Envelope Estimation Research Articles

Assessing the influence of SNR and pre-processing filter bandwidth on the extraction of different muscle co-activation indexes from surface EMG data

Muscle co-activation is the mechanism that regulates simultaneous activity of agonist and antagonist muscles crossing the same joint. During functional movements, robust measurement techniques are required for an accurate determination of muscle co-activation, since various environmental and processing factors in the surface electromyography (sEMG) measurement process might influence the estimation of linear envelope profiles, and therefore the outcome of co-activation evaluated from the signal envelope. The aim of this study is to verify the performance of the co-activation indexes introduced in six different techniques used to assess muscle co-activation. A sensitivity analysis with respect to both noise and pre-processing choices for envelope estimation has been done by using a data-set of simulated sEMG signals. The results show how the indexes are affected by both the level of noise and pre-processing choices. The Vector Coding Technique and the Time-varying Multi-muscle approach perform better than the others in terms of both sensitivity to varying levels of co-activation and robustness to noise.

Effect of estimation error on dual MRC receiver over Hoyt fading channels

ABSTRACTThe effect of antenna correlation and the estimation error on the dual maximal ratio combining receiver over Hoyt fading channels has been analysed in this paper. We have derived the expressions of bit error rate and outage probability to observe the quantum of performance degradation due to estimation error along with correlation. From our analysis, we have concluded that the error in envelope estimation degrades the performance of the receiver up to a great extent than the phase estimation error. Since both antenna correlation and estimation error are unavoidable in practical application, the authors feel that the provided results will assist the design engineers to sustain the quality of service in a wireless receiver.

Widgets and Wodgets: Technology Markets and R&D Spillovers

Estimates of the private and social rates of return to investments in R&D are of high interest to economists, managers and policymakers. An important problem in the literature is that the canonical model used to obtain such estimates only allows R&D to diffuse through spillovers. This is a serious limitation in a world increasingly characterized by active intellectual property (IP) enforcement and monetization. We create a new dataset of interactions in the market for technology between publicly held firms in the U.S. which allows us to generalize the canonical model with both spillovers and market-mediated technology transfers. We obtain four main findings using changes in tax incentives for R&D to identify causal effects. First, R&D accessed through technology markets is an important input in the generation of revenue. Second, conventional spillover estimates are contaminated with technology transfers because the weights traditionally used to capture spillovers are strongly correlated with matching in the market for technology. Third, the private rate of return to R&D is larger in the generalized framework while the wedge between the social and private returns is smaller. Finally, back of the envelope estimates suggest that the gains from trade in the market for technology might be larger than $1 trillion per year, accounting for 10% of total revenue in Compustat.

Model and estimators for partial least squares regression

AbstractPartial least squares (PLS) regression has been a very popular method for prediction. The method can in a natural way be connected to a statistical model, which now has been extended and further developed in terms of an envelope model. Concentrating on the univariate case, several estimators of the regression vector in this model are defined, including the ordinary PLS estimator, the maximum likelihood envelope estimator, and a recently proposed Bayes PLS estimator. These are compared with respect to prediction error by systematic simulations. The simulations indicate that Bayes PLS performs well compared with the other methods.

Quantitative Doppler Analysis Using Conventional Color Flow Imaging Acquisitions.

Interleaved acquisitions used in conventional triplex mode result in a tradeoff between the frame rate and the quality of velocity estimates. On the other hand, workflow becomes inefficient when the user has to switch between different modes, and measurement variability is increased. This paper investigates the use of power spectral Capon estimator in quantitative Doppler analysis using data acquired with conventional color flow imaging (CFI) schemes. To preserve the number of samples used for velocity estimation, only spatial averaging was utilized, and clutter rejection was performed after spectral estimation. The resulting velocity spectra were evaluated in terms of spectral width using a recently proposed spectral envelope estimator. The spectral envelopes were also used for Doppler index calculations using in vivo and string phantom acquisitions. In vivo results demonstrated that the Capon estimator can provide spectral estimates with sufficient quality for quantitative analysis using packet-based CFI acquisitions. The calculated Doppler indices were similar to the values calculated using spectrograms estimated on a commercial ultrasound scanner.

