Equalization Algorithm Research Articles

Photoacoustic graphic equalization and application in characterization of red blood cell aggregates

A photoacoustic (PA) graphic equalization (PAGE) algorithm was developed to characterize the relative size of optical absorbing aggregates. This technique divides the PA signal into frequency bands related to different-sized optical absorbers. Simulations of a material containing optical absorbing microparticles of varying size were used to assess PAGE performance. Experiments were performed on phantom materials containing microspheres of varying size and concentration. Additional experiments were performed using tubes with fresh clotting blood. PA data was obtained using a Vevo LAZR-X system (FUJIFILM VisualSonics Inc). PAGE imaging of phantoms with varying-sized optical absorbers found a 1.5-fold difference in mean image intensity (p < 0.001). Conversely, PA images from these same materials exhibited no intensity changes (p = 0.68). PAGE imaging results from clotting blood exhibited differences for clot sizes in the range 0.30–0.64 mm (p < 0.001). In summary, PAGE imaging can distinguish optical absorbing aggregates of varying size.

Digital Signal Processing for High‐Speed THz Communications

The use of advanced digital signal processing (DSP) technology in the high-speed terahertz (THz) communication can effectively compensate the linear and nonlinear effects of the system and further improve the transmission performance of the system. This paper introduces the principle and application of advanced DSP algorithms such as probability shaping (PS) technology, Volterra series nonlinear compensation algorithm, Kramers-Kronig receiver, look-up table (LUT) pre-distortion compensation technology, pre-equalization and decision-directed least-mean-square equalization algorithm. Combined with DSP algorithms such as PS and LUT pre-distortion, using photon-assisted technology to successfully realize the wireless transmission of vector THz signals higher than 1 Tbit/s on the sub-THz band (D-band) 4 × 4 multiple input multiple output system.

Low Complexity Neural Network Equalization Based on Multi-Symbol Output Technique for 200+ Gbps IM/DD Short Reach Optical System

Nowadays, Neural network (NN) has been proved to be an effective solution for nonlinear equalization in short reach optical systems. However, recent research has mainly focused on implementing more powerful NNs for equalization, while ignoring their adaptability to equalization tasks. In this paper, we propose Multi-Symbol Output (MSO)-Neural Networks (NN) for nonlinear equalization in high-speed short reach optical interconnects. The results show that the proposed MSO design works well on Deep Neural Networks (DNN), Long Short-Term Memory neural networks (LSTM) and Gate Recurrent Unit (GRU), which are the recent NN-based equalization structures. By increasing output symbols of the NNs, the number of slide windows in equalization can be sharply reduced, and so the complexity is reduced. By the same time, more information is brought to the MSO-NNs in back-propagations, therefore performance gain achieved. A 212-Gb/s 1-km Pulse Amplitude Modulation (PAM)-4 optical link is experimentally demonstrated as the target system, and the proposed MSO-NN equalizers are used to compare with traditional equalization algorithms including Volterra Nonlinear Equalizer (VNE) and single-symbol output NNs. Experimental results show that MSO design could help reduce the complexity of NN required for nonlinear equalization in the target system by around 2/3, and the proposed MSO-LSTM performs much better than VNE and 1 dB better than SSO-LSTM at the same time. Based on the proposed MSO-LSTM, transmission with BER under HD-FEC over 1 km NZDSF is achieved with a ROP at -2 dBm. Our work is well expandable and the proposed MSO design can be extended to other NN-based equalizers, which can help reduce complexity and learn more info from the training data and gain performance. The proposed MSO-NNs further enhance the performance and reduces the complexity of NN equalizers, provides assurance for future real-time high-speed short-range optical systems, and brings new ideas to NN-based equalizer design.

Chaotic physical layer encryption scheme based on phase ambiguity for a DMT system.

