Output Signal Of Filter Research Articles

Анализ периодической компоненты выходного сигнала цифрового фильтра в условиях возникновения стохастического резонанса

The periodic component of the output signals of a digital filter based on the Lyapunov characteristic function under the influence of an additive mixture of signal and noise is studied. The possibility of using the level of the periodic component of the output signal to characterize the phenomenon of stochastic resonance in digital devices is shown. The levels of the periodic component of the output signals of a digital filter based on the Lyapunov characteristic function and a linear digital filter at different noise levels at the input are compared. The change of the level of the periodic component of the output signal depending on the noise intensity for these digital filters is shown. The results of using the periodic component of the output signal of a digital filter based on the Lyapunov characteristic function in solving the problem of signal detection on the background of noise are presented.

An Innovative Embedded Processor-Based Signal Phase Shifter Algorithm

Digital filtration is widely used today in many application fields, and with the increased use of low-cost embedded processors, it can be applied to vast areas. A drawback of digital filtration algorithms is the introduction of phase angle shifts in the filtered signals, thereby creating undesirable characteristics in many application fields. In this work, low-pass filters of finite impulse response and infinite impulse response types are designed with an innovative buffering scheme to delay a digitally low-passed signal by an angle ranging from 0° to 180° for real-time signals. The application of the filtration and buffering scheme on a cost-effective embedded processor with limited signal processing capabilities opens the horizons for its applicability in many signal processing fields. In assessing its practicality, the generated filtered output signal is correlated with the original signal (a low-passed version), revealing correlation values reaching 0.99 in certain instances. The novelty of the proposed approach enables its application to a broad-spectrum area of digital signal filtration.

Synthesis of the moving object positioning system

The choice of structure and parameters of the positioning system of a moving object based on a complementary filter using GPS-receiver and accelerometer is considered in order to reduce the effect of significant short-term interference of the GPS receiver when there is a zero offset accelerometer. For the analysis the complementary filter constructed according to the filtration scheme is accepted. Second- and third-order filters are compared. The high efficiency of the filter is shown. It is shown that by choosing a certain structure of a complementary third-order filter, it is possible to eliminate the steady-state component in the filter output signal.

Adaptive Leaderless Consensus for Uncertain High-Order Nonlinear Multiagent Systems With Event-Triggered Communication

This article investigates the leaderless consensus problem for uncertain high-order nonlinear multiagent systems with event-triggered communication. Under a directed graph condition, a fully distributed adaptive control strategy is presented. The main contributions are summarized as follows: 1) globally Lipschitz condition, as required in many existing literatures, is not needed in this article; 2) for each agent, only one filtered output signal is required to be broadcast to its neighbors, which can effectively relieve network transmission burden; 3) the distributed adaptive controllers and event-triggered conditions are co-designed based on a single Lyapunov function such that continuous monitoring of neighbors’ states is not needed; and 4) by introducing an exponential convergence term to the designed triggering condition, Zeno behavior is avoided while all the agents’ outputs can reach asymptotically leaderless consensus. Moreover, to easily implement the designed triggering condition and further reduce the triggering frequency when the consensus errors converge to the neighborhood of origin, a switching type of triggering condition is presented and bounded consensus can be guaranteed. Simulation results are given to validate the presented distributed consensus control scheme.

Filtering method of airborne gravity gradiometer signal based on EMD

Gravity gradiometer sensor performance, platform instability and electronic circuit noise can cause serious influence on the weak gravity gradiometry signal, and the noise level is usually one or several times of gravity gradient signal. This paper put forward a method to resolve this problem according to the characteristics of the signal. First of all, filtrate the gravity gradient signal with a band-passing filter. Then, demodulate the output signal of the band-passing filter, at last, remove the gravity gradiometry noise with the empirical mode decomposition (EMD) method. Then the gravity gradiometer signal was tested on the hardware-in-the-loop simulation platform. The experiment results verify that the various harmonic noise can be removed with the method proposed above, and the gravity gradient signal accuracy and gravity gradient space resolution is further improved, the good properties of the method is of high practice and maneuverability.

