Finite Impulse Response Filter Research Articles

"Enhanced DA Algorithm for FIR Filter Design and FPGA Implementation"

Abstract: This study looks into the difficulties of using the Distributed Arithmetic (DA) method for Finite Impulse Response (FIR) filters on Field-Programmable Gate Arrays (FPGAs). It focuses on how coefficients are represented, balancing precision using fixed-point arithmetic and quantization. The research explores ways to optimize memory use, aiming to store data more efficiently within FPGA resources and reduce memory needs. To speed up computations, it examines how to make accessing lookup tables faster and suggests improvements in design. The study also considers how to manage FPGA resources effectively, balancing latency, throughput, and resource use. It looks at specific improvements to the DA method for FIR filters to make better use of resources and enhance performance. Overall, this work provides insights and solutions for algorithm design, memory use, lookup table speed, and FPGA architecture to make DA-based FIR filter implementations on FPGAs more efficient.

A novel design algorithm for low complexity sparse 2‐D FIR filters

SummaryThe hardware implementation complexity of traditional two‐dimensional (2‐D) finite impulse response (FIR) filters with high order is very high. In this paper, a novel sparse 2‐D FIR filter design algorithm has been proposed by combining the common subexpression elimination technique and sparse filter design algorithms to reduce hardware implementation complexity. First, an initial 2‐D FIR filter with sparse coefficients that meets frequency‐domain specifications is obtained by the sparse filter design algorithm. Then, each filter coefficient is quantized in canonical signed digit, and the sensitivities of all weight‐two subexpressions and isolated non‐zero bits are calculated. Finally, the filter coefficients with low implementation cost are reconstructed via iteratively choosing subexpressions and isolated non‐zero bits in terms of their sensitivities. Simulation results show that the proposed algorithm saves about of adders compared with other low complexity filter design method, which reduces effectively the implementation cost.

Enhanced FPGA linear phase FIR filter with amalgam multiplier

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

Prediction of mild cognitive impairment using EEG signal and BiLSTM network

Mild cognitive impairment (MCI) is a cognitive disease that primarily affects elderly persons. Patients with MCI have impairments in one or more cognitive areas, such as memory, attention, language, and problem-solving. The risk of Alzheimer’s disease development is 10 times higher among individuals who meet the MCI diagnosis than in those who do not have such a diagnosis. Identifying the primary neurophysiological variations between those who are suffering from cognitive impairment and those who are ageing normally may provide helpful techniques to assess the effectiveness of therapies. Event-related Potentials (ERPs) are utilized to investigate the processing of sensory, cognitive, and motor information in the brain. ERPs enable excellent temporal resolution of underlying brain activity. ERP data is complex due to the temporal variation that occurs in the time domain. It is actually a type of electroencephalography (EEG) signal that is time-locked to a specific event or behavior. To remove artifacts from the data, this work utilizes Independent component analysis, finite impulse response filter, and fast Fourier transformation as preprocessing techniques. The bidirectional long short-term memory network is utilized to retain the spatial relationships between the ERP data while learning changes in temporal information for a long time. This network performed well both in modeling and information extraction from the signals. To validate the model performance, the proposed framework is tested on two benchmark datasets. The proposed framework achieved a state-of-the-art accuracy of 96.03% on the SJTU Emotion EEG Dataset dataset and 97.31% on the Chung–Ang University Hospital EEG dataset for the classification tasks.

Design and Implementation of FIR Filters for Biomedical Applications

Abstract: Digital signal processing (DSP) greatly facilitates the continuous capture, monitoring, processing, and analysis of signals in real-world applications, particularly in biomedical or wearable devices. Within DSP systems, the design of Finite Impulse Response (FIR) filters is pivotal. In scenarios demanding intricate calculations and high precision, higher-order filters are employed. Multipliers are crucial components in filter design, consuming significant chip space and introducing extra computation overhead. Designers strive to optimize multipliers to enhance performance. In this project we are going to design pipelined based FIR filter by applying pipelining concept to the FIR filter and following the previous implementations such as the FIR filter with high accuracy compared to existing method. The proposed system architectures are known to be efficient for real signal processing, offering advantages over conventional based designs. The proposed design is anticipated to yield benefits in terms of area, power consumption, and timing performance. The synthesis and simulation of the existing and proposed designs are implemented using Cadence Virtuoso.

