Search results for: system signals

Authors: Vickneswaran Jeyabalan, Andrews Samraj, Loo Chu Kiong

Abstract:

The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.

Keywords: Adaptive autoregressive, adaptive bandpass filter, brain machine Interface, EEG, motor imaginary.

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Authors: Eronini Umez-Eronini

Abstract:

Compressed air energy storage (CAES) coupled with wind farms have gained attention as a means to address the intermittency and variability of wind power. However, most existing studies and implementations focus on bulk or centralized CAES plants. This study presents a dynamic model of a hybrid wind farm with distributed CAES, using air storage tanks and compressor and expander trains at each wind turbine station. It introduces the concept of a distributed CAES with linked air cooling and heating, and presents an approach to scheduling and regulating the production of compressed air and power in such a system. Mathematical models of the dynamic components of this hybrid wind farm system, including a simple transient wake field model, were developed and simulated using MATLAB, with real wind data and Transmission System Operator (TSO) absolute power reference signals as inputs. The simulation results demonstrate that the proposed ad hoc supervisory controller is able to track the minute-scale power demand signal within an error band size comparable to the electrical power rating of a single expander. This suggests that combining the global distributed CAES control with power regulation for individual wind turbines could further improve the system’s performance. The round trip electrical storage efficiency computed for the distributed CAES was also in the range of reported round trip storage electrical efficiencies for improved bulk CAES. These findings contribute to the enhancement of efficiency of wind farms without access to large-scale storage or underground caverns.

Keywords: Distributed CAES, compressed air, energy storage, hybrid wind farm, wind turbines, dynamic simulation.

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Authors: Elnaz Lashgari, Emel Demircan

Abstract:

Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: Electrocardiogram, manifold learning, Laplacian Eigenmaps, running pattern.

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Authors: M. Barański

Abstract:

This paper presents a several diagnostic methods designed to electrical machinesespecially for permanent magnets (PM) machines. Those machines are commonly used in small wind and water systems and vehicles drives.Thosemethodsare preferred by the author in periodic diagnostic of electrical machines. The special attentionshould be paid to diagnostic method of turn-to-turn insulation and vibrations. Both of those methodswere createdinInstitute of Electrical Drives and MachinesKomel. The vibration diagnostic method is the main thesis of author’s doctoral dissertation. This is method of determination the technical condition of PM electrical machine basing on its own signals is the subject of patent application No P.405669. Specific structural properties of machines excited by permanent magnets are used in this method - electromotive force (EMF) generated due to vibrations. There was analysed number of publications which describe vibration diagnostic methods and tests of electrical machines with permanent magnets and there was no method found to determine the technical condition of such machine basing on their own signals.

Keywords: Electrical vehicle, generator, main insulation, permanent magnet, thermography, turn-to- traction drive, turn insulation, vibrations.

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Authors: Soo-young Ye, Gi-Ryon Kim, Dong-Keun Jung, Seong-wan Baik, Gye-rok Jeon

Abstract:

In this paper, algorithm estimating the blood pressure was proposed using the pulse transit time (PTT) as a more convenient method of measuring the blood pressure. After measuring ECG and pressure pulse, and photoplethysmography, the PTT was calculated from the acquired signals. Thereafter, the system to indirectly measure the systolic pressure and the diastolic pressure was composed using the statistic method. In comparison between the blood pressure indirectly measured by proposed algorithm estimating the blood pressure and real blood pressure measured by conventional sphygmomanometer, the systolic pressure indicates the mean error of ±3.24mmHg and the standard deviation of 2.53mmHg, while the diastolic pressure indicates the satisfactory result, that is, the mean error of ±1.80mmHg and the standard deviation of 1.39mmHg. These results are satisfied with the regulation of ANSI/AAMI for certification of sphygmomanometer that real measurement error value should be within the mean error of ±5mmHg and the standard deviation of 8mmHg. These results are suggest the possibility of applying to portable and long time blood pressure monitoring system hereafter.

Keywords: Blood pressure, Systolic, Diastolic, Pulse transit time.

