Search

Recently, maximum Versoria criterion-based adaptive algorithms have been introduced as a new solution for robust adaptive filtering. This paper studies the steady-state tracking analysis of an adaptive filter with maximum Versoria criterion (MVC) in a non-stationary (Markov time-varying) system. Our analysis relies on the energy conservation method. Both Gaussian and general non-Gaussian noise are considered, and for both cases, the closed-form expression for steady-state excess mean square error (EMSE) is derived. Regardless of noise type, unlike the stationary environment, the EMSE curves are not increasing functions of step-size parameter. The validity of the theoretical results is justified via simulation.

The family of least mean square (LMS) based adaptive filtering algorithms suffers from convergence performance limitation due to the sensitivity of such algorithms to the eigenvalue spread of the input correlation matrix. The quasi-Newton family of adaptive filtering algorithms addresses this limitation, but its performance is restricted by the estimation accuracy of the correlation matrix inverse, especially for highly correlated input signals. Furthermore, the convergence rate and the steady-state performance of both LMS and quasi-Newton families are thoroughly depending on their step-sizes. In this paper, a variable step-size regularized quasi-Newton adaptive algorithm is proposed in the context of system identification. In this algorithm, inspired by the matrix inversion lemma, a modified regularized matrix inverse with a time-varying regularization is computed such that during the convergence, the contribution of matrix inverse in the weight update is reduced, resulting in a more noise-robust algorithm. The paper further provides a convergence analysis of the proposed quasi-Newton algorithm, wherein a variable step-size is proposed to achieve a high initial convergence rate and a low steady-state error in the context of system identification applications.

Trace alignment can improve the subsequent side-channel analysis against the trace. Most current trace alignment schemes are, however, typically operated under a high signal-to-noise ratio (SNR), which demands them to be noise reduced before alignment when practical applications in the complex environment. In this paper, we propose a novel strategy for applying adaptive filtering in trace alignment preprocessing under low SNR conditions. The approach selects a trace as the reference signal of the adaptive filter, and the impulse response describing the trace offset is calculated iteratively for each trace. Different from conventional trace alignment methods, the error between the two traces in iteration determines how to eliminate the offset between trace, which eliminate most of the noise effects in the iteration process. In parallel, the filter after iterating will also function as a low-pass filter in the alignment process. Experimental studies based on three side-channel datasets demonstrate the efficacy of the proposed approach. Compared with other alignment methods, with the reasonable computational resource cost and complexity, the average number of traces required has reduced the average number of traces required by 75%, the average confidence has improved by 60%, and the success rate has increased by 72%. Our approach provides great potential for applications in trace alignment preprocessing of side-channel analysis. [ABSTRACT FROM AUTHOR]

In this article, a new decomposition theory, feature mode decomposition (FMD), is tailored for the feature extraction of machinery fault. The proposed FMD is essentially for the purpose of decomposing the different modes by the designed adaptive finite-impulse response (FIR) filters. Benefitting from the superiority of correlated Kurtosis, FMD takes the impulsiveness and periodicity of fault signal into consideration simultaneously. First, a designed FIR filter bank by Hanning window initialization is used to provide a direction for the decomposition. The period estimation and updating process are then used to lock the fault information. Finally, the redundant and mixing modes are removed in the process of mode selection. The superiority of the FMD is demonstrated to adaptively and accurately decompose the fault mode as well as robust to other interferences and noise using simulated and experimental data collected from bearing single and compound fault. Moreover, it has been demonstrated that FMD has superiority in feature extraction of machinery fault compared with the most popular variational mode decomposition. [ABSTRACT FROM AUTHOR]

Cognitive radio is considered a relevant communication paradigm to deal with the increasing demands in modern communications systems. Adaptive schemes are required to recognize channel conditions and to properly adjust main transmission parameters to improve the quality of communications. In this direction, blind algorithms to recover constellation, from phase-modulated signals, represent a means to implement cognitive capabilities to allow automatic modulation recognition (AMR) on receivers. Commonly, the most popular approaches for blind constellation recovery are based on a two-step scheme. The first step uses to equalize channel effects and reduce inter-symbol interference (ISI). The second step carries out constellation recovery utilizing phase locked loop (PLL) systems like the Costas Loop, then to classify the incoming signal. This work proposes a novel single-step blind adaptive filter solution, inspired by an adaptive interference canceler, for joint equalization and constellation symbol recovery from received phase shift keying (PSK) waveforms. Furthermore, we propose new coefficients update mechanisms based on the constant amplitude of PSK signals. The proposed solution exhibits reduced computational complexity compared to the state of the art and a reduced time of convergence. Additionally, the proposed scheme does not require a training sequence to operate properly. The obtained results clearly show that the proposed scheme significantly improves accuracy regarding phase symbol estimation and ISI reduction. [ABSTRACT FROM AUTHOR]

Speaker recognition is challenging in real-world environments. Typically, studies approach noises only in an additive manner. However, real environments commonly present reverberating conditions that worsen speech processing. When not considering reverberation in the system modeling, the system may not be robust when applied to real-world conditions. In this work, we use a slight different approach to simulate reverberation, considering randomized environment conditions. With this approach, each VoxCeleb1 test sample is corrupted by randomly generated conditions, with diverse amplitudes of noise and speech. We generate a corrupted dataset, in which the best speaker recognition model EER degraded from 0.93% to 30.13%. To improve this degradation, we propose using Normalized Kernel Least-Mean-Square (NKLMS) adaptive filter. Through the use of NKLMS, we were able to improve the EER from 30.13% to 1.11%. The results indicate that NKLMS has a great potential for speech enhancement to improve speaker recognition.

