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Nonlinear intensity responses of used devices, resulting in negative effects on the measurement accuracy, are a well-known problem in fringe projection profilometry (FPP). Previous studies have concentrated on estimating the global response parameters of the system through tedious calibration or auxiliary fringe patterns, which weaken the generalization capability and reduce measurement efficiency, respectively. To this end, we introduce a flexible nonlinear error self-correction method based on the minimum phase probability variance without prior calibration or extra projection. First, based on the nonlinear response expression of the system, we derive a pixel-independent parameter estimation model, which reveals that the resulting inverse-wrapped phase can correct nonlinear errors to some extent. Subsequently, an iterative averaging scheme is further developed to maximize the error correction and finalize the optimal phase. In particular, a new phase quality metric, called the phase probability variance, is proposed to guide the iteration process and evaluate the acquired average phases. The proposed method does not require tedious calibration or extra projection while being insensitive to unwrapping schemes, time-varying nonlinear systems, and fringe frequencies, thus greatly improving measurement efficiency and facilitating generalization capabilities. Moreover, the pointwise correction is able to effectively protect the edges and details of the measured object. The results of the simulations and experiments confirm that the proposed method is capable of significantly reducing the phase errors induced by the system's nonlinearity and improving the measurement accuracy. • A flexible nonlinear error self-correction method for the FPP system is presented. • A new phase quality metric called the phase probability variance, is proposed. • This study does not require tedious calibration or extra projection. • This study improves measurement efficiency and promotes the generalization ability. • This study can effectively protect the edges and details of measured objects. [ABSTRACT FROM AUTHOR]

Accurate forecast for water quality is of great importance because it can support water resource management with the future information. In this research, we propose a novel hybrid model by using data decomposition, error correction, and machine learning. In our method, first, the initial forecast is obtained by a prediction model that uses improved complete ensemble empirical mode decomposition with adaptive noise and bidirectional long short-term memory (BLSTM) neural network. Next, a novel error correction framework, which is built by variational mode decomposition and BLSTM neural network, is used to improve forecast accuracy by correcting the initial forecast error. Water quality data of Poyang Lake, China is used to evaluate our model. Results indicate that our model shows highly accurate forecast performance for all of the 9 water quality datasets (the average of mean absolute percentage error (MAPE) of 7 day-ahead forecast is 2.12%; 30 day-ahead forecast is 4.06%). In addition, our model outperforms the competitor models, particularly, compared to the prediction model without error correction framework, the average of MAPE is reduced by 33.33% for 7 day-ahead forecast; 30.48% for 30 day-ahead forecast. This research demonstrates that the proposed error correction framework is an effective tool to improve forecast accuracy for water quality. [Display omitted] [ABSTRACT FROM AUTHOR]

Despite the pervasive use of citation generators and other online citation resources like Purdue's Online Writing Lab (OWL), undergraduate students' bibliographies are still often riddled with errors. This article explores the possibility that these errors are the product of more than just carelessness. After reviewing the literature on students' citation practices and instructional approaches to citation errors, the paper examines the underlying assumptions governing the instructor's unsuccessful error correction practices in a credit-bearing information literacy course. Next, an assessment for learning in-class activity is described that helped uncover some of the obstacles students face that prevent them from using resources like OWL effectively when constructing or correcting their citations. The article ends with an assessment of the activity and students' citations in their final research project. [ABSTRACT FROM AUTHOR]

Accurate cluster photovoltaic power prediction (CPPP) is crucial for the operation and control of renewable energy grid-connected power systems. The traditional modeling strategies for CPPP such as direct aggregation (DA) and statistical upscaling (SU) have limitations such as error accumulation and upscaling factor uncertainty. To address these issues, this paper proposed a novel hybrid approach for CPPP by combining machine learning models with an improved SU technique. Firstly, a robust broad learning system (BLS) model, in which the Generalized Maximum Correntropy Criterion (GMCC) is used to replace the original mean square error (MSE) loss in BLS, is proposed to solve the problem of multiple outliers affecting the prediction accuracy of regional cluster stations, and it is called GBLS. Then, the Relevance Vector Machine (RVM) as an effective nonlinear regression model is further utilized to compensate for the prediction errors obtained by the GBLS to form the hybrid prediction model, namely GBLS-RVM. Moreover, to mitigate the uncertainty associated with scaling factors in traditional SU strategy, a new SU strategy is developed to refine the relationship between the reference station and the cluster sub-region, enabling direct modeling for regional power prediction. Finally, data from two PV clusters in different regions of China are used to validate the effectiveness of the proposed model, and the results show that under the improved SU strategy, the GBLS-RVM model, reduced RMSE by approximately 33.7% compared to the traditional BLS model, and the RMSE decreased by 12.89% and 30.2% when compared to traditional DA and traditional SU strategies. • A novel hybrid method for CPPP is developed by combining ML and improved SU technique. • A BLS with GMCC (GMCC-BLS) as a prediction model is developed to handle the outliers in measurement data. • An innovative error correction strategy based on RVM is proposed to improve prediction reliability. • A new SU technique is used to establish direct relationships between reference stations and subregions. [ABSTRACT FROM AUTHOR]

• Proposes an Adaptive Kernel Correlation Filter algorithm improving tracking precision. • A binocular error correction method improves the synchronization precision of cameras. • A motion capture system evaluates and compensates for binocular measurement errors. • The reconstructed models' average error and similarity are 1.352 mm and 0.996, respectively. • The results contribute to the deformation measurement of flexible mechanisms. In order to measure the kinematic parameters and deformation of flapping wings precisely, this paper presents a wing motion reconstruction method utilizing a binocular vision system (BVS) comprising two action cameras. We designed an Adaptive Kernel Correlation Filter algorithm based on target scaling prediction for wing reconstruction to improve target tracking accuracy and robustness. A binocular error correction method with a startup time difference-induced error model was proposed, which helps the action cameras achieve the same high-precision synchronization as the industrial cameras with external trigger mode. Moreover, an infrared motion capture system is applied to assess and compensate for errors in the BVS. The experimental results show that the maximum value of the average errors of the BVS is 1.352 mm, and the minimum similarity value of the two wings is 0.996. The results contribute to the deformation measurement and optimization of soft materials such as the flapping wings. [ABSTRACT FROM AUTHOR]

• This paper suggests a grating Moiré fringe-based error correction approach that uses an artificial neural network (ANN) and the quartile method (QM). • The approach uses QM for data cleaning operations to eliminate outliers and creates the ANN model with a structure of fully connected layer-normalization layer-fully connected layer for regression training in order to achieve lightweight and stability. • The proposed QM-ANN error correction model's mean absolute error (MAE) can reach 3.3 nm once the QM data has been cleaned and the ANN model has been trained. Many interference factors of the complex and changeable working environment of the grating sensor are the main factors affecting the accuracy; a grating Moiré fringe error correction model based on the quartile method (QM) and artificial neural network (ANN) is proposed in this paper. Firstly, the grating sensor's signals in the working process are collected, and three kinds of interference signals (temperature, humidity and vibration) from the grating sensor working environment are collected simultaneously. Secondly, the QM is used to detect outliers from the collected data and delete data outliers directly. Subsequently, the processed data are input into the ANN model for training. The model is lightweight and stable, because it adopts the cascaded structure of full connection layer-normalization layer-full connection layer, which does not need deploy the complex convolution layer module etc. Finally, the corrected data are output after being processed by the QM-ANN error correction model. The experimental results show that the mean absolute error (MAE) of the proposed QM-ANN error correction model is better than QM-convolutional neural network (CNN), QM-long short-term memory (LSTM) and ANN, CNN, LSTM models without QM. It shows that the proposed model has better robustness and practicability. [ABSTRACT FROM AUTHOR]

