Nonlinear Distortion Research Articles

Robust registration of multi-modal remote sensing images based on multi-dimensional oriented self-similarity features

Registration of multi-modal remote sensing images (MRSI) is crucial for unlocking the full potential of heterogeneous remote sensing imagery. However, achieving accurate registration among MRSI is challenging due to the trade-off between geometric invariance and matching accuracy, caused by differences in signal-to-noise ratio and nonlinear radiometric distortion (NRD) arising from varying imaging mechanisms. To tackle the challenge, this paper proposes a lightweight and hybrid feature-guided registration algorithm for MRSI called the hybrid registration algorithm based on multi-dimensional oriented self-similarity features (MOSS). MOSS leverages the advantages of multi-dimensional oriented self-similarity features to progressively enhance registration performance. In the hybrid feature coarse matching stage, oriented self-similarity features are extracted from MRSI, and their directional information is utilized for feature description to estimate the initial affine transformation. The fine matching under multi-dimensional oriented self-similarity features stage takes the outputs of the coarse matching stage to perform a template-like matching process. To evaluate the performance of MOSS, comprehensive experiments are conducted using six different combinations of MRSI, and seven state-of-the-art registration algorithms are selected for comparison. The experimental results demonstrate that MOSS outperforms the compared methods, with the number of correct matches being at least about 1.6 times higher than the comparison methods. Moreover, MOSS exhibits the lowest root mean square error across all experiments, with an average RMSE of 1.86 pixels, achieving an RMSE within 2 pixels. This highlights its effectiveness in achieving precise alignment and robust registration of MRSI.

Deep Learning-Based SC-FDMA Channel Equalization

It is very challenging to design an effective wireless communication system. That’s because of numerous factors affecting the performance of a typical wireless communication system, such as nonlinear channel distortions and impairments. single carrier frequency division multiple access (SC-FDMA) is a multiple access scheme that is an important part of the long-term evolution (LTE) standard for uplink transmission. An advanced mobile radio system’s multiple access schemes should indeed meet stringent requirements, such as a low bit error rate (BER). In this article, we investigate the equalization problem for nonlinear channel distortions and impairments using deep neural networks (NN). We introduce a novel combined deep neural network channel equalization and symbol detection scheme based on a deep learning (DL) recurrent feedback (RF) long short-term memory (LSTM) neural network to achieve blind equalization and decoding for SC-FDMA systems without knowing the channel state information (CSI). To train the model efficiently, the training data is gathered by simulation, with channel effects and noise treated as a complete black box. CSI and constellation demapping are learned by a deep neural network (DNN) model. Then, the frequency-domain sequences that have been corrupted are implicitly equalized to get the broadcasted signal back. Our specified SC-FDMA system, which uses a quadrature phase-shift keying (QPSK) modulation method and the suggested Deep Learning-based model channel equalizer, performs better than the existing equalizers by an average of 1 to 4 dB at moderate signal-to-noise (SNR) ratios, according to simulation data. A complexity comparison between the proposed and the conventional equalizers was conducted in terms of training time, execution time, and number of operations. On combined channel equalization and symbol detection, the suggested system delivers state-of-the-art performance.

(Why) Do Transparent Hearing Devices Impair Speech Perception in Collocated Noise?

Hearing aids and other hearing devices should provide the user with a benefit, for example, compensate for effects of a hearing loss or cancel undesired sounds. However, wearing hearing devices can also have negative effects on perception, previously demonstrated mostly for spatial hearing, sound quality and the perception of the own voice. When hearing devices are set to transparency, that is, provide no gain and resemble open-ear listening as well as possible, these side effects can be studied in isolation. In the present work, we conducted a series of experiments that are concerned with the effect of transparent hearing devices on speech perception in a collocated speech-in-noise task. In such a situation, listening through a hearing device is not expected to have any negative effect, since both speech and noise undergo identical processing, such that the signal-to-noise ratio at ear is not altered and spatial effects are irrelevant. However, we found a consistent hearing device disadvantage for speech intelligibility and similar trends for rated listening effort. Several hypotheses for the possible origin for this disadvantage were tested by including several different devices, gain settings and stimulus levels. While effects of self-noise and nonlinear distortions were ruled out, the exact reason for a hearing device disadvantage on speech perception is still unclear. However, a significant relation to auditory model predictions demonstrate that the speech intelligibility disadvantage is related to sound quality, and is most probably caused by insufficient equalization, artifacts of frequency-dependent signal processing and processing delays.

