Prediction Filter Research Articles

Toward the Reconstruction of Substorm‐Related Dynamical Pattern of the Radiowave Auroral Absorption

AbstractIn addition to existing empirical models describing the average distributions of energetic electron precipitation into the auroral ionosphere at different activity levels, we develop and test a semiempirical approach to construct dynamical models describing the recurrent features of spatiotemporal development of auroral absorption in the ionosphere during individual substorms. Its key ingredients are (a) usage of linear prediction filter technique to extract from riometer data the response function to the injection of unit magnitude and (b) characterization of injection parameters by midlatitude magnetic variations caused by the substorm current wedge. Using global riometer network we test the method performance for stations in the middle of auroral zone (at corrected geomagnetic latitudes of 65–67°) where generally the absorption amplitude is largest. In this paper we use the midlatitude positive bay index, recently developed by X. Chu and R. McPherron, to drive the model. We evaluate the model performance, discuss the dynamical properties of energetic electron precipitation as revealed by the linear prediction filter response function analyses, and finally, we discuss possible future improvements of this method intended for both science and applications.

Automatically steering cardiac catheters in vivo with respiratory motion compensation

A robotic system for automatically navigating ultrasound (US) imaging catheters can provide real-time intra-cardiac imaging for diagnosis and treatment while reducing the need for clinicians to perform manual catheter steering. Clinical deployment of such a system requires accurate navigation despite the presence of disturbances including cyclical physiological motions (e.g., respiration). In this work, we report results from in vivo trials of automatic target tracking using our system, which is the first to navigate cardiac catheters with respiratory motion compensation. The effects of respiratory disturbances on the US catheter are modeled and then applied to four-degree-of-freedom steering kinematics with predictive filtering. This enables the system to accurately steer the US catheter and aim the US imager at a target despite respiratory motion disturbance. In vivo animal respiratory motion compensation results demonstrate automatic US catheter steering to image a target ablation catheter with 1.05 mm and 1.33° mean absolute error. Robotic US catheter steering with motion compensation can improve cardiac catheterization techniques while reducing clinician effort and X-ray exposure.

Uncertain motion tracking based on convolutional net with semantics estimation and region proposals

In real world, most of the tracking methods suffer from uncertain motion, which may make the tracker failure because of a local search window with the motion smooth assumption. To address this problem, a novel tracking framework based on convolutional net with semantics estimation and region proposals is proposed. Firstly, we present a semantics object proposals generation strategy, including category-level semantics proposals, one-object-level semantics estimation and semantics-contextual proposals generation, to obtain a few of high-quality object-oriented proposals covering uncertain motion. Secondly, combining the globally sparse semantics region proposals prediction and correlation filter prediction, a hybrid semantics tracking algorithm is proposed, which obtains a coarse object location by the decision of multiple response maps. Finally, we learn and train independent correlation filter to estimate the scale of target for a higher tracking accuracy. Extensive experiments on two visual tracking benchmarks and results demonstrate our method achieves state-of-the-art performance.

Adaptive Prediction of a Random Process Using a Sequential Regression Algorithm

Introduction. Adaptive statistical prediction of a random process is relevant to a noise compensation in radar and optical location problems. The shape of the signal reflected from the target is often unknown due to the use of short probing pulses passing during their duration in a distance less than the size of the target. Subtracting the noise forecasted in the previous time point from its current value allows one to compensate for the noise. Aim. Investigation of the problem of adaptive linear prediction of random processes by a non - recursive linear filter implementing a sequential regression algorithm for infinitely and finitely differentiable random processes. Materials and methods . Models of random interferences in the form of infinitely and finitely differentiable random processes were considered. The sequential regression algorithm required to estimate the correlation selection matrix, the selection vector of correlation of the forecast and sample units. Due to random process and its derivative incorrelation, the sparse correlation selection matrix was formed. This factor reduced the number of mathematical operations. Results. The results of numerical calculations and the implementation of random process, its optimal and adaptive prediction obtained during the simulation were presented. The adaptive predictive filter with random process derivative sampling provided at least a one third reduction of the number of mathematical operations in comparison with the transversal predictive filter. Conclusion. An algorithm of sequential regression in predicting a random process and its a priori unknown parameters is the closest to the ideal algorithm of direct matrix inversion. It allows to adapt to the changing process parameters. The number of iterations in non-recursive filtering and the value of attenuation of the estimated linear regression coefficients during the adaptation can be used to adapt to the changes in the parameters of the predicted process.

