High-pass Filter Research Articles

Classification of indexical and segmental features of human speech using low- and high-frequency energya).

The high-frequency region (above 4-5 kHz) of the speech spectrum has received substantial research attention over the previous decade, with a host of studies documenting the presence of important and useful information in this region. The purpose of the current experiment was to compare the presence of indexical and segmental information in the low- and high-frequency region of speech (below and above 4 kHz) and to determine the extent to which information from these regions can be used in a machine learning framework to correctly classify indexical and segmental aspects of the speech signal. Naturally produced vowel segments produced by ten male and ten female talkers were used as input to a temporal dictionary ensemble classification model in unfiltered, low-pass filtered (below 4 kHz), and high-pass filtered (above 4 kHz) conditions. Classification performance in the unfiltered and low-pass filtered conditions was approximately 90% or better for vowel categorization, talker sex, and individual talker identity tasks. Classification performance for high-pass filtered signals composed of energy above 4 kHz was well above chance for the same tasks. For several classification tasks (i.e., talker sex and talker identity), high-pass filtering had minimal effect on classification performance, suggesting the preservation of indexical information above 4 kHz.

Adaptive Technique for Contrast Enhancement of Leading Vehicle Tracks

During movement in various unpaved terrain conditions, the track impressions left over by the leading vehicles provide guiding and safe routes in the area. The delineation of these tracks captured by the images can extend immense support for guidance in real time. These tracks that look like edges in coarse-resolution images take the shape of elongated areas in fine-resolution images. In such a scenario, the high pass and edge detection filters give limited information to delineate these tracks passing through different surroundings. However, the distinct texture of these tracks assists in the delineation of these tracks from their surroundings. Gray level co-occurrence matrix (GLCM) representing the spatial relation of pixels is employed here to define the texture. The authors investigated the influence of different resolutions on the distinguishability of these tracks. The study revealed that texture plays an increasing role in distinguishing objects as the image resolution improves. The texture analysis extended to investigate the track impressions left over by the leading vehicle brings out an ample scope in delineating these tracks. The measures could improve the track contrast even better than conventional techniques. To select the most optimal contrast enhancement measure in a given scenario, authors proposed a quantified measure of track index. An investigation is made on the difference-based track index (TI) representing the mean contrast value of the track vis-à-vis off-track areas. The results show an increase in the quantified contrast from 7.83 per cent to 29.06 per cent. The proposed technique highlights the image with the highest track contrast in a given scenario. The study can lead to onboard decision-making for the rut following vehicles moving in low-contrast terrain.

300-GHz-Band Diplexer for Frequency-Division Multiplexed Wireless Communication

We propose a novel silicon diplexer integrated with filters for frequency-division multiplexing in the 300-GHz band. The diplexer consists of a directional coupler formed of unclad silicon wires, a photonic bandgap-based low-pass filter, and a high-pass filter based on frequency-dependent bending loss. These integrated filters are capable of suppressing crosstalk and providing >15 dB isolation over 40 GHz, which is highly beneficial for terahertz-range wireless communications applications. We have used this diplexer in a simultaneous error-free wireless transmission of 300-GHz and 335-GHz channels at the aggregate data rate of 36 Gbit/s.

Comparison and application of downward continuation methods for potential field data

Downward continuation highlights the details of the gravity and magnetic anomalies and improves the horizontal resolution of potential field data. However, downward continuation acts as a high-pass filter. As depth increases, any high-frequency noise will be quickly amplified and mask the effective information in the continuation field. We review 13 popular downward continuation methods and categorize them into five classes. The methods are compared and discussed in terms of continued depth and noise robustness using synthetic data and then are applied to field data from the Pobei area in Xinjiang Province, China. It is found that the noise robustness of the integral equation approach is improved using the iterative Landweber form. The frequency-domain methods based on high-frequency truncation do not require iterations and have a fast calculation speed. Using the Laplacian of the Gaussian operator to calculate the second-order vertical derivative enhances the noise robustness of the Taylor series expansion-based techniques. Segmentation of the continued depth followed by downward continuation increases the accuracy of extrapolation methods. High-order derivative terms located in the denominator of the expansion series cause instability in the continuous fractional series expansion-based techniques, and the obtained results have to be further proceeded with median filtering.

