Adaptive Digital Filter Research Articles

Feasibility Study on Active Structural Attenuation: Addressing Multiband Vibration in Automotive Vehicles on 2D Asymmetric Structures with a Faulty Horizontal Actuator

This work presents a study on the modeling, analysis, and control of asymmetric source structures, which focuses on a multi-directional active mounting system that aims to consider the location and orientation of an actual automotive powertrain mount. An active mount was created by connecting a PZT (piezo-stack) actuator with a rubber grommet. Additional force necessary for every mount was determined by using forces caused by harmonic stimulation and the control input has the capability to reduce vibrations by engaging in detrimental opposition against the input. In addition, the vibration in the horizontal direction can be reduced with the adjustment of variables that can be modified via the dynamic interconnection of the source frame. This study especially evaluated the effectiveness of vibration reduction without a horizontal active component and determined the feasibility of control. Through sequences of simulated outcomes, it was demonstrated that the implementation of this asymmetric, bi-directional (both horizontally and vertically) active mount may effectively reduce stimulation oscillations. Additionally, a numerical validation was performed to reduce the vibrations generated by the modulation. It was accomplished by observing the system’s response utilizing a digital filter with a normalized least mean square method. The simulations of adaptive digital filters demonstrated that the efficacy of control diminishes when faced with intricate noise and signals, while the attenuation trend stays unaltered.

Sensorless Control of Induction Motor Based on Rotors Slot Harmonics and Digital Adaptive Filters

Sensorless Control of Induction Motor Based on Rotors Slot Harmonics and Digital Adaptive Filters

Echo cancellation using Artificial Intelligence technique

To improve the audio quality in telecommunication, echo cancellation was proposed to prevent signals from re-appearing. Generally speaking, the echo canceler first recognizes the original transmitted signal that appears again due to delay in the transmitted or recieved signal. Then, the echo canceler subtracts the echo in the signals. Commonly, echo cancelers are calledacoustic echo cancellation (AEC) in general, whether they aim for acoustic echo, line echo, or both. However, in modern times, new problems have been emerging which require more innovative, more advanced methods to tackle the echo issue. Thus, the paper reviews methods regarding Artificial Intelligence (AI) to improve the current means for echo cancellation. In this paper, we mainly discuss the approaches to echo cancellation, the identification of echo using adaptive AI and the usage of adaptive filters and neural network for echo cancellation. For instance, deep learning approaches can facilitate the issues of AEC (active echo control) and ANC (active noise control),the optimization of LMS algorithm can be achieved by combining Swarm Intelligence (SI), combining adaptive digital filter and recurrent neural network for acoustic echo cancellation etc. Using AI in echo cancellation can help us achieve high-quality, full-duplex telecommunication in modern telephony system.

Parallel-Cascaded Narrow-Band Adaptive Digital Active EMI Filters for Broadband CM-Noise Reduction in Motor Inverters

Parallel-Cascaded Narrow-Band Adaptive Digital Active EMI Filters for Broadband CM-Noise Reduction in Motor Inverters

Adaptive Delay-Free Filtering Based on IMU for Improving Ship Heave Measurement.

Ship heave motion measurement is crucial for ensuring vessel stability, navigation precision, and maritime engineering safety. In order to achieve accurate heave motion measurement, a method based on an adaptive digital high-pass filter is proposed. The approach involves constructing a ship heave motion model, conducting an analysis of heave motion, determining the optimal cutoff frequency for the adaptive filter based on an analysis of filtering and sensor errors, and designing an adaptive delay-free digital high-pass filter. Through simulation experiments in various sea conditions and platform tests, the method demonstrates superior performance. In comparison to fixed-parameter complementary filters, it exhibits a reduction of over 50% in maximum error and mean square error.

Practical Considerations for Implementing Adaptive Acoustic Noise Cancellation in Commercial Earbuds

Active noise cancellation has become a prominent feature in contemporary in-ear personal audio devices. However, due to constraints related to component arrangement, power consumption, and manufacturing costs, most commercial products utilize fixed-type controller systems as the basis for their active noise control algorithms. These systems offer robust performance and a straightforward structure, which is achievable with cost-effective digital signal processors. Nonetheless, a major drawback of fixed-type controllers is their inability to adapt to changes in acoustic transfer paths, such as variations in earpiece fitting conditions. Therefore, adaptive-type active noise control systems that employ adaptive digital filters are considered as the alternative. To address the increasing system complexity, design concepts and implementation strategies are discussed with respect to actual hardware limitations. To illustrate these considerations, a case study showcasing the implementation of a filtered-x least mean square-based active noise control algorithm is presented. A commercial evaluation board accommodating a low-cost, fixed-point digital signal processor is used to simplify operation and provide programming access. The earbuds are obtained from a commercial product designed for noise cancellation. This study underscores the importance of addressing hardware constraints when implementing adaptive active noise cancellation, providing valuable insights for real-world applications.

