Bolt loosening is a significant concern in structural health monitoring (SHM), and the nonlinear wave modulation (NWM) technique shows promise for its detection. However, temperature variations affect piezoelectric sensors, complicating detection. This study examines thermal stress effects on NWM-based damage detection by simulating a beam with boundary loosening under thermal stress. A temperature compensation method was developed to mitigate these effects. A multibolt structure was also analyzed using a hybrid deep learning model combining convolutional neural networks (CNNs) and Dezert-Smarandache Theory (DSmT) for multisensor data fusion. Results show that thermal stress significantly reduces classification accuracy. At 60 °C, the Damage Index dropped to zero, and CNN classification accuracy fell from 87.5% (at 25 °C) to 27.08%. Applying the compensation algorithm restored CNN accuracy to 87.27%. Statistical tests confirmed the compensated results at 60 °C were comparable to those at 25 °C. The DSmT-based fusion model achieved 98.92% accuracy on average, and even at 60 °C, maintained 98.80% accuracy with compensation for a multibolted plate. It combines the probabilistic outputs of multiple CNNs using the PCR6 rule, which redistributes conflicting evidence to improve decision reliability. This fusion strategy effectively handles uncertainty across sensors and significantly enhances classification robustness under temperature variations. In conclusion, thermal stress adversely impacts SHM damage detection, but the proposed compensation method effectively restores performance. Moreover, integrating multisensor fusion with DSmT greatly enhances accuracy, offering a robust solution for SHM in varying thermal environments.

This paper provides a framework for statistical modeling of genomic sequences. Such a framework can be used a the basis for the synthesize similar sequences. The synthesized sequences could then be used to make for further inference about the genomic sequences. We start by converting the sequence of nucleotides from the genome into a decimal sequence via Huffman coding. Using the HodrickPrescott filter (HP filter) this decimal sequence is decomposed into two components, namely, trend and cyclic. Next, the ARIMA-GARCH statistical modeling approach is applied on the trend component exhibiting heteroskedasticity. The autoregressive integrated moving average (ARIMA) is used to capture the linear characteristics of the sequence, while the generalized autoregressive conditional heteroskedasticity (GARCH) is applied to model the statistical nonlinearity of the genome sequence. This modeling approach allows us to synthesize a given genomic sequence based on its statistical charatceristics. Finally, the probability distribution function (PDF) of a given sequence is estimated using a Gaussian mixture model, and based on the estimated PDF, we determine a new PDF representing sequences that statistically counteract the original sequence. We applied the proposed framework on several genes, as well as on the HIV nucleotide sequence. The corresponding results show some promise.

In this work, a novel learning‐based approach for the design of a compressed sensing measurement matrix is proposed. In contrast with the state‐of‐the‐art methods, the suggested approach takes into account the correlation within entries of each column of the measurement matrix, namely, the inter‐column correlation (ICC). The new method makes use of a rather small number of training sparse signal vectors in a recursive scheme to obtain their corresponding measurement vectors. The latter is exploited to estimate the copula function of measurements which, in turn, is used to generate an arbitrary number of measurement vector ensembles. By using the latter, the autocorrelation of the measurement vectors is estimated precisely and then, the ICC of measurement matrix under design is obtained from the autocorrelation. Given the resulting ICC, the measurement matrix columns are to be generated independently, e.g. by employing a proper random Gaussian vector generator. Performance evaluations using both synthetic and real‐world data confirm the superiority of the proposed approach to the less recent methods.

In this paper, a new approach for the design of measurement matrix, Φ, for compressive sensing (CS) in a generic context is proposed. In accordance with well-known classical CS theory, we take the elements of Φ to be random, yet, we include correlations within the elements of the individual columns of Φ. To this end, a new structure for Φ is proposed where the correlations of interest are controlled by a selectable parameter. We aim at optimizing the proposed Φ with respect to the latter correlation parameter by leveraging an appropriate criterion in a learning-based framework. We evaluate the performance of the proposed Φ and compare it with the state-of-the-art literature including random Φ with independent and identically distributed (i.i.d.) elements. Performance advantage of the proposed approach is validated in different CS scenarios.

