Low Pass FIR Filter Research Articles

Evolution characteristics of an induced electric charge signal and identification method of charge for instability failure in loaded coal

A coal-loaded charge induction monitoring system is developed to effectively forecast the dynamic disasters caused by coal failure. Specifically, a digital finite impulse response (FIR) filter is designed to denoise and filter the signal, and the time-frequency domain evolution of induced charge signals is analyzed during coal failure experiments. The quantitative relationships between the induced electric charge and stress-strain energy, and ultimately, between induced electric charge and coal deformation/failure, are revealed. Ultimately, the electric charge sensor exhibits high signal collection frequency and high sensitivity, and the FIR low-pass filter constructed in MATLAB effectively denoises and filters induced charge signals. The main frequency range of the white noise is 50–500 Hz, and the main frequency of the charge signal induced by coal deformation and failure is concentrated in the range of 0–50 Hz. The optimal distances for monitoring cubic and cylindrical raw coal samples using this sensor are 9 mm and 11 mm, respectively. Notably, strain energy is released faster when it can dissipate more readily, and induced charge pulses become denser when more intense signals produce large fluctuations. A method is proposed to identify coal deformation and failure based on changes in the induced electric charge. This study provides a new means of monitoring the early warning signs of dynamic coal mine disasters. Based on our experimental results and conclusions, a new method is proposed to identify coal deformation and failure based on changes in the induced electric charge. The precursor to the moment of coal failure can be identified by monitoring the amplitude of the induced charge, the dynamic trend of fluctuation, and the cumulative number of induced electric charge pulses during the process of coal deformation.

Filter-processing-assisted Fourier transform Raman spectroscopy for forensic discrimination lubricant machinery oils

The primary objective of this study was to investigate the potential value of filtering algorithms in enhancing spectral model performance. 290 mechanical lubricants oils samples from various applications underwent measurement using Fourier transform Raman spectroscopy (FT-Raman). Subsequently, three discrimination models were developed employing C5.0, Bayes discriminant analysis, and support vector machine algorithms. The emphasis was on evaluating the efficacy of four filter algorithms—Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), Finite Impulse Response (FIR), and Hilbert Transform (HT)—for enhancing model performance. Among these, the FFT filter-SVM model, DWT filter-SVM model, and FIR (low-pass or band-stop filter)-SVM model emerged as optimal choices for identification. These models demonstrated outstanding performance, achieving 100 % accuracy across all 290 samples. Modelling using FT-Raman and chemometrics offered a simpler, more robust, and valuable approach for identifying mechanical lubrication oils across diverse applications, exploiting the potential of filtering algorithms in forensic spectroscopy.

Fine-tuning digital FIR filters with gray wolf optimization for peak performance

The design of optimum filters constitutes a fundamental aspect within the realm of signal processing applications. The process entails the calculation of ideal coefficients for a filter in order to get a passband with a flat response and an unlimited level of attenuation in the stopband. The objective of this work is to solve the FIR filter design problem and to compare the optimal solutions obtained from evolutionary algorithms. The design of optimal FIR low pass (LP), high pass (HP), and band stop (BS) filters is achieved by the utilization of nature-inspired optimization approaches, namely gray wolf optimization ,cuckoo search, particle swarm optimization, and genetic algorithm. The filters are evaluated in terms of their stop band attenuation, pass band ripples, and departure from the anticipated response. In addition, this study compares the optimization strategies applied in the context of algorithm execution time which is achievement of global optimal outcomes for the design of digital finite impulse response (FIR) filters. The results indicate that when the Gray wolf algorithm is applied to the development of a finite impulse response (FIR) filter, it produces a higher level of performance than other approaches, as supported by enhanced design precision, decreased execution time, and achievement of an optimal solution.