The affine projection algorithm with a fixed regularization parameter is subject to a compromise concerning the convergence speed and steady-state misalignment. To address this problem, we propose to employ a variable mixing factor to adaptively combine two different regularization factors in an attempt to put together the best properties of them. The selection of the mixing factor is derived by minimizing the energy of the noise-free a posteriori error, and for the sake of suppressing large fluctuations, a moving-average method is designed for updating the mixing factor. Based on a random walk model, we also prove that the proposed mixing factor is as well available for the non-stationary system. The mathematical analysis including the stability performance, steady-state mean square error, and computational complexity are performed. In practice, we compare with the existing related algorithms in system identification and echo cancellation scenarios, the results illustrate that the proposed algorithm outperforms them with notable margins.

The maximum total complex correntropy (MTCC) algorithm improves the performance of adaptive filtering under the error in variable (EIV) model by integrating both input and output noise information into the total complex correntropy. However, the MTCC algorithm cannot be applied to the widely linear model, directly. Compared with a strictly linear model, the widely linear model provides the complete statistical information of signals for adaptive filtering. This paper first constructs a novel cost function for the widely linear model by incorporating the input and output noise information into the improper complex correntropy. Then, a novel widely linear adaptive filter algorithm named widely linear maximum total improper complex correntropy (WL-MTICC) is proposed using the gradient descent method under the maximum total improper correntropy criterion. Finally, the analysis of local stability and convergence in the mean sense regarding the proposed WL-MTICC algorithm is provided. Simulations are used to show the performance advantage of the proposed WL-MTICC algorithm in the presence of both input and output noises.

The three-phase DSTATCOM is prone to higher dynamics due to grid disturbances. The proportionate affine projection algorithm (PAPA) is an adaptive filter that can be used for DSTATCOM control. In order to overcome the sluggish convergence speed of adaptive filters, PAPA is proposed in this paper. The convergence rate versus the steady-state error is a trade-off in conventional adaptive filters. However, the utilization of two adaptive filters in CSS-PAPA increases the convergence and decreases the steady-state error. The suggested filter has the advantage of having a lower computational cost than a standard adaptive filter. The proposed filter helps the inverter to work as a shunt compensator. The goal of the suggested controller is to adjust for reactive power and unity power factor during faulty conditions. The proposed DSTATCOM controller has undergone experimental validation in the laboratory.

In this work, a proposal is presented to mitigate the voltage ripple effect generated by switched reluctance generator (SRG) in wind energy conversion systems (WECS). Voltage and current oscillations of switched reluctance generators are an expected consequence of the machine’s natural switching operation. This switching operation creates a known frequency component, named stroke frequency. When the SRG is connected to the AC utility grid in WECS, these oscillations may compromise the generated power quality by introducing stroke frequency components in the grid electric current. To solve such issue, this work has as an original contribution the proposal of in-loop adaptive filters applied to mitigate the propagation of the SRG stroke frequency content in the DC-bus voltage of a grid-connected WECS. The notch cascading and moving average filters are implemented at the voltage loop of the voltage source converter in an adaptive form, as function of the SRG speed. Simulation and experimental results show that the waveform distortion of the injected electric current is greatly reduced compared to the conventional non-filtered SRG connection.

Herein, we propose a normalized subband adaptive filter (NSAF) algorithm that adjusts both the step size and regularization parameter. Based on the random-walk model, the proposed algorithm is derived by minimizing the mean-square deviation of the NSAF at each iteration to calculate the optimal parameters. We also propose a method for estimating the uncertainty in an unknown system. Consequently, the proposed algorithm improves performance in terms of tracking speed and misalignment. Simulation results show that the proposed NSAF outperforms existing algorithms in system identification scenarios.

This work presents a method to automatically generate a high performance controller for the permanent magnet synchronous motor (PMSM). The method consists of two components, a nominal system identification and a harmonic component identification. Both identification methods are based on dynamic mode decomposition (DMD). The nominal system identification is used to assign the feedback gaines by matching the desired closed-loop eigenvalues and eigenvectors. The harmonic system identification is used to generate vectors that are multiplied by a delay embedding of the current to predict harmonic components at the next time-step. The method is applied to two experimental test setups, one interior permanent magnet (IPM) and one surface mount permanent magnet (SPM) motor. It is shown that the automatically generated feedback controller is able to achieve a more precise transient response than the traditional rule-based PI controller. It is also shown the harmonic compensation method is able to reduce total demand distortion (TDD) on phase currents better than a traditional adaptive filter approach without the need for gain tuning. This work shows a novel approach to using DMD for the complete system identification of the PMSM, and lays the foundation for using DMD with delay embeddings to analyze and manipulate harmonic signals in a predictive way.

Feedforward active vibration control techniques aim at canceling vibrations occurring in a system by means of introducing intentional forces that generate destructively interfering vibrations. These forces are typically controlled using an adaptive filtering algorithm that tracks the system state in real time and seeks to minimize overall vibration. One important often-overlooked issue in such systems is related with the vibrations introduced by the adaptive control algorithm itself either during its transient learning phase or due to its nonideal characteristic. These vibrations may become very significant in several mechanical structures due to resonance phenomena. In this context, this work is dedicated to the development of an effective scheme for feedforward active vibration control that is capable of dealing with undesirable vibrations introduced by the adaptive controller. A particular version of the filtered-x normalized least-mean-squares algorithm tailored to the proposed scheme is also developed. Results obtained from numerical simulation as well as from an experimental device are presented, aiming to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this work, we study the combination of adaptive filters in colored noise environments. First, a combination framework using delayed weights is introduced to tackle the colored noise. Based on this, delayed convex and affine combinations of two LMS filters are developed, resulting in the so-called Dcvx-LMS and Daff-LMS algorithms. Then, the convergence behaviors of the two algorithms are investigated using standard mean-square deviation analysis. In addition, to speed up the convergence and reduce the computational complexity, we propose delayed combination with periodic feedback, delayed combined-step-size and block implementation methods. Finally, simulation results demonstrate the superiority of our algorithms over previously reported techniques in the presence of colored noise. [ABSTRACT FROM AUTHOR]