• This paper presents an error correction method for curved rail profile measurement based on selective focus, without adding additional hardware. • This paper proposes a method for solving the optimal auxiliary plane through the objective function of the overlap between virtual 3D rail and actual rail. Its advantage is that the optimal auxiliary plane can be solved through a single line laser. • Laboratory experiments have shown that the rail wear error has been reduced to 0.1 mm, while vehicle dynamic experiments have shown that the rail wear error has been reduced to within 0.2 mm. • This paper proposes a virtual 3D rail creation method based on the center of the light strip, providing a new insight into future work on rail profile measurement error correction. Rail profile detection is an important means of railway operation and maintenance. Currently, structured light profile measurement technology is mainly used for vehicle-mounted rail profile measurement. The body attitude of the vehicle will change as it passes by a curve, and the laser plane of the rail profile measurement device would be non-perpendicular to the rail in the local longitudinal direction, resulting in errors in rail profile measurement. To address this issue, an error correction method based on selective focus for curved rail profile measurement is proposed. The virtual 3D rail is generated with the section image of a single-beam laser and projected onto the image plane, which is then combined with the projection data of virtual 3D rail and the actual rail from the rail image to build the optimization function. The optimal auxiliary plane perpendicular to the rail's longitudinal direction is then determined. Finally, error correction is achieved by projecting the measured data onto the optimal auxiliary plane. The effectiveness and accuracy of the proposed method are validated through experiments, demonstrating that regardless of the laser plane's relative pitch or yaw motion with respect to the rail, the corrected profile wear measurement error was under 0.10 mm, satisfying the requirement for rail wear measurement accuracy. In this study, the plane perpendicular to the longitudinal direction of the rail was obtained through selective focus, thus providing a new insight into future work on rail profile measurement error correction. [ABSTRACT FROM AUTHOR]

As the sole orbit data source of space objects for public access, the NORAD catalog provides the basic orbital information in the form of two-line element (TLE) for various space tasks. But the accuracy of orbit prediction (OP) using TLE with the analytical Simplified General Perturbations-4 (SGP4) propagator drops quickly with time, especially for low orbits, significantly limiting the capability of TLEs for advanced space situational awareness (SSA) applications. To enhance the TLE performance over long-duration OP, this paper proposes a data-driven method for improved TLE-based orbit predictions through mining and transferring the orbit error patterns. Two state-of-the-art learning methods, the gradient boosting decision tree (GBDT) and convolutional neural networks (CNN), are applied to model the underlying error patterns, and then the learned models are used as an error corrector to modify the future orbit predictions. Experimental results demonstrate that the time-varying orbit error patterns in the past can be captured by the developed learning framework. As a result, the accuracy of orbit predictions over the future 14 days can be improved by more than 75% in the along-track direction and 90% in the cross-track and radial directions, when the model-predicted errors are used. It also shows that the error pattern learning and application process is computationally efficient, and it could be implemented for near real-time applications. This study demonstrates the promising potential of machine learning (ML)/deep learning (DL) for enhanced SSA capability with the publicly available TLEs. • A machine learning (ML)/deep learning (DL) framework for modeling the TLE orbit prediction (OP) error pattern is developed. • The TLE OP error pattern can be modeled by the ML/DL methods with an accuracy better more than 90%. • The accuracy of future 14 days' TLE OPs can be improved greatly with the error corrections predicted by the ML/DL models. • The computing efficiency of the developed ML/DL framework is high. [ABSTRACT FROM AUTHOR]

[Display omitted] • This study proposes simultaneous in-situ calibration for multiple physical and virtual sensors. • Field application shows virtual sensor errors in building operation. • The importance and effectiveness of simultaneous in-situ calibration are discussed. • Simultaneous in-situ calibration contributes to the virtual sensing-driven digital twins. Digital twin-enabled building operations heavily rely on a building sensor network to collect operational data. Virtual sensing, in combination with physical sensors, has great potential in establishing a reliable and informative sensing environment. However, physical sensors can experience hardware errors or systematic errors due to the working environment within building systems. Virtual sensors are also subject to their model uncertainties arising from input physical sensor errors and changes in system operations. These virtual sensor errors have a negative effect on the in-situ calibration accuracy of corresponding physical sensors. To address these issues, this study proposes a simultaneous in-situ calibration (SIC) method designed to calibrate multiple physical and virtual sensors with varying errors in the context of digital twins. The SIC problem is mathematically formulated, including two types of distance functions (direct and indirect) and defining correction functions to calibrate various types of virtual sensors (such as backup, validation, replacement/virtualization, and observation sensors). Importantly, the proposed method tackles the challenges posed by the unknown calibration environment in operational systems. A field application study was conducted for two physical sensors and three different virtual sensors operating in a district heating substation serving real residential buildings in Korea. The case study shows how the different calibration accuracies for each sensor depend on the calibration approaches, such as physical-targeted (existing Case 1), virtual-targeted (existing Case 2), and simultaneous calibration (novel Case 3). Under the simultaneous errors, only Case 3 satisfied the reference accuracy for all sensors. Specifically, the average MAEs in Case 3 were improved by approximately 79.3%, 75.3%, 63.9%, and 87.5% for the target physical sensor, backup virtual sensor, virtualized sensor, and validation virtual sensor, respectively, compared to the accuracies before calibration. These results demonstrate the importance and effectiveness of simultaneous in-situ calibration of physical and virtual sensors in digital twin-enabled building operations. The proposed calibration can contribute to the synchronization of physical and virtual building systems, thus achieving reliable and informative virtual sensing-driven digital twins. [ABSTRACT FROM AUTHOR]

Wind power forecasting can effectively improve the energy utilization efficiency of a power system and ensure its stable operation. In this study, a novel hybrid multistep prediction model, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), the kernel principal component analysis (KPCA), an enhanced arithmetic optimization algorithm (ENAOA), a bidirectional long short-term memory (BILSTM) neural network, and error correction, was designed for short-term wind power forecasting. First, the collected original wind power data were decomposed into multiple intrinsic mode functions (IMFs) through a secondary decomposition composed of the CEEMDAN and VMD, which eliminated the interactions between different components to achieve denoising. Second, the KPCA was adopted to reduce the dimensionality of the multiple IMFs by extracting the principal components, effectively reducing the complexity of the multidimensional IMF data and improving the forecasting efficiency of the proposed prediction model. Subsequently, an ENAOA was proposed based on the Sobol sequence, adaptive T-distribution, and random walk strategy to optimize the BILSTM parameters. Finally, multiple preprocessed components were predicted by the optimized BILSTM, after which error correction was performed to obtain the final prediction results, which could further reduce the forecast error of the designed prediction model. Based on two sets of data collected from a wind farm in northwest China, the simulation results of 1-step, 4-step, 7-step, and 10-step forecasting revealed that compared with other incomplete models, the various algorithms adopted in the hybrid forecasting model reduced the prediction errors to different degrees, significantly enhanced the wind power prediction performance, and validated the effectiveness and feasibility of the proposed model. • Original wind power data is preprocessed through CEEMNAN and VMD. • KPCA is adopted to extract principal components of several IMFs. • Propose an improved ENAOA optimization algorithm through three improvements. • Error correction is employed to further increase the forecast accuracy. • The hybrid multi-step forecasting model greatly reduces the prediction error. [ABSTRACT FROM AUTHOR]