Area and delay efficient RNS-based FIR filter design using fast multipliers

In today's digital age, speed, and area are the primary design concerns. Increasing the rate at which multiplication and addition are performed has always been a need for developing cutting-edge technologies. Wallace multipliers, and Dadda multipliers, are one of the fastest multipliers used in many processors to accomplish fast arithmetic operations. A novel approach to design a LookUp Table (LUT) multiplier was proposed and implemented in Finite Impulse Response (FIR) filter. To improve the Residue Number System (RNS) based FIR filter's performance, several adders like Carry Look ahead (CLA) adder, Kogge Stone adder (KSA) and proposed adder architectures have been implemented. When compared with the 16 tap with 32 bit proposed adder with LUT multiplier, the hardware resource utilization (Logic Elements) is decreased by 5.97 % and in 32 tap with 16 bit combination, it decreases by 7.60 %.When compared with 32 tap with 4 bit word length, the proposed adder with LUT multiplier in the highlighted combinations, the Fmax is increased by 19.28 % and in 32 tap with 16 bit, it increases by 29.74 %. The Low-pass RNS FIR filter is designed for a cut-off frequency of 50 Hz and generated filter coefficients in MATLAB and implemented to denoising the ECG signal.

Minimized Group Delay FIR Low Pass Filter Design Using Modified Differential Search Algorithm

Designing a finite impulse response (FIR) filter with minimal group delay has proven to be a difficult task. Many research studies have focused on reducing pass band and stop band ripples in FIR filter design, often overlooking the optimization of group delay. While some works have considered group delay reduction, their approaches were not optimal. Consequently, the achievement of an optimal design for a filter with a low group delay value still remains a challenge. In this work, a modified differential search optimization algorithm has been used for the purpose of designing a minimal group delay FIR filter. The results obtained have been compared with the classical techniques and they turned out to be promising.

Online evaluation of resistance spot welding quality and defect classification

Resistance spot welding (RSW) is widely used in industrial product manufacturing, and weld quality is critical to ensure the safety and reliability of mechanical structures. With product quality improvement, the online monitoring of weld quality has become an urgent issue. Therefore, a method for online evaluation of RSW quality and automatic classification of welding defects based on mild steel is proposed. By acquiring the welding current and electrode voltage signals of the welding process in real time, a finite impulse response low-pass filter based on the Blackman window function is designed to perform zero-phase filtering of the dynamic resistance (DR). The influence of three welding parameters on the nugget diameter and shear strength of spot welding of DC01 mild steel was studied through the control variable method to determine the optimum welding parameters. Since the main defects of the RSW process are classified as expulsion defects and incomplete fusion defects, minor adjustments were made to the welding parameters to produce expulsion and incomplete fusion defects. Features in the time domain are extracted from the DR curves signals of normal, expulsion, and incomplete fusion welding spots, and the performance of the extracted features in the classifier is analyzed. Improvements are made to classify normal and expulsion spots with low accuracy, a new method is proposed that can accurately classify the quality of the welding spots. Through online evaluation tests of quality on 200 welding spots, the online evaluation test showed results that closely matched the validation test results. The results of the classification performance tests show that this study can be used for online evaluation of RSW quality and automatic classification of the welding spot defects of the DC01.

Design of Digital Low Pass FIR Filter Using Hybrid Particle Swarm – Grey Wolf Optimization Algorithm

In this study, digital low-pass FIR filter is designed using Hybrid Particle Swarm – Grey Wolf Optimization Algorithm (HPSGWO). The purpose of digital FIR filter design with HPSGWO is to optimized filter coefficients that are closest to the characteristics of the ideal filter. The obtained results are compared with PSO, and GWO which were previously used for FIR filters design in the literature. According to the obtained results, HPSGWO has better filter response and less stop band ripple than other methods.

A 13 µW Analog Front-End with RRAM-Based Lowpass FIR Filter for EEG Signal Detection.