Chaotic encryption is a promising scheme for physical layer security. By solving the multi-dimensional chaotic equations and transforming the obtained results, both bit-level and symbol-level encryption can be realized. One of the mainstream symbol-level encryption solutions is the constellation shifting (CS) scheme, which treats the chaotic sequence as artificial noise and adds it to the QAM signal sequence to achieve encryption. However, this scheme has several technical flaws in practical application, in terms of computational complexity and coexistence with blind equalization algorithm and the probabilistic shaping (PS) technique. In this paper, we propose a novel symbol-level encryption scheme based on phase ambiguity (PA), which converts the two sequences originally used to generate artificial noise into a set of phase rotation keys and complex conjugate keys, so that the encrypted symbols are still on the ideal constellation point coordinates. Simulation verification is carried out in a discrete multi-tone (DMT) system with 64QAM modulation. Results show that the proposed scheme can fully retain the shaping gain brought by the PS technique and avoid the error convergence of the blind equalizer. Moreover, the time required to solve the chaotic equations is only 38% of the CS scheme. Experimental verification is carried out, and the obtained results once again prove the superiority of the proposed encryption algorithm, which is a practical alternative for future physical layer secure optical communications.

Enhancement of Unmanned Aerial Vehicle Image with Shadow Removal Based on Optimized Retinex Algorithm

Images taken by UAVs have shadows due to terrain factors. The image pixel brightness of the shadow areas is compressed, and the information is deficient, which impacts the recognition of image information and thus limits the subsequent image application. Therefore, the shadow removal of the image is crucial. Image enhancement algorithm is capable of improving the whole and partial contrasts of images, highlighting detail information, and removing shadows. Three classical optical image enhancement algorithms are analyzed. The analysis results show that image would be enhanced excessively after the histogram equalization algorithm to the shadow image enhancement. The pixel brightness are compressed by the Mask homogenization algorithm enhancement and uneven brightness in some areas after the enhancement of the traditional Retinex algorithm. Using the Retinex enhancement algorithm, this study proposes a combination algorithm to remove the shadow of the UAV remote sensing image. The proposed algorithm integrates the Retinex algorithm with the two-dimensional (2D) gamma function to remove the brightness colour of the UAV image, so it is capable of removing the shadow area of the UAV image and correcting the uneven darkness attributed to the image enhancement. The acquired UAV image is used to perform the experiment, and it is integrated with the LOG algorithm to extract the enhanced image features. As indicated by the experimental results, the integrated algorithm is proved with better performance to remove the UAV image shadow. The shadow areas of the features cannot be extracted in the original image, but after using the new algorithm to remove the shadow, the ground edge features can be clearly extracted.

Reducing the dynamic range of infrared images based on block-priority equalization and compression of histograms

Objectives. The problem of reducing the dynamic range of infrared images for their reproduction on display devices with a narrow dynamic range is considered. The method of local image histogram equalization based on the integral distribution function of brightness is investigated. To transform the brightness of a pixel, this method uses an approximation of the local alignment values of the nearest blocks of pixels of original image. This in-creases the local contrast of the image, but leads to high computational complexity, which is increasing while block size decreases. The aim of the work is to reduce the computational complexity of adaptive equalization and compression of infrared image histograms while reducing their dynamic range.Methods. Image processing methods are used.Results. To reduce the computational complexity of transforming the dynamic range of infrared images, a block-priority modification of the adaptive histogram equalization method is proposed. The modification is based on the division of the set of image blocks into two subsets of high-priority and low-priority blocks depend-ing on their brightness statistical properties. When interpolating pixel values, high-priority blocks use local alignment values, and low-priority blocks use global alignment values. As a result, the total number of alignment vectors is reduced in proportion to the ratio of subsets sizes and the computational complexity of the dynamic range transformation is reduced.Conclusion. When changing the ratio of the number of high-priority blocks of infrared image pixels to the number of all blocks in the range of 0.25–0.75, the proposed algorithm is more efficient than global and adaptive histogram equalization algorithms.

