Abstract. The challenges associated with wireless vision sensor networks are low energy consumption, less bandwidth and limited processing capabilities. In order to meet these challenges different approaches are proposed. Research in wireless vision sensor networks has been focused on two different assumptions, first is sending all data to the central base station without local processing, second approach is based on conducting all processing locally at the sensor node and transmitting only the final results. Our research is focused on partitioning the vision processing tasks between Senor node and central base station. In this paper we have added the exploration dimension to perform some of the vision tasks such as image capturing, background subtraction, segmentation and Tiff Group4 compression on FPGA while communication on microcontroller. The remaining vision processing tasks i.e. morphology, labeling, bubble remover and classification are processed on central base station. Our results show that the introduction of FPGA for some of the visual tasks will result in a longer life time for the visual sensor node while the architecture is still programmable. I. INTRODUCTIONTypically Vision Sensor Nodes (VSN) in Wireless Vision Sensor Networks (WVSN) consists of a camera for acquiring images, a processor for local image processing and a transceiver for communicating the results to the central base station. Due to the technological development in image sensors, sensor networking, distributed processing, low power processing and embedded systems smart camera networks can perform complex tasks using limited resources such as batteries, a wireless link and with a limited storage facility. Such camera based networks could easily be installed in out-doors areas where there is a limited availability of power, where access is difficult and it is inconvenient to modify the locations of the nodes or frequently change the batteries. VSN have been designed and implemented on microcontroller and microprocessor [1,4]. Often these solutions have high power consumption and moderate processing capabilities. Due to rapid development in the semiconductor technology, the single chip capacity of Field Programmable Gate Array (FPGA) increases greatly while its power consumption decreases tremendously [15]. Presently FPGA chips consist of many cores which makes it ideal candidate for the designing of VSN. As VSN needs to be capable of performing complex image processing such as image compression, which needs a lot of processing. High processing requirement is increased for an increased image size. Attention must be paid to the hardware/software co-design strategy to meet both processing and power requirements of VSN [8]. In [9] the authors designed a novel VSN based on a low cost, low power FPGA plus microcontroller System on Programmable Chip (SOPC). The authors in [10] have implemented a computer vision algorithm in hardware. They have provided a comparison of hardware and software implemented system using the same algorithm. It is c...

This is an accepted version of a paper published in IEEE transactions on circuits and systems for video technology (Print). This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.

Wireless Visual Sensor Network (WVSN) is an emerging field which combines image sensor, on board computation unit, communication component and energy source. Compared to the traditional wireless sensor network, which operates on one dimensional data, such as temperature, pressure values etc., WVSN operates on two dimensional data (images) which requires higher processing power and communication bandwidth. Normally, WVSNs are deployed in areas where installation of wired solutions is not feasible. The energy budget in these networks is limited to the batteries, because of the wireless nature of the application. Due to the limited availability of energy, the processing at Visual Sensor Nodes (VSN) and communication from VSN to server should consume as low energy as possible. Transmission of raw images wirelessly consumes a lot of energy and requires higher communication bandwidth. Data compression methods reduce data efficiently and hence will be effective in reducing communication cost in WVSN. In this paper, we have compared the compression efficiency and complexity of six well known bi-level image compression methods. The focus is to determine the compression algorithms which can efficiently compress bi-level images and their computational complexity is suitable for computational platform used in WVSNs. These results can be used as a road map for selection of compression methods for different sets of constraints in WVSN.

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SUMMARY:Within both the paper and paperboard industries, real time monitoring and measurement of surface roughness of a paper moving at high velocities is an important and challenging area of research. The uniform surface, for an entire production, can be effectively achieved by monitoring and controlling the paper surface roughness, in real time during the manufacturing steps. Presently the majority of paper industries rely on traditional laboratory profilometers. The obvious limitations of lab profilometers are that these are slow, do not measure the quality of entire reels but rather deal with only a few small pieces of samples taken from the end of the reels and it is difficult to make any possible correction in the production lines without knowing the online roughness data. To eradicate the disadvantages associated with conventional measurements, an online prototype instrument has been developed that measures the surface roughness during the manufacturing steps, and is based on a line of light triangulation technique. The prototype technique will be of assistance in ensuring tight process control in order to maintain both a better and a uniform quality throughout the entire production. It measures the whole reel, meter by meter, in traditional units of roughness and is also capable of characterizing the topography in a wide range of wavelength spectra. The article presents the online analyses results obtained from the developed prototype. The real time measurements, in a paperboard pilot mill, have successfully characterized and distinguished 16 different grades of newspaper and paperboard reels including reels which have the same family of quality grades and materials.