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Authors: L. Khoshnevisan, H. R. Momeni, A. Ashraf-Modarres

Abstract:

One of the robust fault detection filter (RFDF) designing method is based on sliding-mode theory. The main purpose of our study is to introduce an innovative simplified reference residual model generator to formulate the RFDF as a sliding-mode observer without any manipulation package or transformation matrix, through which the generated residual signals can be evaluated. So the proposed design is more explicit and requires less design parameters in comparison with approaches requiring changing coordinates. To the best author's knowledge, this is the first time that the sliding mode technique is applied to detect actuator and sensor faults in a real boiler. The designing procedure is proposed in a drum boiler in Synvendska Kraft AB Plant in Malmo, Sweden as a multivariable and strongly coupled system. It is demonstrated that both sensor and actuator faults can robustly be detected. Also sensor faults can be diagnosed and isolated through this method.

Keywords: Boiler, fault detection, robustness, simplified sliding-mode observer.

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Authors: Pogula Rakesh, T. Kishore Kumar

Abstract:

Speech enhancement is a long standing problem with numerous applications like teleconferencing, VoIP, hearing aids and speech recognition. The motivation behind this research work is to obtain a clean speech signal of higher quality by applying the optimal noise cancellation technique. Real-time adaptive filtering algorithms seem to be the best candidate among all categories of the speech enhancement methods. In this paper, we propose a speech enhancement method based on Recursive Least Squares (RLS) adaptive filter of speech signals. Experiments were performed on noisy data which was prepared by adding AWGN, Babble and Pink noise to clean speech samples at -5dB, 0dB, 5dB and 10dB SNR levels. We then compare the noise cancellation performance of proposed RLS algorithm with existing NLMS algorithm in terms of Mean Squared Error (MSE), Signal to Noise ratio (SNR) and SNR Loss. Based on the performance evaluation, the proposed RLS algorithm was found to be a better optimal noise cancellation technique for speech signals.

Keywords: Adaptive filter, Adaptive Noise Canceller, Mean Squared Error, Noise reduction, NLMS, RLS, SNR, SNR Loss.

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Authors: Kexin Jia, Youxin Lu

Abstract:

To make the modulation classification system more suitable for signals in a wide range of signal to noise rate (SNR), a novel method of designing combined classifier based on fuzzy neural network (FNN) is presented in this paper. The method employs fuzzy neural network classifiers and interclass distance (ICD) to improve recognition reliability. Experimental results show that the proposed combined classifier has high recognition rate with large variation range of SNR (success rates are over 99.9% when SNR is not lower than 5dB).

Keywords: Modulation classification, combined classifier, fuzzy neural network, interclass distance.

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Authors: Lalita Gupta, R. P. Singh

Abstract:

Array signal processing involves signal enumeration and source localization. Array signal processing is centered on the ability to fuse temporal and spatial information captured via sampling signals emitted from a number of sources at the sensors of an array in order to carry out a specific estimation task: source characteristics (mainly localization of the sources) and/or array characteristics (mainly array geometry) estimation. Array signal processing is a part of signal processing that uses sensors organized in patterns or arrays, to detect signals and to determine information about them. Beamforming is a general signal processing technique used to control the directionality of the reception or transmission of a signal. Using Beamforming we can direct the majority of signal energy we receive from a group of array. Multiple signal classification (MUSIC) is a highly popular eigenstructure-based estimation method of direction of arrival (DOA) with high resolution. This Paper enumerates the effect of missing sensors in DOA estimation. The accuracy of the MUSIC-based DOA estimation is degraded significantly both by the effects of the missing sensors among the receiving array elements and the unequal channel gain and phase errors of the receiver.

Keywords: Array Signal Processing, Beamforming, ULA, Direction of Arrival, MUSIC

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Authors: M. Satya Sai Ram, P. Siddaiah, M. Madhavi Latha

Abstract:

Vector quantization is a powerful tool for speech coding applications. This paper deals with LPC Coding of speech signals which uses a new technique called Multi Switched Split Vector Quantization (MSSVQ), which is a hybrid of Multi, switched, split vector quantization techniques. The spectral distortion performance, computational complexity, and memory requirements of MSSVQ are compared to split vector quantization (SVQ), multi stage vector quantization(MSVQ) and switched split vector quantization (SSVQ) techniques. It has been proved from results that MSSVQ has better spectral distortion performance, lower computational complexity and lower memory requirements when compared to all the above mentioned product code vector quantization techniques. Computational complexity is measured in floating point operations (flops), and memory requirements is measured in (floats).

