Portion Of Signal Research Articles

Short Term Wind Forecasting Using Machine Learning Models with Noise Assisted Data Processing Method

In order to address the complex and intermittent nature of wind, this thesis proposes an innovative approach to enhance the accuracy of short-term wind forecasting. By leveraging the strengths of different methods while mitigating their weaknesses, a robust hybrid model is developed. The methodology incorporates Empirical Mode Decomposition (EMD), a data-adaptive denoising technique, to break down the signals into meaningful components called Intrinsic Mode Functions (IMFs), along with a residue. However, EMD is known to suffer from the mode mixing problem, where different scales of signals are erroneously mixed within IMFs, causing signal intermittency. To overcome this challenge, Ensemble Empirical Mode Decomposition is introduced, utilizing an ensemble of white noise to establish a uniform reference frame in the time-frequency space. By doing so, the added noise effectively collates signal portions with similar scales into a single IMF. Subsequently, the IMFs and residue obtained from both EMD and Ensemble Empirical Mode Decomposition are fed into a Convolutional Neural Network (CNN). The performance of this hybrid model is then compared against benchmark models such as Bi LSTM and LSTM. Evaluation is conducted based on two critical factors: performance metrics including MSE, MAE, RMSE, MAPE, and loss, as well as the time required for testing. Through a comprehensive analysis of these factors, the superior performance of the hybrid model is determined, thereby enhancing the prospects of reliable wind forecasting.

Groundwater Level Variation Analysis Using Hydrogeophysical Methods in an Area of Campo Sujo in Cerrado, Chapada dos Veadeiros Region, Goiás

Cerrado’s campo sujo areas have been one of the main focuses of anthropic occupation in the Chapada dos Veadeiros region, Brazil’s Central Plateau, as they are easily accessible flattened areas with less dense vegetation. They are usually associated with wetlands representing excellent water reservoirs and groundwater recharge zones. The environmental characterization and analysis of water level variation are essential to investigate the impacts of human occupation. Hydrogeophysics represents one of the main subsurface research tools due to its easy application and efficiency in identifying the water level, with emphasis on Ground-penetrating radar (GPR), and electrical resistivity tomography (ERT). An analysis of GPR sections with 200, 400, and 900 MHz frequency antennas associated with the resistivity model was carried out to identify structures, and map the groundwater level. The overlapping data compose a hydrogeophysical model with good correlation to direct measurements of water level in a monitoring well, and soil horizons mapped in a trench. The GPR proved to be efficient in mapping the water level, mainly about to the survey with a 400 MHz antenna, shown as a horizontal reflector associated with attenuation portions of the reflection signal, registering in profile the lowering of the water level from 1.68 m in May to 3.35 m in August. The resistivity model showed a good correlation with the variations between the mapped soil horizons. The analysis shows that constructing a hydrogeophysical model is an excellent alternative for identifying the water level, and characterizing the shallow subsurface by applying non-invasive techniques. The study area represents a preserved area of campo sujo, and the research data can be used for comparison with future surveys, in addition to representing a base hydrogeophysics methodology that showed promising results for the physiographic characteristics of the area, which can be replicated in regions of similar geoenvironmental aspects.

Subtracting the kinetic Sunyaev-Zeldovich effect from the cosmic microwave background with surveys of large-scale structure

The kinetic Sunyaev-Zeldovich (kSZ) effect will be an important source of cosmological and astrophysical information in upcoming surveys of the cosmic microwave background (CMB). However, the kSZ effect will also act as the dominant source of noise for several other measurements that use small angular scales in CMB temperature maps, since its blackbody nature implies that standard component separation techniques cannot be used to remove it from observed maps. In this paper, we explore the idea of ``de-kSZing'': constructing a template for the late-time kSZ effect using external surveys of large-scale structure, and then subtracting this template from CMB temperature maps in order to remove some portion of the kSZ signal. After building intuition for general aspects of the de-kSZing procedure, we perform forecasts for the de-kSZing efficiency of several large-scale structure surveys, including BOSS, DESI, Roman, MegaMapper, and PUMA. We also highlight potential applications of de-kSZing to cosmological constraints from the CMB temperature power spectrum, CMB lensing reconstruction, and the moving-lens effect. While our forecasts predict achievable de-kSZing efficiencies of 10%--20% at best, these results are specific to the de-kSZing formalism adopted in this work, and we expect that higher efficiencies are possible using improved versions of this formalism.