This brief presents an analog front-end (AFE) for the detection of electroencephalogram (EEG) signals. The AFE is composed of four sections, chopper-stabilized amplifiers, ripple suppression circuit, RRAM-based lowpass FIR filter, and 8-bit SAR ADC. This is the first time that an RRAM-based lowpass FIR filter has been introduced in an EEG AFE, where the bio-plausible characteristics of RRAM are utilized to analyze signals in the analog domain with high efficiency. The preamp uses the symmetrical OTA structure, reducing power consumption while meeting gain requirements. The ripple suppression circuit greatly improves noise characteristics and offset voltage. The RRAM-based low-pass filter achieves a 40 Hz cutoff frequency, which is suitable for the analysis of EEG signals. The SAR ADC adopts a segmented capacitor structure, effectively reducing the capacitor switching power consumption. The chip prototype is designed in 40 nm CMOS technology. The overall power consumption is approximately 13 µW, achieving ultra-low-power operation.

A 3rd-Order FIR Filter Implementation Based on Time-Mode Signal Processing

This paper presents the hardware implementation of a 3rd-order low-pass finite impulse response (FIR) filter based on time-mode signal processing circuits. The filter topology consists of a set of novel building blocks that perform the necessary functions in time-mode including z−1 operation, time addition and time multiplication. The proposed time-mode low-pass FIR filter was designed in a 28 nm Samsung fully-depleted silicon-on-insulator FD-SOI process under 1 V supply voltage with 5 MHz sampling frequency. Simulation results validate the theoretical analysis. The FIR filter achieves a signal-to-noise-plus-distortion ratio (SNDR) of 38.6 dB at the input frequency of 50 KHz consuming around 200 μW.

FPGA implantations of TRNG architecture using ADPLL based on FIR filter as a loop filter

This article describes about the design, implementation, and analysis of a true random number generator (TRNG) employing an all-digital phase-locked loop (ADPLL) based on a finite impulse response (FIR) filter as the digital loop filter and implemented on the Artix 7(XC7A35T-CPG236-1) field programmable gate array (FPGA) board using the Xilinx Vivado v.2015.2 design suite. The coefficients of a 3rd-order broadcast low-pass digital FIR filter are computed using the Keiser window method. The MATLAB-FDA tool is used to calculate the filter coefficient. After reducing the bias in the sequence using the XOR-corrector post-processing approach, the suggested ADPLL-based TRNG designs created an unbiased stochastic random number with an overall throughput of 200 Mbps for both designs. The first proposed FIR-ADPLL-based TRNG design (FAT-1) consumes 0.072 W of power, whereas the second proposed FIR-ADPLL-based TRNG design (FAT-2) consumes 0.074 W. The resulting bitstream is verified for randomness using national institute of standards and technology (NIST) test following post-processing. Digital storage oscilloscope (DSO) is interfaced to the Artrix-7 FPGA board in order to capture the TRNG output waveforms. Both the proposed FIR-based ADPLL-TRNG designs passed the NIST SP 800-22 test, indicating that they are well-suited for a variety of industrial applications, including network security, cybersecurity, banking security, smart cards, radio-frequency identification tags, Internet of Things, and Industrial Internet of Things.

A novel approach to design optimal digital FIR filter based on logistic distribution based approximation

ABSTRACT This work proposes a novel methodology to design FIR lowpass filter (LPF) using logistic probability distribution function. Desired magnitude response of the FIR filter is approximated as sum of equi-spaced logical function waveforms with their mean varying at definite interval. The required number of logistic waveforms and its standard deviation depends on the parameters of designed filter specifications. Simulation results have been compared with optimal Method (Park McClellan) and windowing technique to show the superiority of the proposed method. It is found that ripples in pass band and stop band of the filter has been reduced iteratively, higher stop band attenuation is achieved. The proposed method also provides a close design way to link coefficients of the filter to design specification directly.

