Compensation Approach Research Articles

Global Estimation and Compensation of Linear Effects in Coherent Optical Systems Based on Nonlinear Least Squares

This article proposes a new estimation and compensation approach to mitigate several linear and widely linear effects in coherent optical systems using digital signal processing (DSP) algorithms. Compared to most of the available strategies that employ local estimation and/or compensation algorithms, this approach performs a global impairments estimation and compensation based on nonlinear least squares. The proposed method estimates and compensates for the chromatic dispersion (CD), carrier frequency offset (CFO), in-phase/quadrature (IQ) imbalance, and laser phase noise in two steps. First, it estimates the quasi-static parameters related to the CD, CFO, and both transmitter and receiver IQ imbalance. Second, it estimates both transmitter and receiver lasers’ phases and compensates for all the imperfections by using a zero-forcing equalizer. Simulations show the effectiveness of the approach in terms of statistical performance and computational time. The estimation performance is assessed by computing the Cramér–Rao lower bound, while the detection performance is compared to a modified clairvoyant equalizer.

The influence of Zr4+ doping on the structural and photoluminescence properties of LaPO4:Ce3+/Mn2+ phosphors

A series of LaPO4:Ce3+/Mn2+/xZr4+ (LPOCM:xZr4+) (0% ≤ x ≤ 30%) emission-tunable phosphors were synthesized by a cost-efficient co-precipitation method. The samples presented a monoclinic phase structure and a macroscopic rod-like shape. Photoluminescence (PL) and electron paramagnetic resonance spectroscopy were employed, the Zr4+ concentration-dependent excitation, emission, decay time, and energy transfer (Ce3+ → Mn2+) being discussed in detail. Under UV-excitation of Ce3+, typical green-red PL emission is observed for the LPOCM:xZr4+ phosphors, which is attributed to the 4T1(G) → 6A1(S) transition of the Mn2+ ion. The charge compensation strategy achieved the Mn2+ PL-enhancement and color-tuning effect in LPOCM:xZr4+ phosphors. A strong blue emission with color purity of up to 97% is observed in the Ce3+ singly-doped sample. Cold white light emission can be obtained by doping 5% Zr4+ in LPOCM phosphor under UV irradiation. The corresponding CIE 1931 coordinates were inferred to be (0.309, 0.329), close to the standard white emission (0.330, 0.330). The color was tuned towards whitish-orange/red emissions for higher Zr4+ concentrations (15% and 30%). These findings indicate that the charge compensation approach can greatly improve the PL and color-tunable properties of LPOCM:xZr4+ phosphors.

A 2MHz, low temperature coefficient relaxation oscillator with hybrid temperature compensation and real-time calibration

An RC relaxation oscillator (ROSC) with hybrid temperature compensation as well as the frequency of 2 MHz is proposed in this paper. Frequency stability is enhanced with analog compensation by replicating the path of comparator and level determination logic (LDL), the compensation approach removes the temperature-dependent delay of the comparator and LDL. Since frequency is undesirable at low temperatures (below 0 °C) due to non-zero-temperature-coefficient current and voltage references, as well as weak carrier mobility, digital low temperature compensation is presented to extend the oscillator’s operating temperature range to −40 °C–125 °C. The digital compensation module, after hybrid compensation, is then utilized for real-time oscillator calibration by the same operating principle. The presented oscillator with hybrid compensation and real-time calibration is developed in a standard 0.18-μm CMOS process with the active area of 0.04 mm2, and post-simulation results reveal that after hybrid compensation, the temperature coefficient (TC) of this oscillator is 43.97 ppm/°C in the temperature range of −40 °C–125 °C. The power of this ROSC is 185 μW when the supply voltage is 1.8V.

Proportional-integral-proportional control and compensation design for low-speed motions of permanent magnet synchronous motor driven servomechanism with position-dependent disturbance

ABSTRACT Position-dependent disturbances have a significant impact on the low-speed positional motions of a permanent magnet synchronous motor (PMSM) driven servomechanism; therefore, this study has aimed to develop a control and compensation design, based on the proportional-integral-proportional (PIP) feedback control extensively used in commercialized PMSM drivers, for improving the low-speed positional motions of the PMSM-driven servomechanism without excessively increasing the computational burden on commercialized PMSM drivers and without hardware modifications to commercialized PMSM servomotor packs. The angular position and driving torque signals of the PMSM in low-speed motions were used for a spatial fast Fourier transform analysis to build the position-dependent disturbance model and determine the dominant positional frequencies. Then, a low-speed positional motion control and compensation design was developed that integrates the dynamic compensation, friction compensation, and position-dependent disturbance compensation approaches for the PMSM-driven servomechanism to improve its low-speed positional motion performance. The positional motion experiments performed on a PMSM test bench demonstrated that, in comparison with the PIP feedback control extensively used in commercialized PMSM drivers, the PIP control and compensation designed in this study improved the position and speed responses and suppressed the position and speed fluctuation phenomenon of the PMSM-driven servomechanism during low-speed motions.

Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography.

Simple SummaryThe five-year survival rate of non-small-cell lung cancer (NSCLC), which accounts for 85% of all lung cancer cases, is only 10–20%. A reliable prediction model of overall survival (OS) that integrates imaging and clinical data is required. Overall, 492 patients with NSCLC from two hospitals were enrolled in this study. The compensation method was applied to reduce the variation of imaging features among different hospitals. We constructed a deep learning prediction model, DeepSurv, based on computed tomography radiomics and key clinical features to generate a personalized survival curve for each patient. The results of DeepSurv showed a good performance in discriminating high and low risk of survival. Furthermore, the generated personalized survival curves could be intuitively applied for individual OS prediction in clinical practice. We concluded that the proposed prediction model could benefit physicians, patients, and caregivers in managing NSCLC and facilitate personalized medicine.Patient outcomes of non-small-cell lung cancer (NSCLC) vary because of tumor heterogeneity and treatment strategies. This study aimed to construct a deep learning model combining both radiomic and clinical features to predict the overall survival of patients with NSCLC. To improve the reliability of the proposed model, radiomic analysis complying with the Image Biomarker Standardization Initiative and the compensation approach to integrate multicenter datasets were performed on contrast-enhanced computed tomography (CECT) images. Pretreatment CECT images and the clinical data of 492 patients with NSCLC from two hospitals were collected. The deep neural network architecture, DeepSurv, with the input of radiomic and clinical features was employed. The performance of survival prediction model was assessed using the C-index and area under the curve (AUC) 8, 12, and 24 months after diagnosis. The performance of survival prediction that combined eight radiomic features and five clinical features outperformed that solely based on radiomic or clinical features. The C-index values of the combined model achieved 0.74, 0.75, and 0.75, respectively, and AUC values of 0.76, 0.74, and 0.73, respectively, 8, 12, and 24 months after diagnosis. In conclusion, combining the traits of pretreatment CECT images, lesion characteristics, and treatment strategies could effectively predict the survival of patients with NSCLC using a deep learning model.

Self-temperature compensation approach for fiber specklegram magnetic field sensor based on polarization specklegram analysis

A self-temperature compensation method based on polarization specklegram analysis and magnetic birefringence effect of magnetic fluid (MF) is proposed for a fiber speckle sensor (FSS). The temperature compensation is achieved by measuring the variation difference of the two specklegrams with vertical linear polarization state. A fiber specklegram magnetic field sensor has been designed with this method and the magnetic-birefringence effect of MF. The measurement of magnetic field with high stability and good repeatability has been successfully achieved, which demonstrates the effectiveness of the proposed method. Due to the merits of low cost, ease of implement and system simplicity, the proposed method can promote the practical use of the FSS in long term monitoring.

Distributed Delay Compensation for a Hybrid Simulation System of Space Manipulator Capture

The simulation of the target capture process of space manipulator is of great significance for the manipulator and task verification. By integrating the physical and numerical simulation, the hardware-in-the-loop (HIL) simulation is very suitable to simulate the complicated contact dynamics of the target capture process of the multibody manipulator in three-dimensional space. However, the time delays in the system loop lead to HIL simulation error and even system instability. In this article, a distributed delay compensation approach is proposed for the hybrid simulation system of space manipulator capture. The force delay, calculation delay, actuation delay, and mechanism delay in the system loop are compensated in situ. Compared with lumped compensation, distributed compensation has stronger robustness to the signal noise and better adaptability to the varying contact natural frequency. Simulations and experiments show that the proposed compensation approach can guarantee the system stability and simulation accuracy.

How the Gender Wage Gap for Primary Care Physicians Differs by Compensation Approach : A Microsimulation Study.