Implementation of Digital Filter Using Verilog HDL

Abstract: In the context of Very Large Scale Integration technology, where addition is essential, digital filters are essential elements. The performance of the Finite Impulse Response Filter is highly dependent on the speed of its multiplier unit, making itstand out among the others. In order to improve the effectiveness of the FIR filter, we suggest using a Wallace tree multiplier. This novel method offers improvements over conventional multipliers, with a decrease in latency being one of the main advantages. Significant improvements in latency are obtained by using Xilinx tools and implementing this filter design in Verilog HDL. The Wallace tree multiplier is perfect for FIR filter construction in low-voltage and low-power VLSI applications since it has benefits like higher operating frequency and reduced power consumption. This development might improve the effectiveness and performance of digital filters, especially in settings with limited resources, opening the way for more robust VLSI systems

A new design scheme of two-channel graph filter bank

The existing designs of a two-channel graph filter bank (2c-graph-FB) are polynomial-based and have the numerical calculation problem when designing high performance/order 2c-graph-FB. In this study, a new design scheme is proposed to overcome the numerical calculation problem. The basic idea is adopting the expression form of the traditional symmetric finite impulse response (FIR) filter's frequency response as that of the frequency-domain graph filter of a 2c-graph-FB. Further, for solving the contradiction between the order and the vertex-domain localization in the existing designs as well as the proposed design, the idea of approximate vertex-domain localization is proposed. The designed 2c-graph-FB via our proposed scheme can have strict or approximate vertex-domain localization property. The simulation results reveal that the proposed scheme has no numerical calculation problem. Moreover, compared with the existing designs on the condition of all being able to achieve exceedingly small reconstruction error, the proposed design with the strict localization property has superior frequency selectivity. Among the existing designs (also including the proposed design with strict or approximate localization property) which can achieve exceedingly small reconstruction error, the proposed design scheme with the approximate localization property performs best in terms of the frequency selectivity while having certain localization property.

A PRECISE RESPIRATORY AND HEART RATE DETECTION METHOD FOR MILLIMETER-WAVE RADAR

Millimeter wave radar as a type of noncontact sensor can more conveniently and insensibly obtain breathing and heartbeat signals. However, due to the weak radar echo signal and the influence of noise, there may be phase ambiguity and separation distortion of breathing and heartbeat signals during the processing, which affects the accuracy of detection. In this paper, an enhanced extended differential and cross multiplication (EDACM) algorithm is presented to obtain chest position signals without phase ambiguity. A specific order finite impulse response (FIR) filter with a Blackman window is applied to extract breath signals without waveform distortion from phase signals, and the heartbeat signal without rate distortion is extracted from the phase signal by means of the designed Chebyshev II filter. The measured results show that the proposed method achieves 96.78% accuracy for heart rate detection and 94.44% accuracy for respiratory rate detection, and improves the accuracy of heart rate detection by 2.22% and respiratory rate detection by 4.06% compared to the band-pass filter method under the same parameter conditions.

Time-domain sound field reconstruction using a rigid spherical microphone array.

A time-domain approach for interior spherical near-field acoustic holography is proposed to achieve the low-delay reconstruction of time-domain sound fields using a rigid spherical microphone array. This reconstruction encompasses the incident pressure field, the incident radial particle velocity field, and the total pressure field, which includes scattering. The proposed approach derives time-domain radial propagators through the inverse Fourier transform of their frequency-domain counterparts. These propagators are then applied to the array measurements to obtain the time-domain spherical harmonic coefficients of the interior sound field. Given the fact that the time-domain radial propagators possess finite-time support and exhibit significant high-frequency attenuation characteristics, they can be efficiently implemented using finite impulse response (FIR) filters. The proposed approach processes the signal sample-by-sample through these FIR filters, avoiding a series of issues associated with time-frequency transformations in frequency-domain methods. As a result, the approach offers higher accuracy and lower latency in reconstructing non-stationary sound fields compared to its frequency-domain counterpart and thus holds greater potential for real-time applications. Additionally, owing to the scattering effect of the rigid sphere, the approach avoids the impact of spherical Bessel function nulls and does not require the measurement of particle velocities, which renders the measurements cost effective.

The Design of FIR Filter Based on improved DA Algorithm and its FPGA implementation: REVIEW

The Design of FIR Filter Based on improved DA Algorithm and its FPGA implementation: REVIEW

Adaptive Expectation-Maximization-Based Kalman Filter/Finite Impulse Response Filter for MEMS-INS-Based Posture Capture of Human Upper Limbs.

To obtain precise positional information, in this study, we propose an adaptive expectation-maximization (EM)-based Kalman filter (KF)/finite impulse response (FIR) integrated filter for inertial navigation system (INS)-based posture capture of human upper limbs. Initially, a data fusion model for wrist and elbow position is developed. Subsequently, the Mahalanobis distance is utilized to evaluate the performance of the filter. The integrated filter employs the EM-based KF to enhance noise estimation accuracy when the performance of KF declines. Conversely, upon deterioration in the performance of the EM-based KF, which is evaluated using the Mahalanobis distance, the FIR filter is employed to maintain the effectiveness of the data fusion filter. This research utilizes the proposed EM-based KF/FIR integrated filter to ascertain wrist and elbow positions. The empirical results demonstrate the proficiency of the proposed approach in estimating these positions, thereby overcoming the challenge and highlighting its inherent effectiveness.

