Outlier detection is a well studied problem in various fields. The unique challenges of wireless sensor networks make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we present a new communication technique to find outliers in a wireless sensor network. Communication is minimized through controlling sensor when sensors are allowed to communicate. At the same time, minimal assumptions are made about the nature of the data set as to minimize the loss of generality in the architecture.

BACKGROUND: RNA transcripts from genomic sequences showing dyad symmetry typically adopt hairpin-like, cloverleaf, or similar structures that act as recognition sites for proteins. Such structures often are the precursors of non-coding RNA (ncRNA) sequences like microRNA (miRNA) and small-interfering RNA (siRNA) that have recently garnered more functional significance than in the past. Genomic DNA contains hundreds of thousands of such inverted repeats (IRs) with varying degrees of symmetry. But by collecting statistically significant information from a known set of ncRNA, we can sort these IRs into those that are likely to be functional. RESULTS: A novel method was developed to scan genomic DNA for partially symmetric inverted repeats and the resulting set was further refined to match miRNA precursors (pre-miRNA) with respect to their density of symmetry, statistical probability of the symmetry, length of stems in the predicted hairpin secondary structure, and the GC content of the stems. This method was applied on the Arabidopsis thaliana genome and validated against the set of 190 known Arabidopsis pre-miRNA in the miRBase database. A preliminary scan for IRs identified 186 of the known pre-miRNA but with 714700 pre-miRNA candidates. This large number of IRs was further refined to 483908 candidates with 183 pre-miRNA identified and further still to 165371 candidates with 171 pre-miRNA identified (i.e. with 90% of the known pre-miRNA retained). CONCLUSIONS: 165371 candidates for potentially functional miRNA is still too large a set to warrant wet lab analyses, such as northern blotting, on all of them. Hence additional filters are needed to further refine the number of candidates while still retaining most of the known miRNA. These include detection of promoters and terminators, homology analyses, location of candidate relative to coding regions, and better secondary structure prediction algorithms. The software developed is designed to easily accommodate such additional filters with a minimal experience in Perl.

BACKGROUND: Gene family identification from ESTs can be a valuable resource for analysis of genome evolution but presents unique challenges in organisms for which the entire genome is not yet sequenced. We have developed a novel gene family identification method based on negative selection patterns (NSP) between family members to screen EST-generated contigs. This strategy was tested on five known gene families in Arabidopsis to see if individual paralogs could be identified with accuracy from EST data alone when compared to the actual gene sequences in this fully sequenced genome. RESULTS: The NSP method uniquely identified family members in all the gene families tested. Two members of the FtsH gene family, three members each of the PAL, RF1, and ribosomal L6 gene families, and four members of the CAD gene family were correctly identified. Additionally all ESTs from the representative contigs when checked against MapViewer data successfully identify the gene locus predicted. CONCLUSION: We demonstrate the effectiveness of the NSP strategy in identifying specific gene family members in Arabidopsis using only EST data and we describe how this strategy can be used to identify many gene families in agronomically important crop species where they are as yet undiscovered.

BACKGROUND: Gene duplication events have played a significant role in genome evolution, particularly in plants. Exhaustive searches for all members of a known gene family as well as the identification of new gene families has become increasingly important. Subfunctionalization via changes in regulatory sequences following duplication (adaptive selection) appears to be a common mechanism of evolution in plants and can be accompanied by purifying selection on the coding region. Such negative selection can be detected by a bias toward synonymous over nonsynonymous substitutions. However, the process of identifying this bias requires many steps usually employing several different software programs. We have simplified the process and significantly shortened the time required by condensing many steps into a few scripts or programs to rapidly identify putative gene family members beginning with a single query sequence. RESULTS: In this report we 1) describe the software tools (SimESTs, PCAT, and SCAT) developed to automate the gene family identification, 2) demonstrate the validity of the method by correctly identifying 3 of 4 PAL gene family members from Arabidopsis using EST data alone, 3) identify 2 to 6 CAD gene family members from Glycine max (previously unidentified), and 4) identify 2 members of a putative Glycine max gene family previously unidentified in any plant species. CONCLUSION: Gene families in plants, particularly that subset where purifying selection has occurred in the coding region, can be identified quickly and easily by integrating our software tools and commonly available contig assembly and ORF identification programs.

