Filters For Signal Processing Research Articles

Advancements and Applications of Adaptive Filters in Signal Processing

Advancements and Applications of Adaptive Filters in Signal Processing

An Ultra-Low-Voltage Transconductance Stable and Enhanced OTA for ECG Signal Processing.

In this paper, a rail-to-rail transconductance stable and enhanced ultra-low-voltage operational transconductance amplifier (OTA) is proposed for electrocardiogram (ECG) signal processing. The variation regularity of the bulk transconductance of pMOS and nMOS transistors and the cancellation mechanism of two types of transconductance variations are revealed. On this basis, a transconductance stabilization and enhancement technique is proposed. By using the "current-reused and transconductance-boosted complementary bulk-driven pseudo-differential pairs" structure, the bulk-driven pseudo-differential pair during the input common-mode range (ICMR) is stabilized and enhanced. The proposed OTA based on this technology is simulated using the TSMC 0.18 μm process in a Cadence environment. The proposed OTA consumes a power below 30 nW at a 0.4 V voltage supply with a DC gain of 54.9 dB and a gain-bandwidth product (GBW) of 14.4 kHz under a 15 pF capacitance load. The OTA has a high small signal figure-of-merit (FoM) of 7410 and excellent common-mode voltage (VCM) stability, with a transconductance variation of about 1.35%. Based on a current-scaling version of the proposed OTA, an OTA-C low-pass filter (LPF) for ECG signal processing with VCM stability is built and simulated. With a -3 dB bandwidth of 250 Hz and a power consumption of 20.23 nW, the filter achieves a FoM of 3.41 × 10-13, demonstrating good performance.

Optimizing Energy-Efficient in VLSI Architecture for FIR Filter in Seismic Signal Processing

This project aims to developing an optimized and energy-efficient VLSI design for FIR filters specifically designed for seismic signal processing. The process begins by converting seismic signal dataset values into binary format, and then, simulating them in MATLAB, and generating a coefficient file. This coefficient file is then analyzed using Verilog HDL code and simulated in the Xilinx Vivado 2022.2 tool to assess key parameters such as area, delay, and power. The suggested architecture lessens the complexity of computing, hardware efficiency compared to traditional FIR filters that utilize the CSE technique. This design employs a critical method known as the CSD-based Matrix-Grouped Common Subexpression Elimination (MCSE) algorithm, which significantly reduces the quantity of logic operators (LOs) and logic depths (LDs). Additionally, the design incorporates a Half-Unit Biased (HUB) rounding technique to minimize the truncation errors and cut-set retiming method to reduce the critical-path delay (CPD). This hardware-efficient FIR filter constructs integrates the CSD-based MCSE algorithm, HUB rounding, and cut-set retiming techniques to offer a reconfigurable FIR filter configuration optimized for hardware efficiency, thereby enhancing the real-time alert seismic signal processing. The implementation of an Artix-7 FPGA board including clock gating techniques to further reduces the power consumption.

The Employment of Filters in Electrocardiogram Signal Processing and their Applications

The Employment of Filters in Electrocardiogram Signal Processing and their Applications

Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization

The goal of this study is to explore the effectiveness of applying multi-objective particle swarm optimization (MOPSO) algorithms in the design of infinite impulse response (IIR) filters. Given the widespread application of IIR filters in digital signal processing, the precision of their design plays a significant role in the system’s performance. Traditional design methods often encounter the problem of local optima, which limits further enhancement of the filter’s performance. This research proposes a method based on multi-objective particle swarm optimization algorithms, aiming not just to find the local optima but to identify the optimal global design parameters for the filters. The design methodology section will provide a detailed introduction to the application of multi-objective particle swarm optimization algorithms in the IIR filter design process, including particle initialization, velocity and position updates, and the definition of objective functions. Through multiple experiments using Butterworth and Chebyshev Type I filters as prototypes, as well as examining the differences in the performance among these filters in low-pass, high-pass, and band-pass configurations, this study compares their efficiencies. The minimum mean square error (MMSE) of this study reached 1.83, the mean error (ME) reached 2.34, and the standard deviation (SD) reached 0.03, which is better than the references. In summary, this research demonstrates that multi-objective particle swarm optimization algorithms are an effective and practical approach in the design of IIR filters.