Keywords: Linear predictive Coding, Multi stage vectorquantization, Switched Split vector quantization, Split vectorquantization, Line Spectral Frequencies (LSF).

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Authors: Deepak Goswami, Padma Lochan Hazarika, Kandarpa Kumar Sarma

Abstract:

The customary practice of identifying industrial sickness is a set traditional techniques which rely upon a range of manual monitoring and compilation of financial records. It makes the process tedious, time consuming and often are susceptible to manipulation. Therefore, certain readily available tools are required which can deal with such uncertain situations arising out of industrial sickness. It is more significant for a country like India where the fruits of development are rarely equally distributed. In this paper, we propose an approach based on Artificial Neural Network (ANN) to deal with industrial sickness with specific focus on a few such units taken from a less developed north-east (NE) Indian state like Assam. The proposed system provides decision regarding industrial sickness using eight different parameters which are directly related to the stages of sickness of such units. The mechanism primarily uses certain signals and symptoms of industrial health to decide upon the state of a unit. Specifically, we formulate an ANN based block with data obtained from a few selected units of Assam so that required decisions related to industrial health could be taken. The system thus formulated could become an important part of planning and development. It can also contribute towards computerization of decision support systems related to industrial health and help in better management.

Keywords: Industrial, Health, Classification, ANN, MLP, MSE.

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Authors: Ihor Trots, Yuriy Tasinkevych, Andrzej Nowicki, Marcin Lewandowski

Abstract:

The paper presents the study of synthetic transmit aperture method applying the Golay coded transmission for medical ultrasound imaging. Longer coded excitation allows to increase the total energy of the transmitted signal without increasing the peak pressure. Signal-to-noise ratio and penetration depth are improved maintaining high ultrasound image resolution. In the work the 128-element linear transducer array with 0.3 mm inter-element spacing excited by one cycle and the 8 and 16-bit Golay coded sequences at nominal frequencies 4 MHz was used. Single element transmission aperture was used to generate a spherical wave covering the full image region and all the elements received the echo signals. The comparison of 2D ultrasound images of the wire phantom as well as of the tissue mimicking phantom is presented to demonstrate the benefits of the coded transmission. The results were obtained using the synthetic aperture algorithm with transmit and receive signals correction based on a single element directivity function.

Keywords: Golay coded sequences, radiation pattern, synthetic aperture, ultrasound imaging.

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Authors: Jixin Liu, Zheming Lu

Abstract:

In this paper, a novel multipurpose audio watermarking algorithm is proposed based on Vector Quantization (VQ) in Discrete Cosine Transform (DCT) domain using the codeword labeling and index-bit constrained method. By using this algorithm, it can fulfill the requirements of both the copyright protection and content integrity authentication at the same time for the multimedia artworks. The robust watermark is embedded in the middle frequency coefficients of the DCT transform during the labeled codeword vector quantization procedure. The fragile watermark is embedded into the indices of the high frequency coefficients of the DCT transform by using the constrained index vector quantization method for the purpose of integrity authentication of the original audio signals. Both the robust and the fragile watermarks can be extracted without the original audio signals, and the simulation results show that our algorithm is effective with regard to the transparency, robustness and the authentication requirements

Keywords: Copyright Protection, Discrete Cosine Transform, Integrity Authentication, Multipurpose Audio Watermarking, Vector Quantization.

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Authors: Negar Riazifar, Nigel G. Stocks

Abstract:

This paper proposes strategies in level crossing (LC) sampling and reconstruction that provide alias-free high-fidelity signal reconstruction for speech signals without exponentially increasing sample number with increasing bit-depth. We introduce methods in LC sampling that reduce the sampling rate close to the Nyquist frequency even for large bit-depth. The results indicate that larger variation in the sampling intervals leads to alias-free sampling scheme; this is achieved by either reducing the bit-depth or adding a jitter to the system for high bit-depths. In conjunction with windowing, the signal is reconstructed from the LC samples using an efficient Toeplitz reconstruction algorithm.

