Search results for: Fuzzy input

Authors: Jiashan Tang

Abstract:

In this note, we investigate the blind source separability of linear FIR-MIMO systems. The concept of semi-reversibility of a system is presented. It is shown that for a semi-reversible system, if the input signals belong to a binary alphabet, then the source data can be blindly separated. One sufficient condition for a system to be semi-reversible is obtained. It is also shown that the proposed criteria is weaker than that in the literature which requires that the channel matrix is irreducible/invertible or reversible.

Keywords: Blind source separable, FIR-MIMO system, Binary input, Bezout equality.

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Authors: Tae-Yeong Lee, Eun-Ju Yoo, Won-Yeong Choi, Young-Woo Park

Abstract:

This paper presents a 24 watts SEPIC converter design and control using microprocessor. SEPIC converter has advantages of a wide input range and miniaturization caused by the low stress at elements. There is also an advantage that the input and output are isolated in MOSFET-off state. This paper presents the PID control through the SEPIC converter transfer function using a DSP and the protective circuit for fuel cell from the over-current and inverse-voltage by using the characteristic of SEPIC converter. Then it derives them through the experiments.

Keywords: DC-DC Converter, Fuel-Cell, Microprocessor Control, Military Converter, SEPIC Converter

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Authors: Anna Lower, Michal Lower, Robert Masztalski, Agnieszka Szumilas

Abstract:

The paper presents a method in which the expert knowledge is applied to fuzzy inference model. Even a less experienced person could benefit from the use of such a system, e.g. urban planners, officials. The analysis result is obtained in a very short time, so a large number of the proposed locations can also be verified in a short time. The proposed method is intended for testing of locations of car parks in a city. The paper shows selected examples of locations of the P&R facilities in cities planning to introduce the P&R. The analyses of existing objects are also shown in the paper and they are confronted with the opinions of the system users, with particular emphasis on unpopular locations. The results of the analyses are compared to expert analysis of the P&R facilities location that was outsourced by the city and the opinions about existing facilities users that were expressed on social networking sites. The obtained results are consistent with actual users’ feedback. The proposed method proves to be good, but does not require the involvement of a large experts team and large financial contributions for complicated research. The method also provides an opportunity to show the alternative location of P&R facilities. Although the results of the method are approximate, they are not worse than results of analysis of employed experts. The advantage of this method is ease of use, which simplifies the professional expert analysis. The ability of analyzing a large number of alternative locations gives a broader view on the problem. It is valuable that the arduous analysis of the team of people can be replaced by the model's calculation. According to the authors, the proposed method is also suitable for implementation on a GIS platform.

Keywords: Fuzzy logic inference, P&R facilities, P&R location.

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Authors: N. Tadrisi Parsa, A. R. Vali, R. Ghasemi

Abstract:

Diabetes is a growing health problem in worldwide. Especially, the patients with Type 1 diabetes need strict glycemic control because they have deficiency of insulin production. This paper attempts to control blood glucose based on body mathematical body model. The Bergman minimal mathematical model is used to develop the nonlinear controller. A novel back-stepping based sliding mode control (B-SMC) strategy is proposed as a solution that guarantees practical tracking of a desired glucose concentration. In order to show the performance of the proposed design, it is compared with conventional linear and fuzzy controllers which have been done in previous researches. The numerical simulation result shows the advantages of sliding mode back stepping controller design to linear and fuzzy controllers.

Keywords: Back stepping, Bergman Model, Nonlinear control, Sliding mode control.

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Authors: Bogdan Walek, Cyril Klimes

Abstract:

This paper focuses on testing database of existing information system. At the beginning we describe the basic problems of implemented databases, such as data redundancy, poor design of database logical structure or inappropriate data types in columns of database tables. These problems are often the result of incorrect understanding of the primary requirements for a database of an information system. Then we propose an algorithm to compare the conceptual model created from vague requirements for a database with a conceptual model reconstructed from implemented database. An algorithm also suggests steps leading to optimization of implemented database. The proposed algorithm is verified by an implemented prototype. The paper also describes a fuzzy system which works with the vague requirements for a database of an information system, procedure for creating conceptual from vague requirements and an algorithm for reconstructing a conceptual model from implemented database.