Supervised Contrastive Learning for Voice Activity Detection

The noise robustness of voice activity detection (VAD) tasks, which are used to identify the human speech portions of a continuous audio signal, is important for subsequent downstream applications such as keyword spotting and automatic speech recognition. Although various aspects of VAD have been recently studied by researchers, a proper training strategy for VAD has not received sufficient attention. Thus, a training strategy for VAD using supervised contrastive learning is proposed for the first time in this paper. The proposed method is used in conjunction with audio-specific data augmentation methods. The proposed supervised contrastive learning-based VAD (SCLVAD) method is trained using two common speech datasets and then evaluated using a third dataset. The experimental results show that the SCLVAD method is particularly effective in improving VAD performance in noisy environments. For clean environments, data augmentation improves VAD accuracy by 8.0 to 8.6%, but there is no improvement due to the use of supervised contrastive learning. On the other hand, for noisy environments, the SCLVAD method results in VAD accuracy improvements of 2.9% and 4.6% for “speech with noise” and “speech with music”, respectively, with only a negligible increase in processing overhead during training.

Spectral analysis of ultrasound backscatter for non-invasive measurement of plaque composition

Carotid atherosclerotic plaque composition may be an important indication of patient risk for future cerebrovascular events. Ultrasound spectral analysis has the potential to provide a robust measure of plaque composition in vivo if the backscatter transfer function can be sufficiently isolated from the effects of attenuation from overlying tissue, receive and transmit transfer functions from the ultrasound system and transducer, and diffraction. This study examines the usefulness of the nonlinearly generated second harmonic portion of the backscatter signal and the effects of a variety of attenuation compensation techniques for noninvasively characterizing human carotid plaque using spectral analysis and machine learning. Post-beamformed ultrasound backscatter radiofrequency (RF) data were acquired from 6 normal subjects and 119 carotid endarterectomy patients prior to surgery. Plaque obtained following surgery was histologically processed, and regions of interest (ROI) corresponding to homogenous tissue types (fibrous/fibro-lipidic, hemorrhagic and/or necrotic core and calcified) were selected from RF data. Both the harmonic and fundamental power spectra for each ROI was obtained and normalized by data from a uniform phantom (0.5 dB/cm-MHz slope of attenuation). Additional attenuation compensation approaches were compared to simply using the reference phantom: (1) optimum power spectral shift estimation, (2) one-step adventitial, or (3) two-step adventitial. Spectral parameters extracted from both the fundamental and harmonic estimates of the backscatter transfer function of 363 ROI’s from 152 plaque specimens were used to train and test random forest and support vector machine classification models. The best results came from using spectral parameters derived from both the fundamental and second harmonic bands with a predictive accuracy of 65–68%, kappa statistic of 0.49–0.54, and accuracies of 84% for fibrous, 68–74% for hemorrhagic and/or necrotic core, and 78–81% for calcified ROI’s. The result indicated that the nonlinearly generated second harmonic portion of backscatter is useful for carotid plaque tissue characterization and that a reference phantom approach with a 0.5 dB/cm-MHz slope of attenuation works as well as more complicated approaches.

Freely available dataset of long-lasting surface electromyographic signals during walking

