Finite Impulse Response Filter Research Articles

Five-Axis Trajectory Generation Considering Synchronization and Nonlinear Interpolation Errors

Abstract This paper presents a novel real-time interpolation technique for five-axis machine tools to attain higher speed and accuracy. To realize computationally efficient real-time interpolation of 6-DOF tool motion, a joint workpiece–machine coordinate system interpolation scheme is proposed. Cartesian motion of the tool center point (TCP) is interpolated in the workpiece coordinate system (WCS), whereas tool orientation is interpolated in the machine coordinate system (MCS) based on the finite impulse response filtering. Such an approach provides several advantages: (i) it eliminates the need for complex real-time spherical interpolation techniques, (ii) facilitates efficient use of slower rotary drive kinematics to compensate for the dynamic mismatch between Cartesian and rotary axes and achieve higher tool acceleration, and (iii) mitigates feed fluctuations while interpolating near kinematic singularities. To take advantage of such benefits and realize accurate joint WCS–MCS interpolation scheme, tool orientation interpolation errors are analyzed. A novel approach is developed to adaptively discretize long linear tool moves and confine interpolation errors within user set tolerances. Synchronization errors between TCP and tool orientation are also characterized, and peak synchronization error level is determined to guide the interpolation parameter selection. Finally, blending errors during non-stop continuous interpolation of linear toolpaths are modeled and confined. Advantages of the proposed interpolation scheme are demonstrated through simulation studies and validated experimentally. Overall, proposed technique can improve cycle times up to 10% while providing smooth and accurate non-stop real-time interpolation of tool motion in five-axis machining.

Online interpolation of 5-axis machining toolpaths with global blending

Modern high-speed 5-axis machining toolpaths consist of densely discretized tool center points (TCP) and tool orientation vectors (TOV). This paper presents a global interpolation technique that can blend very densely discretized 5-axis toolpaths within user defined blending tolerance and interpolate them in real-time using minimal computational expense. Instead of geometric splines, Finite Impulse Response (FIR) low-pass filtering is used to blend TCP and TOV and interpolate them smoothly for synchronized 5-axis motion generation. Global blending errors of TCP and TOV are modeled based on path geometry, tool motion kinematics and dynamics of the FIR filter. These global blending errors are confined by modulating the machining feedrate. In other words, machining feedrate is adjusted, i.e. reduced and increased, to control the global blending accuracy along the 5-axis machining toolpath. A windowing scheme is proposed to solve this new feedrate scheduling problem. Since the proposed interpolation method is based on digital filtering (convolution), it enables online (real-time) interpolation of densely discretized 5-axis machining toolpaths in a real-time and at the same time provides control over the frequency spectrum of interpolated trajectories. The proposed global blending strategy delivers significant reduction in machining cycle-time as compared to conventional local blending techniques and generates smoother, frequency-controlled trajectories with a fraction of the computational effort required as compared to spline-based interpolation. Illustrative examples and experimental results are provided to showcase functionality and validate effectiveness of the proposed technique.

Cooperative fault estimation for a class of heterogeneous multi‐agents with stochastic nonlinearities based on finite impulse response filter

AbstractThis article investigates the cooperative fault estimation problem for a class of heterogeneous multi‐agent systems, in which the agent dynamics are governed by linear discrete time‐varying systems with nonidentical dimensions subject to stochastic nonlinearities. A finite impulse response (FIR) filter based fault estimation scheme is developed via relative outputs to estimate the possible faults of the local and neighboring agents simultaneously. An analytical redundancy expressed in terms of all the states in the previous time window is originally established for deriving the fault estimation signal. The prior variance information coupled with fault estimation error in nonlinear form, is fully considered to design performance index through analysis of random matrix inequality. The optimal FIR filter gain is analytically obtained with computational efficiency by searching the minimum point of the relevant matrix trace function. Illustrative examples are finally provided to demonstrate the effectiveness and advantages of the developed results.