Keywords: Alias-free, level crossing sampling, spectrum, trigonometric polynomial.

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Authors: Yi-Cheng Huang, Yan-Chen Shin

Abstract:

This paper proposes method of diagnosing ball screw preload loss through the Hilbert-Huang Transform （HHT） and Multiscale entropy （MSE） process. The proposed method can diagnose ball screw preload loss through vibration signals when the machine tool is in operation. Maximum dynamic preload of 2 %, 4 %, and 6 % ball screws were predesigned, manufactured, and tested experimentally. Signal patterns are discussed and revealed using Empirical Mode Decomposition（EMD）with the Hilbert Spectrum. Different preload features are extracted and discriminated using HHT. The irregularity development of a ball screw with preload loss is determined and abstracted using MSE based on complexity perception. Experiment results show that the proposed method can predict the status of ball screw preload loss. Smart sensing for the health of the ball screw is also possible based on a comparative evaluation of MSE by the signal processing and pattern matching of EMD/HHT. This diagnosis method realizes the purposes of prognostic effectiveness on knowing the preload loss and utilizing convenience.

Keywords: Empirical Mode Decomposition, Hilbert-Huang Transform, Multi-scale Entropy, Preload Loss, Single-nut Ball Screw

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Authors: Maryam Alimardani, Kazuo Hiraki

Abstract:

This study presents a framework for development of a new generation of therapy robots that can interact with users by monitoring their physiological and mental states. Here, we focused on one of the controversial methods of therapy, hypnotherapy. Hypnosis has shown to be useful in treatment of many clinical conditions. But, even for healthy people, it can be used as an effective technique for relaxation or enhancement of memory and concentration. Our aim is to develop a robot that collects information about user’s mental and physical states using electroencephalogram (EEG) and electromyography (EMG) signals and performs costeffective hypnosis at the comfort of user’s house. The presented framework consists of three main steps: (1) Find the EEG-correlates of mind state before, during, and after hypnosis and establish a cognitive model for state changes, (2) Develop a system that can track the changes in EEG and EMG activities in real time and determines if the user is ready for suggestion, and (3) Implement our system in a humanoid robot that will talk and conduct hypnosis on users based on their mental states. This paper presents a pilot study in regard to the first stage, detection of EEG and EMG features during hypnosis.

Keywords: Hypnosis, EEG, robotherapy, brain-computer interface.

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Authors: Ehab F. Badran, Guoxiang Gu

Abstract:

We consider optimal channel equalization for MIMO (multi-input/multi-output) time-varying channels in the sense of MMSE (minimum mean-squared-error), where the observation noise can be non-stationary. We show that all ZF (zero-forcing) receivers can be parameterized in an affine form which eliminates completely the ISI (inter-symbol-interference), and optimal channel equalizers can be designed through minimization of the MSE (mean-squarederror) between the detected signals and the transmitted signals, among all ZF receivers. We demonstrate that the optimal channel equalizer is a modified Kalman filter, and show that under the AWGN (additive white Gaussian noise) assumption, the proposed optimal channel equalizer minimizes the BER (bit error rate) among all possible ZF receivers. Our results are applicable to optimal channel equalization for DWMT (discrete wavelet multitone), multirate transmultiplexers, OFDM (orthogonal frequency division multiplexing), and DS (direct sequence) CDMA (code division multiple access) wireless data communication systems. A design algorithm for optimal channel equalization is developed, and several simulation examples are worked out to illustrate the proposed design algorithm.

Keywords: Channel equalization, Kalman filtering, Time-varying systems.

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Authors: Santanu Chattopadhyay, Gautam Sarkar, Arabinda Das

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This paper presents wavelet based classification of various heart diseases. Electrocardiogram signals of different heart patients have been studied. Statistical natures of electrocardiogram signals for different heart diseases have been compared with the statistical nature of electrocardiograms for normal persons. Under this study four different heart diseases have been considered as follows: Myocardial Ischemia (MI), Congestive Heart Failure (CHF), Arrhythmia and Sleep Apnea. Statistical nature of electrocardiograms for each case has been considered in terms of kurtosis values of two types of wavelet coefficients: approximate and detail. Nine wavelet decomposition levels have been considered in each case. Kurtosis corresponding to both approximate and detail coefficients has been considered for decomposition level one to decomposition level nine. Based on significant difference, few decomposition levels have been chosen and then used for classification.