It is acknowledged that surface electromyography (sEMG) signals vary from subject to subject and even within the same person [1]. In this scenario, it is important to analyze the natural variability associated with muscle activity during free walking to improve the interpretation of sEMG signals in both physiological and pathological conditions. Although few strides might be enough in some applications, the analysis of a larger number of gait cycles is highly recommended to analyze the variability of muscle recruitment during walking. However, free databases including long-lasting sEMG signals during walking are very uncommon to find. To fill this gap, the present study aims to introduce a database composed of long-lasting (around 5 minutes) surface EMG signals recorded from 2011 and 2018 during ground walking of 31 young able-bodied subjects. Synchronized basographic and electrogoniometric data are provided to allow users to achieve a spatial/temporal characterization of the sEMG signals. Basographic, electrogoniometric, and sEMG signals were acquired in a population of 31 young able- bodied subjects at Movement Analysis Lab, Università Politecnica delle Marche, Ancona, Italy. Inclusion criteria: 20 years < age < 30 years; 18.5 Kg/m 2 < body mass index, BMI < 25 Kg/m 2 . Subjects with pathological condition, joint pain, or undergone orthopedic surgery were excluded. All signals were recorded with a sampling rate of 2 kHz and a resolution of 12 bit by the multichannel recording system Step32, Medical Technology, Italy. Probes were applied over gastrocnemius lateralis (GL), tibialis anterior (TA), rectus femoris (RF), vastus lateralis (VL), and hamstrings (Ham) in both legs. Subjects have been asked to walk barefoot on level ground for around 5 min at their natural speed and pace, following an eight-shaped path which includes rectilinear segments and curves, including reversing, acceleration, and deceleration. The data base is composed of a total of 310 sEMG signals of average duration = 258 ± 53 s. Associated basographic and electrogoniometric data are provided. An example of a portion of basographic, electrogoniometric, and sEMG signals in a single stride is depicted in the following Fig. 1. Data are reported in function of gait cycle percentage. The considerable length of the signals makes this dataset very suitable for those studies where the numerosity of the data is essential, such as machine/deep learning approaches, studies for analyzing and quantifying the variability of muscle recruitment during physiological walking, creation of reference dataset in the characterization of pathological condition, and the validation of novel sEMG-based algorithms. This dataset is freely available, consulting the public repository of medical research data PhysioNet [2,3].

Regional Language Speech Recognition from Bone-Conducted Speech Signals through Different Deep Learning Architectures.

Bone-conducted microphone (BCM) senses vibrations from bones in the skull during speech to electrical audio signal. When transmitting speech signals, bone-conduction microphones (BCMs) capture speech signals based on the vibrations of the speaker's skull and have better noise-resistance capabilities than standard air-conduction microphones (ACMs). BCMs have a different frequency response than ACMs because they only capture the low-frequency portion of speech signals. When we replace an ACM with a BCM, we may get satisfactory noise suppression results, but the speech quality and intelligibility may suffer due to the nature of the solid vibration. Mismatched BCM and ACM characteristics can also have an impact on ASR performance, and it is impossible to recreate a new ASR system using voice data from BCMs. The speech intelligibility of a BCM-conducted speech signal is determined by the location of the bone used to acquire the signal and accurately model phonemes of words. Deep learning techniques such as neural network have traditionally been used for speech recognition. However, neural networks have a high computational cost and are unable to model phonemes in signals. In this paper, the intelligibility of BCM signal speech was evaluated for different bone locations, namely the right ramus, larynx, and right mastoid. Listener and deep learning architectures such as CapsuleNet, UNet, and S-Net were used to acquire the BCM signal for Tamil words and evaluate speech intelligibility. As validated by the listener and deep learning architectures, the Larynx bone location improves speech intelligibility.

Selective blockade of rat brain T-type calcium channels provides insights on neurophysiological basis of arousal dependent resting state functional magnetic resonance imaging signals.

A number of studies point to slow (0.1–2 Hz) brain rhythms as the basis for the resting-state functional magnetic resonance imaging (rsfMRI) signal. Slow waves exist in the absence of stimulation, propagate across the cortex, and are strongly modulated by vigilance similar to large portions of the rsfMRI signal. However, it is not clear if slow rhythms serve as the basis of all neural activity reflected in rsfMRI signals, or just the vigilance-dependent components. The rsfMRI data exhibit quasi-periodic patterns (QPPs) that appear to increase in strength with decreasing vigilance and propagate across the brain similar to slow rhythms. These QPPs can complicate the estimation of functional connectivity (FC) via rsfMRI, either by existing as unmodeled signal or by inducing additional wide-spread correlation between voxel-time courses of functionally connected brain regions. In this study, we examined the relationship between cortical slow rhythms and the rsfMRI signal, using a well-established pharmacological model of slow wave suppression. Suppression of cortical slow rhythms led to significant reduction in the amplitude of QPPs but increased rsfMRI measures of intrinsic FC in rats. The results suggest that cortical slow rhythms serve as the basis of only the vigilance-dependent components (e.g., QPPs) of rsfMRI signals. Further attenuation of these non-specific signals enhances delineation of brain functional networks.

