Butterworth High-pass Filter Research Articles

Human hand gesture recognition using fast Fourier transform with coot optimization based on deep neural network

ABSTRACT Hand motion detection is particularly important for managing the movement of individuals who have limbs amputated. The existing algorithm is complex, time-consuming and difficult to achieve better accuracy. A DNN is suggested to recognize human hand movements in order to get over these problems.Initially, the raw input EMG signal is captured then the signal is pre-processed using high-pass Butterworth filter and low-pass filter which is utilized to eliminate the noise present in the signal. After that pre-processed EMG signal is segmented using sliding window which is used for solving the issue of overlapping. Then the features are extracted from the segmented signal using Fast Fourier Transform. Then selected the appropriate and optimal number of features from the feature subset using coot optimization algorithm. After that selected features are given as input for deep neural network classifier for recognizing the hand movements of human. The simulation analysis shows that the proposed method obtain 95% accuracy, 0.05% error, precision is 94%, and specificity is 92%.The simulation analysis shows that the developed approach attain better performance compared to other existing approaches. This prediction model helps in controlling the movement of amputee patients suffering from disable hand motion and improve their living standard.

Development of a procedure for fragmenting astronomical frames to accelerate high frequency filtering

The object of this study is the process of filtering astronomical frames that contain images of potential objects in the Solar System. To contour the image of each such object and recognize it in contrast with the background of the frame, it is necessary to filter the image. Most often, a variety of high-pass filters are used to determine the high-frequency component of the image, which can be removed as a coarse-grained component. Any image filtering is aimed at increasing the signal-to-noise ratio and reducing the dynamic range of the background image. However, the filtering process is quite resource- and time-consuming. This is especially true for systems for parallel processing of series of astronomical frames in real time (online). Therefore, to solve the problem of lack of frame fragmentation, which leads to high consumption of RAM, a procedure for fragmenting astronomical frames has been proposed. Owing to the introduction of a formal connection between the values of frame pixels and fragments, as well as determining their number, it was possible to reduce RAM utilization. Testing was carried out using the following high-pass filters ‒ ideal filter, Butterworth filter, and Gaussian filter. Using the devised procedure for fragmenting astronomical frames has made it possible to reduce the utilization of RAM during filtering. As a result, with parallel processing, this has also made it possible to speed up the high-frequency filtering procedure itself. The procedure devised for fragmenting astronomical frames was tested in practice within the framework of the CoLiTec project. It was implemented in the On-Line Data Analysis System (OLDAS) of the Lemur software. The study showed that when using the devised procedure, RAM utilization was reduced by 7–10 times. And the speed of filtration itself increased by 2–3 times. Accordingly, the processing time for each astronomical frame was reduced by 2–3 times

A Multi Representation Deep Learning Approach for Epileptic Seizure Detection

Epileptic seizures, unpredictable in nature and potentially dangerous during activities like driving, pose significant risks to individual and public safety. Traditional diagnostic methods, which involve labour-intensive manual feature extraction from Electroencephalography (EEG) data, are being supplanted by automated deep learning frameworks. This paper introduces an automated epileptic seizure detection framework utilizing deep learning to bypass manual feature extraction. Our framework incorporates detailed pre-processing techniques: normalization via L2 normalization, filtering with an 80 Hz and 0,5 Hz Butterworth low-pass and high-pass filter, and a 50 Hz IIR Notch filter, channel selection based on standard deviation calculations and Mutual Information algorithm, and frequency domain transformation using FFT or STFT with Hann windows and 50% hop. We evaluated on two datasets: the first comprising 4 canines and 8 patients with 2.299 ictal, 23.445 interictal, and 32.915 test data, 400-5000Hz sampling rate across 16-72 channels; the second dataset, intended for testing, 733 icatal, 4.314 interictal, and 1908 test data, each 10 minutes long, recorded at 400Hz across 16 channels. Three deep learning architectures were assessed: CNN, LSTM, and a hybrid CNN-LSTM model-stems from their demonstrated efficacy in handling the complex nature of EEG data. Each model offers unique strengths, with the CNN excelling in spatial feature extraction, LSTM in temporal dynamics, and the hybrid model combining these advantages. The CNN model, comprising 31 layers, yielded highest accuracy, achieving 91% on the first dataset (precision 92%, recall 91%, F1-score 91%) and 82% on the second dataset using a 30-second threshold. This threshold was chosen for its clinical relevance. The research advances epileptic seizure detection using deep learning, indicating a promising direction for future medical technology. Future work will focus on expanding dataset diversity and refining methodologies to build upon these foundational results.

