Machine Learning-Based Methods for Automatic Compensation of Dead-time Distortion: A Comparative Study

Owing to the ferromagnetism and stray fields of inertial navigation system, component compensation of magnetic field distortion is significant for geomagnetic field vector measurement system. However, traditional scalar compensation methods cannot be used for component compensation of magnetic field distortion, so some improvement works have been researched for component compensation in the study, the major of which is estimating compensation parameters using the Lagrange multiplier method. Experiment results show that the performance of the component compensation method is much better than traditional scalar compensation method. After compensation, the maximal measurement errors of north, vertical, east components and total intensity caused by magnetic field distortion are reduced to 10.4 nT (0.91% of the raw error), 32.5 nT (5.20%), 12.9 nT (1.27%), and 18.4 nT (2.02%), respectively. In addition, compared with previous component compensation method, the proposed method has three advantages: (i) simplified equipment, (ii) easier operation process, and (iii) better generality.

Underwater archaeology relies heavily on photography and video image recording during surveillances and excavations like ordinary archaeological studies on land. All underwater images suffer poor image quality and distortions due to poor visibility, low contrast and blur, caused by differences in refractive indices of water and air, properties of selected lenses and shapes of viewports. In the Yellow Sea (between mainland China and the Korean peninsula), the visibility underwater is far less than 1 m, typically in the range of 30 cm to 50 cm, on even a clear day, due to very high turbidity. For photographing 1 m x 1 m grids underwater, a very wide view angle (180o) fisheye lens with an 8 mm focal length is intentionally used despite unwanted severe barrel-shaped image distortion, even with a dome port camera housing. It is very difficult to map wide underwater archaeological excavation sites by combining severely distorted images. Development of practical compensation methods for distorted underwater images acquired through the fisheye lens is strongly desired. In this study, the source of image distortion in underwater photography is investigated. We have identified the source of image distortion as the mismatching, in optical axis and focal points, between dome port housing and fisheye lens. A practical image distortion compensation method, using customized image processing software, was explored and verified using archived underwater excavation images for effectiveness in underwater archaeological applications. To minimize unusable area due to severe distortion after distortion compensation, practical underwater photography guidelines are suggested.

A new method is proposed for the compensation of color distortion between the right and left sides of a projected image in an LCD projection system. The deviation of the incident beam angle causes the difference in spectral transmittance between the sides of a dichroic mirror, and the color distortion resulting from this difference is analyzed by the colorimetric method. In the compensation method, the variation of the transformation matrix between the Commission Internationale de I’Eclairge (CIE) XYZ tristimulus values and RGB primary values is linearly approximated under reasonable assumptions based on typical LCD projection systems. The proposed method can be simply implemented with an on-line scheme to be practically applicable to projection TV systems and its effectiveness is shown via experimental image examples.

For the past years, the Internet of Things (IoT) supported by 5G technology, has been expanding rapidly across a wide range of services, enabling inter-object connectivity for the automotive industry, consumer electronics, transportation, logistics sectors, and manufacturing. With the increasing ubiquitous usage of various small-sized sensors, manufacturing cost of each element taken remains a critical aspect. Relatively low price of individual elements is the key for enabling tightly connected environment, but may severely affect RF chains quality as well as overall performance. With 5G expansion to the sub-THz bands, power amplifier nonlinearity may significantly limit system performance even in high- grade devices, due to power amplifier design limitations. Multiple studies were done to mitigate nonlinearity impact, both at the transmitter (TX) and receiver (RX) sides. Many solutions propose for evaluation and further compensation of the PA nonlinearity effects, via decision-directed feedback, training or even statistical processing of the received signal. However, with knowledge of the PA nonlinearity function at the receiver side, the processing may be simplified by the application of the reverse function to the equivalent signal in the time domain. In this paper we propose a method for PA nonlinear distortion compensation at the RX side, which can be adjusted for several signal waveforms adopted in 5G NR (New Radio) standard, such as CP-OFDM, DFT-S-OFDM, and others. The simulation results presented demonstrate performance improvement both for the sub-THz PA models and models for the 30–70 GHz band.

The demand for lower uncertainties in force measurement is increasing. Deadweight force standard machines are known to yield the lowest measurement uncertainty in force realization. However, their capacities cannot be extended indefinitely as they become too large in size and prohibitively expensive. Lever amplification of a deadweight force is an option for higher capacities in force realization. This paper describes the performance of a force standard machine with a new method (patent pending) for automatic compensation of lever arm distortion in order to keep the amplification ratio constant.