Resonance characteristics of tsunami in bay of Japan by the Hunga Tonga-Hunga Ha’apai volcano eruption on 15th January 2022

The massive eruption of the Hunga Tonga-Hunga Ha’apai (HTHH) volcano in Tonga on 15 January 2022 at 04:15 UTC had a global impact and triggered an atmospheric wave and a tsunami. We first analyzed observation data from meteorological stations and tide gauges at 12 locations. Low-frequency trends in the observation data were removed by using a high-pass filter. Fourier and wavelet spectral analyses were applied to determine the frequency characteristics of the filtered data. Modal analysis was developed and used to investigate natural oscillation periods based on bathymetry. The results showed that the Lamb wave generated by the atmospheric pressure wave arrived ~ 7 and ~ 44 h after the eruption. The tsunami arrived ~ 11 and ~ 45 h after the eruption, which corresponded to the arrival time of the Lamb wave. The dominant periods of the Lamb waves were ~ 7.7 and ~ 7.5 min, and for the tsunamis they were ~ 9.9 and ~ 28.7 min. The periods derived from the spectral analysis matched the natural oscillation of the eigenperiod derived from the modal analysis, in eight out of the twelve stations. This study provides valuable insight and information regarding the nonseismic and far-field effects of tsunamis generated by volcanic eruptions.

Robust reconfigurable radiofrequency photonic filters based on a single silicon in-phase/quadrature modulator

Abstract Combining integrated photonics and radiofrequency (RF) signals in the optical domain can help overcome the limitations of traditional RF systems. However, it is challenging to achieve environmentally insensitive filtering in wireless communications using integration schemes. In this report, the performance of robust RF filters based on a single silicon in-phase/quadrature modulator with significantly improved temperature and optical carrier wavelength sensitivities, which were suppressed by more than three orders of magnitude compared with those of silicon resonators, was experimentally evaluated. Upconversion and the processing of signals were simultaneously realized on the modulator by setting the relative phases of the arms and the bias voltages. Moreover, the filters can be reconfigured as low-pass, high-pass, band-pass, or band-stop filters. From 25 to 75 °C, the center frequency variation was within 0.2 GHz. From 1500 to 1600 nm, the center frequency variation was within 2 GHz. The proposed scheme allows for filtering and reconfiguration without the use of optical processing modules such as resonators or delay lines, thus providing a novel approach to signal processing and a new robust filter for scenarios with dynamic environments.

Design of Low-Cost Full W-Band 8th Harmonic Mixers for Frequency Extension of Spectrum Analyzer

High-order harmonic mixer is popular for frequency extension of spectrum analyzer (SA) from microwave to millimeter-wave or even terahertz band. The manufactures of SA usually offer expensive harmonic mixers where frequency extension is needed. In this work, low-cost designs of 2-port and 3-port W-band 8th harmonic mixers covering 75–110 GHz are proposed, and design method of two port mixer without frequency diplexer to separate local oscillator (LO) and intermediate frequency (IF) signals are first presented. These two kinds of mixers are compatible with almost all the current SAs with frequency extension options, which provides LO for the external harmonic mixer. The mixers are designed with planar microstrip lines and antiparallel Schottky diodes. The circuit of 2-port mixer includes the input broadband bandpass filter, diodes, output lowpass filter, and matching circuits. As for 3-port mixer, only an extra diplexer is needed to separate the IF signal and LO signal. The diplexer is composed of a planar semi-lumped lowpass and a highpass filter. The planar circuits are easily fabricated with low-cost print circuit board process on polytetrafluoroethylene substrate. The measured conversion loss of 2-port 8th harmonic mixer is from 20 to 26 dB, and 23 to 28 dB for 3-port mixer at full W-band. The good measured results indicate the proposed mixers are simple and effective.

