Equalization Algorithm Research Articles

Concurrent Modified Constant Modulus Algorithm and Decision Directed Scheme With Barzilai-Borwein Method.

At present, in robot technology, remote control of robot is realized by wireless communication technology, and data anti-interference in wireless channel becomes a very important part. Any wireless communication system has an inherent multi-path propagation problem, which leads to the expansion of generated symbols on a time scale, resulting in symbol overlap and Inter-symbol Interference (ISI). ISI in the signal must be removed and the signal restores to its original state at the time of transmission or becomes as close to it as possible. Blind equalization is a popular equalization method for recovering transmitted symbols of superimposed noise without any pilot signal. In this work, we propose a concurrent modified constant modulus algorithm (MCMA) and the decision-directed scheme (DDS) with the Barzilai-Borwein (BB) method for the purpose of blind equalization of wireless communications systems (WCS). The BB method, which is two-step gradient method, has been widely employed to solve multidimensional unconstrained optimization problems. Considering the similarity of equalization process and optimization process, the proposed algorithm combines existing blind equalization algorithm and Barzilai-Borwein method, and concurrently operates a MCMA equalizer and a DD equalizer. After that, it modifies the DD equalizer's step size (SS) by the BB method. Theoretical investigation was involved and it demonstrated rapid convergence and improved equalization performance of the proposed algorithm compared with the original one. Additionally, the simulation results were consistent with the proposed technique.

Retinal vessel segmentation based on an improved deep forest

AbstractTo improve the diagnosis efficiency of diseases related to retinal blood vessels, this article proposes a novel retinal blood vessels segmentation algorithm by combining the advantages of convolutional neural network (CNN) and cascade forest (CF). Firstly, we use the contrast limited adaptive histogram equalization (CLAHE) algorithm to enhance the color fundus retinal image. Secondly, we randomly select some image patches to train the CNN feature extraction module and the CF classification module. Finally, the image patches from the test image are sent to the trained model to complete the retinal blood vessel segmentation. The algorithm is verified on the DRIVE, STARE, and CHASE_DB1 datasets. The sensitivity reaches 0.8206, 0.8762, and 0.7705, the accuracy reaches 0.9531, 0.9611, and 0.9559, and the area under the ROC curve reaches 0.9770, 0.9899, and 0.9767, respectively. The comprehensive performance of our method is better than that of some state‐of‐the‐art methods.

A Performance Comparison of mSE-MMA and mDSE-MMA Adaptive Equalization Algorithm in 16-QAM Signal Transmission

A Performance Comparison of mSE-MMA and mDSE-MMA Adaptive Equalization Algorithm in 16-QAM Signal Transmission

A Performance Comparison of DSE-MMA and QE-MMA Adaptive Equalization Algorithm in Nonconstant Modulus Signal

A Performance Comparison of DSE-MMA and QE-MMA Adaptive Equalization Algorithm in Nonconstant Modulus Signal

Design and implementation of HF signal receiving and processing module based on FPGA

This article designs and implements a receiving processing module based on the Xilinx ZCU102 evaluation board for the signal waveform defined in Appendix C of MIL-STD-188-110C. The focus is on the digital down-conversion, synchronization capture, and adaptive equalization algorithm for this signal processing module. Compare and design two adaptive algorithms, LMS (Least Mean Square) and DLMS (Delayed Least Mean Square). LMS algorithm has better convergence effect than DLMS algorithm, and the calculation efficiency is lower than the DLMS algorithm based on the structure of the systolic array. The signal waveform is tested under CCIR (International Radio Consultative Committee) HF (High Frequency) channel. The experimental results show that the receiving processing module has good feasibility for signals under medium HF channels.

