Blackman Window Research Articles

Evaluating the Impact of Windowing Techniques on Fourier Transform-Preprocessed Signals for Deep Learning-Based ECG Classification

(1) Background: Arrhythmias, or irregular heart rhythms, are a prevalent cardiovascular condition and are diagnosed using electrocardiogram (ECG) signals. Advances in deep learning have enabled automated analysis of these signals. However, the effectiveness of deep learning models depends greatly on the quality of signal preprocessing. This study evaluated the impact of different windowing techniques applied to Fourier transform-preprocessed ECG signals on the classification accuracy of deep learning models. (2) Methods: We applied three windowing techniques—Hamming, Hann, and Blackman—to transform ECG signals into the frequency domain. A one-dimensional convolutional neural network was employed to classify the ECG signals into five arrhythmia categories based on features extracted from each windowed signal. (3) Results: The Blackman window yielded the highest classification accuracy, with improved signal-to-noise ratio and reduced spectral leakage compared to the Hamming and Hann windows. (4) Conclusions: The choice of windowing technique significantly influences the effectiveness of deep learning models in ECG classification. Future studies should explore additional preprocessing methods and their clinical applications.

Improved Sparrow Search Algorithm for Rectangular Planar Array Synthesis

To optimize sparse rectangular planar array antennas under multiple constraints, including array aperture, number of elements, and minimum element spacing, an improved sparrow search algorithm (ISSA) is proposed. Initially, a position distribution matrix is generated using the Blackman window weighting method. The sparrow search algorithm (SSA) is then enhanced by incorporating Kent mapping for population initialization, which improves the initial population’s diversity. Additionally, the strategy for updating the discoverer’s position integrates elements from the sine cosine algorithm (SCA), along with a nonlinear sine learning factor, thereby enhancing global search capability. Finally, a crossover strategy is embedded into the SSA to refine the optimization accuracy by improving the search methodology used by the vigilantes. To verify the efficacy of our approach, we carried out simulation experiments. The results demonstrate that this method significantly enhances the performance of the array antenna by reducing the peak sidelobe level and minimizing the zero-trap depth. These findings validate the reliability and effectiveness of the suggested method.

A New Paradigm for Automated Ground Clutter Removal: Global Regression Filtering

Abstract Ground clutter filtering is an important and necessary step for quality control of ground-based weather radars and this is widely done in the spectral domain via use of the Discrete Fourier Transform (DFT). To accomplish spectral clutter filtering, the I (in-phase) and Q (quadrature) time series are multiplied by a window function, such as the Blackman window, which suppresses clutter leakage. Subsequently, the clutter is filtered from the Doppler spectrum by setting spectral coefficients to zero where there is clutter signal. Recently, it has been shown with simulations that a regression clutter filter is a viable alternative. To demonstrate the regression filter, it is applied to radar data from both National Science Foundation-National Center for Atmospheric Research (NSF-NCAR) S-band polarimetric radar (S-Pol), and NEXRAD (Next Generation Weather Radar). A regression filter can be applied directly to the non-windowed time series data, thus avoiding the signal attenuation from a window function, which is required for spectral domain clutter filters such as GMAP (Gaussian Model Adaptive Processing). It is shown that the regression filter rejects clutter as effectively as the spectral technique, or better, but has the distinct advantage that standard error of estimates of the radar variables are in general improved by about 25% to 50% over a comparable spectral domain filter. The regression filter is straightforward and can be executed in real time.