Bounding Box Stabilization for Visual Object Tracking Using Kalman and FIR Filters

In visual object tracking, the estimation of the trajectory of a moving object is a widely studied problem. In the object tracking process, there are usually variations between the real position of the objet in the scene and the estimated position, that is, the object is not exactly followed throughout its trajectory. These variations can be considered as color measurement noise (CMN) caused by the object and the camera frame movement. In this paper, we treat such differences as Gauss-Markov coloring measurement noise. We use Finite Impulse Response filters and Kalman filter with a recursive strategy in tracking: predict and update. To demonstrate the best performance, tests were carried out with simulated trajectories and with benchmarks from a database available online. The OUFIR and UFIR algorithms showed favorable results with high precision and accuracy in the object tracking task.

Finite impulse response filter based fault estimation with computational efficiency for linear discrete time-varying systems subject to multiplicative noise

The robust fault estimation problem for linear discrete time-varying (LDTV) systems subject to multiplicative noise is investigated by means of finite impulse response (FIR) filter. A novel analytical redundancy, expressed via all states of the previous time window, is originally established to construct the fault estimator. To ensure the satisfactory fault estimation accuracy in stochastic sense under the interference of random uncertainty, a new performance index in forms of matrix trace function is proposed. An easy-to-check necessary and sufficient condition is presented to obtain the optimal filter gain via minimizing the performance index at each time instant. It is analytically demonstrated that, the newly proposed fault estimation algorithm enjoys obvious computational advantages in updating the filter gain, especially as the length of the time window increases for time-varying systems. Simulation results are finally provided to verify its feasibility and superiority.

Frobenius Norm-Based Unbiased Finite Impulse Response Fusion Filtering for Wireless Sensor Networks

This article presents a new approach to designing the Frobenius norm-based weighted unbiased finite impulse response (FIR) fusion filter for wireless sensor networks. The weighted Frobenius norm is employed as a cost function to design a local unbiased FIR filter. The design problem is converted into a constrained optimization problem subject to an equality constraint. The Lagrange multiplier method is used to derive the local FIR filter gain. An alternative Frobenius norm is introduced to determine weights for the local unbiased FIR filters in the design of a global fusion FIR filter. The developed FIR fusion filter is demonstrated to have higher robustness against uncertainties than Kalman filter-based methods, such as the optimal fusion Kalman filter, distributed Kalman filter, and distributed weighted Kalman filter, through a numerical example of moving-target tracking employing seven smart sensors and an experiment with temperature and humidity estimation using eight sensors.

Knowledge-driven feature component interpretable network for motor imagery classification

Objective. The end-to-end convolutional neural network (CNN) has achieved great success in motor imagery (MI) classification without a manual feature design. However, all the existing deep network solutions are purely datadriven and lack interpretability, which makes it impossible to discover insightful knowledge from the learned features, not to mention to design specific network structures. The heavy computational cost of CNN also makes it challenging for real-time application along with high classification performance. Approach. To address these problems, a novel knowledge-driven feature component interpretable network (KFCNet) is proposed, which combines spatial and temporal convolution in analogy to independent component analysis and a power spectrum pipeline. Prior frequency band knowledge of sensory-motor rhythms has been formulated as band-pass linear-phase digital finite impulse response filters to initialize the temporal convolution kernels to enable the knowledge-driven mechanism. To avoid signal distortion and achieve a linear phase and unimodality of filters, a symmetry loss is proposed, which is used in combination with the cross-entropy classification loss for training. Besides the general prior knowledge, subject-specific time-frequency property of event-related desynchronization and synchronization has been employed to construct and initialize the network with significantly fewer parameters. Main results. Comparison of experiments on two public datasets has been performed. Interpretable feature components could be observed in the trained model. The physically meaningful observation could efficiently assist the design of the network structure. Excellent classification performance on MI has been obtained. Significance. The performance of KFCNet is comparable to the state-of-the-art methods but with much fewer parameters and makes real-time applications possible.

