Infinite Impulse Response Filter Research Articles

IIRI-Net: An interpretable convolutional front-end inspired by IIR filters for speaker identification

Learning interpretable filters in Convolutional Neural Networks (CNNs) is an approach that helps to build models with better generalization ability. Interpretable filters can reveal some hidden aspects of the task and help to improve the model. One of the most successful approaches in the field of the speech processing is SincNet, where the model learns some band-pass filters in the first layer of a CNN with a raw waveform as its input. In this paper, similar to SincNet, some meaningful filters are proposed, which here are inspired by Infinite Impulse Response (IIR) filters. The proposed model uses a phase correction process to ensure that phase linearity is satisfied. The effective length of the truncated IIR filter is calculated based on the accumulated energy, and the effect of changing the filter size on the final results has been investigated. The proposed model is evaluated in the speaker identification task on the TIMIT and Librispeech datasets and compared with traditional CNNs and four interpretable kernel-based models. The experimental results show the superiority of the proposed model both in performance and convergence speed. Moreover, some patterns of the speech signal, which lead to uniquely identifying a speaker, are analyzed by examining the spectrum of the learned filters.

Using IIR Filter in Fractional Order Phase Lead Compensation PIMR-RC for Grid-Tied Inverters

Phase lead compensation precision dramatically affects the performance of repetitive controller (RC). Integer order phase lead compensation of proportional integral multi-resonant type RC (PIMR-RC) with low sampling frequency cannot meet the need of fine phase compensation. In addition, the digital control delay affects the actual modeling of the system, and then influences the precision phase compensation. To finely compensate the phase lag of the PIMR-RC system, this paper proposes a fine fractional order phase lead compensation PIMR-RC (FL-PIMR-RC) scheme based on a novel phase lead infinite impulse response (IIR) filter. Its comprehensive synthesis is systematically detailed, including actual modeling considering 1.5-period digital control delay, IIR fractional lead filter design, stability analysis, and parameter optimization. Finally, experiments on a 2 kW prototype grid-tied inverter are successfully carried out to verify the solution.

Peak Cancellation Method With IIR Filters and Its FPGA Implementation

To improve the efficiency of power amplifiers (PA) in communication transmitters, peak cancellation (PC) in the time domain is often implemented on a Field-programmable gate array (FPGA) to reduce the peak-to-average power ratio (PAPR) of the baseband signals. However, to reduce the out-of-band radiation, the existing techniques tend to use high-order finite impulse response (FIR) filters, which increases the input-to-output delay and resource consumption of FPGA. To address this issue, a PC method with the infinite impulse response (IIR) filter is proposed in this brief. The design criteria of the IIR filter for PC is given. The FPGA implementation scheme and resource optimization are discussed. To verify the performance of the proposed method, MATLAB simulation and FPGA implementation are made with a 5G NR signal. The comparison with the state-of-the-art PC methods shows that the proposed technique can reduce the total time delay greatly, with good PC performance and low resource consumption.

Photonic Millimeter Wave Generation and Stabilization in Optically Injected Discrete-Mode Semiconductor Lasers Subject to Photonic Filter Feedback

Photonic millimeter wave signal generation and stabilization based on nonlinear dynamics of optically injected discrete-mode semiconductor lasers with photonic filter feedback are experimentally and numerically studied. The photonic filter is constructed by jointing two ports of a 2×2 optical coupler to form a ring cavity recirculation and is modelled as an infinite impulse response filter. The results show that >30GHz photonic millimeter wave signals can be obtained after optical to electrical conversion of the period-one oscillation output of the optically injected discrete-mode semiconductor laser. More importantly, the linewidth, side peak suppression ratio, as well as the stability of the generated millimeter wave, can be optimized using the photonic filter feedback. A fair comparison of the photonic filter feedback scheme and the single/double optical feedback schemes in terms of optimization performance has been made. The corresponding results demonstrate that the photonic filter feedback scheme has obvious superiority in millimeter wave side peak suppression and stability. The effect of the coupling coefficient as well as the phase variables in the ring cavity has also been discussed in the simulation work and the results qualitatively agree with the experimental observations.

