Finite Impulse Response Filter Research Articles

Denoising ECG Signals using Weighted Iterative UFIR Filtering

The electrocardiogram (ECG) holds paramount importance in diagnosing heart disease, and as it persists leading cause of global mortality. Over the past decades, diverse techniques have emerged for processing ECG signals, with denoising taking a prominent role in enhancing feature extraction. Nonetheless, achieving heightened accuracy remains an enduring challenge. In this study, we introduce an innovative approach involving the application of a weighted unbiased finite impulse response (UFIR) filter. Under the same noise conditions and in terms of the root mean square error (RMSE) and signal-to-noise ratio (SNR), our proposed method showcases worthy performance in comparison to the weighted Savitzky-Golay (SG) filter. This research contributes to the progressive evolution of ECG signal processing, offering the potential for more precise and dependable detection of cardiac diseases.

Implementation of a Finite Impulse Response Filter using PUFs to Avoid Trojans

In the modern era of signal processing, digital filters play an important role in real-time applications such as communication, consumer electronics, digital signal processing, audio, etc. In digital filter design, Finite Impulse Response (FIR) filters are highly preferable due to their linear phase and inherent stability. These filters benefit from being time-invariant and simple to implement with minimal computational requirements. Therefore, the hardware security of FIR filters is essential for good performance and reliable results. On the other hand, there is the possibility of hardware threats, such as tampering, reverse engineering, hardware Trojans, etc., as the design of an FIR filter involves many stages. Such hardware attacks on FIR filters can cause several problems, including performance degradation, leakage of confidential information, lack of stability, etc. This study presents the design and implementation of a Trojan-aware FIR filter using Physical Unclonable Functions (PUFs). The key feature of PUFs is that they generate a unique and unpredictable response for each given challenge. In the proposed design, PUFs were used to generate the FIR filter coefficients that are unique and unpredictable by attackers/trojans to improve security. The security of FIR with PUF was tested using ML-based challenges, and the results showed approximately 30% more reliability and consistency compared to the FIR without PUFs.

AirNN: Over-the-Air Computation for Neural Networks via Reconfigurable Intelligent Surfaces

Over-the-air analog computation allows offloading computation to the wireless environment through carefully constructed transmitted signals. In this paper, we design and implement the first-of-its-kind convolution that uses over-the-air computation and demonstrate it for inference tasks in a convolutional neural network (CNN). We engineer the ambient wireless propagation environment through reconfigurable intelligent surfaces (RIS) to design such an architecture, which we call ’AirNN’. AirNN leverages the physics of wave reflection to represent a digital convolution, an essential part of a CNN architecture, in the analog domain. In contrast to classical communication, where the receiver must <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">react</i> to the channel-induced transformation, generally represented as finite impulse response (FIR) filter, AirNN <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">proactively</i> creates the signal reflections to emulate specific FIR filters through RIS. AirNN involves two steps: first, the weights of the neurons in the CNN are drawn from a finite set of channel impulse responses (CIR) that correspond to realizable FIR filters. Second, each CIR is engineered through RIS, and reflected signals combine at the receiver to determine the output of the convolution. This paper presents a proof-of-concept of AirNN by experimentally demonstrating convolutions with over-the-air computation. We then validate the entire resulting CNN model accuracy via simulations for an example task of modulation classification.

Study on suppression effect of air-conducted sound by bone-conducted sound

This paper proposes a method for suppressing air-conducted (AC) sound by presenting bone-conducted (BC) sound. First, the amplitude and phase of BC tones at six frequencies (250, 500, 1000, 2000, 4000, and 8000 Hz) were examined by using a method of limits to suppress AC tones presented via earphones. On the basis of the results of the first experiment, finite-impulse-response (FIR) filters, dependent upon participants, were then designed as "a compensation filter" to cancel out AC sound by presenting BC sound. Next, whether or not AC complex tones presented via earphones could be suppressed by simultaneously presenting BC complex tones compensated for by FIR filtering was investigated. Finally, whether or not this method could be applied to suppress stationary noise was also investigated. From these investigations, the proposed method was found to adequately suppress AC complex tones, and the average amounts of suppression for the AC and AC complex tones were approximately 5 and 1 dB, respectively. In addition, it was also shown that the proposed method can be used to suppress AC stationary noise but not greatly.

