Improving Indoor Security Surveillance by Fusing Data from BIM, UWB and Video

Indoor physical security, as a perpetual and multi-layered phenomenon, is a time-intensive and labor-consuming task. Various technologies have been leveraged to develop automatic access control, intrusion detection, or video monitoring systems. Video surveillance has been significantly enhanced by the advent of Pan-Tilt-Zoom (PTZ) cameras and advanced video processing, which together enable effective monitoring and recording. The development of ubiquitous object identification and tracking technologies provides the opportunity to accomplish automatic access control and tracking. Intrusion detection has also become possible through deploying networks of motion sensors for alerting about abnormal behaviors. However, each of the above-mentioned technologies has its own limitations. This thesis presents a fully automated indoor security solution that leverages an Ultra-wideband (UWB) Real-Time Locating System (RTLS), PTZ surveillance cameras and a Building Information Model (BIM) as three sources of environmental data. Providing authorized persons with UWB tags, unauthorized intruders are distinguished as the mismatch observed between the detected tag owners and the persons detected in the video, and intrusion alert is generated. PTZ cameras allow for wide-area monitoring and motion-based recording. Furthermore, the BIM is used for space modeling and mapping the locations of intruders in the building. Fusing UWB tracking, video and spatial data can automate the entire security procedure from access control to intrusion alerting and behavior monitoring. Other benefits of the proposed method include more complex query processing and interoperability with other BIM-based solutions. A prototype system is implemented that demonstrates the feasibility of the proposed method

Uncertainty Minimization in Robotic 3D Mapping Systems Operating in Dynamic Large-Scale Environments

This dissertation research is motivated by the potential and promise of 3D sensing technologies in safety and security applications. With specific focus on unmanned robotic mapping to aid clean-up of hazardous environments, under-vehicle inspection, automatic runway/pavement inspection and modeling of urban environments, we develop modular, multi-sensor, multi-modality robotic 3D imaging prototypes using localization/navigation hardware, laser range scanners and video cameras. While deploying our multi-modality complementary approach to pose and structure recovery in dynamic real-world operating conditions, we observe several data fusion issues that state-of-the-art methodologies are not able to handle. Different bounds on the noise model of heterogeneous sensors, the dynamism of the operating conditions and the interaction of the sensing mechanisms with the environment introduce situations where sensors can intermittently degenerate to accuracy levels lower than their design specification. This observation necessitates the derivation of methods to integrate multi-sensor data considering sensor conflict, performance degradation and potential failure during operation. Our work in this dissertation contributes the derivation of a fault-diagnosis framework inspired by information complexity theory to the data fusion literature. We implement the framework as opportunistic sensing intelligence that is able to evolve a belief policy on the sensors within the multi-agent 3D mapping systems to survive and counter concerns of failure in challenging operating conditions. The implementation of the information-theoretic framework, in addition to eliminating failed/non-functional sensors and avoiding catastrophic fusion, is able to minimize uncertainty during autonomous operation by adaptively deciding to fuse or choose believable sensors. We demonstrate our framework through experiments in multi-sensor robot state localization in large scale dynamic environments and vision-based 3D inference. Our modular hardware and software design of robotic imaging prototypes along with the opportunistic sensing intelligence provides significant improvements towards autonomous accurate photo-realistic 3D mapping and remote visualization of scenes for the motivating applications

Controller Synthesis of Multi-Axial Robotic System Used for Wearable Devices

Wearable devices are commonly used in different fields to help improving performance of movements for different groups of users. The long-term goal of this study is to develop a low-cost assistive robotic device that allows patients to perform rehabilitation activities independently and reproduces natural movement to help stroke patients and elderly adults in their daily activities while moving their arms. In the past few decades, various types of wearable robotic devices have been developed to assist different physical movements. Among different types of actuators, the twisted-string actuation system is one of those that has advantages of light-weight, low cost, and great portability. In this study, a dual twisted-string actuator is used to drive the joints of the prototype assistive robotic device. To compensate the asynchronous movement caused by nonlinear factors, a hybrid controller that combines fuzzy logic rules and linear PID control algorithm was adopted to compensate for both tracking and synchronization of the two actuators.;In order to validate the performance of proposed controllers, the robotic device was driven by an xPC Target machine with additional embedded controllers for different data acquisition tasks. The controllers were fine tuned to eliminate the inaccuracy of tracking and synchronization caused by disturbance and asynchronous movements of both actuators. As a result, the synthesized controller can provide a high precision when tracking simple actual human movements