A new comprehensive filtering model for pump shut-in water hammer pressure wave signals during hydraulic fracturing

In the process of fracturing, the position of the fractures can be identified by analyzing the spectrum of the water hammer pressure wave signals after pump shut-in. However, the collected water hammer pressure data contain a lot of noises, which brings difficulty to the spectrum analysis and affect the accuracy of the final results. Removing multiple kinds of noises while protecting useful fracture response is the challenge. However, the filtering model for the water hammer pressure wave signals is absent. This paper proposes a new comprehensive filtering model for pump shut-in water hammer pressure wave signals. The filtering model contains three parts. First, we designed a two-step filtering method, including Hampel filter and FIR low-pass filter, to filter signals in the main channel. Second, multi-frequency-adaptive notch and median filter were combined to construct the signals in the reference channel, which can be used to protect the useful frequency of the water hammer wave signals and pressure response of the fractures from being eliminated. Third, the filtered signal is obtained by adaptive cancellation between the signals in the main channel and the reference channel. After that, the filtering effects of signals with and without fractures were studied by constructing simulated signals and collecting the signals in the laboratory experiment. The signal-to-noise ratio (SNR) gain and the mean square error (MSE) reduction were selected as indicators for the model validation, and the cepstrum was also used as the indicator to verify the preservation of fracture response characteristics. Besides, the noise adaptive range of the model was evaluated by increasing the noise intensity to the constructing signal. This research can provide a new and useful filtering method for water hammer pressure wave signals in the field. • A new comprehensive filter model suitable for water hammer signals was proposed. • For the water hammer pressure wave signal, the SNR gain is about 6.8 dB, and the MSE can be reduced by about 0.05. • The noise adaptive range of the model was evaluated by changing the noise intensity to the constructing signal.

A noise reduction method for force measurements in water entry experiments based on the Ensemble Empirical Mode Decomposition

In this paper a denoising strategy based on the EEMD (Ensemble Empirical Mode Decomposition) is used to reduce the background noise in non-stationary signals, which represent the forces measured in scaled model testing of the emergency water landing of aircraft, generally referred to as ditching. Fuselage ditching tests are performed at a constant horizontal speed of 12 m/s with a controlled vertical motion, resulting in a vertical velocity at the beginning of the impact of 0.45 m/s. The measured data are affected by a large amplitude broadband noise, which has both a mechanical and an electronic origin. The noise sources cannot be easily avoided or removed, since they are associated with the vibrations of the structure of the towing carriage and with the interaction between the measurement chain and the electromagnetic fields. The EEMD noise reduction method is based on the decomposition of the signal into modes and on its partial reconstruction using the residue, the signal-dominant modes and some further modes treated with a thresholding technique, which helps to retain some of the sharp features of the signal. The strategy is developed and tested first on a synthetic signal with a superimposed and known background noise. The method is then verified on the measurement of the inertial force acting on the fuselage when it is moving in air, as in this case the added mass is negligible and the denoised force should equal the product of the mass and the acceleration, both of them being known. Finally, the procedure is applied to denoise the forces measured during the actual ditching experiment. The results are superior to those obtained from other classical filtering methods, such as a moving average filter and a low-pass FIR filter, particularly due to the enhanced capabilities of the EEMD-denoising strategy here developed to preserve the sharp features of the signals and to reduce the residual low-frequency oscillations of spurious origin.

Real-time Optimization and Implementation of Multi-rate Digital FIR Kernel Filter

One ideal performance of this design is in the areas of decimation where a decimation factor of 10, 45-order and pass band ripple of 1dB and interpolation of sampled rates where a sinusoidal signal input produced a ripple free output with interpolation factor of 10, 52-order and stopband attenuation of 60dB. Owing to the multiple samples of filter length of 200, the filter performed down sampling preceded with filtering as well as up sampling preceded with filtering, hence multi-rate filter by allowing a low threshold of frequency of to be passed, blocking a high threshold of and vice versa. There was resampled output increased to 150% preceded by filtering. The filter coefficients for low pass and high pass Digital FIR filter, through the least square regression method, parks McClellan Algorithm and window methods were employed for easy optimization. More so, there was creation of 2-4-5 filter channel banks through the 2nd-level convolution of their down sampling and up sampling filtering techniques during the multi-irate filtering to ensure the design of error-free Digital FIR Filter using MATLAB File editor(M-File) and tool boxes for writing the C-programming of the design. In the analysis, the mean and standard deviation of the low pass Digital FIR Filter output during decimation and interpolation are (0.26, 6.13) and (0.004,1.22) respectively.