Wireless sensor networks, consisting of sensor nodes, monitoring the real-world phenomena generate a huge amount of data. However, the transmission of these measured values consumes most of the battery power of energy constrained sensor nodes. In this paper, threshold based data reduction system (TBDRS) is proposed in order to reduce the transmissions from the sensor node by exploiting the correlation between the measured values. Unlike other systems, TBDRS uses a control mechanism for threshold based switching on the basis of estimation error. This switching allows the system to achieve better trade-off between convergence speed and misadjustment, thereby improving the lifetime of the wireless sensor network by reducing the number of transmissions and computations. Due to the settling time, misadjustment trade-off capability, TBDRS can also be used in disaster management applications where the sensor data variations are more. The performance analysis of the system is evaluated in terms of mean square error. Moreover, the performance of TBDRS is evaluated for different parameters including length, step size and scaling factor. The simulation results validate that the proposed system achieves 70.56% and 83.01% better performance than the state-of-the-art system for an accuracy of 0.1 in terms of data reduction and number of computations, respectively. [ABSTRACT FROM AUTHOR]

The affine combination of two complex-valued least-mean-squares filters (aff-CLMS) addresses the trade-off between fast convergence rate and small steady-state misadjustment error. However, a rigorous analysis of the aff-CLMS algorithm for second-order non-circular inputs is still under investigation. To this end, the focus in this letter is on the full mean-square analysis of the aff-CLMS algorithm, in which the transient analyses of the mixing parameter, as well as the standard and complementary weight-error covariance matrices, are completed. In addition, we derive the closed-form solutions of the steady-state weight-error power and its complementary version of the aff-CLMS. Finally, the effectiveness of the theoretical analysis is supported by computer simulations. [ABSTRACT FROM AUTHOR]

Radio frequency fingerprint (RFF) is an intrinsic physical characteristic of the device caused by hardware imperfection. It can be extracted from wireless signals and has been widely applied for device identification. However, the improving precision of electronic components reduces the distinguishability of RFF, which makes it difficult to identify large amount of devices that are of the same model, especially in a low signal-to-noise ratio (SNR) scenario. In this letter, we propose an artificial radio frequency fingerprint (ARFF) embedding scheme for device identification, which greatly increases the distinguishability of fingerprints and minimally affects the performance of communication. The scheme includes an adaptive filter based RFF extraction method, a principal component analysis (PCA) based ARFF generation algorithm and a proposed ARFF embedding algorithm that eliminates the influence of the device’s original RFF to form a preset ARFF pattern. Experimental results on ZigBee devices demonstrate that our ARFF based scheme achieves 99.91% identification accuracy and simultaneously has little impact on bit error rate. [ABSTRACT FROM AUTHOR]

In this study, a three-phase unified power quality conditioner (UPQC) topology based on a quadruple-active-bridge (QAB) is presented as an attractive solution to power quality issues. It is composed of three single-phase series converters, a three-phase shunt converter and a QAB converter located at the common DC-link. Since the bulky low frequency isolation transformers are replaced by the high frequency ones in QAB, it leads to higher power density, reduced weight and volume. The QAB converter could function as an energy router to provide multiple DC-link voltage access ports in the UPQC, which increase the flexibility of the UPQC. Finally, the MATLAB/Simulink simulation results and hardware-in-the-loop testing verify the correctness and effectiveness of the proposed topology and control method.

The δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is an efficient approach for multiobject tracking in case of high clutter density and low detection probability. However, the formulation of the original δ-GLMB filter requires that the birth δ-GLMB filtering density is known a priori. It is inapplicable for the birth object appearing from unknown positions. To address this problem, an adaptive δ-GLMB filter is proposed to detect and track the birth objects with unknown position information. This adaptive filter establishes the birth δ-GLMB filtering density by using measurements at previous three successive times. Simulation results indicate that the proposed adaptive δ-GLMB filter may efficiently detect and track the multiple objects with unknown positions. Simulation results also demonstrate that the proposed adaptive δ-GLMB filter performs better than the other existing adaptive filters.

In this paper, three easily implemented hardware algorithms, including the adaptive prediction error filter based on the Gram-Schmidt algorithm (GS-APEF), the least mean square adaptive filter and the comb filter, are extensively investigated for artifact denoising on a constructed semi-simulated database with varied ten-fold frequency stimulation. By implementing the GS-APEF in the field-programmable gate array (FPGA) and using the edge noise mitigating technique, a stimulation artifact denoising system is designed to realize real-time stimulation artifact removal under varied ten-fold frequency functional electrical stimulation. Good performance of the artifact denoising is demonstrated in proof-of-concept experiments on able-bodied subjects with a mean correlation coefficient between the root mean square profile of denoised surface electromyography and volitional force of 0.94, verifying the validity of the proposed prototype.

Multiply-Accumulate (MAC) operation is the backbone of Least Mean Squares (LMS) digital adaptive filters. Implementing LMS on hardware platform as a Fully Dedicated Architecture (FDA) multiplier becomes bottleneck for higher order filters, prompting high area, cost and power requirements and hence renders the design unsuited for practical implementation. In this paper, we have proposed a composite design that makes use of Distributed Arithmetic (DA) to replace the bottleneck multiplier with memory units that store Partial Products (PPs) to emulate multiplication. The depth of these memory units tends to exponentially grow as the filter order rises. To manage that, we have used Half Memory algorithm (HM) and Offset Binary Coding (OBC) to refine the structure of PPs such that the memory size is reduced at least by a factor of 4 for the same filter order. The proposed design improves system’s Throughput, Critical Path Delay, Power Consumption and FPGA Resource Utilization. However, it introduces Latency in both the output and update segments of the LMS algorithm. To provide an option between resource utilization and latency, we have suggested a mechanism to halve the originally produced latency by the Parallel Processing of input bit steam w.r.t even and odd bits. Moreover, we have also proposed a method that reduces the latency of update module at the slight expense of other design attributes. The fundamental structure of the proposed design is flexible owing to the dynamic memory structure as well as the option to choose between latency and resource minimization. Simulations have been carried out in Xilinx Vivado and conclusions have been drawn by comparing both FDA and DA based designs. Results for a 16-tap filter indicate a remarkable improvement in Throughput, Area Utilization and Power Consumption by 18%, 5% and 3.5% respectively at the expense of $4\times $ escalated latency. The Half Latency method allowed the latency to drop $2\times $ but with slightly elevated power and area attributes.