• The use of EFCC system for various targeng experiments is presented. • The change of JET wall from Carbon to Tungsten and Beryllium one does not affect the frequency response. • 3D magnec fields can be exploited to probe plasma stability, to study radiaon asymmetries, to control ELM, to compensate spurious magnec fields. This work describes the usage of Error Field Correction Coil (EFCC) system [ Barlow I. et al. 2001 Fusion Eng. Des. 58–59 189 ], which is a set of 4 coils located external to the vessel of the JET device, with the aim of introducing non-axisymmetric n = 1 magnetic field perturbations in various targeted plasma experiments. Besides being used to characterize and correct the intrinsic error field, the EFCCs system has been exploited to i) probe plasma stability in high-β N regimes through the MHD spectroscopy technique, allowing the identification of resistive-wall mode stable operational scenarios, ii) study radiation asymmetry when testing the shattered pellet injector as the prime candidate for ITER disruption mitigation system, and iii) control edge localized modes, permitting a reduction in heat fluxes and carbon erosion of the divertor target plates. [ABSTRACT FROM AUTHOR]

In contrast to conventional methods of optimizing feature-matching results at the data-processing level, we utilize the tightly coupled mode to fuse the information collected by the camera and inertial sensor at the data-gathering level, which contributed to better accuracy and robustness in the feature-matching process. Specifically, the proposed visual-inertial feature tracking method that combines inertial measurement unit (IMU) calibration, feature matching, and prediction algorithms. The approach includes a vision-aided multi-level IMU systemic calibration method and an inertial-aided image feature prediction algorithm, which effectively processes and utilizes information from multiple sensors. Our method addresses not only the influence of image distortion and blur resulting from illumination changes and fast camera motion but also the problem of measurement errors that can arise from the long-term operation of the inertial sensors. Extensive experiments have been conducted to demonstrate that the efficiency of it is superior to the state-of-the-art methods: With the accuracy rate remains the same level, the speed of feature matching is improved by 41.8%. Additionally, when it is applied to simultaneous localization and mapping systems, its localization performances are better than that of the VINS-mono method by 8.1%. • A vision-based multi-layer system-level calibration strategy for IMU is developed. • To determine the predicted position of the feature points, the calibration findings are merged with the feature prediction algorithm based on inertial data. • An error correction approach is provided called "Trust zone" to correct the errors in the location of the expected to feature points. • The zero bias of IMU affects measurement accuracy in the actual workplace has been correct. • A vision-aided online calibration approach is added into the back-end optimization. [ABSTRACT FROM AUTHOR]

The accurate prediction of wind speed is important in satisfying the demands of power grids. However, the prediction of wind speed is challenging because of its randomness and volatility, especially in multi-step cases. This study proposes a novel multi-step wind speed prediction model combining a Weather Research and Forecasting (WRF) simulation and an error correction strategy. First, the WRF model is adopted to predict the wind speed. Variational Mode Decomposition (VMD) is then employed to mine features of the predicted wind speed using the WRF model. The Principal Component Analysis (PCA) method is next used to extract the main components and remove illusive components. Using these principal components and prediction error as the training dataset, Long Short-Term Memory (LSTM) is applied for error correction. The WRF-VMD-PCA-LSTM model is thus developed for the multi-step prediction of wind speed. In a case study of a wind farm located in Sichuan Province, China, the proposed WRF-VMD-PCA-LSTM model outperforms models to which it is compared. The results reveal that the VMD-PCA method effectively extracts features hidden in the numerical WRF output. The proposed model effectively improves the accuracy of multi-step wind speed prediction. • Features hidden in the numerical weather prediction output are extracted by a hybrid VMD-PCA method. • Propose an error correction strategy to improve the prediction accuracy of numerical weather prediction. • The proposed model is effective in multi-step wind speed prediction. [ABSTRACT FROM AUTHOR]

This paper presents three-layer dynamic binary neural networks characterized by ternary connection parameters and the signum activation function. The dynamics is described by a difference equation of binary state variables. Depending on the parameters, the network can generate various binary periodic orbits. We give two main theoretical results. First, when a desired periodic orbit is given, we can set the parameters that guarantee storage and local stability of the periodic orbit. The stability is related to error correction of various binary signals in engineering applications. Second, if a part of the connection parameters becomes zero then stability of the periodic orbit becomes very strong. In this case, all the initial states fall directly into the periodic orbit. [ABSTRACT FROM AUTHOR]

The radiation sensitivity of integrated memory cells increases dramatically as the supply voltage decreases. Although there are some Error Correcting Code (ECC) studies to prevent faults on memories used in space applications, there is no consensus on choosing the best ECC product-type with two-dimensional Hamming to mitigate data faults in memory. This work introduces the Product Code for Space Applications (PCoSA), an ECC product based on Hamming and parity in both rows and columns for use in memory with space-application reliability requirements. The potentialities of PCoSA were evaluated by injecting (i) thirty-six error patterns already available in the literature and (ii) all possible combinations of up to seven bitflips. PCoSA has corrected all cases of the thirty-six error patterns, and it has a correction rate of 100% for any three bitflips, 82.67% for four bitflips, and 69.7% for five bitflips. • ECC for use in memory. • Product Code based on hamming. • High MBU correction rate in memories. [ABSTRACT FROM AUTHOR]

• Causal relationship among waste generation, R&D intensity, and economic growth. • Panel vector error correction model is employed to study the causal relationship. • Regions of old and new EU countries are compared. • Bidirectional causality exists between waste generation and economic growth. • Bidirectional link among waste generation, heating energy, and R&D intensity. The relationship between economic growth and waste generation is a major global concern. Previous studies provided no conclusive evidence as to the causality between these two concepts, which can be attributed to at least two problems. First, R&D intensity is increasingly recognized as being an important determinant of environmental quality. Second, the regional level is considered to be important for the implementation of waste management policies, as regions and municipalities, among others, are responsible for separate collection systems and for establishing and managing treatment facilities. Previous studies failed to reflect the heterogeneity of the regions, which may lead to biased results. To address these problems, the panel vector error correction model was employed to examine the Granger causality in EU regions. The results provide empirical support for the existence of short- and long-run bidirectional causality between waste generation and economic growth in EU regions. A bidirectional link among waste generation, heating energy, and R&D intensity was also observed. The policy implication is that traditional economic development policies are not enough to reduce waste generation in EU regions. Economic tools, such as charges and incentives, and eco-innovation policies should be introduced to promote the region's shift towards a circular economy model. [ABSTRACT FROM AUTHOR]