Keywords: testing, database, relational database, information system, conceptual model, fuzzy, uncertain information, database testing, reconstruction, requirements, optimization

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Authors: Roza Afarin, Saeed Mozaffari

Abstract:

The aim of this paper is image encryption using Genetic Algorithm (GA). The proposed encryption method consists of two phases. In modification phase, pixels locations are altered to reduce correlation among adjacent pixels. Then, pixels values are changed in the diffusion phase to encrypt the input image. Both phases are performed by GA with binary chromosomes. For modification phase, these binary patterns are generated by Local Binary Pattern (LBP) operator while for diffusion phase binary chromosomes are obtained by Bit Plane Slicing (BPS). Initial population in GA includes rows and columns of the input image. Instead of subjective selection of parents from this initial population, a random generator with predefined key is utilized. It is necessary to decrypt the coded image and reconstruct the initial input image. Fitness function is defined as average of transition from 0 to 1 in LBP image and histogram uniformity in modification and diffusion phases, respectively. Randomness of the encrypted image is measured by entropy, correlation coefficients and histogram analysis. Experimental results show that the proposed method is fast enough and can be used effectively for image encryption.

Keywords: Correlation coefficients, Genetic algorithm, Image encryption, Image entropy.

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Authors: Kazeem A. Osuolale, Waheed B. Yahya, Babatunde L. Adeleke

Abstract:

Latin hypercube designs (LHDs) have been applied in many computer experiments among the space-filling designs found in the literature. A LHD can be randomly generated but a randomly chosen LHD may have bad properties and thus act poorly in estimation and prediction. There is a connection between Latin squares and orthogonal arrays (OAs). A Latin square of order s involves an arrangement of s symbols in s rows and s columns, such that every symbol occurs once in each row and once in each column and this exists for every non-negative integer s. In this paper, a computer program was written to construct orthogonal array-based Latin hypercube designs (OA-LHDs). Orthogonal arrays (OAs) were constructed from Latin square of order s and the OAs constructed were afterward used to construct the desired Latin hypercube designs for three input variables for use in computer experiments. The LHDs constructed have better space-filling properties and they can be used in computer experiments that involve only three input factors. MATLAB 2012a computer package (www.mathworks.com/) was used for the development of the program that constructs the designs.

Keywords: Computer Experiments, Latin Squares, Latin Hypercube Designs, Orthogonal Array, Space-filling Designs.

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Authors: C. Ardil

Abstract:

Fuzzy set theory and its extensions (intuitionistic fuzzy sets, picture fuzzy sets, and neutrosophic sets) have been widely used to address imprecision and uncertainty in complex decision-making. However, they may struggle with inherent indeterminacy and inconsistency in real-world situations. This study introduces uncertainty sets as a promising alternative, offering a structured framework for incorporating both types of uncertainty into decision-making processes.This work explores the theoretical foundations and applications of uncertainty sets. A novel decision-making algorithm based on uncertainty set-based proximity measures is developed and demonstrated through a practical application: selecting the most suitable stealth combat aircraft.

The results highlight the effectiveness of uncertainty sets in ranking alternatives under uncertainty. Uncertainty sets offer several advantages, including structured uncertainty representation, robust ranking mechanisms, and enhanced decision-making capabilities due to their ability to account for ambiguity.Future research directions are also outlined, including comparative analysis with existing MCDM methods under uncertainty, sensitivity analysis to assess the robustness of rankings,and broader application to various MCDM problems with diverse complexities. By exploring these avenues, uncertainty sets can be further established as a valuable tool for navigating uncertainty in complex decision-making scenarios.

Keywords: Uncertainty set, stealth combat aircraft selection multiple criteria decision-making analysis, MCDM, uncertainty proximity analysis

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Authors: Raghad Alhusari, Farag Omar, Moustafa Fadel

Abstract:

A traditional greenhouse is a metal frame agricultural building used for cultivation plants in a controlled environment isolated from external climatic changes. Using greenhouses in agriculture is an efficient way to reduce the water consumption, where agriculture field is considered the biggest water consumer world widely. Controlling greenhouse environment yields better productivity of plants but demands an increase of electric power. Although various control approaches have been used towards greenhouse automation, most of them are applied to traditional greenhouses with ventilation fans and/or evaporation cooling system. Such approaches are still demanding high energy and water consumption. The aim of this research is to develop a fuzzy control system that minimizes water and energy consumption by utilizing outside weather conditions and underground heat exchanger to maintain the optimum climate of the greenhouse. The proposed control system is implemented on an experimental model of thermally isolated greenhouse structure with dimensions of 6x5x2.8 meters. It uses fans for extracting heat from the ground heat exchanger system, motors for automatic open/close of the greenhouse windows and LED as lighting system. The controller is integrated also with environmental condition sensors. It was found that using the air-to-air horizontal ground heat exchanger with 90 mm diameter and 2 mm thickness placed 2.5 m below the ground surface results in decreasing the greenhouse temperature of 3.28 ˚C which saves around 3 kW of consumed energy. It also eliminated the water consumption needed in evaporation cooling systems which are traditionally used for cooling the greenhouse environment.

