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An unbiased weighted least square (UWLS) adaptive algorithm is developed and proposed to cooperate with a second-order linear predictor to estimate the frequency of a single tone embed in noise. A new objective function and updating equations are introduced to improve the performance of previous WLS algorithm. Extensive simulations are drawn to verify the performance of the proposed estimator.

Another scheme of a binary frequency phase shift keying (BFSK) signal demodulation has been presented in this paper. The proposed demodulated scheme utilizes an ANC-based adaptive signal processing. The adaptive algorithm which adapts the coefficients of the ANC system will be constrained in adaptation sequence corresponding to each symbol interval of the received BFSK signal. By using this scheme, the system will provide demodulated behavior similar to an integrated and dump correlator of an optimum receiver. The analytical and simulation results have shown that the proposed system can demodulate the received BFSK signal in AWGN channel. Furthermore, the system provides the same performance as the optimum receiver.

A constrained adaptive IIR notch filter (c-ANF) using weighted least square (WLS) algorithm (c-ANF-WLS) is introduced for frequency estimation of a single real tone embed in Gaussian noise. As compared with some previous techniques, the proposed algorithm exhibits better performance in terms of both convergence rate and steady state mean square error (MSE). Computer simulation results are provided to assert the claim.

This paper presents a modified phase based frequency adaptive algorithm (MPBAA). We have modified the original PBAA by using i) an all-pole bandpass filter (BPF) instead of zero-pole BPF ii) only one first-order autoregressive processes (AR(1)) instead of two ARs(1). Moreover, the process of de-composing for real and imaginary parts is not required. Therefore, MPBAA requires less computation than PBAA while maintaining almost the same performances.

Adaptive Sinusoidal interference cancellation (ASIC) based on recursive least square (RLS) algorithm is proposed. RLS algorithm allows us to use a recursion instead of inversion of the autocorrelation matrix to evaluate the optimum weights. The main advantage of RLS algorithm is that it exhibits extremely high rate of convergence. Computer simulations are performed to demonstrate the performance of the proposed algorithm.

This paper has proposed the technique in order to remove salt-and-pepper noise from the corrupted image. The proposed scheme consists of two steps. The first, the adaptive median filter is applied to noisy image for detecting noisy and noise-free pixels. Finally, the output image of previous step will be divided into many non-overlapping windows of which the noise-free pixel values are used to compute the new pixel value by the Shepard method. The experiment results of our approach had shown the better performance than the existing method.

This paper presents a simple lattice-structure complex adaptive linear predictor (LP) to estimate and track an unknown frequency of a noisy complex sinusoid. Instead of a widely used LMS-based algorithm, a weighted least squared (WLS) algorithm is proposed to improve the convergence rate of the predictor. Analytic results for asymptotic bias is derived in closed form and demonstrated that the WLS is asymptotic unbiased estimator.

At present, mobile stations have high data rates and high-speed of mobile stations (MS) such as high-speed trains. Therefore is the cause of frequent handover and handover fails. This makes communication interrupted a challenge to solve. The speed of the mobile station is also an important factor in starting the handover (HO) that should not be ignored. To handle delivery delays, this article proposes a new HO scheme. This scheme uses dynamic HO criteria that use techniques. Link-Going-Down (LGD) to determine the HO process. Starting this technique depends on the delivery time and the speed of the mobile device. Therefore, the HO process begins at the right time and the use of network resources has been improved to be more efficient and reduce interrupt time.

Currently, a mobile network environment has communication high data rate and high-speed of the mobile station (MS) such as a high-speed train. Therefore, it causes frequent handover. Consequently, the handover fails or increases latency in the system. If the system has latency more than 42 ms, it will affect the real-time application. This causes interruptions in communication to be a challenge in solving problems. The speed of the mobile station is also an important factor in getting started handover. To handle the problems of handover latency time, this paper proposes a new handover method. This method applies the dynamic handover threshold to define the starting handover process. This technique is based on the measured received signals strength (RSS) by the Doppler Effect technique, the mobile speed and handover latency time. Consequently, the initial handover process at the right time will reduce the handover time and the efficient use of network resources.

Sign-algorithm (SA) for a constrainted adaptive IIR notch filter (ANF) is very attractive because it is simple to implement. Although the slow convergence speed and biased estimate are two main drawbacks of SA, in some applications, it provides satisfactory results. In this work we propose the bias removal technique to mitigate the biased estimate of SA. Computer simulation has been drawn to show the performance of the proposed technique.