The long time for collecting the data and a considerable amount of data are important technical challenges in microwave imaging for the detection of breast cancer. From the other point of view, compressive sensing (CS) is an interesting representation and analysis of sparse signals. In this study, a new imaging method for monostatic ultra‐wideband microwave imaging of breast cancer using CS is presented. Instead of using all of the conventional radar returned signals, a few received signals, by random choosing the antenna, are sufficient for obtaining reliable images even at high noise levels. Using simulations done, it is shown that sparser images are obtained comparing to the delay‐and‐sum beamforming technique using only a few received signals.

In this paper, a new signal processing algorithm in ultra wideband (UWB) radar as a non-contact sensor with the ability to precisely monitor heartbeat and respiration features is introduced. Exploiting harmonic series representation model that fits completely the return signal from a human chest-wall and wisely engaging an innovative time-varying filter, we propose a new free interference signal that indicates heartbeat features. Using this signal, a novel processing procedure in extraction of heartbeat rate (HR) and heart rate variability (HRV) is proposed. By experimental data, we investigate the applicability of the new approach for real scenarios comparing to other recently proposed procedure in different aspects.

This paper provides a procedure to analyze and model EEG (electroencephalogram) signal as a time series using ARIMA-GARCH to predict an epileptic attack. The heteroskedasticity of EEG signal is examined through the ARCH or GARCH, (Autoregressive conditional heteroskedasticity, Generalized autoregressive conditional heteroskedasticity) test. The best ARIMA-GARCH model in AIC sense is utilized to measure the volatility of the EEG from epileptic canine subjects, to forecast the future values of EEG. ARIMA-only model can perform prediction but the ARCH or GARCH model acting on the residuals of ARIMA attains a considerable improved forecast horizon. First, we estimate the best ARIMA model, then different orders of ARCH and GARCH modelings are examined to determine the best heteroskedastic model of the residuals of the mentioned ARIMA. Using the simulated conditional variance of selected ARCH or GARCH model, we suggest the procedure to predict the oncoming seizures. The results indicate that GARCH modeling determines the dynamic changes of variance well before the onset of seizure. It can be inferred that the prediction capability comes from the ability of the combined ARIMA-GARCH modeling to cover the heteroskedastic nature of EEG signal changes.

One of the most important challenges in target tracking is the modeling of correlated and non-Gaussian random processes. In this paper, a new target tracking approach by means of particle filtering in environments with highly correlated sensors, is discussed. The goal is to provide an accurate model of dependency structure in multivariate observation likelihood function, with non-Gaussian marginals to obtain a new algorithm in tracking problems. The main novelty of our method, termed as Copula-based Sequential Importance Resampling particle filter (CSIR-PF), is an application of the copula theory which is a powerful tool in correlation modeling in statistical theory. The precise model obtained by copulas makes a great improvement in particles' weightings. The performance of our proposed method is evaluated through the simulations of target tracking problems with highly correlated sensors. Results clearly indicate the acceptable performance of CSIR-PF.

In this paper, a novel approach has been proposed for estimating the parameters of symmetric alpha-stable distribution with 1 ≤ α ≤ 2. Alpha-stable distribution can model the statistical behavior of non-Gaussian heavy tailed signals and noises with impulsive components. There are some serious consideration in parameter estimation with stable random variable due to the lack of expression in analytic form for its distribution. In this method, the closed-form expression for parameter estimators is achieved by means of the values of characteristic function (CF) in two different points. We also establish a framework to determine these points in the general case. Performance comparison between the proposed method and the other methods is performed through several simulations and numerical results confirm that our method is efficient and has better performance even with a few number of samples.

In this paper, we present a new algorithm for detection of respiratory movement of a person behind an obstacle by using ultrawideband (UWB) impulse radar. In this scenario, the most significant sign of being alive is the respiratory motions, which is hidden in parameters of the returned signal. This signal, whose statistical characteristics are generally nonstationary, is mixed with both stationary and nonstationary clutters and white and colored noise. In this paper, in an analytical way, the returned signal has been addressed by the Fourier series model with time-varying coefficients, as a fitting model. Then, based on the unconditional orthonormal representation of band-limited signals, a minimum mean square error estimator is introduced to determine the time-varying coefficients of the Fourier series. Using the new representation of the Fourier coefficients, a new approach is suggested that enables us to extract parameters of the respiration in very low signal-to-noise-and-clutter ratio (SNCR) conditions in the presence of both stationary and nonstationary clutters. Getting the most out of the estimator, this approach alleviates the problems associated with clutters and noise, without resorting to match filtering. Hence, a robust and blind respiratory-motion detection (RMD) from stationary or nonstationary received signal is obtained. By experimental data, we demonstrate the applicability of the new approach for the respiratory detection using UWB impulse radar in different aspects.