Improving pulsed laser induced fluorescence distribution function analysis through matched filter signal processing.

Laser induced fluorescence is used to measure argon ion heating during magnetic reconnection in the PHase Space MApping experiment (PHASMA). Sufficient signal-to-noise ratio (SNR) of the processed signal with pulsed laser injection is a delicate balance between saturation of the absorption line and injecting enough laser power to overcome the spontaneous emission of the plasma at the fluorescence wavelength. Averaging over many laser pulses and integrating over the fluorescence lifetime improves the SNR of the processed signal (processed SNR) when the SNR of the laser pulse time series is small (pulse SNR), but for laser powers small enough to avoid saturation, averaging over hundreds of pulses is needed to obtain an appreciable processed SNR over the entire Doppler-broadened absorption line. Here, we describe a matched filter processing method that significantly improves the SNR of the final measurement with fewer shots averaged. Investigation of simulated measurements validated by experimental results suggests that the matched filter method provides up to a 20% improvement in the processed SNR, resulting in less uncertainty in distribution function fits.

Advancements in Filter Technology: Evolution and Applications

This paper explores the historical trajectory and contemporary applications of signal processing filters. From the early days of analog filters to the current digital era, the paper traces their evolution, emphasizing key developments in active RC filters, monolithic integrated op amps, and the transition to switched-capacitor and continuous-time filters. Fundamentals of analog and digital filters are outlined, distinguishing their processing of continuous-time and discrete-time signals. The classification of filters, including low-pass, high-pass, band-pass, and band-stop, is discussed with insights into their specific applications. The paper delves into crucial applications in audio processing, video processing, communication systems, power management, and sensor signal processing, showcasing filters' pivotal role in enhancing signal quality across diverse fields. Addressing emerging technologies, the paper highlights adaptive and deep learning filters, projecting the future trends in filter technology. It anticipates developments in analog integrated filters, emphasizing the growing significance of integrated circuits technology. This comprehensive review aims to deepen readers' understanding and inspire continued innovation in signal processing.

Enhanced FPGA linear phase FIR filter with amalgam multiplier

ABSTRACT Designing high-performance integrated circuits that balance area, speed, and power is increasingly challenging. This study optimises hardware implementation of FIR filters using an innovative Amalgam Multiplier, minimising resource use without compromising performance. The key challenge in designing signal processing hardware is the multiplier design, which significantly affects efficiency. This study explores integrated circuit optimisation, addressing the delicate balance between area utilisation, speed, and power consumption. Focusing on hardware implementation of Finite Impulse Response (FIR) filters in signal processing, the article starts with a conventional FIR filter design using a standard array multiplier which consumed 1.790 W, with 8 s real-time completion. Then, Linear Phase FIR (LP-FIR) filter is designed using Braun and Dadda Multiplier. LP-FIR filter with Braun multiplier consumed 1.436 W power and 26 s of completion time whereas LP-FIR with Dadda multiplier consumed 1.363 W power and 23 s of completion time. Finally, the implementation of novel LP-FIR enhanced by Amalgam multiplier consumed 0.302 W power and 10.29 s of completion time, marking a significant advancement in integrated circuit design.

Some m-fold symmetric bi-univalent function classes and their associated Taylor-Maclaurin coefficient bounds

The Ruscheweyh derivative operator is used in this paper to introduce and investigate interesting general subclasses of the function class Σm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\Sigma_{m}$\\end{document} of m-fold symmetric bi-univalent analytic functions. Estimates of the initial Taylor-Maclaurin coefficients |am+1|\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\vert a_{m+1} \\vert $\\end{document} and |a2m+1|\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\vert a_{2 m+1} \\vert $\\end{document} are obtained for functions of the subclasses introduced in this study, and the consequences of the results are discussed. Additionally, the Fekete-Szegö inequalities for these classes are investigated. The results presented could generalize and improve some recent and earlier works. In some cases, our estimates are better than the existing coefficient bounds. Furthermore, within the engineering domain, the utilization of the Ruscheweyh derivative operator can encompass a broad spectrum of engineering applications, including the robotic manipulation control, optimizing optical systems, antenna array signal processing, image compression, and control system filter design. It emphasizes the potential for innovative solutions that can significantly enhance the reliability and effectiveness of engineering applications.

