Search results for: optical signal acquisition

Authors: Pepin Magnangana Zoko Goyoro, Ibrahim James Moumouni, Sroy Abouty

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Orthogonal Frequency Division Multiplexing (OFDM) is an efficient method of data transmission for high speed communication systems. However, the main drawback of OFDM systems is that, it suffers from the problem of high Peak-to-Average Power Ratio (PAPR) which causes inefficient use of the High Power Amplifier and could limit transmission efficiency. OFDM consist of large number of independent subcarriers, as a result of which the amplitude of such a signal can have high peak values. In this paper, we propose an effective reduction scheme that combines DCT and SLM techniques. The scheme is composed of the DCT followed by the SLM using the Riemann matrix to obtain phase sequences for the SLM technique. The simulation results show PAPR can be greatly reduced by applying the proposed scheme. In comparison with OFDM, while OFDM had high values of PAPR –about 10.4dB our proposed method achieved about 4.7dB reduction of the PAPR with low complexities computation. This approach also avoids randomness in phase sequence selection, which makes it simpler to decode at the receiver. As an added benefit, the matrices can be generated at the receiver end to obtain the data signal and hence it is not required to transmit side information (SI).

Keywords: DCT transform, OFDM, PAPR, Riemann matrix, SLM.

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Authors: Kaumil B. Shah, Hina Chandwani

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The modular multilevel converter (MMC) is one of the advanced topologies for medium and high-voltage applications. In high-power, high-voltage MMC, a large number of switching power devices are required. These switching power devices (IGBT) considerable switching losses. This paper analyzes the performance of different discontinuous pulse width modulation (DPWM) techniques and compares the results against a conventional carrier based pulse width modulation method, in order to reduce the switching losses of an MMC. The DPWM reference wave can be generated by adding the zero-sequence component to the original (sine) reference modulation signal. The result of the addition gives the reference signal of DPWM techniques. To minimize the switching losses of the MMC, the clamping period is controlled according to the absolute value of the output load current. No switching is generated in the clamping period so overall switching of the power device is reduced. The simulation result of the different DPWM techniques is compared with conventional carrier-based pulse-width modulation technique.

Keywords: Modular multilevel converter, discontinuous pulse width modulation, switching losses, zero-sequence voltage.

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Authors: S. H. Oudjana, A. Hellal, I. Hadj Mahammed

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Photovoltaic power generation forecasting is an important task in renewable energy power system planning and operating. This paper explores the application of neural networks (NN) to study the design of photovoltaic power generation forecasting systems for one week ahead using weather databases include the global irradiance, and temperature of Ghardaia city (south of Algeria) using a data acquisition system. Simulations were run and the results are discussed showing that neural networks Technique is capable to decrease the photovoltaic power generation forecasting error.

Keywords: Photovoltaic Power Forecasting, Regression, Neural Networks.

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Authors: Ivayla Trifonova, Naoum Jamous, Holger Schrödl

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A good web design is a prerequisite for a successful business nowadays, especially since the internet is the most common way for people to inform themselves. Web design includes the optical composition, the structure, and the user guidance of websites. The importance of each website leads to the question if there is a way to measure its usefulness. The aim of this paper is to suggest a methodology for the evaluation of web design. The desired outcome is to have an evaluation that is concentrated on a specific website and its target group.

Keywords: Evaluation methodology, factor analysis, target group, web design.

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Authors: Hosein Hamisheh Bahar, Mohammad Hossein Fazel Zarandi, Akbar Esfahanipour

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In this paper, a hybrid expert system is developed by using fuzzy genetic network programming with reinforcement learning (GNP-RL). In this system, the frame-based structure of the system uses the trading rules extracted by GNP. These rules are extracted by using technical indices of the stock prices in the training time period. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In the other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. Finally, the obtained rules are used in a frame-based system implemented in Kappa-PC software. This developed trading system has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in the testing time period shows that the developed system has more favorable performance in comparison with the Buy and Hold strategy.

Keywords: Fuzzy genetic network programming, hybrid expert system, technical trading signal, Tehran stock exchange.

