Equalization Algorithm Research Articles

Multi-Class Liver Cancer Diseases Classification Using CT Images

Abstract Liver cancer is the fourth common cancer in the world and the third leading reason of cancer mortality. The conventional methods for detecting liver cancer are blood tests, biopsy and image tests. In this paper, we propose an automated computer-aided diagnosis technique for the classification of multi-class liver cancer i.e. primary, hepatocellular carcinoma, and secondary, metastases using computed tomography (CT) images. The proposed algorithm is a two-step process: enhancement of CT images using contrast limited adaptive histogram equalization algorithm and extraction of features for the detection and the classification of the different classes of the tumor. The overall achieved accuracy, sensitivity and specificity with the proposed method for the classification of multi-class tumors are 97%, 94.3% and 100% with experiment 1 and 84% all of them with experiment 2, respectively. By automatic feature selection scheme accuracy is deviated maximum by 10.5% from the overall and the ratio features accuracy decreases linearly by 5.5% with 20 to 5 selected features. The proposed methodology can help to assist radiologists in liver cancer diagnosis.

A Simple and Robust Equalization Algorithm for Variable Modulation Systems

This paper proposes a simple and robust variable modulation-decision-directed least mean square (VM-DDLMS) algorithm for reducing the complexity of conventional equalization algorithms and improving the stability of variable modulation (VM) systems. Compared to conventional adaptive equalization algorithms, known information was used as training sequences to reduce the bandwidth consumption caused by inserting training sequences; compared with conventional blind equalization algorithms, the parameters and decisions of the equalizer were determinate, which was conducive to a stable equalization performance. The simulation and implementation results show that the proposed algorithm has a better bit error rate (BER) performance than that of the constant modulus algorithm (CMA) and modified constant modulus algorithm (MCMA) while maintaining the same level of consumption of hardware resources. Compared to the conventional decision-directed least mean square (DDLMS) algorithm, the proposed algorithm only needs to make quadrature phase shift keying (QPSK) symbol decisions, which reduces the computational complexity. In parallel 11th-order equalization algorithms, the operating frequency of VM-DDLMS can reach up to 333.33 MHz.

A low-complexity orthogonal time frequency space modulation method for underwater acoustic communication

Compared with the orthogonal frequency division multiplexing (OFDM) modulation, the orthogonal time frequency space(OTFS) modulation has a lower peak-to-average power ratio. It can effectively resist the time selective fading caused by the Doppler effect and has significant performance advantages over doubly dispersive channels. However, the conventional OTFS linear minimum mean square error (LMMSE) method has a high complexity and is not easy to process in real time. In order to solve this problem, we propose a low-complexity equalization algorithm with infinite norm constraints based on the optimal coordinate reduction. The equalization algorithm not only obtains the optimal solution through a certain number of iterations and avoids direct matrix inversion but also equalizes infinite norm constraints to improve the symbol detection performance gains. At the same time, the OTFS delay-Doppler channel matrix we utilize is sparse and the two-norm squares of each column vector equally reduces the complexity of optimal coordinate descent. Finally, the simulation in the underwater acoustic communication scenario we designed verify the effectiveness of the proposed equalization algorithm. The simulation results show that the performance of the proposed equalization algorithm is close to that of the LMMSE method, while its low complexity is ensured.

Low spatial complexity adaptive artificial neural network post-equalization algorithms in MIMO visible light communication systems.

In this paper, we experimentally propose a feasible and low spatial complexity adaptive artificial neural network (AANN) post-equalization algorithm in MIMO visible light communication (VLC) systems. By introducing the power ratio and the MIMO least mean square (MIMO-LMS) post-equalization algorithm into the structure design process of the artificial neural network (ANN) post-equalization algorithm, we reduced the spatial complexity of the post-ANN equalization algorithm to less than 10%. At the same time, the bit error rate (BER) performance of AANNs did not decrease. Finally, we achieved a data rate of 2.1Gbps in the AANN equalized 16QAM superposition coding modulation (SCM) and carrier-less amplitude-phase (CAP) single-receiver MIMO (SR-MIMO) VLC system.

