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This paper presents new alternative derivations of the well‐known constrained Cramér–Rao bound for estimating parameters that satisfy a set of deterministic and differentiable equality constraints. Specifically, for unbiased parameter estimation, the equality constraints are treated as pseudo‐measurements corrupted by independent zero‐mean Gaussian noise with infinitely small covariance. In this way, the desired constrained Cramér–Rao bound can be established via utilising the property that the Fisher information matrices for independent measurements are additive. This paper also provides a new simple way for deriving the constrained Cramér–Rao bound when the parameter estimate has a specified bias through exploring that the estimation error lies in the null space of the gradient matrix of the constraints and invoking the Cauchy–Schwarz inequality. [ABSTRACT FROM AUTHOR]

SUMMARY This paper proposes a new split digital phase lock loop (DPLL) in the presence of additive wideband Gaussian noise. The proposed new loop incorporates an additional phase modulation input along with its frequency modulation input in the digitally controlled oscillator of the loop. The present loop eliminates the deleterious effects of wideband Gaussian noise. A better output signal-to-noise ratio is achieved using the new split-loop DPLL. In the design of the loop, the interaction between the loop and the radio frequency filters is avoided by using an in-phase and quadrature-phase digitally control oscillator. The proposed split-loop DPLL is simulated using MATLAB (The MathWorks, Natick, MA, USA)/Simulink environment and System Generator, a tool from Xilinx (Xilinx, Inc., San Jose, CA, USA) used for field programmable gate array design, as well as implemented on a Spartan 3E Starter Kit board. The design is implemented on field programmable gate array using the VHDL [Very High Speed Integrated Circuit (VHSIC) Hardware Description Language] language on Xilinx ISE 13.1. The proposed split-loop DPLL proves to be better than the conventional split-loop DPLL from the standpoint of the settling time, the peak time, the rise time, and the peak overshoot as well as from the standpoint of the BER performance for a typical binary phase-shift keying system and validates against the MATLAB/Simulink results. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT Numerical implementation of the gradient of the cost function in a gradient-based full- waveform inversion (FWI) is essentially a migration operator used in wave equation migration. In FWI, minimizing different data residual norms results in different weighting strategies of data residuals at receiver locations prior to back-propagation into the medium. In this paper, we propose different scaling methods to the receiver wavefield and compare their performances. Using time-domain reverse-time migration (RTM), we show that compared to conventional algorithms, this type of scaling is able to significantly suppress non-Gaussian noise, i.e., outliers. Our tests also show that scaling by its absolute norm produces better results than other approaches. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating the period of an unknown periodic function observed in additive Gaussian noise sampled at irregularly spaced time instants in a semiparametric setting. To solve this problem, we propose a novel estimator based on the cumulated Lomb–Scargle periodogram. We prove that this estimator is consistent, asymptotically Gaussian and we provide an explicit expression of the asymptotic variance. Some Monte Carlo experiments are then presented to support our claims. [ABSTRACT FROM AUTHOR]

In this paper, we investigate and compare the asymptotic performance of concatenated convolutional coding schemes over GF(4) over additive white Gaussian noise (AWGN) channels. Both parallel concatenated codes (PCC) and serial concatenated codes (SCC) are considered. We construct such codes using optimal non-binary convolutional codes where optimality is in the sense of achieving the largest minimum distance for a fixed number of encoder states. Code rates of the form k0/(k0 + 1) for k0=1, 8, and 64 are considered, which suite a wide spectrum of communications applications. For all of these code rates, we find the minimum distance and the corresponding multiplicity for both concatenated code systems. This is accomplished by feeding the encoder with all possible weight-two and weight-three input information patterns and monitoring, at the output of the encoder, the weight of the corresponding codewords and their multiplicity. Our analytical results indicate that the SCC codes considerably outperform their counterpart PCC codes at a much lower complexity. Inspired by the superiority of SCC codes, we also discuss a mathematical approach for analysing such codes, leading to a more comprehensive analysis and allowing for further improvement in performance by giving insights on designing a proper interleaver that is capable of eliminating the dominant error patterns. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Elimination of mixed noise consisting of white Gaussian noise and impulsive noise is one of the important problems in image processing. In this paper, a data-dependent α-trimmed mean filter using backpropagation networks is proposed. In order to determine an appropriate value of α for each processing point, the pattern classification capability of the backpropagation networks is used. Further, by processing taking into account the edge portions, both edge preservation and mixed noise elimination are attained simultaneously. Finally, the effectiveness of the proposed method is verified by simulation. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(7): 30–40, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10061 [ABSTRACT FROM AUTHOR]