In this paper, a novel bi-parameter mixture approximation for modeling the statistical properties of heavy-tailed Rayleigh distribution is developed. Heavy-tailed Rayleigh distribution is basically the amplitude PDF of bi-variate isotropic α-stable distribution which appears in the envelope distribution of impulsive interferences and SAR return signals. Closed-form expression for PDF is a serious consideration in signal processing with α-stable distribution. In this case, mixture approximation by means of Rayleigh and a generalization of Cauchy distribution is a simple but comprehensive approach. Several models try to give a model for the mixture ratio based on α. We propose two new methods for mixture ratio estimation based on both α and γ. Numerical results verify that the proposed mixture approximation is closer to the theoretical and empirical heavy-tailed Rayleigh distribution.

Source localization is an important field of research with numerous applications in signal processing and wireless communications. In this paper, we present a received signal strength difference (RSSD) based method to localize a source with unknown transmit power in the presence of sensor position uncertainty. It is an efficient low complexity technique which does not require transmit power estimation as with other approaches. This eliminates the uncertainty due to signal propagation parameter variations. A constrained adaptive weighted least squares technique is presented to obtain a least squares initial estimate (LSIE) of the source location. Then, this estimate is improved using a computationally efficient modified Newton method (MNM) with adaptive weights. The bias of the proposed LSIE-MNM method and the Cramér-Rao lower bound (CRLB) of the RSSD based measurement model are derived to determine the effect of sensor position uncertainties on the source location estimate. Results are presented which show that the proposed method achieves the CRLB when the SNR is sufficiently high.

Passive source localization is an important area of research. In this paper, a received signal strength difference (RSSD) based localization technique is presented for wireless sensor networks. The advantage of this solution is that the transmit power does not have to be known. It is an effective low complexity technique as it eliminates the transmit power estimation required with other approaches. The uncertainties due to the signal propagation parameters are eliminated by using the received signal strength differences from known sensors. This yields a set of equations using signals from the source to sensors with known positions assuming a log-normal path loss model. The constrained weighted least squares (CLS) technique is used for location estimation using these equations. Results are presented which show that the performance of the proposed method achieves the Cramer-Rao lower bound (CRLB) for a sufficiently high signal to noise ratio (SNR).

Passive source location determination is a very active research area. In this paper, we present a received signal strength difference (RSSD) source localization method based on a total least square (TLS) estimator. Due to errors in the data vector and system matrix, the least squares (LS) and weighted least squares (WLS) methods are not applicable as they produce large bias in the location estimation. Therefore, an extension of the LS methods, called total least squares (TLS) is used to solve this problem. Due to the relationship between the data vector and system matrix, a modified TLS method is presented to achieve a closed form estimate. The advantage of this approach is that it does not require transmit power estimation as with other methods, and the complexity is low. The received signal strength difference is used to eliminate uncertainties due to the signal propagation parameters. Performance results are presented which show that the performance of the proposed method comes close to achieving the Cram'er-Rao lower bound.

In this study, monomials with odd integer powers are proposed for blind multiuser detection of code‐division multiple access signals with long spreading codes, in the near–far situation. The performance of the new non‐linear detectors are substantiated asymptotically and confirmed via simulations. It is demonstrated that the proposed non‐linear detectors significantly outperform the matched filter detector in the block fading and Ricean fading channels, at the cost of a small increase in computational complexity. The optimum integer power value for the block fading channel with a limited interferer level, the Ricean channel with a given K factor and asynchronous channel is determined. The proposed multiuser detectors are blind in the sense that they require neither training nor the spreading code of the interferers. The detectors also do not require long convergence time for decision making in contrast to conventional blind multiuser detectors.