Optical three-dimensional coordinate measurement using stereo vision has systematic errors that affect measurement quality. This paper presents a scheme for measuring, modelling and correcting these errors. The position and orientation of a linear stage are measured with a laser interferometer while a stereo vision system tracks target points on the moving stage. With reference to the higher accuracy laser interferometer measurement, the displacement errors of the tracked points are evaluated. Regression using a neural network is used to generate a volumetric error model from the evaluated displacement errors. The regression model is shown to outperform other interpolation methods. The volumetric error model is validated by correcting the three-dimensional coordinates of the point cloud from a photogrammetry instrument that uses the stereo vision system. The corrected points from the measurement of a calibrated spherical artefact are shown to have size and form errors of less than 50 μm and 110 μm respectively. A reduction of up to 30% in the magnitude of the probing size error is observed after error correction is applied. • Error in stereo vision 3D measurement is evaluated using a laser interferometer. • Volumetric error model is developed from 3D errors using neural network. • A photogrammetric error correction scheme is presented for verification. • Up to 30% reduction in the magnitude of probing size error was observed. [ABSTRACT FROM AUTHOR]

On December 27, 2018, the Beidou-3 System (BDS-3) has completed the deployment of 18 Medium Earth Orbit (MEO) satellites combined as a space constellation. In addition to the augmentation information for the new system signals B1C and B2a, the BDS-3 is compatible with the three augmentation information broadcast by the BDS-2 system for B1I and B3I signals: equivalent clock error correction, User Differential Ranging Error (UDRE) and Grid Ionosphere Vertical Error (GIVE). In this paper, the observation data of Beidou monitoring network are used to analyze the pseudo-range observation quality of the smooth transition signals B1I and B3I of BDS-2 and BDS-3. At the same time, the relationship between the equivalent clock error correction and the prediction error of the satellite clock is analyzed by using the Two-Way Satellite Time and Frequency Transfer (TWSTFT) data. The results show that the correlation between the equivalent clock error correction and the monitored clock error by using the TWSTFT data is greater than 60%. We calculate the UDRE by using the equivalent clock error correction. The results show that the satellite equivalent clock error correction can improve the accuracy of User Equivalent Range Error (UERE) by about 50%. This paper also compares the positioning accuracy of the BDS-2 satellites with the BDS-2 satellites combined BDS-3 satellites. The results show that the three-dimensional positioning accuracy is improved by about 30% after the BDS-3 satellites are added. [ABSTRACT FROM AUTHOR]

The pose accuracy of a robot manipulator may be improved by assessing and correcting systematic errors. Both offline and online strategies can be considered. To date, there has not been a solution for the online pose error correction of parallel manipulators. Moreover, offline strategies using indoor-GPS as reference measurement system have not yet been investigated. In this paper an optimization-based kinematic calibration method and an online correction technique are proposed and implemented for a low-cost Stewart Platform. In both cases, an indoor-GPS system was used as reference measurement equipment. Performance of both strategies are compared to a kinematic calibration method based on direct parameter measurement. Pose errors are evaluated for each strategy using a robotic total station. Performance of the optimization-based calibration and the online correction technique were similar and better than the direct parameter measurement calibration. Both techniques resulted in average pose errors less or equal to 0.3 mm and 0.05°. The proposed strategies may be adapted to other similar parallel manipulators and are applicable to large sized equipment. • Novel kinematic calibration technique for Stewart Platforms using indoor-GPS. • Novel online pose-error correction scheme for Stewart Platforms using indoor-GPS. • Novel strategies significantly reduced pose errors of a low-cost Stewart Platform. • Performance similar for both strategies. Average pose errors less or equal to 0.3 mm and 0.05°. [ABSTRACT FROM AUTHOR]

In this paper we study solutions to multivariate stochastic delay differential equations (MSDDEs) and their relation to the discrete-time cointegrated VAR model. In particular, we observe that an MSDDE can always be written in an error correction form and, under suitable conditions, we argue that a process with stationary increments is a solution to the MSDDE if and only if it admits a certain Granger type representation. A direct implication of these results is a complete characterization of the cointegration space. Finally, the relation between MSDDEs and invertible multivariate CARMA equations is used to introduce the cointegrated MCARMA processes. [ABSTRACT FROM AUTHOR]

Recently, there has been increasing interest in designing schemes for quantum computations that are robust against errors. Although considerable research has been devoted to quantum error correction schemes, much less attention has been paid to optimizing the speed it takes to perform a quantum computation and developing computation models that act on decoherence-free subspaces. Speeding up a quantum computation is important, because fewer errors are likely to result. Encoding quantum information in a decoherence-free subspace is also important, because errors would be inherently suppressed. In this paper, we consider quantum computation in a decoherence-free subspace and also optimize its speed. To achieve this, we perform certain single-qubit quantum computations by simply permuting the underlying qubits. In this paper, we make progress in understanding the extent in which quantum computation can be performed by permuting qubits. Namely, we provide two different subgroups of the single qubit Clifford group that can be computed solely by permutations. To make the quantum computation universal for one of the schemes, we rely on resonant couplings motivated from physics. Our first scheme potentially improves the speed in which a quantum computation can be done. We also reduce the problem of finding the existence of permutational Clifford gates to that finding an upper bound for the rank of certain matrices. [ABSTRACT FROM AUTHOR]

This paper emphasizes on NavIC's performance in ionospheric studies over the Indian subcontinent region. The study is performed using data of one year (2017–18) at IIT Indore, a location near the northern crest of Equatorial Ionization Anomaly (EIA). It has been observed that even without the individual error corrections, the results are within ± 20 % of NavIC VTEC estimates observed over the 1° × 1° grid of IPP surrounding the GPS VTEC estimates for most of the time. Additionally, ionospheric response during two distinct geomagnetic storms (September 08 and 28, 2017) at the same location and other IGS stations covering the Indian subcontinent using both GPS and NavIC has also been presented. This analysis revealed similar variations in TEC during the geomagnetic storms of September 2017, indicating the suitability of NavIC to study space weather events along with the ionospheric studies over the Indian subcontinent. [ABSTRACT FROM AUTHOR]

Objectives: The Patient-Reported Outcomes Measurement Information System® (PROMIS) Profile instruments measure health status on 8 PROMIS domains. The PROMIS-Preference (PROPr) score provides a preference-based summary score for health states defined by 7 PROMIS domains. The Profile and PROPr share 6 domains; PROPr has 1 unique domain (Cognitive Function-Abilities), and the Profile has 2 unique domains (Anxiety and Pain Intensity). We produce an equation for calculating PROPr utility scores with Profile data.Methods: We used data from 3982 members of US online survey panels who have scores on all 9 PROMIS domains. We used a 70%/30% split for model fit/validation. Using root-mean-square error and mean error on the utility scale, we compared models for predicting the missing Cognitive Function score via (A) the population average; (B) a score representing excellent cognitive function; (C) a score representing poor cognitive function; (D) a score predicted from linear regression of the 8 profile domains; and (E) a score predicted from a Bayesian neural network of the 8 profile domains.Results: The mean errors in the validation sample on the PROPr scale (which ranges from -0.022 to 1.00) for the models were: (A) 0.025, (B) 0.067, (C) -0.23, (D) 0.018, and (E) 0.018. The root-mean-square errors were: (A) 0.097, (B) 0.12, (C) 0.29, (D) 0.095, and (E) 0.094.Conclusion: Although the Bayesian neural network had the best root-mean-square error for producing PROPr utility scores from Profile instruments, linear regression performs almost as well and is easier to use. We recommend the linear model for producing PROPr utility scores for PROMIS Profiles. [ABSTRACT FROM AUTHOR]