Application of image processing technology in cable detection of underwater robot

Manual inspection of underwater cables has many problems, such as heavy labor intensity, high operation difficulty and low work efficiency. It is more effective to use underwater camera to capture images of underwater cables than manual inspection. The addition of subsea detection and construction equipment raises bottom sediment in the water and causes the water to become turbid and cloudy, it also reduces the contrast of the underwater image and adds irrelevant background information. Therefore, a method of cable location in turbid waters based on image processing is proposed. The method firstly applies the dark channel prior dehazing algorithm. An adaptive histogram equalization algorithm is used to improve the light distribution of underwater image after defogging. Then, we use the local adaptive dynamic threshold segmentation method to achieve the rough segmentation, and the fine segmentation of cable image is carried out by selecting each connected domain region. Finally, the cable pixel coordinates are obtained by the method of center line extraction and frequency domain transformation. Experimental result shows that the proposed algorithm has good effects in terms of clarity.

Research on Hybrid Redundancy Voting Algorithm Based on Fuzzy Theory

The hardware redundancy technology is to reduce the basic reliability in exchange for the high reliability and safety of the system, but the failure rate of the system will increase with the increase of the hardware redundancy. Aiming at the disadvantages of hardware redundancy, the article takes the angle of attack(AOA) as the object, and constructs the analytical value of AOA based on the analytical relationship between the sensors of the flight control system; Introduce an equalization algorithm to eliminate the drift error between redundant signals, further proposes a hybrid redundancy voting algorithm based on hardware redundancy and analysis redundancy. The algorithm calculates the weight of the sensor signal based on the difference between the sensor signal and the analytical value, so that the voting value is closer to the true value, thereby improving the safety of the system. At the end of the article, the feasibility of the voting algorithm is verified by simulation; the analytical study of the AOA proves the effectiveness of the hybrid redundancy design scheme and the voting algorithm.

Modification in the Range of Information Potentials for Blind Algorithms in Impulsive Noise Environments

In this paper a modification approach to the range of influence for information potentials (IP)of blind decision feedback equalizer (DFE) algorithms is presented to cope better with impulsive noise and severe channel distortion. Analysis of the IP range of influence reveals that the range should be applied separately based on the roles under impulsive noise. A new cost function that can manage ranges of information potentials is proposed, and a related DFE algorithm is derived. Simulation results from the environment of a severe multipath channel obtained from shallow-water communication experiments and impulsive noise show that the proposed DFE algorithm yields more than 10 dB in MSE gain by employing the proposed modification toranges of information potentials.

Underwater image enhancement method based on color correction and three-interval histogram stretching

Since the attenuation and absorption of light in the water medium and the influence of the tiny suspended particles, underwater images are degraded and suffer from color cast and low contrast. An underwater image enhancement method based on color correction and three-interval histogram stretching is proposed to handle these issues. Firstly, a color correction method based on sub-interval linear transformation is designed to achieve color correction. Meanwhile, using the three adaptive sub-histograms equalization algorithm to enhance the contrast, and then the resulting images of the above-mentioned methods are combined by the multi-scale fusion. Final experiments demonstrate the proposed method can improve the image contrast, obtain the details of the bright and dark regions of the image, rich color, and finally output high-quality images.

An Efficient Turbo Decoding and Frequency Domain Turbo Equalization for LTE Based Narrowband Internet of Things (NB-IoT) Systems.