Tri-SeizureDualNet: A novel multimodal brain seizure detection using triple stream skipped feature extraction module entrenched dual parallel attention transformer

The timely and accurate detection of epileptic seizures is highly needed to enhance the quality of a patient’s life. The state-of-the-art works to design and utilize many deep learning techniques to detect and predict seizures using EEG, fMRI, and a combination of EEG and fMRI modalities respectively. Whereas the existing models are highly vulnerable to complexity, and overfitting issues due to improper feature analysis. To this end, we design a novel seizure detection model named Triple stream skipped feature extraction module and Dual parallel attention transformer Network (Tri-SeizureDualNet) using EEG and fMRI modalities. The major scope of this research is to enhance the detection accuracy of seizures with better performance. The data acquired for this research is from the CHB-MIT database for EEG and the UW Madison database for fMRI. The designed model tends to perform feature extraction, feature selection, feature engineering, and classification respectively with high feature extraction accuracy and less complexity. Beforehand, we perform pre-processing on both the EEG and fMRI data using ICA, PCA, and high pass filtering. Followed by we perform feature extraction in three streams with skip connections from both modalities to improve the extractor capability and feature extraction accuracy. The extracted features are selected in the feature selection module using the Humming Bird Optimization (HBO) algorithm based on the feature importance ratio. The selected features from both modalities were fused and provided to the Dual Parallel Attention Transformer (DPAT) for feature engineering and context learning respectively. The utilized DPAT is composed of two regularized attention modules with a gating mechanism to picture the feature similarity rate. At last, the softmax layer classifies the features into three classes such as focal onset, general onset, and unknown onset seizures respectively. The experimental results were conducted with two utilized datasets such as CHB-MIT and UW Madison datasets separately for EEG and fMRI data in which the proposed work achieves an overall accuracy rate of 99.67% and AUC rate of 0.989 respectively. The results reveal that the proposed research outperforms the existing works.

Comparative Analysis between Singular Spectral Analysis and Empirical Mode Decomposition for Structural Damage Detection

Context: In recent years, thanks to technological advances in instrumentation and digital signal processing, noninvasive methods to detect structural damage have become increasingly important. Vibration-based structural health monitoring (SHM) techniques allow detecting the presence and location of damage from permanent changes in the fundamental frequencies of signals. A successfully employed method for damage detection is empirical mode decomposition (EMD). Another method, less used in this field of study, is singular spectral analysis (SSA). This paper describes both methods and presents a simulation study aimed at comparing them and identifying which one is more effective in detecting structural damage. Method: The methods of a reference study known as benchmark SHM were applied to facilitate the comparison. To evaluate the effectiveness of both methods, Monte Carlo simulation was employed. To control the random noise and other factors inherent to the simulation, the procedure was repeated 1.000 times for each type of damage. Results: In the case of severe damage, both methods showed a good performance. However, when the damage was slight, the changes in the fundamental frequency were not apparent. However, a significant change in the amplitude level was observed. In this case, SSA obtained the best results. Conclusions: The EMD and SSA methods, together with high-pass filtering, detected severe damage when the acceleration records had low or no noise. When the acceleration records were contaminated with noise, the likelihood of EMD detecting the damage decreased dramatically. One of the advantages of SSA over EMD is that, for moderate or mild damage patterns, the former does not require filters or the use of the Hilbert-Huang transform to detect damage. In general, it was found that SSA was more effective in detecting damage.

Mitigating Worst-case Exozodiacal Dust Structure in High-contrast Images of Earth-like Exoplanets

Detecting Earth-like exoplanets in direct images of nearby Sun-like systems brings a unique set of challenges that must be addressed in the early phases of designing a space-based direct imaging mission. In particular, these systems may contain exozodiacal dust, which is expected to be the dominant source of astrophysical noise. Previous work has shown that it may be feasible to subtract smooth, symmetric dust from observations; however, we do not expect exozodiacal dust to be perfectly smooth. Exozodiacal dust can be trapped into mean-motion resonances with planetary bodies, producing large-scale structures that orbit in lock with the planet. This dust can obscure the planet, complicate noise estimation, or be mistaken for a planetary body. Our ability to subtract these structures from high-contrast images of Earth-like exoplanets is not well understood. In this work, we investigate exozodi mitigation for Earth–Sun-like systems with significant mean-motion resonant disk structures. We find that applying a simple high-pass filter allows us to remove structured exozodi to the Poisson noise limit for systems with inclinations <60° and up to 100 zodis. However, subtracting exozodiacal disk structures from edge-on systems may be challenging, except for cases with densities <5 zodis. For systems with three times the dust of the solar system, which is the median of the best fit to survey data in the habitable zones of nearby Sun-like stars, this method shows promising results for mitigating exozodiacal dust in future Habitable Worlds Observatory observations, even if the dust exhibits significant mean-motion resonance structure.