Keywords: Automation, earth-to-air heat exchangers, fuzzy control, greenhouse, sustainable buildings.

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Authors: Mathew Wakefield, Matthew Mitchell, Lisa Wise, Christopher McCarthy

Abstract:

This paper aims to provide an interpretation of artificial neural networks (ANNs) and explore some of its implications. The interpretation views ANNs as a memory which encodes instances of experience. An experiment explores the behavior of encoding and retrieval of instances from memory. A localised representation ANN is created that allows control over encoding and retrieved memory sample size and is experimented with using the MNIST digits dataset. The relationship between input familiarity, conflict within retrieved samples, and error rates is described and demonstrated to be an effective driver for memory encoding. Results indicate that selective encoding and retrieval samples that allow detection of memory conflicts produce optimal performance, and that error rates are normally distributed with input familiarity and conflict. By using input familiarity and sample consistency to guide memory encoding, the number of encoding trials on the dataset were reduced to 18.33% of the training data while maintaining good recognition performance on the test data.

Keywords: Artificial Neural Networks, ANNs, representation, memory, conflict monitoring, confidence.

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Authors: S. Ben Chaabane, M. Sayadi, F. Fnaiech, E. Brassart

Abstract:

In this paper we propose a new knowledge model using the Dempster-Shafer-s evidence theory for image segmentation and fusion. The proposed method is composed essentially of two steps. First, mass distributions in Dempster-Shafer theory are obtained from the membership degrees of each pixel covering the three image components (R, G and B). Each membership-s degree is determined by applying Fuzzy C-Means (FCM) clustering to the gray levels of the three images. Second, the fusion process consists in defining three discernment frames which are associated with the three images to be fused, and then combining them to form a new frame of discernment. The strategy used to define mass distributions in the combined framework is discussed in detail. The proposed fusion method is illustrated in the context of image segmentation. Experimental investigations and comparative studies with the other previous methods are carried out showing thus the robustness and superiority of the proposed method in terms of image segmentation.

Keywords: Fuzzy C-means, Color image, data fusion, Dempster-Shafer's evidence theory

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Authors: D. Garg, S. Luthra, A. Haleem

Abstract:

Reverse logistics (RL) is supposed to be a systematic procedure that helps in improving the environmental hazards and maintain business sustainability for industries. Industries in Indian are now opting for adoption of RL techniques in business. But, RL practices are not popular in Indian industries because of many barriers for its successful implementation. Therefore, need arises to identify and evaluate the barriers to implement RL practices by taking an Indian industries perspective. Literature review approach and case study approach have been adapted to identify relevant barriers to implement RL practices. Further, Fuzzy Decision Making Trial and Evaluation Laboratory methodology has been brought into use for evaluating causal relationships among the barriers to implement RL practices. Seven barriers out of ten barriers have been categorized into the cause group and remaining into effect group. This research will help Indian industries to manage these barriers towards effective implementing RL practices.

Keywords: Barriers, decision making trial and evaluation laboratory, fuzzy set theory, Indian industries, reverse logistics.

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Authors: M. Padmavathi, R. M. Suresh

Abstract:

Every machine plays roles of client and server simultaneously in a peer-to-peer (P2P) network. Though a P2P network has many advantages over traditional client-server models regarding efficiency and fault-tolerance, it also faces additional security threats. Users/IT administrators should be aware of risks from malicious code propagation, downloaded content legality, and P2P software’s vulnerabilities. Security and preventative measures are a must to protect networks from potential sensitive information leakage and security breaches. Bit Torrent is a popular and scalable P2P file distribution mechanism which successfully distributes large files quickly and efficiently without problems for origin server. Bit Torrent achieved excellent upload utilization according to measurement studies, but it also raised many questions as regards utilization in settings, than those measuring, fairness, and Bit Torrent’s mechanisms choice. This work proposed a block selection technique using Fuzzy ACO with optimal rules selected using ACO.

Keywords: Ant Colony Optimization (ACO), Bit Torrent, Download time, Peer-to-Peer (P2P) network, Performance.