Wavelet DE-noising and Kalman Filtering of MEMS Sensors for Autonomous Latitude Determination

Background: There is a task of autonomous determination of the position latitude of unmoved vehicles. Also, there is another task of the initial value latitude determination as a prepared operation of gimbaled and strap-down inertial navigation systems. For both cases, it is necessary to have an inertial measurement unit (IMU) with triad gyroscopes and triad accelerometers. The output signals of micromechanical gyroscopes and accelerometers have large noise components when using the IMU in the micromachined electromechanical systems (MEMS) technology. Objective: Normally to filter output signals of MEMS sensors, averaging and filtering are used. However, for Kalman filtering, it is necessary to find the exact mathematical model of the sensors and a lot of their initial random characteristics. The study of the possibility of the wavelet transform usage to filter the output signals MEMS accelerometers and gyroscopes for the latitude autonomous determination was conducted in the paper. Method: The wavelet transform approach was used to filter the output signals of MEMS accelerometers and gyroscopes in order to improve the accuracy of the autonomous position latitude determination. The autonomous latitude determination efficiency of IMU based on MEMS gyroscopes and accelerometers has been experimentally confirmed. The projections of the Earth’s angular rate and gravitational acceleration were obtained from the MEMS IMU. After that, the signals of the IMU gyroscopes and accelerometers were filtered, using the wavelet ‘Daubechies’ in decomposition, and averaged. These signals were used in a computational algorithm to determine the latitude. Results: The results showed that Unlike the well-known Kalman filter wavelet de-noising reduced calculation error by almost twice. Conclusion: Wavelet de-noising could be used for output signals filtering of micromechanical gyroscopes and accelerometers for the autonomous determination of position latitude.

Motion Artifacts Suppression From EEG Signals Using an Adaptive Signal Denoising Method

Motion artifacts are one of the most challenging non-physiological noise sources present in the biomedical signal, which can hinder the true performance of EEG-based neuro-engineering applications. Therefore, motion artifact removal from EEG signals can be potentially the utmost protuberant research topic. To solve this issue, a hybrid signal denoising framework which comprises modified empirical mode decomposition (EMD) and an optimized Laplacian of Gaussian (LoG) filter is proposed for the suppression of motion artifacts from EEG signals. The modified-EMD decomposes the single-channel noisy EEG signal into a set of the optimal number of intrinsic mode functions (IMFs). Furthermore, the optimized LoG filter has been applied to the motion artifact intermixed EEG signal which is considered as low-frequency noise likely to present at low-frequency IMFs. This filter performs smoothing of the signal and removes background noises or artifacts from the EEG signal. The denoised signal is reconstructed by adding filtered output signal and high-frequency IMFs. The robustness of the proposed method is demonstrated through simulated and real EEG data with artifacts. The experimental values of the different sets of performance assessment metrics reveal that the proposed algorithm outperforms the existing state-of-the-art EEG signal denoising algorithms for the suppression of motion artifacts from EEG signals.

Modified algorithms for signal nonlinear trend detection

There is a problem of nonlinear (abrupt) signal trend detection in many digital signals processing practical applications. In particular, in the field of biomedical signals processing, the actual task is the elimination of abrupt signal baseline distortions caused by the patient's movements. For processing such signals containing edges and other discontinues, linear filtering based on discrete Fourier or cosine transforms leads to significant smoothing of a signal. Median type algorithms related to nonlinear stable (robust) filters are successfully applied for filtering such signals, in particular, high efficiency is provided by median hybrid filters with finite impulse response (FIR). The article considers simple algorithms of the class of FIR-median hybrid filters used for signal nonlinear trend detection. It is proposed to modify these algorithms by replacing the operation of finding the median of the data in the sliding filter window with the calculation of their myriad, as well as adding weights (number of duplications) to certain window elements. Statistical estimates of filter efficiency according to the mean square error (MSE) criterion for test signals like “step” and “ramp” edges, and triangular peak and parabola have been obtained. The high efficiency of the investigated nonlinear filters for the listed test signals types and the improvements achieved as a result of the proposed filter modifications are shown based on the analysis of the filter output signals and statistical estimates of their quality. Some examples of processing biomedical signals of electroencephalograms which illustrate good quality of noise suppression and signal abrupt changes preservation, and motion artifacts removal without large signal distortions are given.

