Signal Correction Research Articles

A Framework for Reliability Analysis of Combinational Circuits Using Approximate Bayesian Inference

A commonly used approach to compute the error rate at the primary outputs (POs) of a circuit is to compare the fault-free and faulty copies of the circuit using XOR gates. This model results in poor accuracies with nonsampling-based methods for reliability estimation. An alternative is to use a single copy of the circuit with a four-valued representation for each net corresponding to the correct and incorrect signals. One problem in this formulation is the accurate propagation of associated probabilities. We use the framework of Bayesian inference (BI) to address this issue. We derive the conditional probability distribution (CPD) corresponding to the four-valued signals and find the output error rate using various approximate BI techniques. With our formulation, we demonstrate that the output error rate scales with the gate error probabilities. It is guaranteed to be zero when the gate error probability is zero, provided approximate BI algorithms based on sum-product belief propagation (BP) are used. Although inaccuracies increase at very low gate error probabilities, it is able to capture the relative reliability of outputs with respect to each other. We also propose a new method for finding the overall circuit error rate as the partition function for a fixed state of POs. This method provides a significant improvement in accuracy when compared with the existing method using OR gates.

From earth to space: A first deployment of 5G core network on satellite

Recent developments in the aerospace industry have led to a dramatic reduction in the manufacturing and launch costs of low Earth orbit satellites. The new trend enables the paradigm shift of satellite-terrestrial integrated networks with global coverage. In particular, the integration of 5G communication systems and satellites has the potential to restructure next-generation mobile networks. By leveraging the network function virtualization and network slicing, the satellite 5G core networks will facilitate the coordination and management of network functions in satellite-terrestrial integrated networks. We are the first to deploy a 5G core network on a real-world satellite to investigate its feasibility. We conducted experiments to validate the satellite 5G core network functions. The validated procedures include registration and session setup procedures. The results show that the satellite 5G core network can function normally and generate correct signaling.

Computationally Efficient LOB Algorithm for Digitization of Degraded Paper ECG Reports

Purpose: A less computationally intensive methodology is required to digitise paper Electrocardiogram (ECG) records from scanned photographs so that it can be implemented on mobile devices and used in remote places without the necessary hardware or software requirements. Due to numerous degradations inherent in older resting paper ECG recordings, automated digitization of paper ECG records also becomes challenging. Methods: In the proposed method, we present a robust and simple, Level of Binarization (LOB) characteristic curve to binarize paper records. The binarized image is processed using the discontinuous signal correction (DSC) technique and is vertically scanned to extract 1-D ECG signal. Results: The proposed method is tested for paper ECG records having degradations like variable background colour, steep slope, ink evaporation, and folding. The average RMS error and accuracy for 101 test samples, is 0.01 and 98.34 % respectively. The results show that, the LOB-DSC gives better performance when compared to the existing ECG extraction methods. As proposed, the LOB method does not require pre-processing, and the computational complexity is also reduced. Conclusion: An automatic threshold for binarization and estimation of several types of degradations in old ECG records is obtained using the less computationally intensive LOB characteristic curve. This method can be applied in remote locations with low availability of advanced processing devices for ECG signal extraction.

Beat-Notes Acquisition of Laser Heterodyne Interference Signal for Space Gravitational Wave Detection.

In space gravitational wave detection missions, the laser heterodyne interference signal (LHI signal) has a high-dynamic characteristic due to the Doppler shift. Therefore, the three beat-notes frequencies of the LHI signal are changeable and unknown. This may further lead to the unlocking of the digital phase-locked loop (DPLL). Traditionally, fast Fourier transform (FFT) has been used as a method for frequency estimation. However, the estimation accuracy cannot meet the requirement of space missions because of the limited spectrum resolution. In order to improve the multi-frequency estimation accuracy, a method based on center of gravity (COG) is proposed. The method improves the estimation accuracy by using the amplitude of the peak points and the neighboring points of the discrete spectrum. For different windows that may be used for signal sampling, a general expression for multi-frequency correction of the windowed signal is derived. Meanwhile, a method based on error integration to reduce the acquisition error is proposed, which solves the problem of acquisition accuracy degradation caused by communication codes. The experimental results show that the multi-frequency acquisition method is able to accurately acquire the three beat-notes of the LHI signal and meet the requirement of space missions.