Current sensors based on the fluxgate magnetic field meter and feedback zero-field operating principle have excellent current measurement accuracy and linearity in a large range of up to thousands of amperes. An excitation current ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}_{e}$ </tex-math></inline-formula> ) is employed in a magnetic fluxgate sensor to magnetize the magnetic core periodically, which will generate magnetization distortion that affects the performance of the sensor. This article proposes a novel scheme for suppressing the magnetization distortion through electrical compensation. The source of the magnetization distortion is theoretically derived and analyzed. It is found that the magnetization distortion is a function of the excitation current <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}_{e}$ </tex-math></inline-formula> and is not affected by the primary current. Since <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}_{e}$ </tex-math></inline-formula> is a periodical current that is determined by the parameters of the sensor itself, it is feasible to inject a specified periodical current to the output port of the sensor to cancel the magnetization distortion. Compared with the magnetization distortion compensation method with an additional core and coil, this method does not need to pair the cores or coils, which significantly reduces the complexity of the production process and saves space for the sensor. A prototype sensor with electrical compensation is designed, fabricated, and tested. Experimental results show that the compensation setting significantly suppresses the magnetization distortion signal. The amplitude of the basic frequency component is reduced from 24 to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.8 ~\mu \text{V}$ </tex-math></inline-formula> . The other odd harmonics of the magnetization distortion are also reduced. The electrical compensation method works properly in a large current range from −1.5 to 1.5 kA. The scale accuracy of the prototype sensor is less than 5.7 ppm (parts per million). The compensation method can suppress the magnetization distortion to a similar level with operating temperature changes from room temperature to 50 °C.

In a multimode transceiver, the transmitter for one communication standard induces a large interference on the receiver for another one. When this large interference passes through the inherently nonlinear receiver Front-End (FE), it introduces Cross- Modulation (CM) distortion. Increasing the FE linearity to lower the CM distortion leads to unacceptable power consumption for a handheld device. Considering the continuous increase of digital computation power governed by Moore's law an attractive alternative approach is to digitally compensate for the CM distortion. An existing CM compensation method is tailored to single-mode transceivers and requires an auxiliary FE. By using the locally available transmitted interference in the multimode transceiver, we propose a CM compensation method which requires no additional analog hardware. Hence the power consumption and complexity of the multimode transceiver will be reduced significantly.

Echo planar imaging (EPI) is widely used in functional and diffusion‐weighted MRI, but suffers from significant geometric distortions in the phase encoding direction caused by inhomogeneities in the static magnetic field (B0). This is a particular challenge for EPI at very high field (≥7 T), as distortion increases with higher field strength. A number of techniques for distortion correction exist, including those based on B0 field mapping and acquiring EPI scans with opposite phase encoding directions. However, few quantitative comparisons of distortion compensation methods have been performed using human EPI data, especially at very high field. Here, we compared distortion compensation using B0 field maps and opposite phase encoding scans in two different software packages (FSL and AFNI) applied to 7 T gradient echo (GE) EPI data from 31 human participants. We assessed distortion compensation quality by quantifying alignment to anatomical reference scans using Dice coefficients and mutual information. Performance between FSL and AFNI was equivalent. In our whole‐brain analyses, we found superior distortion compensation using GE scans with opposite phase encoding directions, versus B0 field maps or spin echo (SE) opposite phase encoding scans. However, SE performed better when analyses were limited to ventromedial prefrontal cortex, a region with substantial dropout. Matching the type of opposite phase encoding scans to the EPI data being corrected (e.g., SE‐to‐SE) also yielded better distortion correction. While the ideal distortion compensation approach likely varies depending on methodological differences across experiments, this study provides a framework for quantitative comparison of different distortion compensation methods.

A proposal for an image distortion compensation and perspective correction method for a rainbow-type color conical holographic stereograms is presented. The image processing method is accomplished through ray-tracing equations. The approach taken consists in applying an image process to the original set of images to generate a new set of 2-D images. Using this method, the original object perspective is recovered and a full color image without distortion is displayed at the center of the conical holographic stereogram.

PurposeHybrid navigation is a promising technique which combines the benefits of optical or electromagnetic tracking (EMT) and fluoroscopy imaging. Unfortunately, the fluoroscopy system is a source of metallic distortion for the EMT system. In this work, we present a new method for intraoperative calibration and real-time compensation of dynamic field distortions. The method was tested in the presence of a fluoroscopy C-arm, and sub-millimetre errors were obtained after distortion correction.MethodsA hybrid navigation scenario was created by combining the open-source electromagnetic tracking system Anser EMT and a commercial fluoroscopy C-arm. The electromagnetic field generator was placed directly on top of the X-ray collimator, which introduced significant field distortion. Magnetic sensors were placed at known positions to capture the magnetic distortion, and virtual magnetic dipole sources were used to model the distortion magnetic field. The accuracy of the compensated EMT model was tested on a grid of test points.ResultsError reduction was demonstrated from 12.01 to 0.35 mm and from 25.03 to 0.49 mm, for horizontal and vertical sensor orientations, respectively, over a volume of 16 × 16 × 6 cm. It is proposed that such sub-millimetre tracking errors meet the needs of most endoscopic navigation tasks.ConclusionsWe describe a method to model a magnetic field in real time, based on redundant electromagnetic field measurements, and we apply it to compensate for the distortion introduced by a fluoroscopy C-arm. The main limitation of the approach is the requirement for a high number of sensors, with possible occlusion of the operative space. Solutions might come from miniaturisation and wireless sensing.