This paper addresses the main crucial aspects of physical (PHY) layer channel coding in uplink NB-IoT systems. In uplink NB-IoT systems, various channel coding algorithms are deployed due to the nature of the adopted Long-Term Evolution (LTE) channel coding which presents a great challenge at the expense of high decoding complexity, power consumption, error floor phenomena, while experiencing performance degradation for short block lengths. For this reason, such a design considerably increases the overall system complexity, which is difficult to implement. Therefore, the existing LTE turbo codes are not recommended in NB-IoT systems and, hence, new channel coding algorithms need to be employed for LPWA specifications. First, LTE-based turbo decoding and frequency-domain turbo equalization algorithms are proposed, modifying the simplified maximum a posteriori probability (MAP) decoder and minimum mean square error (MMSE) Turbo equalization algorithms were appended to different Narrowband Physical Uplink Shared Channel (NPUSCH) subcarriers for interference cancellation. These proposed methods aim to minimize the complexity of realizing the traditional MAP turbo decoder and MMSE estimators in the newly NB-IoT PHY layer features. We compare the system performance in terms of block error rate (BLER) and computational complexity.

Simplified SVM Equalization Algorithm Based on Single Hyperplane Training Enabled 50Gb/s PAM-4/8 With 10-G Optics in NG-PON System

In this paper, we firstly present a novel equalizer only with a single support vector machine (SVM) training hyperplane to compensate the distortion of multi-class PAM-M signals in low-cost limited-bandwidth next generation passive optical network (NG-PON) system. For this scheme, only single SVM-based hyperplane is trained, and then it is extracted and used to realize the multi-classification task of PAM-M by adjusting the corresponding offset, which would greatly reduce the complexity of equalization algorithm and enhance its application in future high-speed PON system. Moreover, the 50-Gb/s PAM-4/8 per wavelength transmission experiment with 10-G optics in 20km standard single mode fiber (SSMF) PON system are set up and utilized to manifest the feasibility and effectiveness of our scheme. The results show that, compared with the common SVM scheme with multi-classification hyperplane training, our method can reduce M-1 times computational complexity caused by hyperplane training for multiple-level pulse amplitude modulation (PAM-M) signals. Except this, the propose scheme can also maintain almost the same bit error ratio (BER) performance as the common SVM classifier with multi-hyperplane case.

Curvelet and fast marching method‐based technique for efficient artifact detection and removal in dermoscopic images

AbstractEffective detection and removal of the artifacts from dermoscopic images require the analysis of structures within lesions for accurate cancer detection. But the dermoscopic images acquired may be affected by image acquisition noise, variation in texture, color, and irrelevant structure such as hairs, air bubbles, and contour blurring around the lesion. Hence preprocessing is very essential for accurate diagnosis. The proposed algorithm includes the preprocessing, to remove acquisition noise using curvelet transform, illumination correction using contrast limited adaptive histogram equalization (CLAHE) algorithm for image enhancement, Frangi vesselness algorithm is utilized for detection and removal of hairs and vessels like structures present in enhanced image, fast marching inpainting method is applied for repairing the information of lesion from removed hair pixels. The performance of the proposed algorithm is analyzed based on commonly used parameters such as diagnostic accuracy (DA), sensitivity (SE), specificity (SP), and precision (P). The performance is evaluated on PH2 and International Symposium on Biomedical Imaging (ISBI) datasets than compared with existing methods. The proposed algorithm segments the skin lesion with the highest diagnostic accuracy of 81.49% and sensitivity of 93.88% for the PH2 dataset and 75.39% and 79.34% respectively for the ISBI dataset. Results are better than existing methods. The proposed algorithm with improved preprocessing for artifact detection and removal is highly accurate and able to retrieve the hair occluded information.

Artificial Intelligence Pulse Coupled Neural Network Algorithm in the Diagnosis and Treatment of Severe Sepsis Complicated with Acute Kidney Injury under Ultrasound Image.

