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As the speed of sound is interfered easily in the complex environment, the positioning information given by SBL sonar usually has noises. To solve this problem, this paper gives a positioning method based on Kalman filtering, which lays a good foundation for the application of AUV in the complex ocean environment in future, and has a good value on the practical application of engineering. In details, it disposes sensor data to get exact positioning information, and the validity is fully proved by tank experiment of AUV. The experiment results indicate that the positioning precision of SBL sonar is enhanced from 3.99% to 1.41%, at the same time, the designed demand of AUV is satisfied. [ABSTRACT FROM PUBLISHER]

This paper presents an initiative high density design for low profile adapter with medium or low output power. The high frequency quasi-resonant flyback converter with synchronous rectification helps to decrease the whole volume 21% compared with the current smallest product level design. The detailed design consideration is presented and performance is validated by an experimental universal-input off-line 19V/3.42A flyback prototype. [ABSTRACT FROM PUBLISHER]

In this paper, taking a three-phase 12/8 structure SR motor as its control object, the SR motor digital control system is designed based on MCF5213 which integrated the DSP computing power and MCU controller characteristics. This paper adopts the PWM voltage chopping control strategy with fuzzy logic algorithm and the angle position control strategy to realize rotor speed regulation control. The realization method of the system hardware structure and software are also introduced with verified the feasibility of the scheme through the experiment. [ABSTRACT FROM PUBLISHER]

In this paper we present a novel switched-capacitor implementation of short-term synaptic dynamics with simultaneous depression and facilitation. The developed circuit model is a modified version of a model of neurotransmitter release derived from biological measurements. Despite the simplicity of the circuit the rich dynamics of the original model can be delivered. By completely relying on SC techniques for all calculations, our circuit is significantly less sensitive to process variations and easier to calibrate than commonly employed subthreshold circuits. The circuit makes use of a technique for minimizing leakage effects allowing for real-time operation with time constants up to several seconds. Functionality and robustness of the circuit are verified by simulations and comparisons to the original model. [ABSTRACT FROM PUBLISHER]

Modern wireless communication transceiver applications often pose stringent out-of-band rejection requirements which necessitate steep and sharp narrow band rejection profile for microwave filters. The use of transmission zero inserted filters are very common to achieve such a response type. Microstrip filters are popular implementation option because of their compactness and low mass where Q-factor required is not so high. In this paper, a step by step methodology is presented for design and realization of a quadruplet filter with two finite real-frequency symmetric transmission zeros. The filter is realized in microstrip and results are presented. [ABSTRACT FROM PUBLISHER]

Businesses live and die in their ability to correctly invest in areas of competence that delivers value to their customers using the company's unique capabilities, core competencies. There are very scant numbers of practical methods available in literature for determining the core-competencies in small medium enterprise (SME) level. The standard reported practices in the national and regional levels usually use the financial outputs, R&D investments and patent outputs as the main factors to quantify the technological capability in a specific industry or technology. At the firm level, either a benchmarking with the superior companies or a survey from customers shows the core-competencies of the firm respective to the competitors. This study proposes an innovative approach to develop a quantitative method based on customer acceptance (measured through financial outcomes of the products) to define the firm's core-competencies. Method is an indigenous approach which doesn't directly ask from neither customers nor employess/management about the company's competencies. Instead based on rate of success among previous products launched to the market, and common characteristics of the successful products, core-competencies are defined. The proposed Framework uses the simple regression analysis and multivariable regression analysis to recognize the correlation between predictor variables and success variable. This framework has been performed in a small size engineering/manufacturing company and results have been provided. Advantages and limitation of the method will be discussed at the end. [ABSTRACT FROM PUBLISHER]

Customer satisfaction is one of the key performance parameters in the present business scenario of systems like job shop which are characterized by high variety and low volume products. In such systems customer satisfaction strongly depends on factors like delivery time and quality of product/service provided. In order to achieve this, it becomes essential to perform proper planning of resource and establish the framework for setting the relative parameters effects and the significance of the assumed parameters. The contribution of present paper lies in developing capacity planning of resources (in terms of buffers and workstations) and setting the priorities to focus on the assumed decision and system parameters and also in highlighting likely factor-level combinations that would result in near-optimal shop performance. [ABSTRACT FROM PUBLISHER]