The methods of adaptive compensation of cross-polarization distortions are considered in this article

Introduction: radio communication in the decameter range is actively used for communication in remote and hard-to-reach regions of the world. Decameter communication is also a relevant type of radio communication for regions that have suffered serious damage due to natural disasters. The relevance of using decameter radio communication in the above cases is due to the fact that this technology can offer reliable and cheap solutions. The second area of application of decameter radio communications is communications in the Arctic and Antarctic. Practical significance: when receiving signals with a spread spectrum, it becomes necessary to take into account the effect of the frequency dispersion of the ionospheric channel, since it leads to significant distortions of the useful signal and a decrease in the quality of communication in general. An urgent task is to estimate the parameters of the frequency dispersion of the ionospheric channel and to develop algorithms and devices for compensating dispersion distortions of broadband signals. Discussion: this article presents algorithms for estimating and compensating the dispersion distortions of wideband signals in the HF channel. The subbanding estimation method and maximum likelihood estimation (MLE) of the slope of the dispersion characteristic (the slope of DC) are studied. In addition, this article examines the joint MLE of the slope of DC and other parameters of the wideband signal and offers three methods of dispersion distortion compensation. The first method of compensation is a matched filter compensation. The second one is compensation based on the filter bank. Finally, the third method uses a tracking compensator to solve the problem. The experimental results demonstrated in the article were carried out on a one jump track with a length of about 2900 km.

In Pulse-Width-Modulated (PWM) inverters, dead-time and voltage drops lead to a distortion on the average output voltage. This effect is related to the system parameters and to the operating conditions and in many drives applications it can be intolerable. A number of modeling approaches and compensation methods have been proposed in the past literature. A recent approach adopts an accurate physical model of the inverter, aiming at investigating the effects of parasitic capacitances and devices voltage drops by a fitting analytical characteristic. Model parameters are derived from a self-commissioning procedure, based on proper voltage injection and processing, both affecting the accuracy of achievable compensation. In this paper, these issues have been considered and two original enhancements have been proposed and validated, demonstrating the superiority of the results over state-of-the-art. Finally, the effects of the mentioned inverter distortion on the accuracy of current sampling and control loops are also analyzed for the first time, and an original compensation strategy is proposed and validated. Theoretical analysis and developments are fully reported, together with accurate simulations and experimental results based on a commercial drive.

In this paper, we propose a method for blind compensation of a memoryless nonlinear distortion. We assume as prior information that the desired signal admits a sparse representation in a transformed domain that should be known in advance. Then, given that a nonlinear distortion tends to generate signals that are less sparse than the desired one, our proposal is to build a compensating function model that gives rise to a maximally sparse signal. The implementation of this proposal has, as central elements, a criterion built upon an approximation of the $\ell _{0}$ -norm, the use of polynomial functions as compensating structures, and an optimization strategy based on sequential quadratic programming. We provide a theoretic analysis for an $\ell _{0}$ -norm criterion and results considering synthetic data. We also employ the method in an actual application related to chemical analysis via ion-selective electrode arrays.

Heat source-induced thermal error is a primary element influencing the precision of CNC machine tools. A practical and economical approach to mitigating thermal errors is through thermal error compensation. To provide a comprehensive understanding of thermal error modeling and its advancements, this paper systematically reviews machine learning-based methods for thermal error compensation. Thermal error modeling is the most critical step in thermal error compensation, as it directly influences the effectiveness of the compensation due to its accuracy and robustness. With the rapid development of big data and artificial intelligence, machine learning has emerged as a powerful tool in thermal error modeling, leading to significant research progress in recent years. In this paper, an overview of the thermal error modeling methods based on deep learning that have been researched and applied in recent years is presented. Specifically, two methods for reducing thermal errors, namely, thermal error suppression and thermal error compensation, are introduced and analyzed. Second, machine learning-based thermal error modeling methods are categorized into traditional machine learning-driven and deep learning-driven approaches. The application of these two methods in thermal error modeling and compensation is reviewed and summarized in detail. By synthesizing these studies, this paper identifies key challenges and trends in machine learning-based thermal error modeling. Finally, the thermal error modeling methods discussed in this paper are summarized, and future research directions are proposed to further enhance modeling accuracy and robustness.