A novel bacterial foraging technique for edge detection

A new approach for edge detection using a combination of bacterial foraging algorithm (BFA) and probabilistic derivative technique derived from Ant Colony Systems, is presented in this paper. The foraging behavior of some species of bacteria like Escherichia coli can be hypothetically modeled as an optimization process. A group of bacteria search for nutrients in a way that maximizes the energy obtained per unit time spent during the foraging. The proposed approach aims at driving the bacteria through the edge pixels. The direction of movement of the bacteria is found using a direction probability matrix, computed using derivatives along the possible directions. Rules defining the derivatives are devised to ensure that the variation of intensity due to noise is discarded. Quantitative analysis of the feasibility of the proposed approach and its comparison with other standard edge detection operators in terms of kappa and entropy are given. The effect of initial values of parameters of BFA on the edge detection is discussed.     

A novel global optimization technique for high dimensional functions

Several types of line search methods are documented in the literature and are well known for unconstraint optimization problems. This paper proposes a modified line search method, which makes use of partial derivatives and restarts the search process after a given number of iterations by modifying the boundaries based on the best solution obtained at the previous iteration (or set of iterations). Using several high‐dimensional benchmark functions, we illustrate that the proposed line search restart (LSRS) approach is very suitable for high‐dimensional global optimization problems. Performance of the proposed algorithm is compared with two popular global optimization approaches, namely, genetic algorithm and particle swarm optimization method. Empirical results for up to 2000 dimensions clearly illustrate that the proposed approach performs very well for the tested high‐dimensional functions. © 2009 Wiley Periodicals, Inc.     

A novel data distribution technique for host-client type parallelapplications

This paper considers an analytic data distribution for improving the performance of host-client type parallel applications which exhibit serialized communication patterns. The technique involves assuming the serialized communications is enforced, which simplifies data analysis and can provide the basis for real-time dynamic load balancing. This distribution has been tested using a parallel matrix multiplication implementation and a parallel MPEG compression implementation. The key results of this paper are that analytic distribution can reduce execution time and increase scalability of certain parallel applications over typical equal data distributions     

A novel normalization technique for unsupervised learning in ANN

Unsupervised learning is used to categorize multidimensional data into a number of meaningful classes on the basis of the similarity or correlation between individual samples. In neural-network implementation of various unsupervised algorithms such as principal component analysis, competitive learning or self-organizing map, sample vectors are normalized to equal lengths so that similarity could be easily and efficiently obtained by their dot products. In general, sample vectors span the whole multidimensional feature space and existing normalization methods distort the intrinsic patterns present in the sample set. In this work, a novel method of normalization by mapping the samples to a new space of one more dimension is proposed. The original distribution of the samples in the feature space is shown to be almost preserved in the transformed space. Simple rules are given to map from original space to the normalized space and vice versa.     

A novel prototype generation technique for handwriting digit recognition

The aim of this paper is to introduce a novel prototype generation technique for handwriting digit recognition. Prototype generation is approached as a two-stage process. The first stage uses an Adaptive Resonance Theory 1 (ART1) based algorithm to select an effective initial solution, while the second one executes a fine tuning designed to generate the best prototypes.     

一种可应用于小型光伏的MPPT技术

提出了一种基于FOCV方法应用在小型光伏系统中的新型太阳能电池MPPT技术。首先．将太阳能电池阵列改变为全串联、全并联或串并混联的组合方式,相同的环境下,这些阵列在理论上拥有相同的最大输出功率,但需要不同的负载与阻抗相匹配;其次,许多拥有不同阻值的负载电阻可以被选择连接到太阳能电池阵列中,使太阳能电池的输出功率最大化;最后,使用MCU测量每种模式的电压并使用FOCV方法来确定最大的功率状态。     

A novel fault diagnosis technique for wind turbine gearbox

The gearbox is one of the most important parts of a mechanical equipment. The importance of fault diagnosis in rotating machineries for preventing catastrophic accidents and ensuring adequate maintenance has received considerable attention. In this study, a fault diagnosis method based on gearbox vibration signal monitoring is used to differentiate the signal characteristics of different working conditions and improve the accuracy of diagnosis. The time-domain sequence approximate entropy (ApEn) adaptive strategy is used to propose a wind turbine intelligent fault diagnosis algorithm based on a wavelet packet transform (WPT) filter and a cross-validated particle swarm optimized (CPSO) kernel extreme learning machine (KELM). First, the correlation between the parameter requirements of the intelligent diagnosis system and the system complexity analysis is analyzed. Then, the parameters related to the wavelet filter is determined by calculating the ApEn of the time-domain sequence. Finally, a compact wind turbine gearbox test bench is constructed and tested to validate the proposed ApEn-WPT+CPSO-KELM to identify gearbox-related faults for verification. Results show that the proposed ApEn-WPT+CPSO-KELM method can accurately identify four states of the wind turbine gearbox.     

