Filter Coefficients Research Articles

Near-optimal Multiplier-Less Broadband Noise Shaping Filters

Noise shaping (NS) filters reduce the quantization noise power in one or more frequency band(s) while amplifying it in other bands. Narrow-band noise shaping filters are state of the art in audio signal processing, analog-digital and digital-analog conversion, direct digital synthesis, and other applications. However, it is much more difficult to design broadband NS filters. Since NS filters are used in feedback branches, they must therefore be designed direct path free which imposes a constraint on the filter coefficients. This constraint leads to prohibitive large filter coefficients employing state of the art filter design techniques.This paper investigates the theoretical bound for NS filters and shows results about a novel design method for broadband FIR and IIR noise shaping filters and its multiplier-less hardware implementation. The method employed is a purely numerical approximation technique and leads to filter designs close to the discussed theoretical bound. The quantization of the filter coefficients is performed by a Canonical Signed Digit (CSD) representation of the coefficients. Two alternative architectures for the implementation of the filters are discussed. The design technique and the CSD quantization are realized in a MATLAB toolbox. The filters were moreover implemented in VHDL.

Optimized Design of a Variable Fractional Delay Filter With Delay Error Constraints

A novel algorithm for designing variable fractional delay (VFD) filter coefficients is proposed to improve time delay accuracy. The principle of this method is to model the coefficient design as an optimization problem with fractional delay error constraints (FDECs) and use a linear approach to derive a new FDEC expression for solving the problem. The solution of the FDEC expression can be efficiently obtained through linear programming (LP). Compared with existing algorithms, the design examples show the proposed method achieves a 100-fold improvement in delay accuracy while maintaining the frequency response (FR) error at an acceptable level, and yields a 5 dB reduction in FR error with the same level of delay accuracy.

An ultra-high timing resolution pulse generator with spur suppression and correction of errors based on real-time computation

In this paper, a novel ultra-high timing resolution pulse generator is proposed. It is based on the waveform real-time computation method. Through real-time computing and filtering of the waveform samples, a pulse with a 0.1 ps timing resolution pulse could be generated at a 2.5 GSPS sampling rate. Based on the waveform real-time computation method, jitters are injected into the waveform time parameter to break the harmonic components caused by non-integer multiples of the sampling rate and waveform frequency. Waveform spurs are further suppressed using this approach. The pulse error correction is achieved by designing digital filters that complement the waveform distortion features. The complementary digital filters are then combined as Farrow filter coefficients by polynomial fitting. Based on the real-time computation method, pulse width modulation, frequency modulation, and amplitude modulation are easy to realize. The implemented pulse generator has four channels, whose minimum pulse width, edge time, frequency range, and amplitude range are 4, 2.5 ns, 1 μHz–120 MHz, and 50 mVpp–5 Vpp, respectively. All timing resolution and timing accuracy of pulse width, edge time, pulse period, and channel delay are 0.1 and 50 ps, respectively. Timing parameters can be changed continuously without glitches.

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

Optical and Near-infrared Atmospheric Extinction Coefficients at La Silla

We used observations obtained at the Rapid Eye Mount (REM) at the La Silla Observatory between 2016 June 7 UT to determine the extinction coefficients of the REM filters currently in use. The observations were obtain on 6 nights, spanning 2016 June UT through 2016 July 4 UT, over a wide range of airmass values, to determine the mean atmospheric extinction coefficients at the La Silla Observatory site. Coefficients in the REM filter sets—Sloan g′, r′, i′, z′, and J, H and K were determined, where the REM infrared filters closely match those of the J, H, and Ks filters of the 2MASS All-Sky Survey.

