Full Frequency Band Research Articles

Wafer-scale silicon microfabrication technology toward realization of low-cost sub-THz waveguide devices

This paper presents a wafer-scale silicon microfabrication technology for the sub-terahertz (sub-THz) waveguide device mass production. Based on the effective scheme, a WR-5 (140–220 GHz) straight rectangular waveguide and a WR-2.8 (260–400 GHz) rectangular waveguide bandpass filter are implemented as demonstrated examples. The silicon deep reactive ion etching (DRIE) process is employed to etch through the total thickness of the silicon wafer and form the main waveguide channels. Then, a low-temperature thermal compression process is used to bond the trough-etched wafer with the top and bottom metallised silicon wafers to form the closed waveguide structures without any precise alignment process. The fabricated waveguide has the benefit of low transmission loss (0.03–0.05 dB mm−1) at the whole G band. Besides, to measure the fabricated WR-2.8 waveguide filter and solve the measuring equipment standard waveguide difference, silicon micromachined waveguide transitions are explored and fabricated to match two different frequency-band modules for measuring the waveguide filters in the desired full frequency band, which also has a potential application for the different size waveguide conversion. The measured results agree well with the simulated ones. The measured 3 dB bandwidth is 9.3%, with a central frequency of 343 GHz; the average insertion loss (IL) is about 1.6 dB in the pass band, including two extra straight waveguides of 8 mm length on input/output ends and two external waveguide-to-waveguide transitions. The proposed method provides a feasible and cost-effective solution for the mass production of high-performance waveguide devices and integrated systems in sub-THz frequency bands and beyond.

EEG microstates show different features in focal epilepsy and psychogenic nonepileptic seizures.

Electroencephalography (EEG) microstate analysis seeks to cluster the scalp's electric field into semistable topographical EEG activity maps at different time points. Our study aimed to investigate the features of EEG microstates in subjects with focal epilepsy and psychogenic nonepileptic seizures (PNES). We included 62 adult subjects with focal epilepsy or PNES who received video-EEG monitoring at the epilepsy monitoring unit. The subjects (mean age = 42.8 ± 21.2 years) were distributed equally between epilepsy and PNES groups. We extracted microstates from a 4.4 ± 1.0-min, 21-channel resting-state EEG. We excluded subjects with interictal epileptiform discharges during resting-state EEGs. After preprocessing, we derived five main EEG microstates-MS1 to MS5-for the full frequency band (1-30 Hz) and frequency subbands (delta, 1-4 Hz; theta, 4-8 Hz; alpha, 8-12 Hz; beta, 12-30 Hz), using the MATLAB-based EEGLAB toolkit. Statistical features of microstates (duration, occurrence, contribution, global field power [GFP]) were compared between the groups, using logistic regression corrected for age and sex. We detected no differences in microstate parameters in the full frequency band. We found a longer duration (delta: B = -7.680, p = .046; theta: B = -16.200, p = .043) and a higher contribution (delta: B = -7.414, p = .035; theta: B = -7.509, p = .031) of MS4 in lower frequency bands in the epilepsy group. The PNES group showed a higher occurrence of MS5 in the delta subband (B = 3.283, p = .032). In the theta subband, a higher GFP of MS1 was associated with the PNES group (B = 5.674, p = .025), whereas a higher GFP of MS2 was associated with the epilepsy group (B = -6.579, p = .026). Microstate features show differences between patients with focal epilepsy and PNES. EEG microstates could be a promising parameter, helping to understand changes in brain dynamics in subjects with epilepsy, and should be explored as a potential biomarker.

A novel drum-shaped metastructure aided weak signal enhancement method for bearing fault diagnosis

Rolling bearings, extensively utilized in rotating machinery, have critical significance for online fault diagnosis in the domains of industrial equipment maintenance and accident prevention. Presently, fault diagnosis methods heavily rely on identifying the optimal resonance band in the high-frequency range (several kHz) to achieve high signal-to-noise ratio (SNR) fault information. However, these approaches, which necessitate high sampling rate sensing systems and complex algorithm deployments, contradict the practical requirements for cost-effective sensors and edge computing in online diagnosis. To address this contradiction, this paper introduces a novel Drum-shaped Metastructure (DMS) to enhance weak bearing fault signals, thus promoting the detection performance of conventional sensors. The DMS is constructed with a drumhead metastructure consisting of a central block mass attached with four spiral beams, of which its length can be adjusted by a tunable frequency mechanism (TFM) for different frequency responses of interest. The detail of its selective frequency enhancement characteristics is first studied through numerical simulations within a frequency range of 200 Hz to 1000 Hz. Subsequently, the effectiveness of the weak signal enhancement is verified on various bearing test systems, which utilize a prototype DMS fabricated by 3D printing. The results present a significant enhancement in the SNRs of the bearing fault signal, which is achieved by demodulating the full frequency band without the need for complex algorithms. Therefore, the proposed DMS provides a new cost-efficient approach to weak bearing fault diagnosis and online monitoring.