With the improvement in the service accuracy and expansion of the application scope of satellite navigation systems, users now have high demands for system integrity that are directly related to navigation safety. As a crucial index to measure the reliability of satellite navigation systems, integrity is the ability of the system to send an alarm when an abnormity occurs. The new-generation Beidou Navigation Satellite System (BDS-3) prioritized the upgrading of system integrity as an important objective in system construction. Because the system provides both basic navigation and satellite-based augmentation system (SBAS) services by the operational control system, BDS-3 adopts an integrated integrity monitoring and processing strategy that applies satellite autonomous integrity monitoring and ground-based integrity monitoring for both the basic navigation service and SBAS navigation service. BDS-3 also uses an improved and refined integrity parameter system to provide slow, fast and real-time integrity parameters for basic navigation, and provide SBAS-provided integrity information messages in accordance with Radio Technical Commission for Aeronautics (RTCA) specification and dual frequency, multi-constellation (DFMC) specification to support the SBAS signal frequency, single constellation operation and DFMC operation respectively. The performance of BDS-3 system integrity monitoring is preliminarily verified during on-orbit testing in different states, including normal operation, satellite clock failure and satellite ephemeris failure. The results show that satellite autonomous integrity monitoring, ground-based integrity monitoring and satellite-based augmentation all correctly work within the system. Satellite autonomous integrity monitoring can detect satellite clock failure but not satellite orbit failure. However, ground-based integrity monitoring can detect both. Moreover, the satellite-based augmentation integrity system monitors the differential range error after satellite ephemeris and clock error corrections based on user requirements. Compared to the near minute-level time-to-alert capability of ground-based integrity monitoring, satellite autonomous integrity monitoring reduces the system alert time to less than 4 s. With a combined satellite-ground monitoring strategy and the implementation of different monitoring technologies, the BDS-3 integrity of service has been considerably improved. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate changes in refractive and corneal responses in myopic children wearing orthokeratology (ortho-k) lenses with conventional compression factor (CCF, 0.75 D) and increased compression factor (ICF, 1.75 D).Methods: This was a double-blind self-controlled study. Subjects were randomly fitted with CCF in one eye and ICF in the fellow eye. Weekly monitoring of refraction, visual acuity (VA), external ocular health, and corneal responses were performed over one month of lens wear and after discontinuation of lens wear until stabilization was achieved.Results: Twenty-five subjects, aged 9.4 ± 1.0 years, completed one-month lens wear, of whom 23 completed the washout period. The first fit success rates for CCF and ICF were 93% and 96%, respectively. Myopia, unaided VA, central corneal thickness, anterior corneal curvatures (K), apical power (AP), and corneal resistance factor (CRF) all changed significantly during the study period (P <  0.003). Between-eye difference was significant only for myopia reduction (P =  0.001). About 4% and 8% of ICF and CCF eyes were under-corrected. The attempted target was achieved in about 80% of CCF and 60% of ICF eyes at week 4. At the end of the washout period (3 weeks), myopia, K, and AP were significantly different compared to baseline (P <  0.001).Conclusion: Increasing the compression factor did not affect the first fit success rate and external ocular health, but did allow faster correction of refractive error within one month of lens wear. Further investigation is warranted to investigate the long-term effects of ICF on myopia control compared to CCF. [ABSTRACT FROM AUTHOR]

To improve the grating ruling efficiency and success rate, this report documents a method to indirectly measure the error trajectory of space coordinates produced by ruling and indexing systems. The errors were corrected based on the proposed active control system. According to the residual errors after correction, a mathematical model of the full aperture grating diffraction wavefront with high frequency error information was reconstructed in real time. Based on this mathematical model, two gratings were ruled with or without the aid of an error correction system, respectively, and the reconstructed grating diffraction wavefront values were obtained to verify the effectiveness of this method. For larger gratings, the grating diffraction wavefront can also be reconstructed in real time with long time ruling. The experimental result demonstrated that the grating quality can be judged by monitoring the grating diffraction wavefront in real time. [ABSTRACT FROM AUTHOR]

With the growing penetration of wind power, the wind power forecasting is fundamental in aiding the grid scheduling and electricity trading. In this paper, a numerical weather prediction wind speed error correction model based on gated recurrent unit neural networks is proposed for short-term wind power forecasting. Firstly, the standard deviation of numerical weather prediction wind speed error is extracted as weights, and these weights are rearranged according to the numerical weather prediction wind speed time series to get the weight time series. Then, the bidirectional gated recurrent unit neural networks based error correction model is proposed to correct error of numerical weather prediction wind speed with the inputs as numerical weather prediction wind speed, trend and detail terms of the weight time series. The wind power curve model is applied to forecast short-term wind power by using corrected numerical weather prediction wind speed. Finally, the effectiveness of the proposed method is compared with benchmark models by using actual data of wind farm, and the results show that the proposed model outperforms these benchmark models. [ABSTRACT FROM AUTHOR]

• A Chebyshev center model is proposed to localize a target node in its estimative region without range measurements. • An ADMM-based parallel efficient projection algorithm (PEPA) with simple iterations is proposed to solve the model. • The proposed PEPA is applied to localize multiple target nodes via a hierarchical scheme with low communication burden. • The localization performance is improved by a layer-wise error correction mechanism. Localizing target nodes is a fundamental problem for wireless sensor networks (WSNs). Without range measurements, the range-free techniques, which only exploit the connectivity information among nodes, have been widely studied in the past score years. How to achieve a good balance between the localization accuracy and the communication cost has not been well solved. In this paper, we first decide the estimative region of a target node using the information from both its one-hop and two-hop neighboring anchors. Then we establish a Chebyshev center model to localize a single target inside its estimative region via a nonconvex optimization problem, which is solved by the proposed ADMM-based parallel efficient projection algorithm (PEPA). By introducing a hierarchical scheme, the PEPA is further applied to localize multiple targets layer by layer, which enjoys very low communication cost. Moreover, a novel heuristic layer-wise error correction mechanism is proposed to enhance the positioning precision. Simulation results illustrate the advantage of the proposed algorithms against the existing major range-free methods. [ABSTRACT FROM AUTHOR]

In [6] we introduced line Hermitian Grassmann codes and determined their parameters. The aim of this paper is to present (in the spirit of [4]) an algorithm for the point enumerator of a line Hermitian Grassmannian which can be usefully applied to get efficient encoders, decoders and error correction algorithms for the aforementioned codes. [ABSTRACT FROM AUTHOR]