High-Frequency Quantitative Ultrasound for Imaging Prostate Cancer Using a Novel Micro-Ultrasound Scanner

Currently, biopsies guided by transrectal ultrasound (TRUS) are the only method for definitive diagnosis of prostate cancer. Studies by our group suggest that quantitative ultrasound (QUS) could provide a more sensitive means of targeting biopsies and directing focal treatments to cancer-suspicious regions in the prostate. Previous studies have utilized ultrasound signals at typical clinical frequencies, i.e., in the 6-MHz range. In the present study, a 29-MHz, TRUS, micro-ultrasound system and transducer (ExactVu micro-ultrasound, Exact Imaging, Markham, Canada) was used to acquire radio frequency data from 163 patients immediately before 12-core biopsy procedures, comprising 1956 cores. These retrospective data are a subset of data acquired in an ongoing, multisite, 2000-patient, randomized, clinical trial (clinicaltrials.gov NCT02079025). Spectrum-based QUS estimates of effective scatter diameter (ESD), effective acoustic concentration (EAC), midband (M), intercept (I) and slope (S) as well as envelope statistics employing a Nakagami distribution were used to train linear discriminant classifiers (LDCs) and support vector machines (SVMs). Classifier performance was assessed using area-under-the-curve (AUC) values obtained from receiver operating characteristic (ROC) analyses with 10-fold cross validation. A combination of ESD and EAC parameters resulted in an AUC value of 0.77 using a LDC. When Nakagami-µ or prostate-specific antigen (PSA) values were added as features, the AUC value increased to 0.79. SVM produced an AUC value of 0.77, using a combination of envelope and spectral QUS estimates. The best classification produced an AUC value of 0.81 using an LDC when combining envelope statistics, PSA, ESD and EAC. In a previous study, B-mode-based scoring and evaluation using the PRI-MUS protocol produced a maximal AUC value of 0.74 for higher Gleason-score values (GS >7) when read by an expert. Our initial results with AUC values of 0.81 are very encouraging for developing a new, predominantly user-independent, prostate-cancer, risk-assessing tool.

Fourier–Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals

In this paper, a new method has been presented for the time–frequency (TF) representation of non-stationary signals. The existing empirical wavelet transform (EWT) has been enhanced using Fourier–Bessel series expansion (FBSE) in order to obtain improved TF representation of non-stationary signals. We have used the FBSE method for the spectral representation of the analyzed multi-component signals with good frequency resolution. The scale-space based boundary detection method has been applied for the accurate estimation of boundary frequencies in the FBSE based spectrum of the signal. After that, wavelet based filter banks have been generated in order to decompose non-stationary multi-component signals into narrow-band components. Finally, the normalized Hilbert transform has been applied for the estimation of amplitude envelope and instantaneous frequency functions from the narrow-band components and obtained the TF representation of the analyzed non-stationary signal. We have applied our proposed method for the TF representation of multi-component synthetic signals and real electroencephalogram (EEG) signals. The proposed method has provided better TF representation as compared to existing EWT method and Hilbert–Huang transform (HHT) method, especially when analyzed signal possesses closed frequency components and of short time duration.

The costs and benefits of exchange rate protection in China

From 2002 to 2011, China ran large surpluses in both the current and capital accounts of its balance of payments, which the People's Bank of China (PBOC) purchased and held at official foreign reserves to avoid nominal appreciation of the currency. Concurrently, with its massive purchases of foreign exchange, the PBOC compelled commercial banks to buy PBOC ‘sterilisation bonds’ and raised commercial bank reserve requirement ratios to avoid monetisation of its foreign exchange purchases and concomitant upward pressure on the price level (that is, real appreciation of the currency). Sterilising foreign exchange intervention, as China did for a decade, constitutes a violation of the implicit rules of a fixed exchange rate regime and as such can be seen as a mercantilist policy of manipulating the real exchange rate to gain, or avoid losing, international price competitiveness—what Corden (1981) termed ‘exchange rate protection’. This paper sets out the simple theory of the costs and benefits of exchange rate protection and provides back‐of‐the‐envelope estimates of their magnitude in China in the 2000s. It also explores the ‘other side of the story’, the decline in US manufacturing employment in the 2000s, which recent literature attributes to a ‘China Trade Shock’ that allegedly resulted from the US granting Permanent Normal Trade Relations (PNTR) to China in 2001. Here, it is argued that the so‐called China Trade Shock resulted from China's sterilised intervention policy, not the granting of PNTR. The implications of these competing hypotheses are considered in the conclusions.

An improved signal envelope estimation method for analysis of acoustic signals emitted by remotely piloted helicopters

This study presents a new signal envelope estimation method in order to improve the performance of the Empirical Mode Decomposition (EMD), especially in dealing with transient signals. An important application of this method is the analysis of signals containing spikes such as acoustic signals. For this purpose, both the spline interpolation and control point selection algorithm are adapted. To construct signal envelopes, the Catmull-Rom spline interpolation is employed due to its desired features. Also, a new algorithm is utilized in order to select control points prior to the iterative sifting process. Afterward, the proposed method is verified by some benchmark signals. Performance indices indicate that the proposed EMD outperforms the traditional one in handling transient signals. Finally, the proposed method is applied to acoustic signals gathered during some flight tests of a remotely piloted helicopter. The results demonstrate that the proposed method is able to isolate acoustic signals emitted by dissimilar sources.

