help the development and maintenance activities, using the Software Analytics concept. A prototype was implemented containing the main concepts of the proposed model. This prototype was prepared with the contextual information of actual applications under development by a software company and the prototype was evaluated through a case study, where 12 developers used it in their daily activities. By the end of this period, the developers responded a questionnaire, in which the usefulness and the ease of use were measured. The evaluation of the model obtained answers with percentage well placed both in relation to the ease of use as to the usefulness of the system. It can be considered that the consolidation of the contextual information in a single location and the availability of this correlated information in a graphical way, through a dashboard, reached the objective of improving the situational awareness of software developers in maintenance activities.

A long standing debate in psychological science is whether the face signals specific emotions. Basic emotion theory presupposes that there are coordinated facial musculature movements that individuals can identify as relating to a core set of basic emotions. In opposition to this view, the constructionist theory contends that the perception of emotion is a far more intricate process involving semantic knowledge and arousal states. The aim of the current investigation was to explore some of the questions at the crux of this debate. We showed participants video clips of real people in real time, where the face was in motion, much as in everyday life. In study 1 we directly manipulated the effects of context to determine what influences emotion perception – situational information or the face? In support of the basic emotion view, participants identified displays of happiness, anger and sadness irrespective of contextual information provided. Importantly, participants also rated one set of facial movements as more intensely expressing a ‘sad’ face. Study 1 also demonstrated unique context effects in partial support for the constructionist view, suggesting that for some facial expressions, the role of context may be important. In study 2, we explored the possible effects that language has on the perception of emotion. In the absence of linguistic cues, participants used significantly more ‘happy’ and ‘sad’ words to label the basic emotion prototype for happiness and for the ‘sad’ face introduced in study 1. Overall, findings from these studies suggest that although contextual cues may be important for specific scenarios, the face is dominant to the layperson when inferring the emotional state of another.

At the start of the 21st century, the topic of complexity remains a formidable challenge in engineering, science and other aspects of our world. It seems that when disaster strikes it is because some complex and unforeseen interaction causes the unfortunate outcome. Why did the financial system of the world meltdown in 2008-2009? Why are global temperatures on the rise? These questions and other ones like them are difficult to answer because they pertain to contexts that require lengthy descriptions. In other words, these contexts are complex. But we as human beings are able to observe and recognize this thing we call 'complexity'. Furthermore, we recognize that there are certain elements of a context that form a system of complex interactions - i.e., a complex system. Many researchers have even noted similarities between seemingly disparate complex systems. Do sub-atomic systems bear resemblance to weather patterns? Or do human-based economic systems bear resemblance to macroscopic flows? Where do we draw the line in their resemblance? These are the kinds of questions that are asked in complex systems research. And the ability to recognize complexity is not only limited to analytic research. Rather, there are many known examples of humans who, not only observe and recognize but also, operate complex systems. How do they do it? Is there something superhuman about these people or is there something common to human anatomy that makes it possible to fly a plane? - Or to drive a bus? Or to operate a nuclear power plant? Or to play Chopin's etudes on the piano? In each of these examples, a human being operates a complex system of machinery, whether it is a plane, a bus, a nuclear power plant or a piano. What is the common thread running through these abilities? The study of situational awareness (SA) examines how people do these types of remarkable feats. It is not a bottom-up science though because it relies on finding general principles running through a host of varied human activities. Nevertheless, since it is not constrained by computational details, the study of situational awareness provides a unique opportunity to approach complex tasks of operation from an analytical perspective. In other words, with SA, we get to see how humans observe, recognize and react to complex systems on which they exert some control. Reconciling this perspective on complexity with complex systems research, it might be possible to further our understanding of complex phenomena if we can probe the anatomical mechanisms by which we, as humans, do it naturally. At this unique intersection of two disciplines, a hybrid approach is needed. So in this work, we propose just such an approach. In particular, this research proposes a computational approach to the situational awareness (SA) of complex systems. Here we propose to implement certain aspects of situational awareness via a biologically-inspired machine-learning technique called Hierarchical Temporal Memory (HTM). In doing so, we will use either simulated or actual data to create and to test computational implementations of situational awareness. This will be tested in two example contexts, one being more complex than the other. The ultimate goal of this research is to demonstrate a possible approach to analyzing and understanding complex systems. By using HTM and carefully developing techniques to analyze the SA formed from data, it is believed that this goal can be obtained.

Thesis (Ph.D.)--Mississippi State University. Department of Psychology. / Title from title screen. Includes bibliographical references.

Understanding the goal and behaviour of end users is difficult. Moreover, silo thinking is common in large corporations. Needed are methods that support an understanding of the users’ needs as well as improve communication among development departments that require different needs and information. A Persona is a fictional person based on interviews or other data collecting methods, that describes the users’ needs, goals and issues with the product. This thesis describes a Persona method specifically designed for development processes in large corporations. Twenty-nine interviews were made with a standard Persona method in several countries in Europe with end users for a large forklift truck company. Subsequent analysis with a focus to make a method more affective was performed. The resulting method, Quick Persona Method (QPM) presents an affective process involving knowledge sharing among departments. Furthermore, the method is expected to break a silo thinking culture, to be time efficient, usable, understandable and improve communication among departments.

The current driving simulator study investigates the effect of 2 distinct levels of distraction on a drivers’ situational awareness and latent and inherent hazard anticipation. In this study, rear-end crashes were used as the primary crash configuration to target a specific category of crashes due to distraction. The two types of task load used in the experiment was a cognitive distraction (mock cell-phone task) & visual distraction (I-pad task). Forty-eight young participants aged 18-25 years navigated 8 scenarios each in a mixed subject design with task load (cognitive or visual distraction) as a between-subject variable and the presence/absence of distraction representing the within-subject variable. All participants drove 4 scenarios with a distraction and 4 scenarios without any distraction. Physiological variables in the form of Heart rate and heart rate variability was collected for each participant during the practice drives and after each of the 8 experimental drives. After the completion of each experimental drive, participants were asked to fill up a NASA TLX questionnaire which quantifies the overall task load experienced by giving it a score between 1 and 100, where higher scores translate to higher perceived task load. Eye-movements were also recorded for the proportion of latent and inherent hazards anticipated and mitigated for all participants. Standard vehicle data (velocity, acceleration & lane offset) were also collected from the simulator for each participants’ each drive. Analysis of data showed that there was a significant difference in velocity, lane offset and task load index scores across the 2 groups (between-subject factors). The vehicle data, heart rate data and TLX data was analyzed using Mixed subject ANOVA. There was also a logistic regression model devised which showed significant effects of velocity, lane offset, TLX scores and age on a participants’ hazard anticipation abilities. The findings have a major practical implication in reducing drivers’ risk of fatal, serious or near crashes.