Enhancing the performance of consolidated voltage–frequency control of multi‐area hybrid interconnected power systems using electric vehicles

AbstractElectricity consumers desire a reliable and uninterrupted power supply for their equipment. Frequency and voltage are fluctuated due to the increase in intermittent renewable sources in the power systems. To mitigate these problems, a novel multi‐term fractional‐order PID (MFOPID) controller is proposed for improving the combined voltage‐frequency control of a three‐area hydrothermal system (THS) that incorporates a realistic dish‐Stirling solar thermal system (RDSTS). Suitable nonlinearity constraints are considered in both thermal‐hydro units. The proposed controller parameters are optimized using the artificial flora algorithm. Electric vehicle (EV) fleets have been introduced for the first time into the system under consideration. The MFOPID controller outperforms the PID with filter coefficient (PIDN) controller, according to a comparative assessment of the system dynamic responses under various investigations. Investigations exhibit that the system dynamic performance marginally degrades after the incorporation of the RDSTS into all control area of the three‐area THS. The analysis reveals that the assimilation of EVs into the considered system provides better system damping and reduces oscillations and peak deviations. The efficiency of electric vehicles is influenced by battery state‐of‐charge (SOC). The system performance deteriorates when the SOC of the EVs is more than 80% charging and less than 50% discharging at the time of disturbance. Moreover, the MFOPID controller provides a faster injection of EV power into the system during perturbation compared to the PIDN controller. Sensitivity analysis validated the MFOPID controller's sturdiness against numerous physical system conditions.

Maximum negative entropy deconvolution and its application to bearing condition monitoring

Blind deconvolution (BD) has proven to be an effective approach to detecting repetitive transients caused by bearing faults. However, BD suffers from instability issues including excessive sensitivity of kurtosis-guided BD methods to the single impulse and high computational time cost of the eigenvector algorithm-aided BD methods. To address these critical issues, this paper proposed a novel BD method maximizing negative entropy (NE), shortened as maximum negative entropy deconvolution (MNED). MNED utilizes NE instead of kurtosis as the optimization metric and optimizes the filter coefficients through the objective function method. The effectiveness of MNED in enhancing repetitive transients is illustrated through a simulation case and two experimental cases. A quantitative comparison with three existing BD methods demonstrates the advantages of MNED in fault detection and computational efficiency. In addition, the performance of the four methods under different filter lengths and external shocks is compared. MNED exhibits lower sensitivity to random impulse noise than the kurtosis-guided BD methods and higher computational efficiency than the BD methods based on the eigenvalue algorithm. The simulation and experimental results demonstrate that MNED is a robust and cost-effective method for bearing fault diagnosis and condition monitoring.

Graph Diffusion Kernel Maximum Correntropy Criterion Over Sensor Network and Its Performance Analysis

In recent years, graph signal processing has attracted much attention due to its ability to model irregular and interactive data generated by wireless sensor networks (WSNs). However, there is no practical method to deal with the problem of modeling nonlinear systems in non-Gaussian noise environments. Given that the maximum correntropy criterion (MCC) exhibits robustness to impulsive and non-Gaussian noise, this paper introduces it into the graph kernel adaptive filtering algorithm and develops a graph diffusion kernel MCC (GDKMCC) algorithm. To suppress the problem of the infinite growth of the filter coefficient vector of the proposed algorithm, a pre-trained dictionary (PD) method is used in this paper. In addition, the mean-square transient behavior and the convergence condition of the GDKMCC-PD algorithm are also provided under some assumptions. Finally, the simulation results verify the superiority of the proposed algorithm in modeling nonlinear systems under non-Gaussian noise environments and the correctness of the theoretical model.