Research on heart rate extraction method based on mobile phone video.

As an important indicator of human health, heart rate is related to the diagnosis of cardiovascular diseases. In recent years, extracting the heart rate from the mobile phone image has become a research hotspot. However, the illumination intensity of the background, frame rate of the video, and resolution of the image influence heart rate detection accuracy. To overcome these problems, this study proposed a novel heart rate extraction method based on mobile video. Firstly, the mobile phone camera is engaged to record the finger video, the region of interest (ROI) is extracted through the iterative threshold, and the pulse signal is obtained according to the grayscale change of the resolution within the ROI. Then, a low-pass and a high-pass Butterworth filters are exploited to filter out the noise and interframes from the extracted pulse signal. Finally, an improved adaptive peak extraction algorithm is proposed to detect the pulse peaks and the heart rate derived from the difference in pulse peaks. The experimental results show that light intensity, frame rate and resolution all have an influence on the heart rate extraction accuracy, with the most obvious influence of light, the average accuracy of the experiment can reach 99.32% under good lighting conditions, while only 72.23% under poor lighting conditions. In terms of frame rate, increasing the frame rate from 30fps to 60fps, the accuracy is improved by 0.9%. For the resolution, increasing the resolution from 1080p to 2160p, the accuracy is improved by 1.12%. While comparing the proposed method with existing methods, the proposed method has a higher accuracy rate, which has important practical value and application prospects in telemedicine and daily monitoring.

Audio signal based danger detection using signal processing and deep learning

Since there have been more and more incidents of women being harassed in the recent past, girls need to think twice before going out of their houses. Sometimes, they are not even safe in their house or workplace. These circumstance does not change for children of all genders who stay alone because of their working parents or for other reasons. Also, there is no such organized procedure to ensure safety and take women and children out of such violence and harassment. To address this problem, the authors of this paper developed an Android-based automated system to detect danger for women and children using audio from the surroundings. As the Android phone is available to everyone nowadays, they focused on using this device rather than developing a system on some external hardware. Different signal processing methods with deep learning techniques are used for this work. This work also addresses noise from the environment for any chaos and nullifies them using different noise reduction techniques such as Reduce Energy Noise, Reduce Mel Frequency Cepstrum Coefficient (MFCC) up Noise, Reduce Median Noise, Reduce Centroid Noise, Audio DeNoise, Noisereduce by Sainburf et al. & Butterworth high pass filter. The Noisereduce by Sainburg et al. along with the InceptionV3 model architecture turns out to be the best to classify audio with 95.51% accuracy. A new model called AudioViT is introduced. It uses a Visual Transformer and Residual Network to identify the audio signal. The Android application also takes necessary action when any unfavorable situation is detected. Android device users can use this application without any cost, which will pave the way to ensure the safety of women and children.

Mid-Infrared Sheep Segmentation in Highland Pastures Using Multi-Level Region Fusion OTSU Algorithm

In highland pastures, grazing is a common method for managing sheep due to the abundance of grassland resources. However, it is easy for sheep to encounter situations such as stray, deviation and attacks from natural enemies; therefore, the remote monitoring of sheep in the highland pastures is an urgent problem to be solved. This paper proposes a mid-infrared sheep segmentation method based on the multi-level region fusion maximum between-class variance algorithm, i.e., OTSU algorithm, for sheep surveillance. First, a mean adjustment OTSU algorithm is designed to better distinguish the interference areas in the background. Second, the Butterworth high-pass filter is combined with the mean adjustment OTSU segmentation algorithm to remove the high-brightness interference areas in the background with slow gray intensity changes. Finally, after filtering out the large area background and small stray point, the two processed results above are fused with the AND logical operation to obtain a final segmentation result. Our algorithm is evaluated using three objective evaluation indicators: the root mean square error (RMSE), structural similarity index metric (SSIM), and peak signal to noise ratio (PSNR). The RMSE, SSIM, PSNR of highland wetland image are 0.43187, 0.99526, and 29.16353. The RMSE, SSIM, PSNR of sandy land image are 0.87472, 0.98388, and 23.87430. The RMSE, SSIM, PSNR of grassland image are 0.65307, 0.99437, and 30.33159. The results show that our algorithm can meet the requirements for the mid-infrared sheep segmentation in highland pastures.