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Authors: A. Samraj, S. Sayeed, J. E. Raja., J. Hossen, A. Rahman

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Monitoring the tool flank wear without affecting the throughput is considered as the prudent method in production technology. The examination has to be done without affecting the machining process. In this paper we proposed a novel work that is used to determine tool flank wear by observing the sound signals emitted during the turning process. The work-piece material we used here is steel and aluminum and the cutting insert was carbide material. Two different cutting speeds were used in this work. The feed rate and the cutting depth were constant whereas the flank wear was a variable. The emitted sound signal of a fresh tool (0 mm flank wear) a slightly worn tool (0.2 -0.25 mm flank wear) and a severely worn tool (0.4mm and above flank wear) during turning process were recorded separately using a high sensitive microphone. Analysis using Singular Value Decomposition was done on these sound signals to extract the feature sound components. Observation of the results showed that an increase in tool flank wear correlates with an increase in the values of SVD features produced out of the sound signals for both the materials. Hence it can be concluded that wear monitoring of tool flank during turning process using SVD features with the Fuzzy C means classification on the emitted sound signal is a potential and relatively simple method.

Keywords: Fuzzy c means, Microphone, Singular ValueDecomposition, Tool Flank Wear.

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Authors: D. H. Gahane, B. M. Bahirwar, S. V. Moharil

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Efficient luminescence is reported for the first time in Eu2+ activated double Chlorides A2BCl4 (A=Alkali metal, B=Alkaline earth element). A simple wet-chemical preparation is described. Emission intensities are comparable to that of the commercial phosphor. Excitation covers near UV region. These phosphors may be useful for applications like solid state lighting, scintillation detectors and X-ray storage using photo-stimulable phosphors.

Keywords: Alkaline Earth, Chloride, Luminescence.

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Authors: Caixia Gao, Chuanrong Li, Lingli Tang, Lingling Ma, Yaokai Liu, Xinhong Wang, Yongsheng Zhou

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Performances analysis of remote sensing sensor is required to pursue a range of scientific research and application objectives. Laboratory analysis of any remote sensing instrument is essential, but not sufficient to establish a valid inflight one. In this study, with the aid of the in situ measurements and corresponding image of three-gray scale permanent artificial target, the in-flight radiometric performances analyses (in-flight radiometric calibration, dynamic range and response linearity, signal-noise-ratio (SNR), radiometric resolution) of self-developed short-wave infrared (SWIR) camera are performed. To acquire the inflight calibration coefficients of the SWIR camera, the at-sensor radiances (L_i) for the artificial targets are firstly simulated with in situ measurements (atmosphere parameter and spectral reflectance of the target) and viewing geometries using MODTRAN model. With these radiances and the corresponding digital numbers (DN) in the image, a straight line with a formulation of L = G × DN + B is fitted by a minimization regression method, and the fitted coefficients, G and B, are inflight calibration coefficients. And then the high point (L_H) and the low point (L_L) of dynamic range can be described as L_H= (G × DN_H + B) and L_L= B, respectively, where DN_H is equal to 2ⁿ − 1 (n is the quantization number of the payload). Meanwhile, the sensor’s response linearity (δ) is described as the correlation coefficient of the regressed line. The results show that the calibration coefficients (G and B) are 0.0083 W·sr⁻¹m⁻²µm⁻¹ and −3.5 W·sr⁻¹m⁻²µm⁻¹; the low point of dynamic range is −3.5 W·sr⁻¹m⁻²µm⁻¹ and the high point is 30.5 W·sr⁻¹m⁻²µm⁻¹; the response linearity is approximately 99%. Furthermore, a SNR normalization method is used to assess the sensor’s SNR, and the normalized SNR is about 59.6 when the mean value of radiance is equal to 11.0 W·sr⁻¹m⁻²µm⁻¹; subsequently, the radiometric resolution is calculated about 0.1845 W•sr^-1m^-2μm^-1. Moreover, in order to validate the result, a comparison of the measured radiance with a radiative-transfer-code-predicted over four portable artificial targets with reflectance of 20%, 30%, 40%, 50% respectively, is performed. It is noted that relative error for the calibration is within 6.6%.

Keywords: Calibration, dynamic range, radiometric resolution, SNR.