The authors use stochastic resonance (SR) for binary signal detection in the presence of a special type of Gaussian mixture noise. This type of noise naturally exists in signal detection problems in a channel with an interferer, and consequently, the investigations are beneficial for analysing the interferer suppression capability of the linear SR‐based detectors. The authors demonstrate analytically that the region where SR‐based detectors outperform the matched filter (MF) detector coincides with the region where the maximum likelihood (ML) detector outperforms the MF detector. An analytical expression is also obtained for error probability performance of the optimal SR‐based detector and ML detector in an interferer channel.

The performance of mobile satellite networks in built up areas is liable to deterioration since the wireless channel variations significantly affect the link availability over time. Previous statistical models which employ empirical data for estimation of fading parameters, are reliable only for specific environments. In this paper, we incorporate simple physical-statistical characteristics of urban canyons into estimation of shadowing moments which describe the received signal envelope. Our flexible model utilizes analytic fading parameters and accounts for azimuth and elevation angles hence, it facilitates simulation of low earth orbit (LEO) satellite links with good accuracy in areas where channel measurements are not available. Moreover, our novel approach can be effectively used for evaluation of the quality of service (QoS) in terms of bit error outage probability.

This paper introduces a novel Bayesian method for speckle suppression of SAR images. We first analyze the logarithmic transform of the original image by means of the curvelet transform that handles image edges more efficiently than wavelet transform. In a recent work [1], we have shown that due to the statistical properties of the curvelet subbands of SAR images, they can be modelled by two-dimensional Generalized Autoregressive Conditional Heteroscedastic (2D-GARCH) model. Here, we employ a generalization of 2D-GARCH model, called 2D-GARCH Generalized Gaussian (2D-GARCH-GG), to these coefficients. This model preserves the appropriate properties of 2D-GARCH for modeling the curvelet coefficients while extends the dynamic formulation of 2D-GARCH model. Consequently, we design a maximum a-posteriori (MAP) estimator for estimating the clean image curvelet coefficients. Finally, we compare our proposed method with other denoising methods, and quantify the achieved performance improvement.

There are many underwater passive algorithms used to determine the range of a target like a diver, using its acoustic signals which are radiated through underwater environment. In this paper, we introduce a new underwater diver range estimation algorithm based on shallow water cavitation. Through this algorithm we apply diver's bubble oscillation frequency and ocean surface bubble's behaviour as two new carrier frequencies for our proposed method. Then, we utilize a dual frequency technique with the above new carrier frequencies, due to its robustness to shallow water noise, in time-frequency domain, yields to a new passive range estimation algorithm. We demonstrate through real data simulations that this algorithm is suitable for passive diver range estimation in shallow water environment. We used SWeIIEx-96 experiment data to compare our simulation result.

This paper presents an algorithm for speckle reduction of synthetic aperture radar (SAR) images within a framework of multiscale curvelet analysis. First, we introduce a novel method to investigate the presence of 2-D heteroscedasticity based on Lagrange multiplier procedure. Employing this test confirms the heteroscedasticity of SAR image curvelet coefficients. Therefore, we employ a generalization of 2-D generalized autoregressive conditional heteroscedastic (2-D GARCH) model, called 2-D GARCH generalized Gaussian (2-D GARCH-GG), to these coefficients. This model preserves the appropriate properties of 2-D GARCH for modeling the curvelet coefficients while extending the dynamic formulation of 2-D GARCH model. Then, we design a novel Bayesian processor based on employing 2-D GARCH-GG model to estimate the noise-free curvelet coefficients. Experiments carried out on synthetic SAR images, as well as on true SAR images, verify the performance improvement in utilizing the new strategy compared with other established despeckle algorithms.

In this paper we introduce a multiple access scheme in which frequency reuse is achieved using the difference in the signal amplitudes. For this amplitude division multiple access (ADMA) system, a locally optimal multiuser detector (LOMUD) is proposed to detect a weak binary phase shift keying (BPSK) signal in the presence of a similar modulated cochannel interference and additive white Gaussian noise (AWGN). For the proposed detector, there is no need for signal level estimation, which is a computational burden when the desired signal is weak. An analytic expression for the bit error probability of the LOMUD detector is also derived. Furthermore, it is shown that the performance of the proposed detector approaches the lower bound when the signal to interference ratio (SIR) is low enough. We also present some estimators for estimation of the required parameters in the receiver and investigated their efficiencies.