Performance Evaluation of LoRa Communications in Harsh Industrial Environments

LoRa technology is being integrated into industrial applications as part of Industry 4.0 owing to its longer range and low power consumption. However, noise, interference, and the fading effect all have a negative impact on LoRa performance in an industrial environment, necessitating solutions to ensure reliable communication. This paper evaluates and compares LoRa’s performance in terms of packet error rate (PER) with and without forward error correction (FEC) in an industrial environment. The impact of integrating an infinite impulse response (IIR) or finite impulse response (FIR) filter into the LoRa architecture is also evaluated. Simulations are carried out in MATLAB at 868 MHz with a bandwidth of 125 kHz and two spreading factors of 7 and 12. Many-to-one and one-to-many communication modes are considered, as are line of sight (LOS) and non-line of Sight (NLOS) conditions. Simulation results show that, compared to an environment with additive white Gaussian noise (AWGN), LoRa technology suffers a significant degradation of its PER performance in industrial environments. Nevertheless, the use of forward error correction (FEC) contributes positively to offsetting this decline. Depending on the configuration and architecture examined, the gain in signal-to-noise ratio (SNR) using a 4/8 coding ratio ranges from 7 dB to 11 dB. Integrating IIR or FIR filters also boosts performance, with additional SNR gains ranging from 2 dB to 6 dB, depending on the simulation parameters.

Embedded Processing for Extended Depth of Field Imaging Systems: From Infinite Impulse Response Wiener Filter to Learned Deconvolution

Many works in the state of the art are interested in the increase of the camera depth of field (DoF) via the joint optimization of an optical component (typically a phase mask) and a digital processing step with an infinite deconvolution support or a neural network. This can be used either to see sharp objects from a greater distance or to reduce manufacturing costs due to tolerance regarding the sensor position. Here, we study the case of an embedded processing with only one convolution with a finite kernel size. The finite impulse response (FIR) filter coefficients are learned or computed based on a Wiener filter paradigm. It involves an optical model typical of codesigned systems for DoF extension and a scene power spectral density, which is either learned or modeled. We compare different FIR filters and present a method for dimensioning their sizes prior to a joint optimization. We also show that, among the filters compared, the learning approach enables an easy adaptation to a database, but the other approaches are equally robust.

Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture.

To obtain accurate position information, herein, a one-assistant method involving the fusion of extreme learning machine (ELM)/finite impulse response (FIR) filters and vision data is proposed for inertial navigation system (INS)-based human motion capture. In the proposed method, when vision is available, the vision-based human position is considered as input to an FIR filter that accurately outputs the human position. Meanwhile, another FIR filter outputs the human position using INS data. ELM is used to build mapping between the output of the FIR filter and the corresponding error. When vision data are unavailable, FIR is used to provide the human posture and ELM is used to provide its estimation error built in the abovementioned stage. In the right-arm elbow, the proposed method can improve the cumulative distribution functions (CDFs) of the position errors by about 12.71%, which shows the effectiveness of the proposed method.

A spectrum-of-spectrum filtering method to extract direct and multipath arrivals from simulations and measurements

In measurements and numerical modelling of wave propagation, undesired interference between direct and multipath arrivals can be reduced using Fourier-based signal processing methods. Existing methods, such as cepstral analysis and time-signal gating, are not applicable to all cases. Here, an alternative Fourier-based signal processing method is presented, called spectrum-of-spectrum (SoS) filtering. Its main advantage over existing methods is its ability to extract single direct or multipath arrivals for relatively short propagation distances even when subsequent arrivals do not become successively weaker. The method is based on the following steps:•Apply a lowpass filter to the real and imaginary parts of an input frequency spectrum individually, using a digital finite impulse response (FIR) filter in the frequency domain.•Recombine the filtered real and imaginary parts of the frequency spectrum to get the frequency spectrum of the direct arrival.•For extraction of the first multipath arrival, subtract the filtered frequency spectrum from the input frequency spectrum and repeat the previous steps. Repeat multiple times to extract subsequent multipath arrivals.

Robust Design of a Fractional-Delay Repetitive Controller for Uncertain Feedback Control Systems and Its Validation on a Magnetic Bearing Test Rig

This article presents a robust design of a fractional-delay repetitive controller in the frequency domain. The control goal is to accurately suppress the control error caused by a disturbance whose period may be a non-integer multiple of the sampling period. Coefficients of the repetitive controller finite impulse response (FIR) filters are calculated by solving a convex optimization problem that includes constraints limiting both the sensitivity function and the control sensitivity function. The proposed new design algorithm is verified by experiments on a test rig to efficiently damp shaft vibrations of an active magnetic bearing (AMB).

