Methods and Applications Rastislav Lukac Ed.
This paper proposes a general intelligent video surveillance monitoring system to explore and examine some problems in animal behavior analysis particularly in cow behaviors. In this concern, farmers, animal health professionals and researchers have well recognized that analysis of changes in the behavioral patterns of cattle is an important factor for an animal health and welfare management system.
Also, in today dairy world, farm sizes are growing larger and larger, as a result the attention time limits for individual animals smaller and smaller.
Thus, video based monitoring system will become an emerging technology approaching to an era of intelligent monitoring system.
In this context, image processing is a promising technique for such challenging system because it is relatively low cost and simple enough to implement. One of important issues in the management of group-housed livestock is to make early detection of abnormal behaviors of a cow.
Particularly failure in detecting estrus in timely and accurate manner can be a serious factor in achieving efficient reproductive performance. Another aspect is concerned with health management to identify unhealthy or poor health such as lameness through analysis of measured motion data.
Lameness is a one of the biggest health and welfare issue in modern intensive dairy farming.
Although there has been a tremendous amount of methods for detecting estrus, still it needs to improve for achieving a more accurate and practical. Thus in this paper, a general intelligent video surveillance system framework for animal behavior analysis is proposed to be by using i various types of Background Models for target or targets extraction, ii Markov and Hidden Markov models for detection of various types of behaviors among the targets, iii Dynamic Programming and Markov Decision Making Process for producing output results.
As an illustration, a pilot experiment will be performed to confirm the feasibility and validity of the proposed framework.a new class of sparse channel estimation methods based on support vector machines by dongho han a dissertation presented to the graduate school of the university of.
By: Ian West Romsey, Hampshire, and Visiting Scientist at: Ocean and Earth Science, Faculty of Natural and Environment Sciences Southampton University Website hosted by iSolutions, Southampton University Aerial photographs by courtesy of The Channel Coastal Observatory Website archived at the British Library Professor Ian Croudace and Andrew Johnson have made valuable contributions .
Contemporary research on Europa and Juan de Nova atolls in the Mozambique Channel, showed black rats attained high densities in forest in the wet season and lower densities at the beginning of the dry season, while in grassland, black rat densities were lower . * NUES. The student will submit a synopsis at the beginning of the semester for approval from the departmental committee in a specified format.
The student will have to present the progress of the work through seminars and progress reports. Research Article ISSN: CODEN(USA): JCPRC5 Study on Compressed Sensing Sparse Channel Based on Bayesian Estimate in Wireless Sensor Networks Zeyu Sun Department of Computer and Information Engineering, Luoyang Institute of Science and Technology, Henan, any distribution channel estimation.
Thesis on the basis of the original.
Data Compression Explained. Matt Mahoney. Copyright (C) , Dell, initiativeblog.com are permitted to copy and distribute material from this book provided (1) any.