In this paper we propose a novel approach termed as dynamic copula time-frequency distribution (DCTFD) for the construction of positive time-frequency distributions (PTFDs). DCTFD models the dependency, i.e., correlation between time and frequency by a time-varying dependence parameter in copula function, and captures time-frequency dependency/correlation more accurately compared to static copula TFD (CTFD). In addition to presenting the under lying theory of the approach, a set of simulations are proved to demonstrate the advantages over CTFD and other classical methods, namely bivariate distributions.

In this paper, a new method is proposed for delay and Doppler estimation using copula theory. Copula analysis helps to explore the underlying inter-dependence between the desired and undesired parts of a received signal. At first, the probability density function (PDF) of the desired signal is extracted analytically. This analytic result contains the simultaneous influence of delay and Doppler. Our results are also compatible with non-stationary signals. A similar ex traction is done on the dependent set of interference and noise. Then, using copula theory, we estimate the PDF of the received signal and analyze the dependency. This conglomerate PDF is finally used for joint delay and Doppler estimation. In addition to presenting the underlying theory and techniques, a set of simulations demonstrate the advantages.

In this paper, we present a novel non-data aided method for phase recovery in both square and cross quadrature amplitude modulation (QAM) communication systems, based on characteristic function. The proposed method is independent of noise distribution added to the rotated signal. After estimating the gain multiplied to the received signal and compensating its effect, we will estimate the phase offset. The key innovation lies in the use of characteristic function rather than the traditional higher order statistics. We use characteristic function and its estimate (Empirical Characteristic Function-ECF), in order to perform phase estimation. The analytical evaluation of the estimation is provided. Monte Carlo simulation provides feasibility of our new approach.

This paper presents a new method for extracting orthogonal bases from AM-FM signals. Since AM-FM signals are non-stationary, appropriate basis functions are sought provided that instantaneous frequency can be defined meaningfully. With respect to the intrinsic structure of AM-FM signals, these bases should be in generalized exponential format. Expansion by such bases is known as generalized Fourier expansion. So the oscillating modes embedded in signal would be extracted efficiently. In contrast to exiting methods, the proposed technique provides these functions analytically, by utilizing orthogonal rational functions. This method is founded on two main steps. First, the corresponding generating system of AM-FM signal should be determined. A model is developed in this paper based on the concept of linear time-varying systems. Second, this system is expanded by appropriate orthogonal rational functions in complex domain. These functions are tantamount to orthogonal generalized exponential functions in time domain which yield the desired properties.

Speckle suppression is a prerequisite for many synthetic aperture radar (SAR) image-processing tasks. Previously, we introduced a Bayesian-based speckle-suppression method that employed the 2-D generalized autoregressive conditional heteroscedasticity (2D-GARCH) model for wavelet coefficients of log-transformed SAR images. Based on this method, we propose two new Bayesian speckle-suppression approaches in this paper. In the first approach, we introduce a new heteroscedastic model, i.e., the 2D-GARCH Mixture (2D-GARCH-M) model, as an extension of the 2D-GARCH model. This new model can capture the characteristics of wavelet coefficients. Also, the 2D-GARCH-M model introduces additional flexibility in the model formulation in comparison with the 2D-GARCH model, which results in better characterization of SAR image subbands and improved restoration in noisy environments. Then, we design a Bayesian estimator for estimating the clean-image wavelet coefficients based on 2D-GARCH-M modeling. In the second approach, the logarithm of an image is analyzed by means of the curvelet transform instead of wavelet transform. Then, we study the statistical properties of curvelet coefficients, and we demonstrate that the 2D-GARCH model can capture the characteristics of curvelet coefficients, such as heavy tailed marginal distribution, and the dependences among them. Consequently, under the 2D-GARCH model, we design a Bayesian estimator for estimating the clean-image curvelet coefficients. Finally, we compare these methods with other denoising methods applied on artificially speckled and actual SAR images, and we verify the performance improvement in utilizing the new strategies.

This paper presents a new synthesis algorithm to produce flat-top main beams with arbitrary footprint for array elements placed in an arbitrary planar geometry. The general framework of the paper would encompass the patterns produced by circular aperture distribution. It is shown that for patterns generated by circular apertures some efficient simplifying facts based on properties of Bessel functions can be applied. The method shows a high performance in generating optimally flat-top patterns with detailed geometry footprints.