In this study, a voice activity detection technique is designed using features such as short-term energy, periodicity and spectral flatness. The desired results are obtained by using these three features, even at low signal to noise ratio values. In addition, performance of multi-channel noise reduction algorithms such as Wiener speech distortion weighted, spatial prediction, minimum variance distortion-less response are compared using the proposed voice activity detection. Two different audio signals and three different noise types are used in the experiment. Noisy speech and only detection of noisy areas have been performed by proposed voice activity detection algorithm. The filter coefficients have been calculated for each filter algorithm used after detection of noisy speech and only noisy areas. The calculated filter coefficients have been multiplied by the frequency components of the signal received from the reference microphone to obtain an enhanced signal. Segmental signal to noise ratio, an objective method, and mean opinion score as a subjective method have been used to evaluate the performance of the filters. Speech distortion weighted Wiener filter has been found to be the best filter for noise reduction performance.

This paper mainly focuses on the third harmonic current suppression for symmetrical six-phase fault-tolerant permanent magnet synchronous motor drives. First, a brief motor model is established to analyze the source of the third harmonic current. Resonant controller is always appreciated in terms of harmonic current suppression. However, its performance is highly dependent on the precise motor speed, which makes it not suit for variable-speed applications. Fortunately, adaptive filter can skip this problem. Then a current controller based on the adaptive filter is designed to eliminate the third harmonic current. However, the dynamic response of the controller designed is unsatisfactory. Besides, the steady edge of the controller is rather narrow, which limits the operation range of the motor. Then some improvements are made to the structure of the controller as well as the adaptive algorithm. Finally, several groups of experiments are conducted to compare both steady performance and dynamic performance of the controller based on adaptive filter and the improved controller. The experiments results coincide with the analysis and several conclusions are drawn. Besides, the proposed controller also applies to the elimination of other harmonic currents.

The recently proposed affine projection Versoria (APV) algorithm has been widely used over other affine based algorithms due to its robustness against impulsive noises. However, the performance of the APV algorithm suffers from high steady state misalignment. In order to overcome this, we propose affine projection Champernowne adaptive filter (APCMAF) in which instead of taking Versoria function as a cost function we have used the probability density function of the Champernowne distribution as a cost function and data reuse technique. The proposed APCMAF algorithm provides low steady-state misalignment in impulsive noise environment. To verify the performance of the APCMAF algorithm, a set of simulation study has been done in system identification scenarios which confirms that the APCMAF provides better steady state performance with improved convergence performance over other existing algorithms in impulsive noise environments. Further, the bound of learning rate for stable convergence has been also derived and a detailed comparison of computational complexity is also presented. [ABSTRACT FROM AUTHOR]

This brief proposes an enhanced fractional derivative that can prevent the tap weight coefficients from destroying the gradient information, solve the problem caused by the fractional extreme point, and improve the convergence speed with the help of error estimation information and Sign function. Based on this fractional derivative, an enhanced fractional least mean square (EFLMS) filter algorithm is proposed. We analyze the influence of unknown system vector on the convergence performance of the EFLMS algorithm. The computational complexity of the proposed algorithm is also analyzed. Simulation experiments show that the EFLMS algorithm achieves better performance in system identification than the classic least mean square (LMS) algorithm and the existing algorithms based on fractional calculus. [ABSTRACT FROM AUTHOR]

An adaptive PPG (Photoplethysmography) readout system for a dual-channel OLED-OPD flexible sensor is designed and developed with motion artifact (<1Hz) and ambient lighting interference successfully compensated without any additional motion sensors. The compensation is made possible by adopting multi-feedbacks and an additional reference OPD channel to cancel effectively DC drifts. In result, the quality of measured PPG is improved to the level such that long-time, continuous quality monitoring of bio-sign such as heart rate (HR) is possible. The readout is designed with an auto-programmable band-pass trans-impedance amplifier (TIA) of a 100dbΩ gain with a continuous-type DC-current cancellation loop. The rest of the readout consists of a 0.5 Hz low-pass filter, an additional second-order band-pass filter (0.1-10Hz), a difference amplifier, a motion reference channel, an analog multiplexer, a programmable gain amplifier (PGA), a digital control and a programmable DAC-PWM based auto-intensity tuned OLED driver. The readout is fabricated in an area of 9 mm2 via the TSMC 180nm process. The experiment result shows that the developed OLED-OPD readout senses well as small as 1nA current, with a measured dynamic range >90dB (1nA to 100 µA) and input-referred noise of 0.26 nA/√H, with power consumption of 460µW. The DC drift is successfully reduced to 1% of its average. The accuracy for heart rate is 96%. [ABSTRACT FROM AUTHOR]

This article presents a fractional- $N$ digital multiplying delay-locked loop (MDLL) that employs a digital-to-time converter (DTC) to control the reference injection for the fractional- $N$ operation. The presented MDLL features a background two-point DTC calibration that simultaneously corrects the DTC gain and offset errors to achieve a low-jitter and low-spur architecture. The DTC errors are sensed using an embedded time-to-digital converter (TDC) and extracted in the digital domain for reduced implementation overhead. A new TDC dithering and dither noise cancellation technique is used to improve the estimation accuracy of the DTC errors at the presence of TDC quantization error and differential nonlinearity (DNL). In addition, the adaptive dither noise cancellation scheme uses a comb filter to decouple the dither noise and DTC error estimations, allowing the two schemes to operate simultaneously. The proof-of-concept MDLL prototype fabricated in 65-nm CMOS achieves −60-dBc fractional spur and 1.67-ps rms jitter at around 20-MHz offset. With the proposed calibration scheme, a >25-dB spur reduction in reference and fractional spurs is demonstrated. [ABSTRACT FROM AUTHOR]