The physician gender wage gap may be due, in part, to productivity-based compensation models that undervalue female practice patterns. To determine how primary care physician (PCP) compensation by gender differs when applying existing productivity-based and alternative compensation models. Microsimulation. 2016 to 2019 national clinical registry of 1222 primary care practices. Male and female PCPs matched on specialty, years since medical school graduation, practice site, and sessions worked. Net annual, full-time-equivalent compensation for male versus female PCPs, under productivity-based fee-for-service, panel size-based capitation without or with risk adjustment, and hybrid payment models. Microsimulation inputs included patient and visit characteristics and overhead expenses. Among 1435 matched male (n= 881) and female (n= 554) PCPs, female PCP panels included patients who were, on average, younger, had lower diagnosis-based risk scores, were more often female, and were more often uninsured or insured by Medicaid rather than by Medicare. Under productivity-based payment, female PCPs earned a median of $58829 (interquartile range [IQR], $39553 to $120353; 21%) less than male PCPs. This gap was similar under capitation ($58723 [IQR, $42141 to $140192]). It was larger under capitation risk-adjusted for age alone ($74695 [IQR, $42884 to $152423]), for diagnosis-based scores alone ($114792 [IQR, $49080 to $215326] and $89974 [IQR, $26175 to $173760]), and for age-, sex-, and diagnosis-based scores ($83438 [IQR, $28927 to $129414] and $66195 [IQR, $11899 to $96566]). The gap was smaller and nonsignificant under capitation risk-adjusted for age and sex ($36631 [IQR, $12743 to $73898]). Panel attribution based on office visits. The gender wage gap varied by compensation model, with capitation risk-adjusted for patient age and sex resulting in a smaller gap. Future models might better align with primary care effort and outcomes. None.

Improving inkjet printed flexible angle sensor performance by compression–tension sensor pair compensation

This paper introduces a new method for improved integrated angle sensing in the foldable robot joints (hinges), using low-cost inkjet printed sensors and a novel physical intelligence based compensation approach. Silver nanoparticle angle sensors are inkjet printed on a flexible PET substrate as a hinge material and implemented as pairs of compression and tension side folding individuals into a rigid experimental setup. Resulting combined signals improved the individual sensor responses due to their compensating physical characteristics, and gave the far best performances in the existing literature in terms of linearity, sensor life-time, static and cyclic drift, hysteresis and dynamic dependency. Proposed approach is promising for eliminating the major limitations on the printed sensor use in flexible hinges and paving the way to reliable, fully soft and easy-to-fabricate all-integrated foldable robots.

Similarity quantification for linear stochastic systems: A coupling compensator approach

For the formal verification and design of control systems, abstractions with quantified accuracy are crucial. This is especially the case when considering accurate deviation bounds between a stochastic continuous-state model and its finite (reduced-order) abstraction. In this work, we introduce a coupling compensator to parameterize the set of relevant couplings and we give a comprehensive computational approach and analysis for linear stochastic systems. More precisely, we develop a computational method that characterizes the set of possible simulation relations and gives a trade-off between the error contributions on the systems output and deviations in the transition probability. We show the effect of this error trade-off on the guaranteed satisfaction probability for case studies where a formal specification is given as a temporal logic formula.

Digital surface twin for ultra-precision high performance cutting

Increasing productivity of ultra-precision (UP) machining is vital to apply this technology in a broader range of applications. In order to achieve this goal, technologies such as diamond cutting with multiple cutting edges or the application of electromagnetic levitation guides are researched. Due to the additional, influencing factors from these technologies, a digital surface twin is beneficial for predicting surface features and characteristics. In this work, two approaches for generating a digital surface twin are investigated: a surface simulation based on numerical height maps and a dexel-based material removal simulation. Both incorporate multi degree of freedom position data provided by the levitation guide. Fly-cutting experiments are conducted to validate the digital twin approaches. Both approaches are able to predict surface profile, surface roughness and waviness with high accuracy. It is also shown, that the digital twin can support the development of compensation approaches for tool offsets. In conclusion digital surface twins for ultra-precision cutting offer a high potential to support productivity improvement especially in combination with axis position data. Further work will focus on real-time integration of the approaches.

Comparison of super-resolution and noise reduction for passive single-photon imaging

Single-photon sensitive image sensors have recently gained popularity in passive imaging applications where the goal is to capture photon flux (brightness) values of different scene points in the presence of challenging lighting conditions and scene motion. Recent work has shown that high-speed bursts of single-photon timestamp information captured using a single-photon avalanche diode camera can be used to estimate and correct for scene motion thereby improving signal-to-noise ratio and reducing motion blur artifacts. We perform a comparison of various design choices in the processing pipeline used for noise reduction, motion compensation, and upsampling of single-photon timestamp frames. We consider various pixelwise noise reduction techniques in combination with state-of-the-art deep neural network upscaling algorithms to super-resolve intensity images formed with single-photon timestamp data. We explore the trade space of motion blur and signal noise in various scenes with different motion content. Using real data captured with a hardware prototype, we achieved super-resolution reconstruction at frame rates up to 65.8 kHz (native sampling rate of the sensor) and captured videos of fast-moving objects. The best reconstruction is obtained with the motion compensation approach, which achieves a structural similarity (SSIM) of about 0.67 for fast-moving rigid objects. We are able to reconstruct subpixel resolution. These results show the relative superiority of our motion compensation compared to other approaches that do not exceed an SSIM of 0.5.