Scalable In-Band Full-Duplex Phased Arrays: Complexity Reduction and Distributed Processing

Compared to traditional omnidirectional systems, in-band full-duplex (IBFD) phased arrays provide higher antenna gains and offer the ability to electronically steer beams spatially to improve communications/radar links and enable multifunction operation. While a variety of approaches have successfully incorporated self-interference cancellation (SIC) into arrays, their techniques do not scale beyond the limited sizes demonstrated. In addition to providing an overview on state-of-the-art IBFD arrays, this paper presents a novel complexity reduction method that uniquely applies infinite impulse response (IIR) filter modeling for the purpose of compensating for the dispersion effects of the array hardware. Using measured statistical performance, this process is demonstrated for a scalable array design covering 2700 to 3500 MHz, and exhibits a 10x reduction in computations for an array with 1000 elements. Additionally, the distribution of on-array SIC adaptive processing is discussed for aperture-level IBFD designs, including the use of cache-oblivious algorithms that can help facilitate real-time processing and lead to the realization of large-scale IBFD arrays.

Enhancing IIR system identification: Harnessing the synergy of gazelle optimization and simulated annealing algorithms

In the field of digital filter design and system identification, accurately modeling Infinite Impulse Response (IIR) systems is of utmost importance. This paper introduces a new adaptive algorithm that combines the gazelle optimization algorithm with simulated annealing to achieve superior optimization performance for digital IIR filters used in system identification. To evaluate the algorithm's effectiveness, extensive experiments were conducted on two fronts: the challenging CEC2020 benchmark functions and a fifth order IIR system identification problem. The proposed algorithm proves its effectiveness by achieving optimal solutions on the CEC2020 benchmark functions when compared to other well-known optimization algorithms such as arithmetic optimization, particle swarm optimization, differential evolution, sine cosine algorithm, gray wolf optimizer, biogeography-based optimization, salp swarm algorithm, and the original gazelle optimization algorithm. The algorithm's performance is measured by its ability to find the best solutions for these benchmark functions. For the IIR system identification problem, the study considers both the same order and reduced order cases. The system to be identified is represented by a fifth order plant. The algorithm's statistical analysis and convergence behavior are compared to the original gazelle optimization algorithm, artificial hummingbird algorithm, mountain gazelle optimization algorithm, and the widely used artificial bee colony algorithm. These comparisons demonstrate the proposed algorithm's superior capability and efficiency in accurately identifying the parameters of IIR systems.

Artificial hummingbird optimizer as a novel adaptive algorithm for identifying optimal coefficients of digital IIR filtering systems

ABSTRACT This paper introduces the artificial hummingbird algorithm (AHA) as a novel and efficient algorithm for designing digital infinite impulse response (IIR) filtering systems. IIR filters are commonly used in various applications, and their design often involves complex error surfaces and coefficients. The AHA aims to address these challenges by optimizing the IIR model using mean squared error (MSE) cost function. Four benchmark examples are considered for evaluation by comparing both the same and reduced order cases of the IIR model. The results are compared against recent and efficient algorithms named prairie dog optimization, whale optimization algorithm, and artificial bee colony algorithm. The evaluation includes an analysis of the obtained coefficients, statistical measures, and convergence profiles. The findings show that the AHA outperforms the competing algorithms, achieving more accurate models and providing better statistical performance. Additionally, the AHA consistently converges to the lowest MSE values for each case. Further assessments are conducted using various examples from the literature, which validate the AHA's superior ability in digital IIR filtering design. The algorithm demonstrates more accurate identification and achieves lower MSE values compared to alternative methods which highlight the promising potential of the AHA for efficient and precise digital IIR filtering system design.

3P4W Grid Interactive PV-BES System With Seamless Transition Capability

For seamless transition of 3P4W (three phase four-wire) grid interactive solar photovoltaic (PV) system, a suboptimal finite impulse response (FIR) filter with approximation based on infinite impulse response filter (IIR) is employed here. The need for power quality improvement is essential and in order to guarantee an improved PQ, the grid currents are analyzed in covenant with the IEEE-519 standard. The power transfer proficiency with suboptimal filter utilization reduced intermittency associated with renewable sources along with improved power quality. Moreover, with the rise in the usage of distributed energy resources involving renewable energy sources, the need for power quality improvement is paramount. The need for improving PQ, stems from the fact that an uninterrupted supply is essential to the critical loads along with an improved power quality. Therefore, during operating modes such as grid linkage/interruption, the utilization of an efficient control is mandatory. Moreover, the behavior is required to be in agreement with the IEEE-519 and IEEE-1547 standards and in off-grid mode, a voltage controller is utilized for the power transfer. Thus, with adequate performance during the weak grid conditions, corroboration of satisfactory system behavior is obtained.

