Frequency Band Characteristics Research Articles

Application of optical fiber distributed acoustic sensing system in ultrasonic detection

Abstract The optical fiber distributed acoustic sensing system can continuously detect acoustic signals along the optical fiber, with the characteristics of wide response frequency band, large capacity, anti-electromagnetic interference and so on. An ultra-sensitive distributed optical fiber acoustic sensing system (uDAS) was produced with a high response frequency up to 100 kHz and thousands of sensing points. The arrayed ultrasonic detection can be realized by the distributed characteristics of the system. For verifying its ultrasonic detection ability, it is applied to ultrasonic nondestructive testing of structural defects. 20 optical fiber ultrasonic detection units were constructed. Each one is a fiber ring with a diameter of about 1.5 cm and a length of 2 meters. Two cubic cement structures with a side length of 30 cm were manufactured for experiment, one of which had an artificial internal defect. The optical fiber array was attached to the surface of the cement structure, and the ultrasonic signal with a frequency of 40 kHz was excited at a single point on the opposite side. The ultrasonic propagation speed changes on account of the defects such as holes and loose texture in the propagation path. The defects in the structure are identified and located by analyzing the difference of arrival time of waves. It is expected that such sensing system could found important applications in structure defect detection.

A broadband active sound absorber with adjustable absorption coefficient and bandwidth.

Broadband adjustable sound absorbers are desired for controlling the acoustic conditions within enclosed spaces. Existing studies on acoustic absorbers, either passive or active, aim to maximize the sound absorption coefficients over an extended frequency band. By contrast, this paper introduces a tunable acoustic absorber, whose working frequency band and sound absorption characteristics can be defined by users for different applications. The approach leverages an error signal that can be synthesized using a standing wave separation technique. The error signal encodes different target reflection coefficients, leading to arbitrary absorption coefficients between 0 and 1. Experimental validation is conducted in a one-dimensional standing wave tube, demonstrating that the proposed active absorber achieves near-perfect absorption within the 150-1600 Hz frequency range, boasting an average absorption coefficient of 0.98. Adjustable absorption is demonstrated across three octave bands, aligning closely with theoretical predictions. Furthermore, when coupled with a shaping filter, the absorber exhibits spectrally tunable broadband absorption capabilities, selectively reflecting specific frequency bands while effectively absorbing others. These outcomes underscore the versatile tunability of the proposed active acoustic absorber, which is expected to pave the way for personalized regulating of the indoor acoustic environment.

LabVIEW based Mental Status Monitoring System for Cognitive Wellness

This paper delves into the analysis of various cognitive states by identifying the percentages of different types of brain waves, like alpha, beta, delta, and theta, present in an individual's EEG recording. These brain waves carry crucial information about an individual's cognitive health, and accurate readings are essential to classify and represent the signals accurately. To achieve this, the project aimed to develop a robust platform that utilizes LabVIEW software to obtain EEG signals and interpret cognitive health. The acquired data is meticulously preprocessed and filtered to obtain clean EEG signals, which are then subjected to several methods to determine the content of different brain waves present in the filtered EEG signals. Based on these values, the cognitive status of an individual can be accurately determined, providing valuable insights into their cognitive health. Understanding cognitive states through brain wave analysis has been an intriguing area of research, utilizing various electroencephalography (EEG) techniques to interpret neural activity. This paper aims to provide a detailed discussion on how cognitive states are mapped and interpreted based on brain waves. By exploring the characteristics of each brain wave frequency band, we elucidate their implications in cognitive processes such as attention, memory, and relaxation. This research aims to contribute in the advancement of cognitive neuroscience by consolidating current knowledge and identifying potential avenues for future research.

Numerical study on acoustic characteristics of flow boiling in a helical tube

Helically coiled steam generators have advantages of strong heat transfer capacity, compact structure, and low thermal stress, and are widely used in many engineering fields. However, the heat transfer performance is still limited by the critical heat flux, beyond which boiling deterioration is an important cause of safety accidents. Both engineering and experimental studies have found that complex noises could be frequently generated during the occurrence of boiling crisis. In this paper, the acoustic characteristics of both subcooled and saturated flow boiling in a helical tube were numerically investigated, by using the volume of fluid scheme and acoustic model. The distribution of the vapor-liquid phase, flow pattern transition, and acoustic frequency band characteristics during boiling were analyzed under different wall temperatures (varied superheat), inlet subcooling, and flowrates. It’s found that the flow pattern under low mass flowrate was plug flow, slug flow, and wavy flow. All the three flow patterns have unique boiling acoustic features. The plug flow, consisted by a large number of small bubbles, shows the distinct high-frequency band characteristics of the boiling acoustics. The wavy flow, which is composed mainly by large bubbles, exhibits a significant low-frequency band characteristics of the boiling acoustics.