On the minimization of multiplier-adders for powers-of-two filter using a novel right to left (R2L) algorithm

The field of digital signal processing has been receiving increasing attention over the years because of its widespread applications in various fields of science, engineering and technology. In connection to this, design of finite impulse response (FIR) filter has drawn enough attention of researchers throughout the globe. A number of promising developments has been carried out over the last few decades which emphasize on the design of hardware efficient filter structure. In this work, one novel Right to Left (R2L) algorithm is proposed which can minimize the number of multiplier-adders for the powers-of-two FIR filter. The requirement of such adders essentially depends upon the number of such non-zero entries and the word-length of the input signal. Comparative study has been performed amongst few such hardware efficient realizations of digital filters. Finally, the proposed approach has been implemented using Xilinx Plan Ahead 14.7 so as to have a clear understanding about the requirement of different hardware blocks on a field programmable device.

A grayscale compression method to segment bone structures for 2D-3D registration of setup images in non-coplanar radiotherapy

Purpose. To evaluate the performance of an automated 2D-3D bone registration algorithm incorporating a grayscale compression method for quantifying patient position errors in non-coplanar radiotherapy. Methods. An automated 2D-3D registration incorporating a grayscale compression method to segment bone structures was proposed. Portal images containing only bone structures ( Portalbone ) and digitally reconstructed radiographs containing only bone structures ( DRRbone ) were used for registration. First, the portal image was filtered by a high-pass finite impulse response (FIR) filter. Then the grayscale range of the filtered portal image was compressed. Thresholds were determined based on the difference in gray values of bone structures in the filtered and compressed portal image to obtain Portalbone. Another threshold was applied to generate DRRbone when the CT image uses the ray-casting algorithm to generate DRR images. The compression performance was assessed by registering the DRRbone with the Portalbone obtained by compressing the portal image into various grayscale ranges. The proposed registration method was quantitatively and visually validated using (1) a CT image of an anthropomorphic head phantom and its portal images obtained in different poses and (2) CT images and pre-treatment portal images of 20 patients treated with non-coplanar radiotherapy. Results. Mean absolute registration errors for the best compression grayscale range test were 0.642 mm, 0.574 mm, and 0.643 mm, with calculation times of 50.6 min, 42.2 min, and 49.6 min for grayscale ranges of 0–127, 0–63 and 0–31, respectively. For the accuracy validation (1), the mean absolute registration errors for couch angles 0°, 45°, 90°, 270°, and 315° were 0.694 mm, 0.839 mm, 0.726 mm, 0.833 mm, and 0.873 mm, respectively. Among the six transformation parameters, the translation error in the vertical direction contributed the most to the registration errors. Visual inspection of the patient registration results revealed success in every instance. Conclusions. The implemented grayscale compression method successfully enhances and segments bone structures in portal images, allowing for accurate determination of patient setup errors in non-coplanar radiotherapy.

Implementation of FIR Filters through Inner product Units and Parallel Accumulations

Finite Impulse Response (FIR) filters are pivotal in digital signal processing, finding applications in diverse fields like audio processing, telecommunications, and biomedical signal analysis. This work presents an enhanced implementation methodology for FIR filters utilizing inner product computation and parallel accumulations. In the existing, FIR filters are typically implemented using convolution techniques, basic adders, and multipliers, which involve sequential processing and intensive computational resources. This method often leads to latency issues and limits real-time applications. Moreover, traditional implementations suffer from inefficiencies in utilizing hardware resources optimally, leading to suboptimal performance. The proposed methodology overcomes these limitations by leveraging inner product computations and parallel accumulation techniques. By exploiting inherent parallelism in the filtering process, the proposed method significantly reduces latency and enhances throughput.

Efficient monostatic anisotropic point scatterer model for high performance computing

ABSTRACT High performance computing (HPC) electromagnetic (EM) emulators are used to simulate real-time EM wave interactions between numerous radar targets. Radar Cross Section (RCS) data stores reflection profiles of radar targets as a table; however, the needed storage size increases with frequency sampling density, aspect angle sampling density, and number of target types. The large quantity of data often exceeds storage capability and limits manipulation and representation feasibility. The spherical harmonic based monostatic anisotropic point scatterer model is proposed for HPC EM simulations where scattering responses can be computed with finite impulse response (FIR) filters. An efficient algorithm constructing this model with large scale RCS data is discussed. The scatterer position and the reflection profile of each scatterer are solved using least squares methods and particle swarm optimization (PSO). In addition, the function evaluations in PSO are accelerated by taking advantage of the matrix structure, making the algorithm 22 times faster