The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

Self-localizing Smart Cameras and Their Applications

As the prices of cameras and computing elements continue to fall, it has become increasingly attractive to consider the deployment of smart camera networks. These networks would be composed of small, networked computers equipped with inexpensive image sensors. Such networks could be employed in a wide range of applications including surveillance, robotics and 3D scene reconstruction. One critical problem that must be addressed before such systems can be deployed effectively is the issue of localization. That is, in order to take full advantage of the images gathered from multiple vantage points it is helpful to know how the cameras in the scene are positioned and oriented with respect to each other. To address the localization problem we have proposed a novel approach to localizing networks of embedded cameras and sensors. In this scheme the cameras and the nodes are equipped with controllable light sources (either visible or infrared) which are used for signaling. Each camera node can then automatically determine the bearing to all the nodes that are visible from its vantage point. By fusing these measurements with the measurements obtained from onboard accelerometers, the camera nodes are able to determine the relative positions and orientations of other nodes in the network. This localization technology can serve as a basic capability on which higher level applications can be built. The method could be used to automatically survey the locations of sensors of interest, to implement distributed surveillance systems or to analyze the structure of a scene based on the images obtained from multiple registered vantage points. It also provides a mechanism for integrating the imagery obtained from the cameras with the measurements obtained from distributed sensors. We have successfully used our custom made self localizing smart camera networks to implement a novel decentralized target tracking algorithm, create an ad-hoc range finder and localize the components of a self assembling modular robot

Common global architecture applied to automobile electrical distribution systems

Thesis (S.M. in System Design and Management)--Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 111-112).Electrical and electronic components have a prominent role in today's vehicles. Particularly during the last two decades, functionality has been added at an exponential rate, resulting in increased complexity, especially of the Electrical Distribution System (EDS), which is the backbone of the Electrical and Electronic System (EES). Increased content and complexity of electrical systems, together with pressure to reduce the design cycle time - to bring a larger variety of products to the market and at a faster pace - are forcing car companies to re-evaluate their existing electrical development processes. One of the ways that car makers have devised to accomplish this is a common EES architecture strategy, which consists in combining communization, standardization, reusability and best practices to create flexible EES architectural concepts that will be used in a higher number of derivative vehicles. This common architecture has several benefits, the most important being: reduction of development costs and time, which translates in less time for putting the products in the market; architecture, concepts and components reuse; rapid platform modifications, to adapt to market changes and regional preferences. The EES architecture choice for a vehicle is the result of the implementation of the desired functions in hardware and software. Many considerations need to be taken into account: costs, network capabilities, modularity, manufacturing, energy management, weight, among several others. The present work aims to explain these considerations, as well as the elements of the common EES, and in particular their impact on the EDS. Another important aspect for the successful implementation of the common architecture is the EDS development process. Despite the availability of a wide range of software tools, the current EDS approach is intensely manual, relying on design experts to define and maintain the interrelationships and complexities of the core design definition. There is a need to redefine the process, from concept to manufacture using a systems engineering approach, which would yield key benefits, like shorten development time, produce accurate harness manufacturing prints, reduce wiring costs by synchronizing all input and output data. An analysis of the tools and methods for design and validation of wire harnesses will be presented in the last two chapters of this thesis.by Marcia E. Azpeitia Camacho.S.M.in System Design and Managemen

Low-Power Human-Machine Interfaces: Analysis And Design

Human-Machine Interaction (HMI) systems, once used for clinical applications, have recently reached a broader set of scenarios, such as industrial, gaming, learning, and health tracking thanks to advancements in Digital Signal Processing (DSP) and Machine Learning (ML) techniques. A growing trend is to integrate computational capabilities into wearable devices to reduce power consumption associated with wireless data transfer while providing a natural and unobtrusive way of interaction. However, current platforms can barely cope with the computational complexity introduced by the required feature extraction and classification algorithms without compromising the battery life and the overall intrusiveness of the system. Thus, highly-wearable and real-time HMIs are yet to be introduced. Designing and implementing highly energy-efficient biosignal devices demands a fine-tuning to meet the constraints typically required in everyday scenarios. This thesis work tackles these challenges in specific case studies, devising solutions based on bioelectrical signals, namely EEG and EMG, for advanced hand gesture recognition. The implementation of these systems followed a complete analysis to reduce the overall intrusiveness of the system through sensor design and miniaturization of the hardware implementation. Several solutions have been studied to cope with the computational complexity of the DSP algorithms, including commercial single-core and open-source Parallel Ultra Low Power architectures, that have been selected accordingly also to reduce the overall system power consumption. By further adding energy harvesting techniques combined with the firmware and hardware optimization, the systems achieved self-sustainable operation or a significant boost in battery life. The HMI platforms presented are entirely programmable and provide computational power to satisfy the requirements of the studies applications while employing only a fraction of the CPU resources, giving the perspective of further application more advanced paradigms for the next generation of real-time embedded biosignal processing