Analog CMOS Design in Nanometer Regime

There is no doubt that the short-channel effects have affected the analysis and design of modern analog CMOS integrated circuits significantly. Thus, models that take into account these effects must be adopted in order to obtain more accurate results. In this paper, the AC small-signal low-frequency equivalent circuit of the short-channel MOSFET transistor is developed using an appropriate model. The impact of short-channel effects on the operation of basic building blocks of analog integrated circuits such as basic amplifier configurations, cascode stage, differential amplifier and composite transistor is investigated quantitatively. Additionally, the nonlinear distortion is investigated using the adopted model.

Distortion Analysis and Fault Detection in Weakly Non-linear Analog Circuits Using Simplified Volterra Series Method

In this paper a new method to find the parametric faults in weakly nonlinear analog circuit is proposed. Frequency domain volterra kernels are derived to calculate the test parameters i.e., nonlinear distortions and the cross spectral density. Volterra kernels are sensitive to device parameter and the components used to build the circuit. Device parameter and each component value are varied within the tolerance limit using Monte Carlo simulation. For the fault free circuit maximum and minimum values of the 3rd order harmonic, intermodulation distortion and the cross spectral density are measured. During testing, single and multiple faults are injected and the test parameters are measured. If any one or more test parameters are outside the predetermined bounds the circuit is declared to be faulty. This proposed method offers high fault coverage capability. To validate this method CMOS differential amplifier circuit is taken under consideration.

Advancing Fingerprint Template Generation and Matching With Recast Minutiae Clustering and mRBFN

Rapid development of automation in the day-to-day life activity marks up the need of securing bio-metric template and the privacy of rightful owner. Minutiae-based matching is the most popular in the fingerprint recognition system, which greatly suffers from non-linear distortion like translation and rotation. To deal with linear distortion most of the technique proposed in the literature depends upon a reference or singular point. The paper proposes a binary template generation technique which applies an unsupervised clustering technique without fixing the number of clusters. Instead of position and orientation of the minutiae points the cardinality of the clusters are stored and converted into binary template. No spatial pattern information about the fingerprint is stored in the template to protect it from spoofing and information leakage. By the help of modified Radial Basis Function Network (mRBFN) with robust and efficient matching technique the generated templates are matched for authentication. We use MCYT dataset for training the mRBFN. The efficiency of the proposed scheme is evaluated on FVC 2000, FVC 2002 and FVC 2004 dataset.

Reference-free quantitative mass spectrometry in the presence of nonlinear distortion caused by in situ chemical reactions among constituents.

Materials performance is primarily influenced by chemical composition, making compositional analysis (CA) essential in materials science. Traditional quantitative mass spectrometry, which deconvolutes analyte spectra into reference spectra, struggles with reactive systems where spectral variations occur, such as peak shifts and new peak emergences. Additionally, obtaining reference spectra for all pure constituents is often impractical. To address these challenges, I propose nonlinear reference-free quantitative mass spectrometry (NL-RQMS). This method simultaneously determines composition, reference spectra, and nonlinear interaction effects directly from a spectral dataset of mixtures, eliminating the need for prior reference spectra. In a benchmark test on ternary reactive polymers of epoxy and amines, NL-RQMS inferred compositions with an error margin of just 3 wt%, significantly outperforming the 6 wt% error margin of linear RQMS. The inferred interaction terms clearly indicate in situ reactions between epoxy and amine moieties. This framework enables accurate compositional analysis without prior knowledge of the constituents, even in systems with interactive components, and holds significant potential for applications such as grading recycled plastics, where pristine materials, degradation compounds, and stabilizers interact complexly, causing nonlinear spectral distortions.

RF Mismatches and Nonlinear Distortions in Cell-Free Massive MIMO: Impact Analysis and Calibration Performance Analysis

RF Mismatches and Nonlinear Distortions in Cell-Free Massive MIMO: Impact Analysis and Calibration Performance Analysis

Channel estimation and it’s techniques: A survey

In wireless mobile networks, the signal transmitted from the transmitter to the receiver is vulnerable to many performance-degrading factors, such as distortion, noise, fading, and nonlinear distortion, due to the physical nature of the channel. To address these factors, accurate prediction and estimation must be employed to achieve optimized performance in wireless mobile networks. Several channel estimation techniques have been developed. In this article, we provide a review of some of these techniques under various categories, highlighting their strengths and limitations in different operational environments.