Power Optimized VLSI Architecture of Distributed Arithmetic Based Block LMS Adaptive Filter

In this paper, we are presenting a power-efficient Distributed Arithmetic (DA) based Block Least Mean Square (BLMS) Adaptive Digital Filter (ADF). The proposed DA BLMS architecture proposes a shared area-efficient Multiplier Accumulate Block that calculates both the partial filter products and the weight increment terms in the same module. It also uses Multiplexers (MUX) and Demultiplexers (DEMUX) which passes only L out of N inputs, where N and L are the filter length and chosen block size respectively, into the MAC thus helping in achieving the DA functionality along with reduced power consumption. Also, efficient truncation of the obtained error and weight update terms is performed by being able to select the non-zero-bit part of the signal to be fed back. The entire architecture is driven by a single slow clock which reduces the power consumption of the device further. On comparing with the best existing DA BLMS Structures, the proposed architecture uses 15% lesser power, 14% lesser EPS according to ASIC Synthesis, and for a filter length of N=16 and a block size of L=4 respectively.

An adaptive digital filter method for measuring the radial error motion of ultra-precision spindle

To ensure the nanometer-level running accuracy of the ultra-precision spindle, the radial error motion should be precisely measured. However, the asynchronous errors induced by the measurement instrument will mix in the measured data and significantly deteriorate the measurement precision. In this study, a novel adaptive digital filter method (ADFM) is proposed to suppress the asynchronous errors and lower requirements for the precision of measurement instruments based on the Donaldson reversal method. Specifically, the Gaussian mixture model is first used to investigate the characteristics of asynchronous errors and obtain appropriate initial filter parameters. The least mean square algorithm is utilized to adaptively optimize the filter parameters. Monte Carlo simulation results indicate that ADFM can suppress asynchronous errors by more than 90% compared with the traditional methods. Experimental results show that the standard deviation uncertainty is reduced by more than 60%. Besides, the measurement results of two sensors with different precision verify the feasibility of ADFM.

Design of Low Power Temperature Sensor Based on 180 nm Complementary Metal Oxide Semiconductor Technology

CMOS temperature sensor is widely used in power monitoring system, power consumption is an important index. The digital filter power consumption is one of the main sources of the temperature sensor power consumption, and limiting the Digital filter power consumption becomes an important method to realize the low power consumption of the temperature sensor. Based on this, a low power digital filter for CMOS temperature sensors is designed, and a precision adaptive digital filter is proposed, the filter is cascaded by a recursive CIC filter and a FIR filter based on a shift adder, the order of CIC filter and FIR filter can be adjusted according to the difference between the measured temperature and the threshold temperature range set by the user, when the measured temperature is outside the threshold temperature range, the operation unit in the filter is selectively switched off, which makes the power consumption of the filter decrease. For the temperature range that does not need to be monitored, the requirement of temperature measurement accuracy is usually not high, if high-precision temperature monitoring is still carried out, it will have a lot of unnecessary power consumption, in this paper, an adaptive precision digital filter is used to solve the problem. In order to further reduce the power consumption of the temperature sensor, according to the characteristics of the slow change of the temperature signal, a single temperature conversion combined with idle off mode is adopted, the FIR filter power consumption is reduced by 5.5% by optimizing the single temperature conversion operation. The temperature sensor is realized by 180 nm CMOS process. The results show that the sensor can achieve an accuracy of 0.47 °C in the temperature range of −55–115 °C when the measured temperature is in the threshold temperature range, under 1.8 V supply voltage, the power consumption of the digital part of the sensor is 20.15 μw. When the measured temperature is outside the threshold temperature range, the power consumption of the digital part of the sensor can be reduced by 11.3%.

An adaptive digital filter for quasi-Gaussian pulse shaping

The radiation detector output is often shaped into Gaussian or quasi-Gaussian shape to improve the measurement performance. An adaptive digital Step-Gaussian filter is presented for quasi-Gaussian pulse shaping in this paper. It can be applied for both exponential decay input and step input. The transfer function of the filter is established. The amplitude-frequency features of the new filter are compared with the PZC-(RC) n filter and the CR-(RC) n filter. The results indicate that the new filter has the same amplitude-frequency features as the PZC-(RC) n filter and it possesses more effective filter performance than the CR-(RC) n filter. An X-ray fluorescence measurement system based on a FAST-SDD detector is set up for experiments. The proposed filter is verified by the measured pulses that are generated from a manganese sample. The shape of the filter output shows that the Step-Gaussian filter can eliminate the undershoot that exists in the CR-(RC) n filter output. The amplitude spectrums with different shaping times are created to study the shape of the characteristic peak. The results show that the Step-Gaussian filter can cancel the low-energy peak tailing of the 5.89 keV peak. The peak shape parameter, FWTM/FWHM ratio, is also introduced to evaluate the performance of peak shape improvement. The 5.89 keV peaks of the spectrums that are generated by using Step-Gaussian filter are closer to Gaussian distribution than that of the CR-(RC) n filter.

