A fracture enhancement method based on the histogram equalization of eigenstructure-based coherence

Eigenstructure-based coherence attributes are efficient and mature techniques for large-scale fracture detection. However, in horizontally bedded and continuous strata, buried fractures in high grayscale value zones are difficult to detect. Furthermore, middleand small-scale fractures in fractured zones where migration image energies are usually not concentrated perfectly are also hard to detect because of the fuzzy, clouded shadows owing to low grayscale values. A new fracture enhancement method combined with histogram equalization is proposed to solve these problems. With this method, the contrast between discontinuities and background in coherence images is increased, linear structures are highlighted by stepwise adjustment of the threshold of the coherence image, and fractures are detected at different scales. Application of the method shows that it can also improve fracture cognition and accuracy.

Improved Algorithm of Histogram Equalization and Its Actualization

A new improved algorithm of histogram equalization was discussed and actualized by analyzing the traditional algorithm. This improved algorithm has better effect than the traditional one, especially it is used to process poor quality images.

Contrast Limited Adaptive Histogram Equalization for Qualitative Enhancement of Myocardial Perfusion Images

This paper establishes an efficient color space for the contrast enhancement of myocardial perfusion images. The effects of histogram equalization and contrast limited adaptive histogram equalization are investigated and the one which gives good enhancement results is extended to the suitable color space. The color space which gives better results is chosen experimentally. Uniqueness of this work is that contrast limited adaptive histogram equalization technique is applied to the chrominance channels of the cardiac nuclear image, leaving the luminance channel unaffected which results in an enhanced image output in color space.

A New Equalization Method for Lithium-Ion Battery Packs Based on CUK

Aiming at the traditional CUK equalizer can only perform energy equalization between adjacent batteries,if the two single batteries that need to be equalized are far away from each other,there will be the problem of longer energy transmission path and lower equalization efficiency,this paper optimizes the CUK equalizer and optimizes its peripheral selection circuit,which can support the equalization of single batteries at any two positions.The control strategy adopts the open-circuit voltage(OVC)of the battery and the state of charge(SOC)of the battery as the equalization variables,and selects the corresponding equalization variables according to the energy conditions of the two batteries that need to be equalized,and generates the adaptive equalization current with an adaptive PID controller in order to improve the equalization efficiency.Simulation modeling is performed in Matlab/Simulink 2021b,and the experimental results show that the optimized CUK equalizer in this paper improves the equalization time by 25.58%compared with the traditional CUK equalizer.In addition,compared with the mean value difference(MVD)method,the adaptive PID method reduces the equalization time by about 30%in the static and charging and discharging experimental environments,which verifies the superiority of this equalization scheme.

Morphological Filters and Wavelet-based Histogram Equalization Image Enhancement for Weak Target Detection

Image enhancement methods are typically aimed at improvement of the overall visibility of features. Though histogram equalization can enhance the contrast by redistributing the gray levels, it has the drawback that it reduces the information in the processed image. In this paper, we present a new image enhancement algorithm. After histogram equalization is carried out, morphological filters and wavelet-based enhancement algorithm is used to clean out the unwanted details and further enhance the image and compensate for the information loss during histogram equalization. Experimental results show that the morphological filters and wavelet-based histogram equalization algorithm can significantly enhance the contrast and increase the information entropy of the image.

Equalization Reconstruction Algorithm Based on Reference Signal Frequency Domain Block Joint for DTMB-Based Passive Radar

Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small(LSS)target detection,a novel frequency domain block joint equalization algorithm is presented in this article.From the DTMB signal frame structure and channel multipath transmission characteristics,this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block,facilitating concurrent fast Fourier transform(FFT)calculations.Following equalization,an inverse fast Fourier transform(IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal.Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing.In passive radar LSS detection,it effectively suppresses multipath and noise through frequency domain equalization,reducing false alarms and improving the capabilities of weak target detection.

Automatic SOC Equalization Strategy of Energy Storage Units with DC Microgrid Bus Voltage Support

In this paper,an improved sag control strategy based on automatic SOC equalization is proposed to solve the problems of slow SOC equalization and excessive bus voltage fluctuation amplitude and offset caused by load and PV power variations in a stand-alone DC microgrid.The strategy includes primary and secondary control.Among them,the primary control suppresses the DC microgrid voltage fluctuation through the I and II section control,and the secondary control aims to correct the P-U curve of the energy storage system and the PV system,thus reducing the steady-state bus voltage excursion.The simulation results demonstrate that the proposed control strategy effectively achieves SOC balancing and enhances the immunity of bus voltage.The proposed strategy improves the voltage fluctuation suppression ability by approximately 39.4%and 43.1%under the PV power and load power fluctuation conditions,respectively.Furthermore,the steady-state deviation of the bus voltage,△U_(dc) is only 0.01–0.1 V,ensuring stable operation of the DC microgrid in fluctuating power environments.