Moth Flame Optimization Algorithm for Optimal FIR Filter Design

This paper presents the application of Moth Flame optimization (MFO) algorithm to determine the best impulse response coefficients of FIR low pass, high pass, band pass and band stop filters. MFO was inspired by observing the navigation strategy of moths in nature called transverse orientation composed of three mathematical sub-models. The performance of the proposed technique was compared to those of other well-known high performing optimization techniques like techniques like Particle Swarm Optimization (PSO), Novel Particle Swarm Optimization (NPSO), Improved Novel Particle Swarm Optimization (INPSO), Genetic Algorithm (GA), Parks and McClellan (PM) Algorithm. The performances of the MFO based designed optimized FIR filters have proved to be superior as compared to those obtained by PSO, NPSO, INPSO, GA, and PM Algorithm. Simulation results indicated that the maximum stop band ripples 0.057326, transition width 0.079 and fitness value 1.3682 obtained by MFO is better than that of PSO, NPSO, INPSO, GA, and PM Algorithms. The value of stop band ripples indicated the ripples or fluctuations obtained at the range which signals are attenuated is very low. The reduced value of transition width is the rate at which a signal changes from either stop band to pass band of a filter or vice versa is very good. Also, small fitness value in an indication that the values of the control variable of MFO are very near to its optimum solutions. The proposed design technique in this work generates excellent solution with high computational efficiency. This shows that MFO algorithm is an outstanding technique for FIR filter design.

A Feedrate-Constraint Method for Continuous Small Line Segments in CNC Machining Based on Nominal Acceleration

When the CNC machining of continuous small line segments is performed, the direction of the machine tool movement will change abruptly at the corner of adjacent line segments. Therefore, a reasonable constraint on the feedrate at the corner is the prerequisite for achieving high-speed and high-precision machining. To achieve this goal, a feedrate-constraint method based on the nominal acceleration was proposed. The proposed method obtains the predicted value of acceleration during the machining process by the machining trajectory prediction and acceleration filtering. Then, the feedrate at the corner is constrained, according to the predicted acceleration. Specifically, for any corner of adjacent line segments, the proposed method assumes that the CNC machining of a short path centered on the corner is carried out at a constant feedrate. First, the actual machining trajectory is predicted according to the transfer function of the servo system. Then, the nominal acceleration, when the CNC machining is carried out to the corner, is calculated and processed by a low-pass FIR filter. Last, the feedrate-constraint value at the corner is obtained according to the nominal acceleration and the preset normal acceleration. The advantage of the proposed method is that it can be used for different machining paths consisting of long segments or continuous small segments and it has no special requirement for the accuracy of the machining path. As a result, the feedrate-constraint value obtained is reasonable and the smooth machining process can be ensured. The simulation results in both 2D and 3D machining paths show that the proposed method is insensitive to the length of the line segment and the angle of the corner, and the calculated feedrate-constraint value is close to the theoretical value, which has good stability and versatility. In contrast, the feedrate-constraint values obtained by conventional methods change abruptly along the machining path, especially in the 3D simulation, which will damage the machining quality. The experiment was performed on a three-axis CNC machine tool controlled by a self-developed controller, and a free-form surface workpiece was machined by a conventional feedrate-constraint method and the proposed method, respectively. The experimental results showed that the proposed method can make the feedrate of the machining process higher and more stable. Then, machining defects such as overcutting and undercutting can be avoided and the machining quality can be improved. Therefore, the article proposes a new method to constrain the feedrate at the corner of continuous small line segments, which can improve the machining efficiency and quality of the CNC machining.