Biomass derived vegetable oils can be used an alternative fuels for CI engines. In this view the properties measurement of biofuels and its blends with diesel were carried. Biofuels selected for the properties measurement are Undi, Palm and Simarouba. These fuels are added in proportions with pure diesels so as to biodiesel form. These biofuels in transesterfication process are prepared in ethyl and methyl based. The blends of the biofuels are formed in volume proportions of 10, 20, 30, 40, 50 and 100 % with commercial diesel. The various physical and the chemical properties of these biofuels and its blends with diesel were measured. From the measured data of these properties trend lines were plotted and the correlations are found. Also comparative studies of these properties of the blends are discussed in terms of its suitability as fuel in the CI engine with emission characteristics. Also the efforts are made, to study the preparation method and its probable effect on CI engine is discussed. The main objective of this study is to find the suitability of these biofuels and its blends as alternative fuel for CI Engine. [ABSTRACT FROM PUBLISHER]

This paper deals with finding five optimal control parameters input voltage, current, speed, pulse on/off time to maximize metal removal rate (MRR) and minimize surface roughness (SR) on wire edm (electrical discharge machining). Wire edm is an electro thermal production process in which a thin single strand metal wire in conjuction with de-ionised water (used to conduct electricity), allows the wire to cut through metal by the use of heat from electrical sparks. For the purpose to get a best solution to maximize MRR and reduce SR, here we optimize parameters using taguchi method. Also we compare experimental reading with taguchi optimum result to know the optimal solution. [ABSTRACT FROM PUBLISHER]

This paper presents a direct flux and torque control (DTC) of induction motor drive (IMD) for speed regulator (SR) using fuzzy logic controller (FLC). This control method is based on DTC operating principles. The DTC is one of the most excellent strategies of torque and flux ripples control of IMD. The DTC control method has been optimized by using conventional PI controller in the SR loop of IMD. The main drawback of the DTC of IMD using conventional PI controller based SR is high torque, stator flux ripples and speed of IMD is decreasing under transient and steady state operating conditions. This drawback was eliminated using the FLC based SR loop. The FLC based SR control scheme combines the benefits of DTC technique along with FLC technique. Also Comparing with the conventional PI based SR, the FLC based SR is achieved high quality of torque, stator flux, and rated speed of IMD. The stability of IMD during transient and steady state operations is assured through the application of FLC based SR along with DTC. Finally the effectiveness, validity, and performance of the DTC of IMD using both conventional PI and FL controller based SR has been analyzed, studied, compared, and confirmed by simulating results, from the simulation results found that the low torque, stator flux ripples, and rated speed with the FLC technique using MATLAB/SIMULINK. [ABSTRACT FROM PUBLISHER]

This paper presents a multifocus image fusion based on sparse representation theory. In the method, first the source images are decomposed by an overcomplete dictionary that contains prototype signal atoms into their corresponding sparse coefficient. Secondly, the corresponding coefficients of the sources images are combined with the choose-max fusion rule. Finally, the fused image is reconstructed from the combined sparse coefficients and the dictionary. The advantage of the proposed method is that it can simultaneously resolve the image restoration and fusion problem by changing the appropriate criterion in the sparse representation algorithm. The proposed method is tested on several multifocus images and compared with Discrete Wavelet Transform based method. The experiment results show the feasibility and effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Delay tolerant Network (DTN) is new challenging network where end to end delay is more as no end-to-end connectivity. This network is emerged from Mobile Adhoc network. In Internet, data transmission is possible only between those nodes, where TCP/UDP can able to establish connection. This is impossible in DTN where TCP and UDP protocol cant used due no end-to-end connectivity. This makes DTN interesting field for researcher where data transmission takes longer time almost in hours. Like adhoc network, there is no route setup and maintenance phase. The majority of routing schemes that have been proposed earlier are broadly classified as flooding and forwarding strategies. This paper, authors try to evaluate the performance of the most prominent routing protocols designed for Delay Tolerant Networks viz. Direct Delivery, Epidemic, First Contact. These strategies called delay tolerant routing protocols are configured for map based model on day to day movements of all members coming to college by means of any vehicle. Also city traffic is also considered. This scenario is created by considering local map. After study and successful simulations of these protocols, author obtains various results by varying the storage capacity of nodes to store and forward message. With the help of results, the authors proved that the performance of MaxProp routing protocol is outstanding than all other routing protocols under consideration. [ABSTRACT FROM PUBLISHER]