The objective of this study was to explore the diagnosis of severe sepsis complicated with acute kidney injury (AKI) by ultrasonic image information based on the artificial intelligence pulse coupled neural network (PCNN) algorithm. In this study, an algorithm of ultrasonic image information enhancement based on the artificial intelligence PCNN was constructed and compared with the histogram equalization algorithm and linear transformation algorithm. After that, it was applied to the ultrasonic image diagnosis of 20 cases of severe sepsis combined with AKI in hospital. The condition of each patient was diagnosed by ultrasound image performance, change of renal resistance index (RRI), ultrasound score, and receiver operator characteristic curve (ROC) analysis. It was found that the histogram distribution of this algorithm was relatively uniform, and the information of each gray level was obviously retained and enhanced, which had the best effect in this algorithm; there was a marked individual difference in the values of RRI. Overall, the values of RRI showed a slight upward trend after admission to the intensive care unit (ICU). The RRI was taken as the dependent variable, time as the fixed-effect model, and patients as the random effect; the parameter value of time was between 0.012 and 0.015, p=0.000 < 0.05. Besides, there was no huge difference in the ultrasonic score among different time measurements (t = 1.348 and p=0.128 > 0.05). The area under the ROC curve of the RRI for the diagnosis of AKI at the 2nd day, 4th day, and 6th day was 0.758, 0.841, and 0.856, respectively, which was all greater than 0.5 (p < 0.05). In conclusion, the proposed algorithm in this study could significantly enhance the amount of information in ultrasound images. In addition, the change of RRI values measured by ultrasound images based on the artificial intelligence PCNN was associated with AKI.

Reconfigurable Slip Vectoring Control in Four In-Wheel Drive Electric Vehicles

Controllability, maneuverability, fault-tolerance/isolation and safety are significantly enhanced in electric vehicles (EV) equipped with the redundant actuator configuration of four-in-wheel electric motors (4IWM). A highly reconfigurable architecture is proposed and illustrated for the adaptive, nonmodel-based control of 4IWM-EVs. Given the longitudinal force, yaw-moment requests and the reconfiguration matrix, each IWM is given a slip reference according to a Slip Vectoring (SV) allocation strategy, which minimizes the overall slip vector norm. The distributed electric propulsion and the slip vector reference allow for a decentralized online estimation of the four-wheel torque-loads, which are uncertain depending on loading and road conditions. This allows for the allocation of four different torques depending on individual wheel conditions and to determine in which region (linear/nonsaturated or nonlinear/saturated) of the torque/slip characteristics each wheel is operating. Consequently, the 4IWMs can be equalized or reconfigured, including actuator fault-isolation as a special case, so that they are enforced to operate within the linear tire region. The initial driving-mode selection can be automatically adjusted and restored among eighteen configurations to meet the safety requirements of linear torque/slip behavior. Three CarSim realistic simulations illustrate the equalization algorithm, the quick fault-isolation capabilities and the importance of a continuous differential action in a critical double-lane-change maneuver.

State-of-charge dynamic balancing strategy for distributed energy storage system in DC shipboard microgrid

In this paper, a State-of-Charge (SoC) dynamic balancing control strategy considering system communication failure and energy storage capacity difference is proposed to reach the SoC balancing and proper current sharing for distributed energy storage units (DESUs) in DC shipboard microgrid. Firstly, the distributed equalization algorithm is applied in the DESU’s controller when the system communication is normal, each DESU exchanges their SoC information through low bandwidth communication (LBC) and dynamically adjusts the droop coefficient according to the change of their SoC, so as to realize the SoC dynamic balance of the energy storage system. Secondly, the DESU’s controller is switched to the decentralized equalization algorithm with communication failure, each DESU can reach SoC balancing and load current reasonable distribution only according to the local information. After that, a DC bus voltage compensation method is introduced to solve the problem of bus voltage deviation caused by the proposed SoC equalization. Finally, the simulation model comprised of three DESUs is implemented and the results show that the proposed control strategy can make each DESU reach SoC equilibrium state quickly and effectively reduce the bus voltage deviation under the normal and failure communication conditions.