Scoping review on automatic color equalization algorithm

Digital image processing is at the base of everyday applications aiding humans in several fields, such as underwater monitoring, analysis of cultural heritage drawings, and medical imaging for computer-aided diagnosis. The starting point of all such application regards the image enhancement step. A desirable image enhancement step should simultaneously standardize the illumination in the image set, possibly removing bad or not-uniform illumination effects, and reveal all hidden details. In 2002, a successful perceptual image enhancement model, the automatic color equalization (ACE) algorithm, was proposed, which mimics the color and contrast adjustment of the human visual system (HVS). Given its widespread usage, its correlation with the HVS, and since it is easily implementable, we propose a scoping review to identify and classify the available evidence on ACE, starting from the papers citing the two funding papers on the algorithm. The aim of this work is the identification of what extent and in which ways ACE may have influenced the research in the color imaging field. Thanks to an accurate process of papers tagging, classification, and validation, we provide an overview of the main application domains in which ACE was successfully used and of the different ways in which this algorithm was implemented, modified, used, or compared.

A Novel Framework For Automatic Detection Of Plant Leaf Disease Using 2d-Deep Convolutional Neural Network Architecture

The economy of a country largely depends on its agricultural productivity. Hence, identification of plant leaf diseases plays a vital role in the field of agriculture. In this research, we propose a novel framework for the automatic detection of plant leaf disease based on Deep Convolutional Neural Network (DCNN) architecture. The proposed framework involves steps like image restoration, enhancement, clustering, thresholding and classification. The image restoration is performed using a novel filter called 2D Adaptive Hybrid Bilateral Anisotropic Diffusion Filter (2D–AHBAD). This filter is used for the elimination of various noise such as salt and pepper noise, Gaussian noise, random noise, thermal noise, speckle noise etc. Image enhancement is done using Edge Preservation–Modified Histogram Contrast Brightness Equalization (EP-MHCBE) algorithm. The enhanced images are then segmented using clustering and thresholding algorithms. A new technique called Hybrid Fast Fuzzy C Means Improved Expectation Maximization (HFFCM IEM) Clustering technique was used for the computation of clusters. The generated clusters are then segmented based on the Iterative Mean Shift Thresholding (IMST) algorithm. The segmented images are classified using DCNN architecture. A total of 2000 images are used in this framework out of which 1600 images were utilized for training the DCNN architecture. The remaining 400 images were used for testing. The leaf images are categorized into four categories namely, normal, mild, moderate and severe. It was inferred that the proposed AHBAD image restoration algorithm achieved a high PSNR of 54 and very low MSE of 0.0039. Similarly, the proposed DCNN classification system attained a high classification accuracy of 92.25%.

Improved Approach for the Maximum Entropy Deconvolution Problem.

The probability density function (pdf) valid for the Gaussian case is often applied for describing the convolutional noise pdf in the blind adaptive deconvolution problem, although it is known that it can be applied only at the latter stages of the deconvolution process, where the convolutional noise pdf tends to be approximately Gaussian. Recently, the deconvolutional noise pdf was approximated with the Edgeworth Expansion and with the Maximum Entropy density function for the 16 Quadrature Amplitude Modulation (QAM) input but no equalization performance improvement was seen for the hard channel case with the equalization algorithm based on the Maximum Entropy density function approach for the convolutional noise pdf compared with the original Maximum Entropy algorithm, while for the Edgeworth Expansion approximation technique, additional predefined parameters were needed in the algorithm. In this paper, the Generalized Gaussian density (GGD) function and the Edgeworth Expansion are applied for approximating the convolutional noise pdf for the 16 QAM input case, with no need for additional predefined parameters in the obtained equalization method. Simulation results indicate that improved equalization performance is obtained from the convergence time point of view of approximately 15,000 symbols for the hard channel case with our new proposed equalization method based on the new model for the convolutional noise pdf compared to the original Maximum Entropy algorithm. By convergence time, we mean the number of symbols required to reach a residual inter-symbol-interference (ISI) for which reliable decisions can be made on the equalized output sequence.