This paper presents a low peak power consumption BIST based on genetic algorithm (GA) is applied for viterbi decoder error detection and correction, denoted by GAITPGEDC. This method aims at reducing the changes between successive test pattern. Here m-1 test vectors are inserted between two successive n-bit generated by linear feedback shift registers (LFSR), where m and elements of group were optimized by GA. Thus the switching activities of test vectors are greatly reduced in test mode without compromising fault coverage. The proposed structure has the advantages of low test. Experiments conducted on ISCAS'89 benchmark circuits demonstrate that proposed scheme gives better fault coverage and with viterbi decoder error detection and correction was performed with a large reduction in power dissipation during testing. [ABSTRACT FROM PUBLISHER]

Sewer blockages are quite common at present in small as well as big cities worldwide. This has become alarming due to the use of polythene bags and other solid materials which are dumped into the sewer system. The sewer lines get frequently choked leading to their blockage and overflow which results in sanitation problems and creates health hazard for citizens. Detection of blockage point is difficult and a major problem in maintaining the sewer system. The current methods of blockage detection are based on manual visual inspection and inspection through CCD camera based equipments. The main limitation of these systems is that sewage pipeline has to be drained out first. In India and other underdeveloped countries, the CCD camera based equipments are generally not available due to their high cost and other problems and the work has to be done manually by hiring labourers. In this study, robotic equipment with an embedded system has been developed for locating the blockage using ultrasound sensors. These sensors can work in the pipelines of all diameters by carrying these by a suitable size of robotic vehicle and hence can be used easily. It can work in liquid filled sewer pipes of all sizes. This system can also be used for pipe-fault detection of other systems after small changes. [ABSTRACT FROM PUBLISHER]

The Railway Interoperability Laboratory of CEDEX contributes to the process of placing Railway projects into service using reference tools loaded with project data and with real equipment connected. This strategy provides a more efficient and controlled environment, assures the interoperability of the project and facilitates the integration track/ train. The validation of the Laboratory by comparing track and laboratory records makes acceptable the laboratory tests for the safety departments of the Infrastructure Managers and Operators. This communication provides an overview of the process of the laboratory validation. [ABSTRACT FROM PUBLISHER]

Cooperative schemes for critical content distribution over vehicular networks are presented and analyzed. The first scheme is based on unicasting from the base station, whereas the second is based on threshold based multicasting. Long Term Evolution (LTE) is used for long range communications with the base station (BS) and 802.11p is considered for inter-vehicle collaboration on the short range. A high mobility environment with correlated shadowing is adopted. Both schemes are shown to outperform non-cooperative unicasting and multicasting, respectively, when the appropriate 802.11p power class is used. The first scheme achieves the best performance among the compared methods, and a practical approximation of that scheme is shown to be close to optimal performance. [ABSTRACT FROM PUBLISHER]

This paper presents a new approach for reducing transmitter-leakage in UHF RFID readers for mobile devices. In the approach, it is possible to reduce the leakage dynamically by utilizing pilot signals for detecting the leakage and then applying reverse-phase signals for its compensation. Experimental results show more than 30dB of the leakage elimination and 25∼600% of enhancement in the reading range when tested with a 8dBi stationary and a −3.5dBi mobile antennae in an anechoic chamber. [ABSTRACT FROM PUBLISHER]

This paper explores the issues with mapping termination in Grammatical Evolution, and examines approaches that can be used to minimise them. It analyses the traditional approach of reusing the same genetic material, known as wrapping, and shows why this is inefficient with some grammars used in the literature. It suggests the appending of non-coding genetic material to genotype strings, at the start of the run, and shows the benefits of this approach: higher probability of creating terminated individuals, better or similar experimental performance, and a tendency to generate smaller solutions, when compared to the use of wrapping. [ABSTRACT FROM PUBLISHER]