A fast adaptive method to eliminate a single sinusoidal interference of known frequency is proposed. The coupled-oscillator is used to generate the reference signal instead of look-up table. Unlike the least mean square (LMS) based adaptive algorithm, the weighted least squared error criterion is employed instead to evaluate the system parameters. It has been shown that the proposed method can quickly eliminate unwanted interference as compared to previous methods, Moreover, the extensive simulations have been done and revealed that proposed algorithm yields a constant output signal to interference ratio (SIR) of about 20 dB regardless of input SIR.

In this paper, the inverse distance weighting (IDW) is utilized in order to restore contaminated images which are corrupted by the salt-and-pepper noise (SPN). Our method consists of three steps. The first step, the noise candidate pixels are identified by the adaptive median filter. For the second step, the noisy image is divided into many sub-windows. The last step, for each sub-window from previous step, the IDW is applied to calculate the new pixel values that use the neighbour uncorrupted pixels in each sub-region. The results of experiments demonstrate that the proposed technique is better performance than the existing methods.

This paper proposes the high efficient method to remove salt-and-pepper noise from the noisy image. In this paper, we propose two step process which contain the noisy pixels identify and noise restoration. In the first step, the noisy pixels are detected by using the minimal and maximal value of the contaminated image. Finally, the Shepard interpolation method is operated to restore the corrupted pixels on each sliding block. In performance evaluation, it demonstrates the efficiency of the proposed method is better than the existing methods.

The LMS-based indirect frequency estimation algorithm (IFE) is reformulated using weighted least-square error criterion. Theoretical analyses for steady state bias and mean square error (MSE) are addressed. It has been shown that the proposed algorithm outperforms the conventional LMS-based algorithms in terms of convergence speed at the same value of MSE.

An arctangent (AT) based adaptive algorithm for a first-order complex adaptive IIR notch filter (CANF) is proposed in this paper. The objective of this work is to overcome some drawbacks including slow convergence speed and high sensitivity to impulsive noise of the previous gradient and nongradient based adaptive algorithms. The proposed AT algorithm employs the ratio of output to regressor signal as an error criterion where the arctangent value of such a ratio is employed to adjust the filter parameter. It is found that the proposed algorithm provides not only high speed convergence but also high impulsive noise robustness. Moreover, very low bias of the frequency estimate is also obtained. In addition, difference equation for the convergence in the mean and coarse stability bound in mean sense are derived. Computer simulations are conducted to show the performance of the proposed AT algorithm.

A modified inverse tangent (MIT) based adaptive algorithm for a second-order constrained adaptive IIR notch filter (ANF) is proposed in this paper. The objective of this work is to overcome some drawbacks of the gradient based adaptive algorithms (GAs) including slow convergence speed, bias, and high sensitivity to impulsive noise. The proposed MIT algorithm employs the ratio of output signal to internal state as an error criterion where the inverse tangent value of such ratio is employed to adjust the filter parameter. It is found that the proposed algorithm provides not only high speed of convergence but also high impulsive noise robustness. Moreover, unbias of the frequency estimate is also obtained. Computer simulations are conducted to show the performance of the proposed algorithm.

In this paper, the salt-and-pepper noise is removed by Shepard interpolation method. Our method has three steps. The first step, the noise location of the noisy image is detected by applying the adaptive median filter. The second step, the corrupted image is split into sub-regions. The window size of each sub-region is defined by the noise density. Finally, the new pixel value of each noisy pixel which locates in each sub-regions is calculated by Shepard method. The experiment results demonstrate the good performance some recent methods.

In this paper, theoretical analysis for deriving the estimation of mean square error (MSE) at steady-state of an adaptive IIR notch filter using unbiased modified plain gradient (UMPG) algorithm is presented in closed form. Moreover, the stability bound of the algorithm is also derived. To confirm the analytical results, the computer simulations are conducted to corroborate the effectiveness of the UMPG algorithm. Furthermore, the performances of the algorithm are also compared with the modified plain gradient (MPG) algorithm, unbiased plain gradient (UPG) algorithm and simplified lattice algorithm (SLA).

A modified gradient algorithm is developed for improving the convergence speed of a first-order complex adaptive IIR notch filter, which is used for estimating an unknown frequency of a complex sinusoidal signal embedded in white Gaussian noise. The new cost function using new error criterion is presented and analyzed theoretically. The proposed technique can significantly improve the convergence speed as compared with a complex notch filter using plain gradient algorithm. The computer simulations are conducted to demonstrate the validity of the proposed complex adaptive notch filter.