Performance Analysis of Filtered OFDM using MLE for Wireless Communication Networks

Introduction: OFDM has evolved as an effective modulation format for 4G mobile network technologies, Long-Term Evolution, and Worldwide Interoperability for Microwave Access. OFDM is the modulation technique adopted for communication standards such as DSL, wireless LAN, DVB, and DAB. However, because CP-OFDM has considerable out-of-band emission that may interfere with transmissions in adjacent bands and applies a single set of parameters to the whole band to fulfill a given service, it is unable to accommodate the diversity of services. The objective of the research is to develop a novel waveform with better adaptability than CP-OFDM that employs filters to increase spectrum containment. In this view, an FIR filter that uses the Blackman window function to lessen the OOBE is proposed in this manuscript. Filtered OFDM is sensitive to carrier frequency offset (CFO). CFO increases the Inter-Carrier Interference (ICI) in F-OFDM systems. However, research work that combines the CFO compensation technique with filtered OFDM has not been listed in the literature so far. Methods: In this paper, Maximum likelihood estimation is used to cancel the ICI caused by the CFO. The high PAPR of filtered OFDM is one of its drawbacks. The amplitude clipping method is used to reduce the PAPR. The distortion caused by amplitude clipping is prevented by filters used in the FOFDM system. Highlights: • A novel waveform addressing OOBE issues in OFDM is developed for 4G wireless communication networks. • An innovative solution using MLE is proposed to address the challenges faced by the filtered OFDM system due to the CFO. • Article addresses the high PAPR problem of filtered OFDM. Results: The performance of filtered OFDM with amplitude clipping is analyzed in terms of BER, PSD, and PAPR over the AWGN channel. Conclusion: Simulation results show that, when compared to the traditional OFDM system, such a combined method effectively suppresses PAPR while maintaining good BER (Bit Error Rate) and OOBE performance.

Supraharmonics Reconstruction Method Based on Blackman Window and Compressed Sensing

This paper proposes a method that combines window functions with compressed sensing for the detection of ultra-high harmonics in the frequency range of 2–150 kHz. By analyzing the sparsity of the signal, the Bruckmann window function, which is most suitable for the compressed sensing reconstruction process and the characteristics of ultra-high harmonics, is selected. Simulations indicate that, compared to existing methods, the proposed algorithm based on the fusion of the Bruckmann window and compressed sensing achieves a sparser post-observation signal with reduced fluctuations. The robustness and anti-interference capabilities are enhanced, while the harmonic detection accuracy and signal reconstruction performance are significantly improved. The reconstruction error reaches 4.15 × 10−6, the mean squared error percentage (MSE) reaches 4.13 × 10−6, and the signal-to-noise ratio (SNR) is as high as 97.69 dB, marking an increase of 54.11%. This study provides a new theoretical and methodological approach for the analysis and processing of ultra-high harmonics caused by a high proportion of power electronic devices.

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.

Heterogeneous features and deep learning networks fusion-based pest detection, prevention and controlling system using IoT and pest sound analytics in a vast agriculture system

Every country's foundation is its agricultural industry, which drives about half of global economic growth. Accurate farming is crucial for crop evaluation and pest control. Old-fashioned pest detection is unstable and gives inaccurate forecasts. These surveillance tactics are intrusive, expensive, and vulnerable to assumptions. Pest sounds can be caught with minimum effort by utilizing IoT networks. Deep learning algorithms, species distribution range assessments, and nature monitoring can automatically identify and categorize pest sounds. The IoT-driven computerized components in this research's unique pest identification system used machine learning on insect sound recordings. The Butterworth filter, Blackman and Flattop window, Ultraspherical Filter, Rife-Vincent Window, Cosine-Tapered Window, FFT, DFT, STFT, PNCC, RASTA-PLPCC, LSFCC, sound detectors, and PID sensors were employed. The intended study used the HFDLNet for training, testing, and validation, examining 7,200 pest sounds from 72 species to determine their unique characteristics and statistical attributes. The proposed model has 99.87 % accuracy, 99.96 % sensitivity, 99.88 % specificity, 99.96 % recall, 99.93 % F1 score, and 99.98 % precision. This study outperforms Inception-ResNet-v2, FRCNN ResNet-50, Fatser-PestNet, MD-YOLO, YOLOv5m, MAM-IncNet, and Xception. The suggested solution uses IoT networks and pest sound analysis to establish a pest preventive and management strategy and a solar-powered generator to power IoT devices across vast agricultural fields.