Identifiying the PID-N method performance for speed control of BLDC motor propeller on catamaran ships model

Some archipelagic countries must have wide sea. This condition means that there is transportation of goods and people problem. Therefore, an effort has been made to explore renewable energy especially solar energy. In this research, a BLDC (Brush Less Direct Current) motor propeller is applied, and PID-N (Proportional, Integral, and Derivative – Filter Coefficient) method for Catamaran ship speed control. Where PID-N has additional filters such as P, I, and D; however, the advanced performance of this control uses RLS (Recursive Least Square) actuator modeling is the most systematical and flexible design. The experiment includes the speed response of ships with payload and without payload, as well as changes in speed settings. The results show that the ship's speed control using the PID-N has smooth and definite speed control. The PID-N design is systematical, easy and quick for the control designer to change the actuator or sensor performance, if there will be any actuator or sensor replacement with different specifications, which is required in commercial. As it is known, in commercial design, time and method are crucial and has to be effective. The PID-N control method shows its superiority. In detail, the Catamaran speed setting is incremented from 0.4 m/s to 0.6 m/s without payload, the average settling time is 6 seconds with the average error speed 0.071 m/s in the set speed 0.6 m/s. For setting speed increased by 0.4 m/s to 0.6 m/s with payload, settling time gets longer, 7 to 13 seconds with the average error speed 0.01 m/s in the set speed 0.06 m/s. It performs small errors and fasts settling time. The ship model has experimented on an artificial pond, it is related to the proportional small sea wave. The research also shown the ratio of electric power consume toward speed setting is in exponential formula

Hybird RNN based feature extraction for early prediction of CVDs using ECG Signals for type 2 diabetic patients

Diabetes mellitus patients are at an increased risk of cardiovascular illness, and cardiovascular complications are the primary cause of morbidity. Diabetes is linked to both morbidity and mortality. Type-2 Diabetes causes a prothrombotic state that leads to acute coronary syndromes by causing endothelial damage and lowering antiaggregant factors like nitric oxide and prostacyclin, as well as increasing thrombotic substances like fibrinogen and factor VII, and suppressing fibrinolysis with factors like plasminogen activator inhibitors. The accurate identification and diagnosis of CVD (Cardio Vascular Disease) is dependent on the correct detection of the ECG signal from the heart. The ECG signal is extremely important in the early detection of cardiac problems. The ECG signal of diabetic individuals offers vital information about the heart and is one of the most important diagnostic tools used by doctors to identify cardiovascular disorders. The time gap between two consecutive QRS complexes appearing contiguous in an ECG is known as heart rate. The most appealing feature is that HRV (Heart Rate Variability) measurement is non-invasive and repeatable. A number of machine learning techniques have been proposed for the non-invasive automated identification of diabetes. This paper discusses innovative methods for analyzing electrocardiogram (ECG) signals in order to extract important diagnostic information. The ECG signal is first treated using a dual tree complex wavelet transform (DTCWT-SG) with threshold method. Subsequently, the features are extracted from detailed coefficients of DTCWT-SG filter, Eigen vectors by minimum normalization method and Rajan Transform. Main key features are extracted using these three methods. These features are classified and analyzed by different machine learning classifiers. The proposed approach was tested on DICARDIA, MIT-BIH and Physionet database and the performance analysis shows that the hybrid Recurrent Neural Network (RNN) (LSTM+GRU Gated Recurrent Units) achieves better prediction of 98.8% compared to state of art techniques.

A Digital Frequency-Dependent I/Q Imbalance and Group Delay Estimation and Compensation Modules for mmWave Single Carrier Baseband Transceivers

This paper proposes the estimation and compensation modules of digital frequency-dependent (FD) I/Q imbalance and group delay for single carrier (SC) mode with 15 Gbps throughput rate in the millimeter-wave (mmWave) band. The proposed compensation module of a digital recursive filter can mitigate FD I/Q imbalance effects caused by the -3 dB corner imbalance of the reconstruction and anti-aliasing filters of I/Q branches. The proposed recursive filter with the appropriate initial coefficients based on the result of Golay sequence estimation can shorten the convergence time and precisely acquire the desired coefficients. The average image rejection ratio (IRR) performance after compensating by the proposed module can be improved from 21.97 dB to 47.54 dB. Besides, the group delay distortion in reconstruction and anti-aliasing filters will cause intersymbol interference to distort the in-band signal. We employ a digital equalization filter with the filter coefficients derived from the correlation results based on the Golay sequence to compensate the in-band group delay distortion. After the proposed group delay is pre-compensation at TX, the system-level performance of error vector magnitude (EVM) performance is improved by 6.21 dB to -27.78 dB to satisfy the IEEE 802.11ad/ay requirement in 64-QAM mode. The inner RX BER performance with the FD I/Q imbalance and the group delay compensation modules can achieve 3 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> at 23 dB and also satisfy the system requirement at SNR of required 25.52 dB. A four-times parallelism architecture is proposed to work at a clock rate of 625 MHz with 64-QAM mode. The gate count and power consumption of the proposed design are 154.4 K and 16.2 mW, respectively.