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Authors: Muhammad Safi, Abdul Manan

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In recent years, research in aircraft avionics systems (i.e., radars and antennas) has grown revolutionary. Aircraft technology is experiencing an increasing inclination from all mechanical to all electrical aircraft, with the introduction of inhabitant air vehicles and drone taxis over the last few years. This develops an overriding need to summarize the history, latest trends, and future development in aircraft avionics research for a better understanding and development of new technologies in the domain of avionics systems. This paper focuses on the future trends in antennas and phased arrays for avionics systems. Along with the general overview of the future avionics trend, this work describes the review of around 50 high-quality research papers on aircraft communication systems. Electric-powered aircrafts have been a hot topic in the modern aircraft world. Electric aircrafts have supremacy over their conventional counterparts. Due to increased drone taxi and urban air mobility, fast and reliable communication is very important, so concepts of Broadband Integrated Digital Avionics Information Exchange Networks (B-IDAIENs) and Modular Avionics are being researched for better communication of future aircraft. A Ku-band phased array antenna based on a modular design can be used in a modular avionics system. Furthermore, integrated avionics is also emerging research in future avionics. The main focus of work in future avionics will be using integrated modular avionics and infra-red phased array antennas, which are discussed in detail in this paper. Other work such as reconfigurable antennas and optical communication, are also discussed in this paper. The future of modern aircraft avionics would be based on integrated modulated avionics and small artificial intelligence-based antennas. Optical and infrared communication will also replace microwave frequencies.

Keywords: AI, avionics systems, communication, electric aircrafts, Infra-red, integrated avionics, modular avionics, phased array, reconfigurable antenna, UAVs.

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Authors: George Charkoftakis, Panagiotis Liosatos, Nicolas-Alexander Tatlas, Dimitrios Goustouridis, Stelios M. Potirakis

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E-maintenance is a relatively recent concept, generally referring to maintenance management by monitoring assets over the Internet. One of the key links in the chain of an e-maintenance system is data acquisition and transmission. Specifically for the case of a fleet of heavy-duty vehicles, where the main challenge is the diversity of the vehicles and vehicle-embedded self-diagnostic/reporting technologies, the design of the data acquisition and transmission unit is a demanding task. This is clear if one takes into account that a heavy-vehicles fleet assortment may range from vehicles with only a limited number of analog sensors monitored by dashboard light indicators and gauges to vehicles with plethora of sensors monitored by a vehicle computer producing digital reporting. The present work proposes an adaptable internet of things (IoT) sensor node that is capable of addressing this challenge. The proposed sensor node architecture is based on the increasingly popular single-board computer – expansion boards approach. In the proposed solution, the expansion boards undertake the tasks of position identification, cellular connectivity, connectivity to the vehicle computer, and connectivity to analog and digital sensors by means of a specially targeted design of expansion board. Specifically, the latter offers a number of adaptability features to cope with the diverse sensor types employed in different vehicles. In standard mode, the IoT sensor node communicates to the data center through cellular network, transmitting all digital/digitized sensor data, IoT device identity and position. Moreover, the proposed IoT sensor node offers connectivity, through WiFi and an appropriate application, to smart phones or tablets allowing the registration of additional vehicle- and driver-specific information and these data are also forwarded to the data center. All control and communication tasks of the IoT sensor node are performed by dedicated firmware.

Keywords: IoT sensor nodes, e-maintenance, single-board computers, sensor expansion boards, on-board diagnostics

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Authors: Samira Vafaye Eslahi

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BCI (Brain Computer Interface) is a communication machine that translates brain massages to computer commands. These machines with the help of computer programs can recognize the tasks that are imagined. Feature extraction is an important stage of the process in EEG classification that can effect in accuracy and the computation time of processing the signals. In this study we process the signal in three steps of active segment selection, fractal feature extraction, and classification. One of the great challenges in BCI applications is to improve classification accuracy and computation time together. In this paper, we have used student’s 2D sample t-statistics on continuous wavelet transforms for active segment selection to reduce the computation time. In the next level, the features are extracted from some famous fractal dimension estimation of the signal. These fractal features are Katz and Higuchi. In the classification stage we used ANFIS (Adaptive Neuro-Fuzzy Inference System) classifier, FKNN (Fuzzy K-Nearest Neighbors), LDA (Linear Discriminate Analysis), and SVM (Support Vector Machines). We resulted that active segment selection method would reduce the computation time and Fractal dimension features with ANFIS analysis on selected active segments is the best among investigated methods in EEG classification.

Keywords: EEG, Student’s t- statistics, BCI, Fractal Features, ANFIS, FKNN.

