In this paper, a method is provided for suppressing FM noise jammer signal with POLYNOMIAL-PHASE in pulsed radar based on the DISCRETE POLYNOMIAL-PHASE TRANSFORM. In the first step, DPT TRANSFORM is used to estimate POLYNOMIAL-PHASE coefficients and amplitude of jammer signal. In the second step, the jammer signal has been reconstructed using the estimated amplitude and PHASE of the jammer signal, Then, jamming is suppressed efficiently by subtracting the reconstructed jammer signal from the received signal. Mean square error and CRLB for polynomial coefficients and amplitude of jammer signal in presence of combined target signals and the receiver noise, are calculated theoretically for the case of linear frequency modulation, and then are compared with simulations. The detection performance of the proposed method is also shown.

This paper presents a technique to improve mean-squared error of chirp rate and central frequency estimation in high signal to noise ratios in DISCRETE PHASE TRANSFORM and Improved DISCRETE PHASE TRANSFORM methods. The estimation of chirp signal parameters is used in many signal processing fields such as intercept radars, SARs, ISARs, and disrupting enemy radars. Increasing the estimation accuracy of chirp signal parameters in these applications is of great importance. In this paper, in order to increase the estimation accuracy of chirp signal parameters in DISCRETE PHASE TRANSFORM and Improved DISCRETE PHASE TRANSFORM methods, instead of periodogram frequency estimation, the proposed technique is used. Simulation results show that this technique reduces the computational complexity of these methods and increases the estimation accuracy of chirp signal parameters.

The aim of this paper is to introduce the notion of DISCRETE and inverse DISCRETE weighted TRANSFORM. We show that DISCRETE input data can be converted to a continuous approximation through the inverse DISCRETE weighted TRANSFORM.

One of problems of OFDM systems, is the big value of peak to average power ratio. To reduce it, any attempt have been done amongst which, random PHASE updating is an important technique. In contrast to paper, since power variance is computable before IFFT block, the complexity of this method would be less than other PHASE injection methods which could be an important factor. Another interesting capability of random PHASE updating technique is the possibility of applying the variance of threshold power. The operation of PHASE injection is repeated till the power variance reaches threshold power variance. However, this may be a considered as a disadvantage for random PHASE updating technique. The reason is that reaching the mentioned threshold may lead to possible system delay. In this paper, in order to solve the mentioned problem, DCT TRANSFORM is applied on subcarrier outputs before PHASE injection. This leads to reduce the number of required carriers for reaching the threshold value which results in reducing system delay accordingly.

Amongst the approaches proposed to estimate parameters of a chirp signal sequentially, i. e., the central frequency and the chirp rate, algorithms, such as DISCRETE POLYNOMIAL-PHASE TRANSFORM (DPT) and promoted DPT, exhibit acceptable estimation accuracy. Algorithms intended to estimate PHASE parameters sequentially, diminish the order of polynomials in complex exponential power to lower-order polynomials, and then estimate these two parameters using the NLS method in a given single exponential mode. The NLS method, which uses FFT to decrease the computational load of frequency domain search, encounters predicaments. In this work, we assessed the bias of algorithms intended for estimating of PHASE parameters sequentially using the RBF method. The results of investigating the bias of estimators indicated the improved accuracy of the DPT and promoted DPT algorithms in estimation using the RBF method instead of NLS and also than DCFT method.

Analysis and interpretation of medical images are of clinical importance for medical diagnosis and treatment while they also have technical implications for computer vision and pattern recognition. In this context, one of the most fundamental issues is the detection of object boundaries, which is often useful for further processes such as organ/tissue recognition, image registration, motion analysis, measurement of anatomical and physiological parameters, etc. Although one of the best methods of edge detection is based on wavelet TRANSFORM, the standard wavelet TRANSFORM has its own shortcomings such as lack of shift invariant and lack of directional selectivity in sub-bands in multidimensional applications. The DISCRETE complex wavelet TRANSFORM, which is based on complex mother wavelet, not only overcomes these shortcomings but has acceptable redundancy and complexity as well. It is especially useful for multidimensional situations and for high accuracy applications such as medical image processing. In this paper, the shortcomings of ordinary wavelet TRANSFORM are initially investigated and comparisons are made between the standard wavelet and the complex wavelet. Then, the DISCRETE complex wavelet domain is applied for image enhancement and edge detection of noisy images. The simulation results show that our method exhibits a better performance, especially in noisy cases, as compared with the standard wavelet and spatial methods.

This paper deals with the one-dimensional DISCRETE wavelet TRANSFORM (1D DWT) of four scaling coefficients are computed numerically by designing a convolutive operator. The near-zone contribution of the integral is calculated through wavelet TRANSFORM and for the far-zone contribution the classic expansion of the spherical harmonics applied. Finally the geoidal heights are determined over a territory in Canada.