On the Unification of Common Actigraphic Data Scoring Algorithms

Actigraphy is a well-known, inexpensive method to investigate human movement patterns. Sleep and circadian rhythm studies are among the most popular applications of actigraphy. In this study, we investigate seven common sleep-wake scoring algorithms designed for actigraphic data, namely Cole-Kripke algorithm, two versions of Sadeh algorithm, Sazonov algorithm, Webster algorithm, UCSD algorithm and Scripps Clinic algorithm. We propose a unified mathematical framework describing five of them. One of the observed novelties is that five of these algorithms are in fact equivalent to low-pass FIR filters with very similar characteristics. We also provide explanations about the role of some factors defining these algorithms, as none were given by their Authors who followed empirical procedures. Proposed framework provides a robust mathematical description of discussed algorithms, which for the first time allows one to fully understand their operation and basics.

Modified repetitive control based on comb filters for harmonics control in grid-connected applications

Power quality plays an important role in the operation of grid-connected inverters. The injected current should have a minimum harmonic content in order to comply with the local grid codes. This can be challenging due to the fact that the grid voltage can also be distorted. This paper presents the modified repetitive control method for three-phase grid-connected inverters by means of a digital comb filter application. The proposed method provides multiple harmonics compensation, simple implementation and design. It consists in complementing the Proportional-Resonant (PR) control with an appropriately tuned comb filter, which results in mitigation of the output current harmonics and reduced THD. Two types of comb filters have been proposed as a harmonic compensator, namely feedback and feedforward structures. The compensator included an additional FIR low-pass filter for increased robustness. Regardless of the structure chosen the solution is targeted at simplicity of analysis, design and implementation. Furthermore, the compensator based on the feedforward comb filter is casual, hence its implementation on the digital microprocessors is greatly facilitated. The proposed two-stage design of the control loop ensures the stability even in the case of additional poles being introduced by the feedback comb filter. Investigation on frequency variations impact is also included. The research was conducted by means of the following IT solutions: data workflow management environment, code design and generation (Matlab), and user interface design (ControlDesk). The experimental results have demonstrated the effectiveness of the proposed solution for a grid-connected converter operating in a network with distorted supply voltage.

Design and Implementation of Third Order Low Pass Digital FIR Filter using Pipelining Retiming Technique

This paper presents design and implementation of 3rd order low pass digital FIR filter using pipelining retiming technique. Aim of this paper is to apply pipelining retiming technique on low pass digital FIR filter and compare with the existing second order retimed FIR filter and third order broadcast, non-broadcast low pass FIR filter. MATLAB FDA tool is used to calculate digital FIR filter coefficients. Hamming and Kaiser window methods are used to find out the coefficients of the FIR filter. Broadcast and non broadcast third order low pass FIR filter architectures are used for pipelining retiming .Cutset and feed forward pipelining retiming techniques are used to retime the third order low pass digital FIR filter. We design our algorithm in VHDL and implemented on Xilinx Vivado xc7a35tcpg261-1. Area (LUT), speed and power are calculated using Xilinx Vivado 2015.2 tool. Synthesis and simulation results are discussed in this paper. Speed and area (LUT) of proposed third order low pass FIR filter is improved in comparison with existing designs.

Finite impulse response low-pass digital filter based on particle swarm optimisation for image denoising

Noise has very important influence on image quality. Finite Impulse Response (FIR) digital filter is an effective technique for removing image noise. The effectiveness of FIR digital filters designed by traditional methods in image denoising needs to be improved further because of their demerits such as lower accuracy. Therefore, to suppress noise and improve image quality, FIR low-pass digital filter designed by the improved particle swarm optimisation algorithm based on refraction principle (refrPSO) is employed. In the method, firstly, coefficients of FIR low-pass digital filter are mapped to a candidate solution of refrPSO, and then refrPSO is invoked to optimise it. Finally, the designed FIR low-pass digital filter is employed to denoise images. Experimental results of two images with different sizes show that the FIR digital filter designed by refrPSO has better image denoising effect compared with ones designed by window function method, best equivalent ripple approximation method, and PSO.