Direct Adaption Methods for Linear and Circular Antenna Arrays

Introduction. The mitigation of interferences that degrade the performance of navigation systems constitutes one of the most significant problems of contemporary satellite navigation. This problem is conventionally solved using digital adaptive space filters. Depending on a particular radio technical system, the mathematical description of digital signal processing methods may involve specific calculation structures implemented using specific calculation algorithms. For example, the use of centrosymmetric linear and circular antenna arrays in a radio navigation system allows the description of such systems in terms of Toeplitz and circulant sample covariance matrices, respectively, and the inversion of such matrices by means of special numerical methods in order to design a digital filter.Aim. A comparative analysis of the performance of space signal processing algorithms is carried out along with an estimation of Toeplitz and circulant sample covariance matrices and numerical methods of their inversion. The previously obtained results in this field are clarified.Materials and methods. An analysis of algorithm performance was carried out in the MATLAB environment using experimental recordings of satellite navigation signals and jammers obtained by an actual radio technical system.Results. A new expression was derived for estimating circulant sample covariance matrices. Formulae that describe a modification of the Bareiss numerical Toeplitz matrix inversion algorithm for the case of complex Hermitian matrix were introduced. An analysis of the results of computer simulation allowed the algorithms with the highest performance to be indicated. The amount of time taken by the algorithms based on Toeplitz and circulant matrices did not exceed 2.5 10⋅ −3 s and 0.04 s, respectively. The carrier-to-noise ratio in the processed signal was at least 46 dB. Conclusion. The formulae obtained and the algorithms analyzed can be used when implementing adaptive digital filtering of satellite navigation signals.

СИСТЕМА УПРАВЛЕНИЯ ПО СОСТОЯНИЮ ОБЪЕКТА УПРАВЛЕНИЯ С НАБЛЮДАТЕЛЕМ И РЕГУЛЯТОРОМ СОСТОЯНИЯ

The paper proposes the model of a control system based on the controlled object state. The model includes a state observer and a state controller. The reference signal for this control system is the required values of the controlled object state variables. As a state observer of a controlled object in the control system model, the system "extended Kalman filter - adaptive digital filter" (the EKF-ADF system) is used. The structure and operation principle of the control system state controller are described. The adaptive algorithm of the control system state controller is presented.
 The control system state controller with the adaptive algorithm uses the output data of the EKF-ADF system to form the controller output (error) vector. The output data consist of the state estimation vector performed by the EKF of the EKF-ADF system and the vector of the corrected state estimation performed by the ADF of the EKF-ADF system. The adaptive algorithm of the control system state controller takes into account the output data of the EKF-ADF system in such a way as to form the most reliable state controller output vector.
 To confirm the effectiveness of the considered control system, the control process numerical simulation results of a mobile robot with a caterpillar mover are presented: the proposed control system simulation results are compared with the simulation results of the control system that uses the EKF as a state observer.
 The combination of the state observer and the state regulator as part of the proposed control system makes it possible to control dynamic objects with state variables which are inaccessible to direct measurement and have non-periodic external disturbances.

A Systematic Review on Distributed Arithmetic-Based Hardware Implementation of Adaptive Digital Filters

A Systematic Review on Distributed Arithmetic-Based Hardware Implementation of Adaptive Digital Filters

5.2: A Method of Noise Reduction Based on Adaptive Digital Filter for Touch Panel Data Processing

5.2: A Method of Noise Reduction Based on Adaptive Digital Filter for Touch Panel Data Processing

Low-latency monaural speech enhancement with deep filter-bank equalizer.

It is highly desirable that speech enhancement algorithms can achieve good performance while keeping low latency for many applications, such as digital hearing aids, mobile phones, acoustically transparent hearing devices, and public address systems. To improve the performance of traditional low-latency speech enhancement algorithms, a deep filter-bank equalizer (FBE) framework was proposed that integrated a deep learning-based subband noise reduction network with a deep learning-based shortened digital filter mapping network. In the first network, a deep learning model was trained with a controllable small frame shift to satisfy the low-latency demand, i.e., no greater than 4 ms, so as to obtain (complex) subband gains that could be regarded as an adaptive digital filter in each frame. In the second network, to reduce the latency, this adaptive digital filter was implicitly shortened by a deep learning-based framework and was then applied to noisy speech to reconstruct the enhanced speech without the overlap-add method. Experimental results on the WSJ0-SI84 corpus indicated that the proposed DeepFBE with only 4-ms latency achieved much better performance than traditional low-latency speech enhancement algorithms across several objective metrics. Listening test results further confirmed that our approach achieved higher speech quality than other methods.

