On-line Decomposition Research Articles

Adaptive Real-Time Identification of Motor Unit Discharges From Non-Stationary High-Density Surface Electromyographic Signals.

Estimation of the discharge pattern of motor units by electromyography (EMG) decomposition has been applied for neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, most of the methods for EMG decomposition are currently applied offline. Here, we propose an approach for high-density surface EMG decomposition in real-time. A real-time decomposition scheme including two sessions, offline training and online decomposition, is proposed based on the convolutional kernel compensation algorithm. The estimation parameters, separation vectors and the thresholds for spike extraction, are first computed during offline training, and then they are directly applied to estimate motor unit spike trains (MUSTs) during the online decomposition. The estimation parameters are updated with the identification of new discharges to adapt to non-stationary conditions. The decomposition accuracy was validated on simulated EMG signals by convolving synthetic MUSTs with motor unit action potentials (MUAPs). Moreover, the accuracy of the online decomposition was assessed from experimental signals recorded from forearm muscles using a signal-based performance metrics (pulse-to-noise ratio, PNR). The proposed algorithm yielded a high decomposition accuracy and robustness to non-stationary conditions. The accuracy of MUSTs identified from simulated EMG signals was 80% for most conditions. From experimental EMG signals, on average, 12 ± 2 MUSTs were identified from each electrode grid with PNR of 25.0 ± 1.8dB, corresponding to an estimated decomposition accuracy 75%. These results indicate the feasibility of real-time identification of motor unit activities non-invasively during variable force contractions, extending the potential applications of high-density EMG as a neural interface.

Simulation-based Anomaly Detection and Damage Localization: An application to Structural Health Monitoring

We propose a simulation-based decision strategy for the proactive maintenance of complex structures with a particular application to structural health monitoring (SHM). The strategy is based on a data-driven approach which exploits an offline–online decomposition. A synthetic dataset is constructed offline by solving a parametric time-dependent partial differential equation for multiple input parameters, sampled from their probability distributions of natural variation. The collected time-signals, extracted at sensor locations, are used to train classifiers at such sensor locations, thus constructing multiple databases of healthy configurations. These datasets are then used to train one class Support Vector Machines (OC-SVMs) to detect anomalies. During the online stage, a new measurement, possibly obtained from a damaged configuration, is evaluated using the classifiers. Information on damage is provided in a hierarchical manner: first, using a binary feedback, the entire structure response is either classified as inlier (healthy) or outlier (damaged). Then, for the outliers, we exploit the outputs of multiple classifiers to retrieve information both on the severity and the spatial location of the damages. Because of the large number of signals needed to construct the datasets offline, a model order reduction strategy is implemented to reduce the computational burden. We apply this strategy to both 2D and 3D problems to mimic the vibrational behavior of complex structures under the effect of an active source and show the effectiveness of the approach for detecting and localizing cracks.

Moving object detection based on on-line block-robust principal component analysis decomposition

Robust principal component analysis (RPCA) decomposition is widely applied in moving object detection due to its ability in suppressing environmental noises while separating sparse foreground from low rank background. However, it may suffer from constant punishing parameters (resulting in confusion between foreground and background) and holistic processing of all input frames (leading to bad real-time performance). Improvements to these issues are studied in this paper. A block-RPCA decomposition approach was proposed to handle the confusion while separating foreground from background. Input frame was initially separated into blocks using three-frame difference. Then, punishing parameter of each block was computed by its motion saliency acquired based on selective spatio-temporal interesting points. Aiming to improve the real-time performance of the proposed method, an on-line solution to block-RPCA decomposition was utilized. Both qualitative and quantitative tests were implemented and the results indicate the superiority of our method to some state-of-the-art approaches in detection accuracy or real-time performance, or both of them.

Certified dual-corrected radiation patterns of phased antenna arrays by offline–online order reduction of finite-element models

This paper presents a fast numerical method for computing certified far-field patterns of phased antenna arrays over broad frequency bands as well as wide ranges of steering and look angles. The proposed scheme combines finite-element analysis, dual-corrected model-order reduction, and empirical interpolation. To assure the reliability of the results, improved a posteriori error bounds for the radiated power and directive gain are derived. Both the reduced-order model and the error-bounds algorithm feature offline–online decomposition. A real-world example is provided to demonstrate the efficiency and accuracy of the suggested approach.