Hybrid Protection of Digital FIR Filters

A digital finite impulse response (FIR) filter is a ubiquitous block in digital signal processing applications and its behavior is determined by its coefficients. To protect filter coefficients from an adversary, efficient obfuscation techniques have been proposed, either by hiding them behind decoys or replacing them by key bits. In this article, we initially introduce a query attack that can discover the secret key of such obfuscated FIR filters, which could not be broken by the existing prominent attacks. Then, we propose a first of its kind hybrid technique, including both hardware obfuscation and logic locking using a point function for the protection of parallel direct and transposed forms of digital FIR filters. Experimental results show that the hybrid protection technique can lead to FIR filters with higher security while maintaining the hardware complexity competitive or superior to those locked by prominent logic locking methods. It is also shown that the protected multiplier blocks and FIR filters are resilient to existing attacks. The results on different forms and realizations of FIR filters show that the parallel direct form FIR filter has a promising potential for a secure design.

An Efficient Waveform Reconstruction Method for Digital Bandwidth Interleaving Sampling System

Digital bandwidth interleaving (DBI) technology is a method that can simultaneously improve the bandwidth and sampling rate of a sampling system. However, its waveform reconstruction algorithm is very complex, and the realization of real-time waveform reconstruction requires a great number of system parallel computing resources. This paper proposes a frequency-domain waveform reconstruction method for a DBI sampling system to improve the waveform reconstruction efficiency. This method completes digital mixing, filtering, and corrections in the frequency domain, and recovers the waveform from the discrete frequency domain information. This method takes advantage of the computational efficiency advantages of the frequency-domain filtering method compared to the time domain filtering. And it integrates multiple time-domain filter coefficients in the DBI system into a frequency response coefficient, so that one frequency-domain filtering is equivalent to multiple time-domain filtering operations. This paper also introduces a polyphase frequency-domain filtering structure, which has great advantages in computational complexity compared to the classical time-domain parallel polyphase filtering structure. To verify the effeciency of the method, an evaluation platform with 8 GHz bandwidth and 20 GSPS sampling rate is implemented to illustrate its performance. Both the classical time domain method and the proposed frequency domain method are verified in MATLAB and are compared for performance and resource consumption in FPGA. Experimental results show that the proposed method saves 10% look up tables (LUTs), 26% flip-flops(FFs), and 47% digital signal processing (DSP) slices compared to the classical time-domain method. Under the condition that the word length is less than 6 bits, the proposed method improves SFDR by more than 5 dB and SNR by more than 10 dB compared with the classical method.

Attention guided learnable time-domain filterbanks for speech depression detection