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Authors: Anusua Ghosh, Andrew Nafalski, Jeffery Tweedale, Maureen Dollard

Abstract:

This paper presents anapproach of hybridizing two or more artificial intelligence (AI) techniques which arebeing used to fuzzify the workstress level ranking and categorize the rating accordingly. The use of two or more techniques (hybrid approach) has been considered in this case, as combining different techniques may lead to neutralizing each other-s weaknesses generating a superior hybrid solution. Recent researches have shown that there is a need for a more valid and reliable tools, for assessing work stress. Thus artificial intelligence techniques have been applied in this instance to provide a solution to a psychological application. An overview about the novel and autonomous interactive model for analysing work-stress that has been developedusing multi-agent systems is also presented in this paper. The establishment of the intelligent multi-agent decision analyser (IMADA) using hybridized technique of neural networks and fuzzy logic within the multi-agent based framework is also described.

Keywords: Fuzzy logic, intelligent agent, multi-agent systems, neural network, workplace stress.

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Authors: Fasil Endalamaw

Abstract:

Digital magnitude comparators based on Gate Diffusion Input (GDI) implementation technique are high speed and area-efficient, and they consume less power as compared to other implementation techniques. However, they are less efficient for some logic gates and have no full voltage swing. In this paper, we made a performance comparison between the GDI implementation technique and other implementation methods, such as Static CMOS, Pass Transistor Logic (PTL), and Transmission Gate (TG) in 90 nm, 120 nm, and 180 nm CMOS technologies using BSIM4 MOS model. We proposed a methodology (hybrid implementation) of implementing digital magnitude comparators which significantly improved the power, speed, area, and voltage swing requirements. Simulation results revealed that the hybrid implementation of digital magnitude comparators show a 10.84% (power dissipation), 41.6% (propagation delay), 47.95% (power-delay product (PDP)) improvement compared to the usual GDI implementation method. We used Microwind & Dsch Version 3.5 as well as the Tanner EDA 16.0 tools for simulation purposes.

Keywords: Efficient, gate diffusion input, high speed, low power, CMOS.

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Authors: Haiyan Wang

Abstract:

Being main teaching media and major source of comprehensive target language input, teacher talk plays an important role in learners' second-language acquisition. Under the trend of "learner-centered" teaching mode, some researchers think that the best teacher talk means less. But the author holds that, in Chinese second language classroom, it is not advisable to lay too much stress on the formal students' participation, which requires the teacher to say as little as possible and the student to say as much as possible. The emphasis should be put on how to raise teacher talk's quality.

Keywords: Comprehensive language input, "learner-centered" teaching mode, teacher talk, teacher talk's quality.

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Authors: Chunshien Li, Jhao-Wun Hu, Tai-Wei Chiang, Tsunghan Wu

Abstract:

Time series forecasting is an important and widely popular topic in the research of system modeling. This paper describes how to use the hybrid PSO-RLSE neuro-fuzzy learning approach to the problem of time series forecasting. The PSO algorithm is used to update the premise parameters of the proposed prediction system, and the RLSE is used to update the consequence parameters. Thanks to the hybrid learning (HL) approach for the neuro-fuzzy system, the prediction performance is excellent and the speed of learning convergence is much faster than other compared approaches. In the experiments, we use the well-known Mackey-Glass chaos time series. According to the experimental results, the prediction performance and accuracy in time series forecasting by the proposed approach is much better than other compared approaches, as shown in Table IV. Excellent prediction performance by the proposed approach has been observed.

Keywords: forecasting, hybrid learning (HL), Neuro-FuzzySystem (NFS), particle swarm optimization (PSO), recursiveleast-squares estimator (RLSE), time series

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Authors: Hazem M. El-Bakry, Qiangfu Zhao

Abstract:

Recently, neural networks have shown good results for detection of a certain pattern in a given image. In our previous papers [1-5], a fast algorithm for pattern detection using neural networks was presented. Such algorithm was designed based on cross correlation in the frequency domain between the input image and the weights of neural networks. Image conversion into symmetric shape was established so that fast neural networks can give the same results as conventional neural networks. Another configuration of symmetry was suggested in [3,4] to improve the speed up ratio. In this paper, our previous algorithm for fast neural networks is developed. The frequency domain cross correlation is modified in order to compensate for the symmetric condition which is required by the input image. Two new ideas are introduced to modify the cross correlation algorithm. Both methods accelerate the speed of the fast neural networks as there is no need for converting the input image into symmetric one as previous. Theoretical and practical results show that both approaches provide faster speed up ratio than the previous algorithm.