Prediction of COVID-19 using hyperparameter optimized convolutional neural network

All over the world, there are heavy cases of COVID-19 patients those exhibiting the symptoms. In a very short period of time, this pandemic virus has become drastic across the country. A fast detection of corona spread is necessary for both the infected and uninfected person for the further spreading. The preexisting techniques used methods like Linear Regression, Support Vector Machine (SVM) and Naive Bayes are not producing better results. Our aim is to bring out better outcomes and to produce good accuracy. Instead of machine learning we opt for deep learning approaches in our work. Image preprocessing will be done by Histogram Equalization algorithm and further the image classification is done by Convolution Neural Network (CNN) architectures such as VGG-16 and ResNet-50 by using 350 images of X-ray datasets. From the comparison, VGG-16 produce better train and test accuracy of 92% and 98.4% .Hence the accuracy of VGG-16 was further tuned using Hyper Parameter Optimization using Tensor Board which produces better outcomes. © 2021 Karadeniz Technical University. All rights reserved.

Registration and imaging polarimetry of the Fe 6374 Å red coronal line during the 2017 total solar eclipse

ABSTRACT Strict registration is critical for imaging polarimetry; a highly accurate registration approach to the coronal image and imaging polarimetry of the Fe 6374 Å red coronal line have been researched in this paper. In order to improve the registration accuracy, this paper proposes the idea of image enhancement based on blind deconvolution combined with noise-adaptive fuzzy equalization algorithms. After image enhancement, the cross-correlation registration algorithm achieves better results. To diagnose a low-temperature corona, the polarization brightness of the red coronal line data and the Mauna Loa Solar Observatory (MLSO)/KCor data are compared to study the structural features and polarization information of the inner corona. We found that the polarization brightness radiation of the red coronal line has overall features similar to that of the MLSO/KCor data, but it has more detailed features, such as the cavity, polar plume, and interplume characteristics. Active regions, polar plumes, and interplumes show similar performance in red coronal line intensity, polarization brightness, degree of linear polarization, K-corona, and electron density along with the radial height. An interesting finding is that the polarization brightness of the red coronal line changes drastically along with the radial height across the cavity above the solar west limb, while neither the K-corona nor the electron density changes obviously. This seems to be a piece of possible evidence indicating that the red coronal line is not unpolarized in the cavity region.

Tennis‐resource allocation mechanism in software‐defined data center network

With the rapid development of cloud computing, most network resources are usually stored at Data Center Network (DCN) for providing the remote storage services. Under such paradigm, there arises some issues, such as traffic scheduling, energy saving and resource allocation. At present, the resource allocation in DCN has attracted the extensive concern. In order to effectively manage and allocate these resources in DCN, Software‐Defined Networking can be employed to achieve such purpose, by leveraging its natural abilities for the global control and centralized management. Another point, the tennis is a very popular sport to enhance the physical fitness. However, the tennis‐resources are considerably limited as the building of venue is quite expensive. Therefore, it becomes very significant to investigate the tennis‐resource allocation in Software‐Defined DCN (SDCN). Consider that there are many tenants using the tennis‐resources in SDCN, this paper studies the multi‐tenant tennis‐resource allocation scenario. Meanwhile, the Global Superiority Equalization algorithm is devised. At first, the max‐min fairness is used to determine the allocated number of tennis‐resources for each round. Then, the allocation state is adjusted according to the current situation until the final allocation decision is determined. The experiment results show that the proposed tennis‐resource allocation mechanism can reduce the resource allocation cost and improve the resource utilization. Accordingly, SDCN can handle more tennis‐resource requests and increase the benefit of venue provider.

Automated detection of brain tumor disease using empirical wavelet transform based LBP variants and ant-lion optimization

Early detection of brain tumor is a challenging task that assists medical practitioners in disease diagnosis. This article presents a computer-assisted brain tumor detection scheme using empirical wavelet transform based local binary pattern variant features and ant-lion optimization. Firstly, the input magnetic resonance (MR) image is enhanced using brightness preserving dynamic fuzzy histogram equalization (DFHE) algorithm. Then, significant modes from the input brain MR image are extracted using empirical wavelet transform (EWT), and five different local binary variants are extracted from each EWT mode. Further, the ant-lion feature selection algorithm is employed to select discriminating and relevant features and remove redundant descriptors that lower feature dimensionality. Finally, a support vector machine (SVM) classifier is used to detect the input MR image as healthy or tumorous. The proposed algorithm performance is validated on two widely and publicly available databases using different statistical measures including sensitivity, specificity, precision, F1-measure, and accuracy. A substantial set of experiments are carried out to evaluate the effect of individual descriptors, descriptor fusion, number of EWT decomposition levels, and DFHE enhancement on the classification accuracy. The simulation results obtained confirm that the presented approach performance is better as compared to other existing methods in terms of detection rate.