Modified S2 schemes for estimating differential mode group delay in polarization-maintaining few-mode fiber

In the mode-division-multiplexed (MDM) system, the few-mode fiber (FMF) replaces the single-mode fiber (SMF) as the transmission medium, to increase the per-fiber transmission capacity. The polarization-maintaining few-mode fiber (PM-FMF) can greatly enhance the optical communication system, since it can both preserve the polarization state and allow the transmission of multiple modes. To obtain the differential mode group delay (DMGD) value of the PM-FMF, which determines the computational complexity of the equalization algorithm at the receiver, the characterization of the fiber is essential. The conventional S2 method, which can estimate the DMGD of the FMF, cannot be directly applied to the PM-FMF, because it is impossible to determine which of the LP01x and the LP01y mode is the dominant mode. In this work, two new approaches are developed, for the first time to our knowledge, to measure the DMGD in the PM-FMF. The former method is to measure the DMGD in both polarization directions, and LP01x and LP01y are selected as dominant modes, respectively. The latter approach is to realize a simultaneous DMGD measurement of all LP modes, and only LP01y is selected as the dominant mode. It is demonstrated in the experiments that a good agreement has been achieved between the measurement results from both schemes.

Applying Gamma and Histogram Equalization Algorithms for Improving System-performance of Face Recognition-based CNN

In the last few years, many applications have viewed great development, such as smart city applications, social media, smartphones, security systems, etc. In most of these applications, facial recognition played a major role. The work of these applications begins by locating the face within the image and then recognizing the face. The circumstances surrounding the person at the moment of taking the picture greatly affect the accuracy of these applications, especially the inappropriate lighting. Therefore, the stage of preparing the images is very important in the work. To solve this problem, we proposed a system that combines the use of gamma and Histogram Equalization algorithm (HE) to improve the images before starting to detect the face using the Viola-Jones. Then extract the facial features and identify the person using convolutional neural networks. The proposed system achieved a very small error rate and an accuracy during training that reached 100%. Index Terms— Viola-Jones, HE, AHE, Gamma, CNN.

Delay-Doppler domain decision feedback turbo equalization for OTFS modulation

Emerging evidence indicates that orthogonal time–frequency space (OTFS) modulation is a potential candidate modulation scheme for high mobility wireless communications. However, OTFS may experience significant inter-symbol interference (ISI) and inter-Doppler interference (IDI) in the receiver. In this paper, we propose a soft decision feedback turbo equalization for OTFS transmission over delay-Doppler channels to jointly combat both interferences. A novel block decision feedback equalization (BDFE) algorithm is constructed using the band feature of the channels in the delay-Doppler domain. The feedforward and feedback filters are designed by the delay-time channels coefficients. According to the designed filter, an equivalent system model is employed to allow turbo equalization. The posterior probability is established using the soft prior information and feedback filter, and then fed back to the channel decoder as external information. Both theoretical analysis and simulation results demonstrate the effectiveness of the proposed scheme in improving the bit error rate (BER) performance and combat various interference. Numerical simulations are finally provided to justify the validity of the proposed scheme in improving the bit error rate (BER) performance and combating various interference.

The Performance Improvement of VLC-OFDM System Based on Reservoir Computing

Nonlinear effects have been restricting the development of high-speed visible light communication (VLC) systems. Neural network (NN) has become an effective means to alleviate the nonlinearity of a VLC system due to its powerful ability to fit complicated functions. However, the complex training process of traditional NN limits its application in high-speed VLC. Without performance penalty, reservoir computing (RC) simplifies the training process of NN by training only part of the network connection weights, and has become an alternative scheme to NN. For the indoor visible light orthogonal frequency division multiplexing (VLC-OFDM) system, this paper studies the signal recovery effect of the pilot-assisted reservoir computing (PA-RC) frequency domain equalization algorithm. The pilot information is added to the feature engineering of RC to improve the accuracy of channel estimation by traditional least squares (LS) algorithm. The performance of 64 quadrature amplitude modulation (QAM) signal under different transmission rates and peak to peak voltage (Vpp) conditions is demonstrated in the experiments. Compared with the traditional frequency domain equalization algorithms, PA-RC can further expand the Vpp range that meets the 7% hard-decision forward error correction (FEC) limit of 3.8 × 10−3. At the rate of 240 Mbps, the BER of the system is reduced by about 90%, and the utilization rate of the available frequency band of the system reaches 100%. The results show that PA-RC can effectively improve the transmission performance of VLC system well, and has strong generalization ability.

Retrospection of Nonlinear Adaptive Algorithm-Based Intelligent Plane Image Interaction System.