Contrast Enhancement Using Weighted Coupled Histogram Equalization with Laplace Transform

Histogram equalization is a traditional algorithm improving the image contrast,but it comes at the cost of mean brightness shift and details loss.In order to solve these problems,a novel approach to processing foreground pixels and background pixels independently is proposed and investigated.Since details are mainly contained in the foreground,the weighted coupling of histogram equalization and Laplace transform were adopted to balance contrast enhancement and details preservation.The weighting factors of image foreground and background were determined by the amount of their respective information.The proposed method was conducted to images acquired from CVG⁃UGR and US⁃SIPI image databases and then compared with other methods such as clipping histogram spikes,histogram addition,and non⁃linear transformation to verify its validity.Results show that the proposed algorithm can effectively enhance the contrast without introducing distortions,and preserve the mean brightness and details well at the same time.

An Improved Histogram Equalization Method in the Traffic Monitoring Image Processing Field

To process the traffic monitoring image, a local Histogram Equalization method based on fuzzy mathematics was proposed in this paper. In this paper, firstly, we define a function to measure the similarity degree of two images. Then, a suitable Gaussian fuzzy distribution function was chose to generate a 3 × 3 matrix of influential factors. In order to reduce the artificial boundaries, we combined the 3 × 3 influential matrix with a 3 × 3 smooth filter matrix to get the final smooth-influ- ence matrix. Finally, the smooth-influence matrix was used to process the center block image. The simulation results demonstrated that the proposed method can reduce time consumption while improving the image contrast and can get satisfactory results.

Underwater Image Saliency Detection Based on Improved Histogram Equalization

In order to solve the problem of unsatisfactory detection effect of underwater visual saliency map, an image saliency detection algorithm based on improved histogram equalization is proposed. Underwater images are often not clear enough because the refraction of light underwater causes insufficient image resolution. Therefore, in order to solve the existing problems of traditional histogram equalization algorithm, an improved histogram equalization method is proposed to enhance the quality of images, which makes the saliency regions smoother and clearer. In this paper, the simulation experiments were conducted on UIEBD dataset and DLOU_underwater dataset. The experimental results show the effectiveness, robustness and accuracy of the proposed algorithm.

A New Approach for on-Line Blind Equalization and Channel Identification

A new approach for blind equalization and channel identification is proposed in this paper. The equalization scheme is based on over sampling technique and an independent component analysis network. The equalized sequence and its higher order statistics are used to identify the channel parameters. Compared to traditional equalization methods, the proposed approach is with a simple architecture, and does not need learning sequences. Computer simulations show the validity of the proposed method.

A Reduced Search Soft-Output Detection Algorithm and Its Application to Turbo-Equalization

To decrease the complexity of MAP algorithm, reduced state or reduced search techniques can be applied. In this paper we propose a reduced search soft output detection algorithm fully based on the principle of M algorithm for turbo equalization, which is a suboptimum version of the Lee algorithm. This algorithm is called soft output M algorithm (denoted as SO M algorithm), which applies the M strategy to both the forward recursion and the extended forward recursion of the Lee algorithm. Computer simulation results show that, by properly selecting and adjusting the breadth parameter and depth parameter during the iteration of turbo equalization, this algorithm can obtain good performance and complexity trade off.

Blind Equalization Based on RLS Algorithm Using Adaptive Forgetting Factor for Underwater Acoustic Channel

Blind equalization based on adaptive forgetting factor, recursive least squares （RLS） with constant modulus algorithm （CMA）, is investigated. The cost function of CMA is simplified to meet the second norm form to ensure the stability of RLS-CMA, and thus an improved RLS-CMA （RLS-SCMA） is established. To further improve its performance, a new adaptive forgetting factor RLS-SCMA （ARLS-SCMA） is proposed. In ARLS-SCMA, the forgetting factor varies with the output error of the blind equalizer during the iterative process, which leads to a faster convergence rate and a smaller steady-state error. The simulation results prove the effectiveness under the condition of the underwater acoustic channel.

Modulation Recognition in Maritime Multipath Channels:A Blind Equalization-Aided Deep Learning Approach

Modulation recognition has been long investigated in the literature,however,the performance could be severely degraded in multipath fading channels especially for high-order Quadrature Amplitude Modulation(QAM)signals.This could be a critical problem in the broadband maritime wireless communications,where various propagation paths with large differences in the time of arrival are very likely to exist.Specifically,multiple paths may stem from the direct path,the reflection paths from the rough sea surface,and the refraction paths from the atmospheric duct,respectively.To address this issue,we propose a novel blind equalization-aided deep learning(DL)approach to recognize QAM signals in the presence of multipath propagation.The proposed approach consists of two modules:A blind equalization module and a subsequent DL network which employs the structure of ResNet.With predefined searching step-sizes for the blind equalization algorithm,which are designed according to the set of modulation formats of interest,the DL network is trained and tested over various multipath channel parameter settings.It is shown that as compared to the conventional DL approaches without equalization,the proposed method can achieve an improvement in the recognition accuracy up to 30%in severe multipath scenarios,especially in the high SNR regime.Moreover,it efficiently reduces the number of training data that is required.

