The early multi-point leakage source signals of urban gas pipeline are weak and can be easily affected by environmental noise and signal interference between adjacent sources, which causes large leakage positioning error. In this paper, an integrated signal processing method combining VMD, BSS and Relative entropy for multi-point pipeline leakage signal and source positioning is presented. Firstly, VMD and Relative entropy were employed to obtain effective IMF mode components and their features during the decomposition for pipeline leakage signal. Relative entropy was used to improve signal-to-noise ratio and extract the features of leakage signal. Then, BSS was used to decompose multi-point mixed leakage signals so as to obtain independent signal components. Finally, the time difference and wave velocity were, respectively, obtained by calculating the time domain distribution of the independent signal components and the main modal guided wave, so the precise positioning of the pipeline leakage was realized. The results show that the combined method proposed can not only select and extract leakage signal adaptively but also separate single independent signal from multi-point mixed signal, which helps to locate pipeline multi-point leakage sources more accurately.

Due to their flexibility in coverage and resolution, networked robotic cameras become more and more popular in applications such as natural observation, security surveillance, and distance learning. Equipped with a high optical zoom lens, a networked robotic camera can generate a giga-pixel-level panorama to cover its viewable region. As new live frames enter the system, this panorama can be updated as a panorama video. User requests are usually not limited to the current camera frame. A user may request a specific region associated with a specific time window. To satisfy different spatiotemporal requests for multiple concurrent users, we present systems and algorithms to allow the on-demand sharing of the high-resolution panorama video. The high-resolution panorama video is encoded into a patch-based representation to allow efficient storage and on-demand content delivery. We present system architecture, user interface, data representation, and encoding/decoding algorithms. In the experiment, we have implemented the system using the MPEG-2 codec. Experimental results show that our system can not only satisfy different spatiotemporal queries but also significantly reduce computation time and communication bandwidth requirement.

A pan-tilt-zoom robotic camera can provide detailed live video of selected areas of interest within a large potential viewing field. To provide spatial context for human observers, it is desirable to insert the resulting live video into a large spherical panoramic display representing the entire viewing field. Accurate alignment of the video stream within the panoramic display is difficult due to small errors in the robot pan-tilt values and image distortion due to nonlinear projection. Existing image alignment algorithms cannot keep up with rapid changes in camera position. In this paper, we present a constant-time image alignment algorithm based on spherical projection and projection-invariant selective sampling that accurately registers paired images at 25 frames per second on a standard PC. Experiments suggest that the new alignment algorithm is faster than previous algorithms by a factor four or more. In a companion paper, we present a new calibration algorithm based on image variance density that optimally estimates camera pan-tilt parameters

A new generation of inexpensive robotic pan-tilt cameras can maintain high-resolution panoramic displays of natural environments. However, the pan-tilt mechanisms are imprecise: small errors can produce large errors in the panoramic display. It is thus important to accurately estimate pan-tilt values. We present a new calibration algorithm that does not rely on calibration markers or fixed orthogonal edges which are rarely available in natural scenes. Our calibration algorithm uses image variance density to optimally estimate camera pan and tilt values by incrementally refining image registration using overlapping images from prior frames. Experiments suggest that the new calibration algorithm can reduce calibration error by 81%. In a companion paper, we present a new image registration algorithm based on spherical projection that optimally aligns the resulting frames