Capacity Analysis and Throughput Maximization of NOMA with Non-linear Power Amplifier Distortion

Capacity Analysis and Throughput Maximization of NOMA with Non-linear Power Amplifier Distortion

Performance and Computational Efficiency of LMS Adaptive Volterra Equalizers for Nonlinear TWTA Distortion in Satellite Communications

Performance and Computational Efficiency of LMS Adaptive Volterra Equalizers for Nonlinear TWTA Distortion in Satellite Communications

GRiD: Guided Refinement for Detector-Free Multimodal Image Matching.

Multimodal image matching is essential in image stitching, image fusion, change detection, and land cover mapping. However, the severe nonlinear radiometric distortion (NRD) and geometric distortions in multimodal images severely limit the accuracy of multimodal image matching, posing significant challenges to existing methods. Additionally, detector-based methods are prone to feature point offset issues in regions with substantial modal differences, which also hinder the subsequent fine registration and fusion of images. To address these challenges, we propose a guided refinement for detector-free multimodal image matching (GRiD) method, which weakens feature point offset issues by establishing pixel-level correspondences and utilizes reference points to guide and correct matches affected by NRD and geometric distortions. Specifically, we first introduce a detector-free framework to alleviate the feature point offset problem by directly finding corresponding pixels between images. Subsequently, to tackle NRD and geometric distortion in multimodal images, we design a guided correction module that establishes robust reference points (RPs) to guide the search for corresponding pixels in regions with significant modality differences. Moreover, to enhance RPs reliability, we incorporate a phase congruency module during the RPs confirmation stage to concentrate RPs around image edge structures. Finally, we perform finer localization on highly correlated corresponding pixels to obtain the optimized matches. We conduct extensive experiments on four multimodal image datasets to validate the effectiveness of the proposed approach. Experimental results demonstrate that our method can achieve sufficient and robust matches across various modality images and effectively suppress the feature point offset problem.

Joint Tx and Rx Look-Up-Table Based Nonlinear Distortion Mitigation in Reduced State MLSE for 180 Gbit/s PAM-8 IM-DD System

Joint Tx and Rx Look-Up-Table Based Nonlinear Distortion Mitigation in Reduced State MLSE for 180 Gbit/s PAM-8 IM-DD System

Channel Modeling for IRS-Assisted MIMO Systems to Analyze the Effects of Nonlinear Distortions in Wireless Environments

Channel Modeling for IRS-Assisted MIMO Systems to Analyze the Effects of Nonlinear Distortions in Wireless Environments

Behavioral Modeling of Direct-Conversion Receivers for Nonlinear Distortion Mitigation

Behavioral Modeling of Direct-Conversion Receivers for Nonlinear Distortion Mitigation

On the optimum multicarrier performance with memoryless nonlinearities

One of the major problems associated with multicarrier signals is their high envelope fluctuations that lead to implementation difficulties. Therefore, multicarrier signals are usually submitted to nonlinear operations such as clipping to simplify the power amplification, DAC (Digital-to-Analog Conversion) and other digital signal processing operations. However, these nonlinear operations lead to nonlinear noise that usually is associated with performance degradation. Recently, it was shown that the nonlinear distortion associated to bandpass memoryless nonlinear devices not only does not necessarily means performance degradation, but can even lead to performance gains. In this paper we study the impact of non-bandpass memoryless nonlinearities on the performance of multicarrier signals. We consider both nonlinear devices operating on the real-valued multicarrier signals as well as Cartesian nonlinear devices (also denoted as I-Q nonlinearities) operating separately on the real and imaginary parts of the complex envelope of multicarrier signals. We present a theoretical analysis of the potential performance improvements associated with the optimum detection of multicarrier signals, by deriving accurate theoretical expressions for both the average asymptotic gains in ideal AWGN (Additive White Gaussian Noise) channels and the asymptotic gain distribution for frequency-selective channels with multipath Rayleigh fading.

Modified Method of PAPR Reduction Using Clipping and Filtering for Image Transmission with OFDM

Due to the capability of OFDM (Orthogonal Frequency Division Multiplexing) to handle difficult channels, the most agreeable modulation for the multi-carrier scheme in present wireless communications is to improve an all-purpose modulation scheme, especially with high data rates. The image in this research article was transmitted and received on a noisy channel using an OFDM simulation technique. Since the average peak power ratio (PAPR) is one of the main disadvantages of OFDM, a new method has been proposed to reduce the PAPR using the clipping and filtering (CF) method. When the OFDM signal has a high PAPR, it means that many subcarrier components will be added through the operation of IFFT. Also, choosing the type of modulation to examine and getting a perfect type of OFDM system that is used for transmitting the image. Furthermore, signal-to-noise ratio (SNR) was considered to find the PAPR effect on the OFDM signal. The new method was tested to get a reduction of PAPR concerning CF and without CF. This method depends on clipping the signal before transmitting it, by using a method to overcome a nonlinear distortion, and therefore, decrease the bit error rate (BER). Then a filter with multi-stages was used to minimize the noise. This whole process was repeated several times to overcome the difficulties of transmitting/receiving the signal including PAPR. BER and SNR show wonderful outcomes when BPSK is chosen. Control over transmission and reception is also considered to be the type of modulation. All simulation results were defined using an Additive White Gaussian Noise (AWGN) channel.