Design of analog Filter circuit based on Multisim

With the rapid development of electronic technology, the filter circuit has been widely used in various occasions. Therefore, based on the study of various analog filter circuit principles, this paper users the Multisim simulation platform to design the corresponding low-pass and band-pass filters. In order to verify the performance of the filter circuit, the normal signal, noise signal and filter output signal are respectively observed by oscilloscope after noise is introduced, and then the performance is analyzed according to the waveform and parameters. The experimental results show that all the designed filters can achieve good filtering effect.

Sensorless LC Filter Implementation for Permanent Magnet Machine Drive Using Observer-Based Voltage and Current Estimation.

For pulse width modulation (PWM) inverter drives, an LC filter can cascade to a permanent magnet (PM) machine at inverter output to reduce PWM-reflected current harmonics. Because the LC filter causes resonance, the filter output current and voltage are required for the sensorless field-oriented control (FOC) drive. However, existing sensors and inverters are typically integrated inside commercial closed-form drives; it is not possible for these drives to obtain additional filter output signals. To resolve this integration issue, this paper proposes a sensorless LC filter state estimation using only the drive inside current sensors. The design principle of the LC filter is first introduced to remove PWM current harmonics. A dual-observer is then proposed to estimate the filter output current and voltage for the sensorless FOC drive. Compared to conventional model-based estimation, the proposed dual-observer demonstrates robust estimation performance under parameter error. The capacitor parameter error shows a negligible influence on the proposed observer estimation. The filter inductance error only affects the capacitor current estimation at high speed. The performance of the sensorless FOC drive using the proposed dual-observer is comparable to the same drive using external sensors for filter voltage and current measurement. All experiments are verified by a PM machine with only 130 μH phase inductance.

An improved LMS algorithm for mode demultiplexing in frequency domain

In order to solve the problem that the traditional frequency domain least mean square (FD-LMS) algorithm will lose efficacy with the increase of differential mode group delay (DMGD) when the algorithm is used for demultiplexing of the 6×6 mode division multiplexing (MDM) system, an improved FD-LMS demultiplexing algorithm is proposed. By improving the error signal calculation method, the convergence performance of the output signal of the equalization filter is improved, and the steady-state error of the algorithm is reduced. Besides, the equalization performance of the traditional FD-LMS algorithm is compared with the improved FD-LMS algorithm. Simulation results show that the improved FD-LMS algorithm has great advantage over the traditional FD-LMS algorithm in demultiplexing performance on the premise that the computation complexity does not significantly increase. The optical signal to noise ratio (OSNR) penalty of the improved FD-LMS algorithm is 2.6 dB lower than that of traditional FD-LMS algorithm at a transmission distance of 80 km with DMGD is 50 ps/km.

ON APPLICATION OF TAPER WINDOWS FOR SIDELOBE SUPPRESSION IN LFM PULSE COMPRESSION

The output signal of the LFM pulse compression filter is analyzed in order to look into causes of discrepancy between the sidelobe level obtainable using standard taper windows with the literature-reported data. The case of applying window functions in the form of cosine harmonics of the Fourier series, which describes a rather great number of the standard windows, is analyzed in detail. Analytical expressions are presented for the output signal of the chirp-pulse compression filter with such windows and for the amount of loss in the signal-to-noise ratio (SNR). Coefficients of window functions containing 2 cosine harmonics of the Fourier series have empirically been selected that made it possible to improve sidelobe suppression by approximately 0.34 dB for B = 21 and more than by 1 dB for B = 7 as compared with the Hamming window. The obtained results allow concluding that the optimization problem for the window function parameters in the case of small pulse duration-bandwidth products should be solved individually for each specific value of B.