Hybrid Interacting Multiple Model Filtering for Improving the Reliability of Radar-Based Forward Collision Warning Systems.

Automotive forward collision warning (FCW) systems based on radar sensors attracted widespread attention in recent years. To achieve a reliable FCW, it is essential to accurately estimate the position and velocity of a preceding vehicle. To this end, this study proposed a novel estimation algorithm, which is a hybrid of interacting multiple model probabilistic data association (IMM-PDA) and finite impulse response (FIR) filters. Although the IMM-PDA filter is one of the most successful algorithm for tracking a maneuvering target in clutters, it sometimes exhibits divergence owing to modeling errors. In this study, the divergent IMM-PDA filter in the novel algorithm was reset and recovered using an assisting FIR filter. Consequently, this enabled reliable estimation for FCW. The improved reliability of the proposed algorithm was demonstrated through the simulation of preceding vehicle tracking using automotive radars.

An 11.8-fJ/Conversion-Step Noise Shaping SAR ADC with Embedded Passive Gain for Energy-Efficient IoT Sensors.

Herein, we present a noise shaping successive-approximation-register (SAR) analog-to-digital converter (ADC) with an embedded passive gain multiplication technique. The noise shaping moves the in-band quantization noise from the signal band to out-of-band for improved signal-to-noise ratio (SNR). The proposed approach tackles the drawback of the previous active noise shaping (increased power and extra noise) and passive noise shaping (limited noise suppression and signal loss). Both noise shaping and gain multiplication are realized on-chip in an energy-efficient manner without an opamp. This approach uses only capacitors and switches in the finite impulse response (FIR) and infinite impulse response (IIR) filters. A comparator suppressing kickback noise is presented to handle the tradeoff between noise suppression and the filter capacitor size. The energy-efficient merged-capacitor switching (MCS) technique is effectively combined with rail-to-rail swing comparator and thermometer-coded capacitor array, which reduces the settling error in the digital to analog converter (DAC). The process-induced mismatch effect in the capacitive DAC is investigated using a behavioral model of the ADC. Additionally, we propose dynamic element matching (DEM) for the thermometer-coded capacitor array. The ADC is fabricated using a 0.18 μm CMOS process in an area of 0.26 mm2. Consuming 4.1 μW, the ADC achieves a signal-to-noise and distortion ratio (SNDR) of 66.5 dB and a spurious-free dynamic range (SFDR) of 79.1 dB. The figure-of-merit (FoM) of the ADC is 11.8 fJ/conversion-step.

FNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees.

Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis.

A novel filter-bank architecture of 2D-FIR symmetry filters using LUT based multipliers

In this paper, a novel block-based generic filter bank architecture is proposed for various Two Dimensional (2D) symmetry Finite Impulse Response (FIR) filers. It is implemented to prune the area, storage memory, and power consumption of the filter bank. Four 2D symmetrical FIR filter architectures are implemented and explored along with a general filter (without symmetry). The symmetry in coefficients of the filter reduces the number of multiplier blocks (most power-hunger blocks) required for the structure. Hence, a major part of the total power consumption is decreased. Block processing is introduced to improve the throughput of the filter bank. The memory sharing and memory reuse concepts are incorporated to reduce the storage memory of the entire filter bank. Finally, the conventional multiplication process is replaced with memory-based dual-port Look-Up-Table (LUT)-Distributed Arithmetic (DA) multipliers to minimize the power and area of each sub-filter of the filter bank. A generic filter bank architecture is implemented and synthesized in CMOS 45 nm generic library using Genus tools and physical design is carried out by Innovus tools from Cadence. The final layout of the design is generated using proper floor planning, placement, and routing in physical design. The results are compared with the state-of-art works of 2D FIR filters. The significantly improved results are reported in terms of area, power, delay, and memory.

Finite Impulse Response Filter-Based Track Formation for Preceding Vehicle Tracking Using Automotive Radars.

