Fast adaptive exposure correction algorithm based on optimal niche

An adaptive correction algorithm is demonstrated based on weak measurement, which introduces a feedback and an additional interaction to the system and can dynamically adjust the operating point in accordance with the condition of the estimated phase change. Two schemes, fast adaptive correction and slow adaptive correction, are proposed for different conditions of the modulation device. Fast adaptive correction scheme can realize a real-time correction and maintain the high sensitivity. Slow adaptive correction scheme, as a supplement, can correct the distortion of the measured parameter by changing the measuring period. These two schemes are useful for high precision phase estimation with time in modern physics and practical applications, including, but not limited to, timing synchronization, accurate distance measurement, and gravity wave detection. Moreover, we discuss the deviation of the adaptive correction for considering system noise in practical measurement.

The speech enhancement is one of the effective techniques to solve speech degraded by noise. In this paper a fast speech enhancement method for noisy speech signals is presented, which is based on improved Kalman filtering. The conventional Kalman filter algorithm for speech enhancement needs to calculate the parameters of AR (auto-regressive) model, and perform a lot of matrix operations, which usually is non-adaptive. The speech enhancement algorithm proposed in this paper eliminates the matrix operations and reduces the calculating time by only constantly updating the first value of state vector X(n). We design a coefficient factor for adaptive filtering, to automatically amend the estimation of environmental noise by the observation data. Simulation results show that the fast adaptive algorithm using Kalman filtering is effective for speech enhancement.

Low-contrast or uneven illumination in real-world images will cause a loss of details and increase the difficulty of pattern recognition. An automatic image illumination perception and adaptive correction algorithm, termed as GLAGC, is proposed in this paper. Based on Retinex theory, the illumination of an image is extracted through the discrete wavelet transform. Two features that characterize the image illuminance are creatively designed. The first feature is the spatial luminance distribution feature, which is applied to the adaptive gamma correction of local uneven lighting. The other feature is the global statistical luminance feature. Through a training set containing images with various illuminance conditions, the relationship between the image exposure level and the feature is estimated under the maximum entropy criterion. It is used to perform adaptive gamma correction on global low illumination. Moreover, smoothness preservation is performed in the high-frequency subband to preserve edge smoothness. To eliminate low-illumination noise after wavelet reconstruction, the adaptive stabilization factor is derived. Experimental results demonstrate the effectiveness of the proposed algorithm. By comparison, the proposed method yields comparable or better results than the state-of-art methods in terms of efficiency and quality.

With developments in uncooled infrared plane array (UFPA) technology, many new advanced uncooled infrared sensors are used in defensive weapons, scientific research, industry and commercial applications. A major difference in imaging techniques between infrared IRFPA imaging system and a visible CCD camera is that, IRFPA need nonuniformity correction and dead pixel compensation, we usually called it infrared image pre-processing. Two-point or multi-point correction algorithms based on calibration commonly used may correct the non-uniformity of IRFPAs, but they are limited by pixel linearity and instability. Therefore, adaptive non-uniformity correction techniques are developed. Two of these adaptive non-uniformity correction algorithms are mostly discussed, one is based on temporal high-pass filter, and another is based on neural network. In this paper, a new NUC algorithm based on improved neural networks is introduced, and involves the compare result between improved neural networks and other adaptive correction techniques. A lot of different will discussed in different angle, like correction effects, calculation efficiency, hardware implementation and so on. According to the result and discussion, it could be concluding that the adaptive algorithm offers improved performance compared to traditional calibration mode techniques. This new algorithm not only provides better sensitivity, but also increases the system dynamic range. As the sensor application expended, it will be very useful in future infrared imaging systems.

We present a fast adaptive PARAFAC decomposition algorithm with low computational complexity. The proposed algorithm generalizes the Orthonormal Projection Approximation Subspace Tracking (OPAST) approach for tracking a class of third-order tensors which have one dimension growing with time. It has linear complexity, good convergence rate and good estimation accuracy. To deal with large-scale problems, a parallel implementation can be applied to reduce both computational complexity and storage. We illustrate the effectiveness of our algorithm in comparison with the state-of-the-art algorithms through simulation experiments.

SummaryWith the hot issues such as smart city and ecological city put forward, the development of intelligence and informatization of urban space has been established, especially the Internet of Things. With the wide application of location‐based social networks, users can share their location of interest in location. By analyzing users' historical geographic information, location service recommendation can recommend geographic locations to users to help users obtain better access experience. Combined with genetic algorithm (GA) algorithm, a recommendation algorithm based on geographic location service optimization is proposed, which can better recommend to users. Aiming at the problem of slow convergence speed of GA, a fast adaptive genetic algorithm (FAGA) method is proposed to optimize location services. In the experimental part, comparing several functions, FAGA's test effect and convergence are ideal. By comparing FAGA‐least squares support vector machine (LSSVM) algorithm with other methods in location service recommendation, FAGA‐LSSVM method has more advantages.