Probabilistic orthogonal-signal-corrected principal component analysis

Much attention has been paid to reduce redundancy in data and extract discriminative features while preserving much of the relevant information. The technique of orthogonal signal correction (OSC) as a data processing tool removes directions of input features which are not correlated with the output variable. The PCA, on the other hand, has been successfully applied to problems in unsupervised dimensionality reduction of multivariate data. Application of PCA to orthogonal signal corrected data allows the resulting model to easily understand the relationships among variables and control the computational complexity. However, not being based upon a probability model, this procedure undergoes lack in statistical facilities to derive it such as maximum-likelihood estimation or model selection criteria. In this paper, we propose probabilistic orthogonal signal corrected principal component analysis (PO-PCA) which estimates the correct dimensionality based on a Bayesian approach. By formulating OSC and PCA as a probabilistic framework, we demonstrate how the whole procedure is determined by a maximum-likelihood solution of the density function. Similar to the Bayesian information criterion, we penalize the model for its complexity and therefore develop a model selection criterion for each of OSC and PCA, given enough observations. We present the experimental results of PO-PCA on simulation and real-life datasets, which illustrate the desired behavior of our probabilistic approach.

Risk Perceptions and Private Protective Behaviors: Evidence from COVID-19 Pandemic

Abstract We analyze data from a survey we administered during the COVID19 pandemic to investigate the relationship between people's subjective beliefs about risks and their private protective behaviors. On average, people substantially overestimate the absolute level of risk associated with economic activity, but have directionally correct signals about their relative risk based on their demographic characteristics. Subjective risk beliefs are predictive of changes in economic activities independently of government policies. Government mandates restricting economic behavior, in turn, attenuate the relationship between subjective risk beliefs and protective behaviors.

An adaptive control scheme against state‐dependent sensor attacks and input‐dependent actuator attacks in cyber‐physical systems

AbstractThe application of cyber‐physical systems (CPSs) in many areas is threatened by its embedded structure. This paper considers the design of an adaptive controller for linear CPSs against deception attacks. Specifically, a corrective signal is designed to suppress or offset the impact of sensor and actuator attacks, especially when the controller and the correction signal are also attacked. First, an adaptive controller is developed for time‐invariant deception attacks and further extended to time‐varying deception attacks. In each case, state‐independent sensor attacks and input‐independent actuator attacks, as well as state‐dependent sensor attacks and input‐dependent actuator attacks, are discussed. For the considered closed‐loop system with time‐invariant state‐dependent sensor attacks and input‐dependent actuator attacks, the controller the authors developed ensures the asymptotic stability of the systems. For the other three cases of the corresponding system, the controllers are also designed, which ensures uniform ultimate boundedness. Finally, several numerical examples are given to explain the availability of our control scheme.

Conducting a three-pulse DEER experiment without dead time: A review

Double electron-electron resonance spectroscopy (DEER, also known as PELDOR) is used to study spin-spin dipolar interactions between spin labels, at the nanoscale range of distances. The DEER effect is obtained as a signal generated by echo-forming microwave (mw) pulses with an additional mw pump pulse applied at a different frequency. It is important to carry out measurements without artefacts induced by overlap of the pulses in the time scale. Such an experiment without the dead-time effect is achieved using the 4-pulse (4p) DEER method. The analysis of the literature performed here shows however that the 3-pulse (3p) DEER can also be free of the dead time problem, for which there are two possibilities. The first occurs using a specially designed bimodal resonator, for which the two frequencies are completely decoupled. The second possibility, which can be implemented for any commercial spectrometer, involves the signal correction based on an additional “blank” measurement with the pump pulse applied outside the EPR resonance. A detailed comparison of the 3p and 4p DEER data obtained previously by Milov et al. [Appl. Magn. Reson. 41 (2011) 59–67] shows that 3p and 4p approaches give similar results. The advantages of the 3p DEER techniques are discussed.