Construction and Analysis of a New Resting-State Whole-Brain Network Model.

Mathematical modeling and computer simulation are important methods for understanding complex neural systems. The whole-brain network model can help people understand the neurophysiological mechanisms of brain cognition and functional diseases of the brain. In this study, we constructed a resting-state whole-brain network model (WBNM) by using the Wendling neural mass model as the node and a real structural connectivity matrix as the edge of the network. By analyzing the correlation between the simulated functional connectivity matrix in the resting state and the empirical functional connectivity matrix, an optimal global coupling coefficient was obtained. Then, the waveforms and spectra of simulated EEG signals and four commonly used measures from graph theory and small-world network properties of simulated brain networks under different thresholds were analyzed. The results showed that the correlation coefficient of the functional connectivity matrix of the simulated WBNM and empirical brain networks could reach a maximum value of 0.676 when the global coupling coefficient was set to 20.3. The simulated EEG signals showed rich waveform and frequency-band characteristics. The commonly used graph-theoretical measures and small-world properties of the constructed WBNM were similar to those of empirical brain networks. When the threshold was set to 0.22, the maximum correlation between the simulated WBNM and empirical brain networks was 0.709. The constructed resting-state WBNM is similar to a real brain network to a certain extent and can be used to study the neurophysiological mechanisms of complex brain networks.

TMR-high-temperature superconductor composite magnetic sensor and its performance optimization

Tunnel magnetoresistance (TMR), recognized for its high sensitivity and low power consumption, holds significant promise in the domain of weak magnetic field detection. Using superconducting materials as magnetic concentrators can achieve several hundred to even a thousandfold amplification of magnetic fields, making it one of the most effective approaches to enhance the magnetic field resolution of TMR sensors. This paper utilized the flip-chip bonding process to integrate TMR with the high-temperature superconductor YBCO (YBa2Cu3O7-δ), and successfully developed TMR-YBCO composite magnetic sensor. Building upon this foundation, through optimization of the fabrication process and the pioneering use of a structural design incorporating the filling of annular holes with superconducting concentrators, the sensitivity of the sensor was further enhanced. Finally, the magnetic field resolution was increased by 1030 times compared to TMR sensors, reached to 2.9pT/Hz1/2 at 1 Hz. Simultaneously, results from frequency band testing indicated excellent frequency band characteristics, with a frequency response range exceeding kHz.

Improved Test Method for Radio Frequency Band Characteristics of Ferrite Magnetic Materials

Improved Test Method for Radio Frequency Band Characteristics of Ferrite Magnetic Materials

Multi-channel active noise reduction system realizes the local area sound field control of the subway driver's cabin

Aerodynamic noise, air conditioning fan noise, and wheel-rail noise during subway operation have an adverse effect on drivers' physical and emotional well-being and can jeopardize the safe operation of vehicles. On the basis of the original passive acoustic treatment, multi-channel active control technology is used to manage the local sound field in order to further enhance the acoustic comfort of subway drivers' working environment. The multi-channel active noise reduction system installed on the driver's seat is described in this paper. In order to properly prevent the issues of ear pain and obstruction brought on by prolonged use of noise-canceling headphones, the system integrates reference sensors, error sensors, speakers, and a power amplifier within the headrest. The noise in the carriage is picked up using a multi-channel reference signal, and a low delay and anti-interference algorithm is investigated in accordance with the wide frequency band's characteristics and the abrupt change in sound pressure level when the vehicle is running. The test results demonstrate the system's ability to stabilize under situations of abrupt changes in driving noise and in-car broadcast, as well as to successfully minimize noise in the head area of subway drivers.

Inverse design of metasurfaces with customized transmission characteristics of frequency band based on generative adversarial networks.

In recent years, deep learning (DL) has demonstrated significant potential in the inverse design of metasurfaces, and the generation of metasurfaces with customized transmission characteristics of frequency band remains a challenging and underexplored area. In this study, we propose a DL-assisted method for the inverse design of transmissive metasurfaces. The method consists of a generative adversarial network (GAN)-based graph generator, an electromagnetic response predictor, and a genetic algorithm optimizer. By integrating these components, we can obtain customized metasurfaces with desired transmission characteristics of frequency band. We demonstrate the effectiveness of the proposed method through examples of inverse-designed three-layer cascaded transmissive metasurfaces with wideband, dual-band, and stopband responses in the 8∼12 GHz frequency range. Specifically, we realize three different types of dual-band metasurfaces, namely double-wide, front-wide and rear-narrow, and front-narrow and rear-wide configurations. Additionally, we analyze the accuracy and reliability of the inverse design method by employing data from the training dataset, self-defined objectives, and bandwidth-reduced target responses scaled from the wideband type as design inputs. Quantitative evaluation is performed using metrics such as mean absolute error and average precision. The proposed method successfully achieves the desired effect as intended.