Research on the Infrared Thermographic Detection of Concrete under Solar Heating

Infrared thermography for detecting defects in concrete structures is closely related to the heat source and the optimized method of the thermal image. Due to the limitation of the irradiation area of the heat source, it is inefficient to detect the defects in large concrete structures. In this paper, sunlight was employed as a heat source to detect the defects with different sizes and depths in concrete, and the measured infrared images were processed and optimized by an enhancement algorithm. The experimental results showed that the defects in concrete could be rapidly identified under sunlight. The effect of environment, view angle, and boundary can be eliminated by image preprocessing, and the histogram equalization algorithm can increase the detection depth of the defects. The research results can also provide a reference for the infrared detection technology of concrete under the weak heat source.

Channel Estimation and Equalization for Alamouti SF-Coded OFDM-UWA Communications

In this paper, a new channel estimation and equalization algorithm for underwater acoustic (UWA) communications is presented. The proposed algorithm is developed to meet the requirements of underwater time-varying sparse channels that undergo Rayleigh fading. In addition, the algorithm takes into consideration a path-based channel model which describes each received path with significant power by an attenuation factor, a Doppler scale, and a delay. Transmit diversity enabled by Alamouti space-frequency block coding coupled with orthogonal frequency division multiplexing is employed in the form of two transmitters and multiple receivers. The proposed, non-data-aided, expectation-maximization (EM)-based maximum a posteriori probability sparse channel estimation first estimates the channel transfer functions from each transmit antenna to the receiver. Then, the estimation performance is greatly improved by taking into account the sparseness of the UWA channel and refining the estimation based on the sparse solution that best matches the frequency-domain channel estimates obtained during the first phase of the estimation process. Sparse channel path delays and Doppler scaling factors are estimated by a novel technique called delay focusing . After that, slow time-varying, complex-valued channel path gains are estimated using a basis expansion model based on the discrete Legendre polynomial expansion. Computer simulation results show that the resulting channel estimation algorithm can achieve excellent mean-square error and symbol error rate for both generated data and semi-experimental data taken at Sapanca Lake in Turkey and is capable of handling some mismatch due to different fading models.

A Modified Viterbi Equalization Algorithm for Mitigating Timing Errors in Optical Turbulence Channels

In high-speed optical wireless communication (OWC) systems, timing errors can blur the edges of the received symbols, defined by the deviation of a signal’s timing event from its intended occurrence. The timing errors introduce the inter-symbol interference (ISI) to the neighboring symbols. In order to mitigate the degradation, a maximum likelihood (ML) based Timing Error Viterbi Equalization (TEVE) algorithm is proposed, in which the probability density functions (PDF) of the channel conditions and the timing errors are priori information. The mathematical form of each branch metric (BM) is deduced by those statistics in different situations of neighboring signs of timing errors. The summations of BMs form the cumulative metrics (CM). By adopting the add-compare-select iterations, we are able to detect symbols with the largest CMs. The theoretical closed-form average bit error rates (BER) are also deduced and compared in the cases whether the TEVE algorithm is utilized. Experimental results indicate that our proposed method can reduce timing errors’ impairment significantly with an acceptable complexity.

Standard Deviation and Mode Based Bi-Histogram Equalization Algorithm for Image Enhancement

This paper presents an enhancement method for image enhancement in poor lighting condition. Standard deviation and mode are computed on the image histogram. Histogram clipping process is done to limit the rate of over enhancement by mode-based threshold value. The clipped histogram is divided into two segments founded on a threshold value computed by the standard deviation on the image histogram. Two sub-images of the individual histogram are equalized and combined to form the output image that preserves the entropy and produced better contrast enhancement image. The results in terms of Universal Image Quality Index (UIQI), and Entropy, for images, have been simulated on MATLAB. The outcomes produced by the proposed method have been ompared with the results gotten from existing image enhancement methods. It has been observed from the outcomes that proposed method outperform previous methods.

