Bit-error rate performance analysis of spectrum based detector for FSK digital modulation

FSK is widely used digital modulation technique due to its simplicity in implementation using noncoherent detection. Further availability of digital signal processing algorithms such as the FFT (Fast Fourier Transform) and necessary supporting technology makes it possible to implement spectrum based deteetor for FSK. This paper formulates the BER (Biterror rate) performance of the spectrum based detector. Generally, the performance lies between the optimum that is. the coherent detector and the suboptimum that is the noncoherent detector. Verification was performed by computer simulation to confirm the results

Time-Frequency Analysis Of Heart Sounds Using Windowed And Smooth Windowed Wigner-Ville Distribution

Heart sounds and murmurs are time-varying signals that would best be analyzed using time-frequency analysis. Windowed Wigner-Ville distribution (WWVD) and smooth windowed Wigner-Ville distribution (SWWVD) are used to obtain the timefiequency representation (TFR) of the signal. Determination of parameter setting of WWVD and SWWVD will eliminate the cross-terms and improve TFR. The accuracy of TFR will be determined based on the maiulohe width and signal-to-interference ratio. It is found that the most accurate TFR can be achieved using SWWVD

Comparison analysis of stream cipher algorithms for digital communication

The broadcast nature of radio communication such as in the HF (High Frequency) spectrum exposes the transmitted information to unauthorized third parties. Confidentiality is ensured by employing cipher system. For bulk transmission of data, stream ciphers are ideal choices over block ciphers due to faster implementation speed and not introducing error propagation. The stream cipher algorithms evaluated are based on the linear feedback shift register (LFSR) with nonlinear combining function. By using a common key length and worst case conditions, the strength of several stream cipher algorithms are evaluated using statistical tests, correlation attack, linear complexity profile and nonstandard test. The best algorithm is the one that exceeds all of the tests

Effect of Path Loss Propagation Model on the Position Estimation Accuracy of a 3-Dimensional Minimum Configuration Multilateration System

The 3-Dimensional (3-D) position estimation (PE) accuracy of a multilateration (MLAT) system depends on several factors one of which is the accuracy at which the time difference of arrival (TDOA) measurements are obtained. In this paper, signal attenuation is considered the major contributor to the TDOA estimation error and the effect of the signal attenuation based on path loss propagation model on the PE accuracy of the MLAT system is determined. The two path loss propagation models are considered namely: Okumura-Hata and the free space path loss (FSPL) model. The transmitter and receiver parameters used for the analysis are based on actual system used in the civil aviation. Monte Carlo simulation result based on square ground receiving station (GRS) configuration and at selected aircraft positions shows that the MLAT system with the Okumura-Hata model has the highest PE error. The horizontal coordinate and altitude error obtained with the Okumura-Hata are 2.5 km and 0.6 km respectively higher than that obtained with the FSPL mode

Improved signal de-noising in underwater acoustic noise using S-transform: A performance evaluation and comparison with the wavelet transform

Sound waves propagate well underwater making it useful for target locating and communication. Underwater acoustic noise (UWAN) affects the reliability in applications where the noise comes from multiple sources. In this paper, a novel signal de-noising technique is proposed using S-transform. From the time-frequency representation, de-noising is performed using soft thresholding with universal threshold estimation which is then reconstructed. The UWAN used for the validation is sea truth data collected at Desaru beach on the eastern shore of Johor in Malaysia with the use of broadband hydrophones. The comparison is made with the more conventionally used wavelet transform de-noising method. Two types of signals are evaluated: fixed frequency signals and time-varying signals. The results demonstrate that the proposed method shows better signal to noise ratio (SNR) by 4 dB and lower root mean square error (RMSE) by 3 dB achieved at the Nyquist sampling frequency compared to the previously proposed de-noising method like wavelet transform. (C) 2017 Shanghai Jiaotong University. Published by Elsevier B.V

Diurnal Variability Of Underwater Acoustic Noise Characteristics in Shallow Water

The biggest challenge in the underwater communication and target locating is to reduce the effect of underwater acoustic noise (UWAN). An experimental model is presented in this paper for the diurnal variability of UWAN of the acoustic underwater channel in tropical shallow water. Different segments of data are measured diurnally at various depths located in the Tanjung Balau, Johor, Malaysia. Most applications assume that the noise is white and Gaussian. However, the UWAN is not just thermal noise but a combination of turbulence, shipping and wind noises. Thus, it is appropriate to assume UWAN as colored rather than white noise. Site-specific noise, especially in shallow water often contains significant non-Gaussian components. The real-time noise segments are analyzed to determine the statistical properties such as power spectral density (PSD), autocorrelation function and probability density function (pdf). The results show the UWAN has a non-Gaussian pdf and is colored. Moreover, the difference in UWAN characteristics between day and night is studied and the noise power at night is found to be more than at the day time by around (3-8dB)

