Multimedia Fusion for Public Security in Heterogeneous Sensor Networks

Public security is a widespread disastrous phenomenon that constitutes a grave threat. Although information fusion of video sensor networks for public security has been studied extensively, multimedia fusion in heterogeneous sensor networks or its application in public security remains a challenge and central goal in the field of information fusion. In this study, to realize the detection, monitoring, and intelligent alarm of such hazards, we develop a graph-based real-time schema for studying the dynamic structure of heterogeneous sensors for public security. In the proposed schema, data fusion algorithms based on data-driven aspects of fusion are explored to locate the optimal sensing ranges of sensor nodes in a network with heterogeneous targets. In addition, we propose a framework incorporating useful contextual and temporal cues for public security alarm, explore its conceptualizations, benefits, and challenges, and analyze the correlations of the target motion elements in the multimedia sensor stream. The experimental results show that the new method offers a better way of intelligent alarm that cannot be achieved by existing schemes

Wireless Sensor Networks And Data Fusion For Structural Health Monitoring Of Aircraft

This thesis discusses an architecture and design of a sensor web to be used for structural health monitoring of an aircraft. Also presented are several prototypes of critical parts of the sensor web. The proposed sensor web will utilize sensor nodes situated throughout the structure. These nodes and one or more workstations will support agents that communicate and collaborate to monitor the health of the structure. Agents can be any internal or external autonomous entity that has direct access to affect a given system. For the purposes of this document, an agent will be defined as an autonomous software resource that has the ability to make decisions for itself based on given tasks and abilities while also collaborating with others to find a feasible answer to a given problem regarding the structural health monitoring system. Once the agents have received relevant data from nodes, they will utilize applications that perform data fusion techniques to classify events and further improve the functionality of the system for more accurate future classifications. Agents will also pass alerts up a self-configuring hierarchy of monitor agents and make them available for review by personnel. This thesis makes use of previous results from applying the Gaia methodology for analysis and design of the multiagent system

Wireless Sensor Networks And Data Fusion For Structural Health Monitoring Of Aircraft

This thesis discusses an architecture and design of a sensor web to be used for structural health monitoring of an aircraft. Also presented are several prototypes of critical parts of the sensor web. The proposed sensor web will utilize sensor nodes situated throughout the structure. These nodes and one or more workstations will support agents that communicate and collaborate to monitor the health of the structure. Agents can be any internal or external autonomous entity that has direct access to affect a given system. For the purposes of this document, an agent will be defined as an autonomous software resource that has the ability to make decisions for itself based on given tasks and abilities while also collaborating with others to find a feasible answer to a given problem regarding the structural health monitoring system. Once the agents have received relevant data from nodes, they will utilize applications that perform data fusion techniques to classify events and further improve the functionality of the system for more accurate future classifications. Agents will also pass alerts up a self-configuring hierarchy of monitor agents and make them available for review by personnel. This thesis makes use of previous results from applying the Gaia methodology for analysis and design of the multiagent system

Algorithms for sensor validation and multisensor fusion

Existing techniques for sensor validation and sensor fusion are often based on analytical sensor models. Such models can be arbitrarily complex and consequently Gaussian distributions are often assumed, generally with a detrimental effect on overall system performance. A holistic approach has therefore been adopted in order to develop two novel and complementary approaches to sensor validation and fusion based on empirical data. The first uses the Nadaraya-Watson kernel estimator to provide competitive sensor fusion. The new algorithm is shown to reliably detect and compensate for bias errors, spike errors, hardover faults, drift faults and erratic operation, affecting up to three of the five sensors in the array. The inherent smoothing action of the kernel estimator provides effective noise cancellation and the fused result is more accurate than the single 'best sensor'. A Genetic Algorithm has been used to optimise the Nadaraya-Watson fuser design. The second approach uses analytical redundancy to provide the on-line sensor status output μH∈[0,1], where μH=1 indicates the sensor output is valid and μH=0 when the sensor has failed. This fuzzy measure is derived from change detection parameters based on spectral analysis of the sensor output signal. The validation scheme can reliably detect a wide range of sensor fault conditions. An appropriate context dependent fusion operator can then be used to perform competitive, cooperative or complementary sensor fusion, with a status output from the fuser providing a useful qualitative indication of the status of the sensors used to derive the fused result. The operation of both schemes is illustrated using data obtained from an array of thick film metal oxide pH sensor electrodes. An ideal pH electrode will sense only the activity of hydrogen ions, however the selectivity of the metal oxide device is worse than the conventional glass electrode. The use of sensor fusion can therefore reduce measurement uncertainty by combining readings from multiple pH sensors having complementary responses. The array can be conveniently fabricated by screen printing sensors using different metal oxides onto a single substrate

Technologies for safe and resilient earthmoving operations: A systematic literature review