IIR Cascaded-Resonator-Based Complex Filter Banks

The use of a filter bank of IIR filters for the spectral decomposition and analysis of signals has been popular for many years. As such, a new filter-bank resonator-based structure, representing an extremely hardware-efficient structure, has received a good deal of attention. Recently, multiple-resonator (MR)-based and general cascaded-resonator (CR)-based filters have been proposed. In comparison to single-resonator-based analyzers, analyzers with a higher multiplicity of resonators in the cascade provide lower side lobes and a higher attenuation in stopbands. In previous works, it was shown that the CR-based filter bank with infinite impulse response (IIR) filters, which is numerically more efficient than one with finite impulse response (FIR) filters, is suitable for dynamic harmonic analysis. This paper uses the same approach to design complex digital filter banks. In the previous case, the optimization task referred to the frequency responses of harmonic filters. In this work, the harmonic filters of the mother filter bank are reshaped so that the frequency response of the sum (or difference, depending on the parity of the number of resonators in the cascade) of two adjacent harmonic filters is optimized. This way, an online adaptive filter base can be obtained. The bandwidth of the filters in the designed filter bank can be simply changed online by adding or omitting the output signals of the corresponding harmonics of the mother filter.

Characterization of Animal and Vegetable Oil by Attenuated Total Reflectance – Fourier Transform Infrared (ATR-FTIR) Spectroscopy with Supervised Pattern Recognition and Filter Algorithm

The potential of filter algorithms in improving spectral model performance was evaluated. A total of 329 animal and vegetable oil samples were used to collect attenuated total reflectance – Fourier transform infrared (ATR-FTIR) spectra. Fisher discriminant analysis (FDA), support vector machine (SVM), decision tree (DT), K-nearest neighbor analysis (KNN), and multilayer perceptron neural network (MLP) were considered to build models. Three filter algorithms, finite length unit impulse response filter (FIR), infinite length impulse response filter (IIR) and wavelet transform (WT), were evaluated to enhance the performance of the models. The Morlet basis function was the most suitable wavelet transform, accurately classifying 90.3% of the training set and 94.4% of the test set. The MLP algorithms were demonstrated to be superior to the others. The best performance was obtained using low-pass or band-stop filters that provided 100% accuracy with the MLP model. The reported method is demonstrated to be affordable and easy-to-use in forensic analysis.

Performance evaluation of infinite impulse response filter synthesis using parallel-Direct-Ladder form under finiteword-Length effects

Synthesizing a parallel-form IIR filter from a given non-strictly-proper transfer function requires a polynomial long division method, which results in dynamical range problem under finite word-length (FWL) fixed-point implementations. Based on the state-space similarity transformation, in this paper, a synthesis scheme for parallel-form IIR filters is proposed without requiring long division of polynomials operations to reduce dynamical range problems for obtaining parallelized first or second-order sections either in direct-form or coupled-form. The main advantage of the proposed scheme is that the parallelized IIR filter can be synthesized based on state-space normal-form similarity transformation from any digital filter transfer functions. In addition, the proposed two modified coupled-form filters based on the parallelized IIR filter have benefits of less computational consumption and zero-input limit-cycle free, especially for modified second-order coupled forms in contrast with the conventional coupled-form biquad. Finally, two numerical examples are performed to illustrate the effectiveness of the proposed scheme.

Event-based micro vibration measurement using phase correlation template matching with event filter optimization

Condition monitoring of mechanical systems based on vibration is one of the most effective and useful techniques for predictive maintenance. With advancements in image processing and artificial intelligence technologies, vision camera-based condition monitoring has come into the spotlight. An event camera, also called a dynamic vision sensor, operates on a principle different from that of traditional frame-based cameras. However, there is still no standardized method for event camera-based vibration measurement. This study proposes an event filter-based phase correlation template match (EF-PCTM) method, including its optimal design procedure, to measure micro-vibrations using an event camera. In this study, event filter is designed using an infinite impulse response filter and genetic algorithm, and a cost function is defined to improve the performance of EF-PCTM. The performance of the proposed method was verified by two experiments using a rotor system simulator: i) extraction of micro-vibration and ii) fault condition monitoring. The proposed image processing procedure using EF-PCTM makes it possible to measure micro-vibrations, which had been challenging to measure in the past, and calculate the operational deflection shape.

