We present a mechanism called the Markov model mediator (MMM) to facilitate the effective retrieval for content-based image retrieval (CBIR). Different from the common methods in content-based image retrieval, our sto...
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We present a mechanism called the Markov model mediator (MMM) to facilitate the effective retrieval for content-based image retrieval (CBIR). Different from the common methods in content-based image retrieval, our stochastic mechanism not only takes into consideration the low-level image content features, but also learns high-level concepts from a set of training data, such as access frequencies and access patterns of the images. The advantage of our proposed mechanism is that it exploits the structured description of visual contents as well as the relative affinity measurements among the images. Consequently, it provides the capability to bridge the gap between the low-level features and high-level concepts. Our experimental results demonstrate that the MMM mechanism can effectively assist in retrieving more accurate results for user queries.
We develop an Automated Feature Information Retrieval system (AFIRS) for accurate classification of multisource geospatial data, which involves multispectral Landsat imagery, ancillary geographic information system (G...
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We develop an Automated Feature Information Retrieval system (AFIRS) for accurate classification of multisource geospatial data, which involves multispectral Landsat imagery, ancillary geographic information system (GIS) data and other derived features. Two machine learning approaches, i.e., decision tree classifier (DTC) and support vector machine (SVM), are implemented as multisource geospatial data classifiers in the AFIRS. Specifically, we apply the AFIRS to the mapping of United States Department of Agriculture (USDA)'s Conservation Reserve Program (CRP) tracts in Texas County, Oklahoma. CRP is a nationwide program, and recently USDA announced payments of nearly $1.6 billion for new CRP enrollments. It is imperative to obtain accurate CRP maps for effective and efficient management and evaluation of the CRP program. However, most existing CRP maps are inaccurate and little work has been done to improve their accuracy. The proposed AFIRS is capable of handling the complex CRP mapping problem with high accuracy when limited training samples are available. Simulation results show that 5-10% improvements can be obtained by incorporating GIS ancillary data and other derived features in addition to multispectral imagery. This work validates the applicability of machine learning approaches to the complex real-world remote sensing applications.
This paper presents an evaluation of the benefits of streamflows forecasting in long-term hydroelectric scheduling problem. In the approach considered, at each stage of planning a forecast of the future inflows is mad...
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This paper presents an evaluation of the benefits of streamflows forecasting in long-term hydroelectric scheduling problem. In the approach considered, at each stage of planning a forecast of the future inflows is mad...
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This paper presents an evaluation of the benefits of streamflows forecasting in long-term hydroelectric scheduling problem. In the approach considered, at each stage of planning a forecast of the future inflows is made and an operational decision for the following stage is obtained by a deterministic optimization model, in a partial open-loop feedback control framework. The influence of the forecasting model in the performance of this control policy is analysed by simulation using historical inflows record. The effectiveness of the approach was measured using the mean and standard deviation values for hydro generation and operational costs during the planning period, taking into account the hydroelectric plants of the Southeast Brazilian system as a case study.
The aim of this study was to examine the influence of various external factors on the time-frequency (TF) content of surface electromyography (SEMG). The paper reports on a study of various factors like the level of m...
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The aim of this study was to examine the influence of various external factors on the time-frequency (TF) content of surface electromyography (SEMG). The paper reports on a study of various factors like the level of muscle contraction, gender of the subject and inter electrode distance. A group of ten healthy subjects (five males and five females) performed isometric elbow flexions of the right arm at 20, 50 and 80% of their maximal voluntary contraction (MVC). The SEMG signal was recorded using surface electrodes placed at a distance of 18 and 36 mm over the biceps brachii muscle. WVD was used to extract the time frequency (TF) information of the signal and instantaneous frequency (IF) of the signal was extracted. Statistical analysis was performed on the IF values to determine the influence of each of the factors on the signal. The analysis indicates that there is a variation in the SEMG recordings for subjects based on gender, change of inter electrode distance and the level of muscle contraction. The IF at 50% was the highest compared with 20% and 80% MVC, while 20% was the lowest.
In this paper, an end-to-end congestion controlled optimal bandwidth allocation scheme with a transmission rate control mechanism for multimedia transmission is proposed. This rate control mechanism aims at minimizing...
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Automatic video scene change detection is a challenging task. Using audio or visual information alone often cannot provide a satisfactory solution. However, how to combine audio and visual information efficiently stil...
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Multimedia data, typically image data, is increasing rapidly across the Internet and elsewhere. To keep pace with the increasing volumes of image information, new techniques need to be investigated to retrieve images ...
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Designing simple and efficient network protection mechanism is an important requirement of optical networks, which is a challenging task for large mesh networks. We extend our previous work on Hamiltonian cycle protec...
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Designing simple and efficient network protection mechanism is an important requirement of optical networks, which is a challenging task for large mesh networks. We extend our previous work on Hamiltonian cycle protection to large-scale multiple domain inhomogeneous optical networks. We proposed two protection schemes with different trade-off between spare resource usage and failure isolation. Our proposed schemes are simple and competitive in network resource usage.
This article considers a rough neurocomputing approach to the design of the classify layer of a Brooks architecture for a robot control system. In the case of the line-crawling robot (LCR) described in this article, r...
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