Active noise control (ANC) systems are widely used to cancel unwanted noise. However, for high-level noise, the residual error signal cannot be fully eliminated because of the nonlinearity of the secondary path, resulting in the diverging of the adaptive filter. In this letter, we propose a secondary path-decoupled ANC (SPD-ANC) algorithm based on deep learning. Specifically, the secondary path decoupled module consisting of two time-domain convolutional recurrent networks, one for modeling the nonlinear secondary path and the other for modeling the reverse process, is employed to calculate the secondary path-decoupled (SPD) error signal. The control signal is then generated by an adaptive filter that is optimized towards minimizing the SPD error signal. Simulation results indicate that the proposed method outperforms the conventional ANC methods under different conditions. [ABSTRACT FROM AUTHOR]

Non-Gaussian colored input signal and censored observation are encountered frequently in many practical applications of adaptive signal processing. Conventional adaptive algorithms will face convergence performance degradation in such cases. To address the issue, the affine projection algorithm for censored regression (CR-APA) is proposed in this brief. The censored thresholds and the variance of the background noise are regarded as the prior knowledge to compensate for the bias caused by censored observation. By theoretical analysis, the range of the step size is derived to guarantee the stabilization of the CR-APA. Besides, the method of evolving projection order is employed to improve the convergence performance of the CR-APA. Computer simulations in system identification and echo cancellation applications are performed to demonstrate the better convergence performance of the proposed algorithms in censored data processing over competing algorithms, and to verify the improvement of the CR-APA by the method of evolving projection order. The proposed CR-APA and CR-EAPA can be used in engineering applications to make up for the hardware. [ABSTRACT FROM AUTHOR]

Parasitic motion corrupts the measurement of all non-contact sensor systems. It thus hinders deployment of these sensors for such real-world applications where it is impossible to keep the sensor perfectly stationary. In this paper, an adaptive algorithm is presented to remove parasitic vibrations corrupting the measurements of a self-mixing (SM) interferometric laser sensor. Previously, this was achieved by coupling a solid state accelerometer (SSA) with the SM sensor followed by either a pre-calibration based design or a time-domain adaptive filter based system. The proposed method is based on adaptive spectral filtering where filter coefficients are derived based on the parasitic vibrations present in the retrieved target motion. Importantly, it neither requires any pre-calibration procedure nor has any dependence on parameters such as filter-order, convergence criteria, or step-size (required in case of time-domain adaptive filters), thus making the proposed scheme a suitable choice for mass production of SSA-SM sensor systems. The proposed algorithm provides improved mean RMS error of 5.37 nm as opposed to 19.1 nm for least mean square (LMS) filter, 20.22 nm for recursive least square (RLS) filter, and 24.7 nm for pre-calibration based system. This method’s performance has also been characterized and an embedded, real-time, hardware design is also presented. At the end, hardware system results (on experimental data-set) and sensor bounds/bandwidth are also quantified. [ABSTRACT FROM AUTHOR]

Nowadays, adaptive filters in complex domain have received increasing attention since they can be applied to a wider scenario than real valued cases. As two typical information theoretic criteria, the minimum complex kernel risk-sensitive loss (MCKRSL) and maximum complex correntropy criterion (MCCC) have shown robustness to non-Gaussian noises in complex domain adaptive filtering. However, since both criteria cannot consider the probability distribution of error, they are not optimal in the presence of some non-Gaussian noises. This paper firstly defines a complex Shannon entropy in terms of the probability distribution of error. Then, a novel adaptive filter is proposed using the minimum complex Shannon entropy (MCSE) criterion. More significantly, the convergence behavior of the MCSE algorithm is discussed, and thus the steady-state excess mean square error (EMSE) is calculated for theoretical analysis. Finally, simulated results on system identification and channel estimation validate the obtained theoretical results and the merits of the proposed MCSE algorithm. [ABSTRACT FROM AUTHOR]

Source Camera Identification (SCI) has been playing an important role in the security field for decades. With the development of Deep Learning, the performance of SCI has been noteworthily improved. However, most of the proposed methods are forensic only for a single camera identification category, e.g., the camera model identification. For exploiting the coupling between different camera categories, we present a new coding method. That is, we apply the multi-task training method to regress the categories, namely, to classify brands, models and devices synchronously in a single network. Different from the common multi-task method, we obtain the multi-class classification result by just one single label classification. To be specific, we classify the categories in a progressive way that the parent category classification result will be used in the child category classification (a detailed explanation will be given later in the main context). Also, by appropriately increasing the redundancy of the coding method for classifying new camera categories, the training time can be greatly reduced. To better extract camera attributes, we propose an adaptive filter. Additionally, we propose an auxiliary classifier that only focuses on the camera model re-classification, due to the low performance of the main classifier on certain models. Lastly, the extensive experiments show that our methods have a better performance than other existing methods.

This study employs a dual deep neural network (D-DNN) to accurately estimate the absolute longitudinal speed of a vehicle. Accuracy in speed estimation is crucial for vehicle safety, because longitudinal speed is a common parameter employed as a state variable in active safety systems such as anti-lock braking system and traction control system. In this study, DNNs are applied to determine the gain of an adaptive filter to estimate vehicle speed. The used data consists of longitudinal acceleration, wheel speed, filter gain, and estimated vehicle speed. The data generated from Carsim software are collected and preprocessed using a Simulink model. To acquire data with numerous wheel slip patterns, various acceleration and deceleration conditions are applied to four different road conditions. Though, it is challenging to achieve a single DNN model that is optimally cope with the various driving situations. Thus, we adopt two DNN models that were individually trained in low and high acceleration regions. The dual DNN model results in error reductions of 74% and 65%, compared with a single DNN and classical adaptive Kalman filter approaches, respectively.