This paper introduces the application and classification of an adaptive filtering algorithm in the image enhancement algorithm. And the filtering noise reduction impact is compared using MATLAB software for programming, image processing, LMS algorithm, RLS algorithm, histogram equalisation algorithm, and Wiener filtering method filtering noise reduction effect. To optimize the intelligent graphic image interaction system, the proposed nonlinear adaptive algorithm of intelligent graphic image interaction system research is based on the digital filter and adaptive filtering algorithm for simulation experiment. The experimental results of several noise index data filtering algorithms show that the fuzzy coefficient k of LMS index is 0.86, RLS index is 0.91, the histogram equalization index is 0.53, and the Wiener filtering index is 0.62. LMS index of quality index Q is 0.90, RLS index is 0.95, histogram equalization index is 0.58, Wiener filtering index is 0.65. According to the above results, comparing LMS with the RLS method and according to SNR, k, and Q values in the simulation results in the process of processing, it is found that the convergence speed of the RLS algorithm is obviously better than that of the LMS algorithm, and the stability is also good. Additionally, the differential imaging data can provide a strong reference for the clinical diagnosis and qualitative differentiation of TBP and CP, and MSCT is worthy of extensive application in the clinical diagnosis of peritonitis. The processing effect of the image with high similarity to the original image is greatly improved compared with the histogram equalization and Wiener filtering methods used in the simulation.

ИССЛЕДОВАНИЕ АЛГОРИТМА СНИЖЕНИЯ ВЗАИМОНАГРУЖЕНИЯ ДВУХ ПРИВОДОВ, ОДНОВРЕМЕННО УПРАВЛЯЮЩИХ АЭРОДИНАМИЧЕСКИМ РУЛЕМ МАГИСТРАЛЬНОГО САМОЛЕТА

Some civil aircraft assume two actuators simultaneously turning aside the flight control surface(rudder, in some cases aileron or elevator). Thus resulting in onset of the force fighting (mutualloading force). There are several ways to reduce the mutual loading force. One of them is to insertspecific force equalization algorithm in actuators steering. This article deals with effectiveness offorce equalization algorithm as well as its influence on system “2 active actuators - elevator”. Mathematic model of two actuators on elevator has been formed and tested by the developed testing methodthat reproduces the most severe operation conditions. There are several nonlinearities which iscontained in mathematic model: sleeve-travel physical limits, nonlinear effects of fluid foil, rod-travelphysical limits and attachment rigidity. Force equalization algorithm comprises a pressure differentialfeedback loop with PID regulator. The results show that force equalization algorithm is highlyefficient (mutual loading force has been reduced over than 90%) in mode test without external load.However, in test mode with external load it is necessary to introduce a limitation on the magnitude ofthe corrective signal so as decrease the rod drop to level specified in terms of reference. Algorithmhas no effect on stability margin of the system. In conclusion, results of this study could also be usefulto expand the test program for two actuators simultaneously turning aside the flight control surfacesto further simplify the implementation of the force equalization algorithm.

Generalized Approximate Message Passing Equalization for Multi-Carrier Faster-Than-Nyquist Signaling

Multi-carrier faster-than-Nyquist (MFTN) signaling constitutes a promising spectrally efficient non-orthogonal physical layer waveform. In this correspondence, we propose a pair of low-complexity generalized approximate message passing (GAMP)-based frequency-domain equalization (FDE) algorithms for MFTN systems operating in multipath channels. To mitigate the ill-condition of the resultant equivalent channel matrix, we construct block circulant interference matrices by inserting a few cyclic postfixes, followed by truncating the duration of the inherent two-dimensional interferences. Based on the decomposition of the block circulant matrices, we develop a novel frequency-domain received signal model using the two-dimensional fast Fourier transform for mitigating the colored noise imposed by the non-orthogonal matched filter. Moreover, we derive a GAMP-based FDE algorithm and its refined version, where the latter relies on approximations for circumventing the emergence of the ill-conditioned matrices. Our simulation results demonstrate that, for a fixed spectral efficiency, MFTN signaling can significantly improve the bit error rate (BER) performance by jointly optimizing the time- and frequency-domain packing factors. Compared to its Nyquist-signaling counterpart, our proposed MFTN systems employing the refined GAMP equalizer can achieve about 39% higher transmission rates at a negligible BER performance degradation.

