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The accurate measurement of roll angle is the premise of precise guidance and control of controlled high-speed rotating projectile. However, the geomagnetic attitude measurement method based on magnetic sensor will affect the accuracy of magnetic sensor measurement due to the magnetic field interference of the projectile. Aiming at this problem, an online correction method for the magnetic field interference of the projectile is proposed. According to the magnetic field interference that may be encountered during the flight of the high-speed rotating projectile, a magnetic field error model is established, and the magnetic field value measured by the magnetic sensor is corrected and compensated in real time by the flight law constraint of the rotating projectile. The semi-physical simulation experiments show that the method can effectively reduce the measurement error and improve the estimation accuracy of the projectile roll angle. The method is verified by three-axis flight turntable measurement, and the accuracy of geomagnetic attitude roll angle can be improved by 6 times. The method only needs three-axis magnetic sensor to complete the correction, with simple steps, very short correction time, good real-time performance and high accuracy.

With high real-time performance and a good filtering effect, the wavelet transform-based real-time filter is more commonly used in the fusion magnet power supply. Considering the signal characteristics of the fusion magnet power supply, the sliding time window method is widely used to satisfy the requirements of filtering efficiency. In this paper, an updated half-edge extension method is proposed to improve the boundary effect of sliding window data. Moreover, the hardware-based wavelet loopback structure is proposed to avoid the double computation of long sliding window data, thereby improving real-time performance and satisfying the demands of fusion magnet power supply filtering. The proposed half-edge extension method and the wavelet loopback structure are implemented by designed FPGA and experimented on the Experimental and Advanced Superconducting Tokamak (EAST) fusion magnet power supply system to verify the filtering efficiency. According to the experiment's results, the proposed half-edge extension method has a better filtering effect, and the proposed wavelet loopback structure decreases the time by 67.4%, which can satisfy the requirements of the power supply system. [ABSTRACT FROM AUTHOR]

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Structural analysis of signals is an important part of smart energy. It includes adaptive control of the aperiodic, low-frequency, and signal-harmonics levels for relay protection and automation tasks, fault location, and monitoring and diagnostics of electrical systems. For real-time signal processing, adaptive filtering is widely used and elements of artificial intelligence are increasingly being employed. This paper considers feed-forward artificial neural networks (multilayer perceptron) for signal processing. The simplest artificial neural networks (ANNs) can replace neural networks with a more complex structure (convolutional, recurrent), but within the framework of a sequential recurrent algorithm. This makes it possible to control the quality of parameter identification and signal processing at each stage of calculations. The proposed algorithm is tested using the example of sliding window processing of a signal with nonlinear distortions and at CT saturation. It is shown that the amplitude, frequency, and phase of a utility frequency signal with a high level of harmonics and aperiodic component can be identified with an accuracy of a few percent in a time not exceeding a few milliseconds. To improve the accuracy at each calculation step, in addition to ANNs, traditional methods can be used, such as averaging and median smoothing. The presented results demonstrate the possibility of real-time structural analysis of signals. [ABSTRACT FROM AUTHOR]

With high real-time performance and a good filtering effect, the wavelet transform-based real-time filter is more commonly used in the fusion magnet power supply. Considering the signal characteristics of the fusion magnet power supply, the sliding time window method is widely used to satisfy the requirements of filtering efficiency. In this paper, an updated half-edge extension method is proposed to improve the boundary effect of sliding window data. Moreover, the hardware-based wavelet loopback structure is proposed to avoid the double computation of long sliding window data, thereby improving real-time performance and satisfying the demands of fusion magnet power supply filtering. The proposed half-edge extension method and the wavelet loopback structure are implemented by designed FPGA and experimented on the Experimental and Advanced Superconducting Tokamak (EAST) fusion magnet power supply system to verify the filtering efficiency. According to the experiment’s results, the proposed half-edge extension method has a better filtering effect, and the proposed wavelet loopback structure decreases the time by 67.4%, which can satisfy the requirements of the power supply system.