Influence of neuromuscular electrical stimulation pulse waveform on corticomuscular coupling and the brain functional connectivity network

ObjectiveNeuromuscular electrical stimulation (NMES) has been shown to promote functional corticomuscular coupling (FCMC), but its effect on brain functional connectivity network (BFCN) remains uncertain. This study aims to propose a new method to analyze the influence of NMES with different stimulation waveforms on the motor system from two perspectives of FCMC and BFCN. ApproachThe bivariate empirical mode decomposition time-delay maximal information coefficient (BTDMIC) method was used to assess the local frequency band characteristics of the cortex and muscle. Main resultThe experimental results showed that the FCMC increment after NMES was prominent in the β1 (15–25 Hz), β2 (25–35 Hz), and γ1 (35–45 Hz) bands. Additionally, the coupling values of C3 after sine-NMES were highest in the β1 band, while the coupling values after square-NMES were highest in the β2 and γ1 bands. For BFCN, the cortical-cortical coupling of the C3 after square-NMES was high in the C3 → others direction. Moreover, in the α (8–14 Hz) and β1 bands, the clustering coefficient, density, and efficiency after square-NMES were significantly higher than those of the three other frequency bands. ConclusionNMES effectively promoted information exchange between the motor cortices of the brain, and the facilitating effect is particularly pronounced in the case of square-NMES. SignificanceThis study offers a novel perspective for evaluating rehabilitation therapies based on NMES.

Longitudinal wave propagation analysis in Entangled metallic wire materials using an improved wave and finite element method

This paper proposes a novel concept and methodology to study longitudinal wave propagation in entangled metallic wire material (EMWM), a porous structural network consisting of spatial helix wires. The numerical model of EMWM is constructed using representative volume elements (RVE) obtained through X-ray tomography and skeletonization of a standard specimen. By increasing the approximate periodic boundary, the improved wave and finite ele-ment method (WFE) is employed to model EMWM as approximately periodic structures, allowing for numerical simulations of wave propagation characteristics. Frequency bands and resonance modes of EMWM are calculated to analyze the characteristics of one-dimensional wave propagation. The numerical results show that EMWM exhibits unique frequency band characteristics compared to typical periodic porous materials. In the frequency range of 15 to n n 35 kHz, the first pass band corresponds to the tensile-compressive motion of the entire specimen, while the higher pass bands correspond to localized vibrations within the spatial wires. Additionally, the frequency bands can be adjusted across a wide range based on the relative density of EMWM specimens. The improved WFE method and obtained numerical data can facilitate the application of EMWM on high frequency vibration isolation.

Analysis of corticomuscular-cortical functional network based on time-delayed maximal information spectral coefficient

Objective. The study of brain networks has become an influential tool for investigating post-stroke brain function. However, studies on the dynamics of cortical networks associated with muscle activity are limited. This is crucial for elucidating the altered coordination patterns in the post-stroke motor control system. Approach. In this study, we introduced the time-delayed maximal information spectral coefficient (TDMISC) method to assess the local frequency band characteristics (alpha, beta, and gamma bands) of functional corticomuscular coupling (FCMC) and cortico-cortical network parameters. We validated the effectiveness of TDMISC using a unidirectionally coupled Hénon maps model and a neural mass model. Main result. A grip task with 25% of maximum voluntary contraction was designed, and simulation results demonstrated that TDMISC accurately characterizes signals’ local frequency band and directional properties. In the gamma band, the affected side showed significantly strong FCMC in the ascending direction. However, in the beta band, the affected side exhibited significantly weak FCMC in all directions. For the cortico-cortical network parameters, the affected side showed a lower clustering coefficient than the unaffected side in all frequency bands. Additionally, the affected side exhibited a longer shortest path length than the unaffected side in all frequency bands. In all frequency bands, the unaffected motor cortex in the stroke group exerted inhibitory effects on the affected motor cortex, the parietal associative areas, and the somatosensory cortices. Significance. These results provide meaningful insights into neural mechanisms underlying motor dysfunction.