Time-of-flight (ToF) cameras provide depth information and they have been applied in many areas. However, multipath interference (MpI) affects the depth measurements. This article proposes an algorithm to suppress MpI during ToF camera depth measurements by exploiting the amplitude envelope of the camera's transmission wave. More precisely, it uses the fact that the amplitude envelope of the ToF camera's transmission wave is constant. The envelope of the reflected signal in the case of MpI deviates from that of the MpI-free envelope. By minimizing the deviation using an adaptive filter, the depth measurement error can be corrected. The method is applied at the sensor side of the ToF camera and lays the theoretical foundation for MpI suppression in ToF cameras. The simulation and experimental results demonstrate that the proposed algorithm is able to significantly improve the depth measurement accuracy.

To enable full-duplex (FD) in underwater acoustic (UWA) systems, a high level of self-interference cancellation (SIC) is required. This can be achieved by using a combination of SIC methods, including digital SIC. For digital SIC, adaptive filters are used. In time-invariant channels, the SI can be effectively cancelled by classical recursive least-square (RLS) adaptive filters, such as the sliding-window RLS (SRLS) or exponential-window RLS, but their SIC performance degrades in time-varying channels, e.g., in channels with a moving sea surface. Their performance can be improved by delaying the filter inputs. This delay, however, makes the mean squared error (MSE) unsuitable for measuring the SIC performance. In this article, we propose a new evaluation metric, the SIC factor (SICF), which gives better indication of the SIC performance compared to MSE. The SICF can be used to evaluate the performance of digital SIC techniques without the need of implementing a full FD system. A new SRLS adaptive filter based on parabolic approximation of the channel variation in time, named SRLS-P, is also proposed. The SIC performance of the SRLS-P adaptive filter and classical RLS algorithms (with and without the delay) is evaluated by simulation and in lake experiments. The results show that the SRLS-P adaptive filter can significantly improve the SIC performance, compared to the classical RLS adaptive filters.

Recently, a new class of multivector-valued adaptive filters, called geometric-algebra adaptive filters (GA-AFs), have been proposed and applied to 3D registration of point clouds, rotation estimation in computer vision, and so on. To offer a complete theoretical foundation for GA-AFs, we present a transient behavior evaluation of the geometric algebra least mean square (GA-LMS) algorithm. Specifically, the transient mean square deviation (MSD) is obtained by using the results of the mean square theoretical behavior of the GA-LMS. Furthermore, the transient excess mean square error (EMSE) is also given by using the results of MSD. The analytical results rely on the independence theory which has been commonly used in the convergence analysis of real-valued adaptive filter. Considering the non-commutative geometric algebra, the conventional method of analysis is not suitable for the GA-LMS algorithm. Therefore, we propose a novel method to analyze the GA-LMS algorithm under white noise assumption by separating the weight-error array. The obtained theoretical results can be used to accurately predict the transient performance of the GA-LMS. Moreover, the stability condition and steady-state performance are also analyzed. Our proposed steady-state model is more accurate than the previous model. Finally, numerical experiments are presented to confirm the accuracy of the theoretical analysis results. [ABSTRACT FROM AUTHOR]

Noninvasive monitoring of respiratory activity is an emerging research area in biomedical health monitoring. This article describes a neural network-based model, intelligent Photoplethysmography derived Respiration signal Extraction, and Tracking (${i}$ -PRExT). Here, an ensemble empirical mode decomposition (EEMD) is used to select the appropriate intrinsic mode functions (IMFs) through filtering in the respiration band and reconstruct by a linear weighted sum to obtain the photoplethysmography derived respiration (PDR) signal. The weight factors are derived by a multilayer perceptron neural network (MLPNN) fed with respiratory induced amplitude variation (RIAV) features extracted by a deep autoencoder (DAE). The tracking of respiration rate (RR) is done by an adaptive filter-based predictor. ${i}$ -PRExT was tested and validated with BIDMC data set under PhysioNet and 30 volunteers’ data collected under resting condition. The PDRs achieved over 90% correlation and low error (NRMSE~0.2) with reference respiration signal, while RRs have almost 100% correlation even under motion artifact (MA) corrupted photoplethysmography (PPG). The PDR shows improved performance, while RR tracking outperforms the published research on respiration signal extraction based on PPG. [ABSTRACT FROM AUTHOR]

A robust adaptive filter is usually unaffected by spurious disturbances at the error sensor. In an endeavour to improve robustness of the adaptive filter, a novel modified Champernowne function (MCF) is proposed as a robust norm and the corresponding robust Champernowne adaptive filter (CMAF) is derived. To improve modelling accuracy and convergence performance for sparse systems along with being robust, a reweighted zero attraction (RZA) norm is incorporated in the cost function along with MCF and the corresponding RZA-CMAF algorithm is proposed. To further improve filter performance, the CMAF-l0 algorithm is proposed where the l0-norm is approximated using the multivariate Geman-McClure function (GMF). Bound on learning rate for the proposed algorithms is also derived. Extensive simulation study shows the improved robustness achieved by the CMAF algorithm, especially when impulsive noises are present for a longer duration. On the other hand, RZA-CMAF and CMAF-l0 can provide improved convergence performance under sparse and impulsive noise conditions, with CMAF-l0 providing the best performance. [ABSTRACT FROM AUTHOR]

In order to provide convincing artifical touch sensations, humans should be presented with high quality haptic stimuli. In the vibrotactile domain, signals are usually displayed through mechanical actuators. Current high quality actuators exhibit a high dynamic range and have the ability to display a wide range of frequencies. However, fundamentally all actuators introduce distortions into the displayed signals. These distortions are usually nonlinear with additive noise components and they can be detrimental to some vibrotactile application scenarios that require high signal playback precision. To neutralize these distortions, we propose a signal-based equalization setup with adaptive filtering. Such a setup is very general and can be applied to any actuator in a straightforward manner. We introduce a novel adaptive filter based on Volterra and bilinear filter models that is nonlinear and more robust than previous approaches. In simulations and experiments, we show that our filter model is able to consistently outperform existing adaptive filter models and equalize vibrotactile actuators efficiently. [ABSTRACT FROM AUTHOR]