Filtering is a required task in surface metrology for the identification of the components relevant for automated quality control. The calculation of real-time features about the surface is crucial to determining the mechanical and physical properties of the inspected product. The computation efficiency of the filtering operations is a major challenge in surface metrology, as current sensors provide massive volumes of data at very high acquisition rates. To overcome the challenges, this work presents different real-time filtering solutions comparing the performance on the CPU and on the GPU, using modern hardware. The proposed framework is focused on filtering techniques that can be expressed using a finite impulse response (FIR) kernel that includes the Gaussian kernel, the most common filtering technique recommended by ISO and ASME standards. This research work proposes variations of the double FIFO and double circular filters. The filters are transformed into a series of general matrix to matrix multiplications, which can be run extremely efficiently on different architectures. The proposed filtering approach provides superior performance compared with previous works. Additionally, tests are carried out to quantify the performance of the GPU in terms of data transfer and computation capabilities in order to diminish the penalty imposed by data transfer from main memory to the GPU in real-time operations. Based on the results, an efficient batch filtering technique is proposed that can be run on the GPU faster than the CPU even for small profile and kernel sizes, offloading this task from the host CPU for optimal system and application response. [ABSTRACT FROM AUTHOR]

Such tasks as the determination of the level of harmonics upon nonlinear distortions and separation of the amplitude, phase, and signal frequency of fundamental harmonics in many cases require signal processing with a small time delay or in real time. At standard frequencies of signal digitization of 600–2400 Hz, it is difficult to provide accurate determination of signal parameters during an interval amounting to fractions of an industrial frequency period. This paper demonstrates that this can be aided by neural-network algorithms and artificial feedforward neural networks of direct propagation with simple structure. In the case of adequate training and testing of such a neural network, it allows signal parameters to be recovered in fractions of the main frequency period of 50 Hz. It is demonstrated that trained neural network can be used in a regular microcontroller. The algorithm is analyzed using the example of recovery of a fundamental frequency signal upon nonlinear distortions, when the level of the second harmonics exceeds half of the amplitude of the fundamental frequency signal. Estimation errors of recovery of signal amplitude, phase, and frequency are given. It is demonstrated that recovery can be carried out in a sliding window with the duration from units of milliseconds. Herewith, the errors of recovery of signal parameters do not exceed units of percent. The possibility of using the proposed algorithm for real-time signal processing is discussed. The rates of computations are verified and estimated for microprocessors with standard parameters for digital substations. [ABSTRACT FROM AUTHOR]

Surface metrology in automated quality inspection is a field, among many others, affected by noise and thus requiring filtering. In surface metrology, filtering is required to remove undesired information from data in order to extract surface features and relevant properties necessary for quality control. Moreover, filtering requires immediate results, while the product is being manufactured. This way, quick correcting actions can be directly applied to solve possible manufacturing issues. This work proposes different strategies to filter height maps in real-time acquired using laser profilers, the most widely used inspection method in industrial applications. Different models to apply the filtering operations are considered, particularly assessing different alternatives to store previous samples in memory, which are required for data filtering. FIFO, double FIFO, circular and double circular buffers are evaluated. Furthermore, CPU parallelism, SIMD instructions and cache-line friendly data structures are analyzed. The proposed methods are extremely efficient, capable of filtering laser profiles at extremely high acquisition rates. The proposed methods are designed for real-time surface metrology, but they are very likely to find potential applications in different areas. The filters are compared in terms of accuracy and speed, including other well-known filters such as the spline filter. Tests analyze execution time, including cache efficiency and filtering accuracy. Results with synthetic data and real data obtained from steel strips show excellent performance, providing accurate results at very high speeds. [ABSTRACT FROM AUTHOR]

The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate. These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time. Here, we present a coherent analysis procedure that combines the aforementioned features and their benefits. To facilitate utilization of the proposed methodology and to ensure optimal performance, we also introduce a procedure to calculate all necessary parameters. Thereby, we substantially expand the versatility of one of the most widely-used filtering approaches, taking full advantage of its most prevalent extensions. The applicability and superior performance of the proposed methods are demonstrated using simulated and real data. The possible areas of applications for the presented analysis procedure range from movement analysis over medical imaging, brain-computer interfaces to robot navigation or meteorological studies.

Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and residential solar generation. Such information could improve system reliability, economic efficiency, and environmental impact. Rather than installing additional costly sensing and communication infrastructure to obtain additional real-time information, it may be possible to use existing sensing capabilities and leverage knowledge about the system to reduce the need for new infrastructure. In this paper, we disaggregate a distribution feeder's demand measurements into: 1) the demand of a population of air conditioners, and 2) the demand of the remaining loads connected to the feeder. We use an online learning algorithm, Dynamic Fixed Share (DFS), that uses the real-time distribution feeder measurements as well as models generated from historical building- and device-level data. We develop two implementations of the algorithm and conduct case studies using real demand data from households and commercial buildings to investigate the effectiveness of the algorithm. The case studies demonstrate that DFS can effectively perform online disaggregation and the choice and construction of models included in the algorithm affects its accuracy, which is comparable to that of a set of Kalman filters. [ABSTRACT FROM AUTHOR]

The normal digital filter cannot achieve a good filtering result since the characteristics of elec­tro-hydraulic servo system makes the frequency of noistf signal close to the frequency of useful signal. The empirical mode decomposition algorithm is improved to remove these noise signals exactly from data. The definition 1 extreme points is extended, a judging formula of envelope interpolation is given, and a new decomposition end judgment condition is added. The improved EMD method has better effect and decom­position efficiency on the signal from electro hydraulic servo syslem. Il can meet the real-time filtering re­quirements. This new filter based on improved EMD method is used in eleetro-hydraulie servo system, and the normal digital filter compared with the original HMD method. The test results show that this new filter has a good application effect and less consuming time. [ABSTRACT FROM AUTHOR]

Twitter is widely perceived as a potential source of valuable information for responders to mass emergencies. Despite interest in the development of extraction systems for such information, little effort has been put towards systemic methods for obtaining all posts pertaining to a disaster from the live Twitter stream. Researchers rely on keyword-based filters to extract information in spite of evidence that such markers are absent in many informational tweets, and also neglect the topic and traffic dynamics of the relevant tweets as crises progress. Previous work has shown that such practices can often lead to the loss of critical information in the context of a disaster. We introduce an adaptive filter, tailored to the idiosyncrasies of the real-time Twitter feed, intended to extract disaster-related content. Furthermore, we introduce a novel data model based on a three-label classification scheme to describe the composition of the data-stream. We use this model to simulate Twitter streams, modelling various post-disaster scenarios, for the purpose of filter performance evaluation. The filter is able to remove over 85% of the non-crisis content, and achieves a three-fold reduction in the loss of relevant contents compared to the existing approaches. In combination, the method and the model are useful tools for extracting situational awareness and highlight important directions for future work in this area. [ABSTRACT FROM AUTHOR]

The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate. These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time. Here, we present a coherent analysis procedure that combines the aforementioned features and their benefits. To facilitate utilization of the proposed methodology and to ensure optimal performance, we also introduce a procedure to calculate all necessary parameters. Thereby, we substantially expand the versatility of one of the most widely-used filtering approaches, taking full advantage of its most prevalent extensions. The applicability and superior performance of the proposed methods are demonstrated using simulated and real data. The possible areas of applications for the presented analysis procedure range from movement analysis over medical imaging, brain-computer interfaces to robot navigation or meteorological studies. [ABSTRACT FROM AUTHOR]

A novel method for implementing noncausal forward/backward 2-pass recursive digital filters in real time is presented. It is based on a segment-wise block processing scheme without overlapping. Factors that degrade the linearity of the overall system's transfer function are discussed. An analytical condition that corrects the system's linearity is elaborated upon using the state variable approach. A recursive algorithm is developed to compute an implementable condition for real-time filtering. A single first in, first out queue memory is introduced to ensure an organized and continuous data stream into the proposed system. This technique allows real-time, sample-by-sample filtering, and it yields reduced delay and data storage memory compared to previous works. Better performances in total harmonic distortion were also obtained. Experimental results are illustrated. [ABSTRACT FROM AUTHOR]