Frequencies estimation of gearbox vibration signal based on normalized lattice filter with RLS-based algorithm

In this paper, in order to estimate the meshing frequencies of a gearbox online, based on its vibration signal, an adaptive notch filter with a normalized lattice structure is proposed as an estimator. The notch filter exhibits a high degree of robustness and a low level of estimation errors, due to the minimal dynamic range of the node energies of the normalized lattice structure. A novel lattice recursive least square (NLRLS) algorithm, which introduces a correlation value between the vibration signal and the output signal of the estimation filter into the LRLS algorithm, is used as the adaptive algorithm for the notch filter. Theoretical analysis and simulation results show that the NLRLS algorithm exhibits greater stability than the LRLS algorithm for vibration signals where prior information is lacking. The meshing frequency estimation experiment is performed on a two-stage gearbox experimental platform. A triangular cascade structure is used to connect multiple notch filters to achieve multi-frequency estimation. The experimental results show that the notch filters can estimate meshing frequencies quickly and accurately, which verifies its effectiveness.

Full diversity space time block codes with improved power distribution characteristics

In this paper, we introduce a technique for improving power distribution characteristics of space time block codes (STBCs) which include peak to average power ratio (PAPR), average to minimum power ratio (Ave/min), and probability of transmitting “zero” by antenna. We consider STBCs in multiple input multiple output (MIMO) and multiple input single output (MISO) systems that achieve full diversity for linear or maximum likelihood (ML) receivers. It is proved that by multiplying a specific non-singular square matrix by the code matrix, a new code is obtained that achieves full diversity and the power distribution characteristics are improved. The proposed technique is general can be applied to any type of full diversity STBCs such as Toeplitz codes, orthogonal STBCs (OSTBCs), overlapped Alamouti codes (OACs), group orthogonal Toeplitz codes (GOTCs), and embedded Alamouti codes (EACs), and can ease their practical implementation. We systematically present closed-form expressions for the matrices multiplied by Toeplitz codes, OACs, and EACs. Because of the existence of these codes for any number of antennas, the closed-form can be used in 5G which needs huge number of antennas. The performance of the proposed technique is evaluated in terms of bit error rate (BER) and power distribution characteristics. The introduced method provides a trade-off between PAPR, Ave/min, probability of transmitting zero, and BER, to cope with the practical requirements. We also investigate the effect of the proposed method on the PAPR of output signal of shaping filter which is referred to as analog domain PAPR. The results indicate the efficiency of the introduced technique. The proposed method can balance the tradeoff between PAPR, spectral efficiency, and BER.

Differences Amplitude Based Improvement of Minimum Variance Distortionless Response Filter

In this work, the author introduces a new technique for improving the performance of minimum variance distortionless response filter in condition of coherent noise. The proposal algorithm exploits a priori information of differences amplitude to balance power spectral densities of observed noisy signals. The output signal of MVDR filter is then processed by an additional post-filtering, which based speech presence probability to suppress more noise interference and increase quality speech. In experiments using two noisy signal recordings in anechoeic room, the modified MVDR-filter results provides that the suggested algorithm increases speech quality compared to the conventional MVDR filter.

Modified Structures for Hybrid Active Sound Quality Control System Disturbed by Gaussian Random Noise