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Authors: R.V Pawar, P.P.Kajave, S.N.Mali

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The speech signal conveys information about the identity of the speaker. The area of speaker identification is concerned with extracting the identity of the person speaking the utterance. As speech interaction with computers becomes more pervasive in activities such as the telephone, financial transactions and information retrieval from speech databases, the utility of automatically identifying a speaker is based solely on vocal characteristic. This paper emphasizes on text dependent speaker identification, which deals with detecting a particular speaker from a known population. The system prompts the user to provide speech utterance. System identifies the user by comparing the codebook of speech utterance with those of the stored in the database and lists, which contain the most likely speakers, could have given that speech utterance. The speech signal is recorded for N speakers further the features are extracted. Feature extraction is done by means of LPC coefficients, calculating AMDF, and DFT. The neural network is trained by applying these features as input parameters. The features are stored in templates for further comparison. The features for the speaker who has to be identified are extracted and compared with the stored templates using Back Propogation Algorithm. Here, the trained network corresponds to the output; the input is the extracted features of the speaker to be identified. The network does the weight adjustment and the best match is found to identify the speaker. The number of epochs required to get the target decides the network performance.

Keywords: Average Mean Distance function, Backpropogation, Linear Predictive Coding, MultilayeredPerceptron,

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Authors: C. Gunavathi, K. Premalatha

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Microarray technology is universally used in the study of disease diagnosis using gene expression levels. The main shortcoming of gene expression data is that it includes thousands of genes and a small number of samples. Abundant methods and techniques have been proposed for tumor classification using microarray gene expression data. Feature or gene selection methods can be used to mine the genes that directly involve in the classification and to eliminate irrelevant genes. In this paper statistical measures like T-Statistics, Signal-to-Noise Ratio (SNR) and F-Statistics are used to rank the genes. The ranked genes are used for further classification. Particle Swarm Optimization (PSO) algorithm and Shuffled Frog Leaping (SFL) algorithm are used to find the significant genes from the top-m ranked genes. The Naïve Bayes Classifier (NBC) is used to classify the samples based on the significant genes. The proposed work is applied on Lung and Ovarian datasets. The experimental results show that the proposed method achieves 100% accuracy in all the three datasets and the results are compared with previous works.

Keywords: Microarray, T-Statistics, Signal-to-Noise Ratio, FStatistics, Particle Swarm Optimization, Shuffled Frog Leaping, Naïve Bayes Classifier.

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Authors: R. Gunabalan, S. Immanuel Prabakaran, J. Reegan, S. Ganesh

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This paper describes a software approach for modeling inverter fed induction motor drive using Laboratory Virtual Instrument Engineering Workbench (LabVIEW). The reason behind the selection of LabVIEW software is because of its strong graphical interface, flexibility of its programming language combined with built-in tools designed specifically for test, measurement and control. LabVIEW is generally used in most of the applications for data acquisition, test and control. In this paper, inverter and induction motor are modeled using LabVIEW toolkits. Simulation results are presented and are validated.

Keywords: Induction motor, LabVIEW, State model.

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Authors: Adrienne Kline, Jaydip Desai

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Electroencephalogram (EEG) is a noninvasive technique that registers signals originating from the firing of neurons in the brain. The Emotiv EEG Neuroheadset is a consumer product comprised of 14 EEG channels and was used to record the reactions of the neurons within the brain to two forms of stimuli in 10 participants. These stimuli consisted of auditory and visual formats that provided directions of ‘right’ or ‘left.’ Participants were instructed to raise their right or left arm in accordance with the instruction given. A scenario in OpenViBE was generated to both stimulate the participants while recording their data. In OpenViBE, the Graz Motor BCI Stimulator algorithm was configured to govern the duration and number of visual stimuli. Utilizing EEGLAB under the cross platform MATLAB®, the electrodes most stimulated during the study were defined. Data outputs from EEGLAB were analyzed using IBM SPSS Statistics® Version 20. This aided in determining the electrodes to use in the development of a brain-machine interface (BMI) using real-time EEG signals from the Emotiv EEG Neuroheadset. Signal processing and feature extraction were accomplished via the Simulink® signal processing toolbox. An Arduino™ Duemilanove microcontroller was used to link the Emotiv EEG Neuroheadset and the right and left Mecha TE™ Hands.

Keywords: Brain-machine interface, EEGLAB, emotiv EEG neuroheadset, openViBE, simulink.

