The article discusses the algorithm for the joint estimation of the stationary channel factors and signal distortions, such as DC drift, the frequency shift remaining from the demodulation procedure, amplitude and pha...
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ISBN:
(纸本)9781728160726
The article discusses the algorithm for the joint estimation of the stationary channel factors and signal distortions, such as DC drift, the frequency shift remaining from the demodulation procedure, amplitude and phase imbalance (IQ-imbalance). The problem of estimating unknown parameters is solved in two stages, by combining two procedures. The first, based on the polynomial approximation of the generalized communication channel and the linear least squares (MLS) method, estimates the constant components of the signal quadrature, amplitude and phase imbalance, as well as a rough estimate of the channel frequency and factors. The second procedure is synthesized using the Taylor approximation, a modified method of least squares in the form of a functional A.N. Tikhonov and regularization method. It is a nonlinear recursive algorithm for obtaining a more accurate estimate of the frequency and channel factors. This approach allows a sufficiently high estimation accuracy to reduce the complexity of the algorithm compared to using only the second procedure. The resulting algorithm works under conditions of uncorrelated Rayleigh fading in MIMO systems with spatial multiplexing with a priori uncertainty regarding the statistical characteristics of the communication channel (except for the dispersion of additive noise) and the laws of noise distribution, both phase and additive.
In this paper, we focus on the timing analysis of a fault-tolerance technique when a program is written in the form of several modules. If a hardware failure does occur during the ith module execution, the program mus...
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Image moments are image descriptors widely utilized in several image processing, pattern recognition, computer vision, and multimedia security applications. In the era of big data, the computation of image moments yie...
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Image moments are image descriptors widely utilized in several image processing, pattern recognition, computer vision, and multimedia security applications. In the era of big data, the computation of image moments yields a huge memory demand, especially for large moment order and/or high-resolution images (i.e., megapixel images). The state-of-the-art moment computation methods successfully accelerate the image moment computation for digital images of a resolution smaller than 1K x 1K pixels. For digital images of higher resolutions, image moment computation is problematic. Researchers utilized GPU-based parallel processing to overcome this problem. In practice, the parallel computation of image moments using GPUs encounters the non-extended memory problem, which is the main challenge. This paper proposed a recurrent-based method for computing the Polar Complex Exponent Transform (PCET) moments of fractional orders. The proposed method utilized the symmetry of the image kernel to reduce kernel computation. In the proposed method, once a kernel value is computed in one quaternion, the other three corresponding values in the remaining three quaternions can be trivially computed. Moreover, the proposed method utilized recurrence equations to compute kernels. Thus, the required memory to store the pre-computed memory is saved. Finally, we implemented the proposed method on the GPU parallel architecture. The proposed method overcomes the memory limit due to saving the kernel's memory. The experiments show that the proposed parallel-friendly and memory-efficient method is superior to the state-of-the-art moment computation methods in memory consumption and runtimes. The proposed method computes the PCET moment of order 50 for an image of size 2K x 2K pixels in 3.5 seconds while the state-of-the-art method of comparison needs 7.0 seconds to process the same image, the memory requirements for the proposed method and the method of comparison for the were 67.0 MB and 3.4 GB,
Tchebichef polynomials (TPs) play a crucial role in various fields of mathematics and applied sciences, including numerical analysis, image and signal processing, and computer vision. This is due to the unique propert...
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Tchebichef polynomials (TPs) play a crucial role in various fields of mathematics and applied sciences, including numerical analysis, image and signal processing, and computer vision. This is due to the unique properties of the TPs and their remarkable performance. Nowadays, the demand for high-quality images (2D signals) is increasing and is expected to continue growing. The processing of these signals requires the generation of accurate and fast polynomials. The existing algorithms generate the TPs sequentially, and this is considered as computationally costly for high-order and larger-sized polynomials. To this end, we present a new efficient solution to overcome the limitation of sequential algorithms. The presented algorithm uses the parallel processing paradigm to leverage the computation cost. This is performed by utilizing the multicore and multithreading features of a CPU. The implementation of multithreaded algorithms for computing TP coefficients segments the computations into sub-tasks. These sub-tasks are executed concurrently on several threads across the available cores. The performance of the multithreaded algorithm is evaluated on various TP sizes, which demonstrates a significant improvement in computation time. Furthermore, a selection for the appropriate number of threads for the proposed algorithm is introduced. The results reveal that the proposed algorithm enhances the computation performance to provide a quick, steady, and accurate computation of the TP coefficients, making it a practical solution for different applications.
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