Keywords: Fast Pattern Detection, Neural Networks, Modified Cross Correlation

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Authors: Grouz Faten, Sbita Lassaâd

Abstract:

In this paper, Speed Sensorless Indirect Field Oriented Control (IFOC) of a Permanent Magnet Synchronous machine (PMSM) is studied. The closed loop scheme of the drive system utilizes fuzzy speed and current controllers. Due to the well known drawbacks of the speed sensor, an algorithm is proposed in this paper to eliminate it. In fact, based on the model of the PMSM, the stator currents and rotor speed are estimated simultaneously using adaptive Luenberger observer for currents and MRAS (Model Reference Adaptive System) observer for rotor speed. To overcome the sensivity of this algorithm against parameter variation, adaptive for on line stator resistance tuning is proposed. The validity of the proposed method is verified by an extensive simulation work.

Keywords: PMSM, Indirect Field Oriented Control, fuzzy speed and currents controllers, Adaptive Luenberger observer, MRAS.

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Authors: Nataša Živić, Christoph Ruland

Abstract:

Method of Parallel Joint Channel Coding and Cryptography has been analyzed and simulated in this paper. The method is an extension of Soft Input Decryption with feedback, which is used for improvement of channel decoding of secured messages. Parallel Joint Channel Coding and Cryptography results in improved coding gain of channel decoding, which achieves more than 2 dB. Such results are an implication of a combination of receiver components and their interoperability.

Keywords: Block length, Coding gain, Feedback, L-values, Parallel Joint Channel Coding and Cryptography, Soft Input Decryption.

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Authors: J. Menčík

Abstract:

Actual load, material characteristics and other quantities often differ from the design values. This can cause worse function, shorter life or failure of a civil engineering structure, a machine, vehicle or another appliance. The paper shows main causes of the uncertainties and deviations and presents a systematic approach and efficient tools for their elimination or mitigation of consequences. Emphasis is put on the design stage, which is most important for reliability ensuring. Principles of robust design and important tools are explained, including FMEA, sensitivity analysis and probabilistic simulation methods. The lifetime prediction of long-life objects can be improved by long-term monitoring of the load response and damage accumulation in operation. The condition evaluation of engineering structures, such as bridges, is often based on visual inspection and verbal description. Here, methods based on fuzzy logic can reduce the subjective influences.

Keywords: Design, fuzzy methods, Monte Carlo, reliability, robust design, sensitivity analysis, simulation, uncertainties.

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Authors: Lina, Arlends Chris, Bagus Mulyawan, Agus B. Dharmawan

Abstract:

Blood cell analysis plays a significant role in the diagnosis of human health. As an alternative to the traditional technique conducted by laboratory technicians, this paper presents an automatic white blood cell (leukocyte) detection system using Image Stitching and Color Overlapping Windows. The advantage of this method is to present a detection technique of white blood cells that are robust to imperfect shapes of blood cells with various image qualities. The input for this application is images from a microscope-slide translation video. The preprocessing stage is performed by stitching the input images. First, the overlapping parts of the images are determined, then stitching and blending processes of two input images are performed. Next, the Color Overlapping Windows is performed for white blood cell detection which consists of color filtering, window candidate checking, window marking, finds window overlaps, and window cropping processes. Experimental results show that this method could achieve an average of 82.12% detection accuracy of the leukocyte images.

Keywords: Color overlapping windows, image stitching, leukocyte detection.

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Authors: P. Javid, A. Aeenmehr, J. Taghavifar

Abstract:

In this paper, supply air pressure of HVAC system has been modeled with second-order transfer function plus dead-time. In HVAC system, the desired input has step changes, and the output of proposed control system should be able to follow the input reference, so the idea of using model based predictive control is proceeded and designed in this paper. The closed loop control system is implemented in MATLAB software and the simulation results are provided. The simulation results show that the model based predictive control is able to control the plant properly.

Keywords: Air conditioning system, GPC, dead time, Air supply control.

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Authors: P. Silapan, C. Chanapromma, T. Worachak

Abstract:

This article proposes a current-mode square-rooting circuit using current follower transconductance amplifier (CTFA). The amplitude of the output current can be electronically controlled via input bias current with wide input dynamic range. The proposed circuit consists of only single CFTA. Without any matching conditions and external passive elements, the circuit is then appropriate for an IC architecture. The magnitude of the output signal is temperature-insensitive. The PSpice simulation results are depicted, and the given results agree well with the theoretical anticipation. The power consumption is approximately 1.96mW at ±1.5V supply voltages.