In this paper, we introduce a novel noise suppression method for electromyography (EMG) signals, based on statistical modeling of wavelet coefficients. First, we demonstrate that Generalized Autoregressive Conditional Heteroscedasticity (GARCH) effect exists in wavelet coefficients of EMG signals. Then, we use GARCH model for these coefficients. In consequence, we introduce a maximum a-posteriori (MAP) estimator, based on GARCH modeling, for estimating the clean wavelet coefficients. To evaluate the performance of GARCH based method in noise suppression, we compare our proposed method with other wavelet based denoising methods and we verify the performance improvement in utilizing the new strategy.

In this work, a detector scheme for the detection of signal in a group of non-Gaussian narrowband interferences and white Gaussian noise is developed. Since there exists no closed-form probability distribution for this type of disturbance modeling, the key innovation lies in the use of characteristic function rather than the probability distribution to both design and implement the detector. Parameter estimation is performed at first step to find the unknown disturbance parameters. The utilized detector uses these parameters to form an approximately Gaussian distributed test statistic based on the empirical characteristic function of received data. Performance of the detector is investigated by means of both analytical and Monte Carlo simulations.

In this paper, we present a novel Bayesian-based speckle suppression method for Synthetic Aperture Radar (SAR) images within the framework of wavelet analysis. We introduce two-dimensional Generalized Autoregressive Conditional Heteroscedasticity Mixture (2D-GARCH-M) model as an extension of two-dimensional GARCH (2D-GARCH) model and use it for statistical modeling of SAR images subbands. Similar to 2D-GARCH model, this new model can capture heavy tailed marginal distribution and the intrascale dependencies of wavelet coefficients. Also, 2D-GARCH-M model introduces additional flexibility in the model formulation in comparison with 2D-GARCH model, which can result in better characterization of SAR images' subbands and improved restoration in noisy environments. Then, we design a Bayesian estimator for estimating the clean image wavelet coefficients. Finally, we compare our proposed method with various speckle suppression methods applied on actual and synthetic SAR images and verify the performance improvement in utilizing the new strategy.

In this letter we develop a new blind binary level detection in presence of unknown non-Gaussian noise using maximum entropy method (MEM). We show that the performance of the designed detector based on blind maximum entropy (BME) method is near to the one that is designed based on best entropy estimate (BEE) criterion.

This paper presents a Multiplicative Patchwork Method (MPM) for audio watermarking. The watermark signal is embedded by selecting two subsets of the host signal features and modifying one subset multiplicatively regarding the watermark data, whereas another subset is left unchanged. The method is implemented in wavelet domain and approximation coefficients are used to embed data. In order to have an error-free detection, the watermark data is inserted only in the frames where the ratio of the energy of subsets is between two predefined values. Also in order to control the inaudibility of watermark insertion, we use an iterative algorithm to reach a desired quality for the watermarked audio signal. The quality of watermarked signal is evaluated in each iteration using Perceptual Evaluation of Audio Quality (PEAQ) method. The probability of error is also derived for the watermarking scheme and simulation results prove the validity of the analytical derivations. Simulation results show that MPM is robust against various common attacks such as noise addition, filtering, echo, MP3 compression, etc. In comparison to the original patchwork method and its modified versions, and some recent methods, MPM provides more robustness and inaudibility of the watermark insertion.

A novel Bayesian-based speckle suppression method for Synthetic Aperture Radar ( SAR) images is presented that preserves the structural features and textural information of the scene. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. We show that the wavelet coefficients of SAR images have significantly non-Gaussian statistics that are best described by the 2-D GARCH model. By using the 2-D GARCH model on the wavelet coefficients, we are capable of taking into account important characteristics of wavelet coefficients, such as heavy tailed marginal distribution and the dependencies between the coefficients. Furthermore, we use a maximum a posteriori (MAP) estimator for estimating the clean image wavelet coefficients. Finally, we compare our proposed method with various speckle suppression methods applied on synthetic and actual SAR images and we verify the performance improvement in utilizing the new strategy.

