Enhanced grasshopper optimization algorithm with extreme learning machines for motor-imagery classification

In Brain Computer Interface (BCI), achieving a reliable motor-imagery classification is a challenging task. The set of discriminative and relevant feature vectors plays a crucial role in classification. In this article, an enhanced optimization technique is implemented for selecting active feature vectors to enhance motor-imagery classification using Electroencephalography (EEG) signals. After collecting the input EEG signals from BCI competition III-4a and IV-2a databases, the 6th-order butter-worth filter is employed for eliminating base-line wander noise from the raw EEG signals. Further, the Variational Mode Decomposition technique is applied for separating the important signal components from the composite EEG signals, and then, the Higher Order Statistic, kurtosis, skewness, standard deviation, and entropy are utilized for feature extraction. The high-dimensional feature values are given to the Enhanced Grasshopper Optimization Algorithm for optimum feature selection, which are given to the Extreme Learning Machines (ELM) classifier for motor-imagery classification. Finally, in the resulting section, the optimized ELM model achieved 99.48% and 99.12% of accuracy on the BCI competition III-4a and IV-2a databases, where the achieved results are maximum compared to the traditional deep learning models.     

An $\tilde{O}(m^{2}n)$ Algorithm for Minimum Cycle Basis of Graphs

We consider the problem of computing a minimum cycle basis of an undirected non-negative edge-weighted graph G with m edges and n vertices. In this problem, a {0,1} incidence vector is associated with each cycle and the vector space over generated by these vectors is the cycle space of G. A set of cycles is called a cycle basis of G if it forms a basis for its cycle space. A cycle basis where the sum of the weights of the cycles is minimum is called a minimum cycle basis of G. Minimum cycle basis are useful in a number of contexts, e.g. the analysis of electrical networks and structural engineering. The previous best algorithm for computing a minimum cycle basis has running time O(m ^ω n), where ω is the best exponent of matrix multiplication. It is presently known that ω<2.376. We exhibit an O(m ² n+mn ²log n) algorithm. When the edge weights are integers, we have an O(m ² n) algorithm. For unweighted graphs which are reasonably dense, our algorithm runs in O(m ^ω ) time. For any ε>0, we also design an 1+ε approximation algorithm. The running time of this algorithm is O((m ^ω /ε)log (W/ε)) for reasonably dense graphs, where W is the largest edge weight. A preliminary version of this paper appeared in Kavitha et al. (31st International Colloquium on Automata, Languages and Programming (ICALP), pp. 846–857, 2004). T. Kavitha and K.E. Paluch were in Max-Planck-Institut für Informatik, Saarbrücken, Germany, while this work was done.     

Glucose sensing,photocatalytic and antibacterial properties of graphene–ZnO nanoparticle hybrids

A simple and efficient approach was developed to uniformly decorate graphene nanosheets with zinc oxide (ZnO) nanoparticles. A single source precursor, zinc benzoate dihydrazinate complex, has been used for the in situ generation of ZnO nanoparticles onto graphene at a relatively low temperature, 200 °C. Physico chemical analyses such as X-ray diffraction, transmission electron microscopy and X-ray photoelectron spectroscopy revealed that ZnO nanoparticles were finely dispersed on the surface of graphene. ZnO–graphene hybrids were further characterized by Raman spectroscopy and ultraviolet visible spectroscopy and room-temperature photoluminescence. The materials exhibited excellent photocatalytic activity as evident from the degradation of methylene blue in ethanol under UV irradiation. An electrochemical glucose biosensor was fabricated by immobilization of glucose oxidase on the ZnO–graphene hybrids. This biosensor showed improved sensitivity towards glucose as compared to graphene. Also, the hybrids showed significant antibacterial activity against E. coli, gram negative bacteria. This simple and economical preparation strategy may be extended for the preparation of other graphene-based hybrids.     

