Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.

Impressive Gelation in Organic Solvents by Synthetic, Low Molecular Mass, Self-Organizing Urethane Amides of L-Phenylalanine

Synthetic routes leading to 12 L-phenylalanine based mono- and bipolar derivatives (1-12) and an in-depth study of their structure-property relationship with respect to gelation have been presented. These include monopolar systems such as N-[(benzyloxy)carbonyl]-L-phenylalanine-N-alkylamides and the corresponding bipolar derivatives with flexible and rigid spacers such as with 1,12-diaminododecane and 4,4'-diaminodiphenylmethane, respectively. The two ends of the latter have been functionalized with N-[(benzyloxy)carbonyl]-L-phenylalanine units via amide connection. Another bipolar molecule was synthesized in which the middle portion of the hydrocarbon segment contained polymerizable diacetylene unit. To ascertain the role of the presence of urethane linkages in the gelator molecule protected L-phenylalanine derivatives were also synthesized in which the (benzyloxy)carbonyl group has been replaced with (tert-butyloxy)carbonyl, acetyl, and benzoyl groups, respectively. Upon completion of the synthesis and adequate characterization of the newly described molecules, we examined the aggregation and gelation properties of each of them in a number of solvents and their mixtures. Optical microscopy and electron microscopy further characterized the systems that formed gels. Few representative systems, which showed excellent gelation behavior was, further examined by FT-IR, calorimetric, and powder X-ray diffraction studies. To explain the possible reasons for gelation, the results of molecular modeling and energy-minimization studies were also included. Taken together these results demonstrate the importance of the presence of (benzyloxy)carbonyl unit, urethane and secondary amide linkages, chiral purities of the headgroup and the length of the alkyl chain of the hydrophobic segment as critical determinants toward effective gelation.

Multiple contour extraction from graylevel images using an artificial neural network

For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of ail edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.

Genetic analysis of Indian tasar silkmoth (Antheraea mylitta) populations

Indian tasar silkmoth, Antheraea mylitta is an economically important wild silkmoth species distributed across India. A number of morphologically and ethologically well-defined ecotypes are known for this species that differ in their primary food plant specificity. Most of these ecotypes do not interbreed in nature, but are able to produce offspring under captive conditions. Microsatellite markers were developed for A. mylitta, and out of these, ten well-behaved microsatellite loci were used to analyze the population structure of different ecoraces. A total of 154 individual moths belonging to eight different ecoraces, were screened at each locus. Hierarchical analysis of population structure using Analysis of MOlecular VAriance (AMOVA) revealed significant structuring (F-ST = 0.154) and considerable inbreeding (F-IS = 0.505). A significant isolation by distance was also observed. The number of possible population clusters was investigated using distance method, Bayesian algorithm and self organization maps (SOM). The first two methods revealed two distinct clusters, whereas the SOM showed the different ecoraces not to be clearly differentiated. These results suggest that although there is a large degree of phenotypic variation among the different ecoraces of A. mylitta, genetically they are not very different, and the phenotypic differences may largely be a result of their respective ecology.

Metallomicelles as potent catalysts for the ester hydrolysis reactions in water

Synthetic amphiphiles have been employed for the investigation of diverse topics, e.g. membrane mimetics, drug delivery, ion sensing and even in certain separation processes. Metal-complexing amphiphiles comprise an interesting class of compounds possessing multiple utilities. Upon solubilization in water they form metallomicelles. For achieving specific catalysis of a variety of reactions, metallomicelles were utilized by applying the principles of coordination chemistry and self-organizing systems. Because of their certain similarities with the natural enzymes, metallomicelles were synthesized as catalysts for many reactions. In particular the metallomicelles play a catalytic role in reactions involving the hydrolysis of activated carboxylate esters, phosphate esters and amides at ambient conditions near neutral pH. Apart from the hydrolysis reactions, these were exploited to play pertinent role as Lewis acid catalysts in cycloaddition reactions, and in other reactions such as phenolic oxidation in presence of hydrogen peroxide. In this review we emphasize with the help of assorted examples, the design, synthesis of metal-complexing amphiphiles and their aggregation behavior leading to catalytic hydrolysis reactions in aqueous media.

