Chaos Driven Evolutionary Algorithm: a Novel Approach for Optimization

This research deals with the initial investigations on the concept of a chaos-driven evolutionary algorithm Differential evolution. This paper is aimed at the embedding of simple two-dimensional chaotic system, which is Lozi map, in the form of chaos pseudo random number generator for Differential Evolution. The chaotic system of interest is the discrete dissipative system. Repeated simulations were performed on standard benchmark Schwefel’s test function in higher dimensions. Finally, the obtained results are compared with canonical Differential Evolution.

Design and Implementation of Digital Beam Former Architecture for Phased Array Radar

This paper deals with the design and implementation of a digital beam former architecture which is developed for 4/8/12/16 element phased array radar. This technique employs a very high performance FPGA to handle large no of parallel complex arithmetic operations including digital down conversion and filtering. A 3MHz echo signal riding on an IF carrier of 60 MHz is under sampled at 50 MHz and down converted digitally to bring the spectrum to echo signal baseband. After suitable decimation filtering, the I and Q channels are multiplied with Recursive Least Squares based optimized complex weights to form partial beams. The prototype architecture employs techniques of pipelining and parallelism to generate multiple beams simultaneously from a 16 element array within 1 μsec. This can be extended to several number of arrays. The critical components employed in this design are eight 16 bit 125 MS/s ADCs and a very high performance state of the art Xilinx FPGA device Virtex-5 FX 130T having several on-chip resources and 150 MHz clock generators.

Economic Indicators Selection for Property Crime Rates using Grey Relational Analysis and Support Vector Regression

Features selection is very important in the multivariate models because the accuracy of forecasting results produced by the model are highly dependent on these selected features. The purpose of this study is to propose grey relational analysis and support vector regression for features selection. The features are economic indicators that are used to forecast property crime rate. Grey relational analysis selects the best data series to represent each economic indicator and rank the economic indicators according to its importance to the property crime rate. Next, the support vector regression is used to select the significant economic indicators where particle swarm optimization estimates the parameters of support vector regression. In this study, we use unemployment rate, consumer price index, gross domestic product and consumer sentiment index as the economic indicators, as well as property crime rate for the United States. From our experiments, we found that the gross domestic product, unemployment rate and consumer price index are the most influential economic indicators. The proposed method is also found to produce better forecasting accuracy as compared to multiple linear regressions.

A Simulation and Experimental Study on Identification of an Electromechanical System

The current applications in electromechanical energy conversion demand highly accurate speed and position control. For this purpose, a better understanding of the motion characteristics and dynamic behavior of electromechanical systems including nonlinear effects is needed. In this paper, a suitable model of Permanent Magnet Direct Current (PMDC) motor rotating in two directions is developed for identification purposes. Model is parameterized and identified via simulation and using real experimental data. Linear and nonlinear models for the system are built for identification, and the effective nonlinearities in the system, which are Coulomb friction and dead zone, are integrated into the nonlinear model. A Weiner- Hammerstein nonlinear system description is used for identification of the model. MATLAB is selected as the investigating tool, and a simulation model is used to observe the error between the simulated and estimated outputs. Identification of the linear and nonlinear system models using experimental data is performed using the least squares (LS) and recursive least squares (RLS) methods. Performance of the model and identification method with the real time experiments are presented numerically and graphically, revealing the advantages of the proposed nonlinear identification approach.

ERP System Implementation: Benefits and Economic Effectiveness

The enterprise information system offers the service platform to improve the efficiency of enterprise work. Information systems are widely used in different areas and improve the efficiency of enterprise activities. The main purpose of this paper is to present the ERP systems implementation challenges together with identifying the benefits from the implementation and economic effectiveness of ERP systems.

Design an Improved System to Control the Computer Temperature

Temperature is one of the most significant problems that computer users face, especially the users who deal with works that required high-performance processing such as gamers and designers. The traditional PC heat detection methods depend on the information of the CPU and the user should go through a long procedure to check the heat or by installing some third-party programs and in the end, he has to take the action manually. Although some researchers have been talked about the temperature controllers, no system used both software and hardware to control the PC temperature, also there is no consensus on the quick and efficient methods to protect the PC from the overheating problem. The proposed system improved the current solutions by providing better performance in terms of quick responses for safety protection. This paper proposes a novel system for controlling the PC's temperature to increase the safety of computer users.

An Elegant Method of Analysis for the BJT Amplifiers using Floating Admittance Matrix

The Floating Admittance Matrix (FAM) is an elegant, neat, illustrative, and simplified technique for analyzing all configurations of the BJT amplifiers, starting with the maneuvering of the FAM of the phase-splitter circuit. The conventional analysis method requires a small-signal equivalent circuit, and then conventional tools, either KCL, KVL, or Thevenin, Norton, etc., are used for the analysis. The researcher has to guess which conventional tool suites better than the other for any particular circuit, whether active or passive. The proposed technique is equally ell useful for all circuits. In the FAM method, once the device matrix is known rest of the circuit can be embedded in it by inspection. The sum property of this matrix provides a check to know whether FAM has been written correctly to proceed further.

Combining Academic Education With Soft Skills Development: Some Common Aspects of Educational Preparation of IT Professionals and Schoolteachers

In the modern educational process aimed on preparing professionals in the applied areas, it’s crucial, along with purely professional training, to provide students both with solid theoretical background and help them to develop “soft skills” that will facilitate their smooth and efficient adaptation to the industry realities when they start their professional careers. In this paper we consider practical cases of acquiring soft skills through intensive field experience in two areas related to mathematical education.

Semantic and Content-Based Medical Image Retrieval with Proven Pathology for Lung Cancer Diagnosis

In lung cancer computer-aided diagnosis (CAD) systems, having an accurate ground truth is critical and time consuming. Due to lack of ground truth and semantic information, lung CAD systems are not progressing in the manner these are supposed to. In this study, we have explored Lung Image Database Consortium (LIDC) database containing annotated pulmonary computed tomography (CT) scans, and we have used semantic and content-based image retrieval (CBIR) approach to exploit the limited amount of diagnostically labeled data in order to annotate unlabeled images with diagnoses. We evaluated the method by various combinations of lung nodule sets as queries and retrieves similar nodules from the diagnostically labeled dataset. In calculating the precision of this system Diagnosed dataset and computer-predicted malignancy data are used as ground truth for the undiagnosed query nodules. Our results indicate that CBIR expansion is an effective method for labeling undiagnosed images in order to improve the performance of CAD systems while tested on PGIMER data. Also a little knowledge of biopsy confirmed cases can also assist the physician’s as second opinion to mark the undiagnosed cases and avoid unnecessary biopsies

MeLOD

In the last few years, many applications for mobile devices have been developed to support learning experiences both in formal and informal contexts. One of the main limits of these applications concerns the development of learning materials suitable for mobile learning contexts. In fact, learning content must be usually prepared in advance by teachers and maintained during the whole lifespan of the application. In this paper, we present MeLOD, a mobile learning environment, which exploits the huge amount of dataset in the Linked Open Data (LOD) cloud to overcome the previous issue, and provides contextualized and continuously updated information based on students’ location. The position of the student sent by the mobile device is used to interlink Geonames DBpedia and Europeana datasets to provide information about all the interesting cultural heritage sites close to the student. Moreover, students social activities like voting and commenting are used to enhance the knowledge base of the environment and to provide recommendations for next students’ visits.