An electrocardiogram (ECG) records the electrical signal from the heart to check for different heart conditions, but it is susceptible to noises. ECG signal denoising is a major pre-processing step which attenuates the noises and accentuates the typical waves in ECG signals. Researchers over time have proposed numerous methods to correctly detect morphological anomalies. This study discusses the workflow, and design principles followed by these methods, and classify the state-of-the-art methods into different categories for mutual comparison, and development of modern methods to denoise ECG. The performance of these methods is analysed on some benchmark metrics, viz., root-mean-square error, percentage-root-mean-square difference, and signal-to-noise ratio improvement, thus comparing various ECG denoising techniques on MIT-BIH databases, PTB, QT, and other databases. It is observed that Wavelet-VBE, EMD-MAF, GAN2, GSSSA, new MP-EKF, DLSR, and AKF are most suitable for additive white Gaussian noise removal. For muscle artefacts removal, GAN1, new MP-EKF, DLSR, and AKF perform comparatively well. For base-line wander, and electrode motion artefacts removal, GAN1 is the best denoising option. For power-line interference removal, DLSR and EWT perform well. Finally, FCN-based DAE, DWT (Sym6) soft, MABWT (soft), CPSD sparsity, and UWT are promising ECG denoising methods for composite noise removal.

Multiple speaker tracking in distributed microphone array (DMA) network is a challenging task. A critical issue for multiple speaker scenarios is to distinguish the ambiguous observation and associate it to the corresponding speaker, especially under reverberant and noisy environments. To address the problem, a distributed multiple speaker tracking method based on time delay estimation in DMA is proposed in this study. Specifically, the time delay estimated by the generalised cross-correlation function is treated as an observation. In order to distinguish the observation for each speaker, the possible time delays, refer to as candidates, are extracted based on data association technique. Considering the ambient influence, a time delay estimation strategy is designed to calculate the time delay for each speaker from the candidates. Finally, only the reliable time delays in DMA are propagated throughout the whole network by diffusion fusion algorithm and used for updating the speakers' state within the distributed Kalman filter framework. The proposed approach can track multiple speakers successfully in a non-centralised manner under reverberant and noisy environments. Simulation results indicate that, compared with other methods, the proposed method can achieve a smaller root mean square error for multiple speaker tracking, especially in adverse conditions.

Spoken Term Detection (STD) is the process of locating the occurrences of spoken queries in a given speech database. Generally, two methods are adopted for STD: an ASR based sequence matching and ASR-free, feature-based template matching. If a well-performing ASR is available, the former STD method is accurate. However, to build an ASR with consistent performance, several hours of labelled corpora is required. Template matching methods work well for small or chopped utterances. However, in practice, the volume of the search database can be huge, containing sentences of varying lengths. Hence time complexity of template matching techniques will be high, which makes them impractical for realistic search applications. In this work, a two-stage STD system is proposed, which combines the ASR-based phoneme sequence matching in the first stage and feature sequence template matching of selected locations in the second stage. The time complexity of the second stage is reduced by performing DTW-based template matching only at probable query locations identified by the first stage. ‘Split and match’ approach helps to reduce the false-positives in case of longer query words. Effectiveness of the proposed method is demonstrated using English and Malayalam datasets.

In sensor array processing literature, near-field bearing and range estimation algorithms generally use spherical wavefront to model only array sensors’ phase response (sometimes with Fresnel approximation) and assume equality in amplitude response. Ignoring the range dependent amplitudes, though facilitating the algorithmic development, will cause systematic estimation errors due to model mismatch. By taking the spherical wavefront amplitude into account, a new bearing and range estimation algorithm for locating multiple near-field sinusoid sources is presented. With the estimation of the near-field sensor array's response vector, closed-form formulas for bearing and range estimates are derived from its magnitude, or phase, or both. The problem of estimation ambiguity is discussed as well. Cramér-Rao bound is also derived to serve as a benchmark for performance study.

This paper investigates the distributed state estimation for a class of linear time-varying systems with intermittent observations in sensor networks. Unlike the existing studies in distributed state estimation, this work considers the scenario where the cross-covariances between different sensors are unavailable and the measurements for state estimation encounter intermittent observations and/or random losses. For this practical scenario, a new sequential covariance intersection-based Kalman consensus filer (SCIKCF) is then developed. We show that, with the proposed SCIKCF, each sensor can achieve consensus estimates regardless of the order of fusion. Furthermore, the stability of the SCIKCF as well as the boundedness of the estimation error and the corresponding error covariances are analysed. Finally, three examples are performed to verify the effectiveness of the proposed SCIKCF.

The quaternion-valued signals consist of four signal components. The discrete quaternion Fourier transform is to map these four signal components in the time domain to that in the frequency domain. These four signal components in the frequency domain are called the discrete quaternion Fourier transform components. There are a total of 16 inner products among any two discrete quaternion Fourier transform components. The total orthogonal error among the discrete quaternion Fourier transform components is defined based on these 16 inner products. This study aims to find the optimal quaternion number in the discrete quaternion Fourier transforms so that the total orthogonal errors among the discrete quaternion Fourier transform components are minimised. It is worth noting that finding the optimal quaternion number in the discrete quaternion Fourier transform is equivalent to finding the optimal rescaling factors. Since the discrete quaternion Fourier transform components are expressed in terms of the high-order polynomials of the trigonometric functions of the rescaling factors, this optimisation problem is non-convex. To address this problem, a two-stage approach is employed for finding the solution to the optimisation problem. The comparison results show that the authors proposed method outperforms the existing methods in terms of achieving the low total orthogonal error among the discrete quaternion Fourier transform components.