Fast Backprojection Filtration Algorithm in Circular Cone-Beam Computed Tomography

In order to meet the heavy backprojection calculation of the backprojection filtration (BPF) algorithm in circular cone-beam computed tomography (CT), a fast backprojection method is developed, which uses the integral operator of the fixed scanning angle integral interval. The proposed method combines with Hilbert filtration and the filter of the Blackman window, and then, the fast BPF (F-BPF) algorithm is obtained. The experimental results of simulation data and real data demonstrate that the proposed algorithm is fast enough for high-quality reconstructed images only with half-circle projection data.

A New Nonlinear Ion Drift Model of Memristor Element and its Versatile Analog Reconfigurable Realizations

While using polynomial functions to define window functions is an initial approach in studying the memristor element, it is susceptible to generating imaginary results. However, using window functions, including the trigonometric function, is a current field of research on the memristor element. This paper uses the trigonometric Blackman window function to present a new memristor element model and investigates its nonlinear ion drift model properties. The motivation of this study is the usage of the trigonometric Blackman window function, which presents a more detailed definition and leads to more accurate results in windowing operations. The Blackman window function can address the issues of border locking and terminal state. Numerical simulations have verified this proposed structure. Additionally, the analog realizations of the memristor element constructed with the Blackman window function have been achieved on a Field Programmable Analog Array, which offers fast prototyping, serving as an alternative approach for emulating memristors.

Online evaluation of resistance spot welding quality and defect classification

Resistance spot welding (RSW) is widely used in industrial product manufacturing, and weld quality is critical to ensure the safety and reliability of mechanical structures. With product quality improvement, the online monitoring of weld quality has become an urgent issue. Therefore, a method for online evaluation of RSW quality and automatic classification of welding defects based on mild steel is proposed. By acquiring the welding current and electrode voltage signals of the welding process in real time, a finite impulse response low-pass filter based on the Blackman window function is designed to perform zero-phase filtering of the dynamic resistance (DR). The influence of three welding parameters on the nugget diameter and shear strength of spot welding of DC01 mild steel was studied through the control variable method to determine the optimum welding parameters. Since the main defects of the RSW process are classified as expulsion defects and incomplete fusion defects, minor adjustments were made to the welding parameters to produce expulsion and incomplete fusion defects. Features in the time domain are extracted from the DR curves signals of normal, expulsion, and incomplete fusion welding spots, and the performance of the extracted features in the classifier is analyzed. Improvements are made to classify normal and expulsion spots with low accuracy, a new method is proposed that can accurately classify the quality of the welding spots. Through online evaluation tests of quality on 200 welding spots, the online evaluation test showed results that closely matched the validation test results. The results of the classification performance tests show that this study can be used for online evaluation of RSW quality and automatic classification of the welding spot defects of the DC01.

Influence of electrode apodization weighted structure on the performance of SAW temperature sensors

In order to improve the comprehensive performance of the surface acoustic wave (SAW) temperature sensors and reduce clutter interference, the optimized Chebyshev window function, Blackman window function and Hamming window function are used to carry out the apodization weighting design for interdigital transducer (IDT) and reflective grating respectively. The influence of electrode apodization weighted structure on the performance of SAW temperature sensors is systematically studied through experiments. The results show that the apodization weighted design of IDT and reflective gate can effectively improve the resonance peak intensity and reduce the side lobe interference; the IDT and the reflection grating are designed with apodization weighting at the same time, compared with the single reflection grating weighting, the volatility of the side lobe of the Chebyshev window function and the Hamming window function increases, the volatility of the side lobe of the Blackman window function decreases; after electrode apodization optimization, the resonance frequency, Q value of the SAW sensor and the sensitivity to temperature increases; using a hamming window optimized electrode structure, the peak value of S11 is −36.73 dB and the Q value is 15 030. In order to ensure the signal processing ability and comprehensive performance of the sensor, an optimized Hamming window should be used for the electrode design of the SAW sensors.