Integrated Sidelobe Cancellation and Linear Prediction Kalman Filter for Joint Multi-Microphone Speech Dereverberation, Interfering Speech Cancellation, and Noise Reduction

In multi-microphone speech enhancement, reverberation as well as additive noise and/or interfering speech are commonly suppressed by deconvolution and spatial filtering, e.g., using multi-channel linear prediction (MCLP) on the one hand and beamforming, e.g., a generalized sidelobe canceler (GSC), on the other hand. In this paper, we consider several reverberant speech components, whereof some are to be dereverberated and others to be canceled, as well as a diffuse (e.g., babble) noise component to be suppressed. In order to perform both deconvolution and spatial filtering, we integrate MCLP and the GSC into a novel architecture referred to as integrated sidelobe cancellation and linear prediction (ISCLP), where the sidelobe-cancellation (SC) filter and the linear prediction (LP) filter operate in parallel, but on different microphone signal frames. Within ISCLP, we estimate both filters jointly by means of a single Kalman filter. We further propose a spectral Wiener gain post-processor, which is shown to relate to the Kalman filter's posterior state estimate. The presented ISCLP Kalman filter is benchmarked against two state-of-the-art approaches, namely first a pair of alternating Kalman filters respectively performing dereverberation and noise reduction, and second an MCLP+GSC Kalman filter cascade. While the ISCLP Kalman filter is roughly $M^2$ times less expensive than both reference algorithms, where $M$ denotes the number of microphones, it is shown to perform similarly as compared to the former, and to outperform the latter.

Reactive Collision Avoidance Algorithm for UAV Using Bounding Tube Against Multiple Moving Obstacles

A reactive collision avoidance algorithm is proposed to enable safe autonomous flight in the presence of multiple dynamic obstacles. A position-controlled hexacopter equipped with a visual sensor obtaining obstacle information is considered as an Unmanned Aerial Vehicle (UAV) platform. The proposed method centers on the concept of bounding tube which intrinsically extends the static bounding box to incorporate forthcoming movement of the obstacles into the collision avoidance framework. The processing pipeline consists of separate components for each of the sequential tasks in obstacle sensing and tracking. Computation of a spherical bounding box for each obstacle is followed by discrete-time Kalman filtering for prediction of obstacle trajectory to detect potential collision. If the current course of UAV turns out highly likely to end in collision with any of the obstacles, the vehicle steers to an aiming point chosen from among the bundle of candidates produced by constructing a bounding tube that takes account of predicted obstacle motion. The bounding-tube-based aiming point generation extends seamlessly to the case with multiple moving obstacles through running in series with multi-obstacle track management that combines hierarchical clustering of sensory data points for obstacle identification and a simple geometric method for data association. Numerical simulations are conducted to verify the performance of the proposed collision avoidance algorithm.

A Signal Adaptive Prediction Filter for Video Coding Using Directional Total Variation: Mathematical Framework and Parameter Selection.

In this paper we combine video compression and modern image processing methods. Iterative filter methods for prediction signals based on classic inpainting methods are introduced and extensive parameter tests are described. In order to construct an alternative prediction filter for video coding, techniques originally employed for inpainting are applied. Thereby, the structures of the underlying prediction were incorporated into the filter construction making it signal adaptive. The resulting optimization problem is solved using the so-called Alternating Direction Method of Multipliers (ADMM). The undertaken novel parameter tests are described and it is shown that they improve the coding efficiency of the tool. The suggested filter is embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. Overall, the proposed filter method obtains average bitrate savings of 1.35% at an average encoder runtime increase of 28% and decoder runtime increase of 38%. UHD test sequences achieve bitrate savings of up to 3.66% for Random Access.

Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration

The INS/GNSS integration is the commonly used technique for hypersonic vehicle navigation. However, owing to the complicated flight dynamics with high maneuverability and large flight envelope, the dynamic model of INS/GNSS integration inevitably exists errors which degrades the navigation performance of a hypersonic vehicle seriously. In this paper, a new model predictive based unscented Kalman filter (MP-UKF) is proposed to address this problem. The MP-UKF employs the concept of model predictive filter for the establishment of a dynamic model error estimator, and it subsequently compensate the model error estimation to UKF for nonlinear state estimation. Since the MP-UKF could predict the dynamic model error persistently and correct the filtering procedure of UKF online, it improves the UKF adaptiveness and is promising for the performance enhancement of INS/GNSS integration for hypersonic vehicle navigation. Simulation results and comparison analysis have been conducted to demonstrate the effectiveness of the proposed method.