Uncertainty Region Discovery and Model Refinement for Domain Adaptation in Road Detection

Road detection is an important task in intelligent transportation systems. In regard to the domain adaptation, although self-training methods generate pseudo-labels for retraining the model, redundancy and noise in pseudo-labels lead to limited improvement. We propose that necessary annotations are required to effectively handle this challenge. First, we introduce the classifier discrepancy to discover and annotate uncertainty regions in the target domain. Then, we also design a recurrent teacher–student module to consider both prior knowledge and correction signals, avoiding the risk of suboptimal solution entrapment. Experiments on public data sets show that our approach is competitive with state-of-the-art approaches.

Intensified Partial Least Squares Fault Diagnosis Method based on CARS

Partial Least Squares (PLS) is a fault diagnosis model used in statistical process monitoring. PLS decomposes the data space into a quality correlated subspace and an uncorrelated subspace, and monitors the state of both subspaces by setting the appropriate statistics. However, post-processing methods such as modified-PLS (MPLS) and efficient projection to latent structures (EPLS) gradually lose their ability to detect faults when low-intensity faults that do not affect quality. In order to eliminate the drawbacks of post-processing methods and improve the stability of algorithms under different conditions, this paper introduces the competitive and adaptive reweighted sampling method (CARS) into the field of fault diagnosis and proposes a fault filtering method combining CARS and orthogonal signal correction algorithm (OSC). First, an initial screening of the variable space was completed using CARS method. OSC algorithm is then used to remove quality- uncorrelated components. Next, the variable space is completely decomposed into a quality-correlated and an irrelevant subspace using intensified partial least squares method (IPLS). Finally, the validity of the model was verified using the simulations.

A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location

In this paper, a cutting-edge video target tracking system is proposed, combining feature location and blockchain technology. The location method makes full use of feature registration and received trajectory correction signals to achieve high accuracy in tracking targets. The system leverages the power of blockchain technology to address the challenge of insufficient accuracy in tracking occluded targets, by organizing the video target tracking tasks in a secure and decentralized manner. To further enhance the accuracy of small target tracking, the system uses adaptive clustering to guide the target location process across different nodes. In addition, the paper also presents an unmentioned trajectory optimization post-processing approach, which is based on result stabilization, effectively reducing inter-frame jitter. This post-processing step plays a crucial role in maintaining a smooth and stable track of the target, even in challenging scenarios such as fast movements or significant occlusions. Experimental results on CarChase2 (TLP) and basketball stand advertisements (BSA) datasets show that the proposed feature location method is better than the existing methods, achieving a recall of 51% (27.96+) and a precision of 66.5% (40.04+) in the CarChase2 dataset and recall of 85.52 (11.75+)% and precision of 47.48 (39.2+)% in the BSA dataset. Moreover, the proposed video target tracking and correction model performs better than the existing tracking model, showing a recall of 97.1% and a precision of 92.6% in the CarChase2 dataset and an average recall of 75.9% and mAP of 82.87% in the BSA dataset, respectively. The proposed system presents a comprehensive solution for video target tracking, offering high accuracy, robustness, and stability. The combination of robust feature location, blockchain technology, and trajectory optimization post-processing makes it a promising approach for a wide range of video analytics applications, such as surveillance, autonomous driving, and sports analysis.