Security of Optical Beam Splitter in Quantum Key Distribution

The optical beam splitter is an essential device used for decoding in quantum key distribution. The impact of optical beam splitters on the security of quantum key distribution was studied, and it was found that the realistic device characteristics closely influence the error rate introduced by the wavelength-dependent attack on optical beam splitters. A countermeasure, combining device selection and error rate over-threshold alarms, is proposed to protect against such attacks. Beam splitters made of mirror coatings are recommended, and the variation of splitting ratio should be restricted to lower than 1 dB at 1260–1700 nm. For the partial attack scenario where the eavesdropper attacks only a portion of the quantum signal, a modified secure key rate formula is proposed to eliminate the revealed information of the attacked portion. Numerical results show that the QKD system adopting this countermeasure exhibits good performance with a secure key rate of over 10 kbps at 100 km and a maximum transmission distance of over 150 km, with only a small difference from the no-attack scenario. Additionally, a countermeasure to monitor the light intensity of different wavelengths is proposed to protect against the wavelength-dependent attack on optical beam splitters.

Battery state-of-charge estimation using machine learning analysis of ultrasonic signatures

The potential of acoustic signatures to be used for State-of-Charge (SoC) estimation is demonstrated using artificial neural network regression models. This approach represents a streamlined method of processing the entire acoustic waveform instead of performing manual, and often arbitrary, waveform peak selection. For applications where computational economy is prioritised, simple metrics of statistical significance are used to formally identify the most informative waveform features. These alone can be exploited for SoC inference. It is further shown that signal portions representing both early and late interfacial reflections can correlate highly with the SoC and be of predictive value, challenging the more common peak selection methods which focus on the latter. Although later echoes represent greater through-thickness coverage, and are intuitively more information-rich, their presence is not guaranteed. Holistic waveform treatment offers a more robust approach to correlating acoustic signatures to electrochemical states. It is further demonstrated that transformation into the frequency domain can reduce the dimensionality of the problem significantly, while also improving the estimation accuracy. Most importantly, it is shown that acoustic signatures can be used as sole model inputs to produce highly accurate SoC estimates, without any complementary voltage information. This makes the method suitable for applications where redundancy and diversification of SoC estimation approaches is needed. Data is obtained experimentally from a 210 mAh LiCoO2/graphite pouch cell. Mean estimation errors as low as 0.75% are achieved on a SoC scale of 0–100%.

Design and simulation of a compact graphene-based plasmonic D flip-flop

In this paper, a compact graphene-based plasmonic D flip-flop is presented where the graphene waveguides are sandwiched between two SnO2 layers. The structure includes two input ports CLK and D, and two output ports Q and Q'. The input signals transmit through the waveguides and interfere with other signals. A portion of input signal D appears at port Q, and another portion interferes with the incoming signal from port CLK then is guided toward port Q'. The commercial Lumerical software is used to solve Maxwell’s equations and simulate the propagation of terahertz waves. The finite difference time domain method is used to calculate the electric and magnetic components of waves throughout the structure. The structure is studied at the transverse magnetic mode, and the perfectly matched layer is assumed at boundaries. The wavelength is assumed from 12.7 µm to 13.1 µm, and the area of the structure is 0.83 µm2. The small area of the designed flip-flop is an essential feature for use in optical integrated circuits. The contrast ratio of outputs Q and Q' is about 7.3 dB and 13 dB, respectively which is more than the previous work. In comparison to the last work which had one port for signal Q, two output ports for signals Q and Q' are designed in this work. This feature is needed for employing the flip-flop at sequential circuits.

Operando electrochemical Kerr Gated Raman Spectroscopy to Probe the High States of Charge in Graphite Electrodes for Li-Ion Batteries