A hybrid active sound quality control system, in which a hybrid feedforward and feedback structure is applied, can not only be used in cases where the line-spectrum noise is obtained easily with reference sensors, but it can also improve the comfortability of noise and eliminate unexpected Gaussian random noise. However, the traditional structure for a hybrid active sound quality control system, whereby a reference signal in the feedback control structure is synthesized by the output signals of the feedforward control filter, feedback control filter, and line-spectrum noise cancellation control filter, introduces couplings of the three control filters. To remove the coupling interactions of the feedforward and feedback control structures and to reduce the complexity of the control system, two modified structures with less computational complexity or a smaller increase in computation are investigated in this paper. The first one involves a simplified structure in which the reference signal in the feedback control structure is replaced by the summation of the residual error signal and the output signal of the line-spectrum noise cancellation control filter, and the second one is a modified structure which integrates the output signals of the feedback control filter and the line-spectrum noise cancellation control filter for the reference signal in the feedback control structure. Numerical simulations are carried out to show the performance of the modified structures. The results illustrate that the two modified structures have the ability to cancel Gaussian random noise and to reduce or enhance the amplitude of line-spectrum noise to promote sound quality. Moreover, a simplified structure with a new leaky filtered-x least mean square (FxLMS) algorithm is proposed to upgrade the noise reduction performance and elevate stability in the feedback control structure. The effectiveness of the proposed algorithm also is proven by the simulation results.

Epilepsy analysis using open source EDF tools for information science and data analytics

SummaryElectroencephalogram (EEG) is the signals that measure the electrical variances of brain using metal electrodes. We observe the EEG signals by using European Data Format (EDF) BROWSER and EEG STUDIO. By using EDF BROWSER, we can get the mean and frequency from the filtered output signal using band‐pass filter. Using EDF BROWSER, we can also perform Root Mean Square (RMS) and signal samples. Using EEG STUDIO, we can analyze the average frequency and standard deviation. Epileptic seizure prediction and detection are done by spike detection, frequency domain analysis, and nonlinear methods. EEG signal contains different artifacts like electrooculography (EOG), EKG, and electrocardiogram (ECG). ECG signals are produced by heart. EOG signals are produced by eyes. EMG signals are produced by muscle coordination.

Toward Special-Relativity-on-a-Chip: analogue of Einstein velocity addition using optical add-drop filter (OADF)

ABSTRACTWe present an optical analogue of a central concept (not a specific relativistic effect) in Special Relativity (SR) that underlies many relativistic effects. This concept is the Einstein Velocity Addition (EVA) which plays a fundamental role in the development of SR. The photonic circuit analogue is between the EVA, and the complex electric field, through the output signal of the optical add-drop filter (OADF). The OADF is one of the key building blocks for wavelength filtering in photonic integrated circuits (PICs or photonic chip). This analogue strengthens the vision toward a Special-Relativity-on-a-Chip. The analogy is established by equating the two relativistic normalized velocities in EVA with the two transmission coupling coefficients of the two directional couplers in the OADF. Furthermore, the generating angle in the EVA is associated with the single-pass phase shift of the OADF. We analyze its magnitude and directional angle in detail. Lastly, other important consequences are also briefly discussed.

Blind signal deconvolution based on pulsed neuron model

In this paper, we consider the vector-matrix model of a pulsed neuron, focused on solving problems of digital signal processing. We extend the application domain of the model to the blind signal deconvolution problem. To achieve this goal we propose an unsupervised learning algorithm, which maximizes the absolute value of the normalized kurtosis of the output signal of the deconvolution filter using vector-matrix model of a pulsed neuron. To show the validity of the proposed learning algorithm, some examples of deconvolution of speech signals distorted by reverberation are presented.

Adaptive output-feedback synchronisation of electromechanical systems under time-varying communication delays

ABSTRACT This paper deals with the position synchronisation problem for a class of electromechanical systems, which can be represented by Euler–Lagrange equations, where velocities of agents are not measurable. The agents share their position errors and filter-output signals, instead of actual velocity errors, over a directed communication network, in the presence of time-varying delay. Besides, balanced communication topology is also not required. The proposed adaptive control mechanism ensures the position synchronisation among agents, as well as the tracking of each agent to given desired trajectory, under the effects of time-varying communication delay, parametric uncertainty and unknown external disturbance, simultaneously. By employing a classical Lyapunov approach and Lyapunov–Krasovskii functionals, it is concluded that the suggested control structure yields semi-global asymptotic convergence of all closed-loop errors, including synchronisation errors. The simulation results, on a six-marine vessel network system, are presented to validate the feasibility of the proposed control scheme.