Excellence in Research and Innovation for Humanity

International Science Index

Commenced in January 1999 Frequency: Monthly Edition: International Paper Count: 24

Computer, Electrical, Automation, Control and Information Engineering

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  • 24
    A Comparative Analysis of Zotero and Mendeley Reference Management Software

    This paper presents a comparison of the reference management software between Zotero and Mendeley and the results were drawn by comparing the two software’s. The novelty of this paper is the comparative analysis of the software and it has shown that Mendeley can import more information from the Google Scholar for the researchers. This finding can help to know researchers to use the reference management software.

    Tool for Fast Detection of Java Code Snippets

    This paper presents general results on the Java source code snippet detection problem. We propose the tool which uses graph and subgraph isomorphism detection. A number of solutions for all of these tasks have been proposed in the literature. However, although that all these solutions are really fast, they compare just the constant static trees. Our solution offers to enter an input sample dynamically with the Scripthon language while preserving an acceptable speed. We used several optimizations to achieve very low number of comparisons during the matching algorithm.

    Combined Automatic Speech Recognition and Machine Translation in Business Correspondence Domain for English-Croatian

    The paper presents combined automatic speech recognition (ASR) of English and machine translation (MT) for English and Croatian and Croatian-English language pairs in the domain of business correspondence. The first part presents results of training the ASR commercial system on English data sets, enriched by error analysis. The second part presents results of machine translation performed by free online tool for English and Croatian and Croatian-English language pairs. Human evaluation in terms of usability is conducted and internal consistency calculated by Cronbach's alpha coefficient, enriched by error analysis. Automatic evaluation is performed by WER (Word Error Rate) and PER (Position-independent word Error Rate) metrics, followed by investigation of Pearson’s correlation with human evaluation.

    Reference Management Software: Comparative Analysis of RefWorks and Zotero

    This paper presents a comparison of reference management software between RefWorks and Zotero. The results were drawn by comparing two software and the novelty of this paper is the comparative analysis of software and it has shown that ReftWorks can import more information from the Google Scholar for the researchers. This finding could help to know researchers to use the reference management software.

    Theoretical Exploration for the Impact of Accounting for Special Methods in Connectivity-Based Cohesion Measurement

    Class cohesion is a key object-oriented software quality attribute that is used to evaluate the degree of relatedness of class attributes and methods. Researchers have proposed several class cohesion measures. However, the effect of considering the special methods (i.e., constructors, destructors, and access and delegation methods) in cohesion calculation is not thoroughly theoretically studied for most of them. In this paper, we address this issue for three popular connectivity-based class cohesion measures. For each of the considered measures we theoretically study the impact of including or excluding special methods on the values that are obtained by applying the measure. This study is based on analyzing the definitions and formulas that are proposed for the measures. The results show that including/excluding special methods has a considerable effect on the obtained cohesion values and that this effect varies from one measure to another. For each of the three connectivity-based measures, the proposed theoretical study recommended excluding the special methods in cohesion measurement.

    Evaluation of Short-Term Load Forecasting Techniques Applied for Smart Micro Grids

    Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is easy yet effective to apply Load Forecasting at larger geographic scale, i.e. Smart Micro Grids, wherein the lower available grid flexibility makes accurate prediction more critical in Demand Response applications. This paper analyses the application of short-term load forecasting in a concrete scenario, proposed within the EU-funded GreenCom project, which collect load data from single loads and households belonging to a Smart Micro Grid. Three short-term load forecasting techniques, i.e. linear regression, artificial neural networks, and radial basis function network, are considered, compared, and evaluated through absolute forecast errors and training time. The influence of weather conditions in Load Forecasting is also evaluated. A new definition of Gain is introduced in this paper, which innovatively serves as an indicator of short-term prediction capabilities of time spam consistency. Two models, 24- and 1-hour-ahead forecasting, are built to comprehensively compare these three techniques.