High-Accuracy Absolute-Position-Aided Code Phase Tracking Based on RTK/INS Deep Integration in Challenging Static Scenarios

Many multi-sensor navigation systems urgently demand accurate positioning initialization from global navigation satellite systems (GNSSs) in challenging static scenarios. However, ground blockages against line-of-sight (LOS) signal reception make it difficult for GNSS users. Steering local codes in GNSS basebands is a desirable way to correct instantaneous signal phase misalignment, efficiently gathering useful signal power and increasing positioning accuracy. Inertial navigation systems (INSs) have been used as effective complementary dead reckoning (DR) sensors for GNSS receivers in kinematic scenarios, resisting various forms of interference. However, little work has focused on whether INSs can improve GNSS receivers in static scenarios. Thus, this paper proposes an enhanced navigation system deeply integrated with low-cost INS solutions and GNSS high-accuracy carrier-based positioning. First, an absolute code phase is predicted from base station information and integrated solutions of the INS DR and real-time kinematic (RTK) results through an extended Kalman filter (EKF). Then, a numerically controlled oscillator (NCO) leverages the predicted code phase to improve the alignment between instantaneous local code phases and received ones. The proposed algorithm is realized in a vector-tracking GNSS software-defined radio (SDR). Results of the time-of-arrival (TOA) and positioning based on real-world experiments demonstrated the proposed SDR.

Carrier Characteristic Bias Estimation between GNSS Signals and Its Calibration in High-Precision Joint Positioning

A distinctive feature of modern Global Navigation Satellite System (GNSS) signals is that they transmit multiple signal components at the same carrier frequencies. The idea of joints across the signal channels from the same carrier frequency and even across different frequencies has been presented in many studies for tracking purposes. Carrier joint tracking is required on the premise that the frequency and phase relationship between signals are nominal values, and the bias of carrier characteristics between signals is drowned in noise as the signal reaches the ground, which requires high-gain receiving equipment to restore the original signal. The space signal-quality monitoring and evaluation system built by the National Timing Center of the Chinese Academy of Sciences is based on a 40 m dish antenna, which can automatically track a single satellite and achieve a high-fidelity reception of navigation signals to a certain extent, realizing fine signal quality monitoring (SQM) of GNSS satellites. Based on this platform, we discuss four types of time distributions of the combined signals among different signal components and provide a method to estimate the carrier characteristic bias between GNSS signals. We derived the correction method of carrier characteristic bias in the joint reception by the joint tracking mathematical model. Under the conditions of narrow correlation and unobstructed case, the carrier characteristic deviation does not vary significantly with the correlator interval and the satellite elevation angle. Based on the results of stability analysis, it is recommended that the receivers should update the carrier frequency bias correction number of the intra-frequency signal and carrier phase bias correction number of the intra-frequency signal monthly. The carrier phase deviation correction number of the inter-frequency signal is performed daily. The measured data from satellites show that the phase accumulation error of the joint tracking carrier loop can be eliminated to achieve long-term stable tracking after frequency bias correction. After the carrier phase bias correction, the joint positioning accuracy of the B2a and B2b signals was improved by 0.81%, and those of the B1C, L1C, E1C, and B2a signals were improved by 0.35%, 0.04%, 0.20%, and 0.11%, respectively. The positioning accuracy improvement effect of inter-frequency signals was greater than that of intra-frequency signals after carrier phase correction.

A non-destructive determination of protein content in potato flour noodles using near-infrared hyperspectral imaging technology

Hyperspectral imaging (HSI) with the near-infrared (NIR) bands (900–2250 nm) was employed to investigate the non-destructive prediction of protein content in potato flour noodles. The protein content (8.906–10.515 %) of 120 potato flour noodles was studied to establish the prediction model. Partial least squares regression (PLSR) model was established based on the spectra of potato flour noodles to predict the protein content showing high performance. After optimization, orthogonal signal correction (OSC) was used to preprocess the original spectra, and the competitive adaptive reweighted sampling algorithm (CARS) was chosen to select characteristic wavelengths, therefore, OSC-CARS-PLSR was established. Next, 77 samples were selected as the calibration set, and the remaining 38 samples were used as the prediction set. The coefficient of determination (R2) and the root mean square error (RMSE) were used to evaluate the performance of the model. OSC-CARS-PLSR showed a high performance with R2 values of 0.9606 and 0.8925 and RMSE values of 0.070 % and 0.1385 % in the calibration set and prediction set, respectively. The visualization image was used to identify protein distribution in potato flour noodles. Overall, the results indicate that HSI technology could accurately predict the protein content in potato flour noodles providing a rapid and non-destructive method to detect protein and other compositions in grains and foods.