In order to improve the lifetime of lithium ion (Li-ion) cells, methods to reliably probe the cell state of charge are essential to track lithium inventory loss and understand the relation to key degradation processes in the cell. NMR and X-ray/neutron scattering techniques have been demonstrated to probe the full range of intercalation from pure graphite through to stage 1 LiC6.1-3 While Raman spectroscopy/microscopy has been proven a powerful tool for the probing the earlier stages 4 and 3 of Li intercalation into graphite electrodes, the process is plagued by significant growth in competing fluorescence/emission signals as the intercalation proceeds through the lower stages between Li0.5C6 and LiC6. Coupled with a loss of Raman scattering intensity due to increased conductivities of the lithiated graphite,4, 5 the graphite bands also become swamped by the growth of overlapping fluorescence/emission signals, becoming difficult to reliably observe/assign. Consequently, diagnosing the state of charge of the highly lithiated graphite electrode by Raman spectroscopy is challenging.To overcome this, we report in this work the use of operando electrochemical Kerr gated Raman spectroscopy to track the critical changes in the graphitic bands during the Li intercalation process into a graphite negative electrode. Kerr gated Raman spectroscopy is a fluorescence suppression technique that exploits the varied time domains of the Raman scattering and fluorescence/emission processes. We have previously reported on the use of Kerr gated Raman spectroscopy as an effective tool to measure the Raman spectra of highly fluorescing, degraded/aged battery electrolyte materials based on the lithium hexafluorophosphate salt.6 In this work, a dedicated operando measurement cell was designed to permit observation of the graphite working electrode in the Li|graphite half-cell during electrochemical Li intercalation. Therein, the Kerr gated Raman spectra are compared with conventional continuous wave Raman spectroscopy, outlining the benefits and limitations of both methodologies.Importantly, owing to the efficacy of the Kerr gate in filtering out a significant portion of the problematic emission signals, the graphitic Raman bands could be observed even during the transitions through intercalation stages 2 and 1 (i.e., through Li0.5C6 to LiC6) with much greater clarity than has been achieved by conventional Raman techniques. K. Märker, C. Xu and C. P. Grey, J. Am. Chem. Soc., 142, 17447 (2020). S. Taminato, M. Yonemura, S. Shiotani, T. Kamiyama, S. Torii, M. Nagao, Y. Ishikawa, K. Mori, T. Fukunaga, Y. Onodera, T. Naka, M. Morishima, Y. Ukyo, D. S. Adipranoto, H. Arai, Y. Uchimoto, Z. Ogumi, K. Suzuki, M. Hirayama and R. Kanno, Sci. Rep., 6, 28843 (2016). A. H. Whitehead, K. Edström, N. Rao and J. R. Owen, J. Power Sources, 63, 41 (1996). M. Inaba, H. Yoshida, Z. Ogumi, T. Abe, Y. Mizutani and M. Asano, J. Electrochem. Soc., 142, 20 (1995). L. J. Hardwick, H. Buqa and P. Novák, Solid State Ionics, 177, 2801 (2006). L. Cabo-Fernandez, A. R. Neale, F. Braga, I. V. Sazanovich, R. Kostecki and L. J. Hardwick, Phys. Chem. Chem. Phys., 21, 23833 (2019).

Combating False Data Injection Attacks on Human-Centric Sensing Applications

The recent prevalence of machine learning-based techniques and smart device embedded sensors has enabled widespread human-centric sensing applications. However, these applications are vulnerable to false data injection attacks (FDIA) that alter a portion of the victim's sensory signal with forged data comprising a targeted trait. Such a mixture of forged and valid signals successfully deceives the continuous authentication system (CAS) to accept it as an authentic signal. Simultaneously, introducing a targeted trait in the signal misleads human-centric applications to generate specific targeted inference; that may cause adverse outcomes. This paper evaluates the FDIA's deception efficacy on sensor-based authentication and human-centric sensing applications simultaneously using two modalities - accelerometer, blood volume pulse signals. We identify variations of the FDIA such as different forged signal ratios, smoothed and non-smoothed attack samples. Notably, we present a novel attack detection framework named Siamese-MIL that leverages the Siamese neural networks' generalizable discriminative capability and multiple instance learning paradigms through a unique sensor data representation. Our exhaustive evaluation demonstrates Siamese-MIL's real-time execution capability and high efficacy in different attack variations, sensors, and applications.