Mitigation of SOA-Induced Nonlinearity With the Aid of Deep Learning Neural Networks

Semiconductor optical amplifier (SOA) is a promising solution for future O-band optical amplification. However, the SOA-induced nonlinearity will affect the quality of the signals and cause bit errors. Nowadays, the neural network is a hot topic in communication networks and has been considered a practical algorithm for signals equalization. In this paper, we investigate the performance of the three types of neural networks: deep neural networks (DNN), convolutional neural network (CNN), and long short term memory (LSTM) neural networks for the compensation of the SOA-induced nonlinearity. We first compare the fitting ability of the three neural networks on the saturated response of SOA. Based on the simulation result, LSTM has the best nonlinear expressiveness for the SOA-induced nonlinearity. Subsequently, we do some experimental validations in an intensity modulation and direct-detection (IM/DD) system, where the 100 Gbit/s pulse-amplitude-modulation-4 (PAM4) signals are transmitted over 80 km at O-band using an 18 GHz electro-absorption modulated laser (EML). The bit-error-ratio (BER) results show that the cell states in LSTM play a crucial role in nonlinearity compensation. Furthermore, LSTM offers the best equalization performance and can achieve a receiver sensitivity of −18.0 dBm.

Research on Equalization Technology of Broadband Satellite Communication Channel

Satellite channel is the representative non-linear channel with limited frequency band. The random change of the channel causes the communication stability to be affected. With the development of satellite communication technology, the bandwidth of satellite communication has increased and the data rate has also increased significantly, resulting in more serious code interference, which has become a key factor affecting communication quality. Finding an efficient equalization technology to eliminate channel inter-code interference is the key to ensuring satellite broadband communications. This article first briefly discusses the principle of eliminating inter-symbol interference and common equalizer algorithms, and summarizes the main factors that affect the performance of channel equalization algorithms. On this basis, an improved LSTM blind equalization algorithm is proposed.

Assessment of Different Channel Equalization Algorithms for a Converged OFDM-Based 5G mm-wave A-RoF System at 60 GHz

In this article, we simulate a converged 5G mm-wave analogue radio-over-fiber (A-RoF) system at 60 GHz, and perform offline signal processing to equalize the dispersive optical link with the three most frequently employed algorithms, i.e., the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA) and the adaptive median filtering (AMF), which are implemented in Matlab. The performances of the different algorithms are compared for various optical fiber lengths with respect to the EVM values obtained before and after equalization. In the case of QPSK in OFDM subcarriers, it is observed that the CMA algorithm performs better than the LMS and MF algorithms, with 2% and 1.4% EVM improvement respectively, while for 16QAM in OFDM subcarriers it is observed that the LMS algorithm has a very small improvement of 0.2% EVM compared to the MF algorithm, while CMA is not suitable for 16QAM modulation in the proposed converged 5G mm-wave A-RoF system at 60 GHz.

3D Texture Reconstruction of Abdominal Cavity Based on Monocular Vision SLAM for Minimally Invasive Surgery