Characteristic analysis and data comparative of linear and nonlinear low-frequency sweep in vibroseis

Abstract Low-frequency seismic data plays a crucial role in seismic data processing and seismic wave inversion. At present, there are two methods to realize the low-frequency excitation of vibrators: one is exciting low-frequency vibrators by linear sweep signals, and the other is exciting conventional vibrators by nonlinear low-frequency sweep signals. The cost of exploration using low-frequency vibroseis is high, and it is challenging to obtain sufficient low-frequency information using traditional vibrators. To this end, this paper comparatively studies the low-frequency sweep signal characteristics and data effects of low-frequency and traditional vibrators. Therefore, three kinds of linear and nonlinear low-frequency sweep signal are designed. Theoretical analysis shows that there are certain differences between linear and nonlinear signal in design methods, signal shapes, etc., but after correlation calculations the signal spectra reflecting the seismic response and the related wavelet shapes are basically consistent. Besides, the actual force signal data shows that the linear and nonlinear harmonic distortion are basically equivalent. Finally, based on the forward simulation of three sweep signals and the comparative analysis of field test data, it can be considered that the linear and nonlinear low-frequency sweep signals of vibrators have almost the same denoising ability under the basic conditions of the spectrum and wavelet. Both can achieve low-frequency excitation and obtain rich low-frequency information, and the quality of seismic data is basically the same, so they can be applied in practical production.

Generalized memory polynomial input neural networks for nonlinear modeling in digital predistortion applications

AbstractThe power amplifiers (PAs) will embody the nonlinear distortion if they work at saturation so as to increase efficiency. The neural network (NN)‐based digital predistortion (DPD) is widely applied for the linearization of the PA in wireless communication. However, the current NN‐based DPD schemes only focus on designing the NN hidden layer and ignore improving the NN input layer, which loses the degree of freedom to fit the DPD. Therefore, this paper first proposes the generalized memory polynomial (GMP) input NN structure. The training data of GMP‐NN outputs are obtained by iterative learning control. The experimental results show that the GMP‐NN presents a great advantage in performance compared with the widely used GMP model and recurrent NN (RNN) model. From the testing data, it is observed that the minimum mean square error (MMSE) of the GMP‐NN is 4 dB lower than the conventional GMP model and is nearly 8 dB lower than the RNN model. The proposed GMP‐NN model is also verified in the DPD application, which shows 2.61 dB improvement in comparison with the GMP DPD model.

Показатели качества потребления энергии в коллекторном и асинхронном тяговых приводах электровозов переменного тока

Purpose: To conduct an experimental study of the indicators of electrical energy consumption quality for serially produced freight electric locomotives of alternating current 2(3)ES5K with a collector traction drive and electric locomotives 2(3)ES5S with an asynchronous traction drive, to provide their comparison and comparative analysis. Methods: The main technical characteristics of the collector traction electric drive (TED) NB-514E and asynchronous TED DTA-1100 are presented. A measurement scheme for voltage and current on the power winding of the transformer is proposed. The monitoring of parameters has been carried out directly on electric locomotives using specialized metrological equipment. The processing of experimental data is based on decomposing the original signal into harmonic components using Fast Fourier Transform (FFT). Results: Parameters of voltage and current harmonics, phase shifts, as well as the values of active and reactive power of the corresponding harmonics have been obtained. An analysis of the spectral composition of voltage and current has been performed. Comparative analysis of the results obtained for electric locomotives with collector and asynchronous traction drives in traction mode has shown that the power factor is 0.65 and 0.99, respectively; and the coefficient of non-linear current distortions is 0.53 and 0.15. A conclusion has been drawn regarding significantly higher quality of electrical energy consumption in the asynchronous traction drive. Practical significance: Based on the specific values of the indicators of electrical energy consumption quality in collector and asynchronous traction drives in traction mode, it is possible to proceed to obtaining economic assessments for the transition of the locomotive fleet of Russian Railways (JSCo “RZD”) to nextgeneration electric locomotives of the 2(3)ES5S series.