This article proposes an unbiased plain gradient algorithm for a second-order adaptive IIR notch filter with constrained poles and zeros. The proposed algorithm employs removing a dominant parameter that produces inherent bias. By using this technique, the performances are improved with slight expense in computational complexity. In this paper, theoretical analysis for deriving the estimations of bias and mean square error (MSE) at steady state are presented in closed form. Moreover, the stability bound of the algorithm is also derived. To confirm the analytical results, the computer simulations are provided to corroborate the effectiveness of the proposed algorithm. Furthermore, the performances of the algorithm are also compared with the plain gradient (PG) and modified plain gradient (MPG) algorithms.

This paper deals with estimating the frequency of sinusoidal signal buried in broad band noise. The simple unbiased indirect frequency estimation (IFE) algorithm is proposed for a second-order adaptive finite impulse response (FIR) notch filter with constrained zeros. The technique of estimating input noise variance is employed to remove the bias existing in the estimated filter parameter. Also, the difference equation for the convergence of the expectation of the estimated parameter and the closed-form of steady state estimation mean square error (MSE) are derived. In addition, extensive simulations are conducted to corroborate the efficiency of the proposed estimator with the theoretical analysis.

Modified sign algorithm (MSA) for a second-order constrained adaptive IIR notch filter (ANF) is proposed and analyzed in this paper. Difference equations for the convergence in the mean and of the mean square, and steady-state estimation bias and MSE are derived in closed form. Moreover, a coarse stability for the MSA is carried out. Computer simulations are conducted to corroborate the theoretical analysis.

Modified indirect and direct plain gradient (MIPG and MDPG) algorithms using new error criteria for second-order adaptive IIR (infinite-impulse response) notch filters (ANFs) with constrained poles and zeros are proposed in this paper. The proposed techniques are based on modifying error functions of the standard indirect and direct ANFs. In this work, the proposed error criteria are modified as the mean value of the product of the two signals which are, respectively, produced by the FIR (finite-impulse response) and IIR sections of the ANFs. Due to a non-zero slope straight line-like characteristic of the new cost functions, the gradient based adaptive algorithm rapidly converges to an optimum solution resulting in faster convergence speed than that of the conventional method. The closed form expressions for steady-state estimation bias and mean square error (MSE) of the proposed algorithms are also carried out. In addition, extensive simulations of the proposed algorithms compared to other methods are provided to support the theoretical analyses. Although, there are some limitations in the proposed algorithms, however, the demonstrated results obtained from tested algorithms at the same condition confirm that the proposed MIPG and MDPG algorithms are superior to others in convergent speed.

This work deals with the problem of the frequency estimation of a sinusoidal signal corrupted by broad-band noise. The direct frequency estimation based adaptive algorithm for a second-order adaptive finite impulse response (FIR) notch filter (AFNF) is thus proposed. The proposed algorithm employs the bias removal technique to remove the bias existing in the estimated parameter. The performances including the rate of convergence and the mean square error (MSE) can be easily controlled by using only one parameter, i.e., step size parameter. Moreover, the proposed filter is simple to implement and suitable for real-time applications. In addition, the difference equations for the convergence in the mean and mean square, and the closed form expressions for the steady-state estimation bias and MSE are also carried out. Finally, the simulation results are provided to confirm the theoretical analysis.

This paper presents a new digital frequency modulation scheme using digital sinusoidal oscillator, By multiplying an information signal with the oscillator coefficient, positions of the system poles are varied according to the strength of the incoming signals. As a result, the frequency modulation is obtained. The advantage of this system is that its frequency deviation and bandwidth are directly defined by the initial frequency condition of the system and are independent of the signal strength. Thus, the signal bandwidth is easily controlled. Computer simulation results are provided to confirm that the proposed system is the frequency modulator.

This paper presents an alternative method to generate the digital frequency modulation (FM) signal. By summing an information signal m(k) with the oscillating frequency ω 0 and putting the result into the cosine function of digital oscillator coefficient which is −2 cos (ω 0 +m(k)), the FM signal is obtained. Computer simulations are conducted to show the validity of the proposed FM signal generator

The gradient algorithm used absolute value of error signal as a criterion to adjust the filter coefficient of adaptive IIR notch filter is presented in this paper. The objective is to estimate or track the frequency of a single sinusoidal signal corrupted by additive white Gaussian noise. The proposed algorithm can solve the problem of selecting an initial condition as met in the previous method. Moreover, it also has a high convergence speed.