Nonlinear predictive filter based fault diagnosis of oxygen generation system by using electrolytic water in space station

The oxygen generation system using electrolytic water is one of the important systems in space station which supplies the oxygen for the astronauts from the condensate water, i.e., Sabatier reduction reaction water and urine treated water. During the operation of the space station, the normal operation of the system directly affects the safety of astronauts and the operating costs of the space station, so the fault diagnosis of the system has become a research hotspot. This paper systematically describes the fault diagnosis method of the oxygen generation system using electrolytic water, including the establishment of the fault mode and effects analysis, mathematical model of the key components and the estimation algorithm of unmeasured parameters. For the two direct discriminant indicators of the oxygen production and hydrogen production, because they are unmeasurable and their dynamic models are difficult to be established, this paper proposes a cascade algorithm of nonlinear predictive filtering and low-pass filter to solve the problem. Firstly, the oxygen and hydrogen production are estimated by the predictive filter. And then, the high frequency noise introduced by measurement error is eliminated when the oxygen and hydrogen production signals entered in a low-pass filter. Through the simulation analysis of the normal and fault mode, the correctness and effectiveness of the estimation algorithm are illustrated.

Design of MELPe-Based Variable-Bit-Rate Speech Coding with Mel Scale Approach Using Low-Order Linear Prediction Filter and Representing Excitation Signal Using Glottal Closure Instants

In this paper, we propose a variable-bit-rate speech codec-based on mixed excitation linear prediction enhanced (MELPe) with an average bit rate of 2 kbps and with a better representation of excitation signal. The order of the prediction filter in MELPe coding architecture is reduced from 10 to 7 without affecting the perceptual quality of the decoded speech by using psychoacoustic Mel scale. An efficient two-split vector quantization is developed with weighted Euclidean distance measure for Mel scale-based linear predictive coding (Mel-LPC), and it requires only 18 bits/frame. The instantaneous pitch or epoch that is vital for many speech processing applications is preserved in this codec by including it in the excitation signal used for reconstructing the voiced speech. The quantization scheme developed for glottal closure instants (GCIs) causes an increase in the bit requirement for voiced frames by 4–25 bits depending on the position of GCIs. To compensate for that, the Mel-LPC order for both silence and unvoiced frames has been brought down to 4 without compromising the perceptual quality of reconstructed speech. The lowered bit budget for unvoiced frame is 41 bits/frame, and for silence, it is 31 bits/frame. Further reduction of 10 bits for silence frame is obtained by reducing the number of transmitted parameters and by tuning the quantization bit requirement for each. For categorizing the speech frames at the entry of the encoder, a neural network-based voiced/unvoiced/silence classification algorithm using five-dimensional feature set is created. The experimental results show that the proposed coding scheme operates at an average bit rate of 2 kbps, which is less than the bit rate of MELPe (2.4 kbps), but with a better perceptual score. In addition to all these, the incorporation of Mel-LPC gives a better performance in the estimation of formants and GCIs.

Streamlined MRM method transfer between instruments assisted with HRMS matching and retention-time prediction

Multiple reaction monitoring (MRM) mode using liquid-chromatography tandem mass spectrometry (e.g., LC-QqQ-MS/MS) has been extensively employed in the small molecule analysis with trace levels in complex samples owing to its high sensitivity. However, most of the reported MRM methods are developed using authentic standards, which are often costly yet not readily available. To address this question, a practical platform for the MRM method transfer between different LC-QqQ-MS/MS instruments, assisted by the high-resolution mass spectrometry (LC-HRMS) and retention time (RT) prediction, has been developed in this study. The reported platform can take advantage of both the high sensitivity of LC-MRM method and ion transition pairs from the previous publications. LC-HRMS can provide the accurate mass measurement of the compounds, though high-quality MS/MS fragments are usually difficult to obtain for chemicals at trace levels. Retention time matching and peaks matching between both instrumental platforms rule out isobaric candidates. With an additional retention time prediction filter from quantitative structure retention relationship (QSRR) model based on random forest feature selection (Pearson r2 = 0.63), identification of small molecules is achieved at a high confidence level without using authentic standards. The developed platform has been validated with robustness by examining spiked environmental chemicals in sludge water samples, biological urine, and cell extracts.

