Traditionally, video acquisition, coding and analysis have been designed and optimized as independent tasks. This has a negative impact in terms of consumed resources, as most of the raw information captured by conventional acquisition devices is discarded in the coding phase, while the analysis step only requires a few descriptors of salient video characteristics. Recent Compressive Sensing literature has partially broken this paradigm by proposing to integrate sensing and coding in a unified architecture composed by a light encoder and a more complex decoder, which exploits sparsity of the underlying signal for efficient recovery. However, a clear understanding of how to embed video analysis in this scheme is still missing. In this paper, we propose a joint compressive video coding and analysis scheme and, as a specific application example, we consider the problem of object tracking in video sequences. We show that, weaving together compressive sensing and the information computed by the analysis module, the bit-rate required to perform reconstruction and tracking of the foreground objects can be considerably reduced, with respect to a conventional disjoint approach that postpones the analysis after the video signal is recovered in the pixel domain.These findings suggest that considerable gains in performance can be potentially obtained in video analysis applications, provided that a joint analysis-aware design of acquisition, coding and signal recovery is carried out.

In a typical video analysis framework, video sequences are decoded and reconstructed in the pixel domain before being processed for high level tasks such as classification or detection. Nevertheless, in some application scenarios, it might be of interest to complete these analysis tasks without disclosing sensitive data, e.g. the identity of people captured by surveillance cameras. In this paper we propose a new coding scheme suitable for video surveillance applications that allows tracking of video objects without the need to reconstruct the sequence, thus enabling privacy protection. By taking advantage of recent findings in the compressive sensing literature, we encode a video sequence with a limited number of pseudo-random projections of each frame. At the decoder, we exploit the sparsity that characterizes background subtracted images in order to recover the location of the foreground object. We also leverage the prior knowledge about the estimated location of the object, which is predicted by means of a particle filter, to improve the recovery of the foreground object location. The proposed framework enables privacy, in the sense it is impossible to reconstruct the original video content from the encoded random projections alone, as well as secrecy, since decoding is prevented if the seed used to generate the random projections is not available.

Many deadlock prevention approaches have been suggested in the literature for Petri net models of flexible manufacturing systems, based on siphon enumeration and control. With medium and large problem dimensions, such methods often require both an excessive computational load and extremely large control sub-nets, making them unfeasible or impractical. In this work, a simple approach is proposed for the design of sub-optimal but compact controllers. The approach is based on the anticipated allocation of a sub-set of resources that decouples the deadlock prevention problem in two much smaller and simpler problems, each devoted to the deadlock prevention for a sub-set of resources only. The application of the two designed control sub-nets to the original Petri net together with resource anticipation ensures deadlock prevention. A heuristic algorithm is also provided for the selection of a suitable resource partition, in order to maximize the control quality and performance. Several illustrative benchmark examples are provided

Information-rich data sets bring several challenges in the areas of visualization and analysis, even when associated with node-link network visualizations. This paper presents an integration of multi-focus and multi-level techniques that enable interactive, multi-step comparisons in node-link networks. We describe NetEx, a visualization tool that enables users to simultaneously explore different parts of a network and its thematic data, such as time series or conditional probability tables. NetEx, implemented as a Cytoscape plug-in, has been applied to the analysis of electrical power networks, Bayesian networks, and the Enron e-mail repository. In this paper we briefly discuss visualization and analysis of the Enron social network, but focus on data from an electrical power network. Specifically, we demonstrate how NetEx supports the analytical task of electrical power system fault diagnosis. Results from a user study with 25 subjects suggest that NetEx enables more accurate isolation of complex faults compared to an especially designed software tool.

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