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Authors: Salama Meghriche, Amer Draa, Mohammed Boulemden

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Heart failure is the most common reason of death nowadays, but if the medical help is given directly, the patient-s life may be saved in many cases. Numerous heart diseases can be detected by means of analyzing electrocardiograms (ECG). Artificial Neural Networks (ANN) are computer-based expert systems that have proved to be useful in pattern recognition tasks. ANN can be used in different phases of the decision-making process, from classification to diagnostic procedures. This work concentrates on a review followed by a novel method. The purpose of the review is to assess the evidence of healthcare benefits involving the application of artificial neural networks to the clinical functions of diagnosis, prognosis and survival analysis, in ECG signals. The developed method is based on a compound neural network (CNN), to classify ECGs as normal or carrying an AtrioVentricular heart Block (AVB). This method uses three different feed forward multilayer neural networks. A single output unit encodes the probability of AVB occurrences. A value between 0 and 0.1 is the desired output for a normal ECG; a value between 0.1 and 1 would infer an occurrence of an AVB. The results show that this compound network has a good performance in detecting AVBs, with a sensitivity of 90.7% and a specificity of 86.05%. The accuracy value is 87.9%.

Keywords: Artificial neural networks, Electrocardiogram(ECG), Feed forward multilayer neural network, Medical diagnosis, Pattern recognitionm, Signal processing.

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Authors: Ani Grubišić, Slavomir Stankov, Branko Žitko

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This paper describes a concept of stereotype student model in adaptive knowledge acquisition e-learning system. Defined knowledge stereotypes are based on student's proficiency level and on Bloom's knowledge taxonomy. The teacher module is responsible for the whole adaptivity process: the automatic generation of courseware elements, their dynamic selection and sorting, as well as their adaptive presentation using templates for statements and questions. The adaptation of courseware is realized according to student-s knowledge stereotype.

Keywords: Adaptive e-learning systems, adaptive courseware, stereotypes, Bloom's knowledge taxonomy.

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Authors: Mehmet Ozcan

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This study is designed to investigate errors emerged in written texts produced by 30 Turkish EFL learners with an explanatory, and thus, qualitative perspective. Erroneous language elements were identified by the researcher first and then their grammaticality and intelligibility were checked by five native speakers of English. The analysis of the data showed that it is difficult to claim that an error stems from only one single factor since different features of an error are triggered by different factors. Our findings revealed two different types of errors: those which stem from the interference of L1 with L2 and those which are developmental ones. The former type contains more global errors whereas the errors in latter type are more intelligible.

Keywords: Contrastive analysis, Error analysis, Language acquisition, Language transfer, Turkish

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Authors: Rami N. Khushaba, Adel Al-Jumaily

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The myoelectric signal (MES) is one of the Biosignals utilized in helping humans to control equipments. Recent approaches in MES classification to control prosthetic devices employing pattern recognition techniques revealed two problems, first, the classification performance of the system starts degrading when the number of motion classes to be classified increases, second, in order to solve the first problem, additional complicated methods were utilized which increase the computational cost of a multifunction myoelectric control system. In an effort to solve these problems and to achieve a feasible design for real time implementation with high overall accuracy, this paper presents a new method for feature extraction in MES recognition systems. The method works by extracting features using Wavelet Packet Transform (WPT) applied on the MES from multiple channels, and then employs Fuzzy c-means (FCM) algorithm to generate a measure that judges on features suitability for classification. Finally, Principle Component Analysis (PCA) is utilized to reduce the size of the data before computing the classification accuracy with a multilayer perceptron neural network. The proposed system produces powerful classification results (99% accuracy) by using only a small portion of the original feature set.

Keywords: Biomedical Signal Processing, Data mining andInformation Extraction, Machine Learning, Rehabilitation.

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Authors: C. Gunavathi, K. Premalatha

Abstract:

Tumor classification is a key area of research in the field of bioinformatics. Microarray technology is commonly used in the study of disease diagnosis using gene expression levels. The main drawback of gene expression data is that it contains thousands of genes and a very few samples. Feature selection methods are used to select the informative genes from the microarray. These methods considerably improve the classification accuracy. In the proposed method, Genetic Algorithm (GA) is used for effective feature selection. Informative genes are identified based on the T-Statistics, Signal-to-Noise Ratio (SNR) and F-Test values. The initial candidate solutions of GA are obtained from top-m informative genes. The classification accuracy of k-Nearest Neighbor (kNN) method is used as the fitness function for GA. In this work, kNN and Support Vector Machine (SVM) are used as the classifiers. The experimental results show that the proposed work is suitable for effective feature selection. With the help of the selected genes, GA-kNN method achieves 100% accuracy in 4 datasets and GA-SVM method achieves in 5 out of 10 datasets. The GA with kNN and SVM methods are demonstrated to be an accurate method for microarray based tumor classification.