Beyond low-pass filtering on large-scale graphs via Adaptive Filtering Graph Neural Networks

Graph Neural Networks (GNNs) have emerged as a crucial deep learning framework for graph-structured data. However, existing GNNs suffer from the scalability limitation, which hinders their practical implementation in industrial settings. Many scalable GNNs have been proposed to address this limitation. However, they have been proven to act as low-pass graph filters, which discard the valuable middle- and high-frequency information. This paper proposes a novel graph neural network named Adaptive Filtering Graph Neural Networks (AFGNN), which can capture all frequency information on large-scale graphs. AFGNN consists of two stages. The first stage utilizes low-, middle-, and high-pass graph filters to extract comprehensive frequency information without introducing additional parameters. This computation is a one-time task and is pre-computed before training, ensuring its scalability. The second stage incorporates a node-level attention-based feature combination, enabling the generation of customized graph filters for each node, contrary to existing spectral GNNs that employ uniform graph filters for the entire graph. AFGNN is suitable for mini-batch training, and can enhance scalability and efficiently capture all frequency information from large-scale graphs. We evaluate AFGNN by comparing its ability to capture all frequency information with spectral GNNs, and its scalability with scalable GNNs. Experimental results illustrate that AFGNN surpasses both scalable GNNs and spectral GNNs, highlighting its superiority.

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.

Enhancing Speech Signal Quality through Noise Reduction for Improved Communication

Abstract: Speech is a fundamental and reliable mode of human communication, conveying not only linguistic content but also essential cues about the speaker, including language, emotion, gender, and identity. However, in contemporary telephone and audio-based communication systems, speech signals are highly susceptible to the deleterious effects of ambient and Gaussian noise. To detach this noise from the signal, many Digital Audio Filters can be employed, including the Audio Weighting Filter, low-pass and High-pass filter, Band Pass Filter, and Band Stop Filter. This paper implements and compares various digital filters for audio signal enhancement using MATLAB Simulink and compares the outcome to identify the best filter for this specific purpose.

Dual-branch convolutional neural network for robust camera model identification

Identifying the model of the camera used for image capture is very useful in image forensics. To exploit complementary camera model specific features, we propose a dual-branch convolutional neural network (CNN) for camera model identification (CMI), where one branch directly uses the three-channel RGB image and the other uses a noise image obtained via high-pass filtering. For scalability, the approach operates on cropped image patches and majority voting is used for image-level CMI. We conducted extensive experiments to evaluate the proposed method on multiple datasets and compare its performance against prior approaches. For quantifying CMI accuracy, we use existing patch and image level (accuracy) metrics and also a new metric that we propose for assessing the robustness of image-level camera model estimates. Importantly, our evaluations and performance comparisons include: (a) datasets that are more representative of real-world application scenarios of current interest, where the images have undergone unknown processing as a result of sharing on social media platforms, and (b) cross-dataset scenarios where the evaluation is performed on a dataset different from and not necessarily represented by the training dataset. Our results demonstrate that the proposed approach offers improvements over the prior techniques, with particularly significant gains in accuracy for the social media dataset and for cross-dataset robustness assessment. The significant improvements over prior approaches that have used a single RGB or noise branch support our hypothesis that the proposed dual-branch architecture provides a convenient mechanism to introduce a favorable inductive bias in CNN architectures for CMI.

Denoising task-related fMRI: Balancing noise reduction against signal loss.