Data-Driven Feature Selection Framework for Approximate Circuit Design

The ever-growing data scale and computation complexity raise tremendous concerns about computer systems’ efficiency (i.e., lower hardware overhead and power consumption). Orthogonal to the advancement in semiconductor manufacturing technologies, approximate computing provides an alternative paradigm to reduce the hardware cost and power dissipation by relaxing computation quality for error-resilient applications. Voltage over-scaling (VOS) and approximate logic design (ALD) have become two mainstream approaches of approximate computing due to their superior performance in efficiency-critical designs. VOS reduces the power in quadratic by scaling down supply voltage while ALD saves hardware overhead by redesigning an approximate version of a given circuit (e.g., trimming less significant circuitry). However, these primitive approximate circuits (PAC) inevitably introduce notable errors and require additional error compensation circuits (ECC) to preserve computation accuracy. In existing works of ECC design, there lacks a systematic method that can generalize well to different approximate computing approaches. In this paper, we present a data-driven feature selection framework for approximate circuit design, which is applicable to both VOS and ALD. We propose novel algorithms that profoundly analyze the correlation between input data and output errors and select the most critical features to generate compensation circuits. Extensive evaluations are performed over a variety of circuits using approximate finite impulse response (FIR) filters and the prevalent approximate computing benchmark AxBench. The experimental results show that the proposed approach achieves superior compensation performance, boosting the circuit accuracy while only introducing trivial area overhead.

Maximum Complex Correntropy Criterion Adaptive IIR Filtering Based on Gauss-Newton Approach

Adaptive infinite impulse response (IIR) filters have received noticeable consideration for its lower computational complexity compared with adaptive finite impulse response (FIR) filters recently. At present, the adaptive IIR algorithms are almost using the well-established stochastic gradient descent method, but the convergence rate is less than satisfactory. In this paper, to pursue the fast convergence speed of Gauss-Newton (GN) method, we present a GN algorithm for adaptive IIR filters with complex coefficients based on the complex correntropy measure. Besides, the steady-state excess mean square error is provided. Simulations are performed to show the eximious performance of this algorithm.

Exploration of optimal functional Trojan-resistant hardware intellectual property (IP) core designs during high level synthesis

Hardware Trojans that have the capability to change the computed functional output in intellectual property (IP) cores, integrated into computing systems can be a vital reliability concern in the context of correct system operation. Therefore, determining an optimal Trojan-resistant hardware design architecture that considers multi-objective orthogonal parameters such as area and delay is crucial. This paper presents a novel exploration of optimal hardware IP core design methodology with Trojan defense capability (i.e., detection and isolation) during high level synthesis (HLS) that provides isolation of functional Trojan in a system design to ensure reliable and correct functional behavior. The proposed methodology is robust and provides the capability to yield the correct output value using HLS-based triple modular redundancy (TMR) logic and a distinct multivendor allocation policy. Therefore, the proposed HLS methodology can generate an optimal hardware IP core/system-on-chip (SoC) design with functional Trojan defense capability. The paper presents an overall flow of the proposed methodology along with a demonstrative case study on designing optimal Trojan resistant finite impulse response filter (FIR) hardware SoC design. Results of the proposed approach are evaluated in terms of design cost, convergence time, security and optimality analysis, and comparison with prior works. The proposed approach is able to generate fully functional Trojan-resistant optimal SoC designs with minimum overhead, as evident from optimality analysis and design cost.

A novel tool path smoothing algorithm of 6R manipulator considering pose-dependent dynamics by designing asymmetrical FIR filters

The joint dynamic constraints of robot manipulators are normally set to conservative constant values to meet the dynamic tolerances at different poses, which leads to incomplete utilization of the joint drive capability. To fully utilize the drive capability of joint drives, this paper proposes an asymmetrical finite impulse response (FIR) filter-based tool path smoothing algorithm, which considers the pose-dependent dynamics of the robot with 6 rotational (6R) joints. To analyze the pose-dependent motor drive capabilities, a simplified dynamics model is established by considering both the tangential and joint dynamic constraints. An asymmetrical FIR filter-based tool path smoothing algorithm is also proposed to realize different constraints of the acceleration and the deceleration limits at different positions of the linear segments. The tool tip position and the tool orientation commands are respectively smoothed in the Cartesian coordinate system and spherical coordinate system, with the constraints of the tool path deviations in the task frame. The synchronization of the position and the orientation is realized by adjusting the parameters of FIR filters. The simulation and experimental results show that the proposed algorithm guarantees the corner error tolerances of the tool tip position and the tool orientation. Moreover, the proposed method improves the robot motion efficiency by more than 10 % through the full utilization of the joint drive capability compared with the traditional tool path smoothing algorithm based on symmetrical FIR filters.