Energy Transferring Dynamic Equalization for Battery Packs

The equivalent circuit model of battery and the analytic model of series battery uniformities are setup. The analysis shows that it is the key to maintain small voltage difference between cells in order to improve uniformities. Therefore a new technique combining low voltage difference, big current charging and bi-directional charge equalizer system is put forward and designed. The test shows that the energy transferring dynamic equalization system betters the series battery uniformities and protection during charging and discharging, improves the battery performance and extends the use life of series battery.

A Modified T/2 Fractionally Spaced Coordinate Transformation Blind Equalization Algorithm

When T/2 Fractionally Spaced blind Equalization Algorithm based Constant Modulus Algorithm (T/2-FSE- CMA) is employed for equalizing higher order Quadrature Amplitude Modulation signals (QAM), it has disadvantages of low convergence speed and large Mean Square Error (MSE). For overcoming these disadvantages, a Modified T/2 Fractionally Spaced blind Equalization algorithm based on Coordinate Transformation and CMA (T/2-FSE-MCTCMA) was proposed by analyzing the character of 16QAM signal constellations. In the proposed algorithm, real and imaginary parts of input signal of T/2 fractionally spaced blind equalizer are equalized, respectively, and output signals of equalizer are transformed to the same unit circle by coordinate transformation method, a new error function is defined after making coordinate transformation and used to adjust weight vector of T/2 fractionally spaced blind equalizer. The proposed algorithm can overcome large misjudgments of T/2 fractionally spaced blind equalization algorithm for equalizing multi-modulus higher order QAM. Simulation results with underwater acoustic channel models demonstrate that the proposed T/2-FSE-MCTCMA algorithm outperforms T/2 Fractionally Spaced blind Equalization algorithm bas-ed on Coordinate Transformation and CMA (T/2-FSE-CTCMA) and the T/2-FSE-CMA in convergence rate and MSE.

Soft Direct-Adaptation Based Bidirectional Turbo Equalization for MIMO Underwater Acoustic Communications

This paper proposes a soft direct-adaptation based bidirectional turbo equalizer for multiple-input multiple-output underwater acoustic communication systems. Soft, rather than hard, direct-adaptation based equalizer combined with the fast self-optimized least mean square algorithm is employed to achieve a faster convergence rate, and the second-order phase-locked loop is embedded into the equalizer to track the time-varying channel. Meanwhile, by utilizing a weighted linear combining scheme, the conventional soft direct-adaptation based equalizer is combined with the time-reversed soft direct-adaptation based equalizer to exploit bidirectional diversity and mitigate error propagation. Both the simulation and experimental results demonstrate that the soft direct-adaptation based bidirectional turbo equalizer outperforms the single-direction soft direct-adaptation based turbo equalizer, and achieves a faster convergence rate than the hard direct-adaptation based bidirectional turbo equalizer.

Face anti-spoofing algorithm combined with CNN and brightness equalization

Face anti-spoofing is a relatively important part of the face recognition system,which has great significance for financial payment and access control systems.Aiming at the problems of unstable face alignment,complex lighting,and complex structure of face anti-spoofing detection network,a novel method is presented using a combination of convolutional neural network and brightness equalization.Firstly,multi-task convolutional neural network(MTCNN)based on the cascade of three convolutional neural networks(CNNs),P-net,R-net,and O-net are used to achieve accurate positioning of the face,and the detected face bounding box is cropped by a specified multiple,then brightness equalization is adopted to perform brightness compensation on different brightness areas of the face image.Finally,data features are extracted and classification is given by utilizing a 12-layer convolution neural network.Experiments of the proposed algorithm were carried out on CASIA-FASD.The results show that the classification accuracy is relatively high,and the half total error rate(HTER)reaches 1.02%.

An Improved Equalization with Real Interference Prediction Scheme of the FBMC/OQAM System

FBMC/OQAM transmission system has a better spectral efficiency than OFDM.However,its orthogonality condition is only considered in the real field.In the presence of fading channels,the real orthogonality of FBMC/OQAM might be lost,which calls for new equalization schemes.In this paper,an improved equalizer with real interference prediction(ERIP)scheme of FBMC/OQAM is proposed.We analyze the correlation between the real and the neighboring imaginary interference components taking Doppler shifts into account,and derive the improved ERIP scheme.The simulation results show that the proposed scheme can outperform the original ERIP and the one-tap equalization in time-varying multipath scenarios with an affordable complexity.

Chip-level space-time equalization receiver scheme for MIMO HSDPA systems

A chip-level space-time equalization receiver scheme is proposed for multiple-input multiple-output high-speed downlink packet access (MIMO HSDPA) systems to jointly combat the co-channel interference and the inter-code interference. A fractional sample equalizer is also derived to further improve the performance of the receiver. Performance analysis and the calculation of the output signal to interference ratio (SINR) at each receiver antenna are presented to help direct the design of equalization weight in a more optimal manner. System simulations demonstrate the significant performance gain over conventional Rake receiver and high potential of MIMO HSDPA for high-data-rate packet transmission.