Low power CMOS Gm-C based low pass filter for front end neural signal processing

The sub 100 µV voltage levels and sub 100 Hz frequency range makes the processing of most popular signal electroencephalograph (EEG) for brain functionality analysis, a complex task. The low frequency content of EEG (useful signals below 70 Hz) is commonly used for diagnosis of various brain related disorders making low-pass filter (LPF) a key block in front-end processing as noise reduction and resolution enhancement is crucial for precise recovery of these information. This paper is aimed to design reduced transconductance (Gm) based low power and small area CMOS LPF with cutoff frequency (fc) around 70 Hz. The proposed design is simulated using Cadence virtuoso tool and gives cut-off frequency of 72.958 Hz with low output noise of 3.0609 µV/√Hz and power consumption of 264.060 nW at operating voltage of 0.4 V. The simulation results show linearity of performance over -40 to 100 °C. Layout of circuit takes up area of 86.74×81.21 µm and post layout simulation shows 5% variation in power consumption as compared to pre layout simulations.

Reconfigurable integrated photonic filters for optical signal processing using a silicon photonic platform.

We present a systematic photonic filter design approach by deploying pole-zero optimization. The filter transfer function is derived from its specifications by formulating closed-form optimization objective functions and subsequently translating them into optical design parameters. Two distinct filter examples, namely Chebyshev and elliptic filters, are considered for the design and validation. A compact reconfigurable three-pole photonic filter is fabricated on a silicon photonic platform to illustrate the proposed design technique including transmission tunability. Integrated thermal phase shifters coupled with micro-ring resonators are used to reconfigure filter responses. A well-matched experimental demonstration is presented to validate the proposed tuning method. We achieved a sharp out-of-band edge rejection of at least 20 and 40 dB for the elliptic and Chebyshev filter, respectively.

Hardware Implementation of FIR Filter for ECG Signal Processing: Design, Optimization, and Performance Analysis on an FPGA

Electrocardiogram (ECG) signals are commonly used to diagnose heart-related diseases. However, noise induced during the measurement process can affect the accuracy of the diagnosis. Digital filters, such as the Finite Impulse Response (FIR) filter, are widely used to filter out noise from the ECG signal. Nevertheless, the processing speed of software-based FIR filters is slow for large ECG datasets due to serial processing. This paper presents a hardware implementation of the FIR filter for ECG signal processing to overcome the processing speed issue. The filter is designed using the Kaiser Window method and implemented on the Intel Cyclone IV Field Programmable Gate Array (FPGA). The filter is first designed in MATLAB to obtain the filter coefficients where the ECG data were obtained from Physionet database. From the difference equation, we designed the signal flow graph (SDFG) and then mapped into hardware logics to enable parallel processing. Simulation results of the software (MATLAB) and hardware (FPGA) implementations are obtained and compared. The results show that the FPGA-based FIR filter can process the ECG signal up to 1,250 times faster than software implementations. To further optimize the design and reduce hardware cost, we introduce optimized designs by applying the operation scheduling and constrained resource allocation techniques. The maximum operating frequency, logic utilization, and power consumption of each design were analysed and compared. This study demonstrates that custom-designed hardware logic for digital signal processing can significantly outperform software implementations due to its parallel processing capabilities. The proposed optimization techniques reduce the hardware cost while maintaining high processing speed and accuracy. The hardware implementation of the FIR filter for ECG signal processing has numerous applications in diagnosing heart-related diseases and real-time monitoring of ECG signals in critical care settings.