Fast square-root RLS adaptive filtering algorithms

Fast adaptive phase unwrapping algorithm based on improved bucket sorting

A fast complex-valued adaptive filtering algorithm based on the Euclidean direction set (EDS) method is presented in this paper. A mathematical analysis of the complex-valued EDS (CEDS) algorithm is also performed in this paper. It is shown that the CEDS algorithm has a computational complexity of O ( N ) for each system update, and that the CEDS converges to the true solution. Computer simulations illustrate that this complex algorithm is very efficient in complex adaptive FIR and IIR filtering.

As a new display technology, Mini‐LED has more and more broad prospects. However, there are some difficult problems, such as Mass Transfer, yield, Uniformity of brightness and chroma. In order to improve the uniformity, especially the low grayscale, we propose a fine full grayscale correction method, which includes precise pixel‐by‐pixel brightness and chroma extraction algorithm and level‐by‐level full grayscale correction algorithm. To ensure the uniformity of the interior of the cabinet and between the cabinets, the full‐color viewing angle compensation and adaptive cabinet edge seam correction algorithm is also proposed, which can realize the arbitrary splicing of the cabinets.

In this paper, a novel combined leakage CMA and direct decision (DD) adaptive algorithm using automatic iteration step adaptive blind equalizer is presented. In contrast to known CMA algorithm, the novel algorithm can approach the performance of mean-square error and fast convergent rate. For adaption of filters, efficient automatic iteration step and DD algorithm is present. The novel algorithm is lower computational complexity withO(N) and N is the filter order. Furthermore, the relation of novel algorithm to CMA algorithm is investigated. Finally, it is shown that proposed combined LCMA-DD algorithm is robust against amplitude and phase offset with fast convergence and mean-square error.

In this paper, a two-dimensional (2-D) fast adaptive filtering algorithm based on the exponentially weighted least squares errors is developed for the non linear canonical piecewise quadratic (CPWQ) model. This algorithm take advantages of the non linear CPWQ modeling and the fast convergence of the exponentially weighted least squares adaptive algorithm. The simulation results show that the proposed method gives good results in image restoration. Experimental comparison with the canonical piecewise linear (CPWL) model shows the superiority of the non linear filter in term of image enhancement.

A fast adaptive algorithm is developed for training optimal weighted order statistic (WOS) filters under the mean absolute error (MAE) criterion. This algorithm is implemented without use of threshold decomposition and can be applied to arbitrary real-valued signals directly. It can easily be extended to finite impulse response (FIR)-WOS hybrid filters. An interesting application of adaptive WOS and FIR WOS hybrid filters is presented. >

The control of each individual bend and overall process is presented in Part 1 of the paper. In Part 2 of the paper, statistical methods are used to analyze and improve 3-D tube bending accuracy. The relationship between bending process error and tube geometry error is obtained with Monte Carlo simulation. For the same tube tolerance requirement, the required process tolerance varies in a large range based on tube geometry. Among the three bending errors: bend angle, bend plane and distance between bends, bend angle error has the largest influence on tube error. For a tube with multiple bends, the overall tube geometry error can be minimized by intentionally modifying the nominal values of the bends to be made based on the errors in the existing bends. The required modification of the bending commands is calculated with an adaptive bend correction algorithm.

The article focuses on the development and implementation of an effective methodology for measuring high-precision parts on coordinate measuring machines (CMM). The proposed approach addresses the challenges associated with complex geometry measurements under variable environmental conditions by combining advanced mathematical modeling techniques with adaptive error compensation algorithms. The mathematical foundation is based on the application of tensor formalism in Riemannian space, which allows for more precise model-ing of geometric errors using Christoffel symbols and covariant derivatives of the error po-tential. This approach significantly improves the accuracy of spatial positioning. A refined stochastic error model, incorporating Stratonovich integrals and fractional Brownian motion, provides a more accurate description of random processes occurring in the measurement sys-tem. To enhance overall measurement accuracy, an adaptive correction algorithm is pro-posed, based on Itô stochastic differential equations with Fourier-Bessel series expansion, which ensures efficient compensation of systematic errors. In addition, the measurement tra-jectory optimization is formulated as a variational problem, taking into account holonomic and non-holonomic constraints, enabling the optimal positioning strategy. A thermoelastic deformation model based on sixth-rank tensors and Green’s functions is developed to account for temperature-induced deformations, ensuring the reliability of measurements under ther-mal instability. Experimental verification confirmed the effectiveness of the proposed method-ology, demonstrating a 15-20% reduction in systematic errors, a 10-15% decrease in random errors, and an approximately 20% improvement in the accuracy of measurement uncertainty estimation. The proposed methodology combines theoretical advancements with practical so-lutions, providing a robust tool for improving the accuracy and reliability of coordinate measurements in industrial metrology applications.