A new family of Differential Evolution mutation strategies (DE/nrand) that are able to handle multimodal functions, have been recently proposed. The DE/nrand family incorporates information regarding the real nearest neighborhood of each potential solution, which aids them to accurately locate and maintain many global optimizers simultaneously, without the need of additional parameters. However, these strategies have increased computational cost. To alleviate this problem, instead of computing the real nearest neighbor, we incorporate an index-based neighborhood into the mutation strategies. The new mutation strategies are evaluated on eight well-known and widely used multimodal problems and their performance is compared against five state-of-the-art algorithms. Simulation results suggest that the proposed strategies are promising and exhibit competitive behavior, since with a substantial lower computational cost they are able to locate and maintain many global optima throughout the evolution process. [ABSTRACT FROM PUBLISHER]

This paper presents the addition of an adaptive stepsize value and a local search operator to the modified bacterial foraging algorithm (MBFOA) to solve constrained optimization problems. The adaptive stepsize is used in the chemotactic loop for each bacterium to promote a suitable sampling of solutions and the local search operator aims to promote a better trade-off between exploration and exploitation during the search. Three MBFOA variants, the original one, another with only the adaptive stepsize and a third one with both, the adaptive stepsize and also the local search operator are tested on a set of well-known benchmark problems. Furthermore, the most competitive variant is compared against some representative nature-inspired algorithms of the state-of-the-art. The results obtained provide evidence on the utility of each added mechanism, while the overall performance of the approach makes it a viable option to solve constrained optimization problems. [ABSTRACT FROM PUBLISHER]

This paper discusses a particle swarm optimization (PSO) with switched topology and its application to the multi-solution problems. First, we introduce a deterministic PSO characterized by normalized deterministic parameters and a canonical form system equation. This system is convenient to grasp effects of parameters on the stability. Second, we investigate effects of the average distance of several the swarm topologies on the search capability. Especially, we introduce the switched topology where any information is not transmitted from the edge if the switch is off. Third, we consider an application to exploring multiple periodic points in simple dynamical systems. Performing numerical experiments for typical examples, the algorithm performance is investigated. [ABSTRACT FROM PUBLISHER]

ABC is an optimization technique, used in finding the best solution from all feasible solutions. However, there is still an insufficiency in ABC regarding improvement in exploitation and convergence speed. In order to improve the performance of ABC we used mean mutation operator (MMO), which uses a linear combination of Gaussian and Cauchy distributions. This convoluted distribution produces larger mutations than the Gaussian distribution, and smaller mutations than the Cauchy distribution, which in simpler words justifies/balances exploration and exploitation in ABC. Experiments are conducted on a set of 6 benchmark functions. The results demonstrate good performance of proposed variant in solving numerical optimization problems when compared with three ABC-based algorithms. [ABSTRACT FROM PUBLISHER]

We present a novel multi-class classifier that strikes a balance between the nearest-subspace classifier, which assigns a test sample to the class that minimizes the distance between the test sample and its principal projection in the selected class, and a collaborative representation based classifier, which classifies a sample to the class that minimizes the distance between the collaborative components of the test sample by using all training samples from all classes as the dictionary and its projection in the selected class. In our formulation, the sparse representation based classifier [1] and nearest subspace classifier become special cases under different regularization parameters. We show that the classification performance can be improved by optimally tuning the regularization parameter, which can be done at almost no extra computational cost. We give extensive numerical examples for digit identification and face recognition with performance comparisons of different choices of collaborative representations, in particular when only a partial observation of the test sample is available via compressive sensing measurements. [ABSTRACT FROM PUBLISHER]