Abstract: Continuous recording of Visual Evoked Potentials (VEPs) and functional Magnetic Resonance Imaging (fMRI) exploits the VEPs high temporal resolution and the fMRI high spatial resolution. In this work, we present a new method of continuous VEPs/fMRI recording to study visual function in seven normal subjects. Our real-time artifact filtering is characterized by a procedure based on an analytical study of echo-planar imaging (EPI) sequence parameters related electro-encephalogram (EEG)-artifact shapes. The magnetic field artifacts were minimized by using a dedicated amagnetic device and by a subtraction algorithm that takes into account the EPI sequence parameters. No significant decrease in signal-to-noise ratio was observed in case of EEG recording simultaneously with MR acquisition; similarly, transient and steady-state VEPs parameters were comparable during fMRI acquisition and in the off-phase of fMRI recording. We also applied this method to one patient with optic neuritis, and, compared with controls, found different results. We suggest that our technique can be reliably used to investigate the function of human visual cortex and properly correlate the electrophysiological and functional neuroimaging related changes. [Copyright &y& Elsevier]

A standard EEG equipment was modified in order to work properly during ultra-fast MRI acquisitions. Changes include: amagnetic electrode cap, optical fiber link, shielding box for EEG amplifier, twisted low metal mass cable. The effects of the RF pulse and time varying magnetic fields were minimized by using a correct head cap wires locked environment montage and then removed with a simple subtraction algorithm during EEG/fMRI acquisition. Ballistocardiogram artifact was also minimized at suitable levels by using a comfortable head position and immobilization. [Copyright &y& Elsevier]

The purpose of this study was the development of a real-time filtering procedure of MRI artifacts in order to monitor the EEG activity during continuous EEG/fMRI acquisition. The development of a combined EEG and fMRI technique has increased in the past few years. Preliminary “spike-triggered” applications have been possible because in this method, EEG knowledge was only necessary to identify a trigger signal to start a delayed fMRI acquisition. In this way, the two methods were used together but in an interleaved manner. In real simultaneous applications, like event-related fMRI study, artifacts induced by MRI events on EEG traces represent a substantial obstacle for a right analysis. Up until now, the methods proposed to solve this problem are mainly based on procedures to remove post-processing artifacts without the possibility to control electrophysiological behavior of the patient during fMRI scan. Moreover, these methods are not characterized by a strong “prior knowledge” of the artifact, which is an imperative condition to avoid any loss of information on the physiological signals recovered after filtering. In this work, we present a new method to perform simultaneous EEG/fMRI study with real-time artifacts filtering characterized by a procedure based on a preliminary analytical study of EPI sequence parameters-related EEG-artifact shapes. Standard EEG equipment was modified in order to work properly during ultra-fast MRI acquisitions. Changes included: high-performance acquisition device; electrodes/cap/wires/cables materials and geometric design; shielding box for EEG signal receiver; optical fiber link; and software. The effects of the RF pulse and time-varying magnetic fields were minimized by using a correct head cap wires-locked environment montage and then removed during EEG/fMRI acquisition with a subtraction algorithm that takes in account the most significant EPI sequence parameters. The on-line method also allows a further post-processing utilization. [Copyright &y& Elsevier]

According to the characteristics of inertial sensor signal processing, this paper proposed the signal processing system and method based on sparse representation. Through the K-SVD algorithm of signal learning training to obtain the dictionary, and in order to reduce the amount of computation to meet the real-time, as far as possible to reduce the size of the dictionary, the simulation results show that, under certain precision condition, the minimum size of the dictionary is 3 X 10. Then, in the dictionary under the sparse representation and reconstruction of signals, while changing the input signal, it could achieve signal real-time filtering. The simulation results show that the proposed method can effectively eliminate the noise, improve the accuracy of output signals, and improve SNR up to 4.5 dB. The filtering method has more advantages than the traditional method, and provides a new method for inertial sensor signal processing. [ABSTRACT FROM AUTHOR]