Deposition of zinc–zinc phosphate composite coatings on steel by cathodic electrochemical treatment

The present work aims at the development of an energy-efficient and eco-friendly approach for the deposition of zinc phosphate coatings on steel. The study describes the possibility of preparing zinc–zinc phosphate composite coatings by cathodic electrochemical treatment using dilute phosphoric acid as an electrolyte and zinc as an anode. The methodology enables the preparation of coatings with different proportions of zinc and zinc phosphate by suitably varying the applied current density, pH, and treatment time. Adhesion of the coating on mild steel and adhesion of paint film on the phosphate coating were found to be good. The surface morphology of the coatings exhibited platelet-type features and small white crystals (agglomerated at some places) which represented zinc and zinc phosphate, respectively. An increase in current density (from 20 to 50 mA/cm²) increased the size of the zinc crystals, and coatings prepared at 40 and 50 mA/cm² resembled that of electrodeposited zinc. Since the proportions of zinc and zinc phosphate could be varied with applied current density, pH, and treatment time, it would be possible to use this methodology to prepare coatings that would offer different degrees of corrosion protection.     

Simulation Studies of Computation and Data Scheduling Algorithms for Data Grids

Data Grids seek to harness geographically distributed resources for large-scale data-intensive problems. Such problems, involving loosely coupled jobs and large data-sets, are found in fields like high-energy physics, astronomy and bioinformatics. A variety of factors need to be considered for effective scheduling of resources in such environments: e.g., resource utilization, response time, global and local allocation policies and scalability. We propose a general and extensible scheduling architecture that addresses these issues. Within this architecture we develop a suite of job scheduling and data replication algorithms that we evaluate using simulations for a wide range of parameters. Our results show that it is important to evaluate the combined effectiveness of replication and scheduling strategies, rather than study them separately. More specifically, we find that scheduling jobs to locations that contain the data they need and asynchronously replicating popular data-sets to remote sites, works rather well.     

A study on new benzotriazole derivatives as inhibitors on copper corrosion in ground water

The corrosion inhibition properties of newly synthesized 1-(2-pyrrole carbonyl)-benzotriazole (PCBT) and 1-(2-thienyl carbonyl)-benzotriazole (TCBT) in combination with the non-ionic surfactant Triton X-100 (TX-100) on metallic copper were studied in ground water environment and the results were compared with benzotriazole (BTA). Various electrochemical studies such as open circuit potential (OCP), potentiodynamic polarization, ac impedance and cyclic voltammetric (CV) were made. Surface analytical techniques like FT-IR and XRD were also performed. The results indicated that PCBT is a better corrosion inhibitor for copper and the formulation consisting of PCBT and TX-100 offered improved inhibition efficiency (IE) in a synergistic manner.     

Modeling Packet Delay in Multiplexed Video Traffic

In this paper a Markov chain based characterization of a single video source is used to model multiplexed video traffic and the resulting packet delay. The multiplexed video stream of statistically identical sources is represented using a reduced order approximation of the superposed Markov generator matrix. It is shown that inadequate spectral content in the single source generator matrix is manifested as an underestimate of the packet delay probabilities for the multiplexed stream. A new method for simplifying the generator matrix for the multiplexed video stream that amply models its spectral content is given. The simulated and calculated results using the aforementioned model are shown be in good agreement.     

Clustering Gene Expression Data Through Modified Agglomerative M-CURE Hierarchical Algorithm

Gene expression refers to the process in which the gene information is used in the functional gene product synthesis. They basically encode the proteins which in turn dictate the functionality of the cell. The first step in gene expression study involves the clustering usage. This is due to the reason that biological networks are very complex and the genes volume increases the comprehending challenges along with the data interpretation which itself inhibit vagueness, noise and imprecision. For a biological system to function, the essential cellular molecules must interact with its surrounding including RNA, DNA, metabolites and proteins. Clustering methods will help to expose the structures and the patterns in the original data for taking further decisions. The traditional clustering techniques involve hierarchical, model based, partitioning, density based, grid based and soft clustering methods. Though many of these methods provide a reliable output in clustering, they fail to incorporate huge data of gene expressions. Also, there are statistical issues along with choosing the right method and the choice of dissimilarity matrix when dealing with gene expression data. We propose to use a modified clustering algorithm using representatives (M-CURE) in this work which is more robust to outliers as compared to K-means clustering and also able to find clusters with size variances.