A modified partitioned frequency domain equalization algorithm for 3-LP mode multiplexed systems with few mode fibers

In recent years, mode division multiplexing (MDM) which use the few mode fibers(FMF) as transmission link has attracted widespread attention and many MDM systems were demonstrated. However, some drawbacks arising with MDM cannot be ignored, such as mode coupling, differential mode group delay(DMGD) and etc. Fortunately, many of them can be conveniently mitigated by digital equalization at a low cost. In order to solve the problems of slow convergence speed and poor equalization effect of digital equalizer in the polarization division multiplexed (PDM)-MDM system (PDM-MDM), a modified partitioned frequency domain equalization algorithm is proposed in this paper. In the proposed algorithm, the filter length is divided into several partitions and consequently each partitions’ average power model was rebuilt with its individual length. The performance of 3-LP MDM system is investigated in terms of convergence speed, constellation diagram and symbol error rate(SER). From the simulation results, the modified partitioned frequency domain algorithm can get better SER performance by about an order of magnitude and improve the convergence rate by about 10 % compared to traditional algorithm.

Modulation format identification under stringent bandwidth limitation based on an artificial neural network

A new method based on an artificial neural network (ANN) and asynchronous amplitude histograms (AAHs) is designed for modulation formats identification (MFI) in a bandwidth-limited intensity modulation direct detection (IM/DD) system. The proposed method recognizes modulation format features of AAHs and the signal is extracted before equalization algorithms such as FFE and CMA for limited bandwidth and chromatic dispersion (CD). Three IM/DD modulation formats, non-return-to-zero (NRZ), 4-level pulse amplitude modulation (PAM4), and 8-level pulse amplitude modulation (PAM8) are recognized. The numerical simulation results manifest that MFI accuracy can reach up to 95% with a 30-40% bandwidth limitation. Furthermore, we evaluate the effectiveness of the method in the experiment, and 100% identification accuracy can be achieved even when the system is under 36.76% bandwidth limitation. The proposed method is implemented in DSP without extra hardware and is promising for optical performance monitoring in next-generation elastic optical networks (EONs).

Research on Intelligent Image Recognition Technology Based on Equalization Algorithm

In the image edge distortion correction, the straight-line projection-derived edge fit is poor, resulting in large correction errors. Therefore, an image edge distortion correction algorithm based on the equalization algorithm is proposed in the paper, where the adaptive threshold wavelet denoising algorithm is adopted to implement adaptive transformation of various scale parameters to complete noise removal. According to the results of image denoising, Fisher vector coding is used to optimize the results of the equalization algorithm to extract the edge distortion of the image. Besides, based on the edge distortion shape extraction results, the correction objective optimization function is obtained to analyze the edge fracture situation, realizing the linear projection derivative edge fitting. Meanwhile, by determining the image edge error evaluation function, the method of image edge distortion correction is judged to achieve the purpose of image edge distortion correction. Therefore, taking noisy barrel distortion and pincushion distortion images as the research object, the experimental analysis is carried out. The results show that the algorithm proposed in the paper can effectively correct the barrel distortion and pincushion distortion of the image, achieving high-precision and high-efficiency image edge distortion correction.

Time Reverse Equalization Algorithm for 16 QAM Coherent Optical Communication Systems

The high speed rotation of state of polarization (RSOP) is a key issue for 16 quadrature amplitude modulation (QAM) coherent optical polarization division multiplexing (PDM) communication systems in strong lightning weather. To reduce the bit error rate (BER) caused by the high speed of RSOP, the historical information is first proposed to correct the demodulation process in coherent optical communication systems. By adding the time reverse error term into the cost function, a new equalization algorithm is proposed for coherent optical communication systems. Compared with the conventional Multi-modulus Algorithm (MMA), simulation results show that the average BER and steady-state error (SSE) of the new algorithm is reduced by 33.47% and 4.08%, respectively. Moreover, the average tolerance of RSOP of the new algorithm is increased by 28.57% compared with the MMA in 16 QAM coherent optical PDM communication systems.