    A New Approach to Predicting Physical Biometrics from Behavioural Biometrics

    A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

    A Video-Based Observation and Analysis Method to Assess Human Movement and Behaviour in Crowded Areas

    Human movement in the real world provides important information for developing human behaviour models and simulations. However, it is difficult to assess ‘real’ human behaviour since there is no established method available. As part of the AUNTSUE (Accessibility and User Needs in Transport – Sustainable Urban Environments) project, this research aimed to propose a method to assess human movement and behaviour in crowded areas. The method is based on the three major steps of video recording, conceptual behavior modelling and video analysis. The focus is on individual human movement and behaviour in normal situations (panic situations are not considered) and the interactions between individuals in localized areas. Emphasis is placed on gaining knowledge of characteristics of human movement and behaviour in the real world that can be modelled in the virtual environment.

    Heuristic for Accelerating Run-Time Task Mapping in NoC-Based Heterogeneous MPSoCs

    In this paper, we propose a new packing strategy to find a free resource for run-time mapping of application tasks to NoC-based Heterogeneous MPSoC. The proposed strategy minimizes the task mapping time in addition to placing the communicating tasks close to each other. To evaluate our approach, a comparative study is carried out for a platform containing single task supported PEs. Experiments show that our strategy provides better results when compared to latest dynamic mapping strategies reported in the literature.

    Estimation of Component Reusability through Reusability Metrics

    Software reusability is an essential characteristic of Component-Based Software (CBS). The component reusability is an important assess for the effective reuse of components in CBS. The attributes of reusability proposed by various researchers are studied and four of them are identified as potential factors affecting reusability. This paper proposes metric for reusability estimation of black-box software component along with metrics for Interface Complexity, Understandability, Customizability and Reliability. An experiment is performed for estimation of reusability through a case study on a sample web application using a real world component.

    Designing Intelligent Adaptive Controller for Nonlinear Pendulum Dynamical System

    This paper proposes the designing direct adaptive neural controller to apply for a class of a nonlinear pendulum dynamic system. The radial basis function (RBF) neural adaptive controller is robust in presence of external and internal uncertainties. Both the effectiveness of the controller and robustness against disturbances are importance of this paper. The simulation results show the promising performance of the proposed controller.

    Frequency Controller Design for Distributed Generation by Load Shedding: Multi-Agent Systems Approach

    Frequency stability of microgrids under islanded operation attracts particular attention recently. A new cooperative frequency control strategy based on centralized multi-agent system (CMAS) is proposed in this study. Based on this strategy, agents sent data and furthermore each component has its own to center operating decisions (MGCC).After deciding on the information, they are returned. Frequency control strategies include primary and secondary frequency control and disposal of multi-stage load in which this study will also provide a method and algorithm for load shedding. This could also be a big problem for the performance of micro-grid in times of disaster. The simulation results show the promising performance of the proposed structure of the controller based on multi agent systems.

    Information Retrieval: A Comparative Study of Textual Indexing Using an Oriented Object Database (db4o) and the Inverted File

    The growth in the volume of text data such as books and articles in libraries for centuries has imposed to establish effective mechanisms to locate them. Early techniques such as abstraction, indexing and the use of classification categories have marked the birth of a new field of research called "Information Retrieval". Information Retrieval (IR) can be defined as the task of defining models and systems whose purpose is to facilitate access to a set of documents in electronic form (corpus) to allow a user to find the relevant ones for him, that is to say, the contents which matches with the information needs of the user. Most of the models of information retrieval use a specific data structure to index a corpus which is called "inverted file" or "reverse index". This inverted file collects information on all terms over the corpus documents specifying the identifiers of documents that contain the term in question, the frequency of each term in the documents of the corpus, the positions of the occurrences of the word... In this paper we use an oriented object database (db4o) instead of the inverted file, that is to say, instead to search a term in the inverted file, we will search it in the db4o database. The purpose of this work is to make a comparative study to see if the oriented object databases may be competing for the inverse index in terms of access speed and resource consumption using a large volume of data.