Keywords: F-Test, Gene Expression, Genetic Algorithm, k- Nearest-Neighbor, Microarray, Signal-to-Noise Ratio, Support Vector Machine, T-statistics, Tumor Classification.

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Authors: Ali Habiboghli, Rouhollah Mostafaei, Vasif Nabiyev

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Optical network uses a tool for routing called Latin router. These routers use particular algorithms for routing. For example, we can refer to LDF algorithm that uses backtracking (one of CSP methods) for problem solving. In this paper, we proposed new approached for completion routing table (DRA&CRA algorithm) and compare with pervious proposed ways and showed numbers of backtracking, blocking and run time for DRA algorithm less than LDF and CRA algorithm.

Keywords: Latin Router, Constraint Satisfaction Problem, Wavelength Routing.

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Authors: Karchung, S. Ruangsinchaiwanich

Abstract:

This paper presents open-loop vector control method of induction motor with space vector pulse width modulation (SVPWM) technique. Normally, the closed loop speed control is preferred and is believed to be more accurate. However, it requires a position sensor to track the rotor position which is not desirable to use it for certain workspace applications. This paper exhibits the performance of three-phase induction motor with the simplest control algorithm without the use of a position sensor nor an estimation block to estimate rotor position for sensorless control. The motor stator currents are measured and are transformed to synchronously rotating (d-q-axis) frame by use of Clarke and Park transformation. The actual control happens in this frame where the measured currents are compared with the reference currents. The error signal is fed to a conventional PI controller, and the corrected d-q voltage is generated. The controller outputs are transformed back to three phase voltages and are fed to SVPWM block which generates PWM signal for the voltage source inverter. The open loop vector control model along with SVPWM algorithm is modeled in MATLAB/Simulink software and is experimented and validated in TMS320F28335 DSP board.

Keywords: Electric drive, induction motor, open-loop vector control, space vector pulse width modulation technique.

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Authors: Fahad Alhussein, Huaping Liu

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This paper compares the multipath mitigation performance of code correlation reference waveform receivers with variable and fixed window width, for binary offset carrier and multiplexed binary offset carrier signals typically used in global navigation satellite systems. In the variable window width method, such width is iteratively reduced until the distortion on the discriminator with multipath is eliminated. This distortion is measured as the Euclidean distance between the actual discriminator (obtained with the incoming signal), and the local discriminator (generated with a local copy of the signal). The variable window width have shown better performance compared to the fixed window width. In particular, the former yields zero error for all delays for the BOC and MBOC signals considered, while the latter gives rather large nonzero errors for small delays in all cases. Due to its computational simplicity, the variable window width method is perfectly suitable for implementation in low-cost receivers.

Keywords: Correlation reference waveform receivers, binary offset carrier, multiplexed binary offset carrier, global navigation satellite systems.

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Authors: S. Bouharati, I. Bouharati, C. Benzidane, F. Alleg, M. Belmahdi

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Sensors possess several properties of physical measures. Whether devices that convert a sensed signal into an electrical signal, chemical sensors and biosensors, thus all these sensors can be considered as an interface between the physical and electrical equipment. The problem is the analysis of the multitudes of saved settings as input variables. However, they do not all have the same level of influence on the outputs. In order to identify the most sensitive parameters, those that can guide users in gathering information on the ground and in the process of model calibration and sensitivity analysis for the effect of each change made. Mathematical models used for processing become very complex. In this paper a fuzzy rule-based system is proposed as a solution for this problem. The system collects the available signals information from sensors. Moreover, the system allows the study of the influence of the various factors that take part in the decision system. Since its inception fuzzy set theory has been regarded as a formalism suitable to deal with the imprecision intrinsic to many problems. At the same time, fuzzy sets allow to use symbolic models. In this study an example was applied for resolving variety of physiological parameters that define human health state. The application system was done for medical diagnosis help. The inputs are the signals expressed the cardiovascular system parameters, blood pressure, Respiratory system paramsystem was done, it will be able to predict the state of patient according any input values.