Research Progress of Blood Pressure Measurement by Oscillometric Method

Blood pressure is an important physiological index that can reflect the function of the heart and blood vessels. Compared with other methods, oscillometric measurement has the advantages of strong noise immunity, simple operation and low cost, which is the most widely used technology for blood pressure measurement. In this paper, we reviewed and discussed the key technologies of measuring blood pressure, such as pre-processing method of oscillation envelope and blood pressure estimation technique. The latter include traditional wave method, waveform morphology method, the methods based on the model and machine learning. At last, we have a look forward to its future development direction.

Functional envelope for model-free sufficient dimension reduction

In this article, we introduce the functional envelope for sufficient dimension reduction and regression with functional and longitudinal data. Functional sufficient dimension reduction methods, especially the inverse regression estimation family of methods, usually involve solving generalized eigenvalue problems and inverting the infinite-dimensional covariance operator. With the notion of functional envelope, essentially a special type of sufficient dimension reduction subspace, we develop a generic method to circumvent the difficulties in solving the generalized eigenvalue problems and inverting the covariance directly. We derive the geometric characteristics of the functional envelope and establish the asymptotic properties of related functional envelope estimators under mild conditions. The functional envelope estimators have shown promising performance in extensive simulation studies and real data analysis.

EEG-assisted Modulation of Sound Sources in the Auditory Scene.

Noninvasive EEG (electroencephalography) based auditory attention detection could be useful for improved hearing aids in the future. This work is a novel attempt to investigate the feasibility of online modulation of sound sources by probabilistic detection of auditory attention, using a noninvasive EEG-based brain computer interface. Proposed online system modulates the upcoming sound sources through gain adaptation which employs probabilistic decisions (soft decisions) from a classifier trained on offline calibration data. In this work, calibration EEG data were collected in sessions where the participants listened to two sound sources (one attended and one unattended). Cross-correlation coefficients between the EEG measurements and the attended and unattended sound source envelope (estimates) are used to show differences in sharpness and delays of neural responses for attended versus unattended sound source. Salient features to distinguish attended sources from the unattended ones in the correlation patterns have been identified, and later they have been used to train an auditory attention classifier. Using this classifier, we have shown high offline detection performance with single channel EEG measurements compared to the existing approaches in the literature which employ large number of channels. In addition, using the classifier trained offline in the calibration session, we have shown the performance of the online sound source modulation system. We observe that online sound source modulation system is able to keep the level of attended sound source higher than the unattended source.

Determination of reachable flight regions: a zonotopic approach

The general problem addressed in this paper is that of the estimation and prediction of the aircraft flight maneuvering envelope. This work is intended to be further used as the basis of a new approach for predictive safe flight envelope monitoring and early notification of developing off-nominal flight situations. The proposed method combines: i) a Zonotopic Kalman Filter (ZKF) to estimate the set of possible states related to the open-loop flight dynamics of an aircraft, ii) reachable tube computations based on a closed-loop model to predict the future evolution of an aircraft over a specified time window and based on some assumptions about future control activities. The reachable tubes over the moving anticipatory temporal window are initialized and computed at each sample time based on the estimated state set resulting from the ZKF prediction/correction observation scheme which takes into account bounded state and measurement uncertainties. The aircraft benchmark CALC (Civilian Aircraft Landing Challenge) jointly proposed by ONERA (French Aerospace lab) and AIRBUS has been used to provide some preliminary simulation results.

Intracardiac electrogram envelope detection during atrial fibrillation using fast orthogonal search.

The performance of any intracardiac electrogram processing method is limited by the accuracy of its activation detection approach. The most common activation detection approaches in the literature aim to find the highest peak in the activation envelope disregarding the start and end points. However, the duration of the activation can be used to extract useful information such as wave collisions. In this work, we propose a novel orthogonal based approach for fast and accurate estimation of the start and end of the activations (activation envelope) in intracardiac recordings during atrial fibrillation. Wavelet decomposition of the signals was used to create a pool of basis functions for the proposed modeling method. The database included 24 recordings of approximate length of 6s obtained from atrial endocardium of 5 patients who underwent catheter ablation therapy. The start and end of activations in each electrogram was manually annotated by an expert electrophysiologist and the annotations were used as a gold standard to calculate the performance of our envelope detection method. The results show promising performance and excellent robustness to training data for our proposed method with respect to envelope estimation error.