This paper is concerned with a direct sequence spread spectrum system making use of automatic repeat request (ARQ) schemes and operating in the presence of narrow-band (NB) interference and additive white Gaussian noise. In this study, we evaluate the system's throughput efficiency when a suppression filter is employed at the receiver to mitigate the effect of NB interference. Specifically, we investigate how the performance is influenced by various parameters such as the number of filter taps, processing gain, type of interference, and the signal power-to-interference power ratio. The simulation results show that using the suppression filter significantly improves the throughput performance of the hybrid ARQ schemes in the presence of NB interference. Copyright © 2002 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper derives two new formulae for BER estimation when the transmission system is subject to both intersymbol interference and additive Gaussian noise. The approach undertaken here was oriented to find a simple formula giving close estimation of the true BER, which may be useful for the transmission system designer for rapid evaluation of system performance. The obtained results are compared with commonly used BER estimations. Copyright © 2001 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, the authors propose a data-dependent weighted average filter (Video-DDWA: Video Data-Dependent Weighted Average) aimed at restoration of dynamic images deteriorated due to Gaussian additive noises. As proposed by the authors, this filter is based on the data-dependent processing, in which the filter weight is varied by multiple local information items derived from the data proximal to the processing point in a still image, with extension of this processing from a spatial filter to a temporal-spatial filter; and then the weight of adjacent frames is determined by detecting presence or absence of motion from the motion information such as new local information. There are several conventional methods of restoration of dynamic images that involve motion compensation with subsequent spatiotemporal filter processing, but they all have limitations as to the filter restoration performance due to noise-affected deterioration of the estimation accuracy of the motion vector. The proposed data-dependent filter using the motion information has among others the following advantages: (1) Because it involves detection of the motion degree at which noise influence is suppressed, it enables restoration of dynamic images featuring a high noise cancellation performance in still areas, without motion deterioration due to filter processing in motion areas—in other words, with processing that does not cause deterioration of movement and (2) the computing load is lower than that with motion-compensated filters. As compared to the conventional methods using motion compensation, this method enables attaining a high restoration performance not only for signals with a low S/N ratio at which the accuracy of the motion compensation estimation vector starts decreasing, but also for signals with a wide range of S/N ratios. The authors demonstrate with various application examples that this method is efficient for restoration of dynamic images. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 84(4): 1–10, 2001 [ABSTRACT FROM AUTHOR]

An important problem in image processing is noise removal that preserves edges in images degraded by white Gaussian noise. In this paper, we propose a data-dependent weighted mean filter using counter propagation networks. This method achieves high-level performance in the removal of white Gaussian noise while preserving the edges. Coefficients of the filter and features to change them are adjusted and decided by using a layered neural network and self-organizing network, respectively. This allows a robust filter to be obtained. Analysis of this network enables the derivation of a simpler filter structure. Using this fact, we show an example of analysis and simplification, and obtained a filter capable of processing at high speed by fewer computations. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 84(3): 42–51, 2001 [ABSTRACT FROM AUTHOR]

This paper proposes differential parallel combinatory spread spectrum (DPC/SS) communication systems; one of the parallel combinatory spread spectrum (SS) communication systems the authors have been studying so far. The parallel combinatory SS system is a spectral efficient SS system in which the system transforms several bits of data into a combination of pseudonoise (PN) sequence with modulation of positive and negative signs and transmits them. The proposed DPC/SS system applies differential encoding to the positive or negative sign modulation of the PN sequences, and then they are transmitted. In the receiver, the combination of a transmitted sequence set is estimated by noncoherent detection and the coded sign is demodulated by differential detection. Thus, the receiver does not need to regenerate the phase of the carrier frequency. The proposed systems will be applicable to a mobile cellular system in which a large phase variation interferes with the communications. This paper evaluates the error performances of the DPC/SS systems in additive white Gaussian noise (AWGN) channels and frequency-nonselective, slowly Rayleigh fading channels. It also evaluates the effect of diversity in the Rayleigh fading channels. In the AWGN channel, the DPC/SS system is superior by 1 dB to the differential phase-shift keying (DPSK) system and to the BPSK system in terms of required signal-to-noise ratio at 10 bit error rate (BER) at the same spectral efficiency. In the Rayleigh fading channel, diversity reception in the DPC/SS system gains more improvement in the BER performance, as the number of branches increases, than that obtained in a direct-sequence spread spectrum system with DPSK. [ABSTRACT FROM AUTHOR]