Keywords: Arrhythmia, congestive heart failure, discrete wavelet transform, electrocardiogram, myocardial ischemia, sleep apnea.

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Authors: C. Panya-Isara, T. Kulworawanichpong, P. Pao-La-Or

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Piezoelectric transformers are electronic devices made from piezoelectric materials. The piezoelectric transformers as the name implied are used for changing voltage signals from one level to another. Electrical energy carried with signals is transferred by means of mechanical vibration. Characterizing in both electrical and mechanical properties leads to extensively use and efficiency enhancement of piezoelectric transformers in various applications. In this paper, study and analysis of electrical and mechanical properties of multi-layer piezoelectric transformers in forms of potential and displacement distribution throughout the volume, respectively. This paper proposes a set of quasi-static mathematical model of electromechanical coupling for piezoelectric transformer by using a set of partial differential equations. Computer-based simulation utilizing the three-dimensional finite element method (3-D FEM) is exploited as a tool for visualizing potentials and displacements distribution within the multi-layer piezoelectric transformer. This simulation was conducted by varying a number of layers. In this paper 3, 5 and 7 of the circular ring type were used. The computer simulation based on the use of the FEM has been developed in MATLAB programming environment.

Keywords: Multi-layer Piezoelectric Transformer, 3-D Finite Element Method (3-D FEM), Electro-mechanical Coupling, Mechanical Vibration

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Authors: Andrey V. Timofeev

Abstract:

The practical efficient approach is suggested to estimate the high-speed objects instant bounds in C-OTDR monitoring systems. In case of super-dynamic objects (trains, cars) is difficult to obtain the adequate estimate of the instantaneous object localization because of estimation lag. In other words, reliable estimation coordinates of monitored object requires taking some time for data observation collection by means of C-OTDR system, and only if the required sample volume will be collected the final decision could be issued. But it is contrary to requirements of many real applications. For example, in rail traffic management systems we need to get data of the dynamic objects localization in real time. The way to solve this problem is to use the set of statistical independent parameters of C-OTDR signals for obtaining the most reliable solution in real time. The parameters of this type we can call as «signaling parameters» (SP). There are several the SP’s which carry information about dynamic objects instant localization for each of COTDR channels. The problem is that some of these parameters are very sensitive to dynamics of seismoacoustic emission sources, but are non-stable. On the other hand, in case the SP is very stable it becomes insensitive as rule. This report contains describing of the method for SP’s co-processing which is designed to get the most effective dynamic objects localization estimates in the C-OTDR monitoring system framework.

Keywords: C-OTDR-system, co-processing of signaling parameters, high-speed objects localization, multichannel monitoring systems.

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Authors: Jason Chien-Hsun Tseng

Abstract:

This paper evaluates performances of an adaptive noise cancelling (ANC) based target detection algorithm on a set of real test data supported by the Defense Evaluation Research Agency (DERA UK) for multi-target wideband active sonar echolocation system. The hybrid algorithm proposed is a combination of an adaptive ANC neuro-fuzzy scheme in the first instance and followed by an iterative optimum target motion estimation (TME) scheme. The neuro-fuzzy scheme is based on the adaptive noise cancelling concept with the core processor of ANFIS (adaptive neuro-fuzzy inference system) to provide an effective fine tuned signal. The resultant output is then sent as an input to the optimum TME scheme composed of twogauge trimmed-mean (TM) levelization, discrete wavelet denoising (WDeN), and optimal continuous wavelet transform (CWT) for further denosing and targets identification. Its aim is to recover the contact signals in an effective and efficient manner and then determine the Doppler motion (radial range, velocity and acceleration) at very low signal-to-noise ratio (SNR). Quantitative results have shown that the hybrid algorithm have excellent performance in predicting targets- Doppler motion within various target strength with the maximum false detection of 1.5%.

Keywords: Wideband Active Sonar Echolocation, ANC Neuro-Fuzzy, Wavelet Denoise, CWT, Hybrid Algorithm.

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Authors: Úrsula B. Ferraz, Paulo F. Seixas, Webber E. Aguiar

Abstract:

This paper presents a dynamic model for mechanical loads of an electric drive, including angular misalignment and including load unbalance. The misalignment model represents the effects of the universal joint between the motor and the mechanical load. Simulation results are presented for an induction motor driving a mechanical load with angular misalignment for both flexible and rigid coupling. The models presented are very useful in the study of mechanical fault detection in induction motors, using mechanical and electrical signals already available in a drive system, such as speed, torque and stator currents.

Keywords: Angular misalignment, fault modeling, unbalance, universal joint.

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Authors: Hema C. R., Paulraj M. P., S. Yaacob, A. H. Adom, R. Nagarajan

Abstract:

Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification

Keywords: Motor Imagery, Brain Machine Interfaces, Neural Networks, Particle Swarm Optimization, EEG signal processing.

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Authors: Ihor Trots, Yuriy Tasinkevych, Andrzej Nowicki, Marcin Lewandowski

Abstract:

In the paper the study of synthetic transmit aperture method applying the Golay coded transmission for medical ultrasound imaging is presented. Longer coded excitation allows to increase the total energy of the transmitted signal without increasing the peak pressure. Moreover signal-to-noise ratio and penetration depth are improved while maintaining high ultrasound image resolution. In the work the 128-element linear transducer array with 0.3 mm inter-element spacing excited by one cycle and the 8 and 16- bit Golay coded sequences at nominal frequency 4 MHz was used. To generate a spherical wave covering the full image region a single element transmission aperture was used and all the elements received the echo signals. The comparison of 2D ultrasound images of the tissue mimicking phantom and in vitro measurements of the beef liver is presented to illustrate the benefits of the coded transmission. The results were obtained using the synthetic aperture algorithm with transmit and receive signals correction based on a single element directivity function.

Keywords: Golay coded sequences, radiation pattern, signal processing, synthetic aperture, ultrasound imaging.

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Authors: Chen Sun, Nemai Chandra Karmakar

Abstract:

A novel direction-of-arrival (DOA) estimation technique, which uses a conventional multiple signal classification (MUSIC) algorithm with periodic signals, is applied to a single RF-port parasitic array antenna for direction finding. Simulation results show that the proposed method gives high resolution (1 degree) DOA estimation in an uncorrelated signal environment. The novelty lies in that the MUSIC algorithm is applied to a simplified antenna configuration. Only one RF port and one analogue-to-digital converter (ADC) are used in this antenna, which features low DC power consumption, low cost, and ease of fabrication. Modifications to the conventional MUSIC algorithm do not bring much additional complexity. The proposed technique is also free from the negative influence by the mutual coupling between elements. Therefore, the technique has great potential to be implemented into the existing wireless mobile communications systems, especially at the power consumption limited mobile terminals, to provide additional position location (PL) services.

Keywords: Direction-of-arrival (DOA) estimation, electronically steerable parasitic array radiator (ESPAR), multiple single classifications (MUSIC), position location.

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Authors: Sajjad Farashi

Abstract:

Neurons in the nervous system communicate with each other by producing electrical signals called spikes. To investigate the physiological function of nervous system it is essential to study the activity of neurons by detecting and sorting spikes in the recorded signal. In this paper a method is proposed for considering the spike sorting problem which is based on the nonlinear modeling of spikes using exponential autoregressive model. The genetic algorithm is utilized for model parameter estimation. In this regard some selected model coefficients are used as features for sorting purposes. For optimal selection of model coefficients, self-organizing feature map is used. The results show that modeling of spikes with nonlinear autoregressive model outperforms its linear counterpart. Also the extracted features based on the coefficients of exponential autoregressive model are better than wavelet based extracted features and get more compact and well-separated clusters. In the case of spikes different in small-scale structures where principal component analysis fails to get separated clouds in the feature space, the proposed method can obtain well-separated cluster which removes the necessity of applying complex classifiers.

Keywords: Exponential autoregressive model, Neural data, spike sorting, time series modeling.