Depression, as a global mental health problem, is lacking effective screening methods that can help with early detection and treatment. This paper aims to facilitate the large-scale screening of depression by focusing on the speech depression detection (SDD) task. Currently, direct modeling on the raw signal yields a large number of parameters, and the existing deep learning-based SDD models mainly use the fixed Mel-scale spectral features as input. However, these features are not designed for depression detection, and the manual settings limit the exploration of fine-grained feature representations. In this paper, we learn the effective representations of the raw signals from an interpretable perspective. Specifically, we present a joint learning framework with attention-guided learnable time-domain filterbanks for depression classification (DALF), which collaborates with the depression filterbanks features learning (DFBL) module and multi-scale spectral attention learning (MSSA) module. DFBL is capable of producing biologically meaningful acoustic features by employing learnable time-domain filters, and MSSA is used to guide the learnable filters to better retain the useful frequency sub-bands. We collect a new dataset, the Neutral Reading-based Audio Corpus (NRAC), to facilitate the research in depression analysis, and we evaluate the performance of DALF on the NRAC and the public DAIC-woz datasets. The experimental results demonstrate that our method outperforms the state-of-the-art SDD methods with an F1 of 78.4% on the DAIC-woz dataset. In particular, DALF achieves F1 scores of 87.3% and 81.7% on two parts of the NRAC dataset. By analyzing the filter coefficients, we find that the most important frequency range identified by our method is 600–700Hz, which corresponds to the Mandarin vowels /e/ and /eˆ/ and can be considered as an effective biomarker for the SDD task. Taken together, our DALF model provides a promising approach to depression detection.

An adaptive constrained multi-channel active noise control filter design approach using convex cone optimization

In current practical active noise control (ANC) applications, solutions to three main technical challenges need to be sought: the requirement of a stable controller when acoustic feedback paths exist, a larger quiet zone, and adaptation to a time-varying environment. The imposed controller stability constraints can significantly increase the computational requirements and negatively impact the adaptation of ANC filters in real time. Multi-channel systems are usually needed if a larger quiet zone is required while the coupling between different channels can greatly lower the convergence rate of adaptive algorithms. In the current work, a multi-channel adaptive constrained ANC filter design method is proposed. The recently proposed convex cone formulation for ANC design is adopted to reduce the computational time of solving the constrained ANC filter design problem. The optimal filter coefficients are then redesigned continuously using convex optimization when the environment is time-varying. The experimental result shows that, compared with the traditional leaky FxLMS method, the proposed method has a faster convergence rate and a better steady-state noise control performance.

High Performance FPGA Implementation of Single MAC Adaptive Filter for Independent Component Analysis

Blind source separation (BSS) is the process of extracting sources from mixed data without or with limited awareness of the sources. This paper uses field programmable gate array (FPGA) to create an effective version of the Blind source separation algorithm (ICA) with a single Multiply Accumulate (MAC) adaptive filter and to optimize it. Recently, space research has paid a lot of attention to this technique. We address this problem in two sections. The first approach is ICA, which seeks a linear revolution that can enhance the mutual independence of the mixture to distinguish the source signals from mixed signals. The second is a powerful flexible finite impulse response (FIR) filter construction that makes use of a MAC core and is adaptable. The adjustable coefficient filters have been used in the proposed study to determine the undiscovered system utilizing an optimal least mean square (LMS) technique. The filter tap under consideration in this paper includes 32 taps, and hardware description language (HDL) and FPGA devices were used to carry out the analysis and synthesis of it. When compared to the described architecture, the executed filter architecture uses 80% fewer resources and increases clock frequency by nearly five times, and speed is increased up to 32%.

Mitigation of AGC Problem of the RES Integrated Hydro-Thermal System Using FACTS and INEC Based AHVDC with ESS Considering the 3DOF-TIDN Controller

This article highlights the mitigation of automatic generation control (AGC) problem considering various FACTS devices and inertia emulation control (INEC) strategies based accurate model of HVDC links using various energy storage systems (ESS). The proposed system comprises a three-area hydro-thermal system integrated with renewable energy sources (RES) such as a realistic dish-Stirling solar thermal system (RDSTS) and a precise wind turbine system (PWTS). A maiden effort has been made to design a novel secondary controller called three degrees of freedom tilt-integral-derivative with filter coefficient (3DOF-TIDN) for the proposed system, and their parameters are successfully optimized by the bird swarm algorithm (BSA). The comparative analysis of system dynamics corresponding to the proposed 3DOF-TIDN controller exhibits betters dynamics over 3DOF-PID and 2DOF-PID controllers. The system dynamic performances are also evaluated considering RDSTS and PWTS in all the areas, and it has been evident that the incorporation of RDSTS and PWTS improves system dynamics. The system with AHVDC link shows quickly damped out the system oscillation. Moreover, the study on inertia emulation using various ESS such as redox flow battery (RFB), ultra-capacitor (UC) and battery energy storage (BES) systems inferred that the system’s initial response with RFB is better than others. Furthermore, the integration of various FACTS devices such as thyristor control phase shifter (TCPS), static synchronous series capacitor (SSSC) and Gate-Controlled Series Capacitor (GCSC) revealed that the system dynamics marginally enhanced with GCSC than TCPS and SSSC.