The Maximum Versoria Criterion (MVC) based adaptive algorithm has recently gained the attention of researchers because of its robustness against impulsive interference and reduced complexity. In this brief, the steady-state Excess Mean Square Error (EMSE) analysis of the standard MVC algorithm is done using the energy conservation relation. For the Gaussian noise case, quadratic equation that can provide the exact value of theoretical steady state EMSE is obtained without any assumptions other than the conventional ones and the Price’s theorem. For the non-Gaussian case, an approximate solution using Taylor’s expansion is derived. Experimental results validate the obtained theoretical findings. [ABSTRACT FROM AUTHOR]

The relative position sensor of a high-speed maglev train is an important part of train location and speed detection for motor traction, but its output signal is not only related to position but also related to the suspension gap of the maglev train. The fluctuation of the suspension gap will affect the amplitude of the output signal of the sensor (i.e., the suspension wave signal is coupled with the output signal). The prediction normalization method currently used can eliminate the effect of the suspension fluctuation to a certain extent, but there is a limitation. Aiming at this problem, this paper analyzes the frequency characteristics of the suspension gap fluctuation caused by track irregularities and proposes a gap estimation algorithm based on the adaptive filter. The Kalman filter is used to estimate the gap and then the output signal is compensated according to the numerical relationship between the gap and the output signal of the sensor, so as to achieve the decoupling between the gap and the position measurement. Finally, the effectiveness of the method is proved by the comparison experiments.

The vibration signals extracted from gearbox are usually masked in heavy background noise. Analyzing the feature components of these signals is challenging and crucial to incipient fault diagnosis. The Kurtogram has been verified as a very powerful and practical tool in the mechanical fault diagnosis due to its advantages in detecting and characterizing transients in signals. However, the accuracy of the kurtogram is limited because it is based on short-time Fourier transform (STFT) or FIR filter in extracting transient characteristics from a noisy signal. Therefore, it is necessary to develop a more accurate filter to overcome its shortcomings and further improve its fault detection accuracy. Wavelet transform has been widely applied in the past as a powerful tool to analyze the non-stationary signals, and an alpha-stable distribution model is often used to describe the statistical features of non-Gaussian signals. In this paper, a Morlet wavelet filter is optimized based on the kurtogram and the alpha parameter of the alpha-stable distribution model. Through the analysis of simulation signals at different fault degrees and operating conditions, it can be concluded that alpha-stable distribution has better performance than kurtosis in measuring the non-Gaussian characteristics of an impulsive gear fault signal. The results obtained from the simulation and practical experiments confirm the superiority of the proposed method for incipient gear fault diagnosis.

Electrocardiogram (EKG) based classification of out-of-hospital cardiac arrest (OHCA) rhythms is important to guide treatment and to retrospectively elucidate the effects of therapy on patient response. OHCA rhythms are grouped into five categories: ventricular fibrillation (VF) and tachycardia (VT), asystole (AS), pulseless electrical activity (PEA), and pulse-generating rhythms (PR). Clinically these rhythms are grouped into broader categories like shockable (VF/VT), non-shockable (AS/PEA/PR), or organized (ORG, PEA/PR). OHCA rhythm classification is further complicated because EKGs are corrupted by cardiopulmonary resuscitation (CPR) artifacts. The objective of this study was to demonstrate a framework for automatic multiclass OHCA rhythm classification in the presence of CPR artifacts. In total, 2133 EKG segments from 272 OHCA patients were used: 580 AS, 94 PR, 953 PEA, 479 VF, and 27 VT. CPR artifacts were adaptively filtered, 93 features were computed from the stationary wavelet transform analysis, and random forests were used for classification. A repeated stratified nested cross-validation procedure was used for feature selection, parameter tuning, and model assessment. Data were partitioned patient-wise. The classifiers were evaluated using per class sensitivity, and the unweighted mean of sensitivities (UMS) as a global performance metric. Four levels of clinical detail were studied: shock/no-shock, shock/AS/ORG, VF/VT/AS/ORG, and VF/VT/AS/PEA/PR. The median UMS (interdecile range) for the 2, 3, 4, and 5-class classifiers were: 95.4% (95.1-95.6), 87.6% (87.3-88.1), 80.6% (79.3-81.8), and 71.9% (69.5-74.6), respectively. For shock/no-shock decisions sensitivities were 93.5% (93.0-93.9) and 97.2% (97.0-97.4), meeting clinical standards for artifact-free EKG. The UMS for five classes with CPR artifacts was 5.8-points below that of the best algorithms without CPR artifacts, but improved the UMS of latter by over 19-points for EKG with CPR artifacts. A robust and accurate approach for multiclass OHCA rhythm classification during CPR has been demonstrated, improving the accuracy of the current state-of-the-art methods.

This article presents a grid-connected single-stage photovoltaic (PV) system using an adaptive normalized notch filter (ANNF). An unknown time-varying fundamental frequency is precisely estimated from a normalized notch filter and consequently, the fundamental load component is extracted using this algorithm. The convergence of the algorithm is independent of both amplitude and the frequency of the fundamental component, and therefore, this algorithm is robust to these changes in the load current. The adaptive estimation of fundamental load current with perturbations on load/grid side system is well analyzed. The ANNF based control strategy is not only efficient in fundamental extraction, however, it also achieves the objectives of supplying real power to the grid, grid currents balancing, and power factor correction. Moreover, this algorithm is robust to harmonic/ distorted/ unbalanced grid voltages caused at far radial ends in the weak distribution network. Elaborated simulation results are presented to show the effectiveness of the strategy. A prototype is developed to validate this control strategy at various perturbations. Under all operating conditions, test results are found satisfactory. [ABSTRACT FROM AUTHOR]