Recently, the differential detection scheme has attracted considerable attention as a demodulated scheme for digital mobile communications because of its robustness in the frequency nonselective fading channel. A problem of the differential detection scheme is that its bit error rate (BER) performance in the additive white Gaussian noise (AWGN) channel is inferior to that of the coherent detection scheme. To solve this problem, many methods to improve BER performance of the differential detection in the AWGN channel have been proposed. However, those methods require complex signal processings. This paper proposes a novel differential detection scheme as a simpler improvement method which estimates the differential phase of the transmitted signal by a sequence estimation scheme using the Viterbi algorithm. The proposed scheme is simplified in a manner such that its signal processing is composed of only additions and subtractions on the phase information of the received signal. Results of computer simulation show that BER performance of the proposed scheme is close to that of the ideal coherent detection in the AWGN channel. In addition, the trellis diagram of the Viterbi algorithm can be reduced without performance degradation introducing the signal processing similar to the Scarece State Transition (SST) type Viterbi decoding. The proposed scheme also is useful in the case of the fast Rician fading channel and the slow Rayleigh fading channel. [ABSTRACT FROM AUTHOR]

This paper proposes a novel type of edge-preserving smoothing filter to be applied to an image corrupted by impulsive and white Gaussian noise. This filter is based on a weighted mean filter having two kinds of coefficient for impulsive and white Gaussian noise. These coefficients can be varied adaptively by local features in the window. Fuzzy control laws that can be used for the implementation of the proposed filter are described. To reduce the computational burden, the fuzzy inference model proposed by Ichihashi and coworkers is employed. Finally, we demonstrate the effectiveness of the proposed technique by using results from a simulation. 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 3, 80(10): 19–27, 1997 [ABSTRACT FROM AUTHOR]

This paper investigates the effect of channel-to- channel interference in optical frequency division multiplexing (FDM)-homodyne detection systems that use optical filters for densely-spaced optical FDM systems. Power penalties at BER = 10-9 for double-sideband (DSB), single-sideband (SSB), and vestigial-sideband (VSB) signals were evaluated by approximating the channel-to-channel interference as Gaussian noise. The authors investigated four cases: where no optical filter was used; where an optical channel selection filter was used at the receiver, where an optical filter suppressing spectrum broadening was used for each channel before multiplexing at the transmitter; and where both of these filters were used. It has been shown that using an optical filter before multiplexing is effective for reducing channel spacing. The optical filterings are effective especially for SSB and VSB signals. Extremely densely-spaced multiplexing with a channel spacing of less than twice the bit rate can be achieved with a power penalty of less than 0.1 dB. [ABSTRACT FROM AUTHOR]

We propose a sort of double sparsity dictionary (DSD) to deal with random noise of seismic exploration, which consists of shearlet transform and data‐driven tight frame (DDTF). We train the DDTF dictionary in the domain of shearlet transform to improve the robustness of the dictionary. Furthermore, the function of hard‐thresholding is applied to find dictionary coefficients and cut small shearlet coefficients. Finally, we verified the reliability of the proposed approach by a synthetic data denoising example. [ABSTRACT FROM AUTHOR]

Joint detection and maximum-likelihood (ML) classification of linear modulations based on observations collected over an unknown flatfading additive Gaussian noise channel is considered. It is assumed that some of the observations are subject to data failures, in which case the receiver acquires only noise. Expectation-maximisation algorithm is employed to compute the ML estimates of the unknown channel parameters, which are then substituted into the corresponding likelihood expressions to perform hypothesis testing. Numerical simulations indicate that a suboptimal classifier, which is ignorant to data failures, exhibits extremely poor performance in the presence of high failure rates. On the other hand, the proposed classifier demonstrates comparable performance with that of the clairvoyant classifier which is assumed to have a priori knowledge of the channel parameters and data failures. [ABSTRACT FROM AUTHOR]

Chaotic multi-dimensional time series (MDTS) exist in some fields such as stock markets and life sciences. To effectively extract the desired information from the measured MDTS, it is important to pre-process data to reduce noise. On the basis of dictionary learning, a method to remove noise is proposed, and the proposed approach is shown to be very effective in the case of MDTS. An MDTS is first considered as a whole, namely an image, and then the method is applied on it. Compared with traditional methods, the proposed approach can utilise the information among the different dimensional time series to improve noise reduction. Using the Lorenz data superimposed by the Gaussian noise as an example, the simulation results have validated the mathematical framework and the performance. [ABSTRACT FROM AUTHOR]