Application of a coarse-to-fine minimum entropy deconvolution method for rotating machines fault detection

Due to the severe working condition and long-term service, the key rotating parts including the bearing and gearbox, are susceptible to damage. Blind deconvolution which can eliminate the influence of the transfer path and enhance the fault-related feature is widely used for machinery fault diagnosis. Not only the objective function but the initial filter can influence the convergence properties of the deconvolution algorithm. For example, in practical application, the classical MED trends to converge to fault-unrelated outliers if the initial filter coefficients are not appropriate. Motivated by this, the effect of initialization on the convergence of MED is firstly investigated. Subsequently, a coarse-to-fine MED (CFMED) is designed to highlight the fault-induced repetitive impulses. Specifically, the procedure of pre-iterating is used to search for the convergence direction and coarsely update the initial filter. Then, the dropout step is introduced to drop out the redundant candidates and reserve the expected solution by quantitatively measuring the sparsity of all possible solutions in the frequency domain. Meanwhile, CFMED is more robust to filter size than MED. Finally, the simulation and experimental data with bearing and planetary gearbox fault verify CFMED is more suitable for the fault diagnosis of rotating machines compared with original MED.

Admittance Remodeling Strategy of Grid-Connected Inverter Based on Improving GVF

With the continuous enhancement of weak grid characteristics, the negative effects of grid voltage feedforward (GVF) and PLL on grid-connected inverters become more and more serious and are coupled. Therefore, it is difficult to effectively solve the system stability problem by only improving the PLL structure. Firstly, based on an improved phase-locked loop structure (CCF-PLL) with complex coefficient filter and considering the influence of GVF, the output admittance model of a grid-connected inverter is established. Through stability analysis, it is found that conventional GVF leads the total output admittance phase of the inverter, thus reducing the system stability margin under the weak grid. Then, an improved admittance remodeling strategy of the grid-connected inverter is proposed. An all-pass filter is introduced into the GVF loop to correct the phase of the total output admittance of the inverter, and the phase margin is used as the constraint to design the control parameters, which effectively improves the stability of the system under the weak grid. Finally, the simulation results show that, compared with traditional GVF, the proposed strategy can obviously improve the distortion of grid-connected current waveforms and improve system stability.

Assessment of stability of distributed FxLMS active noise control systems

This paper addresses the assessment of the stability of distributed active noise control (ANC) systems, which are designed to cancel acoustic noise at given points in space. These systems distribute the control task across several simple acoustic nodes that generate the control signals by filtering a noise reference signal. The coefficients of each node filter are iteratively calculated by the filtered-X LMS algorithm. The nodes remain stable when the adaptive filters computed in each node converge to finite values. However, the acoustic coupling among nodes can cause instability (i.e., divergence). Collaboration among nodes is required to avoid this phenomenon. It is shown that the properties of the system are summarized in a system matrix and that the system remains stable when the real parts of all of the eigenvalues of this system matrix are positive. However, computation of all of the eigenvalues is computationally expensive. In this paper, we propose a fast method for checking the positiveness of the real parts of the system matrix eigenvalues. It is shown that the proposed method is faster than the direct calculation of eigenvalues and it assesses the stability/instability of the ANC systems without any false stability outcomes and more accurately than existing alternatives.