Quantum computers can solve problems that are inefficiently solved by classical computers, such as integer factorization. A fully programmable quantum computer requires a quantum control microarchitecture that connects the quantum software and hardware. Previous research has proposed a Quantum Instruction Set Architecture (QISA) and a quantum control microarchitecture, which targets Noisy Intermediate-Scale Quantum (NISQ) devices without fault-tolerance. However, fault-tolerant (FT) quantum computing requires FT implementation of logical operations, and repeated quantum error correction, possibly at runtime. Though highly patterned, the amount of required (physical) operations to perform logical operations is ample, which cannot be well executed by existing quantum control microarchitectures. In this paper, we propose a control microarchitecture that can efficiently support fault-tolerant quantum computing based on the rotated planar surface code with logical operations implemented by lattice surgery. It highlights a two-level address mechanism which enables a clean compilation model for a large number of qubits, and microarchitectural support for quantum error correction at runtime, which can significantly reduce the quantum program codesize and present better scalability. [ABSTRACT FROM AUTHOR]

• This study investigates the (mis)alignment between teachers' beliefs of WCF practice and their actual WCF practice. • The results indicate a degree of alignment between teachers' beliefs and practice but only in certain instances. • Most teachers mistakenly believed they primarily provide direct feedback while providing more indirect feedback in practice. • WCF was more frequent for local than global issues, despite teachers believing they offered more WCF on global issues. • Time constraints, excess workload and perceptions of students' attitude to WCF appear responsible for the misalignment. Written corrective feedback (WCF) refers to the feedback provided by writing teachers on students' writing. While much attention has been given to the (in)effectiveness of WCF on writing accuracy or skills in L2 writing research, there has been relatively less research on whether teachers' beliefs regarding WCF are aligned with their actual correction practices. This study investigates the (mis)alignment between teachers' beliefs of their WCF practice and their actual practice, using questionnaire and interview data from five English writing teachers in a Chinese EFL context who provided WCF on 100 student essays. The results indicate a degree of alignment between teachers' beliefs and practice in certain instances. However, three key areas of misalignment between beliefs and WCF practice were found, namely (1) most teachers mistakenly believed they primarily provide direct feedback while providing more indirect feedback in practice; (2) the teachers believed they often indicated the total number of errors in the margin, despite never doing so in practice, and; (3) WCF on local issues received more attention than global issues, despite teachers believing they offered more feedback on global issues. Contextual issues including time constraints, excess workload and perceptions of students' attitude to WCF were found to result in the eventual misalignment between beliefs and WCF practice. To resolve this issue, it is recommended that students and teachers cooperate to achieve the ultimate goals of written feedback, while in-service teachers should undertake periodic explicit professional training on WCF provision. [ABSTRACT FROM AUTHOR]

• The relation between structural public balance adjustment and poverty is explored. • Poverty is measured through the AROPE indicator following the threshold approach. • An error correction dynamic panel data technique is employed. • Results suggest that restrictive measures increase poverty and social exclusion. • The results are valid both for the whole population and for men and women separately. After the 2007 financial crisis, some Eurozone countries were subject to adjustment programs, whose main content was the reduction of public expenditure in its structural component. The aim of the paper is to examine, through an error correction dynamic panel data technique, the relationship between structural public balance adjustment and poverty in selected Eurozone countries in the period from 2005 to 2015. The results suggest that restrictive measures increase poverty and social exclusion while expansive measure decrease it, eithe r when considering the population as a whole or when considering men and women separately. The introduction of additional variables considered to be relevant for poverty into the empirical model does not change the main conclusions of the paper. The objective of sound public finance seems to be gained at the expense of a higher number of people living in poverty. [ABSTRACT FROM AUTHOR]

There is a growing demand for power from day to day. At present, the development achieved in power production from wind is highly significant. In this work, an optimized nonlinear neural network architectural model integrated with a modified firefly algorithm and particle swarm optimization is proposed to perform multistep wind-speed forecasting for specific target sites. Considering these aspects, this paper intends to predict wind speed, as its influence is high in generating wind power. The weights and bias values of the nonlinear neural network model are optimized employing the proposed optimization algorithm in order to achieve the minimum-error criterion. The computed results establish the effectiveness of the forecasting accuracy and the minimal error rate in comparison with existing methods available in the literature. [ABSTRACT FROM AUTHOR]

Scientific writing is difficult. It is even harder for those for whom English is a second language (ESL learners). Scholars around the world spend a significant amount of time and resources proofreading their work before submitting it for review or publication. In this paper we present a novel machine learning based application for proper word choice task. Proper word choice is a generalization the lexical substitution (LS) and grammatical error correction (GEC) tasks. We demonstrate and evaluate the usefulness of applying bidirectional Long Short Term Memory (LSTM) tagger, for this task. While state-of-the-art grammatical error correction uses error-specific classifiers and machine translation methods, we demonstrate an unsupervised method that is based solely on a high quality text corpus and does not require manually annotated data. We use a bidirectional Recurrent Neural Network (RNN) with LSTM for learning the proper word choice based on a word's sentential context. We demonstrate and evaluate our application in various settings, including both a domain-specific (scientific), writing task and a general-purpose writing task. We perform both strict machine and human evaluation. We show that our domain-specific and general-purpose models outperform state-of-the-art general context learning. As an additional contribution of this research, we also share our code, pre-trained models, and a new ESL learner test set with the research community. [ABSTRACT FROM AUTHOR]

This paper investigated DRAM DIMM errors using field records in replacement network servers. Large DRAM samples of about 40 K were collected over a 2.5 years period from 23 different server types, included various DIMMs from three different DRAM manufacturers with densities between 4 and 128 GB, and speeds between 1066 and 2400 Mbps. Errors that occurred during system operation were classified as either correctable (CE) or uncorrectable (UE) errors based on error correction code (ECC) schemes built into the servers. Of the collected DIMMS, 24% had recorded errors, where CE-only, UE-only, and UE and CE together comprised 28%, 43%, and 29% of recorded errors, respectively. Since UEs can cause large-scale failures, systems are replaced upon any UE occurrence. Approximately half UE-only DIMMs had 1 UE error. In contrast, many DIMMs had billions of CE errors, where a faulty location may be repetitively accessed. Such drastic differences in UE and CE counts help explain the importance of ECC and error mitigation schemes. Comparative analyses of errors were made over the manufacturers and operating speeds. After reasonable adjustments for repetitive counts of errors, failure in time (FIT) differences were up to 38% over manufacturers. Higher speed DIMMs generally had higher FIT with 2400 Mbps DIMMs exhibiting 6.7 times FIT of 1066 Mbps DIMMs. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the existence of pollution haven hypothesis (PHH) and test the casual relationship between inward foreign direct investment (FDI) and environmental pollutions, as well as other potential influencing factors of environmental performance. To this end, a panel vector error correction model (VECM) and panel co-integration test is conducted for two country groups - the BRICS (i.e., Brazil, Russia, India, China, and South Africa) and the MINT (i.e., Mexico, Indonesia, Nigeria, and Turkey) - during 1982–2014. Empirical results illustrate a bidirectional and positive causality between FDI inflows and GDP per capita for both groups, implying a virtuous circle of FDI-growth nexus. More importantly, both the BRICS and MINT countries illustrate bidirectional and negative causality between FDI inflows and environmental pressures (i.e., per capita energy consumption for BRICS and per capita carbon emissions for MINT). Such findings indicate the pollution haven hypothesis does not stand in this case. In addition, it is also found that the opening degree of trade can be promoted by FDI inflows, which has rarely been deeply analyzed in prior studies. Concluding remarks and policy suggestions are proposed in the last section. Image 1 • Vector error correction model are employed in this study. • A comparative analysis of BRICS and MINT countries. • Results refute the pollution haven hypothesis. • A virtuous circle exist between FDI and economic growth. • Trade openness can be promoted by FDI inflows. [ABSTRACT FROM AUTHOR]