On an Active High-Pass Network with Equal Capacitors

This short article fills up a gap in a previous publication on the design of an active Butterworth high-pass filter employing equal capacitors.

One active element implementation of fractional-order Butterworth and Chebyshev filters

Fractional-order low-pass and high-pass Butterworth and Chebyshev filters with cut-off frequencies determined independently of their order are approximated in this work. The already available schemes in the literature have a cut-off frequency pre-determined by the pole frequency value, and hence the filter’s order. The derivation of rational integer-order approximation functions, which approximate the frequency behavior of the filters, is performed through the utilization of a magnitude curve-fitting based method. The form of the transfer function is common for all filter functions, providing design versatility. Implementation is performed using just one active element, leading to a structure with minimized active component count. The evaluation of the accuracy of the employed approximation method is performed through numerical and experimental results, while the evaluation of the behavior of the active circuit topology is done using circuit simulations.

Enhancement and Restoration of Scratched Murals Based on Hyperspectral Imaging—A Case Study of Murals in the Baoguang Hall of Qutan Temple, Qinghai, China

Environmental changes and human activities have caused serious degradation of murals around the world. Scratches are one of the most common issues in these damaged murals. We propose a new method for virtually enhancing and removing scratches from murals; which can provide an auxiliary reference and support for actual restoration. First, principal component analysis (PCA) was performed on the hyperspectral data of a mural after reflectance correction, and high-pass filtering was performed on the selected first principal component image. Principal component fusion was used to replace the original first principal component with a high-pass filtered first principal component image, which was then inverse PCA transformed with the other original principal component images to obtain an enhanced hyperspectral image. The linear information in the mural was therefore enhanced, and the differences between the scratches and background improved. Second, the enhanced hyperspectral image of the mural was synthesized as a true colour image and converted to the HSV colour space. The light brightness component of the image was estimated using the multi-scale Gaussian function and corrected with a 2D gamma function, thus solving the problem of localised darkness in the murals. Finally, the enhanced mural images were applied as input to the triplet domain translation network pretrained model. The local branches in the translation network perform overall noise smoothing and colour recovery of the mural, while the partial nonlocal block is used to extract the information from the scratches. The mapping process was learned in the hidden space for virtual removal of the scratches. In addition, we added a Butterworth high-pass filter at the end of the network to generate the final restoration result of the mural with a clearer visual effect and richer high-frequency information. We verified and validated these methods for murals in the Baoguang Hall of Qutan Temple. The results show that the proposed method outperforms the restoration results of the total variation (TV) model, curvature-driven diffusion (CDD) model, and Criminisi algorithm. Moreover, the proposed combined method produces better recovery results and improves the visual richness, readability, and artistic expression of the murals compared with direct recovery using a triple domain translation network.

Fusion classification of stroke patients' biosignals by weighted cross-validation-based feature selection (W-CVFS) method

A multi-source information fusion-based disease class classification of stroke patients was implemented to address the low classification accuracy of pure input motion and electromyographic signals. sEMG sensor MYO arm ring and wearable wireless motion sensor Shimmer were used as data acquisition devices. The Butterworth high-pass filter filtering and envelope thresholding method detected the activity segment. Detection and FIR filtering using the window function method remove interference from the motion signal. A weighted cross-validation-based feature selection (W-CVFS) method is proposed for feature fusion selection. The top 10 features selected by the W-CVFS method and all 18 features are input to the deep neural network for training and testing, and the feature classification result of the W-CVFS method is 79.17%, which is better than the existing mRMR method (66.67%) and ILFS method (62.50%). The classificatip9uon accuracy of multi-source information fusion was 95.385%, which was higher than that of a single input motion signal or sEMG. The experiments showed that the proposed method can retain the features that have more influence on the classification results and can improve the classification accuracy of the rehabilitation model for stroke patients.

