Procoding blind estimation4/7/2023 ![]() A comprehensive validation stage considers synthetic and experimental FLIM datasets of ex vivo atherosclerotic plaques and human breast cancer cell samples that highlight the advantages of the proposed BDE algorithm under different noise and initial conditions in the iterative scheme and parameters of the proposal. After convergence, the final stage computes the fluorescence impulse response at all spatial points. First, the iterative methodology relies on a least-squares solution for the instrument response, and quadratic programming for the scaling coefficients applied just to a subset of the measured fluorescence decays to initially estimate the instrument response to speed up the convergence. Our proposal searches for the samples of the instrument response with a global perspective, and the scaling coefficients of the basis functions locally at each spatial point. Specifically, we formulate the semi-blind cascaded channel estimation as a trilinear. The algorithms are used to estimate an unknown vector of interest (such as temperature, sound, pressure, motion, pollution, etc. Hence, due to the nonlinear nature of the estimation process, an alternating least-squares scheme iteratively solves the approximation problem. In this paper, we investigate semi-blind cascaded channel estimation for RIS-aided massive MIMO systems, in which the receiver simultaneously estimates the channel coefficients and the partially unknown transmit signal with a small number of pilot sequences. The apparatus and method for blind block recursive estimation in adaptive networks, such as a wireless sensor networks, uses recursive algorithms based on Cholesky factorization (Cholesky) or singular value decomposition (SVD). In the approximation cost function, there is a bilinear dependence on the decision variables. Our blind deconvolution estimation (BDE) algorithm is formulated as a quadratic approximation problem, where the decision variables are the samples of the instrument response and the scaling coefficients of the basis functions. A linear combination of a base conformed by Laguerre functions models the fluorescence impulse response of the sample at each spatial point in our formulation. An iterative methodology is proposed to address the blind deconvolution problem departing from a dataset of FLIM measurements. In such cases, a blind deconvolution approach is required. In many applications, however, the instrument response is not available. Our techniques are blind in the sense that they do not require the transmission of pilot symbols, nor the estimation of the instantaneous state of the channel making them bandwidth efficient. The selection is based on the gradients of the patches. Our approach includes the process of selecting low-rank patches without high frequency components from a single noisy image. Time-deconvolution of the instrument response from fluorescence lifetime imaging microscopy (FLIM) data is usually necessary for accurate fluorescence lifetime estimation. In this paper, we propose a patch-based noise level estimation algorithm and suggest that the noise level parameter should be tuned according to the scene complexity.
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