Weighted envelope estimation to handle variability in model selection

SummaryEnvelope methodology can provide substantial efficiency gains in multivariate statistical problems, but in some applications the estimation of the envelope dimension can induce selection volatility that may mitigate those gains. Current envelope methodology does not account for the added variance that can result from this selection. In this article, we circumvent dimension selection volatility through the development of a weighted envelope estimator. Theoretical justification is given for our estimator, and the validity of the residual bootstrap for estimating its asymptotic variance is established. A simulation study and real-data analysis illustrate the utility of our weighted envelope estimator.

The ecoacoustics of a kelp forest: Measurement and prediction (Ecklonia radiata)

Despite of over a century of intensive research on kelp (brown macroalgae of the order Laminariales), very few acoustic studies have been undertaken and much remains unknown about their species-specific acoustic properties, which are nevertheless essential for surveying and managing the respective habitats. In this paper, we discuss results of an in-situ, acoustic and ecological investigation of species Ecklonia radiata which forms dense aggregations at Canoe bay, Tasmania (FORTES 12). The experiment measured extensively the time‐varying Green’s function (0.2–20 kHz) between pairs of distant source and receivers together with physical and biogeochemical variables of the ecosystem. Propagation and scattering features are interpreted in the light of other results obtained by the authors including ray-based estimation of the channel impulse response envelope, finite‐element scattering model of a whole E. radiata thallus, and a study of production from diel oxygen modelling, oxygen exchange, and electron tran...

Glottal Vocoding With Frequency-Warped Time-Weighted Linear Prediction

Linear prediction (LP) is a prevalent source-filter separation method of speech production. One of the drawbacks of conventional LP-based approaches is the biasing of estimated formants by harmonic peaks. Methods such as discrete all-pole modeling and weighted LP have been proposed to overcome this problem, but they all use a linear frequency scale. This study proposes a new LP technique, frequency-warped time-weighted linear prediction (WWLP), to provide spectral envelope estimates robust to harmonic peaks that work on a warped frequency scale that approximates the sensitivities of the human auditory system. Experiments are performed within the context of vocoding in statistical parametric speech synthesis. Subjective listening test results show that WWLP-based spectral envelope modeling increases quality over previously developed methods.

Improved local mean decomposition for modulation information mining and its application to machinery fault diagnosis

Local mean decomposition (LMD) has been developed for modulation information mining. Its performance is dependent on boundary condition, envelope estimation, and stopping criterion. This paper proposes a soft sifting stopping criterion that enables LMD to achieve a self-adaptive stop for each sifting process. In the proposed method, we define an objective function that considers two characteristics, namely, the root mean square and the excess kurtosis, of the target signal. To optimize this objective function, a heuristic mechanism is proposed to automatically determine the optimal number of sifting iterations. Experimental results on simulated signals demonstrate the effectiveness of the proposed soft sifting stopping criterion for improving the accuracy of LMD, and finally the proposed method is applied to modulation information mining for gear fault diagnosis.

Estimation of amplitude and standard deviation of noisy sinusoidal signals

A simple method to estimate the amplitude and standard deviation of sinusoidal signals corrupted with additive Gaussian noise is proposed. For this, a two-parameter model is developed by sorting the samples of the signal. This reduced parametric model allows robust parameter estimation, even if the phase function of the sinusoid is nonlinear, discontinuous, and unknown. The functionality and performance of the proposed method are analyzed by several computer simulations; the used GNU Octave program is provided. The proposed method can be useful for unbiased envelope estimation in fringe pattern normalization among other potential applications.

An efficient wavelet-based automated R-peaks detection method using Hilbert transform

Machine-aided detection of R-peaks is becoming a vital task to automate the diagnosis of critical cardiovascular ailments. R-peaks in Electrocardiogram (ECG) is one of the key segments for diagnosis of the cardiac disorder. By recognizing R-peaks, heart rate of the patient can be computed and from that point onwards heart rate variability (HRV), tachycardia, and bradycardia can also be determined. Most of the R-peaks detectors suffer due to non-stationary behaviors of the ECG signal. In this work, a wavelet transform based automated R-peaks detection method has been proposed. A wavelet-based multiresolution approach along with Shannon energy envelope estimator is utilized to eliminate the noises in ECG signal and enhance the QRS complexes. Then a Hilbert transform based peak finding logic is used to detect the R-peaks without employing any amplitude threshold. The efficiency of the proposed work is validated using all the ECG signals of MIT-BIH arrhythmia database, and it attains an average accuracy of 99.83%, sensitivity of 99.93%, positive predictivity of 99.91%, error rate of 0.17% and an average F-score of 0.9992. A close observation of the simulation and validation indicates that the suggested technique achieves superior performance indices compared to the existing methods for real ECG signal.