Automotive Ethernet is considered to be the backbone of future in-vehicle data communication. The IEEE 802.3bu standard specifies the use of one unshielded twisted pair of copper cables to simultaneously carry data and energy via Power over Data Lines (PoDL). One main challenge in combined data and energy transfer is the distortion of the data channel due to wideband interference from power signals present in the Automotive environment. This paper proposes the design of a novel enhanced coupling network for PoDL that features a noise cancelling current source controlled by an adaptive finite impulse response (FIR) filter for the active attenuation of interfering power signals. The concept of active attenuation was proven for a resistive network, an average attenuation of 92 dB was measured up to 120 MHz. Further analyses show that the cancellation performance heavily relies on low latency of the active filter. It is derived that the provided attenuation decreases exponentially with the frequency for a given time delay and a novel latency management concept called compressed summation is proposed. The approach maximizes the cancellation performance of the proposed filter architecture by exploiting the knowledge of the ideal filter function and effective latency to define the FIR-filter weights. [ABSTRACT FROM AUTHOR]

The detection performance of high-frequency surface wave radar (HFSWR) is highly susceptible to radio frequency interference (RFI). The fact that external RFIs will turn into a chirp signal and the fractional Fourier transform (FRFT) is a potent tool to analyze the chirp signal, motivates us to use FRFT in interference mitigation. In this letter, two new RFI suppression schemes are proposed, of which one is based on hybrid used of FRFT and autoregressive (AR) linear prediction algorithm, another is based on joint FRFT and recursive least squares (RLS) adaptive filtering algorithm. The simulation result indicates that the proposed schemes can effectively mitigate external RFIs even when the interferences cover Bragg peaks. The experimental result shows that the proposed schemes can effectively suppress RFI without losing echoes, and the signal to noise-plus-interference ratio is improved by 6 dB on average after RFI suppression. [ABSTRACT FROM AUTHOR]

Sparse least mean square (LMS) algorithms employ approximations of sparseness constraints as a zero-point attraction term that forces small tap weights towards the origin when unknown systems to be identified are sparse. Recently, the online linearized Bregman iteration (OLBI) algorithm appreciated soft thresholding techniques based on an $L_{1}$ -norm regularization in reducing a steady-state error. Although the soft thresholding successfully improves accuracy of the adaptive filter for sparse systems, this brief is limited to the $L_{1}$ -norm regularization. In sparse representation, the $L_{0}$ -norm regularization can theoretically yield the sparsest representation and lead to the promising performance in adaptive filters. In this regard, we introduce a $L_{0}$ -norm based LMS algorithm by exploiting a hard thresholding through a variable splitting method. The proposed algorithm preserves the behavior of large tap weights and strongly enforces small tap weights to zero by relaxation of $L_{0}$ -norm regularization. We also provide the mean stability conditions and theoretical mean-square performance of the proposed algorithm. Experimental results show that the proposed algorithm achieves superior convergence performance compared with conventional sparse algorithms. [ABSTRACT FROM AUTHOR]

A slide window variational adaptive Kalman filter is presented in this brief based on adaptive learning of inaccurate state and measurement noise covariance matrices, which is composed of the forward Kalman filtering, the backward Kalman smoothing, and the online estimates of noise covariance matrices. By imposing an approximation on the smoothing posterior distribution of slide window state vectors, the posterior distributions of noise covariance matrices can be analytically updated as inverse Wishart distributions by exploiting the variational Bayesian method, which avoids the fixed-point iterations and achieves good computational efficiency. Simulation comparisons demonstrate that the proposed method has better filtering accuracy and consistency than the existing cutting-edge method. [ABSTRACT FROM AUTHOR]

A wideband analog/RF interference cancellation technique that is able to autonomously adapt itself to time-varying interference channels and to scale its dc power based on the number of multiple-input–multiple-output (MIMO) array elements is presented. Least-mean square (LMS) adaptive circuitry is co-designed with and partially embedded in a wideband RF/analog interference canceller and a gain-boosted mixer-first receiver (RX), significantly reducing the system complexity and dc power while achieving rapid (microsecond-scale) adaptation. In addition, the proposed canceller is able to digitally adjust its transconductance and coupling strength at the RX side, essentially trading its dc power with the element-level RX noise figure (NF). At the array level, the NF can be lowered by averaging uncorrelated noise across elements. A prototype 0.5-to-2.5-GHz full-duplex (FD) MIMO RX is designed and fabricated using a 65-nm CMOS process. With the adaptive circuitry fully integrated and consuming 14 mW, the cancellers adapt themselves to an unknown channel in 1 $\mu \text{s}$ , providing >20-dB RF/analog interference cancellation across 20-MHz bandwidth (BW) with antenna voltage-standing-wave ratio (VSWR) up to 2:1. Simultaneous self- and cross-interference cancellation in our FD MIMO RX is also demonstrated using the integrated adaptation circuitry, achieving 24-dB overall interference cancellation across 20-MHz BW. The FD MIMO RX with self- and cross-interference cancellation is power-scalable—the NF-power scalability of the FD RX enables a nearly constant canceller dc power per element, despite a quadratic increase of cancellers. [ABSTRACT FROM AUTHOR]

Many phase-locked loop (PLL) or frequency-locked loop (FLL) use quadrature signals (directly or indirectly) as the input variables. Generalized integrator (GI) is a popular quadrature signal generator (QSG) available in the literature. GI is also widely used in various industrial applications. In addition to being a QSG, GI also works as a kind of bandpass filter. However, due to structural limitations, the dynamic tuning range is limited for the standard GI. The limitation arises from using only one gain in the direct-phase estimation dynamics, while quadrature phase estimation dynamics does not use direct feedback of the filter estimation error. Some attempts have already been made to overcome this limitation by adding direct feedback of the filter estimation error to quadrature-phase dynamics as well. However, we have demonstrated in this article that this kind of implementation has some frequency domain limitations. In this article, we propose a novel GI-type adaptive filter using coordinate transformation. The resulting structure maintains the same kind of filtering property of the standard GI at the transformed coordinates level while at the same time enhancing the dynamic tuning range of standard GI. Details of the proposed technique, stability analysis, and discussion on gain tuning are provided in this article. Finally, comparative experimental results are provided with respect to GI-FLL to show the dynamic performance improvement. Experimental results demonstrate the suitability of the proposed technique. [ABSTRACT FROM AUTHOR]