Receiver Design for OTFS with a Fractionally Spaced Sampling Approach

The recent emergence of orthogonal time frequency space (OTFS) modulation as a novel PHY-layer mechanism is more suitable in high-mobility wireless communication scenarios than traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have analyzed OTFS performance using theoretical and ideal baseband pulseshapes, a challenging and open problem is the development of effective receivers for practical OTFS systems that must rely on non-ideal pulseshapes for transmission. This work focuses on the design of practical receivers for OTFS. We consider a fractionally spaced sampling (FSS) receiver in which the sampling rate is an integer multiple of the symbol rate. For rectangular pulses used in OTFS transmission, we derive a general channel input-output relationship of OTFS in delay-Doppler domain without the common reliance on impractical assumptions such as ideal bi-orthogonal pulses and on-the-grid delay/Doppler shifts. We propose two equalization algorithms: iterative combining message passing (ICMP) and turbo message passing (TMP) for symbol detection by exploiting delay-Doppler channel sparsity and the channel diversity gain via FSS. We analyze the convergence performance of TMP receiver and propose simplified message passing (MP) receivers to further reduce complexity. Our FSS receivers demonstrate stronger performance than traditional receivers and robustness to the imperfect channel state information knowledge.

Fast tracking of polarization impairments using DSP algorithms in fiber for a coherent optical communication system

AbstractIn a 100G coherent optical fiber communication system, the equalization algorithm of polarization effects in fibers is generally integrated in digital signal processing (DSP) using a multiple input/multiple output (MIMO) equalizer to overcome the impairment due to the polarization effects in fiber. However, it was found in some extreme scenarios, such as the lightning strikes near the fiber cables, the Kerr effect and Faraday effect would induce very fast time‐varying state‐of‐polarization (SOP) and transient variation of principal‐state‐of‐polarization (PSP) of polarization mode dispersion (PMD) which would result in the coherent receivers unlocking. Faced with the difficulty, we propose a 3‐parameter RSOP model and design a window‐split structured Kalman filter equalization scheme instead of conventional MIMO equalizer to cope with this extreme scenario of large PMD combined with ultra‐fast rotation of SOP in presence of residual chromatic dispersion (RCD). The proposed equalization method has an excellent performance and can cope with RCD in the range of ±820 ps/nm under RSOP from 100 krad/s to 3 Mrad/s and DGD = 200 ps.

Experimental phantom evaluation to identify robust positron emission tomography (PET) radiomic features

BackgroundRadiomics analysis usually involves, especially in multicenter and large hospital studies, different imaging protocols for acquisition, reconstruction, and processing of data. Differences in protocols can lead to differences in the quantification of the biomarker distribution, leading to radiomic feature variability. The aim of our study was to identify those radiomic features robust to the different degrading factors in positron emission tomography (PET) studies. We proposed the use of the standardized measurements of the European Association Research Ltd. (EARL) accreditation to retrospectively identify the radiomic features having low variability to the different systems and reconstruction protocols. In addition, we presented a reproducible procedure to identify PET radiomic features robust to PET/CT imaging metal artifacts. In 27 heterogeneous homemade phantoms for which ground truth was accurately defined by CT segmentation, we evaluated the segmentation accuracy and radiomic feature reliability given by the contrast-oriented algorithm (COA) and the 40% threshold PET segmentation. In the comparison of two data sets, robustness was defined by Wilcoxon rank tests, bias was quantified by Bland–Altman (BA) plot analysis, and strong correlations were identified by Spearman correlation test (r > 0.8 and p satisfied multiple test Bonferroni correction).ResultsForty-eight radiomic features were robust to system, 22 to resolution, 102 to metal artifacts, and 42 to different PET segmentation tools. Overall, only 4 radiomic features were simultaneously robust to all degrading factors. Although both segmentation approaches significantly underestimated the volume with respect to the ground truth, with relative deviations of −62 ± 36% for COA and −50 ± 44% for 40%, radiomic features derived from the ground truth were strongly correlated and/or robust to 98 radiomic features derived from COA and to 102 from 40%.ConclusionIn multicenter studies, we recommend the analysis of EARL accreditation measurements in order to retrospectively identify the robust PET radiomic features. Furthermore, 4 radiomic features (area under the curve of the cumulative SUV volume histogram, skewness, kurtosis, and gray-level variance derived from GLRLM after application of an equal probability quantization algorithm on the voxels within lesion) were robust to all degrading factors. In addition, the feasibility of 40% and COA segmentations for their use in radiomics analysis has been demonstrated.

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