We will present an algorithm for a high resolution spectral estimation based on central solution of Nevanlinna-Pick interpolation. The approach is very well suited to short data records and shows superior performance, compared to traditional approaches, especially in detecting and resolving spectral lines buried in colored non-Gaussian noise.

In this paper, we introduce a new nonlinear detector to improve the performance of a weak BPSK system in the presence of like-modulated cochannel interference and additive white Gaussian noise (AWGN) with time varying characteristics. In our scheme we use locally optimal (LO) detection rule in conjunction with maximum entropy method (MEM) for probability density function (PDF) estimation of the observation noise. We utilize MEM based on moment generating function (MGF) constraints instead of moments, as a new criterion. The estimated PDF based on MEM-MGF is quite close to the true PDF and yields better uniform approximation, especially in the tail of PDF. The results indicate that the new nonlinear detector outperforms conventional matched filter, and approaches the optimal receiver when desired signal is weak. For the new detector, there is no need for any signal level estimation, which is a computational burden when these parameters are time varying.

In this paper, we propose a novel detector for multiplicative watermarking. The decoder extracts the watermark data by comparing the variances of the watermarked signal and the original signal. Due to the use of the variance test, the decoder works independent of the distribution of the host signal. This is the major advantage of this decoder per other decoders. The decoder is optimized under the Additive White Gaussian Noise channel by calculating the best threshold value. In order to show the optimal performance of this decoder on audio signals, a Maximum likelihood (ML) decoder is also introduced by considering a Gaussian distribution for the host signal. Furthermore, PEAQ algorithm is used for controlling the inaudibility of the watermark data insertion. Using this algorithm, the watermark strength factor updates automatically every 200 ms, according to the quality which is desired for the watermarked signal. Simulation results showed that the proposed decoder is extremely robust to the common audio watermarking attacks and slightly better than the ML decoder under Additive White Gaussian Noise attack.

In synchronous CDMA, having more users than the signature length results in a singular correlation matrix. However, with a careful design of the correlations and with the help of the binary feature of user signals, good performance in a slightly overloaded system can still be achieved. However, the singularity of the correlation matrix does make the direct implementation of many multiuser detectors impossible. With the help of successive cancellation, taking inverse of a singular matrix is avoided.

In space time block coded (STBC) MIMO-OFDM, most receivers assume the channel noise to be Gaussian, this paper considers the semi-blind detection of symbols in non-Gaussian channels. Our aim is to introduce a new receiver for STBC MIMO-OFDM transmission over Rayleigh fading channels using Zernike moments which apply either k-nearest neighbor (KNN) or support vector machine (SVM) for classification. The detrimental effects of multipath on the transmitted symbols are alleviated using invariance to rotation, and scaling properties of the Zernike moments. The performance of the new method is shown in symmetric α-stable (SaS) and Middleton noise environments.

In this paper we introduce a new nonlinear detector to improve the performance of spread spectrum receiver in the presence of narrow-band interference. The new detector is blind in the sense that no training data is required. In our scheme we use maximum likelihood (ML) detection rule in conjunction with maximum entropy method (MEM) for probability density function (PDF) estimation of the observation noise. We use MEM with a new approach based on fractional moments instead of integer moments. The estimated PDF based on fractional moments is quite close to the true PDF. The results indicate that the new nonlinear detector outperforms conventional matched filter, and well known locally optimal (LO) detector.

In this paper, we propose a new method for practical non-Gaussian and non-stationary underwater ambient noise modeling and direction-finding approach. In this application, measurement of ambient noise in natural environment shows that noise can sometimes be significantly non-Gaussian and time-varying features such as variances. Therefore, signal processing algorithms such as direction-finding that are optimized for Gaussian noise, may degrade significantly in this environment. Generalized autoregressive conditional heteroscedasticity (GARCH) models are feasible for heavy tailed PDFs and time-varying variances of stochastic process and also has flexible forms. We use a more realistic GARCH (1,1) based noise model in the maximum likelihood approach for the estimation of direction-of-arrivals (DOAs) of impinging sources and show using experimental data that this model is suitable for the additive noise in an underwater environment