An efficient high performance reconfigurable canonical sign digit architecture for software defined radio

Software defined radios (SDRs) are highly motivated for wireless device modelling due to their flexibility and scalability over alternative wireless design options. The evolutionary structure of finite impulse response (FIR) filters was designed for a proposed reconfigurable canonical sign digit (CSD) approach. Considering the complex trade-off, this is accomplished with many FIR taps, which is a challenging assignment. On the baseband processing side, design is given with parameterization-controlled FIR filter tap selection. Optimal processing models to overcome the reconfigurable design issues associated with the SDR system for a multi-standard wireless communication system root cosine filter standard are often used to implement multiple FIR channelization topologies, each of which is tied to a particular in-phase and quadrature (IQ) symbol. Additionally, it demonstrates the viability of using a multi-modulation baseband modulator in the SDR system for next-generation wireless communication systems to maximise adaptability with the least amount of computational complexity overhead. The proposed multiplier-less FIR filter-based reconfigurable baseband modulator, according to the experimental results, offers a 6% complexity reduction and a 47% improvement in performance efficiency over the current SDR system.

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

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

An IoT and Machine Learning-based Neonatal Sleep Stage Classification

Sleep, in neonates, is used to access the quality of brain and physical development. Typically, neonatal sleep has been divided into three stages: active sleep (AS), quiet sleep (QS), and intermediate sleep (IS). Polysomnography (PSG) is considered a gold standard to classify sleep. To address this issue, over the past two decades, researchers proposed multiple algorithms for automatic sleep stage classification. These algorithms work achieved outstanding results i.e. quiet sleep detection still, lacks in many aspects. One major drawback of the existing research is amalgamation of awake and active sleep into low voltage irregular (LVI) state. This amalgamation corrupts 40% of the overall EEG signal. For this reason, we proposed an algorithm for neonatal sleep-wake classification using machine learning. The proposed research is divided into three steps. Firstly, the EEG signal was pre-processed using finite impulse response filter to remove the noise and artifacts. Clean EEG signal is then divided into 4560 30-sec segments. Then, twenty prominent EEG features were extracted from time, frequency, and spatial domain. After feature extraction, support vector machine was used for sleep stage classification. The propounded study outperformed all the existing algorithms for sleep-wake classification with a mean accuracy of 83.7%. Four-fold cross-validation was used to validate the overall dataset. Multiple other performance matrices i.e. sensitivity, specificity, Kappa were calculated to prove the efficacy of the proposed study. Statistical results show that the proposed study can be used as a real-time neonatal sleep and Awake classification algorithm, as this did not use prior post-processing techniques.

Design of efficient alterable bandwidth FIR filterbank for hearing aid system

The lifestyle of any person depends on his ability to hear his surrounding sounds or voices. This is because each of our sense organs works interrelated. Without an ability to proper hearing, he/she cannot respond to the other person leading to a very less or no communication to the society. Although he has a good talent and knowledge within, he cannot be able to communicate or express unless he has a good hearing ability. Also, he faces emotive problems caused by a reduction in self-confidence. So, a requirement of designing an efficient hearing aid is much necessary to resolve the hearing loss problem. In this paper, design of an efficient alterable ten band Finite Impulse Response filter bank for the hearing aid is achieved. Among the various windowing techniques available, Hamming window is preferred to design the FIR filter owing to its relatively sharp main lobe width and also the first few lobes have good attenuation. In this filterbank, variability in the subband bandwidths is achieved by simply altering each subband's bandwidth and center frequency. Unlike fixed filterbank, the proposed design provides the advantage of matching most of the audiograms of different hearing-impaired persons more accurately with simple procedure leading to low cost and low hardware complexity. Also using an FIR filter in the filterbank leads to highly stable design. Thus, a design of highly qualitative filterbank compared to fixed bandwidth designs with high audiogram matching and less matching error is implemented.

Chaotic properties of an FIR filtered Hénon map

When chaotic signals are used in practical communication systems, it is essential to control and eventually limit the spectral bandwidth occupied by these signals. One way to achieve this goal is to insert a discrete-time filter into a nonlinear map that generates chaotic signals. However, this can completely change the dynamic properties of the original map. Considering this situation, this paper presents a series of numerical experiments aimed at obtaining the Lyapunov exponents of the signals generated by the two-dimensional Hénon map with a set of prototypical finite impulse response (FIR) filters added in the feedback loop. Our results show that the number of filter coefficients and the location of the zeros have a significant and complex impact on the behavior of the generated signals. Therefore, FIR filters should be carefully designed to preserve or suppress chaos in practical applications.