Noise reduction in elastograms using temporal stretching with multicompression averaging

In most of biomedical signal processing applications the presence of interference signals is inevitable. Processing of lung sounds is not an exemption. In this case the heart sounds are unavoidable and sometimes a severe disturbing noise source. Previous studies indicated that the performance of any interference cancellation scheme relies strongly on an adequate time delay estimation between the reference and the primary signals. Here, a new procedure for time delay estimation using the multi-resolution framework is presented. The proposed methodology possesses such key benefits as the multiple looks information of a signal at different bands, robustness in presence of noise and a validation procedure for the delay estimates. The procedure is applicable to a broad range of problems where time delay estimation is required. Results obtained with lung sounds show the promise of the scheme for the time delay estimation and subsequent interference cancellation.

Accurate localization in harsh indoor environments has long been a challenging problem due to the presence of multipath. Since joint direction-of-arrival (DOA) and time delay (TD) estimation has the capability to separate the line-of-sight signal from multipath signals in the TD space, it has recently become a key technique for accurate indoor localization in next-generation WiFi and 5G networks. This paper addresses the problem of joint azimuth, elevation, and TD estimation of multiple reflections of a known signal. First, we propose an efficient approximate maximum likelihood algorithm for this problem, which updates the DOA and TD parameters alternatingly. This algorithm applies to arbitrarily distributed (planar or 3-D) arrays. Then, we present the closed-form Cramer–Rao bound for joint DOA and TD estimation, based on which we provide further analysis to show the benefit of joint DOA and TD estimation over DOA-only estimation. Although the benefit of joint estimation has been empirically shown long ago, our analysis is the first theoretical proof of it. Finally, simulation results have been provided to demonstrate the theoretical finding and the effectiveness of the new algorithm. Matlab code for the new algorithm is available at https://github.com/FWen/JADE.git .

Time-delay estimation (TDE) sensors can be used to effectively locate a transmitting source. Our aim is to use distributed TDE sensors that are relatively inexpensive. The latter is the result of two constraints: a single analog to digital converter is used for sampling the sum of two sensor output signals, and the sampling interval is substantially larger than the practically required time delay resolution. This approach leads to the requirement for sub-sample interval delay estimation from overlapping different echoes of a received signal. No specific a priori assumptions are made regarding the nature of the received signal. We adapt the time-frequency ratio of mixtures (TFRM) approach for blind source separation for our TDE sensor. Our experimental set-up consists of two microphones placed at non-equal distances from an acoustic source. The TDEs are then used for source localization.

A time delay estimation-based PD-sliding mode control method is proposed to implement high performance trajectory tracking control for a hybrid robot used for automobile electro-coating conveying. First, to exempt the difficulty of real-time dynamic control from calculations of hybrid robot dynamics equations due to its complex mechanism, the time delay estimation-based dynamic model is established by linearizing the dynamic model with time delay estimation technology. Then, a sliding mode control method for the hybrid robot based on time delay estimation-based linearized dynamic model is presented for solving the problem of time delay estimation errors and overcoming the external interferences dramatically. Moreover, the calculation on system dynamics of sliding mode control for the hybrid robot is avoided by substituting the PD control item for the equivalent item of sliding mode control. Lyapunov stability theorem is used to prove the stability of the proposed control law. Finally, the PD control and the proposed control method for the hybrid robot are simulated by MATLAB, and the experiment is carried out on the prototype. The results demonstrate that the proposed control method is independent of complex dynamic model which could avoid the calculation on system dynamics of sliding mode control for the hybrid robot, and it could realize high-accuracy trajectory tracking control compared to PD control under external disturbances.KeywordsHybrid robotPD controlSliding mode controlTime delay estimation

Despite its high-level of robustness and versatility, the human sensorimotor control system regularly encounters and manages various noises, non-linearities, uncertainties, redundancies, and delays. These delays, which are critical to biomechanical stability, occur in various parts of the system and include sensory, signal transmission, CNS processing, as well as muscle activation delays. Despite the relevance of accurate estimation and prediction of the various time delays, the current literature reflects major discrepancy with regards to existing prediction and estimation methods. This scoping review was conducted with the aim of characterizing and categorizing various approaches for estimation of physiological time delays based on PRISMA guidelines. Five data bases (EMBASE, PubMed, Scopus, IEEE and Web of Science) were consulted between the years of 2000 and 2022, with a combination of four related categories of keywords. Scientific articles estimating at least one physiological time delay, experimentally or through simulations, were included. Eventually, 46 articles were identified and analyzed with 20 quantification and 16 qualification questions by two separate reviewers. Overall, the reviewed studies, experimental and analytical, employing both linear and non-linear models, reflected heterogeneity in the definition of time delay and demonstrated high variability in experimental protocols as well as the estimation of delay values. Most of the summarized articles were classified in the high-quality category, where multiple sound analytical approaches, including optimization, regression, Kalman filter and neural network in time domain or frequency domain were used. Importantly, more than 50% of the reviewed articles did not clearly define the nature of the estimated delays. This review presents and summarizes these issues and calls for a standardization of future scientific works for estimation of physiological time-delay.