A simple and efficient method for canceling sinusoidal interference is presented in this paper. The interference of known frequency is modeled as a sum of unknown amplitude and zeros phase sine and cosine components. The amplitude of those components are sequentially estimated interms of Wiener-Hopf solutions using cumulative average system. Additionally, in order to avoid any look-up tables for generating the sine and cosine signals, the coupled-digital oscillator is employed instead. It has been demonstrated that the proposed method can efficiently and rapidly cancel sinusoidal interference as compared with the previous method.

Bias removal in a modified indirect plain gradient (MIPG) algorithm is presented in this paper. The proposed technique can remove the bias existing in the estimated parameter with little increasing of computation. In addition, steady state analysis of the convergence behavior and bias are derived in closed form. Computer simulations are conducted to corroborate the theoretical analysis and the effectiveness of the proposed algorithm.

A modification of error function of a modified indirect plain gradient algorithm (MIPG) is presented in this paper. The objective is to improve the stability of the MIPG. In this modification, we have derived a new adaptive algorithm, namely, improved MIPG algorithm (IMIPG). The methodology of modifying is the sum of the two error functions. The former is of the MIPG algorithm and later is of the plain gradient (PG) algorithm. The IMIPG algorithm not only has fast convergence speed but it also provides high stability, especially when the signal frequency is close to 0 or π.

The Phase/Amplitude shift keying modulator using digital oscillator is presented in this work. The proposed method comprises of digital line coder, digital differentiator and digital oscillator. The line coding signal is transformed into impulse signal to excite the oscillator to generate the desired PSK or ASK signal. The advantages of this technique are simple, high accuracy and robust to noise. It is robust to noise because all components are digital implementation.

A very simple method to demodulate the digital frequency modulated (FM) signal is presented in this paper. A unit delay operator, multiplier, lowpass filter, arccosine function, and adder are employed. The proposed method is easily adjusted and controlled. The computer simulations are conducted to demonstrate the performance of the proposed technique.

This paper presents the unbiased gradient algorithm for the equation error adaptive IIR notch filter (UEANF). The proposed algorithm employs the bias removal technique to remove the bias existing in the estimated parameter, resulting in obtaining good estimate of the parameter. Moreover, the convergence property is improved as compared with the conventional adaptive IIR notch filter (ANF). The computer simulations are provided to confirm the performances of the proposed technique.

An unbiased plain gradient (UPG) algorithm for a constrained adaptive IIR notch filter (ANF) is presented in this paper. The proposed algorithm employs the bias removal technique to remove the bias existing in the estimate of the filter parameter, resulting in good estimation. The computer simulation results have shown that the proposed technique is unbiased.

An unbiased constrained direct adaptive IIR notch filter is presented in this paper. The proposed algorithm employs the bias removal technique to remove the bias existing in the estimate of the filter parameter, resulting in good estimate of filter parameter. The theoretical analysis for the difference equation for the convergence in the mean is derived in closed form and confirmed by the computer simulations.

The inverse tangent based second-order adaptive IIR notch filter (ITANF) is presented in this paper. It is well known that the gradient-based adaptive IIR notch filter (ANF) has inherent low convergence speed due to the flattened error function. Moreover, the magnitude of error function depends on magnitude of sinusoid which implies that the speed of convergence of the gradient-based adaptive algorithm also depends on the magnitude of an input signal. To improve such drawback, the new inverse tangent based adaptive algorithm for a second order IIR notch filter is therefore proposed. The proposed algorithm employs the ratio of output to input signals as an error criterion where the inverse tangent value of the ratio is employed to adapt the filter parameter. It is found that the proposed algorithm provides not only high speed convergence but also high impulse noise robustness. The simulation results confirm that the performance of the proposed algorithm has been improved over the conventional ANF.

In this paper, new solution analysis of difference equation for amplitude controlled digital oscillators based on multi-time variables technique is presented. In general, when the forced input is static signal, the natural response depends only on the value of forced response at initial time. Alternatively for dynamic forced input, the magnitude of natural response gained by using such assumption is different from the simulation results. Thus, the multi-time variables technique for dynamic forced response is therefore introduced. Based on this technique, it is found that the forced response at any arbitrary time is occurred in a term of natural response. This implies that the amplitude of natural response is therefore directly controlled by forced response. To confirm the theoretical analysis, simulation results have been demonstrated and agreed well with the analysis results.