Adaptive Predictive Filter "a"

Modern machinery has quite necessity of accuracy, fast and robust sensors/actuators in order to solve more complicated tasks as actual life demands, such as given by air compressor systems, pumps and turbines that are used, because of mechanical movement transmission. Notwithstanding, while sensors can’t get fast transduction, information from physical variables can be lost even also due to disturbances. By other side, actuators need fast information owing to send a control signal over mechanical systems. In this work is proposed a filter as the part inside of sensors based in nanostructures. Therefore, because of geometrical and material properties from nanostructures, sensors/actuators can separate perturbation based in electromagnetic signals that are coupled in the main information to be processed.

Compensated closed-loop Kalman filtering for nonlinear systems

In this paper, we introduce new state estimation algorithm for nonlinear systems with the measurement loss at output channels. The goal of proposed methodology is to compensate for the output state-measurement losses through an advanced scheme that relies on the nonlinear prediction (NLP) and Kalman filter. Using NLP subsystem, based on Exponential Autoregressive (EXPAR) model, a compensated observation signal is first regenerated and then it is delivered to nonlinear discrete filtering or linearized Kalman filter in the form of closed-loop. Two proposed scheme versions (EXPAR-Substitution and EXPAR-Elimination) are presented in this paper. A two-phase permanent magnet synchronous motor (PMSM) case study is exposed to loss of measurements and then the loss is reconstructed using EXPAR-based schemes to demonstrate some capable results of the proposed algorithm. Simulation results of the proposed algorithm are found to be superior compared to other algorithms in the literature.

Continuous Telugu Speech Recognition on T-LPC and DNN Techniques

This paper deals with the basic application speech recognition. There are many languages in the world but one of the regional language is Telugu. Recognition of this language helps in many applications for 8 crores of people stay in AP and Telangana states. Recognition is done by recording the speech signals and database creation. Pre-processing is done by 2 stage DNN (seep neural networks) where denoising, framing is done. The preprocessed signal features are extracted using TLPC(teager energy operator linear prediction filter). The features extracted are classified using DNN which generates adequate results. The results are obtained for continuous speech of Telugu language.

PROSPECTIVE BIOLOGICAL ACTIVE COMPOUNDS AMONG 7-SUBSTITUTED OF 3-BENZYL-8-PROPYLXANTHINES FOR TREATMENT OF METABOLIC SYNDROME PATHOLOGIES

Aim – the search for biologically active compounds with diuretic and hypoglycemic action among 7-substituted of 3-benzyl-8-propylxanthines, which can be used for the treatment of pathologies of “metabolic syndrome”. Materials and methods. 30 new chemical compounds among derivatives of 7-substituted of 3-benzyl-8-propylxanthines by PASS prediction filter were chosen for diuretic and hypoglycemic activity researches. Diuretic, hypoglycemic activity and for the most active compounds – the acute toxicity were studied in vivo. The molecular docking, which is based on EADock DSS mechanism of the modulation displayed the interaction between some of functional groups inside discovered xanthine ligands and known receptors and enzymes presented in Ligand-protein Database of Swiss Institute of Bioinformatics. Results. It has been shown that synthesized compounds displayed strong diuretic and medium hypoglycemic activities. The molecular docking modulation (SwissDock) of interaction of xanthine derivatives hits with proposed receptors and enzymes revealed prospective of using 7-substituted of 3-benzyl-8-propylxanthines as potential drugs for treatment of metabolic syndrome pathologies. Conclusion. The diuretic activity of the new 23 compounds of 7-substituted of 3-benzyl-8-propylxanthines and hypoglycemic activity of the new 7 derivatives of 7-substituted of 3-benzyl-8-propylxanthines was studied. Results of performed investigation illustrate that 7-substituted of 3-benzyl-8-propylxanthines demonstrate biological activity comparable to standard drugs. We also proposed probable molecular targets for the most active compounds by molecular docking method. It was shown that derivatives of 7-substituted of 3-benzyl-8-propylxanthines can be used for metabolic syndrome disorders prevention.