Automotive radars, which are used for preceding vehicle tracking, have attracted significant attention in recent years. However, the false measurements that occur in cluttered roadways hinders the tracking process in vehicles; thus, it is essential to develop automotive radar systems that are robust against false measurements. This study proposed a novel track formation algorithm to initialize the preceding vehicle tracking in automotive radar systems. The proposed algorithm is based on finite impulse response filtering, and exhibited significantly higher accuracy in highly cluttered environments than a conventional track formation algorithm. The excellent performance of the proposed algorithm was demonstrated using extensive simulations under real conditions.

Design of variable fractional delay FIR filter using the high-order derivative sampling method

In this study, a design algorithm for a variable fractional delay finite impulse response (FIR) filter based on piecewise osculating interpolation (POI) is proposed. The proposed algorithm can use higher-order derivative information of the input sampling sequence. To improve the performance of the proposed POI algorithm at high frequencies, two frequency-domain optimization algorithms (including derivative weighted least-squares and derivative minimax algorithm) are proposed. Meanwhile, a one-by-one increase scheme is introduced for optimizing the polynomial orders, such that a given bound is satisfied exactly. Additionally, through derivation of an analytical formula, a two-level Farrow structure is developed to implement the proposed algorithms. As shown, the implementation structure has fixed coefficients and only requires multiplication and addition operations. The proposed algorithms are validated and compared with existing algorithms, and it is shown that the proposed algorithms outperform existing algorithms.

Multirate Adaptive Equalization

Finite Impulse Response (FIR) filter model emulates the Inter Symbol Interference (ISI) in a wireless communication channel. An equalizer, typically an Infinite Impulse Response (IIR) filter, behaves as an inverse filter to the FIR filter to remove the effects of the ISI. IIR filters are generally avoided due to tractability issues, and an FIR filter, with an adaptive signal processing algorithm to minimize the error due to the ISI, is deployed at the receiver. However, the filter is observed to quickly reach a steady state where further iterations do not yield a reduction in the error. This can be attributed to relatively slow variations in the steady state error which prevent further reduction of the errors. This work focuses on converting the low frequency error variations to high frequency variations by the use of multirate signal processing. As such, the steady state error can be damped as well, providing further reduction in the error and an enhanced adaptive filter performance.

Low Area and Power-Efficient FPGA Implementation of Improved AM-CSA-IIR Filter Design for the DSP Application

Digital Signal Processing (DSP) grew enormously in the past few decades and it was extensively practiced for numerous engineering applications like biomedical signal processing, radar signal processing, adaptive antenna design, intelligent control, etc. In DSP, the Finite Impulse Response (FIR) filter and Infinite Impulse Response (IIR) filter plays a vital role in the design of a complex signal processing system. Generally, The FIR filters are stable, linear in phase, with fewer finite-precision errors and easy to implement. However, most of the IIR filters are used in signal processing applications due to the less computational complexity which is compared to the FIR filters. Moreover, the IIR filter requires only fewer filter coefficients and requires only less amount storage registers. In this research, an IIR filter is implemented with Array multiplier and Carry Skip Adder (CSA) to improve hardware utilization. The IIR filter input is generated randomly which is stored in Random Access Memory (RAM). Similarly, the coefficient is generated by utilizing the Parks-McClellan algorithm where the generated value is stored in Read Only Memory (ROM). The proposed AM-CSA IIR (Array Multiplier-Carry Skip Adder Infinite Impulse Response) filter performances are evaluated in terms of filter output, Input Output Block (IOB), Look Up Table (LUT), Flip Flops (FF), slices, delay, and fractional delay. Overall the proposed design attained LUT= 115 (0.0564%), FF=40 (0.01%) and IOB= 22 (3.77%) and reduced fractional delay of about 0.0057ns.