Preprocessing fMRI data requires striking a fine balance between conserving signals of interest and removing noise. Typical steps of preprocessing include motion correction, slice timing correction, spatial smoothing, and high-pass filtering. However, these standard steps do not remove many sources of noise. Thus, noise-reduction techniques, for example, CompCor, FIX, and ICA-AROMA have been developed to further improve the ability to draw meaningful conclusions from the data. The ability of these techniques to minimize noise while conserving signals of interest has been tested almost exclusively in resting-state fMRI and, only rarely, in task-related fMRI. Application of noise-reduction techniques to task-related fMRI is particularly important given that such procedures have been shown to reduce false positive rates. Little remains known about the impact of these techniques on the retention of signal in tasks that may be associated with systemic physiological changes. In this paper, we compared two ICA-based, that is FIX and ICA-AROMA, two CompCor-based noise-reduction techniques, that is aCompCor, and tCompCor, and standard preprocessing using a large (n = 101) fMRI dataset including noxious heat and non-noxious auditory stimulation. Results show that preprocessing using FIX performs optimally for data obtained using noxious heat, conserving more signals than CompCor-based techniques and ICA-AROMA, while removing only slightly less noise. Similarly, for data obtained during non-noxious auditory stimulation, FIX noise-reduction technique before analysis with a covariate of interest outperforms the other techniques. These results indicate that FIX might be the most appropriate technique to achieve the balance between conserving signals of interest and removing noise during task-related fMRI.

Titania Nanoparticle-Stimulated Ultralow Frequency Detection and High-Pass Filter Behavior of a Flexible Piezoelectric Nanogenerator: A Self-Sustaining Energy Harvester for Active Motion Tracking.

A significant driving force for the fabrication of IoT-compatible smart health gear integrated with multifunctional sensors is the growing trend in fitness and the overall wellness of the human body. In this work, we present an autonomous motion and activity-sensing device based on the efficacious nucleation of the polar β-phase in an electroactive polymer. Representatively, we investigate the nucleating effect of TiO2 nanoparticles on weight-modulated PVDF-HFP films (PT-5, PT-10, and PT-15) and subsequently prototype a sensing device with the film that demonstrates superior β-phase nucleation. The PT-10 film, with an optimal polar β-phase, shows the highest remnant polarization (2Pr) and energy density of 0.36 μC/cm2 and 22.3 mJ/cm3, respectively, at 60 kV/cm. The films mimic a high pass filter at frequencies above 10 KHz with very low impedance and high ac conductivity values. The frequency-dependent impedance studies reveal an effective interfacial polarization between TiO2 nanoparticles and PVDF-HFP, explicitly observed in the low-frequency region. Consequently, the sensor fabricated with PT-10 as the sensing layer exhibits ultralow frequency detection (25 Hz) resulting from the blood flow muscle oxygenation. The device successfully senses voluntary joint movements of the human body and actively tracks a range of motions, from brisk walking to running. Additionally, through repetitive human finger-tapping motion, the nanogenerator lights up multiple light-emitting diodes in series and charges capacitors of varying magnitudes under 50 s. The real-time human motion sensing and movement tracking modalities of the sensor hold promise in the arena of smart wearables, sports biomechanics, and contact-based medical devices.

Comparison of filtering methods for real-time extraction of the volitional EMG component in electrically stimulated muscles

ObjectiveRecorded electromyograms (EMG) of electrically stimulated muscles can contain both an exogenous-evoked potential (M-wave) and an endogenous, or volitional, component. This study evaluated the effectiveness of three filtering methods (i.e., high-pass, adaptive, and comb), commonly used in neurorehabilitation, in extracting the volitional component of simulated and experimental EMG during upper-limb tasks. MethodsVolitional EMG and M-wave were simulated through a physiological model of muscle recruitment, comprising of a motor neuron pool and associated muscle fibres, superimposed to a stimulation artefact. Experimental EMG data during different levels of volitional muscle contraction in isometric and dynamic tasks were recorded from five unimpaired individuals. Electrical stimulation artefact was removed with different techniques (i.e., none, removing samples, blanking, and interpolation) to assess filter performance across time and frequency domains, and information content (i.e., Kolmogorov-Smirnov D-value). ResultsThe experimental results agreed with the simulations, wherein the adaptive filter outperformed the other filters when using no artefact removal or removing artefact samples from the signal, while for the blanking and interpolation artefact removal methods, the adaptive and comb filters outperformed the high-pass filter. ConclusionThe adaptive and comb filters best estimated volitional muscle activity in electrically stimulated muscles. SignificanceResults from this study will enable the enhanced design of real-time neuroprosthesis control.

A scintillation dosimeter with real-time positional tracking information for in vivo dosimetry error detection in HDR brachytherapy.