    EUDIS-An Encryption Scheme for User-Data Security in Public Networks

    The method of introducing the proxy interpretation for sending and receiving requests increase the capability of the server and our approach UDIV (User-Data Identity Security) to solve the data and user authentication without extending size of the data makes better than hybrid IDS (Intrusion Detection System). And at the same time all the security stages we have framed have to pass through less through that minimize the response time of the request. Even though an anomaly detected, before rejecting it the proxy extracts its identity to prevent it to enter into system. In case of false anomalies, the request will be reshaped and transformed into legitimate request for further response. Finally we are holding the normal and abnormal requests in two different queues with own priorities.

    A Tree Based Association Rule Approach for XML Data with Semantic Integration

    The use of eXtensible Markup Language (XML) in web, business and scientific databases lead to the development of methods, techniques and systems to manage and analyze XML data. Semi-structured documents suffer due to its heterogeneity and dimensionality. XML structure and content mining represent convergence for research in semi-structured data and text mining. As the information available on the internet grows drastically, extracting knowledge from XML documents becomes a harder task. Certainly, documents are often so large that the data set returned as answer to a query may also be very big to convey the required information. To improve the query answering, a Semantic Tree Based Association Rule (STAR) mining method is proposed. This method provides intentional information by considering the structure, content and the semantics of the content. The method is applied on Reuter’s dataset and the results show that the proposed method outperforms well.

    Motion Capture Based Wizard of Oz Technique for Humanoid Robot

    The paper focus on robotic telepresence system build around humanoid robot operated with controller-less Wizard of Oz technique. Proposed solution gives possibility to quick start acting as a operator with short, if any, initial training.

    The Impact of the Number of Neurons in the Hidden Layer on the Performance of MLP Neural Network: Application to the Fast Identification of Toxic Gases

    In this work, neural networks methods MLP type were applied to a database from an array of six sensors for the detection of three toxic gases. The choice of the number of hidden layers and the weight values are influential on the convergence of the learning algorithm. We proposed, in this article, a mathematical formula to determine the optimal number of hidden layers and good weight values based on the method of back propagation of errors. The results of this modeling have improved discrimination of these gases and optimized the computation time. The model presented here has proven to be an effective application for the fast identification of toxic gases.

    Gaussian Density and HOG with Content Based Image Retrieval System – A New Approach

    Content-based image retrieval (CBIR) uses the contents of images to characterize and contact the images. This paper focus on retrieving the image by separating images into its three color mechanism R, G and B and for that Discrete Wavelet Transformation is applied. Then Wavelet based Generalized Gaussian Density (GGD) is practical which is used for modeling the coefficients from the wavelet transforms. After that it is agreed to Histogram of Oriented Gradient (HOG) for extracting its characteristic vectors with Relevant Feedback technique is used. The performance of this approach is calculated by exactness and it confirms that this method is wellorganized for image retrieval.

    Optimal Grid Scheduling Using Improved Artificial Bee Colony Algorithm

    Job Scheduling plays an important role for efficient utilization of grid resources available across different domains and geographical zones. Scheduling of jobs is challenging and NPcomplete. Evolutionary / Swarm Intelligence algorithms have been extensively used to address the NP problem in grid scheduling. Artificial Bee Colony (ABC) has been proposed for optimization problems based on foraging behaviour of bees. This work proposes a modified ABC algorithm, Cluster Heterogeneous Earliest First Min- Min Artificial Bee Colony (CHMM-ABC), to optimally schedule jobs for the available resources. The proposed model utilizes a novel Heterogeneous Earliest Finish Time (HEFT) Heuristic Algorithm along with Min-Min algorithm to identify the initial food source. Simulation results show the performance improvement of the proposed algorithm over other swarm intelligence techniques.