This study addresses the problem of direction-of-arrival (DOA) estimation of coherent signals via sparse parameter estimation. Since many sparse methods provide good performances regardless of signal correlations and array geometry, they can be considered as candidates for DOA estimation of coherent signals impinging on a sensor array with arbitrary geometry. However, their straightforward applications require high computational loads especially for two-dimensional (2D) DOA estimation. Two efficient methods based on sparse parameter estimation are herein presented; one is a combined approach of sparse estimation and the RELAX algorithm extended for 2D DOA estimation and the other relies on the adaptive 2D grid refinement and power update control. Numerical simulations are performed to demonstrate the efficiency of the proposed methods using a uniform circular array for both 1D and 2D DOA estimation cases. It is shown that sparse asymptotic minimum variance (SAMV)-RELAX, a combined approach of SAMV and RELAX, outperforms SAMV and multi-stage SAMV in 2D scenarios without suffering from plateau effects for off-grid signals and that its computational load is significantly lower than those of SAMV and multi-stage SAMV. In addition, SAMV-RELAX does not require the difficult selection of grid parameters for fine DOA estimation unlike the multi-stage approach.

Continuous monitoring of the blood glucose levels is essential and critical for controlling diabetes and its complications. With the improvement of the measurement accuracy of the acquisition devices developed in recent decades, developing the optical-based methods for performing the non-invasive blood glucose estimation for the consumer applications becomes very important. The authors’ previous work is based on the heart rate variability of the electrocardiogram and the existing method is based on applying the random forest to the features extracted from the photoplethysmogram. However, the accuracies of these two methods are not very high. In this study, a joint empirical mode decomposition and exponential function estimation approach is proposed for estimating the mean value of a photoplethysmogram acquired from a wearable non-invasive blood glucose device. Also, the exponential function fitting approach is employed for estimating the blood glucose levels via an L ₁ norm formulation. The computer numerical simulation results show that the estimation accuracy based on their proposed method is higher than that based on the state-of-the-art methods. Therefore, their proposed method can be employed for performing blood glucose estimation effectively.

To solve the problem of high-order moment Gaussian distribution (HGD) noise in state estimation, a fusion filter for Markov jump linear systems (MJLSs) with high-order moment information obtained from sensor data is designed. To obtain high-order moment information, the multi-sensor MJLS is converted to a single-mode system composed of high-order moment components by using a cumulant generating function. Next, a filter design based on Bayesian theory is established to achieve state estimation with a high-order moment information form according to the transformed single-mode deterministic system. Subsequently, a high-order moment fusion technique based on entropy theory is proposed to obtain a more accurate estimation result of the state by using the high-order moment information obtained from various sensors. Comparing the first- and second-order moment information obtained by traditional Gaussian distribution, the HGD introduces higher-order moment information and makes the fusion process more reasonable. In this way, a more precise and reasonable performance of the state estimation is achieved, depending on the sensor fusion technique. To confirm the effectiveness and advantages of the proposed method, a numerical simulation example is provided with various fusion methods. Thus, the performance of the proposed fusion filter design is verified.

A deep learning approach to estimate the mean Doppler velocity and spectral width in weather radars is presented. It can operate in scenarios with and without the presence of ground clutter. The method uses a deep neural network with two branches, one for velocity and the other for spectral width estimation. Different network architectures are analysed and one is selected based on its validation performance, considering both serial and parallel implementations. Training is performed using synthetic data covering a wide range of possible scenarios. Monte Carlo realisations are used to evaluate the performance of the proposed method for different weather conditions. Results are compared against two standard methods, pulse-pair processing (PPP) for signals without ground clutter and Gaussian model adaptive processing (GMAP) for signals contaminated with ground clutter. Better estimates are obtained when comparing the proposed algorithm against GMAP and comparable results when compared against PPP. The performance is also validated using real weather data from the C-band radar RMA-12 located in San Carlos de Bariloche, Argentina. Once trained, the proposed method requires a moderate computational load and has the advantage of processing all the data at once, making it a good candidate for real-time implementations.

The traditional least-squares based diffusion least mean squares is not robust against outliers present in either desired data or input data. The diffusion minimum generalised rank (GR) norm algorithm proposed in the earlier works of the authors was able to effectively estimate the parameter of interest in presence of outliers in both desired and input data. However, this manuscript deals with the robust distributed estimation over distributed networks exploiting sparsity underlying in the system model. The proposed algorithm is based on both GR norm and compressive sensing, where GR norm ensures robustness against outliers in input as well as desired data. The techniques from compressive sensing endow the network with adaptive learning of the sparse structure form the incoming data in real-time and it also enables tracking of the sparsity variations of the system model. The mean and mean square convergence of the proposed algorithm are analysed and the conditions under which the proposed algorithm outperforms the unregularised diffusion GR norm algorithm are also investigated. The proposed algorithms are validated for three different applications namely distributed parameter estimation, tracking and distributed power spectrum estimation.