ANAUDIO DENOISING APPROACH USING HYBRID MODIFIED FAST WAVELET TRANSFORM METHOD

Noise includes all unwelcome ambient sounds. If a sound is perceived as delightful music or annoying noise, it will be perceived as having the same decibel level. The fundamental drawback of noise in an audio signal is that it degrades the signal's quality during transmission over the communication system. Real-time audio signals are gathered for the present study using a microphone. The additive white Gaussian noise of AWGN is blended with a genuine audio source. The Median, Finite Impulse Response filter with Wavelet Transform approach is a hybrid filter for denoising audio signals that incorporates the Median Filter, FIR Filter, and Wavelet Transform techniques (MFWT). In this study Peak Signal to Noise Ratio and Mean Square Error (MSE) are used to assess MFWT performance(PSNR). In terms of SNR (82.04) and RMSE score, MFWT performs better than the median filter, FIR filter, and Wavelet transform (0.055).

Low energy, long sustainable and high-speed FIR filter based on truncated multiplier with SCG-HSCG adder

In advancement technology and applications like processing sound signals, processing image signals, software define radio signals and so on. In processing of digital systems in all type of devices may be wide range of contraptions and clamors. The result of design the all the athematic devices and performing the addition and multiplication operations need larger space in very large scale implementation. Here, the latest proposed methodology progressed in the design of Finite impulse response filter by redesign the multipliers and adders to introduce new unsigned and signed truncated multiplier and new SCG-HSCG adders. Also different III-Vth semiconductor materials can be used to get high speed filtering operation. In this way to decreases the partials part reduction of design truncated multiplier have more number of adders. The new design handles to replace the SCG-HSCG Adders. This design decreases the logic gates in operations of athematic, additions and multiplications. It also gives the more efficient in the sign and unsigned designs of different digital signal processing applications. This proposed new finite impulse response filter has designed with help of VHDL and Xilinx FPGA-S6LX9. The efficiency of proposed filter with truncated multiplier and SCG-HSCG adder, is compare with the existing all CSLA – Carry Select adder in terms of area, delay, and power. The novel FIR filter design reduces power distribution to the environment while increasing hardware device performance. Furthermore, these kinds of designs take up less space, which means they use less energy and last longer.

Measurement Matrix Design for Sample-Efficient Binary Compressed Sensing

This paper investigates the problem of recovering a binary-valued signal from compressed measurements of its convolution with a known finite impulse response filter. We show that it is possible to attain optimum sample complexity for exact recovery (in absence of noise) with a computationally efficient algorithm. We achieve this by adopting an algorithm-measurement co-design strategy where the measurement matrix is designed as a function of the filter, such that the recovery of binary signals with arbitrary sparsity is possible by using a sequential decoding algorithm. Such a filter-dependent sampler design can overcome the computational challenges associated with enforcing binary constraints, and enable us to operate in “extreme compression” regimes, where the number of measurements can be much smaller than the sparsity level.

On-The-Fly CNC interpolation using frequency-domain FFT-based filtering

Finite Impulse Response filtering is increasingly becoming the interpolation method of choice in modern computer numerically controlled (CNC) machining centres. Recently, On-The-Fly (OTF) interpolation was introduced using direct convolution to adaptively change the FIR flter time constant throughout the toolpath. In this paper, it is shown that computational advantages can be gained by using frequency domain methods instead. This paper introduces a novel on-the-fly CNC interpolation method using Fast Fourier Transforms (FFTs). The presented OTF FFT method demonstrates up to 10 times increase in computational speed than the direct convolution OTF method. The effectiveness of the proposed method is validated in simulation based case studies.

Iterative Greedy LMI for Sparse Control

In this letter, we propose a novel method to find matrices that satisfy sparsity and LMI (linear matrix inequality) constraints at the same time. This problem appears in sparse control design such as sparse representation of the state feedback gain, sparse graph representation with fastest mixing, and sparse FIR (finite impulse response) filter design, to name a few. We propose an efficient algorithm for the solution based on Dykstra's projection algorithm. We then prove a convergence theorem of the proposed algorithm, and show some control examples to illustrate merits and demerits of the proposed method.