Making numerical program analysis fast

Numerical abstract domains are a fundamental component in modern static program analysis and are used in a wide range of scenarios (e.g. computing array bounds, disjointness, etc). However, analysis with these domains can be very expensive, deeply affecting the scalability and practical applicability of the static analysis. Hence, it is critical to ensure that these domains are made highly efficient. In this work, we present a complete approach for optimizing the performance of the Octagon numerical abstract domain, a domain shown to be particularly effective in practice. Our optimization approach is based on two key insights: i) the ability to perform online decomposition of the octagons leading to a massive reduction in operation counts, and ii) leveraging classic performance optimizations from linear algebra such as vectorization, locality of reference, scalar replacement and others, for improving the key bottlenecks of the domain. Applying these ideas, we designed new algorithms for the core Octagon operators with better asymptotic runtime than prior work and combined them with the optimization techniques to achieve high actual performance. We implemented our approach in the Octagon operators exported by the popular APRON C library, thus enabling existing static analyzers using APRON to immediately benefit from our work. To demonstrate the performance benefits of our approach, we evaluated our framework on three published static analyzers showing massive speed-ups for the time spent in Octagon analysis (e.g., up to 146x) as well as significant end-to-end program analysis speed-ups (up to 18.7x). Based on these results, we believe that our framework can serve as a new basis for static analysis with the Octagon numerical domain.

Parametric analytical preconditioning and its applications to the reduced collocation methods

In this paper, we extend the recently developed reduced collocation method [3] to the nonlinear case, and propose two analytical preconditioning strategies. One is parameter independent and easy to implement, the other one has the traditional affinity with respect to the parameters, which allows an efficient implementation through an offline–online decomposition. Overall, preconditioning improves the quality of the error estimation uniformly on the parameter domain, and speeds up the convergence of the reduced solution to the truth approximation.

A space-time hp-interpolation-based certified reduced basis method for Burgers' equation

We present a space-time interpolation-based certified reduced basis method for Burgers' equation over the spatial interval (0, 1) and the temporal interval (0, T] parametrized with respect to the Peclet number. We first introduce a Petrov–Galerkin space-time finite element discretization which enjoys a favorable inf–sup constant that decreases slowly with Peclet number and final time T. We then consider an hp interpolation-based space-time reduced basis approximation and associated Brezzi–Rappaz–Raviart a posteriori error bounds. We describe computational offline–online decomposition procedures for the three key ingredients of the error bounds: the dual norm of the residual, a lower bound for the inf–sup constant, and the space-time Sobolev embedding constant. Numerical results demonstrate that our space-time formulation provides improved stability constants compared to classical L2-error estimates; the error bounds remain sharp over a wide range of Peclet numbers and long integration times T, in marked contrast to the exponentially growing estimate of the classical formulation for high Peclet number cases.

Real-Time Motor Unit Identification From High-Density Surface EMG

This study addresses online decomposition of high-density surface electromyograms (EMG) in real time. The proposed method is based on the previously published Convolution Kernel Compensation (CKC) technique and shares the same decomposition paradigm, i.e., compensation of motor unit action potentials and direct identification of motor unit (MU) discharges. In contrast to previously published version of CKC, which operates in batch mode and requires ∼10 s of EMG signal, the real-time implementation begins with batch processing of ∼3 s of the EMG signal in the initialization stage and continues on with iterative updating of the estimators of MU discharges as blocks of new EMG samples become available. Its detailed comparison to previously validated batch version of CKC and asymptotically Bayesian optimal linear minimum mean square error (LMMSE) estimator demonstrates high agreement in identified MU discharges among all three techniques. In the case of synthetic surface EMG with 20 dB signal-to-noise ratio, MU discharges were identified with average sensitivity of 98%. In the case of experimental EMG, real-time CKC fully converged after initial 5 s of EMG recordings and real-time and batch CKC agreed on 90% of MU discharges, on average. The real-time CKC identified slightly fewer MUs than its batch version (experimental EMG, 4 MUs versus 5 MUs identified by batch CKC, on average), but required only 0.6 s of processing time on regular personal computer for each second of multichannel surface EMG.