The low resolution of images has been one of the major limitations in recognising humans from a distance using their biometric traits, such as face and iris. Superresolution has been employed to improve the resolution and the recognition performance simultaneously, however the majority of techniques employed operate in the pixel domain, such that the biometric feature vectors are extracted from a super-resolved input image. Feature-domain superresolution has been proposed for face and iris, and is shown to further improve recognition performance by capitalising on direct super-resolving the features which are used for recognition. However, current feature-domain superresolution approaches are limited to simple linear features such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are not the most discriminant features for biometrics. Gabor-based features have been shown to be one of the most discriminant features for biometrics including face and iris. This paper proposes a framework to conduct super-resolution in the non-linear Gabor feature domain to further improve the recognition performance of biometric systems. Experiments have confirmed the validity of the proposed approach, demonstrating superior performance to existing linear approaches for both face and iris biometrics. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a discriminative low-rank dictionary learning algorithm for sparse representation. Sparse representation seeks the sparsest coefficients to represent the test signal as linear combination of the bases in an over-complete dictionary. Motivated by low-rank matrix recovery and completion, assume that the data from the same pattern are linearly correlated, if we stack these data points as column vectors of a dictionary, then the dictionary should be approximately low-rank. An objective function with sparse coefficients, class discrimination and rank minimization is proposed and optimized during dictionary learning. We have applied the algorithm for face recognition. Numerous experiments with improved performances over previous dictionary learning methods validate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Representative data in terms of a set of selected samples is of interest for various machine learning applications, e.g. dimensionality reduction and classification. The best-known techniques probably still are k-Nearest Neighbors (kNN) and its variants. Recently, richer representations have become popular. Examples are methods based on l1-regularized least squares (Sparse Representation (SR)) or l2-regularized least squares (Collaborative Representation (CR)), or on l1-constrained least squares (Local Linear Embedding (LLE)). We propose Iterative Nearest Neighbors (INN). This is a novel sparse representation that combines the power of SR and LLE with the computational simplicity of kNN. We test our method in terms of dimensionality reduction and classification, using standard benchmarks such as faces (AR), traffic signs (GTSRB), and PASCAL VOC 2007. INN performs better than NN and comparable with CR and SR, while being orders of magnitude faster than the latter. [ABSTRACT FROM PUBLISHER]

Constructing a good graph to represent data structures is critical for many important machine learning tasks such as clustering and classification. This paper proposes a novel non-negative low-rank and sparse (NNLRS) graph for semi-supervised learning. The weights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse matrix that represents each data sample as a linear combination of others. The so-obtained NNLRS-graph can capture both the global mixture of subspaces structure (by the low rankness) and the locally linear structure (by the sparseness) of the data, hence is both generative and discriminative. We demonstrate the effectiveness of NNLRS-graph in semi-supervised classification and discriminative analysis. Extensive experiments testify to the significant advantages of NNLRS-graph over graphs obtained through conventional means. [ABSTRACT FROM PUBLISHER]

The choice of the over-complete dictionary that sparsely represents data is of prime importance for sparse coding-based image super-resolution. Sparse coding is a typical unsupervised learning method to generate an over-complete dictionary. However, most of the sparse coding methods for image super-resolution fail to simultaneously consider the geometrical structure of the dictionary and corresponding coefficients, which may result in noticeable super-resolution reconstruction artifacts. In this paper, a novel sparse coding method is proposed to preserve the geometrical structure of the dictionary and the sparse coefficients of the data. Moreover, the proposed method can preserve the incoherence of dictionary entries, which is critical for sparse representation. Inspired by the development on non-local self-similarity and manifold learning, the proposed sparse coding method can provide the sparse coefficients and learned dictionary from a new perspective, which have both reconstruction and discrimination properties to enhance the learning performance. Extensive experimental results on image super-resolution have demonstrated the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a temporal super resolution approach for quasi-periodic image sequence such as human gait. The proposed method effectively combines example-based and reconstruction-based temporal super resolution approaches. A periodic image sequence is expressed as a manifold parameterized by a phase and a standard manifold is learned from multiple high frame-rate sequences in the training stage. In the test stage, an initial phase for each frame of an input low frame-rate image sequence is estimated based on the standard manifold at first, and the manifold reconstruction and the phase estimation are then iterated to generate better high frame-rate images in the energy minimization framework that ensures the fitness to both the input images and the standard manifold. The proposed method is applied to low frame-rate gait recognition and experiments with real data of 100 subjects demonstrate a significant improvement by the proposed method, particularly for quite low frame-rate videos (e.g., 1 fps). [ABSTRACT FROM PUBLISHER]

Reconstruction- and example-based super-resolution (SR) methods are promising for restoring a high-resolution (HR) image from low-resolution (LR) image(s). Under large magnification, reconstruction-based methods usually fail to hallucinate visual details while example-based methods sometimes introduce unexpected details. Given a generic LR image, to reconstruct a photo-realistic SR image and to suppress artifacts in the reconstructed SR image, we introduce a multi-scale dictionary to a novel SR method that simultaneously integrates local and non-local priors. The local prior suppresses artifacts by using steering kernel regression to predict the target pixel from a small local area. The non-local prior enriches visual details by taking a weighted average of a large neighborhood as an estimate of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate that the proposed method can produce high quality SR recovery both quantitatively and perceptually. [ABSTRACT FROM PUBLISHER]