A Wide Frequency Response Fabry–Pérot Acoustic Sensor Based on the Self-Stabilization System

A self-stabilization system based on the operating point is built for measuring the membrane-free optical acoustic sensor constructed of a rigid Fabry-Pérot (F-P) cavity. The system can directly lock and track the quadrature point of the sensor’s spectrum to realize broadband acoustic detection with high sensitivity. Compared with the demodulation system based on phase modulation spectrum technology, the system is simple in construction and has the advantage of fast real-time demodulation rate and wide detection bandwidth. The experimental results show the acoustic sensor achieves a wide frequency response of 700 Hz-411 kHz with a flatness of ±1.78 dB, and a sensitivity of 31.83 mV/Pa, while the maximum detectable sound pressure reaches 136.9 dB. The sensor performs well in broadening the frequency response range, which includes a medium and high frequency range from 700 Hz to 20 kHz for noise monitoring, as well as an ultrasound range from 20 to 411 kHz for aeroacoustics and photoacoustic imaging. The improvement of the frequency band characteristics of the sensor in this research will further contribute to the development of application fields.

Single-ended protection method for hybrid HVDC transmission line based on transient voltage characteristic frequency band

Hybrid high-voltage direct current (HVDC) transmission has the characteristic of long transmission distance, complex corridor environment, and rapid fault evolution of direct current (DC) lines. As high fault current can easily cause irreversible damage to power devices, rapid and reliable line protection and isolation are necessary to improve the security and reliability of hybrid HVDC transmission system. To address such requirement, this paper proposes a single-ended protection method based on transient voltage frequency band characteristics. First, the frequency characteristics of the smoothing reactor, DC filter, and DC line are analyzed, and the characteristic frequency band is defined. A fault criterion is then constructed based on the voltage characteristic frequency band energy, and faulty pole selection is performed according to the fault voltage characteristic frequency band energy ratio. The proposed protection method is verified by simulation, and the results show that it can rapidly and reliably identify internal and external faults, accurately select faulty poles without data communication synchronization, and has good fault-resistance and anti-interference performance.

154 Comparison of Chronic and Intraoperative LFP and ECoG Recordings in Humans With Parkinson’s Disease

INTRODUCTION: Advances in implantable neurotechnologies have enabled chronic invasive long-term recordings of field potentials from depth leads or paddle-type electrocorticography (ECoG) leads. The relationship between these long-term recordings and those obtained acutely in the operating room has not been studied. METHODS: Human subjects (n=6) with movement disorders (Parkinson’s disease, dystonia) were permanently implanted with quadripolar DBS leads, and quadripolar ECoG leads over primary motor and sensory cortices, attached to a Medtronic Summit RC+S investigational device in the chest. Recordings were acquired intraoperatively using a NeuroOmega (Alpha Omega, Inc) recording system customized for research use. RC+S recordings were acquired between 1 and 345 days post implantation. The same processing pipeline was applied to both data sets, and average power spectra in canonical frequency bands (delta, theta, alpha, beta, gamma) were compared using rank sum tests within each subject. RESULTS: Power spectra show similar power law scaling phenomena between intraoperative and chronic recordings. Disease specific frequency band characteristics such as beta peaks could be seen in both chronic and intraoperative recordings, however, within subjects these were not always consistently seen. CONCLUSIONS: We present within-patient comparisons of signal characteristics (power spectra) of intraoperative recordings from a research grade recording system, with wirelessly streamed data from a chronic implantable device, using the same brain leads. Power spectra have similar characteristics between the intraoperative and long term recordings, however, there are distinct differences between the intraoperative and longer term clinic recordings, reinforcing the utility of tracking neural signals over time after the effects of lead insertion on the motor circuit have diminished.

External UHF Sensor for GIS PD Insulation Defect from Resin Hole

In view of the effective perception demand of PD (Partial Discharge) insulation defect of in-service GIS (Gas Insulated Switchgear), the external UHF (Ultra High Frequency, 0.3−3 GHz) sensing technology for GIS PD insulation defect from resin hole is studied in this paper. Firstly, rectangular waveguide theory is used to analyze the electromagnetic wave propagation characteristics from rectangular resin hole in GIS disc insulator. It is found that the energy of leaked electromagnetic wave mainly concentrates in the frequency band above 1 GHz. Then based on this frequency band characteristics, a cavity-backed bowtie sensor for GIS PD external detection is designed optimized by HFSS finite element software, in band of 1–3 GHz, its maximum VSWR is 7, the minimum is 1.1, the average is 2.9, and in band of 0.3–1 GHz, the maximum and minimum VSWR is 42.8 and 7, the average is 20.6. Finally, a sensor prototype is made, its sensitivity experiment is carried out on 220 kV GIS, experimental results show that the designed sensor can stably detect the PD electromagnetic wave signal leaked from the resin hole, and the signal energy in frequency band range is consistent with the theoretical analysis results.