A novel approach for estimating constant time delay through the use of neural networks (NN) is introduced. A desired reference signal and a delayed, damped, and noisy replica of it are both filtered by a fourth-order digital infinite impulse response (IIR) filter. The filtered signals are normalized with respect to the highest values they achieve and then applied as input for an NN system. The output of the NN is the estimated time delay. The network is first trained with one thousand training data set in which each data set corresponds to a randomly chosen constant time delay. The estimated time delay obtained by the NN is an accurate estimate of the exact time-delay values. Even in the case of noisy data, the estimation error obtained was a fraction of the sampling time interval. The delay estimates obtained by the NN are comparable to the estimated delay values obtained by the cross-correlation technique. The main advantage of using this technique is that accurate estimation of time delay results from performing one pass of the filtered and normalized data through the NN. This estimation process is fast when compared to the classical techniques utilized for time-delay estimation. Classical techniques rely on generating the computationally demanding cross-correlation function of the two signals. Then a peak detector algorithm is utilized to find the time at which the peak occurs.

The use of active vibration control may induce a delay leading to detrimental degradation of the performance of active vibration control. This is particularly true in the case of mechanical systems subjected to friction-induced vibration and noise for which such time-delays can lead to the appearance of undesirable instability. Furthermore, conducting a stability analysis of time-delay systems and estimation of the critical time delay are challenging, due to the infinite nature of the characteristic (quasi) polynomial of the associated closed-loop system, having an infinite number of roots.The objective of this paper is to discuss a strategy for the estimation of the critical time delay for the problem of Friction-Induced Vibration and noisE (FIVE). To achieve such an objective, the prediction of the stability analysis of time delay systems and the estimation of the associated critical time delay are first performed by applying the frequency sweep test and the eigenvalue problem approximation using the Taylor series expansion of the delayed term. In a second time, a mixed approach is proposed to predict effectively the real critical time delay of autonomous controlled systems subjected to friction-induced vibration. The efficiency of the proposed approach is illustrated by numerical examples for the prediction of self-sustaining vibrations of a phenomenological model with two degrees of freedom for which it is possible to provide a clear understanding and illustration of the phenomena involved and observed.

Supply of uninterrupted power is of present need in progressing India. The most of these interruptions occur mainly due to failures in insulations of electrical apparatus. Therefore, the present work aimed at developing an early warning system for electrical distribution substations by predicting insulation breakdown. This paper, focused on time delay estimation in between the UHF signals received at multiple sensors placed around the substation. The delay estimation is most critical and essential part in detection and localization of electrical component having weakness or some defects in insulation and act as PD source. Unfortunately, the radiated UHF signals are highly contaminated with noise, form mobile, TV, and Radio broadcastings. Previous works on time delay estimation in the area mainly depends on time domain based estimation procedures, such as first peak (FP), cumulative energy (CE), and cross correlation (CC). However, recent study reveals that, each PD sources having less variant and almost unique frequency domain based signature. Therefore, in the work, Generalized Cross Correlation (GCC) which is a frequency domain based correlation technique for estimation of time delay has been investigated in details. In the work different weighing functions for the GCC, such as Smoothed Coherence Transform (SCOT), the Phase Transform (PHAT), the Eckart Filter (EF) and the Maximum Likelihood (ML) estimator. The study reveals that the GCC with ML function provide most accurate and precise estimation of time delay.

In this paper we present a highly accurate, spline-based time delay estimator (TDE) that directly determines subsample time delay estimates from sampled data. The algorithm uses cubic splines to produce a continuous time representation of a reference signal and then computes an analytical matching function between this reference and a delayed signal. The location of the minima of this function yields estimates of the time delay. We also present a more computationally efficient formulation of this algorithm, which is based on FIR filtering to determine the cubic spline coefficients, polynomial approximation to determine the time delay estimates and adaptive search of the delay estimate. The proposed algorithm is particularly useful in applications such as blood flow estimation and tissue elasticity estimation since it can include companding with little additional computational cost.