Characterization of Failure Modes in CFRP Composites - An ANN Approach

Carbon fiber reinforced polymer (CFRP) composite specimens with different thickness, geometry, and stacking sequences were subjected to fatigue spectrum loading in stages. Another set of specimens was subjected to static compression load. On-line acoustic Emission (AE) monitoring was carried out during these tests. Two artificial neural networks, Kohonen-self organizing feature map (KSOM), and multi-layer perceptron (MLP) have been developed for AE signal analysis. AE signals from specimens were clustered using the unsupervised learning KSOM. These clusters were correlated to the failure modes using available a priori information such as AE signal amplitude distributions, time of occurrence of signals, ultrasonic imaging, design of the laminates (stacking sequences, orientation of fibers), and AE parametric plots. Thereafter, AE signals generated from the rest of the specimens were classified by supervised learning MLP. The network developed is made suitable for on-line monitoring of AE signals in the presence of noise, which can be used for detection and identification of failure modes and their growth. The results indicate that the characteristics of AE signals from different failure modes in CFRP remain largely unaffected by the type of load, fiber orientation, and stacking sequences, they being representatives of the type of failure phenomena. The type of loading can have effect only on the extent of damage allowed before the specimens fail and hence on the number of AE signals during the test. The artificial neural networks (ANN) developed and the methods and procedures adopted show significant success in AE signal characterization under noisy environment (detection and identification of failure modes and their growth).

Regional Flood Frequency Analysis using Two-level Clustering Approach

Clustering techniques are used in regional flood frequency analysis (RFFA) to partition watersheds into natural groups or regions with similar hydrologic responses. The linear Kohonen's self‐organizing feature map (SOFM) has been applied as a clustering technique for RFFA in several recent studies. However, it is seldom possible to interpret clusters from the output of an SOFM, irrespective of its size and dimensionality. In this study, we demonstrate that SOFMs may, however, serve as a useful precursor to clustering algorithms. We present a two‐level. SOFM‐based clustering approach to form regions for FFA. In the first level, the SOFM is used to form a two‐dimensional feature map. In the second level, the output nodes of SOFM are clustered using Fuzzy c‐means algorithm to form regions. The optimal number of regions is based on fuzzy cluster validation measures. Effectiveness of the proposed approach in forming homogeneous regions for FFA is illustrated through application to data from watersheds in Indiana, USA. Results show that the performance of the proposed approach to form regions is better than that based on classical SOFM.

Self Assessment-Based Decision Making for Multiagent Cooperative Search

This paper addresses a search problem with multiple limited capability search agents in a partially connected dynamical networked environment under different information structures. A self assessment-based decision-making scheme for multiple agents is proposed that uses a modified negotiation scheme with low communication overheads. The scheme has attractive features of fast decision-making and scalability to large number of agents without increasing the complexity of the algorithm. Two models of the self assessment schemes are developed to study the effect of increase in information exchange during decision-making. Some analytical results on the maximum number of self assessment cycles, effect of increasing communication range, completeness of the algorithm, lower bound and upper bound on the search time are also obtained. The performance of the various self assessment schemes in terms of total uncertainty reduction in the search region, using different information structures is studied. It is shown that the communication requirement for self assessment scheme is almost half of the negotiation schemes and its performance is close to the optimal solution. Comparisons with different sequential search schemes are also carried out. Note to Practitioners-In the futuristic military and civilian applications such as search and rescue, surveillance, patrol, oil spill, etc., a swarm of UAVs can be deployed to carry out the mission for information collection. These UAVs have limited sensor and communication ranges. In order to enhance the performance of the mission and to complete the mission quickly, cooperation between UAVs is important. Designing cooperative search strategies for multiple UAVs with these constraints is a difficult task. Apart from this, another requirement in the hostile territory is to minimize communication while making decisions. This adds further complexity to the decision-making algorithms. In this paper, a self-assessment-based decision-making scheme, for multiple UAVs performing a search mission, is proposed. The agents make their decisions based on the information acquired through their sensors and by cooperation with neighbors. The complexity of the decision-making scheme is very low. It can arrive at decisions fast with low communication overheads, while accommodating various information structures used for increasing the fidelity of the uncertainty maps. Theoretical results proving completeness of the algorithm and the lower and upper bounds on the search time are also provided.

RIGIDITY OF HOLOMORPHIC MAPS BETWEEN FIBER SPACES

In the study of holomorphic maps, the term ``rigidity'' refers to certain types of results that give us very specific information about a general class of holomorphic maps owing to the geometry of their domains or target spaces. Under this theme, we begin by studying when, given two compact connected complex manifolds X and Y, a degree-one holomorphic map f :Y -> X is a biholomorphism. Given that the real manifolds underlying X and Y are diffeomorphic, we provide a condition under which f is a biholomorphism. Using this result, we deduce a rigidity result for holomorphic self-maps of the total space of a holomorphic fiber space. Lastly, we consider products X = X-1 x X-2 and Y = Y-1 x Y-2 of compact connected complex manifolds. When X-1 is a Riemann surface of genus >= 2, we show that any non-constant holomorphic map F:Y -> X is of a special form.