Keywords: CFTA, Current-mode, Square-rooting Circuit

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Authors: Neeraj K. Chasta

Abstract:

This paper presents an idea for analog current comparison which compares input signal and reference currents with high speed and accuracy. Proposed circuit utilizes amplification properties of common gate configuration, where voltage variations of input current are amplified and a compared output voltage is developed. Cascaded inverter stages are used to generate final CMOS compatible output voltage. Power consumption of circuit can be controlled by the applied gate bias voltage. The comparator is designed and studied at 180nm CMOS process technology for a supply voltage of 3V.

Keywords: Current Mode, Comparator, High Resolution, High Speed.

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Authors: Kapil Dev Sharma, Kirat Pal, Costas Psychalinos

Abstract:

A new first order all-pass filter topology realized using current controlled current conveyors (CCCIIs) is introduced in this paper. Offered benefits are the high-impedance of the input node, the absence of external resistors because of the usage of CCCIIs with positive and negative intrinsic resistances, the presence of only grounded capacitors, and the capability of electronic adjustment of the phase shift through a single bias current. The correct operation of the introduced topology is conformed through simulation results, while its behavior is evaluated through comparison results.

Keywords: Active filters, All-pass filters, Analog signal processing, Current conveyors.

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Authors: Dulal Acharjee, Punyaban Patel

Abstract:

For collecting data from all sensor nodes, some changes in Dynamic Source Routing (DSR) protocol is proposed. At each hop level, route-ranking technique is used for distributing packets to different selected routes dynamically. For calculating rank of a route, different parameters like: delay, residual energy and probability of packet loss are used. A hybrid topology of DMPR(Disjoint Multi Path Routing) and MMPR(Meshed Multi Path Routing) is formed, where braided topology is used in different faulty zones of network. For reducing energy consumption, variant transmission ranges is used instead of fixed transmission range. For reducing number of packet drop, a fuzzy logic inference scheme is used to insert different types of delays dynamically. A rule based system infers membership function strength which is used to calculate the final delay amount to be inserted into each of the node at different clusters. In braided path, a proposed 'Dual Line ACK Link'scheme is proposed for sending ACK signal from a damaged node or link to a parent node to ensure that any error in link or any node-failure message may not be lost anyway. This paper tries to design the theoretical aspects of a model which may be applied for collecting data from any large hanging iron structure with the help of wireless sensor network. But analyzing these data is the subject of material science and civil structural construction technology, that part is out of scope of this paper.

Keywords: Metallic corrosion, Multi Path Routing, DisjointMPR, Meshed MPR, braided path, dual line ACK link, route rankingand Fuzzy Logic.

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Authors: Ahmad M. Sarhan, Omar I. Al Helalat

Abstract:

In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addition to the input matrix, are fed to an ANN. Simulation results are provided and show that the proposed system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions.

Keywords: ANN, Backpropagation, Gaussian, LMS, MSE, Neuron, standard deviation, Widrow-Hoff rule.

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Authors: Myungsook Klassen

Abstract:

Training neural networks to capture an intrinsic property of a large volume of high dimensional data is a difficult task, as the training process is computationally expensive. Input attributes should be carefully selected to keep the dimensionality of input vectors relatively small. Technical indexes commonly used for stock market prediction using neural networks are investigated to determine its effectiveness as inputs. The feed forward neural network of Levenberg-Marquardt algorithm is applied to perform one step ahead forecasting of NASDAQ and Dow stock prices.

Keywords: Stock Market Prediction, Neural Networks, Levenberg-Marquadt Algorithm, Technical Indexes

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Authors: V.Manikandan, N.Devarajan

Abstract:

The main objective of this work is to provide a fault detection and isolation based on Markov parameters for residual generation and a neural network for fault classification. The diagnostic approach is accomplished in two steps: In step 1, the system is identified using a series of input / output variables through an identification algorithm. In step 2, the fault is diagnosed comparing the Markov parameters of faulty and non faulty systems. The Artificial Neural Network is trained using predetermined faulty conditions serves to classify the unknown fault. In step 1, the identification is done by first formulating a Hankel matrix out of Input/ output variables and then decomposing the matrix via singular value decomposition technique. For identifying the system online sliding window approach is adopted wherein an open slit slides over a subset of 'n' input/output variables. The faults are introduced at arbitrary instances and the identification is carried out in online. Fault residues are extracted making a comparison of the first five Markov parameters of faulty and non faulty systems. The proposed diagnostic approach is illustrated on benchmark problems with encouraging results.

Keywords: Artificial neural network, Fault Diagnosis, Identification, Markov parameters.

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