Real-Time Implementation of a Frequency Shifter for Enhancement of Heart Sounds Perception on VLIW DSP Platform

Auscultation of heart sounds is important to perform cardiovascular assessment. External noises may limit heart sound perception. In addition, heart sound bandwidth is concentrated at very low frequencies, where the human ear has poor sensitivity. Therefore, the acoustic perception of the operator can be significantly improved by shifting the heart sound spectrum toward higher frequencies. This study proposes a real-time frequency shifter based on the Hilbert transform. Key system components are the Hilbert transformer implemented as a Finite Impulse Response (FIR) filter, and a Direct Digital Frequency Synthesizer (DDFS), which allows agile modification of the frequency shift. The frequency shifter has been implemented on a VLIW Digital Signal Processor (DSP) by devising a novel piecewise quadratic approximation technique for efficient DDFS implementation. The performance has been compared with other DDFS implementations both considering piecewise linear technique and sine/cosine standard library functions of the DSP. Piecewise techniques allow a more than 50% reduction in execution time compared to the DSP library. Piecewise quadratic technique also allows a more than 50% reduction in total required memory size in comparison to the piecewise linear. The theoretical analysis of the dynamic power dissipation exhibits a more than 20% reduction using piecewise techniques with respect to the DSP library. The real-time operation has been also verified on the DSK6713 rapid prototyping board by Texas Instruments C6713 DSP. Audiologic tests have also been performed to assess the actual improvement of heart sound perception. To this aim, heart sound recordings were corrupted by additive white Gaussian noise, crowded street noise, and helicopter noise, with different signal-to-noise ratios. All recordings were collected from public databases. Statistical analyses of the audiological test results confirm that the proposed approach provides a clear improvement in heartbeat perception in noisy environments.

Adaptive dispersion compensation using a photonic integrated circuit finite impulse response filter.

Optical equalization can be used for chromatic dispersion compensation in optical communication systems to improve the system performance; however, optical signal processing (OSP) is generally specifically designed for transmission channels, that is non-adaptive to dynamic transmission distortions compared with digital signal processing (DSP). In this contribution, we demonstrate optical equalization using a photonic integrated circuit (PIC) filter for chromatic dispersion compensation, with static and adaptive techniques: (a) the static optical equalizer is calibrated based on the known fiber dispersion and length, by using the fractional delay reference method; (b) the adaptive optical equalizer is updated iteratively to compensate transmission impairments based on a least-mean squares (LMS) algorithm. Experimental results show that both the static optical equalizer and the adaptive optical LMS equalizer can give an 18-dB Q-factor for a 14-Gbd QPSK signal transmitting over 30 km. To highlight the capability of the optical equalizers, we use simulations to show the improvement in dispersion compensating characteristics by implementing additional taps.

Gaussian Mixture Bandpass Filter Design for Narrow Passband Width by Using a FIR Recursive Filter

Bandpass filters (BPFs) are very important to extract target signals and eliminate noise from the received signals. A BPF of which frequency characteristics is a sum of Gaussian functions is called the Gaussian mixture BPF (GMBPF). In this research, we propose to implement the GMBPF approximately by the sum of several frequency components of the sliding Fourier transform (SFT) or the attenuated SFT (ASFT). Because a component of the SFT/ASFT can be approximately realized using the finite impulse response (FIR) recursive filters, its calculation complexity does not depend on the length of the impulse response. The property makes GMBPF ideal for narrow bandpass filtering applications. We conducted experiments to demonstrate the advantages of the proposed GMBPF over FIR filters designed by a MATLAB function with regard to the computational complexity.

Application of turbulence filters on PCBoom predictions for two NASA flight test campaigns

In preparation for the NASA X-59 community noise tests, the effects of turbulence on predictions of sonic boom levels are studied. This work builds on previous studies by including the effects of turbulence via finite impulse response filters implemented in the PCBoom prediction program. The resulting turbulized sonic boom predictions are compared to measured boom metric levels from previous NASA flight tests: Quiet Supersonic Flights 2018 and Carpet Determination in Entirety Measurements Phase I. To determine the factors driving differences between predictions and measured levels, least absolute shrinkage and selection operator (Lasso) regression is used to create a reduced-order model for each flight test campaign. [Work supported by NASA Langley Research Center through Analytical Mechanics Associates.]

