Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence

Moving object detection and segmentation in urban environments from a moving platform

This paper proposes an effective approach to detect and segment moving objects from two time-consecutive stereo frames, which leverages the uncertainties in camera motion estimation and in disparity computation. First, the relative camera motion and its uncertainty are computed by tracking and matching sparse features in four images. Then, the motion likelihood at each pixel is estimated by taking into account the ego-motion uncertainty and disparity in computation procedure. Finally, the motion likelihood, color and depth cues are combined in the graph-cut framework for moving object segmentation. The efficiency of the proposed method is evaluated on the KITTI benchmarking datasets, and our experiments show that the proposed approach is robust against both global (camera motion) and local (optical flow) noise. Moreover, the approach is dense as it applies to all pixels in an image, and even partially occluded moving objects can be detected successfully. Without dedicated tracking strategy, our approach achieves high recall and comparable precision on the KITTI benchmarking sequences.This work was carried out within the framework of the Equipex ROBOTEX (ANR-10- EQPX-44-01). Dingfu Zhou was sponsored by the China Scholarship Council for 3.5 year’s PhD study at HEUDIASYC laboratory in University of Technology of Compiegne

Di-μ-sulfato-κ4 O:O′-bis­[diaqua­(1H-imidazo[4,5-f][1,10]phenanthroline-κ2 N 7,N 9)cobalt(II)] dihydrate

In the centrosymmetric dinuclear title compound, [Co2(SO4)2(C13H8N4)2(H2O)4]·2H2O, the CoII atom is coord­in­ated by two N atoms from two 1H-imidazo[4,5-f][1,10]phenanthroline ligands, two O atoms from two sulfate anions and two O atoms from water mol­ecules in a distorted octa­hedral geometry. The Co⋯Co separation is 5.1167 (7) Å. The coordinated and uncoordinated water mol­ecules engage in N—H⋯O and O—H⋯O hydrogen-bonding inter­actions