Application of the Savitzky-Golay filter in multi-spectral signal processing

Multi-spectral signals are the result of the interaction between electromagnetic energy and the test material, which is then displayed by the signal fluctuation pattern of the test material. Signal fluctuations are inaccuracies in the peak amplitude of a signal caused by noise in the data. This fluctuation pattern reflects the properties of the test material, especially in this case H2O. To overcome this problem, it is necessary to use the right filter to smooth the signal and reduce the noise in the data so that the fluctuation pattern obtained is clearer and more accurate. This research involves the segmentation of HF fluctuation patterns, followed by the application of a Savitzky-Golay filter for signal smoothing. Signal quality is assessed objectively by calculating the Signal to Noise Ratio (SNR) and Mean Square Error (MSE). The research results show that the Savitzky-Golay filter succeeded in reducing noise and producing clearer fluctuation patterns. The SNR value varies, with the largest value reaching 16.6146 dB, and the smallest value being 3.0171 dB. This research contributes to a new method, namely the Savitzky-Golay adaptive filter, to identify multi-spectral signal fluctuation patterns more effectively, thereby enabling more accurate identification of fluctuation patterns. Apart from that, this research also provides insight into the characteristics of H2O which can be identified through fluctuation patterns, especially in certain segments with high amplitude. This method has potential for applications in various fields, especially in precise multi-spectral signal analysis.

Parameter estimation of biased harmonic signal

This paper deals with the problem of asymptotically estimating amplitude, frequency and phase of a sinusoidal signal by adopting the theory of invariant relations proposed in analytical mechanics [8] and further investigated in [9, 10] (see also [11]). The problem of frequency, amplitude and phase estimation of a sinusoidal signal has attracted a remarkable research attention in the past and current literature. The reasons of this interest rely on several engineering applications where an effective and robust solution to this problem is crucial. To mention few, it is worth mentioning problems of harmonic disturbance compensation in automatic control, design of phase-looked loop circuits in telecommunication, adaptive filtering in signal processing, etc. In principle, the method of least squares, Fourier analysis, Laplace transform provide a potential solution to the corresponding problems. However, these methods may not be suitable, for example, for control algorithms with real-time data processing. The goal of this paper is to suggest a further contribution to this task by showing how to solve the problem at hand through the observer’s theory. The method of invariant relations is used for the asymptotically observation scheme design. This aproach is based on dynamical extension of original system and construct of appropriate invariant relations, from which the unknowns variables can be expressed as a functions of the known quantities on the trajectories of extended system. The final synthesis is carried out from the condition of obtaining asymptotic estimates of unknown parameters. It is shown that an asymptotic estimate of the unknown states can be obtained by rendering attractive an appropriately selected invariant manifold in the extended state space. The asymptotic convergence of the estimates of the sought phase vector components to their true value is proved. The simulation results demonstrate the effectiveness of the proposed method of solving the state observation problem of the harmonic oscillator. It should be noted that a more general approach, which forms an appropriate method for solving observation problems for nonlinear dynamical systems due to the synthesis of invariant manifold, was proposed as a modification of the I&I method (Input and Invariance) of stabilization of nonlinear systems in [12, 13].

Freeform direct-write and rewritable photonic integrated circuits in phase-change thin films.

Photonic integrated circuits (PICs) with rapid prototyping and reprogramming capabilities promise revolutionary impacts on a plethora of photonic technologies. We report direct-write and rewritable photonic circuits on a low-loss phase-change material (PCM) thin film. Complete end-to-end PICs are directly laser-written in one step without additional fabrication processes, and any part of the circuit can be erased and rewritten, facilitating rapid design modification. We demonstrate the versatility of this technique for diverse applications, including an optical interconnect fabric for reconfigurable networking, a photonic crossbar array for optical computing, and a tunable optical filter for optical signal processing. By combining the programmability of the direct laser writing technique with PCM, our technique unlocks opportunities for programmable photonic networking, computing, and signal processing. Moreover, the rewritable photonic circuits enable rapid prototyping and testing in a convenient and cost-efficient manner, eliminate the need for nanofabrication facilities, and thus promote the proliferation of photonics research and education to a broader community.