We developed the height correction algorithm based on the Monin–Obukhov similarity theory for application in Weather Research and Forecasting (WRF) model outputs to correct the 10-m height definition and match weather station observations. Thirty simulations were conducted for April 2012 to validate the height correction algorithm against observations such as the automated surface observing system and automatic weather stations. The WRF model simulations were constructed with two nested domains with horizontal grid spacings of 10 and 1 km. The root mean square error of the diagnosed 10-m wind speed after applying the height correction algorithm was consistently reduced compared to the one before applying the algorithm over the innermost domain during the analysis period. The impact of the height correction on diagnosed wind speed was greater when the environment was unstable and the magnitude of the roughness length was large. Evaluation showed that a strong synoptic forcing condition results in a significant reduction in the bias of wind speed. • Developed the height correction algorithm based on Monin–Obukhov similarity theory. • The 10-m height definition in the model is corrected to match the height of observation. • Alleviated positive bias in wind speed of weather research and forecasting model. [ABSTRACT FROM AUTHOR]

• Does not require pre-correction and additional images. • Insensitive to changes in height difference compared to the HT method. • Compared with the PHE method, there is no discretization error. Phase errors caused by the gamma nonlinear response of the phase shift profilometry (PSP) severely affect 3D measurement accuracy. To tackle this, a simple phase error compensation technique based on polar angle average is presented in this paper to improve the accuracy. Wrapped phase values are regarded as polar angles, and a polar axis distribution map can be obtained in the polar coordinate. Practically, the polar axis distribution (PAD) of the undistorted wrapped phase is uniform. When there are nonlinear effects in an PSP, the polar axis is unevenly distributed. The difference between adjacent polar angles is computed and then rearranged with specifically labels. After that, the even PAD diagram is generated, and the smooth phase can be further recovered. Compared with conventional nonlinearity correction methods, the proposed method corrects the error without any auxiliary conditions. Its effectiveness and robustness are verified in simulations and experiments. [ABSTRACT FROM AUTHOR]

• Two measuring units are attached to the lower limb for pedestrian positioning. • A new approach is developed to address the problem of heading error. • A geometric constraint model is proposed to improve the positioning accuracy. • Zero-velocity update method is combined with Coriolis theorem for error correction. • The effectiveness of the proposed method is verified by experiments. In pedestrian inertial navigation system, the traditional zero-velocity update (ZUPT) method establishes a velocity constraint. Based on the Kalman filter algorithm, the velocity error is utilized to correct the position drift. However, the yaw error cannot be observed. In this study, two low-cost magnetometers/inertial measurement units (MIMUs) are tied to the foot and shank, respectively. To improve the positioning accuracy, two error correction methods are developed. Firstly, the yaw angle coordinate is chosen as a Kalman filter measurement vector. Secondly, a geometric constraint is proposed. It includes degree of freedom constraint, angle constraint, and position vector constraint. Finally, different indoor and outdoor experiments are employed to verify the effectiveness of the proposed method. The average distance error percentage of the proposed method is less than 2%, and the heading bias is less than 5° within 5.05 min. Thus, the estimated paths are more in line with the actual trajectories. [ABSTRACT FROM AUTHOR]

Accurate prognostic for the remaining useful life (RUL) of lithium-ion batteries (LIBs) is extremely crucial to the stable operation and timely maintenance of a battery system. Nevertheless, battery lifespan is difficult to measure due to the capacity regeneration in non-linear and unstable degradation trend. To increase the prediction accuracy, the Time Varying Filter-based Empirical Mode Decomposition (TVF-EMD) is innovatively introduced to decompose the original capacity data into subseries. Meanwhile, the complexities of the subseries are measured by the Box-counting dimension (BCD). Moreover, Fast Ensemble Empirical Mode Decomposition (FEEMD) is exploited to further decompose the most complex subseries. Additionally, Bidirectional Gated Recurrent Unit (BiGRU) is established for (sub-)subseries prognosis. The prediction performance is further strengthened by an error correction method (ECM). Eventually, the effectiveness of the proposed prognosis framework is verified on two battery datasets. The experimental results illustrate that the maximum root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the proposed hybrid framework are merely 1.917, 0.434 and 0.706% respectively. Compared with two decomposition methods, MAE can be reduced by at least 22.73%, and a reduction of not less than 7.4% in RMSE is achieved. • A new hybrid methodis proposed for the remaining useful life forecasting. • The secondary decomposition method employed can enhance the composition ability. • The box-counting dimension is utilized to choose the most complicated sub-layers. • An error correction scheme is proposed to further enhance prediction performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a modified K-means algorithm to mitigate both linear and nonlinear impairments in the multiple input single output (MISO) visible light communication (VLC) system. By presenting a novel dimming control scheme at the transmitter, various superimposed constellation structures are generated at the receiver end. Meanwhile, a table is generated, after looking up the superimposed signals in this table; we separate them to recover the transmitted signals. Then we can realize the spatial multiplexing. Experiments demonstrate that we achieve a data rate of 1.5 Gb ∕ s with the measured bit error rates (BERs) is under 7% pre-forward error correction (pre-FEC) limit of 3.8 × 10−3 in 1.3-m indoor. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in codes that can correct adjacent bit errors in memories. This is due to the increasing percentage of radiation induced errors that affect multiple cells due to technology scaling. The cells affected by the errors are physically close and in many cases adjacent. This means that in the absence of interleaving, adjacent bits will be likely to be affected by an error. A number of Single Error Correction and Double Adjacent Error Correction (SEC-DAEC) codes have been proposed to deal with those errors. These codes have a low overhead in terms of number of additional parity check bits compared to Double Error Correction (DEC) codes. A problem is that they can introduce a significant penalty in terms of delay compared to a Single Error Correction (SEC) or a Single Error Correction and Double Error Detection (SEC-DED) code. This is due to the more complex decoding that needs to check approximately twice the number of syndrome patterns. In this paper, low delay SEC-DAEC codes are presented and the parity check matrices for 16, 32 and 64 bit data words are provided. To reduce the delay, a number of techniques are used in the construction of the code and in the implementation of the decoder. The evaluation shows that this results in significant savings compared to existing SEC-DAEC codes. Therefore, the proposed codes can be useful for high speed designs on which adjacent error correction is required. [ABSTRACT FROM AUTHOR]