In this paper, we introduce a new method to improve the detection performance of weak unknown radar signals in the presence of unknown clutter. We use maximum entropy (MAXENT) probability density function (PDF) estimation with a new approach based on a few sampled fractional moments (FM). These moments; i.e, their fractional orders, are obtained from the observed sample variates. Using the fractional moments instead of the integer moments the estimated PDF is quite close to the true PDF. The test statistics is a fractional polynomial of very low order of the received samples

Because of the limited frequency spectrum and an increasing demand for cellular radio communication services, the problem of channel assignment becomes increasingly important. The channel assignment problem is to assign frequencies to requested calls in a cellular radio network subject to electromagnetic compatibility constraints such that required bandwidth is minimized. In this paper, we propose a new approach to solve the channel assignment problem by modifying the dynamic programming algorithm. We use node color ordering of cells and row wise ordering of calls. The performance of the proposed method is evaluated by solving six well-known benchmarks in channel assignment problems. Results show that this method can find more solutions with minimum required bandwidth in comparison with the other algorithms investigated in this paper. Advantages of our method are number of obtained solutions, independency of generating an initial random solution, and convergence

This paper describes a new technique that gives high performance based on GARCH (generalized autoregressive conditional heteroskedastic) time series modeling incorporating past variances to predict future variances. This is particularly suitable since no transformation on the speech signal is performed, rather we have a new statistical feature extraction, moreover, the speech signals are among nonstationary processes whose variances are heteroskedastic; e.g., time varying. Therefore, we provide a new parametric speech modeling using GARCH coefficients. The features resulting from GARCH modeling are used for recognition of isolated digits 1 to 10 in the Persian language. The results show a significant improvement in the recognition accuracy compared to results based on Mel-frequency cepstrum coefficients (MFCC).

A new constant false alarm rate (CFAR) for the marine environment based on a stability in sea clutter state-space model is proposed. In this CFAR, based on observation samples in the reference window, a maximum a posteriori (MAP) estimation for clutter power in the test cell is utilized for setting a threshold. The compound K model is the best model for high resolution sea clutter that includes a phenomenological insight into clutter formation, and it is most frequently cited in the literature. The compound model for sea clutter is considered to be the multiplication of two independent processes. This multiplicative model is linearized to form a state-space model for the sea clutter evolution. This state-space model is established in the logarithmic domain. By introducing a stable distribution for innovations in the state-space model as a priori, we derive a new MAP estimator for the clutter power. The performance of the new CFAR, named SLMAP-CFAR (stable linear MAP CFAR), is then compared to the existing LMAP.

There has been great interest in particle filtering as a sequential Bayesian estimation method for dynamic systems. The problem of detection in flat fading channels in the presence of autoregressive conditional heteroscedasticity (ARCH) noise is addressed. An auxiliary particle filtering (APF) algorithm is presented for the estimation of the transmitted sequence and ARCH noise variance. We introduce the ARCH process for the purpose of noise modelling. The applicability of the model is based on a variance change over time compatible to a large extent with non-stationarity and non-Gaussianity of wireless fading channels. We show, through simulation, the superiority of the proposed algorithm over the bootstrap particle filter (PF) even in the presence of the new noise model; i.e., the ARCH process.

We propose a new method for modeling practical non-Gaussian and non-stationary noise in array signal processing. GARCH (generalized autoregressive conditional heteroscedasticity) models are introduced as the feasible model for the heavy tailed probability density functions (PDFs) and time varying variances of stochastic processes. We use the GARCH noise model in the maximum likelihood approach for the estimation of directions-of-arrival (DOAs). Our analysis exploits time varying variance and spatially non-uniform noise in sensor array signal processing. We show through simulations that this GARCH modeling is suitable for high-resolution source separation and noise suppression in a non-Gaussian environment.

The conventional single user detector in DS/CDMA (direct sequence code division multiple access) systems involves multiple access interference and near-far effect which cause the limitation in capacity. The complexity of optimum multiuser detectors also grows up exponentially with the number of users. There has been a lot of interest in suboptimal multiuser detectors with less complexity and reasonable performance. In this paper, we apply decision based neural network (DBNN), fuzzy decision neural network (FDNN), and discriminative learning and backpropagation neural networks employing multilayer perceptron for detection of signals of users in DS/CDMA systems in additive white Gaussian noise (AWGN) channel. We also show that FDNN and discriminative learning neural net have almost the same performance.