The depth information of abdominal tissue surface and the position of laparoscope are very important for accurate surgical navigation in computer-aided surgery. It is difficult to determine the lesion location by empirically matching the laparoscopic visual field with the preoperative image, which is easy to cause intraoperative errors. Aiming at the complex abdominal environment, this paper constructs an improved monocular simultaneous localization and mapping (SLAM) system model, which can more accurately and truly reflect the abdominal cavity structure and spatial relationship. Firstly, in order to enhance the contrast between blood vessels and background, the contrast limited adaptive histogram equalization (CLAHE) algorithm is introduced to preprocess abdominal images. Secondly, combined with AKAZE algorithm, the Oriented FAST and Rotated BRIEF(ORB) algorithm is improved to extract the features of abdominal image, which improves the accuracy of extracted symmetry feature points pair and uses the RANSAC algorithm to quickly eliminate the majority of mis-matched pairs. The medical bag-of-words model is used to replace the traditional bag-of-words model to facilitate the comparison of similarity between abdominal images, which has stronger similarity calculation ability and reduces the matching time between the current abdominal image frame and the historical abdominal image frame. Finally, Poisson surface reconstruction is used to transform the point cloud into a triangular mesh surface, and the abdominal cavity texture image is superimposed on the 3D surface described by the mesh to generate the abdominal cavity inner wall texture. The surface of the abdominal cavity 3D model is smooth and has a strong sense of reality. The experimental results show that the improved SLAM system increases the registration accuracy of feature points and the densification, and the visual effect of dense point cloud reconstruction is more realistic for Hamlyn dataset. The 3D reconstruction technology creates a realistic model to identify the blood vessels, nerves and other tissues in the patient’s focal area, enabling three-dimensional visualization of the focal area, facilitating the surgeon’s observation and diagnosis, and digital simulation of the surgical operation to optimize the surgical plan.

Effect of optical pulse shaping and adaptive equalization on the performance of 100G DP-QPSK WDM system

Abstract Dual polarization quadrature phase shifting keying (DP-QPSK) modulation format along with coherent receiver helps in increasing the data carrying capability of existing optical networks without any major change in existing transmission infrastructure. The various linear and nonlinear fiber effects, frequency and phase errors are corrected in electrical domain at the receiver end with digital back propagation algorithms (DBP) instead of in-line compensation. In such a case the selection of optimum values of system parameters make the task easier for DBP algorithms. This paper highlights the importance of finding optimum operating point of continuous modulus algorithm (CMA) for better adaptive equalization (AE). The paper also discusses the optical pulse shaping using Gaussian optical band-pass filter to improve the spectral characteristics of DP-QPSK signal.

Performance Evaluation of Machine Learning-Based Channel Equalization Techniques: New Trends and Challenges

Wireless communication systems have evolved and offered more smart and advanced systems like ad hoc and sensor-based infrastructure fewer networks. These networks are evaluated with two fundamental parameters including data rate and spectral efficiency. To achieve a high data rate and robust wireless communication, the most significant task is channel equalization at the receiver side. The transmitted data symbols when passing through the wireless channel suffer from various types of impairments, such as fading, Doppler shifts, and Intersymbol Interference (ISI), and degraded the overall network performance. To mitigate channel-related impairments, many channel equalization algorithms have been proposed for communication systems. The channel equalization problem can also be solved as a classification problem by using Machine Learning (ML) methods. In this paper, channel equalization is performed by using ML techniques in terms of Bit Error Rate (BER) analysis and comparison. Radial Basis Functions (RBFs), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Functional Link Artificial Neural Network (FLANN), Long-Short Term Memory (LSTM), and Polynomial-based Neural Networks (NNs) are adopted for channel equalization.

Error Handling Method for Improving BDS Monitoring Accuracy of Bridge Deformation

The existing high‐tech bridge deformation monitoring technologies mostly use the GPS system. To eliminate the dependence on this system, China has independently developed and built the Beidou satellite navigation system. In this study, an improved algorithm for differently modulated signals is proposed to increase the accuracy of satellite monitoring and minimise the intercode interference into the output signals of the Beidou bridge deformation monitoring system. It includes a modified recurrent least square constant modulus algorithm (RLS‐CMA) developed using MATLAB software. The numbers of iterations for four modulated signals are determined through various blind equalisation and intersymbol interference (ISI) algorithms as well as by performing error vector magnitude comparison simulations without equalisation, with CMA equalisation, and with enhanced RLS‐CMA equalisation. At high signal‐to‐noise ratios, the correction speed and anti‐intercode interference capability of the improved RLS‐CMA are greater than those of the CMA. Moreover, the lower the error vector amplitude, the higher is the accuracy of received constellations. The error vector amplitude achieved by the RLS‐CMA equalisation algorithm is lower than those of the CMA equalisation and nonequalisation algorithms, while its accuracy is increased by 17%. Hence, the improved RLS‐CMA can eliminate ISI, increase the accuracy of satellite monitoring, and satisfy the requirements of theoretical analysis, calculation accuracy, and engineering error.