Cleaned Hydrophone Array Logging Data Aids Identification of Wellbore Leaks

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201512, “Enhanced Wellbore-Leak Localization With Estimation and Removal of Guided Wave Noise Using Array Hydrophone Logging Data,” by Yao Ge, Ruijia Wang, and Yi Yang Ang, Halliburton, et al. The paper has not been peer reviewed. Leaks in wellbore tubulars emit acoustic waves in the borehole that can be captured by a hydrophone array. Processing the array data yields location and energy level of the wellbore leaks; however, the hydrophones also may capture other coherent noise propagating as guided waves along the borehole. The complete paper describes an approach to estimate and subsequently remove the guided-wave noise from the hydrophone array data to improve the accuracy of leak-source locations. Estimating the propagation direction and amplitude of leak-induced guided waves aids logging operations to locate a leak source efficiently. Noise Logging and Well Integrity Well integrity has become a focused area for most operators given the long life cycle and complex structure of a wellbore. Noise-logging tools have been developed to detect flow or leak in a wellbore. A typical noise-logging tool consists of one or two acoustic sensors logged through the depth of the well that produce the amplitude and frequency spectrum-log of the received acoustic signals. A noise-logging tool usually operates in two modes—the continuous (or dynamic) mode and the stationary mode. In the continuous mode, the tool usually is logged through all accessible depths at a constant speed. Based on the data from the continuous pass, specific depths are identified and additional stationary mode passes are performed to stop the tool at selected depths and acquire higher-quality data. A major issue of the data from continuous logging is the presence of road noise, which is created by the centralizer, cable, or any part of the tool strings scratching against the casing or tubing. To reduce the effect of the road noise, a high-pass filter usually is applied to remove the signal from the lower-frequency range corresponding to the range of the road noise. However, this procedure could affect the quality of the leak signal when a portion of the leak signal lies in the lower-frequency range. The logging data taken when the tool is stationary for 1 minute or more at a target depth is free from road noise, but this approach prolongs logging time and limits depth resolution. Aside from road noise, other forms of propagating noise exist in the wellbore, such as noise from surface or downhole equipment or propagating noise induced by a leak source. Road noise and all forms of propagating noises are referred to as guided-wave noise in the complete paper. The guided waves usually propagate along the borehole’s axis for a long distance with low attenuation. However, traditional noise-logging tools with one or more omnidirectional hydrophones are unable to distinguish guided-wave noise from leak signals. Recent advances in noise-logging-tool design use array hydrophones to provide not only depth but also radial location of downhole leaks. The array hydrophones also enable an advanced array-processing method to measure and suppress the guided-wave noise.

Analysis of Atmospheric Radiosulfur at Natural Abundance by a New-Type Liquid Scintillation Counter Equipped with Guard Compensation Technology

High-sensitivity measurements of radiosulfur (cosmogenic 35S; half-life: 87.4 days) at natural abundance using ultra-low-level liquid scintillation counter (LSC) methods have been developed and optimized in the last decade, providing new details in space, atmospheric, and hydrological sciences. These LSC methods heavily rely on instruments conventionally equipped with 650 kg lead blocks that passively shield cosmic and environmental background radiation, but this type of instrument is not commercially available anymore, hindering further applications of 35S. To solve this problem, we extended the methods to a new-type LSC equipped with new mathematics-based active shielding techniques (Guard Compensation Technology; GCT). The counting efficiency of the new-type LSC for low-35S activity samples (e.g., natural samples) is low and highly variable because a portion of true signals from 35S decay events was undesirably removed by GCT. We therefore developed a new data processing protocol to determine 35S activities accurately and precisely in the range between ∼1 and ∼13 disintegrations per minute, and its validity was tested by working standards with known 35S activities. As an application example, we measured concentrations of 35S in sulfate aerosols collected in Guangzhou, a megacity in subtropical South China. The obtained values are within the range of previously reported data from various mid-latitude sampling sites. Based on these results, we conclude that our protocol allows the continuing utility of 35S measurements using a new-type LSC for a deeper understanding of the atmospheric sulfur cycle and its influences on the environment, climate, and public health.

All-optical tunable wavelength conversion in opaque nonlinear nanostructures

Abstract We demonstrate a simple, femtosecond-scale wavelength tunable, subwavelength-thick nanostructure that performs efficient wavelength conversion from the infrared to the ultraviolet. The output wavelength can be tuned by varying the input power of the infrared pump beam and/or relative delay of the control beam with respect to the pump beam, and does not require any external realignment of the system. The nanostructure is made of chalcogenide glass that possesses strong Kerr nonlinearity and high linear refractive index, leading to strong field enhancement at Mie resonances. Although, as many other materials, chalcogenide glasses absorb in the ultraviolet range, fundamental phase-locking mechanism between the pump and the inhomogeneous portion of the third-harmonic signal enables ultraviolet transmission with little or no absorption.