Sequential delay-free strategy for sliding mode position observer considering analog and digital implementations of PMSM drives

ABSTRACT This paper proposes a sequential delay-free strategy to avoid side effects of the delays arising from both analog and digital implementations of permanent magnet synchronous machine (PMSM) drives on the accuracy of sliding mode position observers used in the high-speed conditions. First, after establishing the sliding mode observer (SMO) used for position estimation, the principles of the analog delay, low-pass filter (LPF) delay and execution delay together with their impacts are presented at length, posing the necessity of investigating the delay-free techniques. Then, novel current-based, position-based and speed-prediction-based compensation strategies are developed to deal with the three kinds of delays, respectively. Because the proposed compensation strategies are achieved based on the direct signal compensation theories, they are simple to be understood and implemented in practice. Third, the implementation sequence of the sequential delay compensation-based SMO is illustrated clearly. Thanks to the proposed delay compensation approaches, not only the position estimation accuracy but also the current regulation process of the closed-loop control can be improved. Finally, experiment is conducted on a three-phase 8000 rpm PMSM to verify the effectiveness of the proposed delay compensation-based SMO algorithms.

Variability-Aware Memristive Crossbars—A Tutorial

Memristor crossbar architecture is one of the most popular circuit configurations due to its wide range of practical applications. The crossbar architecture can emulate the weighted summation operation, called multiply and accumulate operation (MAC). The errors to MAC computing get introduced due to a range of crossbar variability. We broadly group the variability in three categories: (1) device-to-device variations, (2) programming nonlinearity, and (3) those from peripheral circuits. This tutorial provides insights into the variability and compensation approaches that can be adopted to reduce its impact when designing for practical applications with crossbars.

A receptance-based vibration control with dead-zone compensation for systems with input delay

This paper presents a novel compensation approach for receptance-based second-order systems with long input delay and unknown dead-zone input nonlinearity. The approach is based on a filtered Smith predictor for systems with long input delay based on the receptance model. An innovative discrete-time adaptive strategy approach for dealing with unknown input dead-zone is also based on a receptance model realization. The receptance-based second-order model is used on several fundamental applications, including active control vibration of mechanical vibrating systems. In the proposed approach, a filtered prediction error that considers the effects of time delay can be applied to pursue state feedback design for active vibration control purposes. The dead-zone compensation is treated by a discrete-time state observer, which is based on displacement, velocity, and control effort signals to estimate the unknown parameters to be used on an adaptive algorithm. The main contribution of this work is to combine unknown dead-zone adaptive mechanism and a receptance-based time delay compensation in a unified design. Some numerical examples illustrate the effectiveness of the innovative proposed approach.

Microfluidics Temperature Compensating and Monitoring Based on Liquid Metal Heat Transfer.

Microfluidic devices offer excellent heat transfer, enabling the biochemical reactions to be more efficient. However, the precision of temperature sensing and control of microfluids is limited by the size effect. Here in this work, the relationship between the microfluids and the glass substrate of a typical microfluidic device is investigated. With an intelligent structure design and liquid metal, we demonstrated that a millimeter-scale industrial temperature sensor could be utilized for temperature sensing of micro-scale fluids. We proposed a heat transfer model based on this design, where the local correlations between the macro-scale temperature sensor and the micro-scale fluids were investigated. As a demonstration, a set of temperature-sensitive nucleic acid amplification tests were taken to show the precision of temperature control for micro-scale reagents. Comparations of theoretical and experimental data further verify the effectiveness of our heat transfer model. With the presented compensation approach, the slight fluorescent intensity changes caused by isothermal amplification polymerase chain reaction (PCR) temperature could be distinguished. For instance, the probability distribution plots of fluorescent intensity are significant from each other, even if the amplification temperature has a difference of 1 °C. Thus, this method may serve as a universal approach for micro–macro interface sensing and is helpful beyond microfluidic applications.