Data processing techniques for ion and electron-energy distribution functions

Retarding field energy analyzers and Langmuir probes are routinely used to obtain ion and electron-energy distribution functions (IEDF and EEDF). These typically require knowledge of the first and second derivatives of the current–voltage characteristics, both of which can be obtained using analog and numerical techniques. A frequent problem with electric-probe plasma diagnostics is the noise from the plasma environment and measurement circuits. This poses challenges inherent to differentiating noisy signals, which often require prior filtering of the raw current–voltage data before evaluating the distribution functions. A review of commonly used filtering and differentiation techniques is presented. It covers analog differentiator circuits, polynomial fitting (Savitzky–Golay filter and B-spline fitting), window filtering (Gaussian and Blackman windows) methods as well as the AC superimposition and Gaussian deconvolution routines. The application of each method on experimental datasets with signal-to-noise ratios ranging from 44 to 66 dB is evaluated with regard to the dynamic range, energy resolution, and signal distortion of the obtained IEDF and EEDF as well as to the deduced plasma parameters.

An Improved Constant Modulus Algorithm for Blind Signal Adaptation in Wireless Communications

While the Constant Modulus Algorithm (CMA) is the most often used blind signal equalization or adaptation technique, it converges slowly and produces excessive Side Lobe Level (SLL) radiation, which wastes energy and interferes with other equipment. Additionally, interfering signals may be picked up by side lobes, increasing the noise level in the receiver. A normalized constant-modulus method with adjustable step size was devised, which considerably increased its convergence rate. The Blackman window was also applied to the CMA to lower the SLL. The designed beamformer improved convergence rate by 40%, as observed by lower Mean Square Error (MSE) to Signal to Interference plus Noise Ratio (SINR) values. As a result, the beam former is a viable alternative in an environment where channel conditions are constantly changing. Furthermore, the use of the Blackman window had the ability to minimize the Peak Side Lobe Level (PSLL) and resulted in a 5 dB improvement over CMA for the SLL rejection gain. As compared to the conventional CMA, the Improved CMA displayed the highest reduction in PSLL with quick convergence capabilities and saved a lot of energy wastage due to side lobes minimization. These characteristics make the ICMA a potential choice for advanced wireless applications

Determination of Harmonic Parameters in Pathological Voices—Efficient Algorithm

The harmonic parameters Autocorrelation, Harmonic to Noise Ratio (HNR), and Noise to Harmonic Ratio are related to vocal quality, providing alternative measures of the harmonic energy of a speech signal. They will be used as input resources for an intelligent medical decision support system for the diagnosis of speech pathology. An efficient algorithm is important when implementing it on low-power devices. This article presents an algorithm that determines these parameters by optimizing the window type and length. The method used comparatively analyzes the values of the algorithm, with different combinations of window and size and a reference value. Hamming, Hanning, and Blackman windows with lengths of 3, 6, 12, and 24 glottal cycles and various sampling frequencies were investigated. As a result, we present an efficient algorithm that determines the parameters using the Hanning window with a length of six glottal cycles. The mean difference of Autocorrelation is less than 0.004, and that of HNR is less than 0.42 dB. In conclusion, this algorithm allows extraction of the parameters close to the reference values. In Autocorrelation, there are no significant effects of sampling frequency. However, it should be used cautiously for HNR with lower sampling rates.

Auditory Brainstem Responses at 6 and 8 kHz in Infants With Normal Hearing.