The Stability of LCL-Type Grid-Tied Inverter Based on Repetitive Control and Grid Voltage Feed-Forward

The grid voltage feed-forward can improve the dynamic performance of the repetitive controller (RC), but it will seriously weaken the stability under non-ideal grid conditions. This article focuses on the stability and dynamic characteristics of a grid-tied inverter with RC and grid voltage feed-forward. The internal model filter of RC uses an infinite impulse response (IIR) filter to improve the steady-state performance. First, an equivalent digital control model with RC and feed-forward is established, the parameters of RC are designed, and the system’s stability is analyzed based on the small gain theorem. Then, it is verified that the stability of RC in the presence of grid impedance can be weakened by the feed-forward term, thus a better feed-forward scheme is required. In addition, the solution for weak grid condition is proposed, and different feed-forward algorithms are compared from various perspectives, which is concluded that the improved proportional and first-order differential feed-forward scheme has a better performance. Finally, the validity of the controller is verified by the experimental prototype of a single-phase <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LCL$ </tex-math></inline-formula> -type grid-tied inverter.

Heuristic adaptive threshold detection method for neuronal spikes

AbstractIn recent years, the development of microelectrode arrays and multichannel recordings has provided opportunities for high‐precision detection in signal processing. The study of neuronal frontal potentials has been rapidly emerging as an important component in brain‐computer interface and neuroscience research. Neuronal spike detection provides a basis for neuronal discharge analysis and nucleus cluster identification; its accuracy depends on feature extraction and classification, which affect neuronal decoding analysis. However, improving the detection accuracy of spike potentials in highly noisy signals remains a problem. IThe authors propose a heuristic adaptive threshold spike‐detection algorithm that removes noise and reduces the phase shift using a zero‐phase Butterworth infinite impulse response filter. Next, heuristic thresholding is applied to obtain spike points, remove repetitions, and achieve robust spike detection. The proposed algorithm achieved an average accuracy of 95.40% using extracellular spiked datasets and effectively detected spikes.

Low-Power Current Integrating Flat-Passband Infinite Impulse Response Filter for Sensor Read-Out Integrated Circuit in 65-nm CMOS Technology

Low-power current integrating infinite impulse response filter having flat-passband for sensor read-out integrated circuit is proposed. In a current integrating filter, passband flatness degradation is inevitable due to sinc-like filtering characteristics. In this paper, by proposing a high order infinite impulse response filter architecture, flat-passband characteristic can be achieved. By implementing a filter architecture with a flat passband, the required sampling frequency can be lowered, which in turn can reduce power consumption. Moreover, the proposed high order infinite impulse response filter architecture has a high degree of freedom on adjusting input sample weights. The proposed integrated circuit is implemented in TSMC 65-nm CMOS process and operated on 1.2 V supply voltage.

Design and implementation of a least-mean-square adaptive notch filter

The task of removing sinusoidal components from observed signals can be accomplished by using a notch filter with a specific attenuation at a particular frequency. In some applications, however, such as acoustic feedback control, the frequency at which attenuation is required is unknown and possibly time-varying, and hence an adaptive notch filter is a more appropriate solution. Transitioning from a fixed notch filter to an adaptive one is by no means trivial and involves the understanding of a range of digital signal processing (DSP) topics from pole-zero placement techniques for designing infinite impulse response filters to optimal and adaptive filtering algorithms. In the signal processing algorithms and implementation graduate course taught at KU Leuven (Belgium), we study the design of an adaptive notch filter, which is based on a constrained biquadratic IIR representation, andwhose parameters are updated using a least-mean-square algorithm. Students also have to implement the algorithm on a 16-bit DSP TMS320C5515. In this presentation, we will discuss the design and implementation challenges of this adaptive notch filter and how it serves as an illustrative example/homework problem where several aspects of DSP are interwoven.