The use of surface electromyography in muscle fatigue assessments–a review

The developments in physiological studies have established the importance of muscle fatigue estimation in various aspects including neurophysiological and medical research, rehabilitation, ergonomics, sports injuries and human-computer interaction. Surface electromyography signals are commonly used in muscle fatigue assessment. Techniques of surface EMG signal processing used to quantify muscle fatigue are not only based on time domain and frequency domain, but also on time–frequency domain. The developments of different signal analysis to extract different indices for muscle fatigue assessments are reviewed in this paper. Several indices in time, frequency, and time-frequency representations for muscle fatigue assessments have been identified. However the sensitivity of those indices needs to be investigated. Minimizing this issue becomes the objective of the recent research in muscle fatigue assessments

Real-Time Power Quality Disturbances Detection and Classification System

Power quality disturbances present noteworthy ramifications on electricity consumers, which can affect manufacturing process, causing malfunction of equipment and inducing economic losses. Thus, an automated system is required to identify and classify the signals for diagnosis purposes. The development of power quality disturbances detection and classification system using linear time-frequency distribution (TFD) technique which is spectrogram is presented in this paper. The TFD is used to represent the signals in time-frequency representation (TFR), hence it is handy for analyzing power quality disturbances. The signal parameters such as instantaneous of RMS voltage, RMS fundamental voltage, total waveform distortion (TWD), total harmonic distortion (THD) and total non-harmonic distortion (TnHD) are estimated from the TFR to identify the characteristic of the signals. The signal characteristics are then served as the input for signal classifier to classify power quality disturbances. Referring to IEEE Std. 1159-2009, the power quality disturbances such as swell, sag, interruption, harmonic and interharmonic are discussed. Standard power line measurements, like voltage and current in RMS, active power, reactive power, apparent power, power factor and frequency are also calculated. To verify the performance of the system, power quality disturbances with various characteristics will be generated and tested. The system has been classified with 100 data at SNR from 0dB to 40dB and the outcomes imply that the system gives 100 percent accuracy of power quality disturbances classification at 34dB of SNR. Since the low absolute percentage error present, the system achieves highly accurate system and suitable for power quality detection and classification purpose

Power Quality Analysis Using Frequency Domain Smooth - Windowed Wigner - Ville Distribution

Power quality has become a great concern to all electricity consumers. Poor power quality can cause equipment failure, data and economical losses. An automated monitoring system is needed to ensure signal quality, reduce diagnostic time and rectify failures. This paper presents the analysis of power quality signals using frequency domain smooth-windowed Wigner-Ville distribution (FDSWWVD). The power quality signals focused are swell, sag, interruption, harmonic, interharmonic and transient based on IEEE Std. 1159-2009. The TFD represents signal jointly in time-frequency representation (TFR) with good frequency and time resolution. Thus, it is very appropriate to analyze the signals that consist of multi-frequency components and magnitude variations. However, there is no fixed kernel of the TFD can be used to remove cross-terms for all types of signals. A set of performance measures is defined and used to compare the TFRs to identify and verify the TFD that operated at optimal kernel parameters. The result shows that FDSWWVD offers good performance of TFR and appropriate for power quality analysis

Enhancing PDC Functional Connectivity Analysis for Subjects with Dyslexia Using Artifact Cancellation Techniques

The neurobiological origin of dyslexia allows the study of this disorder by examining functional con- nectivity between regions of the brain. During rest-state or at task completion, Electroencephalograms (EEG) are used to observe brain signals. By using Partial Directed Coherence (PDC) analysis, the correct anal- ysis of functional connectivity was assessed. In spite of that, the estimation of functional connectivity can be inaccurate due to the presence of artifacts. Several methods have been employed by researchers to remove artifacts, including Moving Average Filters (MAF), Wiener Filters (WF), Wavelet Transforms (WT), and hybrid filters. Despite this, no research has been con- ducted on the effects of artifact removal methods on functional connectivity. Consequently, Artifact Can- cellation (AC) algorithms are developed to reduce the effects of eye blinks, eye movements, and muscle move- ments on functional connectivity estimation. In this work, the denoising filters discussed earlier are utilized as part of the AC algorithm. Additionally, a compar- ison was conducted to determine the effectiveness of the filters. According to the results, AC-MAF removed all artifacts with the least computational complexity after improving the MAF. In order to test its efficacy in real-world conditions, it was applied to the real signals recorded while children with dyslexia were participat- ing in rapid automatized naming activities. Utilizing the PDC approach, the developed algorithm accurately assessed functional connectivity