Normative auditory brainstem response (ABR) data for infants and young children are available for 0.25-4 kHz, limiting clinical assessment to this range. As such, the high-frequency hearing sensitivity of infants and young children remains unknown until behavioral testing can be completed, often not until late preschool or early school ages. The purpose of this study was to obtain normative ABR data at 6 and 8 kHz in young infants. Participants were 173 full-term infants seen clinically for ABR testing at 0.4-6.7 months chronological age (M = 1.4 months, SD = 1.0), 97% of whom were ≤ 12 weeks chronological age. Stimuli included 6 and 8 kHz tone bursts presented at a rate of 27.7/s or 30.7/s using Blackman window gating with six cycles (6 kHz) or eight cycles (8 kHz) rise/fall time and no plateau. Presentation levels included 20, 40, and 60 dB nHL. The ABR threshold was estimated in 5- to 10-dB steps. As previously observed with lower frequency stimuli, ABR waveforms obtained in response to 6 and 8 kHz tone bursts decreased in latency with increasing intensity and increasing age. Latency was shorter for 8-kHz tone bursts than 6-kHz tone bursts. Data tables are presented for clinical reference for infants ≤ 4 weeks, 4.1-8 weeks, and 8.1-12 weeks chronological age including median ABR latency for Waves I, III, and V and the upper and lower boundaries of the 90% prediction interval. Interpeak Latencies I-III, III-V, and I-V are also reported. The results from this study demonstrate that ABR assessment at 6 and 8 kHz is feasible for young infants within a standard clinical appointment and provide reference data for clinical interpretation of ABR waveforms for frequencies above 4 kHz.

MmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm

Millimeter-wave SAR (Synthetic Aperture Radar) imaging is widely studied as a common means of RF (Radio Frequency) imaging, but there are problems of the ghost image in Sparsely-Sampled cases and the projection of multiple targets at different distances. Therefore, a robust imaging algorithm based on the Analytic Fourier Transform is proposed, which is named mmSight. First, the original data are windowed with Blackman window to take multiple distance planes into account; then, the Analytic Fourier Transform that can effectively suppress the ghost image under Sparsely-Sampled is used for imaging; finally, the results are filtered using a Mean Filter to remove spatial noise. The experimental results show that the proposed imaging algorithm in this paper, relative to other algorithms, can image common Fully-Sampled single target, hidden target, and multiple targets at the same distance, and solve the ghost image problem of single target in the case of Sparsely-Sampled, as well as the projection problem of multiple targets at different distances; the Image Entropy of the mmSight is 4.6157 and is on average 0.3372 lower than that of other algorithms. Compared with other algorithms, the sidelobe and noise of the Point Spread Function are suppressed, so the quality of the image obtained from imaging is better than that of other algorithms.

Kernel-Windowed SDFT Based Frequency-Locked Loop for Capacitance Sensing on an FPGA

Sliding discrete Fourier transform with kernel windowing technique is proposed for frequency estimation of periodic signals that can be applied to capacitance sensing. SDFT is an efficient technique to compute <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> -point DFT of a signal by sliding the rectangular window one sample at a time. In the proposed work Hanning and Blackman windows replace rectangular window of SDFT to reduce the spectral leakage and increase the mainlobe width of the spectra. The concept of increased mainlobe width of the SDFT spectra has been exploited to increase the operating range of the frequency-locked loop. Kernels of these windows have been computed with certain coefficients and incorporated in the FLL to increase the operating range which increases the estimation input signal frequency. Designed FLL with Hanning window having bin index <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k} = 1$ </tex-math></inline-formula> estimates frequency from 4 to 56 kHz while with Blackman window estimates frequency from 6 to 75 kHz. The proposed scheme with kernel windowing has been implemented in FPGA and verified for enhanced performance. The experimental investigation on the proposed technique is validated for discrete capacitance sensing with maximum frequency estimation error of 0.054 and liquid level measurement of 0 - 13.5 cm with a sensitivity of 4.435 pF/mm. This method provides wide range of frequency estimation which results in wide range of capacitance measurement.