Kalman filter/smoother-based design and implementation of digital IIR filters

Recently, a unified framework was proposed for forward-backward filtering and penalized least-squares optimization. It was shown that forward-backward filtering can be presented as instances of penalized least-squares optimization. In other words, the output of a zero-phase digital infinite-impulse response (IIR) filter can be computed by solving a constrained optimization problem, in which the weight controlling the constraint is directly related to cutoff frequencies with closed-form equations. It was also shown that a zero-phase digital IIR filter can be formed as an optimal smoothing Wiener filter for a random process obtained from an autoregressive (AR) or AR-moving average (ARMA) model driven by input (innovation) noise in presence of an observation noise.In this paper, the problem of zero-phase digital IIR filtering is re-examined using Kalman filter/smoother. The paper shows that every zero-phase digital IIR filter can be viewed as a special case of an optimal smoothing Wiener filter. Based on the fact that the formulations of the optimum filter by Wiener and Kalman are equivalent in steady state, we present a Kalman filter/smoother framework to the design and implementation of digital IIR filters. As an example, the zero-phase digital Butterworth filter is designed using Kalman smoother and compared with the traditional design (forward filtering and backward smoothing) method.

Stable and Widely Tunable Optoelectronic Oscillator Using a Cascade Microwave Photonic Filter

A widely tunable optoelectronic oscillator (OEO) with stable output based on a cascade microwave photonic filter (MPF) is proposed and demonstrated. The cascade MPF is implemented by a combination of a finite impulse response (FIR) filter based on shifted dispersion-induced power fading and an infinite impulse response (IIR) filter based on a double-parallel recirculating delay lines (DP-RDLs). Through adjusting the time delay difference between the upper and lower connecting arms of the dual-input Mach-Zehnder modulator within FIR filter, the center frequency of cascade MPF is changed and the oscillation frequency of the OEO can be tuned. The theoretical analysis and verified experiment of the proposed OEO is performed. Tunable oscillation signals from 6.1 to 11.98 GHz with an average step size of 490 MHz are generated. Due to the introduce of the IIR filter, the stable performance of the OEO is obtained, where the maximum frequency drift at 7.48 GHz is observed to be 1.817 kHz over 1 hour. The phase noise of the structure is also investigated

DESIGN AND IMPLEMENTATION OF EFFICIENT DIGITAL FILTERS USING LABVIEW

In digital signal processing, the purpose of a filter is to remove unnecessary components in the signal, such as random noise. An infinite impulse response (IIR) filter is an iteration-type filter. The current pattern resulting from the IIR filter is based on the current input and output patterns. Several methods are available to detect digital IIR filters. Filters are widely used in signal processing and communication device approaches such as in channel equalization; noise reduction; radar systems; audio, speech signal, video, and biomedical signal processing, and economical, electrocardiogram, electroencephalogram, electromyography, and electrical circuit analyses. In this study, we used the Laboratory Virtual Instrument Engineering Workbench (LabVIEW) software program to analyze the design and implementation of an efficient digital filter, the results of which are presented in this paper. The important objective of this task was to design and examine different IIR filters-namely, Chebyshev, Butterworth, and Elliptic filters-using LabVIEW for the simulation processes. LabVIEW is used by graphical programming environmental engineers to implement automated research, certification, and product testing systems. In this work, the LabVIEW graphical programming tool was used because it gives accurate results in real time compared to other available simulation tools.

A VCO-Based 2nd-Order Δ2–ΔΣ Modulator for Small-Size High Energy-Efficient Current Sensing Front-End

In this letter, a 2nd-order 2 -Σ modulator consisting of a voltage-controlled-oscillator-based quantizer (VCOQ) and a current digital-to-analog converter (DAC) with a pulse width modulator (PWM) is presented for the precise acquisition of a wide range photocurrent in an area-and energy-efficient form factor. The proposed 2-modulation realized by the 2nd-order infinite impulse response (IIR) filter on the feedback significantly attenuates the magnitude of input signals, enhancing the DR and linearity. Moreover, an additional differentiator followed by the VCOQ features the negative feedback loop in the 2nd-order Σ modulator, improving noise shaping with no additional current DAC noise. In addition, a 1-bit PWM current DAC substituting the multi-bit current DAC is devised to mitigate the noise from the current DAC, realizing the high resolution of 1 pA with 500 Hz bandwidth. The prototype chip fabricated in a 110-nm CMOS occupies 0.0308 mm2 and achieves Walden FoM of 4.15 pJ/conv.