Universal Graph Filter Design Based on Butterworth, Chebyshev, and Elliptic Functions

Graph filters are crucial tools in processing the spectrum of graph signals. In this paper, we propose to design universal IIR graph filters with low computational complexity by using three kinds of functions, which are Butterworth, Chebyshev, and elliptic functions, respectively. Specifically, inspired by the classical analog filter design method, we first derive the zeros and poles of graph frequency responses. With these zeros and poles, we construct the conjugate graph filters to design the Butterworth high-pass graph filter, Chebyshev high-pass graph filter, and elliptic high-pass graph filter, respectively. On this basis, we further propose to construct a desired graph filter of low pass, band pass, and band stop by mapping the parameters of the desired graph filter to those of the equivalent high-pass graph filter. Furthermore, we propose to set the graph filter order given the maximum passband attenuation and the minimum stopband attenuation. Our numerical results show that the proposed graph filter design methods realize the desired frequency response more accurately than the autoregressive moving average graph filter design method, the linear least-squares fitting-based graph filter design method, and the Chebyshev FIR polynomial graph filter design method.

Robust Identification of the QRS-Complexes in Electrocardiogram Signals Using Ramanujan Filter Bank-Based Periodicity Estimation Technique

Plausibly, the first computerized and automated electrocardiogram (ECG) signal processing algorithm was published in the literature in 1961, and since then, the number of algorithms that have been developed to-date for the detection of the QRS-complexes in ECG signals is countless. Both the digital signal processing and artificial intelligence-based techniques have been tested rigorously in many applications to achieve a high accuracy of the detection of the QRS-complexes in ECG signals. However, since the ECG signals are quasi-periodic in nature, a periodicity analysis-based technique would be an apt approach for the detection its QRS-complexes. Ramanujan filter bank (RFB)-based periodicity estimation technique is used in this research for the identification of the QRS-complexes in ECG signals. An added advantage of the proposed algorithm is that, at the instant of detection of a QRS-complex the algorithm can efficiently indicate whether it is a normal or a premature ventricular contraction or an atrial premature contraction QRS-complex. First, the ECG signal is preprocessed using Butterworth low and highpass filters followed by amplitude normalization. The normalized signal is then passed through a set of Ramanujan filters. Filtered signals from all the filters in the bank are then summed up to obtain a holistic time-domain representation of the ECG signal. Next, a Gaussian-weighted moving average filter is used to smooth the time-period-estimation data. Finally, the QRS-complexes are detected from the smoothed data using a peak-detection-based technique, and the abnormal ones are identified using a period thresholding-based technique. Performance of the proposed algorithm is tested on nine ECG databases (totaling a duration of 48.91 days) and is found to be highly competent compared to that of the state-of-the-art algorithms. To the best of our knowledge, such an RFB-based QRS-complex detection algorithm is reported here for the first time. The proposed algorithm can be adapted for the detection of other ECG waves, and also for the processing of other biomedical signals which exhibit periodic or quasi-periodic nature.

ESTIMATION OF RELATIONSHIP BETWEEN INFORMATION COMPONENTS AND NOISE OF ACOUSTIC EMISSION SIGNALS

In this article, a method for processing acoustic information is presented to assess the correlation relationship of information components and noise of acoustic emission (AE) signals. The method is based on a polynomial approximation of bidirectional Butterworth high and low pass filters. The operability of the processing method on full-scale samples of the noisy AE signal is analyzed and the evaluation of the received processing is carried out on the basis of quantitative indicators. Bidirectional implementation of high-pass filters improves the quality of processing when compared with a low-pass filter. To assess the correlation relationship using the considered processing method, fragments of the information component and noise are isolated from the noisy signal. Based on the selected components, a high correlation relationship between AE information signals and noise has been established.