New technique for detection and height estimation of buried metallic objects

A new technique for the detection and height estimation of buried metallic objects is developed. This technique is bused on the phase characteristics of both transmitted and received signals. A discrete Fourier transform via a fast Fourier transform algorithm is used to compute the phase characteristic. The height of the buried metallic object is estimated by detecting the transition frequency and measuring the phase difference between the transition frequency and the next frequency component in the spectrum of the received signal. Computer simulation studies show that this technique is efficient, reliable, and provides sufficient accuracy.     

A new technique for producing large-area as-deposited zero-stressLPCVD polysilicon films: the MultiPoly process

Polysilicon films deposited by low-pressure chemical vapor deposition (LPCVD) exhibit tensile or compressive residual stresses, depending on the deposition temperature. Polysilicon films composed of alternating tensile and compressive layers can display any overall stress value between those of the individual layers, including a state of zero overall residual stress, depending on the relative thickness of each layer. The residual stress gradient can be similarly controlled by the layer thicknesses and distribution. This has been demonstrated with a ten-layer near-zero stress (<10 MPa), near-zero stress gradient (⩽0.2 MPa/μm) polysilicon film, containing flat cantilever beams whose length-thickness ratios exceed 150. Using multilayer deposition to control the stresses and stress gradients of polysilicon films is termed the MultiPoly process     

A novel GWO optimized pruning technique for inexact circuit design

Improving area efficiency of modern computing systems is the main task in today's digital design. circuit design innovations are necessary to keep the power budget under control. Inexact or approximate circuit design is a computing solution that trades circuit precision for cost reduction. In this work present a new pruning technique which using grey wolf optimizer for pruning. This optimization mimics the social behavior of grey wolves whereas it follows the leadership hierarchy and attacking strategy. The probabilistic pruning complexity cost is optimized at each computation to maintain a tradeoff between minimum area, error and area delay product. The parameters like Area, delay and area delay product measures are utilized to analyze the potency of GWO rule with existing one. The observed results convey that the GWO based pruning provides promising results compared to conventional pruning techniques     

A novel CRT-based watermarking technique for authentication of multimedia contents

Digital watermarking techniques have been proposed as a solution to the problem of copyright protection of multimedia data. In this paper, we propose a novel Chinese remainder theorem (CRT)-based technique for digital watermarking. The use of CRT for this purpose provides additional security along with resistance to some familiar attacks. We have shown that this technique is quite resilient to addition of the noise. We have compared performance of the proposed technique with recently reported two singular value decomposition (SVD)-based watermarking techniques and shown its superior performance in terms of tampering assessment function (TAF), computational efficiency and peak signal to noise ratio (PSNR). For example, the embedding time of the proposed CRT-based scheme is 6 and 3 times faster than the SVD-based Schemes 1 and 2, respectively. This technique can also be applied to document, audio and video contents.     

A novel feedforward control technique for a flexible dual manipulator

This paper proposes a feedforward control technique for two flexible links attached to one motor to suppress residual vibrations in a point-to-point motion. In the proposed method, we attempt to express the trajectory of the joint angle using a combination of cycloidal and polynomial functions, which enables the easy generation of a smooth motion. The generated trajectory depends on the coefficients of the polynomial function. To minimize the residual vibrations of the two flexible links, the coefficients are tuned using a particle swarm optimization algorithm, which is a type of a metaheuristic algorithm. The optimal trajectory obtained by this approach can suppress residual vibrations; i.e., multimode vibration control can be realized. Simulations and experiments are performed to evaluate the applicability and effectiveness of the proposed method.     