Time delay estimation is the basis of target location of multi-sensor network, while the basic analysis of the signal is of key importance to the time delay estimation. In this paper, a time delay analysis system is designed by virtual instrument development tools. The analysis system contains: virtual oscilloscope, virtual spectrum analyzer, virtual filter and virtual time delay analyzer. Using this analysis system on a set of field collected acoustic target signal, spectrum analysis and filtering is made. Then the time delay estimation has been carried out for several sampling data segments selected by certain rules, and curve of the time delay vs. time is given. Analysis shows the main frequency of the target signal: 85Hz, 13Hz, 170Hz and 125Hz. The regular pattern of the time delay curve is consistent with the target motion. Application reveals that the system is helpful in the research of time delay estimation.

Magnitude squared coherence (MSC) and time delay are two important quantities needed for passive detection and localization of a radiating source using several sensors. This paper presents a novel approach to estimating the MSC and time delay from the outputs of two sensors. It first models the MSC as the product of the transfer functions of two auto-regressive moving-average (ARMA) filters. The ARMA coefficients are then determined by a least squares, matrix pseudoinverse solution to the estimation equation. Time delay estimation follows as a by-product since both phase and MSC estimates are now available. Simulation results obtained from this modeling approach are compared against those estimated from the traditional periodograms. In the majority of cases studied, the ARMA approach appears to be superior, giving smaller bias and variances in both MSC and time delay estimations.

Relative navigation of spacecrafts may be accomplished by observing X-ray sources and indirectly determining the spacecraftspsila relative position. In this approach, two spacecrafts lock on a known pulsar which irradiates X-ray waveforms that reach them with a differential time delay that is proportional to the distance between the spacecrafts. By observing different pulsar sources geometrically distributed over the galactic disc, it is possible to determine the spacecraftspsila relative inertial position. Our goal is to estimate their relative position by Time Delay Estimation (TDE) between the detected signals. Although there are several off-line TDE methods, like the basic cross-correlation (BCC) and the generalized cross-correlation (GCC) techniques, in this work we formulate TDE as a channel estimation problem and apply adaptive filtering techniques to estimate the time delay online. There are certain benefits in using adaptive filters, especially when the underlying parameters like signalspsila statistics are unknown or change over time. We study different adaptive algorithms and show how they are able to efficiently deliver accurate delay estimates at reduced computational complexity and in real time.

Neuro-fuzzy based time-delay estimation using DCT coefficients

Time-delay estimation (TDE) using ground penetrating radar (GPR) is of great importance in roadway surveys. The conventional GPR methods apply a uniform sampling strategy for TDE, which requires numerous frequency sampling points, leading to lengthy data acquisition time and large data storage, especially for ultra-wideband (UWB) radar. Moreover, detecting the overlapped backscattered echoes from the thin layer of roadways remains a challenge in TDE, due to the limited resolution of GPR and the characteristics of GPR signals. To address these issues, we derive a co-prime sampling strategy-based TDE for thin layers in roadway survey by exploiting off-grid sparse Bayesian learning (OGSBL), referred to co-prime-OGSBL. In our scheme, the sampling rate of GPR signals with a co-prime sampling strategy is greatly reduced compared with the uniform sampling, which therefore reduces the data acquisition burden and computational complexity. The estimation performance of time delays and thickness is also enhanced with OGSBL by utilizing radar pulse, co-prime sampling, and noncircularity of GPR signals. Both simulation and experimental results demonstrate the efficiency and accuracy of the proposed method in the estimation of time delays and thickness.

The localization of acoustic reflectors is a fundamental component in various applications, including room acoustics analysis, sound source localization, and acoustic scene analysis. Time delay estimation (TDE) is essential for determining the position of reflectors relative to a sensor array. Traditional TDE algorithms generally yield time delays that are integer multiples of the operating sampling period, potentially lacking sufficient time resolution. To achieve subsample TDE accuracy, various interpolation methods, including parabolic, Gaussian, frequency, and sinc interpolation, have been proposed. This paper presents a comprehensive study on time delay interpolation to achieve subsample accuracy for acoustic reflector localization in reverberant conditions. We derive the Whittaker-Shannon interpolation formula from the previously proposed sinc interpolation in the context of short-time windowed TDE for acoustic reflector localization. Simulations show that sinc and Whittaker-Shannon interpolation outperform existing methods in terms of time delay error and positional error for critically sampled and band limited reflections. Performance is evaluated on real-world measurements from the MYRiAD dataset, showing that sinc and Whittaker-Shannon interpolation consistently provide reliable performance across different sensor-source pairs and loudspeaker positions. These results can enhance the precision of acoustic reflector localization systems, vital for applications, such as room acoustics analysis, sound source localization, and acoustic scene analysis.