A Digitally Assisted Multiplexed Neural Recording System With Dynamic Electrode Offset Cancellation via an LMS Interference-Canceling Filter

This article presents a low-power (LP) area-efficient implantable neural recording system that supports high-density neural implant (HDNI) applications. The system uses a time-division multiple access method to record from 16-neural electrodes simultaneously. A least mean squares (LMSs) algorithm is used to cancel the slowly varying electrode offsets from all channels simultaneously by using a single-tap digital adaptive filter (AF). The presented technique is fabricated in 65-nm CMOS technology and achieves a per-channel area of 0.00248 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ; 68% of which is digital circuitry (and is thus scalable with technology). The overall system consumes 3.38 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> per channel while achieving 2.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{V}_{\mathrm {rms}}$ </tex-math></inline-formula> of input referred noise (IRN) in 10 kHz of bandwidth. The proposed system has a noise efficiency factor (NEF) of 1.83 and is fully integrated on-chip.

IMPLEMENTATION OF DIGITAL FILTERS USING NEURAL NETWORKS WHEN THE OBJECT MOVEMENT DYNAMICS ARE NOT KNOWN

Considered: Neural network-based algorithms to obtain an optimal digital filter. Recurrent neural networks can be used to implement an adaptive digital filter considering the characteristics of both interference and useful signal. The developed program trains recurrent neural networks and as a result obtains the coefficients of recursive digital filters. The efficiency of the proposed algorithm is confirmed with numerical experiments results. Keywords : digital filter, recursive filters, recurrent neural networks

A Real-Time Wearable Physiological Monitoring System for Home-Based Healthcare Applications

The acquisition of physiological data are essential to efficiently predict and treat cardiac patients before a heart attack occurs and effectively expedite motor recovery after a stroke. This goal can be achieved by using wearable wireless sensor network platforms for real-time healthcare monitoring. In this paper, we present a wireless physiological signal acquisition device and a smartphone-based software platform for real-time data processing and monitor and cloud server access for everyday ECG/EMG signal monitoring. The device is implemented in a compact size (diameter: 30 mm, thickness: 4.5 mm) where the biopotential is measured and wirelessly transmitted to a smartphone or a laptop for real-time monitoring, data recording and analysis. Adaptive digital filtering is applied to eliminate any interference noise that can occur during a regular at-home environment, while minimizing the data process time. The accuracy of ECG and EMG signal coverage is assessed using Bland–Altman analysis by comparing with a reference physiological signal acquisition instrument (RHS2116 Stim/Recording System, Intan). Signal coverage of R-R peak intervals showed almost identical outcome between this proposed work and the RHS2116, showing a mean difference in heart rate of 0.15 ± 4.65 bpm and a Wilcoxon’s p value of 0.133. A 24 h continuous recording session of ECG and EMG is conducted to demonstrate the robustness and stability of the device based on extended time wearability on a daily routine.

On Minimal Second-order IIR Bandpass Filters with Constrained Poles and Zeros

In this paper, several forms of infinite impulse response (IIR) bandpass filters with constrained poles and zeros are presented and compared. The comparison includes the filter structure, the frequency ranges and a number of controlled parameters that affect computational efforts. Using the relationship between bandpass and notch filters, the two presented filters were originally developed for notch filters. This paper also proposes a second-order IIR bandpass filter structure that constrains poles and zeros and can be used as a minimal parameter adaptive digital second-order filter. The proposed filter has a wider frequency range and more flexibility in the range values of the adaptation parameters.

Control of solar PV‐integrated battery energy storage system for rural area application

The inaccessibility of a utility grid is the challenge for rural and remote areas. This work presents the application of solar photovoltaic (PV) integrated battery energy storage (BES) for rural area electrification. The addition of a BES at DC link, is realised by means of a DC–DC bidirectional converter. The BES is discharged/charged in accordance with the solar PV generation and load variations. This converter control also maintains the voltage for the maximum power point tracking (MPPT) with perturb and observe (P & O) control at the DC link. The voltage source converter (VSC) works by means of voltage control algorithm in a solar PV-BES system. The system manages the power for the load network with frequency and voltage regulation by the non-ideal discrete proportional and resonant controller (PR). The fundamental component from the load current, is acquired using an adaptive digital filter, which improves the power quality. The utilization of buck-boost converter with an optimum BES rating as related to the system, when it is linked directly at the VSC DC link. The system's operations at steady state and dynamic circumstances i.e. solar insolation change and load variation, load disconnection, are authenticated with test results on a developed prototype.