A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet

EEG-based emotion recognition research has become a hot research topic. However, many studies focus on identifying emotional states from time domain features, frequency domain features, and time-frequency domain features of EEG signals, ignoring the spatial information and frequency band characteristics of the EEG signals. In this paper, an emotion recognition method based on multi-band EEG topology maps is proposed by combining the frequency domain features, spatial information, and frequency band characteristics of multi-channel EEG signals. In this method, multi-band EEG topology maps are introduced to present EEG signals, and a novel emotion recognition network, ERENet, is proposed to recognize emotional states from multi-band EEG topology maps. The results on the DEAP dataset show that the performance of ERENet outperforms that of most of the current methods.

Exploring Sex Differences in Key Frequency Bands and Channel Connections for EEG-based Emotion Recognition.

Previous studies have demonstrated the existence of sex differences in emotion recognition by comparing the performance of same-sex and cross-sex training strategies. However, the EEG properties behind the sex differences have not been fully explored. To fill this research gap, we aim to investigate the sex differences in key frequency bands and channel connections of EEG signals. The single-modality attentive simple graph convolutional network (ASGC) is applied to three datasets SEED, SEED-IV and SEED-V under subject-dependence conditions. The classification rates are 90.86 ±4.84%, 83.14 ± 8.84% and 78.33±7.83%, respectively. The adjacency matrices learned by ASGC indicate that females and males have similar channel-connection patterns, but the degree of importance of channel connections varies by sex. Additionally, by comparing the classification results of 5 frequency bands, we find that males and females represent similar frequency band characteristics, i.e., high-frequency bands achieve better performance, indicating that these frequency bands are more related to emotion processing. Finally, we conduct the cross-subject experiment using ASGC and find that the same-sex strategy outperforms the cross-sex strategy, which is consistent with previous studies. The results also imply that males may be more suitable for sex generalization. However, this finding needs the support of more samples and advanced algorithms.

Powergram: A novel auto-regressive power spectrumbasedfrequency band selection method and its applicationingearfault diagnosis

Abstract Fast kurtogram (FK) is an effective method for resonance demodulation analysis, which segments the frequency spectrum of raw signal by constructing a tree-shaped band-pass filter bank based on alternate dichotomy or trisection method, then the modulation information is extracted via selecting the maximum kurtosis of components. Though FK has a high computation efficiency, it can not adaptively segment the frequency band due to its 1/3 binary tree structure. Besides, the different characteristics of frequency band filtered signal cannot be comprehensively reflected due to the use of a single kurtosis index in FK. To overcome the above shortcomings of FK, a novel frequency band selection method named Powergram is proposed based on auto-regressive (AR) power spectrum. First of all, the AR power spectrum is computed to overcome the influence of noise and irrelevant components on spectrum segmentation. Second, the minima point nearest to the midpoint of two adjacent maxima is selected as the segmentation boundary, where the maximum segmentation level is preset in advance. Third, a new fusion indicator is constructed by weighting the indexes of kurtosis, correlation coefficient, and spectral negentropy. After that, the frequency band with largest fusion indicator is selected as the optimal demodulation frequency band (ODFB). Then, the envelope spectrum of ODFB filtered signal is analyzed to identify the fault characteristic frequency and complete the fault diagnosis. Finally, Powergram is applied to the simulated and tested data of faulty gear via comparing it with existing FB-based FK and Autogram approaches. The analysis results show that the introduced approach is better than the comparison methods in ODFB selection and fault characteristic extraction.

Design and experimental analysis of UHF sensor spiral antenna for partial discharge detection of distribution network switchgear

The ultra high frequency (UHF) sensor antenna for detecting partial discharge (PD) in distribution network switchgear is studied. Because of the long period of insulation deterioration in the switchgear, more attention is paid to the middle and later stage of insulation deterioration, and the frequency of PD signal that needs to be paid attention to is wider. Among the commonly used UHF antennas, Archimedes spiral antenna has a wider working frequency band and excellent low-frequency working characteristics, Therefore, Archimedes spiral antennas of UHF Sensors with different outer diameters are designed by using ANSYS HFSS. The influence of the outer diameter of Archimedes spiral antenna on the S11 parameters of Archimedes spiral antenna is studied, and the partial discharge signal acquisition experiments are carried out. The results show that the partial discharge characteristic signals collected by Archimedes spiral antenna with different outer diameters are different. This provides a theoretical basis for the design of UHF sensor antenna used in switch cabinet.