Multi-Objective Sparsity Maximum Mode De-Composition: A New Method for Rotating Machine Fault Diagnosis on High-Speed Train Axle Box

Signal decomposition techniques generally have two limitations. First, for adaptive correlated kurtogram (ACK) and variational mode decomposition (VMD), etc., the definition of the decomposition target has no direct relationship with the features of fault impacts, i.e., the decomposition is not guided by the health indices used to characterize bearing faults, resulting in fault impacts cannot be completely separated from vibration data. Second, it is hard to accurately determine the decomposition stop parameter (the number of final output modes), resulting in frequent over- or under-decomposition. All these are not conducive to bearing fault detection in engineering practice. In this paper, a new decomposition method, multi-objective sparse maximum mode decomposition (MOSMMD), is proposed. Firstly, by analyzing the Fourier spectrum of vibration data, MOSMMD constructs multiple finite impulse response filters covering different frequency bands to obtain initial candidate modes. Then, aiming at maximizing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L2</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L1</i> norm of envelope spectrum (ES), the upper and lower cutoff frequencies of each filter are adjusted adaptively through iterative optimization, and the frequency bands covered by each mode are adjusted accordingly. Finally, by using that fault feature harmonics have periodic natures in ES, the redundant modes are eliminated and only the modes with obvious fault features are output. Compared with VMD and ACK, the decomposition target of MO-SMMD is directly related to the fault features, i.e., cyclostationarity of fault impacts, which makes the extraction of fault impacts more accurate. Moreover, the number of final output modes of MOSMMD is determined adaptively and does not need to be set in advance as an input parameter. Also, MOSMMD can decompose fault modes from vibration data without prior fault information, even when processing concurrent fault signals. Simulation and experiments demonstrate the superiority of the proposed MOSMMD.

Localization of underground pipe jacking machinery: A reliable, real-time and robust INS/OD solution

Accurate positioning is important for guidance control in pipeline construction, but traditional approaches fail in GPS-denied environments. This paper addresses the underground localization problem for the pipe jacking machine by presenting an Inertial Navigation System (INS)/ODometry (OD) scheme. It is a loosely coupled navigation system that combines an INS and an OD to provide reliable, real-time, and robust location estimation in an enclosed and restricted space. The combination is achieved by a multi-sensor fusion algorithm to avoid the divergence of standalone INS. Compared with the traditional localization technologies, the advantage of the INS/OD scheme is that there is no performance degradation or service failure despite severe blockage of the thick soil layer due to the selection of dead reckoning-based sensors. Moreover, an Unbiased Finite Impulse Response (UFIR) filter serves as the fusion mechanism to improve the robustness against a prior knowledge of noise, considering unknown and time-varying noise statistics in practice. The INS/OD scheme is verified through a numerical simulation and employed on-site in a real underground sewage pipeline construction project. Despite the construction site being more than 10 meters deep underground, the INS/OD scheme continues to perform reliably and robustly in real-time, significantly benefiting the engineering guidance enterprise.

Implementation of distributed arithmetic-based symmetrical 2-D block finite impulse response filter architectures.

Background: This paper presents an efficient two-dimensional (2-D) finite impulse response (FIR) filter using block processing for two different symmetries. Architectures for a general filter (without symmetry) and two symmetrical filters (diagonal and quadrantal symmetry) are implemented. The proposed architectures need fewer multipliers because of the symmetry of the filter coefficients. Methods: A distributed arithmetic (DA)- based multiplication method is used in the proposed architecture. A dual-port memory-based lookup table (DP-MLUT) is used in the multiplication instead of lookup-table (LUT) to reduce the area and power of the FIR filter. The filter's throughput is increased by using block processing. Memory reuse and memory sharing methods are introduced, which reduces the need for many registers and hence the circuit complexity. The architectures are written in Verilog Hardware Description Language and synthesized using Genus Synthesis tool-19.1 in 45nm technology with a generic library of Cadence vendor constraints. The synthesis tool generates the area, delay, and power reports. Power consumption of architectures is calculated with an image size of 64 X 64 and at 20 MHz frequency. Results: Compared to existing architectures, the synthesis results show improvements in power, area, area delay product (ADP), and power delay product (PDP). The proposed MLUT-based 2-D block Quadrantal Symmetry Filter (QSF) for length 8 with block size 4 consumes 58.94% less power, occupies 59.5% less area, 48.44% less ADP and 47.78% less PDP compared to best existing methods. Conclusions: A novel DA-based 2-D block FIR filter architecture with various symmetries is realized. Symmetry is incorporated into the filter coefficients to minimize the number of multipliers. The LUT size is optimized by odd multiples or even multiples storage techniques. Also, the overall area of the architecture is decreased by DP-LUT-based multipliers. The proposed filter architecture is area-power-efficient. It is best suited for applications that have fixed coefficients.