Beyond the lens : communicating context through sensing, video, and visualization

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 101-103).Responding to rapid growth in sensor network deployments that outpaces research efforts to understand or relate the new data streams, this thesis presents a collection of interfaces to sensor network data that encourage open-ended browsing while emphasizing saliency of representation. These interfaces interpret, visualize, and communicate context from sensors, through control panels and virtual environments that synthesize multimodal sensor data into interactive visualizations. This work extends previous efforts in cross-reality to incorporate augmented video as well as complex interactive animations, making use of sensor fusion to saliently represent contextual information to users in a variety of application domains, from building information management to real-time risk assessment to personal privacy. Three applications were developed as part of this work and are discussed here: DoppelLab, an immersive, cross-reality browsing environment for sensor network data; Flurry, an installation that composites video from multiple sources throughout a building in real time, to create an interactive and incorporative view of activity; and Tracking Risk with Ubiquitous Smart Sensing (TRUSS), an ongoing research effort aimed at applying real-time sensing, sensor fusion, and interactive visual analytic interfaces to construction site safety and decision support. Another project in active development, called the Disappearing Act, allows users to remove themselves from a set of live video streams using wearable sensor tags. Though these examples may seem disconnected, they share underlying technologies and research developments, as well as a common set of design principles, which are elucidated in this thesis. Building on developments in sensor networks, computer vision, and graphics, this work aims to create interfaces and visualizations that fuse perspectives, broaden contextual understanding, and encourage exploration of real-time sensor network data.by Gershon Dublon.S.M

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Digital Twin Techniques for Power Electronics-Based Energy Conversion Systems : A Survey of Concepts, Application Scenarios, Future Challenges, and Trends

The steady increase in energy demands has led to ever-increasing “energy generation.” This, coupled with the need for higher efficiency, flexibility, and reliability, has boosted the use of power electronics in power and energy systems. Therefore, power electronics-based energy conversion systems (PEECSs) have become prominent in power generation, power transmission, and end user applications. Given the relevance of such systems, and by considering their trend of digitalization, it is crucial to establish digital and intelligent PEECSs. To this end, digital twins (DTs) can be adopted, as they integrate many cuttingedge information techniques to realize the life cycle management of complex systems by constructing real-time mappings of them. In this article, existing DT techniques for PEECSs are reviewed. The concept, system layers, and key technologies of DTs are described first. Some application cases of DTs are then elaborated. Finally, future trends and challenges of DTs are discussed to provide a valuable reference for subsequent research.acceptedVersionPeer reviewe

Dynamic Reconfiguration in Camera Networks: A Short Survey

There is a clear trend in camera networks towards enhanced functionality and flexibility, and a fixed static deployment is typically not sufficient to fulfill these increased requirements. Dynamic network reconfiguration helps to optimize the network performance to the currently required specific tasks while considering the available resources. Although several reconfiguration methods have been recently proposed, e.g., for maximizing the global scene coverage or maximizing the image quality of specific targets, there is a lack of a general framework highlighting the key components shared by all these systems. In this paper we propose a reference framework for network reconfiguration and present a short survey of some of the most relevant state-of-the-art works in this field, showing how they can be reformulated in our framework. Finally we discuss the main open research challenges in camera network reconfiguration

Automatic Fire Detection Using Computer Vision Techniques for UAV-based Forest Fire Surveillance

Due to their rapid response capability and maneuverability, extended operational range, and improved personnel safety, unmanned aerial vehicles (UAVs) with vision-based systems have great potentials for forest fire surveillance and detection. Over the last decade, it has shown an increasingly strong demand for UAV-based forest fire detection systems, as they can avoid many drawbacks of other forest fire detection systems based on satellites, manned aerial vehicles, and ground equipments. Despite this, the existing UAV-based forest fire detection systems still possess numerous practical issues for their use in operational conditions. In particular, the successful forest fire detection remains difficult, given highly complicated and non-structured environments of forest, smoke blocking the fire, motion of cameras mounted on UAVs, and analogues of flame characteristics. These adverse effects can seriously cause either false alarms or alarm failures. In order to successfully execute missions and meet their corresponding performance criteria and overcome these ever-increasing challenges, investigations on how to reduce false alarm rates, increase the probability of successful detection, and enhance adaptive capabilities to various circumstances are strongly demanded to improve the reliability and accuracy of forest fire detection system. According to the above-mentioned requirements, this thesis concentrates on the development of reliable and accurate forest fire detection algorithms which are applicable to UAVs. These algorithms provide a number of contributions, which include: (1) a two-layered forest fire detection method is designed considering both color and motion features of fire; it is expected to greatly improve the forest fire detection performance, while significantly reduce the motion of background caused by the movement of UAV; (2) a forest fire detection scheme is devised combining both visual and infrared images for increasing the accuracy and reliability of forest fire alarms; and (3) a learning-based fire detection approach is developed for distinguishing smoke (which is widely considered as an early signal of fire) from other analogues and achieving early stage fire detection

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor

Swarm Based Implementation of a Virtual Distributed Database System in a Sensor Network

The deployment of unmanned aerial vehicles (UAVs) in recent military operations has had success in carrying out surveillance and combat missions in sensitive areas. An area of intense research on UAVs has been on controlling a group of small-sized UAVs to carry out reconnaissance missions normally undertaken by large UAVs such as Predator or Global Hawk. A control strategy for coordinating the UAV movements of such a group of UAVs adopts the bio-inspired swarm model to produce autonomous group behavior. This research proposes establishing a distributed database system on a group of swarming UAVs, providing for data storage during a reconnaissance mission. A distributed database system model is simulated treating each UAV as a distributed database site connected by a wireless network. In this model, each UAV carries a sensor and communicates to a command center when queried. Drawing equivalence to a sensor network, the network of UAVs poses as a dynamic ad-hoc sensor network. The distributed database system based on a swarm of UAVs is tested against a set of reconnaissance test suites with respect to evaluating system performance. The design of experiments focuses on the effects of varying the query input and types of swarming UAVs on overall system performance. The results show that the topology of the UAVs has a distinct impact on the output of the sensor database. The experiments measuring system delays also confirm the expectation that in a distributed system, inter-node communication costs outweigh processing costs