Three-parameter prestack seismic inversion based on L1-2 minimization

Prestack inversion has become a common approach in reservoir prediction. At present, the critical issue in the application of seismic inversion is the estimation of elastic parameters in the thin layers and weak reflectors. To improve the resolution and the accuracy of the inversion results, we introduced the difference of [Formula: see text] and [Formula: see text] norms as a nearly unbiased approximation of the sparsity of a vector, denoted as the [Formula: see text] norm, to the prestack inversion. The nonconvex penalty function of the [Formula: see text] norm can be decomposed into two convex subproblems via the difference of convex algorithm, and each subproblem can be solved efficiently by the alternating direction method of multipliers. Compared with the [Formula: see text] norm regularization, the [Formula: see text] minimization can reconstruct reflectivities more accurately. In addition, the [Formula: see text]-[Formula: see text] predictive filtering was introduced to guarantee the lateral continuity of the location and the amplitude of the reflectivity series. The generalized linear inversion and [Formula: see text]-[Formula: see text] predictive filtering are combined for stable elastic impedance inversion results, and three parameters of P-wave velocity, S-wave velocity, and density can be inverted with the Bayesian linearized amplitude variation with offset inversion. The inversion results of synthetic and real seismic data demonstrate that the proposed method can effectively improve the resolution and accuracy of the inversion results.

Widely linear denoising of multicomponent seismic data

ABSTRACTSeismic data processing is a challenging task, especially when dealing with vector‐valued datasets. These data are characterized by correlated components, where different levels of uncorrelated random noise corrupt each one of the components. Mitigating such noise while preserving the signal of interest is a primary goal in the seismic‐processing workflow. The frequency‐space deconvolution is a well‐known linear prediction technique, which is commonly used for random noise suppression. This paper represents vector‐field seismic data through quaternion arrays and shows how to mitigate random noise by proposing the extension of the frequency‐space deconvolution to its hypercomplex version, the quaternion frequency‐space deconvolution. It also shows how a widely linear prediction model exploits the correlation between data components of improper signals. The widely linear scheme, named widely‐linear quaternion frequency‐space deconvolution, produces longer prediction filters, which have enhanced signal preservation capabilities shown through synthetic and field vector‐valued data examples.

A resonant frequency retrieving method for low Q-factor materials based on resonant ultrasound spectroscopy

Resonant ultrasound spectroscopy (RUS) allows identification of the elastic properties of solid materials vibrating under an ultrasonic excitation from the measurement of their inherent frequencies. Retrieving the resonant frequencies is therefore a key signal processing step in RUS, which is generally addressed using a linear prediction filter. In this study, the Empirical Mode Decomposition (EMD) was proposed to retrieve the inherent resonant frequencies of materials with low Q-factor (quality factor). EMD was used to decompose the frequency response of the tested sample into intrinsic mode functions (IMF). The relevant IMF was selected from which the resonant frequencies could be computed. A bovine cortical bone sample was measured and its resonant frequencies were identified with EMD and with linear prediction for comparison. The elastic constants were also derived using both approaches. The number of resonant frequencies (45) extracted with EMD was larger than the number of frequencies (26) identified using the classical linear prediction approach. In particular, EMD proved to be more effective in detecting resonance in the higher frequency range (i.e., between 235 kHz and 400 kHz), i.e., on the weak excitation side where the spectral amplitude is low. The number of measured frequencies matching with the calculated ones was also larger for EMD (39) compared to linear prediction (17). If these results are confirmed in further studies on more samples, EMD combined with RUS, by improving the extraction of resonant frequencies for low Q-factor materials, may be considered to be useful not only to improve the reliability of the estimation of elastic parameters, but also to extend the application range of RUS.

Evaluation of the signal frequency with adaptive noise suppression in systems of acoustic sounding of the atmosphere

The paper deals with the mathematical modeling of acoustic noise using models of autoregression and synthesis of autoregressive prediction filters for whitening and adaptive noise suppression. It is shown that the use of adaptive notch filters significantly reduces the effect of acoustic interference on the estimated peak frequency of the useful signal in acoustic sounding systems and significantly reduces its standard deviation.

Indoor INS UWB-based human localization with missing data utilizing predictive UFIR filtering

A combined algorithm for the loosely fused ultra wide band &#x0028 UWB &#x0029 and inertial navigation system &#x0028 INS &#x0029 -based measurements is designed under the indoor human navigation conditions with missing data. The scheme proposed fuses the INS- and UWB-derived positions via a data fusion filter. Since the UWB signal is prone to drift in indoor environments and its outage highly affects the integrated scheme reliability, we also consider the missing data problem in UWB measurements. To overcome this problem, the loosely-coupled INS &#x002F UWB-integrated scheme is augmented with a prediction option based on the predictive unbiased finite impulse response &#x0028 UFIR &#x0029 fusion filter. We show experimentally that, the standard UFIR fusion filter has higher robustness than the Kalman filter. It is also shown that the predictive UFIR fusion filter is able to produce an acceptable navigation accuracy under temporary missing UWB-data.