Optimum Extrapolation Techniques for Two-Dimensional Antenna Array Tapered Beamforming

Optimizing antenna arrays is essential for achieving efficient beamforming with very low sidelobe level (SLL) where adopting tapered window functions is one of the straightforward efficient techniques for achieving this goal. Recently, two-dimensional (2D) beamforming has been extensively required for many applications; therefore, this paper proposes two extrapolation techniques applied to one-dimensional (1D) tapered functions to efficiently feed 2D antenna arrays using cross-linear and adaptive radial tapering techniques. The first proposed 2D cross-linear tapering technique determines the 2D tapering coefficients by Hadamard multiplication of two right-angled grids of repeated 1D functions, while the second proposed adaptive radial tapering technique locates the antenna element in the 2D array in terms of its radial distance with respect to the array center, then converts this distance to an element index in a virtual 1D tapering window to determine the element weighting value. The adaptive radial tapering technique is optimized for achieving the minimum SLLs. The two proposed techniques are analyzed and discussed, where it is found that the adaptive radial tapering provides deeper SLLs compared to the cross-linear tapering technique. The two extrapolation techniques are examined for four window functions including triangular (Bartlett), Hamming, cosine-square, and Blackman windows, and the simulation results show that for extrapolating the Blackman window using adaptive radial tapering, a –50 dB SLL can be achieved which is independent on the array size, while cross-linear tapering provides –35 dB and –41 dB SLLs for and antenna arrays, respectively.

Direct synthesis of time domain pseudo-random 3D electromagnetic response with a band-limited source

Methods based on pseudo-random coded emission technology can effectively suppress the noise in electromagnetic exploration and improve the depth and resolution of the detection. Additionally, the synthesis of electromagnetic response signals excited by pseudo-random code sources can provide not only theoretical data for data processing but also intermediate results for evaluations of field data. Currently, pseudo-random signal simulations ignore the presence of source effects generated by band-limited sources in the Earth's impulse response. Furthermore, the simulation methods are mainly completed based on one-dimensional and two-dimensional formats and thus cannot accurately reflect the pseudo-random response of three-dimensional geoelectric structures. In this paper, 3D pseudo-random electromagnetic signals with a band-limited source were synthesized according to the propagating routine of the electromagnetic field. First, Maxwell's Equations were solved using a three-dimensional time-domain finite volume method. To avoid the singularity of the source and incorporate source effect response, the Gaussian function was introduced to simulate the band-limited impulse sources. The Earth's impulse response was obtained, which contained source effects under three-dimensional geoelectric structures. Then, ideal pseudo-random electromagnetic signals could be composed by convoluting the Earth's impulse response with a pseudo-random source waveform. Since the transmitter and receiver instruments were band-limited, a low-pass filter with a Blackman window was introduced to simulate the instrument's system response. The simulated pseudo-random signals were obtained by adding noise and the ideal pseudo-random electromagnetic signals were filtered using a low-pass filter. By comparison with signals acquired in the field, the correctness of the pseudo-random signal synthesis with source effects was successfully verified.

Design of FIR digital filters using Semi-ellipse window

A fixed window function which is similar in shape to a semi-ellipse is proposed. The semi–ellipse which has its major axis to be equal to the window length and the minor axis at unity produced about 4.2 dB lower ripple ratio than the rectangular window. The proposed window function is derived from the equation of an ellipse in the explicit and parametric forms. First of all, the spectral characteristic of the proposed window is studied in terms of spectral parameters and compared with other fixed windows like Rectangular, Bartlett, Hann, Hamming and Blackman windows. The window simulation results reveal that the proposed window produced comparable spectral characteristic with existing standard fixed windows. Secondly, the paper presents the application of the proposed window in a digital filter design. The filter analysis comparison results with other fixed windows namely Bartlett, Von Hann, Hamming, and Kaiser window, an adjustable window, confirm that filter design with the proposed window exhibits good spectral characteristic, and can be used to design better filter than the Bartlett window using less than half the Bartlett’s filter length for a fixed transition width. The similicity of its coefficients formulation and design algorithm makes it a good choice for digital filter design applications.