Keywords: Sensors, Sensivity, fuzzy logic, analysis, physiological parameters, medical diagnosis.

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Authors: Rodrigo Chandia, Mauricio Papa

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Efforts to secure supervisory control and data acquisition (SCADA) systems must be supported under the guidance of sound security policies and mechanisms to enforce them. Critical elements of the policy must be systematically translated into a format that can be used by policy enforcement components. Ideally, the goal is to ensure that the enforced policy is a close reflection of the specified policy. However, security controls commonly used to enforce policies in the IT environment were not designed to satisfy the specific needs of the SCADA environment. This paper presents a language, based on the well-known XACML framework, for the expression of authorization policies for SCADA systems.

Keywords: Access policy specification, process control systems, network security.

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Authors: Gabriel Sitler, Yousef Sardahi, Asad Salem

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This paper presents an optimal and robust sliding mode controller (SMC) to regulate the position of the knee joint angle for patients suffering from knee injuries. The controller imitates the role of active orthoses that produce the joint torques required to overcome gravity and loading forces and regain natural human movements. To this end, a mathematical model of the shank, the lower part of the leg, is derived first and then used for the control system design and computer simulations. The design of the controller is carried out in optimal and multi-objective settings. Four objectives are considered: minimization of the control effort and tracking error; and maximization of the control signal smoothness and closed-loop system’s speed of response. Optimal solutions in terms of the Pareto set and its image, the Pareto front, are obtained. The results show that there are trade-offs among the design objectives and many optimal solutions from which the decision-maker can choose to implement. Also, computer simulations conducted at different points from the Pareto set and assuming knee squat movement demonstrate competing relationships among the design goals. In addition, the proposed control algorithm shows robustness in tracking a standard gait signal when accounting for uncertainty in the shank’s parameters.

Keywords: Optimal control, multi-objective optimization, sliding mode control, wearable knee exoskeletons.

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Authors: M. Ahmadi, M. Kafil, H. Ebrahimi

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Nowadays, induction motors have a significant role in industries. Condition monitoring (CM) of this equipment has gained a remarkable importance during recent years due to huge production losses, substantial imposed costs and increases in vulnerability, risk, and uncertainty levels. Motor current signature analysis (MCSA) is one of the most important techniques in CM. This method can be used for rotor broken bars detection. Signal processing methods such as Fast Fourier transformation (FFT), Wavelet transformation and Empirical Mode Decomposition (EMD) are used for analyzing MCSA output data. In this study, these signal processing methods are used for broken bar problem detection of Mobarakeh steel company induction motors. Based on wavelet transformation method, an index for fault detection, CF, is introduced which is the variation of maximum to the mean of wavelet transformation coefficients. We find that, in the broken bar condition, the amount of CF factor is greater than the healthy condition. Based on EMD method, the energy of intrinsic mode functions (IMF) is calculated and finds that when motor bars become broken the energy of IMFs increases.

Keywords: Broken bar, condition monitoring, diagnostics, empirical mode decomposition, Fourier transform, wavelet transform.

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Authors: Stefano Straullu, Silvio Abrate, Antonino Nespola, Paolo Savio, Roberto Gaudino

Abstract:

Two completely different approaches for a Gigabit Ethernet compliant stream transmission over 50m of 1mm PMMA SI-POF have been experimentally demonstrated and are compared in this paper. The first solution is based on a commercial RC-LED transmission and a careful optimization of the physical layer architecture, realized during the POF-PLUS EU Project. The second solution exploits the performance of an edge-emitting laser at the transmitter side in order to avoid any sort of electrical equalization at the receiver side.

Keywords: Gigabit Ethernet, Home Networking, Step-Index Polymer Optical Fiber (SI-POF)

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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: K. A. Laptinskiy, S. A. Burikov, A. M. Vervald, S. A. Dolenko, T. A. Dolenko

Abstract:

The article presents the results of the application of artificial neural networks to separate the fluorescent contribution of nanodiamonds used as biomarkers, adsorbents and carriers of drugs in biomedicine, from a fluorescent background of own biological fluorophores. The principal possibility of solving this problem is shown. Use of neural network architecture let to detect fluorescence of nanodiamonds against the background autofluorescence of egg white with high accuracy - better than 3 ug/ml.

Keywords: Artificial neural networks, fluorescence, data aggregation.

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