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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.

Fasciolosis is an economically important disease for livestock, as well as being zoonotic. Recent figures on the prevalence of this disease have caused alarm concerning its potential for an increased prevalence in the future. The prevalence of fascioliosis has been documented from different regions of the world, helping us identify areas where future research needs to be focused. This manuscript is a review of the current status of the disease, the pathogenic species involved, diagnostic techniques (with new modifications and comparative specificity, sensitivity, and rapidity of these tests), chemotherapy, and vaccination. This also encompasses inaccurate reports on vaccination and drug development as well as the latest technologies to find promising candidates for drugs and vaccines. Drugs with lower efficacy have been used on some farms which lead to exacerbation of the clinical disease, presumably due to the development of drug resistance. Future studies should be focused on (1) the use of the most reliable diagnostic tests for periodic monitoring of the disease, (2) insights of the ecobiology and transmission dynamics of the snail intermediate host and the best possible methods of their control, (3) in vitro and in vivo testing of chemotherapeutic compounds using sensitive methods, and (4) the identification of novel drug and vaccine candidates using modern molecular markers. This approach may help increase the reliability of chemotherapeutic agents and control nuisance, ultimately reducing the economic losses attributable to the livestock industry around the world.

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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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Abstract-Wireless Visual Sensor Network (WVSN) is formed by deploying many Visual Sensor Nodes (VSNs) in the field. The VSNs acquire images of the area of interest in the field, perform some local processing on these images and transmit the results using an embedded wireless transceiver. The energy consumption on transmitting the results wirelessly is correlated with the information amount that is being transmitted. The images acquired by the VSNs contain huge amount of data due to many kinds of redundancies in the images. Suitable bi-level image compression standards can efficiently reduce the information amount in images and will thus be effective in reducing the communication energy consumption in the WVSN. But compression capability of the bi-level image compression standards is limited to the underline compression algorithm. Further data reduction can be achieved by detecting Region of Interest (ROI) in the bilevel images and then coding these ROIs using bi-level image compression method. We explored the compression performance of the lossless ROI detection and coding method for various kinds of changes such as different shapes, locations and number of objects in the continuous set of frames. The CCITT Group 4, JBIG2 and Gzip are used for coding the detected ROIs. We concluded that CCITT Group 4 is a better choice for coding the ROIs in the Bi-level images because of its comparatively good compression performance and less computational complexity. This paper is intended to be a resource for the researchers interested in reducing the amount of data in the bi-level images for energy constrained WVSNs.

PreprintThis is the submitted version of a paper published in IEEE transactions on circuits and systems for video technology (Print).