Wireless capsule endoscopy video segmentation using an unsupervised learning approach based on probabilistic latent semantic analysis with scale invariant features

Since wireless capsule endoscopy (WCE) is a novel technology for recording the videos of the digestive tract of a patient, the problem of segmenting the WCE video of the digestive tract into subvideos corresponding to the entrance, stomach, small intestine, and large intestine regions is not well addressed in the literature. A selected few papers addressing this problem follow supervised leaning approaches that presume availability of a large database of correctly labeled training samples. Considering the difficulties in procuring sizable WCE training data sets needed for achieving high classification accuracy, we introduce in this paper an unsupervised learning approach that employs Scale Invariant Feature Transform (SIFT) for extraction of local image features and the probabilistic latent semantic analysis (pLSA) model used in the linguistic content analysis for data clustering. Results of experimentation indicate that this method compares well in classification accuracy with the state-of-the-art supervised classification approaches to WCE video segmentation.     

An impatient evolutionary algorithm with probabilistic tabu search for unified solution of some NP-hard problems in graph and set theory via clique finding.

Many graph- and set-theoretic problems, because of their tremendous application potential and theoretical appeal, have been well investigated by the researchers in complexity theory and were found to be NP-hard. Since the combinatorial complexity of these problems does not permit exhaustive searches for optimal solutions, only near-optimal solutions can be explored using either various problem-specific heuristic strategies or metaheuristic global-optimization methods, such as simulated annealing, genetic algorithms, etc. In this paper, we propose a unified evolutionary algorithm (EA) to the problems of maximum clique finding, maximum independent set, minimum vertex cover, subgraph and double subgraph isomorphism, set packing, set partitioning, and set cover. In the proposed approach, we first map these problems onto the maximum clique-finding problem (MCP), which is later solved using an evolutionary strategy. The proposed impatient EA with probabilistic tabu search (IEA-PTS) for the MCP integrates the best features of earlier successful approaches with a number of new heuristics that we developed to yield a performance that advances the state of the art in EAs for the exploration of the maximum cliques in a graph. Results of experimentation with the 37 DIMACS benchmark graphs and comparative analyses with six state-of-the-art algorithms, including two from the smaller EA community and four from the larger metaheuristics community, indicate that the IEA-PTS outperforms the EAs with respect to a Pareto-lexicographic ranking criterion and offers competitive performance on some graph instances when individually compared to the other heuristic algorithms. It has also successfully set a new benchmark on one graph instance. On another benchmark suite called Benchmarks with Hidden Optimal Solutions, IEA-PTS ranks second, after a very recent algorithm called COVER, among its peers that have experimented with this suite.     

A class of call-fail-safe and distribution-effective multi-band multi-carrier traffic allocation methods for 3GB wireless systems

For better capacity and higher availability, present day third generation (3G) wireless systems based on the code division multiple access(CDMA) technology are evolving to operate on multiple carriers (frequencies) spread over multiple bands. In order to provide better quality of service, the 3GB (3rd generation and beyond) systems need to distribute calls equitably to different carriers on different base-stations accessible to the mobiles irrespective of the bands or carriers on which those mobiles initiated their calls. However, there is a risk of call failure when a call originated on a carrier in a band is migrated to another carrier in a different band, particularly because of the differences in the radio coverage of the base-stations operating in different bands. This paper presents a class of methods that offer equal robustness against call failures and varying degrees of call distribution effectiveness. For call distribution, these methods employ an enhanced carrier capacity measure (ECM) proposed in this paper. ECM augments the gross capacities of the carriers (to house calls) with pre-configured biases specific to the mobile users. We develop here an intuitively appealing distribution-effectiveness measure based on the ECM for comparing the methods. Relative performances of the proposed methods with respect to call failure rate and distribution effectiveness are established by means of simulation results for calls originating anywhere in the cell coverage area as well as calls originating exclusively near the cell boundaries. The latter results help to study the effect of mobility on the performances of the algorithms.