To evaluate the performance of an electromagnetic (EM)-tracked scintillation dosimeter in detecting source positional errors of IVD in HDR brachytherapy treatment. Two different scintillator dosimeter prototypes were coupled to 5 degrees-of-freedom (DOF) EM sensors read by an Aurora V3 system. The scintillators used were a 0.3 × 0.4 × 0.4 mm3 ZnSe:O and a BCF-60 plastic scintillator of 0.5 mm diameter and 2.0 mm in length (Saint-Gobain Crystals). The sensors were placed at the dosimeter's tip at 20.0 mm from the scintillator. The EM sampling rate was 40/s while the scintillator signal was sampled at 100 000/s using two photomultiplier tubes from Hamamatsu (series H10722) connected to a data acquisition board. A high-pass filter and a low-pass filter were used to separate the light signal into two different channels. All measurements were performed with an afterloader unit (Flexitron-Elekta AB, Sweden) in full-scattered (TG43) conditions. EM tracking was further used to provide distance/angle-dependent energy correction for the ZnSe:O inorganic scintillator. For the error detection part, lateral shifts of 0.5 to 3 mm were induced by moving the source away from its planned position. Indexer length (longitudinal) errors between 0.5 to 10 mm were also introduced. The measured dose rate difference was converted to a shift distance, with and without using the positional information from the EMsensor. The inorganic scintillator had both a signal-to-noise-ratio (SNR) and signal-to-background-ratio (SBR) close to 70 times higher than those of the plastic scintillator. The mean absolute difference from the dose measurement to the dose calculated with TG-43U1 was 1.5% ±0.7%. The mean absolute error for BCF-60 detector was 1.7% when compared to TG-43 calculations formalism. With the inorganic scintillator and EM tracking, a maximum area under the curve (AUC) gain of 24.0% was obtained for a 0.5-mm lateral shift when using the EMT data with the ZnSe:O. Lower AUC gains were obtained for a 3-mm lateral shifts with both scintillators. For the plastic scintillator, the highest gain from using EM tracking information occurred for a 0.5-mm lateral shift at 20 mm from the source. The maximal gain (17.4%) for longitudinal errors was found at the smallest shifts (0.5 mm). This work demonstrates that integrating EM tracking to in vivo scintillation dosimeters enables the detection of smaller shifts, by decreasing the dosimeter positioning uncertainty. It also serves to perform position-dependent energy correction for the inorganic scintillator,providing better SNR and SBR, allowing detection of errors at greater distances from thesource.

Ultra-fast ultrasound blood flow velocimetry for carotid artery with deep learning

Accurate measurement of blood flow velocity is important for the prevention and early diagnosis of atherosclerosis. However, due to the uncertainty of parameter settings, the autocorrelation velocimetry methods based on clutter filtering are prone to incorrectly filter out the near-wall blood flow signal, resulting in poor velocimetric accuracy. In addition, the Doppler coherent compounding acts as a low-pass filter, which also leads to low values of blood flow velocity estimated by the above methods. Motivated by this status quo, here we propose a deep learning estimator that combines clutter filtering and blood flow velocimetry based on the adaptive property of one-dimensional convolutional neural network (1DCNN). The estimator is operated by first extracting the blood flow signal from the original Doppler echo signal through an affine transformation of the 1D convolution, and then converting the extracted signal into the desired blood flow velocity using a linear transformation function. The effectiveness of the proposed method is verified by simulation as well as in vivo carotid artery data. Compared with typical velocimetry methods such as high-pass filtering (HPF) and singular value decomposition (SVD), the results show that the normalized root means square error (NRMSE) obtained by 1DCNN is reduced by 54.99 % and 53.50 % for forward blood flow velocimetry, and 70.99 % and 69.50 % for reverse blood flow velocimetry, respectively. Consistently, the in vivo measurements demonstrate that the goodness-of-fit of the proposed estimator is improved by 8.72 % and 4.74 % for five subjects. Moreover, the estimation time consumed by 1DCNN is greatly reduced, which costs only 2.91 % of the time of HPF and 12.83 % of the time of SVD. In conclusion, the proposed estimator is a better alternative to the current blood flow velocimetry, and is capable of providing more accurate diagnosis information for vascular diseases in clinical applications.

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.