Hand gesture recognition based improved multi-channels CNN architecture using EMG sensors

With the continuous development of sensor and computer technology, human-computer interaction technology is also improving. Gesture recognition has become a research hotspot in human-computer interaction, sign language recognition, rehabilitation training, and sports medicine. This paper proposed a method of hand gestures recognition which extracts the time domain and frequency domain features from surface electromyography (sEMG) by using an improved multi-channels convolutional neural network (IMC-CNN). The 10 most commonly used hand gestures are recognized by using the spectral features of sEMG signals which is the input of the IMC-CNN model. Firstly, the third-order Butterworth low-pass filter and high-pass filter are used to denoise the sEMG signal. Secondly, effective sEMG signal segment from denoised signal is applied. Thirdly, the spectrogram features of different channels’ sEMG signals are merged into a comprehensive improved spectrogram feature which is used as the input of IMC-CNN to classify the hand gestures. Finally, the recognition accuracy of IMC-CNN model, three single channel CNN of IMC-CNN model, SVM, LDA, LCNN and EMGNET are compared. The experiment was carried out on the same dataset and the same computer. The experimental results showed that the recognition accuracy, sensitivity and accuracy of the proposed model reached 97.5%, 97.25% and 96.25% respectively. The proposed method not only has high average recognition accuracy on MYO collected dataset, but also has high average recognition accuracy on NinaPro DB5 dataset. Overall, the proposed model has more advantages in accuracy and efficiency than that of the comparison models.

A Comparison Study for Cardiac Arrhythmias Noise Cancellation Techniques

The electrocardiogram (ECG), which represents the electrical activity of the heart, is always chosen as the basic signal for diagnosing cardiac abnormalities and detecting the patient's states. Generally, the various filters are applied (preprocessing) to denoise the artifacts from ECG signal. This step makes the decision making and diagnosis simpler and faster. The most common noise sources are power line interference and baseline wandering. This article discusses different filtering techniques used to denoise the ECG signals. Electrocardiogram signals were downloaded from the MIT-BIH Arrhythmia Database. The MIT-BIH Database was the first set of standard test materials that is generally available to evaluate arrhythmia detection. To remove baseline wander, 4 filters were designed. The filters are a fourth-order high-pass Butterworth filter, a second-order finite impulse response Hamming window, an finite impulse response rectangular window with length L, and an finite impulse response Hanning window. To extract the power line interference (60 Hz), 4 filters were designed: a second-order digital bandpass Butterworth filter, a second-order finite impulse response filter, a Chebyshev filter, and an finite impulse response notch filter with Hanning window. Finally, 5 parameters were used to evaluate filter performance; the parameters are frequency response, phase response, average filter delay (group delay), phase delay, and signal-to-noise ratio. The results show that, for baseline wander filters, a fourth-order high-pass Butterworth filter removes more noise and has no time delay. Power line interference filters have the same signal-to-noise ratio approximately.

Bionic artificial synaptic floating gate transistor based on MXene

The ability to simulate biological neurobehavior with electronic devices has now attracted widespread attention. In the past few decades, people have tried a variety of device structures to simulate biological synaptic functions. Due to floating gate thin-film transistors' robust charge blocking and the existence of the tunneling layer, the trapped charge can be stored in a non-volatile manner, which has been considered one of the most suitable device structures for manufacturing artificial synapses. In this work, a photoelectric-stimulated artificial synaptic thin-film transistor was proposed. The MXene and self-assembled titanium dioxide on the surface serves as the floating gate layer and the tunneling layer, respectively. Moreover, the typical synaptic behaviors of synaptic transistors, such as excitatory postsynaptic current and paired-pulse facilitation, have been proven through electrical and optical pulse tests. Finally, through the simulation based on Butterworth's high-pass filter, the input images could be successfully sharpened. The characteristics of these photoelectric synapses reveal the huge potential of this device in neuromorphic vision applications. This work provides a very feasible solution for the application of artificial synaptic devices to image processing and recognition.

Speckle reduction in digital holography with non-local means filter based on the Pearson correlation coefficient and Butterworth filter.