Superpixel spectral unmixing framework for the volumetric assessment of tissue chromophores: A photoacoustic data-driven approach

The assessment of tissue chromophores at a volumetric scale is vital for an improved diagnosis and treatment of a large number of diseases. Spectral photoacoustic imaging (sPAI) co-registered with high-resolution ultrasound (US) is an innovative technology that has a great potential for clinical translation as it can assess the volumetric distribution of the tissue components. Conventionally, to detect and separate the chromophores from sPAI, an input of the expected tissue absorption spectra is required. However, in pathological conditions, the prediction of the absorption spectra is difficult as it can change with respect to the physiological state. Besides, this conventional approach can also be hampered due to spectral coloring, which is a prominent distortion effect that induces spectral changes at depth. Here, we are proposing a novel data-driven framework that can overcome all these limitations and provide an improved assessment of the tissue chromophores. We have developed a superpixel spectral unmixing (SPAX) approach that can detect the most and less prominent absorber spectra and their volumetric distribution without any user interactions. Within the SPAX framework, we have also implemented an advanced spectral coloring compensation approach by utilizing US image segmentation and Monte Carlo simulations, based on a predefined library of optical properties. The framework has been tested on tissue-mimicking phantoms and also on healthy animals. The obtained results show enhanced specificity and sensitivity for the detection of tissue chromophores. To our knowledge, this is a unique framework that accounts for the spectral coloring and provides automated detection of tissue spectral signatures at a volumetric scale, which can open many possibilities for translational research.

Credible commitments in contract relations: Where are the limits of the possible?

Credible commitments are the most important condition for contracts sustainability. The effects and ways of ensuring the commitments credibility are discussed in the new institutional economics and game theory. At the same time, there is a number of unresolved issues related to the microeconomic aspects of obligations’ reliability in contracts regarding specific resources. The article delves into various approaches to determining credible commitments in contractual relations at the micro level in the context of bilateral (multilateral) opportunism. The methodological basis of the study is the transaction costs economics. The paper shows the limitations of the compensation approach in modeling credible liabilities and explains the importance of relational credible commitments for ensuring sustainability and effectiveness of contracts. The author proposes an extended model of hostages in contracts using specific resources as a way of solving the “ugly princess” problem. Based on the specification of the temporal structure of contracting, we develop a game-theoretic model that takes into account the possibilities of opportunism on the part of both the pledger and the pledgee as an investor in specific assets. The study demonstrates the consequences of the wrong hostage chosen, as well as the characteristics of the interaction equilibrium due to the correct choice of it with an indication of the residual opportunism problem. The research provides a rationale why the promisee and the potential purchaser of full rights to an asset acting as a hostage are not always the same actor. The approaches to dealing with credible commitments in the context of renegotiations and multilateral contracts are outlined. The research results can serve as a basis for designing contracts requiring reciprocal collateral.

An Efficient Translational Motion Compensation Approach for ISAR Imaging of Rapidly Spinning Targets

For inverse synthetic aperture radar (ISAR) imaging of rapidly spinning targets, the large migration through range cells (MTRC) results in weak coherence between adjacent echoes, which makes the conventional envelope alignment method unable to be applied. By analyzing the correlation between the echoes, a translational motion compensation (TMC) method for rapidly spinning targets is proposed. Firstly, the rotation period of the target is estimated by the incoherent accumulation method for the echo signal after range compression. Secondly, Kalman filtering is performed on the shift values required to maximize the correlation coefficient of the echoes with one rotation period difference in azimuth time to obtain the relative translational motion of the radar and the target. Finally, a translational compensation function is constructed according to the results of Kalman filtering to compensate the phase items caused by translational motion. Furthermore, the covariance matrix of observation noise required by Kalman filtering is also provided. This method is used to achieve high-precision envelope alignment, and the effectiveness of the proposed method is validated by simulations.

Advanced part design method for springback minimization of stamped sheet metal car body components

The requirements for high-strength and low-weight car body structures prove challenging for the stamping process and cause growing problems with the elastic springback. Compensation approaches such as the geometrical adjustment of the stamping dies by the amount of and in opposite direction to the springback can be an effective solution in many cases. However, this approach can only lead to satisfactory results if the scatter of the springback is considered, controlled and in the best case reduced. The reduction of the scatter can be provoked by adaption of the part design. Therefore, different methods such as the topology optimization method, the so-called line-of-force method and shape optimization can be applied. In the present work an advanced topology optimization method is suggested. Based on the decomposition of the springback deformation into membrane and bending components, the most efficient optimization of the part design is found relying on unconventional topology optimization. For the proposed adapted topology optimization approach the excellent results are also confirmed when applied to the industry-relevant case of a truck rear panel.