Compositional pattern producing networks: A novel abstraction of development

Natural DNA can encode complexity on an enormous scale. Researchers are attempting to achieve the same representational efficiency in computers by implementing developmental encodings, i.e. encodings that map the genotype to the phenotype through a process of growth from a small starting point to a mature form. A major challenge in in this effort is to find the right level of abstraction of biological development to capture its essential properties without introducing unnecessary inefficiencies. In this paper, a novel abstraction of natural development, called Compositional Pattern Producing Networks (CPPNs), is proposed. Unlike currently accepted abstractions such as iterative rewrite systems and cellular growth simulations, CPPNs map to the phenotype without local interaction, that is, each individual component of the phenotype is determined independently of every other component. Results produced with CPPNs through interactive evolution of two-dimensional images show that such an encoding can nevertheless produce structural motifs often attributed to more conventional developmental abstractions, suggesting that local interaction may not be essential to the desirable properties of natural encoding in the way that is usually assumed.

A novel signal diagnosis technique using pseudo complex-valued autoregressive technique

In this paper, a new method of biomedical signal classification using complex- valued pseudo autoregressive (CAR) modeling approach has been proposed. The CAR coefficients were computed from the synaptic weights and coefficients of a split weight and activation function of a feedforward multilayer complex valued neural network. The performance of the proposed technique has been evaluated using PIMA Indian diabetes dataset with different complex-valued data normalization techniques and four different values of learning rate. An accuracy value of 81.28% has been obtained using this proposed technique.     

Investigations of SU-8 removal from metallic high aspect ratio microstructures with a novel plasma technique

First promising investigations of SU-8 removal experiments with a novel plasma etching technique are presented. The basic idea of this technique is to separate the highly effective generation of chemical radicals (e.g. oxygen radicals) using a traveling wave reactor (TWR) microwave source with water cooled plasma zone from the chemical reaction with the resist polymer. The etching tool operates in a remote and downstream mode with very high radical density allowing precise thermal management of the substrates on the chuck giving controlled process conditions without deviation in temperature, and generally preventing ion bombardment, at least resulting in gentle processing without jeopardizing the integrity of the metal structures. Very good removal of SU-8 with very few residues and very high etching rates up to 10 μm per minute are observed in first experiments which are offering chances to get even more than 20 μm per minute. The etching process is isotropic, and the rate stays stable during the whole removing process even for very thick films of 1 mm and more. First application examples of SU-8 removal are demonstrating the great potential of the presented microwave plasma based technique not only for the cleaning of metallic microparts but also for other more sensitive materials which is demonstrated by SU-8 removal from graphite X-ray mask substrates.     

A novel multiseed nonhierarchical data clustering technique

Clustering techniques such as K-means and Forgy as well as their improved version ISODATA group data around one seed point for each cluster, It is well known that these methods do not work well if the shape of the cluster is elongated or nonconvex. We argue that for a elongated or nonconvex shaped cluster, more than one seed is needed, In this paper a multiseed clustering algorithm is proposed. A density based representative point selection algorithm is used to choose the initial seed points. To assign several seed points to one cluster, a minimal spanning tree guided novel technique is proposed. Also, a border point detection algorithm is proposed for the detection of shape of the cluster. This border in turn signifies whether the cluster is elongated or not, Experimental results show the efficiency of this clustering technique.     

A novel multi-dimensional multiple image encryption technique

Multimedia Tools and Applications - This paper proposes a novel secure and fast multiple image encryption technique to encrypt multiple images of arbitrary sizes. In the proposed technique, a group...     

A novel differential evolution mapping technique for generic combinatorial optimization problems

Differential evolution is primarily designed and used to solve continuous optimization problems. Therefore, it has not been widely considered as applicable for real-world problems that are characterized by permutation-based combinatorial domains. Many algorithms for solving discrete problems using differential evolution have been proposed, some of which have achieved promising results. However, to enhance their performance, they require improvements in many aspects, such as their convergence speeds, computational times and capabilities to solve large discrete problems. In this paper, we present a new mapping method that may be used with differential evolution to solve combinatorial optimization problems. This paper focuses specifically on the mapping component and its effect on the performance of differential evolution. Our method maps continuous variables to discrete ones, while at the same time, it directs the discrete solutions produced towards optimality, by using the best solution in each generation as a guide. To judge its performance, its solutions for instances of well-known discrete problems, namely: 0/1 knapsack, traveling salesman and traveling thief problems, are compared with those obtained by 8 other state-of-the-art mapping techniques. To do this, all mapping techniques are used with the same differential evolution settings. The results demonstrated that our technique significantly outperforms the other mapping methods in terms of the average error from the best-known solution for the traveling salesman problems, and achieves promising results for both the 0/1 knapsack and the traveling thief problems.