A new equation-error based gradient algorithm, which is derived from standard PG (plain gradient) algorithm, is proposed for a second-order adaptive IIR notch filter with constrained poles and zeros. The proposed technique employs the technique of minimizing an unconstrained error function to remove the bias in an estimated coefficient. It is demonstrated that the suggested method gives unbiased IIR system parameter estimation.

This article, variable momentum factor to improve performance of momentum LMS is presented. The momentum factor of algorithm is varied according to a gradient decent designed to reduce the square estimation error during each iteration. So this proposed algorithm has high convergence rate and low steady state misadjustment. Simulation results showed good performance of the algorithm in both stationary and nonstationary environments.

A statistical analysis of the equation error adaptive IIR notch filter (EANF) is presented in this paper. Analytical results reveal that if the filter is excited with a noisy sinusoid, the EANF will become a biased estimator. In addition, the obtained results show that the bias depends on the input noise power and input frequency but it is independent of the step-size parameter whereas the MSE (mean square error) depends on the bias and step-size parameter. However, these analytical results will be invalid when the signal to noise ratio (SNR) is less than 10 dB.

The dynamic structure linear predictive coder (DLPC) is proposed. The analysis filter structure is dynamic, i.e., the order number is varied according to the analyzing gain. It is adjusted so as to the achieved gain is less than the reference gain value. The main advantage is that the DLPC provides low bit-rate of transmission. Also, the subjective and objective measurements show that the DLPC yields relative quality of the synthesized speech compared to that of the conventional LPC coding.

A direct estimation based adaptive IIR notch filter (DANF) based on state-space structure and discrete Kalman filtering is represented in this paper. It is well known that the state-space description not only provides a more detail explanation of the system characteristic compared to the input-output description but also has robustness to finite word-length effects. In addition, by using the discrete Kalman filtering, the fluctuation of an estimated parameter of the filter is reduced. In this paper, sinusoidal signal detection is employed to evaluate the performance of the proposed filter compared to the state-space structure of the DANF (SDANF). The computer simulation results confirm that the performance of the proposed filter has been improved over the SDANF.

The new inverse tangent based adaptive algorithm for a second-order constrained adaptive IIR notch filter is presented in this paper. The proposed algorithm employs the ratio of output to input signals as an error criterion where the inverse tangent value of the ratio is used to adapt the filter parameter. It is found that the proposed algorithm provides not only high speed convergence but also high impulse noise robustness. The simulation results confirm that the performance of the proposed algorithm has been improved over the conventional gradient based technique.

A modified adaptive IIR notch filter based on direct frequency estimation algorithm is presented. The proposed system uses the automatic gain control and the Kalman filtering technique to improve the convergence speed and reduce the steady-state mean square error (MSE) of the estimated frequency. The simulation results confirm that the performance of the proposed system has been improved over the conventional direct and indirect adaptive IIR notch filters.

In this paper, a simplify second order IIR adaptive notch (ANF) algorithm based on least mean p-power (LMP) error criterion is presented. The quantizing gradient technique is used to detect high convergence speed and high impulsive noise robustness. This algorithm reduces an amount of multiplication computational equal to the length of input data. It is proved here that the convergence speed, variance, bias and noise robustness do not depend on p values. For practical application with most desirable purposes, the value of p=1 is chosen, the results of computer simulation and application on TMS320C50 digital signal processing starter kit are given to demonstrate the convergence speed and noise robustness performance.

The best adaptive algorithm requires fast convergence speed, low variance, unbias and low steady-state mean square error (MSE) in both low and high signal-to-noise ratio (SNR) situations. We have proposed a robust variable step-size LMS-like algorithm (VS-LMS-L) for a second-order adaptive IIR notch filter for frequency detection in radar, sonar and communication systems. This algorithm is compared with the conventional LMS-like algorithm called the plain gradient algorithm (PG). The time-varying step-size /spl mu/(n) is adjusted by using the square of the time-averaged estimate of autocorrelation of the present output signal y(n) and the past one y(n-1). This technique can reject the effect of the uncorrelated noise sequence on the step-size update, resulting in a small MSE due to the small final /spl mu/(n). Moreover, this algorithm can also improve the convergence speed by comparison with the PG at the same MSE value.