    Colour Image Compression Method Based On Fractal Block Coding Technique

    Image compression based on fractal coding is a lossy compression method and normally used for gray level images range and domain blocks in rectangular shape. Fractal based digital image compression technique provide a large compression ratio and in this paper, it is proposed using YUV colour space and the fractal theory which is based on iterated transformation. Fractal geometry is mainly applied in the current study towards colour image compression coding. These colour images possesses correlations among the colour components and hence high compression ratio can be achieved by exploiting all these redundancies. The proposed method utilises the self-similarity in the colour image as well as the cross-correlations between them. Experimental results show that the greater compression ratio can be achieved with large domain blocks but more trade off in image quality is good to acceptable at less than 1 bit per pixel.

    Highly Optimized Novel High Speed Low Power Barrel Shifter at 22nm Hi K Metal Gate Strained Si Technology Node
    This research paper presents highly optimized barrel shifter at 22nm Hi K metal gate strained Si technology node. This barrel shifter is having a unique combination of static and dynamic body bias which gives lowest power delay product. This power delay product is compared with the same circuit at same technology node with static forward biasing at ‘supply/2’ and also with normal reverse substrate biasing and still found to be the lowest. The power delay product of this barrel sifter is .39362X10-17J and is lowered by approximately 78% to reference proposed barrel shifter at 32nm bulk CMOS technology. Power delay product of barrel shifter at 22nm Hi K Metal gate technology with normal reverse substrate bias is 2.97186933X10-17J and can be compared with this design’s PDP of .39362X10-17J. This design uses both static and dynamic substrate biasing and also has approximately 96% lower power delay product compared to only forward body biased at half of supply voltage. The NMOS model used are predictive technology models of Arizona state university and the simulations to be carried out using HSPICE simulator.
    Driver Fatigue State Recognition with Pixel Based Caveat Scheme Using Eye-Tracking
    Driver fatigue is an important factor in the increasing number of road accidents. Dynamic template matching method was proposed to address the problem of real-time driver fatigue detection system based on eye-tracking. An effective vision based approach was used to analyze the driver’s eye state to detect fatigue. The driver fatigue system consists of Face detection, Eye detection, Eye tracking, and Fatigue detection. Initially frames are captured from a color video in a car dashboard and transformed from RGB into YCbCr color space to detect the driver’s face. Canny edge operator was used to estimating the eye region and the locations of eyes are extracted. The extracted eyes were considered as a template matching for eye tracking. Edge Map Overlapping (EMO) and Edge Pixel Count (EPC) matching function were used for eye tracking which is used to improve the matching accuracy. The pixel of eyeball was tracked from the eye regions which are used to determine the fatigue state of the driver.
    Segmentation of Noisy Digital Images with Stochastic Gradient Kernel
    Image segmentation and edge detection is a fundamental section in image processing. In case of noisy images Edge Detection is very less effective if we use conventional Spatial Filters like Sobel, Prewitt, LOG, Laplacian etc. To overcome this problem we have proposed the use of Stochastic Gradient Mask instead of Spatial Filters for generating gradient images. The present study has shown that the resultant images obtained by applying Stochastic Gradient Masks appear to be much clearer and sharper as per Edge detection is considered.
    Fuzzy Inference System Based Unhealthy Region Classification in Plant Leaf Image
    In addition to environmental parameters like rain, temperature diseases on crop is a major factor which affects production quality & quantity of crop yield. Hence disease management is a key issue in agriculture. For the management of disease, it needs to be detected at early stage. So, treat it properly & control spread of the disease. Now a day, it is possible to use the images of diseased leaf to detect the type of disease by using image processing techniques. This can be achieved by extracting features from the images which can be further used with classification algorithms or content based image retrieval systems. In this paper, color image is used to extract the features such as mean and standard deviation after the process of region cropping. The selected features are taken from the cropped image with different image size samples. Then, the extracted features are taken in to the account for classification using Fuzzy Inference System (FIS).