Graph Filters for Signal Processing and Machine Learning on Graphs

Graph Filters for Signal Processing and Machine Learning on Graphs

A reconfigurable floating-gate-transistor-capacitor filter for analog signal processing

This paper introduces a fully reconfigurable floating-gate-transistor-capacitor (FGT-C) filter that exhibits compactness and power efficiency. The proposed filter incorporates a biquadratic section, enabling the generation of lowpass (LP) and bandpass (BP) outputs with adjustable filter parameters, including gains, natural frequency, quality factor, and DC levels for input and output signals. Moreover, the FGT-C filter topology demonstrates a modular structure, facilitating easy cascading and scalability of the biquadratic sections to implement high-order frequency responses. A prototype chip has been fabricated in the 0.35 μm CMOS process, where each biquadratic section occupies an area of 0.0313 mm2. The chip measurements were conducted with a supply voltage of 1.8 V and both output voltage levels programmed at 0.9 V. The biquadratic FGT-C filter exhibits a power consumption of 118.4 nW and achieves a spurious free dynamic range of 43 dB in a 10 kHz bandwidth setting. It is suitable for low-power analog signal processing in large-scale field programmable analog arrays.

Supervised Reciprocal Filter for OFDM Radar Signal Processing

In this paper, we address the problem of the range-Doppler map evaluation in continuous wave radar exploiting OFDM signals. This stage is usually implemented by resorting to a suboptimal batches algorithm and a typical choice is to fragment the signal in batches with length equal to the OFDM symbol length and to apply at each batch an appropriate range compression strategy: typically, either Matched Filter (MF) or Reciprocal Filter (RF). The former provides the best performance against noise-limited scenarios, whereas the latter against clutter-limited scenarios, thanks to its high Peak SideLobe Level (PSL). Using “OFDM fragmentation” requires symbol synchronization and sets constraints on the coherent processing chain; moreover, we show that it provides a Signal-to-Noise Ratio (SNR) loss both when using MF and RF. Therefore, we investigate the case of “non-OFDM fragmentation”, which does not require synchronization and avoids setting constraints on the processing chain. Specifically, we address the case of batch lengths longer than a single OFDM symbol that can potentially reduce the SNR loss at long ranges. We find that this is effective for the MF, but causes an even higher SNR loss for the direct application of the RF filter, which still provides a low level of sidelobes. Aiming at preserving the potential benefits of the RF over the MF against the clutter-limited scenarios, we propose some modified versions of the RF for the non-OFDM fragmentation case, which are shown to offer a trade-off between SNR losses and sidelobes level control. The effectiveness of the proposed approaches is demonstrated both by providing theoretical performance prediction expressions and by using simulated analyses. To this purpose, a case study is considered for a passive radar exploiting DVB-T transmissions

Designing FIR Filter using Distributed Arithmetic

Abstract: Distributed arithmetic technique is employed in this paper to develop the FIR filter. To eliminate undesired signal and noise, FIR filters are frequently employed as digital filters in digital signal processing. This method also calls for shift registers and an accumulator and stores the FIR filter coefficients in a look-up table (LUT).By using this method, shift and accumulate can take the place of the multipliers in the FIR filter. The size of the LUT can be reduced for larger filter coefficients by dividing it into any number of LUTs, which are made up of taps that are FIR filter coefficients. FIR filter is created in MATLAB using the DA technique, and manual computation yields the same result

Method EWMA (Exponentially Weighted Moving Average) as a Filter to Fine and Remove Noise on Time Series Data

The Moving Average method is the most common filter in Digital Signal Processing, the Moving Average method is optimal for reducing unstable value fluctuations due to interference by maintaining accurate readings. Therefore it is often used for signal coding in the context of time (Smith, 1999). The kind of moving average method is the Exponentially Weighted Moving Average (EWMA) which is often applied to a time sequence of random variables, by calculating the weighted average of the sequence by applying a weight that decreases geometrically with the length of observation (Perry, 2011). this method can be applied to tools/instruments that use data continuously in a certain time so that it will require a filter so that the fluctuations in the data are not extreme which will result in changes in the output response being softer so as not to damage the device on the output. This study uses the EWMA method to eliminate noise in the processing of sensor readings. The results of the study, the system is able to monitor realtime ph and temperature data. In fish farming, measuring and monitoring water temperature and ph is important, if it is not regularly measured and monitored it can cause various problems such as pH and temperature that are not optimal for fish growth and health. The research method to be carried out is R&D, EWMA will be applied to a multi-sensor circuit then processed by a data logger device to then be displayed on serial data on its output.