Algorithms for Complexing an Inertial Navigation System with Angular Acceleration Sensors

In this paper the problem of increasing the accuracy of inertial navigation system of an aircraft in the absence of high-precision additional information sensors, such as GPS, has been studied. It is proposed to install angular acceleration sensors on the gyrostabilized platform of the inertial navigation system. The use of signals from the angular acceleration sensors made it possible to generate correction signals for the inertial navigation system. Correction algorithms have been developed in the structure of the inertial navigation system and in its output signal. The effectiveness of the developed algorithms has been demonstrated using semi-natural simulation with the Ts060K inertial navigation system.

Phase-Discontinuity Correction Method With an Overlapped Signal Structure in Stepped-Carrier OFDM Radar With Dual Transmitters

A phase-discontinuity correction method for a stepped-carrier orthogonal frequency-division multiplexing (SC-OFDM) radar is proposed. Correction is accomplished using an overlapped signal structure in both the time and frequency domains, made possible by implementing a radar system with dual transmitters (DTs). The overlap in the frequency domain is used to correct phase discontinuities in the frequency steps, and the overlap in the time domain allows lowering the effective measurement time loss due to guard intervals. The subband phase discontinuity of DTs is extracted by cross-correlating a received subsymbol with the frequency-overlapped subsymbol from the next frequency step. After the gain and phase values at the maximum correlation output are obtained, subband and subsymbol correction processes are applied alternately for each frequency step in a chained manner. Simulated range-Doppler maps are presented without and with phase-discontinuity corrections using the proposed signal structure. Also, an SC-OFDM radar system with DTs is implemented to prove the concept. The results show that phase discontinuities among the frequency steps in a block can be readily corrected using the proposed signal structure and correction method without precalibration using a reference target.

Application of MIA-QSAR in Designing New Protein P38 MAP Kinase Compounds Using a Genetic Algorithm

Multivariate image analysis quantitative structure-activity relationship (MIA-QSAR) study aims to obtain information from a descriptor set, which are image pixels of two-dimensional molecule structures. In the QSAR study of protein P38 mitogen-activated protein (MAP) kinase compounds, the genetic algorithm application for pixel selection and image processing is investigated. There is a quantitative relationship between the structure and the pIC50 based on the information obtained. (The pIC50 is the negative logarithm of the half-maximal inhibitory concentration ( IC50 ), so pIC50 = −log IC50 .) Protein P38 MAP kinase inhibitors are used in the treatment of malignant tumors. The development of a model to predict the pIC50 of these compounds was performed in this study. To accomplish this, the molecules were first plotted and fixed in the same coordinates in ChemSketch. Then, the images were processed in the MATLAB program. Partial least squares (PLS) model, orthogonal signal correction partial least squares (OSC-PLS) model, and genetic algorithm partial least squares (GA-PLS) model methods are used to generate quantitative models, and pIC50 prediction is performed. The GA-PLS model has the highest predictive power for a series of statistical parameters such as root mean square error of prediction (RMSEP) and relative standard errors of prediction (RSEP). Finally, the molecular junction (docking) was done for predicted molecules in quantitative structure activity relationship (QSAR) with an appropriate receptor and acceptable results were obtained. These results are good and proper for the prediction of compounds with better properties.