Resilience engineering relates to the ability of a system to anticipate, prepare, and respond to predicted and unpredicted disruptions. It necessitates the use of monitoring and object detection technologies to ensure system safety in excavation systems. Given the increased investment and speed of improvement in technologies, it is necessary to review the types of technology available and how they contribute to excavation system safety. A systematic literature review was conducted which identified and classified the existing monitoring and object detection technologies, and introduced essential enablers for reliable and effective monitoring and object detection systems including: 1) the application of multisensory and data fusion approaches, and 2) system-level application of technologies. This study also identified the developed functionalities for accident anticipation, prevention and response to safety hazards during excavation, as well as those that facilitate learning in the system. The existing research gaps and future direction of research have been discussed

Enhanced online programming for industrial robots

The use of robots and automation levels in the industrial sector is expected to grow, and is driven by the on-going need for lower costs and enhanced productivity. The manufacturing industry continues to seek ways of realizing enhanced production, and the programming of articulated production robots has been identified as a major area for improvement. However, realizing this automation level increase requires capable programming and control technologies. Many industries employ offline-programming which operates within a manually controlled and specific work environment. This is especially true within the high-volume automotive industry, particularly in high-speed assembly and component handling. For small-batch manufacturing and small to medium-sized enterprises, online programming continues to play an important role, but the complexity of programming remains a major obstacle for automation using industrial robots. Scenarios that rely on manual data input based on real world obstructions require that entire production systems cease for significant time periods while data is being manipulated, leading to financial losses. The application of simulation tools generate discrete portions of the total robot trajectories, while requiring manual inputs to link paths associated with different activities. Human input is also required to correct inaccuracies and errors resulting from unknowns and falsehoods in the environment. This study developed a new supported online robot programming approach, which is implemented as a robot control program. By applying online and offline programming in addition to appropriate manual robot control techniques, disadvantages such as manual pre-processing times and production downtimes have been either reduced or completely eliminated. The industrial requirements were evaluated considering modern manufacturing aspects. A cell-based Voronoi generation algorithm within a probabilistic world model has been introduced, together with a trajectory planner and an appropriate human machine interface. The robot programs so achieved are comparable to manually programmed robot programs and the results for a Mitsubishi RV-2AJ five-axis industrial robot are presented. Automated workspace analysis techniques and trajectory smoothing are used to accomplish this. The new robot control program considers the working production environment as a single and complete workspace. Non-productive time is required, but unlike previously reported approaches, this is achieved automatically and in a timely manner. As such, the actual cell-learning time is minimal

NETWORK TRAFFIC CHARACTERIZATION AND INTRUSION DETECTION IN BUILDING AUTOMATION SYSTEMS

The goal of this research was threefold: (1) to learn the operational trends and behaviors of a realworld building automation system (BAS) network for creating building device models to detect anomalous behaviors and attacks, (2) to design a framework for evaluating BA device security from both the device and network perspectives, and (3) to leverage new sources of building automation device documentation for developing robust network security rules for BAS intrusion detection systems (IDSs). These goals were achieved in three phases, first through the detailed longitudinal study and characterization of a real university campus building automation network (BAN) and with the application of machine learning techniques on field level traffic for anomaly detection. Next, through the systematization of literature in the BAS security domain to analyze cross protocol device vulnerabilities, attacks, and defenses for uncovering research gaps as the foundational basis of our proposed BA device security evaluation framework. Then, to evaluate our proposed framework the largest multiprotocol BAS testbed discussed in the literature was built and several side-channel vulnerabilities and software/firmware shortcomings were exposed. Finally, through the development of a semi-automated specification gathering, device documentation extracting, IDS rule generating framework that leveraged PICS files and BIM models.Ph.D

On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced

Context-Aware Sensor Fusion For Securing Cyber-Physical Systems

The goal of this dissertation is to provide detection and estimation techniques in order to ensure the safety and security of modern Cyber-Physical Systems (CPS) even in the presence of arbitrary sensors faults and attacks. We leverage the fact that modern CPS are equipped with various sensors that provide redundant information about the system\u27s state. In such a setting, the system can limit its dependence on any individual sensor, thereby providing guarantees about its safety even in the presence of arbitrary faults and attacks. In order to address the problem of safety detection, we develop sensor fusion techniques that make use of the sensor redundancy available in modern CPS. First of all, we develop a multidimensional sensor fusion algorithm that outputs a bounded fusion set which is guaranteed to contain the true state even in the presence of attacks and faults. Furthermore, we provide two approaches for strengthening sensor fusion\u27s worst-case guarantees: 1) incorporating historical measurements as well as 2) analyzing sensor transmission schedules (e.g., in a time-triggered system using a shared bus) in order to minimize the attacker\u27s available information and impact on the system. In addition, we modify the sensor fusion algorithm in order to provide guarantees even when sensors might experience transient faults in addition to attacks. Finally, we develop an attack detection technique (also in the presence of transient faults) in order to discard attacked sensors. In addition to standard plant sensors, we note that modern CPS also have access to multiple environment sensors that provide information about the system\u27s context (e.g., a camera recognizing a nearby building). Since these context measurements are related to the system\u27s state, they can be used for estimation and detection purposes, similar to standard measurements. In this dissertation, we first develop a nominal context-aware filter (i.e., with no faults or attacks) for binary context measurements (e.g., a building detection). Finally, we develop a technique for incorporating context measurements into sensor fusion, thus providing guarantees about system safety even in cases where more than half of standard sensors might be under attack