Gravitational Waves from a Core-Collapse Supernova: Perspectives with Detectors in the Late 2020s and Early 2030s

We studied the detectability and reconstruction of gravitational waves from core-collapse supernova multidimensional models using simulated data from detectors predicted to operate in the late 2020s and early 2030s. We found that the detection range will improve by a factor of around two with respect to the second-generation gravitational-wave detectors, and the sky localization will significantly improve. We analyzed the reconstruction accuracy for the lower frequency and higher frequency portion of supernova signals with a 250 Hz cutoff. Since the waveform’s peak frequencies are usually at high frequencies, the gravitational-wave signals in this frequency band were reconstructed more accurately.

Neural Processing of Naturalistic Echolocation Signals in Bats.

Echolocation behavior, a navigation strategy based on acoustic signals, allows scientists to explore neural processing of behaviorally relevant stimuli. For the purpose of orientation, bats broadcast echolocation calls and extract spatial information from the echoes. Because bats control call emission and thus the availability of spatial information, the behavioral relevance of these signals is undiscussable. While most neurophysiological studies, conducted in the past, used synthesized acoustic stimuli that mimic portions of the echolocation signals, recent progress has been made to understand how naturalistic echolocation signals are encoded in the bat brain. Here, we review how does stimulus history affect neural processing, how spatial information from multiple objects and how echolocation signals embedded in a naturalistic, noisy environment are processed in the bat brain. We end our review by discussing the huge potential that state-of-the-art recording techniques provide to gain a more complete picture on the neuroethology of echolocation behavior.

Spectral Changes in Skin Blood Flow Do Not Reflect Pressure Manipulations

Skin blood flow is commonly assessed by laser Doppler flowmetry. It has been suggested that pathophysiological conditions can be assessed by analysis of frequency domains of the laser Doppler signals. The frequency range of 0.06‐0.15 Hz has been associated with myogenic control of skin blood flow. We tested the hypothesis that changes in blood pressure would be detectable by short‐time Fourier transform of the myogenic portion of the laser Doppler signal. Laser Doppler sensors were placed on the right ventral forearm of fourteen healthy young volunteers (8 males, 6 females, age 30±6 years, mean ± SD) and a photoplethysmography sensor on the middle finger of the right arm. Measurements of skin blood flow were made with the right arm supported at heart level. Intravascular pressure of the forearm was manipulated by positioning the arm ~50˚ above the heart level to decrease pressure and lowering the arm ~50˚ below the heart level to increase pressure. Short‐time Fourier analyses were computed over the range 0.06 to 0.15 Hz. There were no statistically significant differences (p = 0.526) in spectral densities for baseline (5±7 x 10‐4dB, median ± SD), arm above heart (7±54 x 10‐4dB), and arm below heart (7±14 x 10‐4dB). The results demonstrate that spectral density in the myogenic frequency range is unchanged by manipulation of intravascular pressure. We conclude that spectral analysis of laser Doppler signals in the myogenic range does not reflect changes in blood pressure. These findings suggest that myogenic changes in the skin vasculature cannot be inferred from spectral analysis of laser Doppler signals.

Automated classification of eight different Electroencephalogram (EEG) bands using hybrid of Fast Fourier Transform (FFT) with machine learning methods

Analysing and processing the EEG dataset is crucial. Countless actions have been taken to ensure that the researcher in brain studies always achieves informative data and produces notable findings. There are several standard procedures to produce an informative result in analysing the EEG data. However, the techniques used in each standard procedure might be different for the researcher or data analyst because they have their preferences to suit the purpose of their experiments to adapt with the dataset collected. Not only the current manual method is time-consuming, but the main challenges are that researchers need to analyse only a small portion of the brain signals that are the most relevant to be observed through the analysis of several bands such as Very low, Delta, Theta, Alpha-1, Alpha-2, Beta-1, Beta-2, and Gamma. Therefore, one of the best alternatives is to automate the process of classifying the eight bands and extract the most relevant features. Hence, this paper proposed an automated classification method and feature extraction method through hybridising Fast Fourier Transform (FFT) with three different machine learning methods (KNN, SVM, and ANN) that can improve the efficiency of EEG analysis. Based on the result, the FFT + SVM method gives a 100% accuracy and successfully classified the bands into different of eight EEG bands accurately.