BACKGROUND: Clustering the ESTs from a large dataset representing a single species is a convenient starting point for a number of investigations into gene discovery, genome evolution, expression patterns, and alternatively spliced transcripts. Several methods have been developed to accomplish this, the most widely available being UniGene, a public domain collection of gene-oriented clusters for over 45 different species created and maintained by NCBI. The goal is for each cluster to represent a unique gene, but currently it is not known how closely the overall results represent that reality. UniGene's build procedure begins with initial mRNA clusters before joining ESTs. UniGene's results for soybean indicate a significant amount of redundancy among some sequences reported to be unique mRNAs. To establish a valid non-redundant known gene set for Glycine max we applied our algorithm to the clustering of only mRNA sequences. The mRNA dataset was run through the algorithm using two different matching stringencies. The resulting cluster compositions were compared to each other and to UniGene. Clusters exhibiting differences among the three methods were analyzed by 1) nucleotide and amino acid alignment and 2) submitting authors conclusions to determine whether members of a single cluster represented the same gene or not. RESULTS: Of the 12 clusters that were examined closely most contained examples of sequences that did not belong in the same cluster. However, neither the two stringencies of PECT nor UniGene had a significantly greater record of accuracy in placing paralogs into separate clusters. CONCLUSION: Our results reveal that, although each method produces some errors, using multiple stringencies for matching or a sequential hierarchical method of increasing stringencies can provide more reliable results and therefore allow greater confidence in the vast majority of clusters that contain only ESTs and no mRNA sequences.

No abstract available.

A new systolic algorithm which computes image differences in run-length encoded (RLE) format is described. The binary image difference operation is commonly used in many image processing applications including automated inspection systems, character recognition, fingerprint analysis, and motion detection. The efficiency of these operations can be improved significantly with the availability of a fast systolic system that computes the image difference as described in this paper. It is shown that for images with a high similarity measure, the time complexity of the systolic algorithm is small and, in some cases, constant with respect to the image size. A formal proof of correctness for the algorithm is also given.

We propose a new fingerprint image compression scheme based on the hybrid model of an image. Our scheme uses the essential steps of a typical automated fingerprint identification system (AFIS) such as enhancement, binarization and thinning to encode fingerprint images. The decoding process is based on reconstructing a hybrid surface by using the gray values on ridges and valleys. In this compression scheme, the ridge skeleton is coded efficiently by using differential chain codes. The valley skeleton is derived from the ridge skeleton and the gray values along the ridge and valley skeletons are encoded using the discrete cosine transform. The error between the original and the replica is also encoded to increase the quality. One advantage of our approach is that original features such as end points and bifurcation points can be extracted directly from compressed image even for a very high compression ratio. Another advantage is that the proposed scheme can be integrated to a typical AFIS easily. The algorithm has been applied to various fingerprint images, and high compression ratios like 63:1 have been obtained. A comparison to wavelet/scalar quantization (WSQ) has been also made.

The importance of the inspection process has been magnified by the requirements of the modern manufacturing environment. In electronics mass-production manufacturing facilities, an attempt is often made to achieve 100% quality assurance of all parts, subassemblies, and finished goods. A variety of approaches for automated visual inspection of printed circuits have been reported over the past two decades. In this survey, algorithms and techniques for the automated inspection of printed circuit boards are examined. A classification tree for these algorithms is presented and the algorithms are grouped according to this classification. This survey concentrates mainly on image analysis and fault detection strategies; these also include state-of-the-art techniques. A summary of the commercial PCB inspection systems is also presented.

A simple and yet effective method for finding the borders of tumors is presented as an initial step towards the diagnosis of skin tumors from their color images. The method makes use of an adaptive color metric from the red, green, and blue planes that contains information for discriminating the tumor from the background. Using this suitable coordinate transformation, the image is segmented. The tumor portion is then extracted from the segmented image and borders are drawn. Experimental results that verify the effectiveness of this approach are given.

In this paper, the problem of assigning N parallel tasks onto a hypercube with N processors is considered. Three heuristics are developed to map arbitrary process graphs, in a one-to-one fashion, onto a hypercube architecture, in order to minimize the communication cost between processors:bit-toggling (BT), bit-swapping (BS), and node-swapping (NS). The effectiveness of the methods is evaluated using a number of sample process graphs, and compared with the results obtained from simulated annealing (SA). With respect to solution quality, between the three approaches proposed, the node-swapping technique is superior.

Finite-element discretization produces linear equations in the form Ax=b, where A is large, sparse, and banded with proper ordering of the variables x. The solution of such equations on distributed-memory message-passing multiprocessors implementing the hypercube topology is addressed. Iterative algorithms based on the conjugate gradient method are developed for hypercubes designed for coarse-grained parallelism. The communication requirements of different schemes for mapping finite-element meshes onto the processors of a hypercube are analyzed with respect to the effect of communication parameters of the architecture. Experimental results for a 16-node Intel 80386-based iPSC/2 hypercube are presented and discussed.>

The processor allocation problem is addressed in the context of the parallelization of a finite element modeling program on a processor mesh. A heuristic two-step, graph-based mapping scheme with polynomial-time complexity is developed: 1) initial generation of a graph partition for nearest-neighbor mapping of the finite element graph onto the processor graph, and, 2) a heuristic boundary refinement procedure to incrementally alter the initial partition for improved load balancing among the processors. The effectiveness of the approach is gaged both by estimation using a model with empirically determined parameters, as well as implementation and experimental measurement on a 16 node hypercube parallel computer.