On-line Decomposition Technique of Dioxins in Exhaust Gas using Solid Radiation

On-line Decomposition Technique of Dioxins in Exhaust Gas using Solid Radiation

Distributed Manufacturing Networks

The authors consider optimization problems expressed in Decision Guidance Query Language that may involve linear arithmetic constraints, as well as finite domain and binary variables. They focus on Distributed Manufacturing Network optimization problems in which only a part of the problem is dynamic, i.e., the demand for the output products in a manufacturing network, whereas the rest of the problem is static, i.e., the connectivity graph of the assembly processes and the cost functions of machines. The authors propose the Online Decomposition Algorithm based on offline preprocessing that optimizes each static problem component for discretized values of shared constraint variables, and approximate the optimal aggregated utility functions. The Online Decomposition Algorithm uses the pre-processed approximated aggregated cost functions to decompose the original problem into smaller problems, and utilizes search heuristics for the combinatorial part of the problem based on the pre-processed look-up tables. They also conduct an initial experimental evaluation which shows that the Online Decomposition Algorithm, as compared with Mixed Integer Linear Programming, provides an order of magnitude improvement in terms of both computational time and the quality of found solutions for a class of problems for which pre-processing is possible.

Productivity and priming: Morphemic decomposition in Arabic

Word formation in Arabic involves the interleaving of two abstract morphemes—a root consisting exclusively of consonants and conveying semantic meaning, and a word pattern comprised primarily of vowels and conveying phonological and morpho-syntactic information. In masked and cross-modal priming experiments, we probed the processing relationship between these two morphemes during word recognition by examining the roles of word pattern and root productivity (family size) in producing word-pattern priming in Arabic deverbal nouns. Co-varying word pattern and root productivity in a 2×2 design, we found that priming was determined entirely by the productivity of the root. Even very productive word patterns did not prime if they appeared in the context of an unproductive root. This pattern of results, which is identical in cross-modal and masked priming, indicates the importance of the root in driving the on-line decomposition of Arabic surface forms into their constituent morphemes.

Reduced Basis Method and Error Estimation for Parametrized Optimal Control Problems with Control Constraints

We propose a Reduced Basis method for the solution of parametrized optimal control problems with control constraints for which we extend the method proposed in Dede, L. (SIAM J. Sci. Comput. 32:997, 2010) for the unconstrained problem. The case of a linear-quadratic optimal control problem is considered with the primal equation represented by a linear parabolic partial differential equation. The standard offline---online decomposition of the Reduced Basis method is employed with the Finite Element approximation as the "truth" one for the offline step. An error estimate is derived and an heuristic indicator is proposed to evaluate the Reduced Basis error on the optimal control problem at the online step; also, the indicator is used at the offline step in a Greedy algorithm to build the Reduced Basis space. We solve numerical tests in the two-dimensional case with applications to heat conduction and environmental optimal control problems.

A Laplace transform certified reduced basis method; application to the heat equation and wave equation

We present a certified reduced basis (RB) method for the heat equation and wave equation. The critical ingredients are certified RB approximation of the Laplace transform; the inverse Laplace transform to develop the time-domain RB output approximation and rigorous error bound; a (Butterworth) filter in time to effect the necessary “modal” truncation; RB eigenfunction decomposition and contour integration for Offline–Online decomposition. We present numerical results to demonstrate the accuracy and efficiency of the approach.

Model reduction of semiaffinely parameterized partial differential equations by two-level affine approximation

We propose an improvement to the reduced basis method for parametric partial differential equations. An assumption of affine parameterization leads to an efficient offline–online decomposition when the problem is solved for many different parametric configurations. We consider an advection–diffusion problem, where the diffusive term is non-affinely parameterized and treated with a two-level affine approximation given by the empirical interpolation method. The offline stage and a posteriori error estimation is performed using the coarse-level approximation, while the fine-level approximation is used to perform a correction iteration that reduces the actual error of the reduced basis approximation while keeping the same certified error bounds.

Online detection of an emotional response of a horse during physical activity

The objective of this research was to develop a non-invasive method to detect an emotional response of a horse to novelty during physical activity. Two horses performed 20 trials each, in which the horse’s heart rate (HR) and physical activity were continuously measured. The relationship between the horse’s physical activity and HR was described by a mathematical model allowing online decomposition of the horse’s HR into a physical component and a component containing information about its emotional state. Exposure to the novel object resulted in an increase in the emotional component of HR, which allowed automatic detection of an emotional response of the horse in 33/40 trials. In the remaining seven trials no stable model could be built or data were missing. The results show that model-based decomposition of HR can be a useful tool for quantification of certain aspects of temperament.