This Letter presents a non-local means filter based on the Pearson correlation coefficient and Butterworth high-pass filter. In the method, the new gray value of the denoising pixel is equal to the weighted sum of the surrounding pixel values. We use the Pearson correlation coefficient between the pixels to calculate the weight of the surrounding pixels to the denoising pixel, then use Butterworth high-pass filter to optimize. Experimental results show that the method effectively reduces the speckle noise of digital holography and the image details are also very rich. At the same time, its performance is still better when compared with methods such as BM3D.

Comparing Different Filter-Parameter for Pre-Processing of Brain-Stimulation Evoked Motor Potentials.

Background: Brain stimulation motor-evoked potentials (MEPs) are transient signals and not periodic signals, and thus, they differ significantly in their properties from classical surface electromyograms. Unsuitable pre-processing of MEPs due to inappropriate filter settings leads to distortions. Filtering of extensor carpi radialis MEPs with transient signal characteristics of 20 subjects was examined. The effects of a 1st-order Butterworth high-pass filter (HPF) with different cut-off frequencies 1 Hz, 20 Hz, 40 Hz, and 80 Hz and a 5 Hz Butterworth high-pass filter with degrees 1st, 2nd, 4th, 8th-order are investigated for the filter output. Results: The filtering of the MEPs with an inappropriate filter setting led to distortions on the parameters peak-to-peak amplitudes of the MEP (MEPpp) and the absolute integral of the MEP (MEParea). The lowest distortions of all of the examined filter parameters were revealed after filtering with the lowest filter order and the lowest cut-off frequency. The 1st-order 1 Hz HPF calculation results in a difference of −0.53% (p < 0.001) for the MEPpp and −1.94% (p < 0.001) for the MEParea. Significance: Reproducibility is a major concern in science, including brain stimulation research. Only the filtering of the MEPs with appropriate filter settings led to mostly undistorted MEPs.

A combined algorithm for denoising GNSS-RTK positioning solutions with application to displacement monitoring of a super-high-rise building

Given that global navigation satellite system (GNSS)-based real-time kinematic (GNSS-RTK) monitoring accuracy is easily interfered with by residual errors, a combined algorithm is proposed for signal denoising with application to the GNSS-RTK positioning solutions of a super-high-rise building; namely, the Tianjin Radio and Television Tower in China. The proposed denoising algorithm is a combination of the Butterworth high-pass filter and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), after which the intrinsic mode function components are selected based on the effective coefficient, the correlation coefficient and the power spectral density. After signal filtering, the structural dynamic deformation is 36.0 mm in the horizontal directions and 48.4 mm in the up-direction. Two orders of natural frequencies of the Tianjin Radio and Television Tower are successfully identified (i.e. 0.1583 Hz and 1 Hz). In addition, the relative error of the fundamental frequency is less than 0.44% compared with previous studies. The results reveal the reliability of the combined Butterworth–CEEMDAN algorithms for dealing with GNSS-RTK measurements. Moreover, the improvement in the GNSS-RTK sampling frequency is conducive to extracting more helpful monitoring information. Furthermore, the dynamic deformation data in this paper indicate that the Tianjin Radio and Television Tower is operating normally.

CNTFET‐based active grounded inductor using positive and negative current conveyors and applications

AbstractIn this work, we design and simulate novel 32 nm carbon nanotube field effect transistor (CNTFET) as well as complementary metal oxide semiconductor (CMOS)‐based negative class AB second generation current conveyors (CC) (CNTFET‐CCII and CMOS‐CCII−). The comparative analyses of various performance measuring parameters of both these CCII's have been performed. A significant improvement in current and voltage bandwidths, terminal X and Y impedances at lesser total harmonic distortions and reduced power consumption have been observed in the proposed CNTFET‐based CCII− in comparison to its CMOS‐based counterpart. Further, a CNTFET‐based active grounded inductor (AGI) has been designed and simulated for the first time using the proposed CNTFET‐CCII− and our recently designed CNTFET‐CCII+ and has been compared with the CMOS‐based AGI. To validate the performance of the simulated AGI's, single input multi output current mode filter and third‐order high pass Butterworth filter have also been designed and simulated. The simulation results reveal that the performance of the CNTFET‐AGI‐based applications are close to ideal response with less power consumption and temperature insensitivity with reduced active chip die area of 0.16 μm2 and can be efficiently used for low voltage, low power, and high frequency applications.