Quantum computers capable of solving classically intractable problems are under construction, and intermediate-scale devices are approaching completion. Current efforts to design large-scale devices require allocating immense resources to error correction, with the majority dedicated to the production of high-fidelity ancillary states known as magic-states. Leading techniques focus on dedicating a large, contiguous region of the processor as a single "magic-state distillation factory" responsible for meeting the magic-state demands of applications. In this work we design and analyze a set of optimized factory architectural layouts that divide a single factory into spatially distributed factories located throughout the processor. We find that distributed factory architectures minimize the space-time volume overhead imposed by distillation. Additionally, we find that the number of distributed components in each optimal configuration is sensitive to application characteristics and underlying physical device error rates. More specifically, we find that the rate at which T-gates are demanded by an application has a significant impact on the optimal distillation architecture. We develop an optimization procedure that discovers the optimal number of factory distillation rounds and number of output magic states per factory, as well as an overall system architecture that interacts with the factories. This yields between a 10x and 20x resource reduction compared to commonly accepted single factory designs. Performance is analyzed across representative application classes such as quantum simulation and quantum chemistry. [ABSTRACT FROM AUTHOR]

This work presents a half-select free 12T SRAM cell with Data-Dependent Feedback Cutting approach to improve the write ability and isolated read path to enhance the read stability. The enhanced read and write ability is 1.95 × and 2.84 × larger respectively than that of the conventional 6T cell at 0.4 V. The half-select free behavior of proposed cell using the cross-point read/write structure facilitates the bit-interleaving memory architecture to effectively reduce the multi-bits soft error occurrence. The incorporated PMOS stacking effect in inverter pairs of the proposed cell offers the reduced leakage power which is 0.59 × to that of 6T, at 0.4 V supply. To further minimize the leakage power at array level, the bit lines between two adjacent cells have been shared that consumes only 0.38 × leakage power than that of the conventional 6T array for a 1 KB macro. Moreover, a Reconfigurable FPGA architecture is proposed for low power applications. The simulated static and active power consumption of 12T SRAM based reconfigurable FPGA is 0.22 × and 0.45 × when compared with the regular 12T FPGA. Finally, a Double Adjacent-bits Error Detection and Correction (DAEDC) scheme is suggested for the proposed bit-interleaved 12T SRAM array, to reduce the soft error effects. [ABSTRACT FROM AUTHOR]

Convolution Neural Networks are now widely used in image processing, object detection, video detection, and other classification tasks. Thus the acceleration of CNN is also widely researched for its complex computation features and data dependence. To achieve high energy efficiency, we proposed a CNN accelerator with approximate computing techniques. In this paper, two main aspects are studied: the hardware-compatible network compression algorithms, and the approximate computing units and architectures with hardware resource scheduling strategies. For the algorithm approximation part, we introduce a dynamic layered CNN structure for different scales of input, the convolution kernel shrinking strategy with layer-by-layer quantization to compress networks, and the Winograd Minimum Filter algorithm to decrease operations in convolution layers. For the architecture part, two types of approximate multipliers are innovated as iterative multipliers, and multi-port SRAM integrated LUT based multipliers. Approximate adders with error correction logic are also designed. Based on the approximate computing units, the Convolution Neural Processing Unit named CNPU is proposed with reconfigurable datapath designs for the mapping of different tasks. By the work on the algorithm, the CNPU architecture and the datapath design, we propose a high energy efficient reconfigurable CNN accelerator with approximate computing named ARA (Approximate computing based Reconfigurable Architecture). Implemented under TSMC 45 nm process, our accelerator achieves 1.92TOPS/W@ 1.1 V, 200 MHz and 3.72TOPS/W@ 0.9 V, 40 MHz in energy-efficiency, which is 1.51 ∼ 4.36 times better than the state-of-the-art accelerators. [ABSTRACT FROM AUTHOR]

Highlights • Two methods for signal denoising is proposed. • Both signal and noise are sparse but in different domains. • The proposed methods are fast. Graphical abstract Abstract In this paper, we propose two methods for signal denoising where both signal and noise are sparse but in different domains. First, an optimization problem is proposed which is non-convex and NP-hard due to the existence of ℓ 0 norm in its cost function. Then, we propose two approaches to approximate and solve it. We also provide the proof of convergence for the proposed methods. The problem addressed in this paper arises in some applications for example in image denoising where the noise is sparse, signal reconstruction in the case of random sampling where the random mask is unknown, and error detection and error correction in the case of missing samples. The experimental results indicate that the proposed methods suitably achieve better performance than the state-of-the-art methods even with lower CPU-time. [ABSTRACT FROM AUTHOR]

Nowadays, multiple-error occurrence because of multiple transient faults is highly probable in digital circuits especially used in outer space environments. Therefore, concerning such applications, the processing systems performing operations such as addition and multiplication are of great importance and should be redesigned, properly. In this paper, some multiple-error resilient carry look-ahead adders are proposed more efficient than the existing designs. In this manner, at first, two new customized fault-tolerant voters are proposed and analyzed to be used for multiple-error correction/detection in the carry look-ahead adders utilizing a distributed partial triple modular redundancy along with a parity prediction scheme. Then, the new voters are exploited and incorporated into two types of grouped carry look-ahead adder architectures to obtain different designs with a compromise between the area overhead and reliability. Simulation results show that, as well as a complete single error correction/detection, the proposed voters lead to 8–15% area overhead reduction in different carry look-ahead adders while increasing multiple-error correction probability up to 17% and 26% utilizing the first and second proposed voters, respectively, compared to the best existing design. Furthermore, the total multiple error correction/detection probability is improved in different adder architectures in many cases. • Two new customized fault-tolerant voters are proposed with different error correction/detection probabilities and overheads. • All single errors inside these voters will be either corrected or detected. • Compared to previous voters in CLAs, proposed voters have higher fault-tolerance quality while requiring lower overheads. • Two different CLAs, simple grouped and two-level grouped are proposed, evaluated, and compared to the best existing design. • Designed 64-bit CLAs can correct or detect multiple errors incurred by 10 concurrent faults with 90% probability. [ABSTRACT FROM AUTHOR]

Single-event-upset (SEU) is becoming an increasingly important threat to the reliable operation of electronic integrated circuits. To cope with an SEU in a sequential element which is one of the most critical memory components in the ICs, many methods have been devised. However, existing methods can only restore an error that occurs in the sequential element, and they cannot deal with an error occurring in the restoration hardware itself. Furthermore, most existing methods expose an incorrect output while restoring an error, which could lead to an error in another sequential circuit. In this paper, we propose a new cost-effective transient-error correction system with real-time logic switching (RTLS) inspired from attractor-conversion characteristics of a cancer cell. A novel circuit architecture combining a sequential element with RTLS, which is totally different from those of traditional sequential elements, has been developed. The RTLS-based transient-error correction circuit can restore an SEU on any location of the circuit including the restoration hardware as far as the SEU is sustained within a typical time duration. Moreover, the RTLS-based transient-error correction system can autonomously switch its own transient-error correction logic when the circuit changes its normal state. Thus, the proposed system can restore any erroneous state to a normal state irrespective of the current state of a system, and it can also maintain a correct output while restoring an error. Based on simulation and experimental analysis in comparison with the existing methods, we have verified that the RTLS-based transient-error correction system can ensure higher reliability with lower hardware overhead. • A transient-error correction circuit inspired from biological processes is devised. • An integrated circuit having both data storage and error restoration is developed. • The proposed circuit system can recover from any error-induced states in real time. • The extensive simulation for addressing environmental variations is performed. [ABSTRACT FROM AUTHOR]