Temporal Signature Modeling and Analysis

A vast amount of digital satellite and aerial images are collected over time, which calls for techniques to extract useful high-level information, such as recognizable events. One part of this thesis proposes a framework for streaming analysis of the time series, which can recognize events without supervision and memorize them by building the temporal contexts. The memorized historical data is then used to predict the future and detect anomalies. A new incremental clustering method is proposed to recognize the event without training. A memorization method of double localization, including relative and absolute localization, is proposed to model the temporal context. Finally, the predictive model is built based on the method of memorization. The Edinburgh Pedestrian Dataset , which offers about 1000 observed trajectories of pedestrians detected in camera images each working day for several months, is used as an example to illustrate the framework. Although there is a large amount of image data captured, most of them are not available to the public. The other part of this thesis developed a method of generating spatial-spectral-temporal synthetic images by enhancing the capacity of a current tool called DIRISG (Digital Imaging and Remote Sensing Image Generation). Currently, DIRSIG can only model limited temporal signatures. In order to observe general temporal changes in a process within the scene, a process model, which links the observable signatures of interest temporally, should be developed and incorporated into DIRSIG. The sub process models could be categorized into two types. One is that the process model drives the property of each facet of the object changing over time, and the other one is to drive the geometry location of the object in the scene changing as a function of time. Two example process models are used to show how process models can be incorporated into DIRSIG

Advances in crowd analysis for urban applications through urban event detection

The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined

Convergence of Intelligent Data Acquisition and Advanced Computing Systems

This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions