Search results for: signal analysis

Authors: Fang Ye, Han Yu, Yibing Li

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Radio interference is one of the major concerns in using the global positioning system (GPS) for civilian and military applications. Interference signals are produced not only through all electronic systems but also illegal jammers. Among different types of interferences, continuous wave (CW) interference has strong adverse impacts on the quality of the received signal. In this paper, we make more detailed analysis for CW interference effects on GPS signal quality. Based on the C/A code spectrum lines, the influence of CW interference on the acquisition performance of GPS receivers is further analysed. This influence is supported by simulation results using GPS software receiver. As the most important user parameter of GPS receivers, the mathematical expression of bit error probability is also derived in the presence of CW interference, and the expression is consistent with the Monte Carlo simulation results. The research on CW interference provides some theoretical gist and new thoughts on monitoring the radio noise environment and improving the anti-jamming ability of GPS receivers.

Keywords: GPS, CW interference, acquisition performance, bit error probability, Monte Carlo

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Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

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Authors: Hung Chih Hsieh, Cheng Hao Chang, Yun Hsiang Chang, Yu Lin Chang

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In the applications of the spectrometer, the stray light that comes from the environment affects the measurement results a lot. Hence, environment and instrument quality control for the stray reduction is critical for the spectral reflectance measurement. In this paper, a simple and practical method has been developed to correct a spectrometer's response for measurement errors arising from the environment's and instrument's stray light. A sinusoidal modulated light intensity signal was incident on a tested sample, and then the reflected light was collected by the spectrometer. Since a sinusoidal signal modulated the incident light, the reflected light also had a modulated frequency which was the same as the incident signal. Using the three-parameter sine curve fitting algorithm, we can extract the primary reflectance signal from the total measured signal, which contained the primary reflectance signal and the stray light from the environment. The spectra similarity between the extracted spectra by this proposed method with extreme environment stray light is 99.98% similar to the spectra without the environment's stray light. This result shows that we can measure the reflectance spectra without the affection of the environment's stray light.

Keywords: spectrometer, stray light, three-parameter sine curve fitting, spectra extraction

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Authors: Zahid Ullah, Atlas Khan

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This abstract focuses on the advancements in mathematical modeling and optimization techniques that play a crucial role in enhancing the efficiency, reliability, and performance of these systems. In this era of rapidly evolving technology, mathematical modeling and optimization offer powerful tools to tackle the complex challenges faced by control, signal processing, and energy systems. This abstract presents the latest research and developments in mathematical methodologies, encompassing areas such as control theory, system identification, signal processing algorithms, and energy optimization. The abstract highlights the interdisciplinary nature of mathematical modeling and optimization, showcasing their applications in a wide range of domains, including power systems, communication networks, industrial automation, and renewable energy. It explores key mathematical techniques, such as linear and nonlinear programming, convex optimization, stochastic modeling, and numerical algorithms, that enable the design, analysis, and optimization of complex control and signal processing systems. Furthermore, the abstract emphasizes the importance of addressing real-world challenges in control, signal processing, and energy systems through innovative mathematical approaches. It discusses the integration of mathematical models with data-driven approaches, machine learning, and artificial intelligence to enhance system performance, adaptability, and decision-making capabilities. The abstract also underscores the significance of bridging the gap between theoretical advancements and practical applications. It recognizes the need for practical implementation of mathematical models and optimization algorithms in real-world systems, considering factors such as scalability, computational efficiency, and robustness. In summary, this abstract showcases the advancements in mathematical modeling and optimization techniques for control, signal processing, and energy systems. It highlights the interdisciplinary nature of these techniques, their applications across various domains, and their potential to address real-world challenges. The abstract emphasizes the importance of practical implementation and integration with emerging technologies to drive innovation and improve the performance of control, signal processing, and energy.

Keywords: mathematical modeling, optimization, control systems, signal processing, energy systems, interdisciplinary applications, system identification, numerical algorithms

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Authors: Zerihun Ketema Tadesse, Dabbu Suman Reddy

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Brain-Computer Interfaces (BCIs) give the possibility for disabled people to communicate and control devices. This work aims at developing steady-state visual evoked potential (SSVEP)-based BCI for patients with limb disabilities. In hospitals, devices like nurse emergency call devices, lights, and TV sets are what patients use most frequently, but these devices are operated manually or using the remote control. Thus, disabled patients are not able to operate these devices by themselves. Hence, SSVEP-based BCI system that can allow disabled patients to control nurse calling device and other devices is proposed in this work. Portable LED visual stimulator that flickers at specific frequencies of 7Hz, 8Hz, 9Hz and 10Hz were developed as part of this project. Disabled patients can stare at specific flickering LED of visual stimulator and Emotiv EPOC used to acquire EEG signal in a non-invasive way. The acquired EEG signal can be processed to generate various control signals depending upon the amplitude and duration of signal components. MATLAB software is used for signal processing and analysis and also for command generation. Arduino is used as a hardware interface device to receive and transmit command signals to the experimental setup. Therefore, this study is focused on the design and development of Steady-state visually evoked potential (SSVEP)-based BCI for limb disabled patients, which helps them to operate and control devices in the hospital room/wards.

Keywords: SSVEP-BCI, Limb Disabled Patients, LED Visual Stimulator, EEG signal, control devices, hospital room/wards

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Authors: Khalida S. Rijab, Mohammed E. Safi, Ayad A. Ibrahim

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When electrical electrodes are mounted on the skin surface of the muscle, a signal is detected when a skeletal muscle undergoes contraction; the signal is known as surface electromyographic signal (EMG). This signal has a noise-like interference pattern resulting from the temporal and spatial summation of action potentials (AP) of all active motor units (MU) near electrode detection. By appropriate processing (Decomposition), the surface EMG signal may be used to give an estimate of motor unit action potential. In this work, a denoising technique is applied to the MUAP signals extracted from the spatial filter (IB2). A set of signals from a non-invasive two-dimensional grid of 16 electrodes from different types of subjects, muscles, and sex are recorded. These signals will acquire noise during recording and detection. A digital fourth order band- pass Butterworth filter is used for denoising, with a tuned band-pass frequency of suitable choice of cutoff frequencies is investigated, with the aim of obtaining a suitable band pass frequency. Results show an improvement of (1-3 dB) in the signal to noise ratio (SNR) have been achieved, relative to the raw spatial filter output signals for all cases that were under investigation. Furthermore, the research’s goal included also estimation and reconstruction of the mean shape of the MUAP.

Keywords: EMG, Motor Unit, Digital Filter, Denoising

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Authors: A. Moawad, K. C. Yao, A. Mansour, R. Gautier

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The detecting of an occupied frequency band is a major issue in cognitive radio systems. The detection process becomes difficult if the signal occupying the band of interest has faded amplitude due to multipath effects. These effects make it hard for an occupying user to be detected. This work mitigates the missed-detection problem in the context of cognitive radio in frequency-selective fading channel by proposing blind channel estimation method that is based on scattering transform. By initially applying conventional energy detection, the missed-detection probability is evaluated, and if it is greater than or equal to 50%, channel estimation is applied on the received signal followed by channel equalization to reduce the channel effects. In the proposed channel estimator, we modify the Morlet wavelet by using its first derivative for better frequency resolution. A mathematical description of the modified function and its frequency resolution is formulated in this work. The improved frequency resolution is required to follow the spectral variation of the channel. The channel estimation error is evaluated in the mean-square sense for different channel settings, and energy detection is applied to the equalized received signal. The simulation results show improvement in reducing the missed-detection probability as compared to the detection based on principal component analysis. This improvement is achieved at the expense of increased estimator complexity, which depends on the number of wavelet filters as related to the channel taps. Also, the detection performance shows an improvement in detection probability for low signal-to-noise scenarios over principal component analysis- based energy detection.

Keywords: channel estimation, cognitive radio, scattering transform, spectrum sensing

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Authors: Bowei Yuan, Shi Li, Liuyang Song, Huaqing Wang, Lingli Cui

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Convolutional neural networks (CNN) have received extensive attention in the field of fault diagnosis. Many fault diagnosis methods use CNN for fault type identification. However, when the amount of raw data collected by sensors is massive, the neural network needs to perform a time-consuming classification task. In this paper, a mechanical fault diagnosis method based on vibration signal down-sampling and the improved one-dimensional convolutional neural network is proposed. Through the robust principal component analysis, the low-rank feature matrix of a large amount of raw data can be separated, and then down-sampling is realized to reduce the subsequent calculation amount. In the improved one-dimensional CNN, a smaller convolution kernel is used to reduce the number of parameters and computational complexity, and regularization is introduced before the fully connected layer to prevent overfitting. In addition, the multi-connected layers can better generalize classification results without cumbersome parameter adjustments. The effectiveness of the method is verified by monitoring the signal of the centrifugal pump test bench, and the average test accuracy is above 98%. When compared with the traditional deep belief network (DBN) and support vector machine (SVM) methods, this method has better performance.

Keywords: fault diagnosis, vibration signal down-sampling, 1D-CNN

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Authors: Cemil Turan, Mohammad Shukri Salman

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The convergence rate of the least-mean-square (LMS) algorithm deteriorates if the input signal to the filter is correlated. In a system identification problem, this convergence rate can be improved if the signal is white and/or if the system is sparse. We recently proposed a sparse transform domain LMS-type algorithm that uses a variable step-size for a sparse system identification. The proposed algorithm provided high performance even if the input signal is highly correlated. In this work, we investigate the performance of the proposed TD-LMS algorithm for a large number of filter tap which is also a critical issue for standard LMS algorithm. Additionally, the optimum value of the most important parameter is calculated for all experiments. Moreover, the convergence analysis of the proposed algorithm is provided. The performance of the proposed algorithm has been compared to different algorithms in a sparse system identification setting of different sparsity levels and different number of filter taps. Simulations have shown that the proposed algorithm has prominent performance compared to the other algorithms.

Keywords: adaptive filtering, sparse system identification, TD-LMS algorithm, VSSLMS algorithm

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Authors: Ju-Hong Lee, Ching-Wei Liao

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Adaptive beamforming using antenna array of sensors is useful in the process of adaptively detecting and preserving the presence of the desired signal while suppressing the interference and the background noise. For conventional adaptive array beamforming, we require a prior information of either the impinging direction or the waveform of the desired signal to adapt the weights. The adaptive weights of an antenna array beamformer under a steered-beam constraint are calculated by minimizing the output power of the beamformer subject to the constraint that forces the beamformer to make a constant response in the steering direction. Hence, the performance of the beamformer is very sensitive to the accuracy of the steering operation. In the literature, it is well known that the performance of an adaptive beamformer will be deteriorated by any steering angle error encountered in many practical applications, e.g., the wireless communication systems with massive antennas deployed at the base station and user equipment. Hence, developing effective signal processing techniques to deal with the problem due to steering angle error for array beamforming systems has become an important research work. In this paper, we present an effective signal processing technique for constructing an adaptive beamformer against the steering angle error. The proposed array beamformer adaptively estimates the actual direction of the desired signal by using the presumed steering vector and the received array data snapshots. Based on the presumed steering vector and a preset angle range for steering mismatch tolerance, we first create a matrix related to the direction vector of signal sources. Two projection matrices are generated from the matrix. The projection matrix associated with the desired signal information and the received array data are utilized to iteratively estimate the actual direction vector of the desired signal. The estimated direction vector of the desired signal is then used for appropriately finding the quiescent weight vector. The other projection matrix is set to be the signal blocking matrix required for performing adaptive beamforming. Accordingly, the proposed beamformer consists of adaptive quiescent weights and partially adaptive weights. Several computer simulation examples are provided for evaluating and comparing the proposed technique with the existing robust techniques.

Keywords: adaptive beamforming, robustness, signal blocking, steering angle error

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Authors: Jing Niu, Jun X. Wang

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Arterial stiffness predicts mortality and morbidity, independently of other cardiovascular risk factors. And it is a major risk factor for age-related morbidity and mortality. The non-invasive industry gold standard measurement system of arterial stiffness utilizes pulse wave velocity method. However, the desktop device is expensive and requires trained professional to operate. The main objective of this research is the proof of concept of the proposed non-invasive method which uses measurement of magnetic flux disturbance and electrocardiogram (ECG) signal for measuring arterial stiffness. The method could enable accurate and easy self-assessment of arterial stiffness at home, and to help doctors in research, diagnostic and prescription in hospitals and clinics. A platform for assessing arterial stiffness through acquisition and analysis of radial artery pulse waveform and ECG signal has been developed based on the proposed method. Radial artery pulse waveform is acquired using the magnetic based sensing technology, while ECG signal is acquired using two dry contact single arm ECG electrodes. The measurement only requires the participant to wear a wrist strap and an arm band. Participants were recruited for data collection using both the developed platform and the industry gold standard system. The results from both systems underwent correlation assessment analysis. A strong positive correlation between the results of the two systems is observed. This study presents the possibility of developing an accurate, easy to use and affordable measurement device for arterial stiffness assessment.

Keywords: arterial stiffness, electrocardiogram, pulse wave velocity, Magnetic Flux Disturbance

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Authors: Beata Zima, Octavio A. Márquez Reyes, Masoud Mohammadgholiha, Jochen Moll, Luca de Marchi

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This document presents the approach of using compressed sensing in signal encoding and information transferring within a guided wave sensor network, comprised of specially designed frequency steerable acoustic transducers (FSATs). Wave propagation in a damaged plate was simulated using commercial FEM-based software COMSOL. Guided waves were excited by means of FSATs, characterized by the special shape of its electrodes, and modeled using PIC255 piezoelectric material. The special shape of the FSAT, allows for focusing wave energy in a certain direction, accordingly to the frequency components of its actuation signal, which makes available a larger monitored area. The process begins when a FSAT detects and records reflection from damage in the structure, this signal is then encoded and prepared for transmission, using a combined approach, based on Compressed Sensing Matching Pursuit and Quadrature Amplitude Modulation (QAM). After codification of the signal is in binary chars the information is transmitted between the nodes in the network. The message reaches the last node, where it is finally decoded and processed, to be used for damage detection and localization purposes. The main aim of the investigation is to determine the location of detected damage using reconstructed signals. The study demonstrates that the special steerable capabilities of FSATs, not only facilitate the detection of damage but also permit transmitting the damage information to a chosen area in a specific direction of the investigated structure.

Keywords: data compression, ultrasonic communication, guided waves, FEM analysis

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Authors: Nor Asrina Binti Ramlee

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Voltage sag, voltage swell, high-frequency noise and voltage transients are kinds of disturbances in power quality. They are also known as power quality events. Equipment used in the industry nowadays has become more sensitive to these events with the increasing complexity of equipment. This leads to the importance of distributing clean power quality to the consumer. To provide better service, the best analysis on power quality is very vital. Thus, this paper presents the events detection focusing on voltage sag and swell. The method is developed by applying time domain signal analysis using wavelet transform approach in MATLAB. Four types of mother wavelet namely Haar, Dmey, Daubechies, and Symlet are used to detect the events. This project analyzed real interrupted signal obtained from 22 kV transmission line in Skudai, Johor Bahru, Malaysia. The signals will be decomposed through the wavelet mothers. The best mother is the one that is capable to detect the time location of the event accurately.

Keywords: power quality, voltage sag, voltage swell, wavelet transform

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Authors: Hamza Nejib, Okba Taouali

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This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one.

Keywords: online prediction, KAF, signal processing, RKHS, Kernel methods, KRLS, KLMS

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Authors: Mohammad Beyki, Justus Pawlak, Robert Patzke, Franz Renz

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In the context of planetary exploration, the MIMOS II (miniaturized Mössbauer spectrometer) serves as a proven and reliable measuring instrument. The transmission behaviour of the electronics in the Mössbauer spectroscopy is newly developed and optimized. For this purpose, the overall electronics is split into three parts. This elaboration deals exclusively with the first part of the signal chain for the evaluation of photons in experiments with gamma radiation. Parallel to the analysis of the electronics, a new method for the stability consideration of linear and non-linear systems is presented: The extended method of Lyapunov’s stability criteria. The design helps to weigh advantages and disadvantages against other simulated circuits in order to optimize the MIMOS II for the terestric and extraterestric measurment. Finally, after stability analysis, the controller design according to Ackermann is performed, achieving the best possible optimization of the output variable through a skillful pole assignment.

Keywords: Mössbauer spectroscopy, electronic signal amplifier, light processing technology, photocurrent, trans-impedance amplifier, extended Lyapunov method

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Ramón Aparicio-García, Gustavo Juárez Gracia, Jesús Álvarez Cedillo

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A research line of the computer science that involve the study of the Human-Computer Interaction (HCI), which search to recognize and interpret the user intent by the storage and the subsequent analysis of the electrical signals of the brain, for using them in the control of electronic devices. On the other hand, the affective computing research applies the human emotions in the HCI process helping to reduce the user frustration. This paper shows the results obtained during the hardware and software development of a Brain Computer Interface (BCI) capable of recognizing the human emotions through the association of the brain electrical activity patterns. The hardware involves the sensing stage and analogical-digital conversion. The interface software involves algorithms for pre-processing of the signal in time and frequency analysis and the classification of patterns associated with the electrical brain activity. The methods used for the analysis and classification of the signal have been tested separately, by using a database that is accessible to the public, besides to a comparison among classifiers in order to know the best performing.

Keywords: affective computing, interface, brain, intelligent interaction

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Authors: R. Latif, W. Jenkal, A. Toumanari, A. Hatim

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This paper presents an efficient method of electrocardiogram signal denoising based on a hybrid approach. Two techniques are brought together to create an efficient denoising process. The first is an Adaptive Dual Threshold Filter (ADTF) and the second is the Discrete Wavelet Transform (DWT). The presented approach is based on three steps of denoising, the DWT decomposition, the ADTF step and the highest peaks correction step. This paper presents some application of the approach on some electrocardiogram signals of the MIT-BIH database. The results of these applications are promising compared to other recently published techniques.

Keywords: hybrid technique, ADTF, DWT, thresholding, ECG signal

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Authors: Jung-In Baik, Seung-Jun Yu, Young-Min Ko, Hyoung-Kyu Song

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This paper proposes an efficient relaying algorithm to obtain diversity for improving the reliability of a signal. The algorithm achieves time or space diversity gain by multiple versions of the same signal through two routes. Relays are separated between a source and destination. The routes between the source and destination are set adaptive in order to deal with different channels and noises. The routes consist of one or more relays and the source transmits its signal to the destination through the routes. The signals from the relays are combined and detected at the destination. The proposed algorithm provides a better performance than the conventional algorithms in bit error rate (BER).

Keywords: multi-hop, OFDM, relay, relaying selection

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Authors: Mohamed El Morsy, Gabriela Achtenová

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Research on damage of gears and gear pairs using vibration signals remains very attractive, because vibration signals from a gear pair are complex in nature and not easy to interpret. Predicting gear pair defects by analyzing changes in vibration signal of gears pairs in operation is a very reliable method. Therefore, a suitable vibration signal processing technique is necessary to extract defect information generally obscured by the noise from dynamic factors of other gear pairs. This article presents the value of cepstrum analysis in vehicle gearbox fault diagnosis. Cepstrum represents the overall power content of a whole family of harmonics and sidebands when more than one family of sidebands is present at the same time. The concept for the measurement and analysis involved in using the technique are briefly outlined. Cepstrum analysis is used for detection of an artificial pitting defect in a vehicle gearbox loaded with different speeds and torques. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers introduce the load on the flanges of the output joint shafts. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. Also, a method for fault diagnosis of gear faults is presented based on order cepstrum. The procedure is illustrated with the experimental vibration data of the vehicle gearbox. The results show the effectiveness of cepstrum analysis in detection and diagnosis of the gear condition.

Keywords: cepstrum analysis, fault diagnosis, gearbox, vibration signals

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Authors: Nileshkumar Vaishnav, Aditya Tatu

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An algebraic framework for processing graph signals axiomatically designates the graph adjacency matrix as the shift operator. In this setup, we often encounter a problem wherein we know the filtered output and the filter coefficients, and need to find out the input graph signal. Solution to this problem using direct approach requires O(N3) operations, where N is the number of vertices in graph. In this paper, we adapt the spectral graph partitioning method for partitioning of graphs and use it to reduce the computational cost of the filtering problem. We use the example of denoising of the temperature data to illustrate the efficacy of the approach.

Keywords: graph signal processing, graph partitioning, inverse filtering on graphs, algebraic signal processing

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Authors: Maryam Ebrahimpour, Amir Ebrahimi

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This paper presents the steady-state amplitude analysis of MOS Colpitts oscillator with short channel device. The proposed method is based on a large signal analysis and the nonlinear differential equations that govern the oscillator circuit behaviour. Also, the short channel effects are considered in the proposed analysis and analytical equations for finding the steady-state oscillation amplitude are derived. The output voltage calculated from this analysis is in excellent agreement with simulations for a wide range of circuit parameters.

Keywords: colpitts oscillator, CMOS, electronics, circuits

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Authors: Jen-Fa Huang, Yu-Wei Chiu, Jhe-Ren Cheng

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Visible light communication (VLC) technique has become useful method via LED light blinking. Several issues on indoor mobile robot positioning with LED blinking are examined in the paper. In the transmitter, we control the transceivers blinking message. Orthogonal Walsh codes are adopted for such purpose on auto-correlation function (ACF) to detect signal sequences. In the robot receiver, we set the frame of time by 1 ns passing signal from the transceiver to the mobile robot. After going through many periods of time detecting the peak value of ACF in the mobile robot. Moreover, the transceiver transmits signal again immediately. By capturing three times of peak value, we can know the time difference of arrival (TDOA) between two peak value intervals and finally analyze the accuracy of the robot position.

Keywords: Visible Light Communication, Auto-Correlation Function (ACF), peak value of ACF, Time difference of Arrival (TDOA)

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Authors: Ying Han, Yuanxiang Chen, Yongtao Huang, Jia Fu, Kaile Li, Shangjing Lin, Jianguo Yu

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In long-haul and high-speed optical transmission system, the orthogonal frequency division multiplexing (OFDM) signal suffers various linear and non-linear impairments. In recent years, researchers have proposed compensation schemes for specific impairment, and the effects are remarkable. However, different impairment compensation algorithms have caused an increase in transmission delay. With the widespread application of deep neural networks (DNN) in communication, multi-impairment compensation based on DNN will be a promising scheme. In this paper, we propose and apply DNN to compensate multi-impairment of 16-QAM coherent optical OFDM signal, thereby improving the performance of the transmission system. The trained DNN models are applied in the offline digital signal processing (DSP) module of the transmission system. The models can optimize the constellation mapping signals at the transmitter and compensate multi-impairment of the OFDM decoded signal at the receiver. Furthermore, the models reduce the peak to average power ratio (PAPR) of the transmitted OFDM signal and the bit error rate (BER) of the received signal. We verify the effectiveness of the proposed scheme for 16-QAM Coherent Optical OFDM signal and demonstrate and analyze transmission performance in different transmission scenarios. The experimental results show that the PAPR and BER of the transmission system are significantly reduced after using the trained DNN. It shows that the DNN with specific loss function and network structure can optimize the transmitted signal and learn the channel feature and compensate for multi-impairment in fiber transmission effectively.

Keywords: coherent optical OFDM, deep neural network, multi-impairment compensation, optical transmission

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Authors: Ayad Dalloo, Sulaf Dalloo

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Electrocardiogram (ECG) recording is prone to complications, on analysis by physicians, due to noise and artifacts, thus creating ambiguity leading to possible error of diagnosis. Such drawbacks may be overcome with the advent of high resolution Methods, such as Discrete Wavelet Analysis and Digital Signal Processing (DSP) techniques. This ECG signal analysis is implemented in three stages: ECG preprocessing, features extraction and classification with the aim of realizing high resolution ECG diagnosis and improved detection of abnormal conditions in the heart. The preprocessing stage involves removing spurious artifacts (noise), due to such factors as muscle contraction, motion, respiration, etc. ECG features are extracted by applying DSP and suggested sloping method techniques. These measured features represent peak amplitude values and intervals of P, Q, R, S, R’, and T waves on ECG, and other features such as ST elevation, QRS width, heart rate, electrical axis, QR and QT intervals. The classification is preformed using these extracted features and the criteria for cardiovascular diseases. The ECG diagnostic system is successfully applied to 12-lead ECG recordings for 12 cases. The system is provided with information to enable it diagnoses 15 different diseases. Physician’s and computer’s diagnoses are compared with 90% agreement, with respect to physician diagnosis, and the time taken for diagnosis is 2 seconds. All of these operations are programmed in Matlab environment.

Keywords: ECG diagnostic system, QRS detection, ECG baseline removal, cardiovascular diseases

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Authors: Tahir Saleem, Mohammad Usman, Nadeem Khan

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GPS signals are used for navigation and positioning purposes by a diverse set of users. However, this project intends to utilize the reflected GPS L5 signals for location of target in a region of interest by generating an image that highlights the positions of targets in the area of interest. The principle of bi-static radar is used to detect the targets or any movement or changes. The idea is confirmed by the results obtained during MATLAB simulations. A matched filter based technique is employed in the signal processing to improve the system resolution. The simulation is carried out under different conditions with moving receiver and targets. Noise and attenuation is also induced and atmospheric conditions that affect the direct and reflected GPS signals have been simulated to generate a more practical scenario. A realistic GPS L5 signal has been simulated, the simulation results verify that the detection and imaging of targets is possible by employing reflected GPS using L5 signals and matched filter processing technique with acceptable spatial resolution.

Keywords: GPS, L5 Signal, SAR, spatial resolution

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Authors: Hiroyuki Aoki

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The optoacoustic effect is the energy conversion phenomenon from light to sound. In recent years, this optoacoustic effect has been utilized for an imaging agent to visualize a tumor site in a living body. The optoacoustic imaging agent absorbs the light and emits the sound signal. The sound wave can propagate in a living organism with a small energy loss; therefore, the optoacoustic imaging method enables the molecular imaging of the deep inside of the body. In order to improve the imaging quality of the optoacoustic method, the more signal intensity is desired; however, it has been difficult to enhance the signal intensity of the optoacoustic imaging agent because the fundamental mechanism of the signal generation is unclear. This study deals with the mechanism to generate the sound wave signal from the optoacoustic imaging agent following the light absorption by experimental and theoretical approaches. The optoacoustic signal efficiency for the nano-particles consisting of metal and polymer were compared, and it was found that the polymer particle was better. The heat generation and transfer process for optoacoustic agents of metal and polymer were theoretically examined. It was found that heat generated in the metal particle rapidly transferred to the water medium, whereas the heat in the polymer particle was confined in itself. The confined heat in the small particle induces the massive volume expansion, resulting in the large optoacoustic signal for the polymeric particle agent. Thus, we showed that heat confinement is a crucial factor in designing the highly efficient optoacoustic imaging agent.

Keywords: nano-particle, opto-acoustic effect, in vivo imaging, molecular imaging

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Authors: G. R. Kavitha, T. S. Indumathi

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With the constantly rising demands of the multimedia services, the requirements of long haul transport network are constantly changing in the area of optical network. Maximum data transmission using optimization of the communication channel poses the biggest challenge. Although there has been a constant focus on this area from the past decade, there was no evidence of a significant result that has been accomplished. Hence, after reviewing some potential design of optical network from literatures, it was understood that optical signal to noise ratio was one of the elementary attributes that can define the performance of the optical network. In this paper, we propose a framework termed as ECON (Enhanced Constraint-based Optical Network) that primarily optimize the optical signal to noise ratio using ROADM. The simulation is performed in Matlab and optical signal to noise ratio is extracted considering the system matrix. The outcome of the proposed study shows that optimized OSNR as compared to the existing studies.

Keywords: component, optical network, reconfigurable optical add-drop multiplexer, optical signal-to-noise ratio

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Authors: Yuyan Yi, Jingyi Zheng, Nedret Billor

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Independent component analysis (ICA) is one of the most commonly used blind source separation (BSS) techniques for signal pre-processing, such as noise reduction and feature extraction. The main parameter in the ICA method is the number of independent components (IC). Although there have been several methods for the determination of the number of ICs, it has not been given sufficient attentionto this important parameter. In this study, wereview the mostused methods fordetermining the number of ICs and providetheir advantages and disadvantages. Further, wepropose an improved version of column-wise ICAByBlock method for the determination of the number of ICs.To assess the performance of the proposed method, we compare the column-wise ICAbyBlock with several existing methods through different ICA methods by using simulated and real signal data. Results show that the proposed column-wise ICAbyBlock is an effective and stable method for determining the optimal number of components in ICA. This method is simple, and results can be demonstrated intuitively with good visualizations.

Keywords: independent component analysis, optimal number, column-wise, correlation coefficient, cross-validation, ICAByblock

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Authors: T. Bensana, S. Mekhilef

Abstract:

The methodology of vibration based condition monitoring technology has been developing at a rapid stage in the recent years suiting to the maintenance of sophisticated and complicated machines. The ability of wavelet analysis to efficiently detect non-stationary, non-periodic, transient features of the vibration signal makes it a demanding tool for condition monitoring. This paper presents a methodology for fault diagnosis of rolling element bearings based on wavelet envelope power spectrum technique is analysed in both the time and frequency domains. In the time domain the auto-correlation of the wavelet de-noised signal is applied to evaluate the period of the fault pulses. However, in the frequency domain the wavelet envelope power spectrum has been used to identify the fault frequencies with the single sided complex Laplace wavelet as the mother wavelet function. Results show the superiority of the proposed method and its effectiveness in extracting fault features from the raw vibration signal.

Keywords: preventive maintenance, fault diagnostics, rolling element bearings, wavelet de-noising

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