Non-Gaussian Quality Relevant Process Monitoring Based on Higher-Order Statistics Projection to the Latent Structure and Independent Signal Correction

A novel statistical model based on the higher-order statistics projection to the latent structure (HPLS) is proposed, which uses a combination of higher-order statistics (mutual information and differential entropy) instead of covariance to extract latent variables. This model not only has an explicit representation similar to the projection to latent structure model but can also capture the non-Gaussian process features. Furthermore, in order to reduce the redundant components contained in the latent variables independent of the quality variables, a novel strategy called independent signal correction is proposed. Finally, the novel independent signal correction HPLS (ISC-HPLS) model and the corresponding process monitoring strategy are developed. This model considers higher-order statistics that may involve certain key features of non-Gaussian processes and eliminates redundant components. Experimental results from synthetic numerical simulations and the Tennessee-Eastman process benchmark test verified the effectiveness of the proposed method.

N-glycosylation on Asn-57 is required for the correct HAI-2 protein folding and protease inhibitory activity.

Hepatocyte growth factor activator inhibitor (HAI)-2 is an integral membrane Kunitz-type serine protease inhibitor that regulates the proteolysis of matriptase and prostasin in a cell-type selective manner. The cell-type selective nature of HAI-2 function depends largely on whether the inhibitor and potential target enzymes are targeted to locations in close proximity. The N-glycan moiety of HAI-2 can function as a subcellular targeting signal. HAI-2 is synthesized with 1 of 2 different N-glycan modifications: one of oligomannose-type, which largely remains in the endoplasmic reticulum/GA, and another of complex-type, which is targeted toward the apical surface in vesicle-like structures, and could function as an inhibitor of matriptase and prostasin. HAI-2 contains 2 putative N-glycosylation sites, Asn-57 and Asn-94, point mutations of which were generated and characterized in this study. The protein expression profile of the HAI-2 mutants indicates that Asn-57, and not Asn-94, is responsible for the N-glycosylation of both HAI-2 species, suggesting that the form with oligomannose-type N-glycan is the precursor of the form with complex-type N-glycan. Unexpectedly, the vast majority of non-glycosylated HAI-2 is synthesized into multiple disulfide-linked oligomers, which lack protease inhibitory function, likely due to distorted conformations caused by the disarrayed disulfide linkages. Although forced expression of HAI-2 in HAI-2 knockout cells artificially enhances HAI-2 oligomerization, disulfide-linked HAI-2 oligomers can also be observed in unmodified cells. These results suggest that N-glycosylation on Asn-57 is required for folding into a functional HAI-2 with full protease suppressive activity and correct subcellular targeting signal.

Stress estimation by the prefrontal cortex asymmetry: Study on fNIRS signals

BackgroundFunctional near-infrared spectroscopy (fNIRS) is a non-invasive technique frequently used to measure the brain hemodynamic activity in applications to evaluate affective disorders and stress. Using two wavelengths of light, it is possible to monitor relative changes in the concentrations of oxyhemoglobin and deoxyhemoglobin. Besides, the spatial asymmetry in the prefrontal cortex activity has been correlated with the brain response to stressful situations. MethodsWe measured prefrontal cortex activity with a NIRS multi-distance device during a baseline period, under stressful conditions (e.g., social stress), and after a recovery phase. We calculated a laterality index for the contaminated brain signal and for the brain signal where we removed the influence of extracerebral hemodynamic activity by using a short channel. ResultsThere was a significant right lateralization during stress when using the contaminated signals, consistent with previous investigations, but this significant difference disappeared using the corrected signals. Indeed, exploration of the susceptibility to contamination of the different channels showed non-homogeneous spatial patterns, which would hint at detection of stress from extracerebral activity from the forehead. LimitationsThere was no recovery phase between the social and the arithmetic stressor, a cumulative effect was not considered. ConclusionsExtracerebral hemodynamic activity provided insights into the pertinence of short channel corrections in fNIRS studies dealing with emotions. It is important to consider this issue in clinical applications including modern monitoring systems based on fNIRS technique to assess emotional states in affective disorders.