Speciation analysis of organoarsenical compounds in biological matrices by coupling ion chromatography to atomic fluorescence spectrometry with on-line photooxidation and hydride generation

The optimisation of an on-line decomposition based on UV photooxidation for the analysis of organoarsenic species by coupling cation-exchange chromatography and atomic fluorescence spectrometry with hydride generation, is described. In this study, special consideration is given to the compatibility of mobile phases with post-column treatments. Results show that the most commonly used mobile phase, aqueous pyridine solutions, decreases species conversion efficiency, leading to a significant loss of sensitivity. New fully-compatible chromatographic conditions are proposed to separate arsenobetaine, arsenocholine, trimethylarsine oxide and tetramethylarsonium ion within 20min. The very low absolute limits of detection, 4–12pg(As), allow speciation at trace levels. Analysis of a certified reference fish tissue (DORM-2) and other seafood samples (French and Chilean oysters and mussel) highlights the robustness and the accuracy of the optimised system.

Fluorimetric determination of arsanilic acid by flow-injection analysis using on-line photo-oxidation.

A flow-injection-fluorimetric method for the determination of arsanilic acid is proposed. The assay is based on the on-line decomposition of arsanilic acid in the presence of peroxydisulfate on irradiation with UV light. The arsenate generated in the photochemical reaction was reacted with molybdate in dilute nitric acid to form arsenomolybdic acid, which oxidised thiamine to thiochrome. The thiochrome was monitored fluorimetrically at 440 nm with excitation at 375 nm. The calibration graph was linear in the range 0.10-10.8 microg mL(-1) with a correlation coefficient of 0.999. The detection limit was 0.01 microg mL(-1) and the sample throughput was 55 samples h(-1). The applicability of the method was demonstrated by determining arsanilic acid in animal foodstuffs and water.

On-line analysis of the photoacoustic spectral signal using wavelet transform

An on-line wavelet transform (WT) algorithm was proposed and successfully used in processing the photoacoustic (PA) signal. By investigating the spectra of three rare earth compounds, it was indicated that after on-line decomposition by WT, the noise in the PA spectra was cleanly removed, and the resolved information in the overlapping peaks was easily retrieved. The effect of different sampling rate on resolved results was also investigated. The experimental results showed that the position of peaks after decomposition agreed with the corresponding intramolecular energy transition bands, which demonstrated that WT is a useful and efficient tool for the study of energy bands of rare earth compounds using PA spectroscopy.

Ion-exchange separation of eight arsenic compounds by high-performance liquid chromatography–UV decomposition–hydride generation–atomic fluorescence spectrometry and stability tests for food treatment procedures

A novel separation for cationic arsenic compounds on a polymer-based cation-exchange column was developed using an ion-pairing reagent (3-carboxy-4-hydroxybenzenesulphonic acid) in the mobile phase. An existing anion-exchange separation was used for anionic arsenic compounds. Combining both separation techniques, eight environmentally important arsenic compounds can be determined using on-line decomposition in a UV reactor prior to hydride generation (HG) and atomic fluorescence spectrometry (AFS). The method was applied for testing the stability of arsenic compounds (in aqueous media) related to food treatment procedures. Boiling and microwave treatment gave no degradation, whereas γ-irradiation and dry heating resulted in partial decomposition of several arsenic compounds. No health hazards are to be expected when these data are extrapolated to commercial or domestic food treatment procedures.

On-line microwave-assisted sample decomposition for lead determination in fish slurry samples by electrothermal atomic absorption spectrometry

An on-line microwave-assisted decomposition procedure for the determination of lead in fish is proposed. 300 µL slurry and 400 µL of a 6 mol L-1 HNO3 solution were simultaneously injected, and the mixture was positioned inside a microwave oven. The decomposed sample inside the flushing solution was collected in the autosampler cup of a graphite furnace. The proposed procedure covered the 2.5 to 25 µg L-1 Pb range and presented a detection limit of 0.72 µg L-1 Pb. Precision expressed as RSD, was 10.5% (n = 20) for repeatability and 14.3% (n = 10) for reproducibility. Accuracy was assessed using standard reference material, and also by comparing the results to a nitric-perchloric decomposition procedure.