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Authors: Jean François Chatelain, Imed Zaghbani, Gilbert Lebrun, Kaml Hasni

Abstract:

Severe damages may occur during the drilling of carbon fiber reinforced plastics (CFRP). In practice, this damage is limited by adding a backup support to the drilled parts. For some aeronautical parts with curvatures, backing up parts is a demanding process. In order to simplify the operation, this research studies the effect of using a configurable setup to support parts on the resulting quality of drilled holes. The test coupons referenced in this study are twenty four-plies unidirectional laminates made of carbon fibers and epoxy resin. Different signals were measured during the drilling process for these laminates, including the thrust force, the displacement and the acceleration. The processing of these signals demonstrated that the damage is due to the combination of two main factors: the spring-back of the thin part and the thrust force. The results found were confirmed for different feeds and speeds. When the distance between supports is increased, it is observed that the spring-back increases but the thrust force decreases. The study proves the feasibility of unsupported drilling of thin CFRP laminates without creating any observable damage.

Keywords: CFRP, Damage, Drilling, Flexible setup.

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Authors: Eleonora Tudora, Adriana Alexandru

Abstract:

The radio frequency identification (RFID) is a technology for automatic identification of items, particularly in supply chain, but it is becoming increasingly important for industrial applications. Unlike barcode technology that detects the optical signals reflected from barcode labels, RFID uses radio waves to transmit the information from an RFID tag affixed to the physical object. In contrast to today most often use of this technology in warehouse inventory and supply chain, the focus of this paper is an overview of the structure of RFID systems used by RFID technology and it also presents a solution based on the application of RFID for brand authentication, traceability and tracking, by implementing a production management system and extending its use to traders.

Keywords: RFID, RFID Tag, Electronic Product Code (EPC), EPC network, Object Naming Service (ONS), Authentication, Traceability.

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Authors: E. Irankhah, M. Zarif, E. Mazrooei Rad, K. Ghandehari

Abstract:

Alzheimer's prevalence is on the rise, and the disease comes with problems like cessation of treatment, high cost of treatment, and the lack of early detection methods. The pathology of this disease causes the formation of protein deposits in the brain of patients called plaque amyloid. Generally, the diagnosis of this disease is done by performing tests such as a cerebrospinal fluid, CT scan, MRI, and spinal cord fluid testing, or mental testing tests and eye tracing tests. In this paper, we tried to use the Medial Temporal Atrophy (MTA) method and the Leave One Out (LOO) cycle to extract the statistical properties of the three Fz, Pz, and Cz channels of ERP signals for early diagnosis of this disease. In the process of CT scan images, the accuracy of the results is 81% for the healthy person and 88% for the severe patient. After the process of ERP signaling, the accuracy of the results for a healthy person in the delta band in the Cz channel is 81% and in the alpha band the Pz channel is 90%. In the results obtained from the signal processing, the results of the severe patient in the delta band of the Cz channel were 89% and in the alpha band Pz channel 92%.

Keywords: Alzheimer's disease, image and signal processing, medial temporal atrophy, LOO Cycle.

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Authors: Montri Phothisonothai, Masahiro Nakagawa

Abstract:

The objective of this paper is to characterize the spontaneous Electroencephalogram (EEG) signals of four different motor imagery tasks and to show hereby a possible solution for the present binary communication between the brain and a machine ora Brain-Computer Interface (BCI). The processing technique used in this paper was the fractal analysis evaluated by the Critical Exponent Method (CEM). The EEG signal was registered in 5 healthy subjects,sampling 15 measuring channels at 1024 Hz.Each channel was preprocessed by the Laplacian space ltering so as to reduce the space blur and therefore increase the spaceresolution. The EEG of each channel was segmented and its Fractaldimension (FD) calculated. The FD was evaluated in the time interval corresponding to the motor imagery and averaged out for all the subjects (each channel). In order to characterize the FD distribution,the linear regression curves of FD over the electrodes position were applied. The differences FD between the proposed mental tasks are quantied and evaluated for each experimental subject. The obtained results of the proposed method are a substantial fractal dimension in the EEG signal of motor imagery tasks and can be considerably utilized as the multiple-states BCI applications.

Keywords: electroencephalogram (EEG), motor imagery tasks, mental tasks, biomedical signals processing, human-machine interface, fractal analysis, critical exponent method (CEM).

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