Exploring Frequency Band-Based Biomarkers of EEG Signals for Mild Cognitive Impairment Detection.

Mild Cognitive Impairment (MCI) is often considered a precursor to Alzheimer's disease (AD), with a high likelihood of progression. Accurate and timely diagnosis of MCI is essential for halting the progression of AD and other forms of dementia. Electroencephalography (EEG) is the prevalent method for identifying MCI biomarkers. Frequency band-based EEG biomarkers are crucial for identifying MCI as they capture neuronal activities and connectivity patterns linked to cognitive functions. However, traditional approaches struggle to identify precise frequency band-based biomarkers for MCI diagnosis. To address this challenge, a novel framework has been developed for identifying important frequency sub-bands within EEG signals for MCI detection. In the proposed scheme, the signals are first denoised using a stationary wavelet transformation and segmented into small time frames. Then, four frequency sub-bands are extracted from each segment, and spectrogram images are generated for each sub-band as well as for the full filtered frequency band signal segments. This process produces five different sets of images for five separate frequency bands. Afterwards, a convolutional neural network is used individually on those image sets to perform the classification task. Finally, the obtained results for the tested four sub-bands are compared with the results obtained using the full bandwidth. Our proposed framework was tested on two MCI datasets, and the results indicate that the 16-32 Hz sub-band range has the greatest impact on MCI detection, followed by 4-8 Hz. Furthermore, our framework, utilizing the full frequency band, outperformed existing state-of-the-art methods, indicating its potential for developing diagnostic tools for MCI detection.

Differgram: A convex optimization-based method for extracting optimal frequency band for fault diagnosis of rotating machinery

The extraction of fault resonance bands from a full frequency band has always stood as a classical and effective strategy for fault diagnosis in rotating machinery. Among the existing techniques for capturing fault frequency bands, methods such as fast kurtogram and its variants have been prevalent. However, these methods encounter two main limitations. Firstly, they are subject to the constraints of index performance, potentially leading to the identification of incorrect frequency bands. Secondly, they do not fully harness the potential of healthy signals, thus hindering accurate fault frequency band localization. While approaches like SKRgram and accugram endeavor to leverage healthy signals to highlight differences between healthy and faulty signals, SKRgram falls short in thoroughly exploring the significance of healthy signals. Accugram relies on classifier accuracy to pinpoint fault frequency bands, rendering it less robust and less interpretable. To capitalize more effectively on healthy signals' potential, this study proposed a novel approach called differgram based on the convex optimization model. By inputting both healthy and faulty signals into the differgram, optimal fault frequency bands can be extracted. By solving the constructed convex optimization model, the optimal difference frequency bands between healthy and faulty signals are automatically identified. The method is substantiated through mathematical proofs and physical explanations, enhancing its interpretability. Through simulations and experimental validation, the proposed diffetergram demonstrates its efficiency in the extraction of fault frequency bands. In comparison to fast kurtogram, SKRgram, and accugram, the differgram showcases heightened robustness and noise immunity.

Alterations in brain network functional connectivity and topological properties in DRE patients.

The study aimed to find the difference in functional network topology on interictal electroencephalographic (EEG) between patients with drug-resistant epilepsy (DRE) and healthy people. We retrospectively analyzed the medical records as well as EEG data of ten patients with DRE and recruited five sex-age-matched healthy controls (HC group). Each participant remained awake while undergoing video-electroencephalography (vEEG) monitoring. After excluding data that contained abnormal discharges, we screened EEG segments that were free of artifacts and put them together into 20-min segments. The screened data was bandpass filtered to different frequency bands (delta, theta, alpha, beta, and gamma). The weighted phase lag index (wPLI) and the network properties were calculated to evaluate changes in the topology of the functional network. Finally, the results were statistically analyzed, and the false discovery rate (FDR) was used to correct for differences after multiple comparisons. In the full frequency band (0.5-45 Hz), the functional connectivity in the DRE group during the interictal period was significantly lower than that in the HC group (p < 0.05). Compared to the HC group, in the full frequency band, the DRE group exhibited significantly decreased clustering coefficient (CC), node degree (D), and global efficiency (GE), while the characteristic path length (CPL) significantly increased (p < 0.05). In the sub-frequency bands, the functional connectivity of the DRE group was significantly lower than that of the HC group in the delta band but higher in the alpha, beta, and gamma bands (p < 0.05). The statistical results of network properties revealed that in the delta band, the DRE group had significantly decreased values for D, CC, and GE, but in the alpha, beta, and gamma bands, these values were significantly increased (p < 0.05). Additionally, the CPL of the DRE group significantly increased in the delta and theta bands but significantly decreased in the alpha, beta, and gamma bands (p < 0.05). The topology structure of the functional network in DRE patients was significantly changed compared with healthy people, which was reflected in different frequency bands. It provided a theoretical basis for understanding the pathological network alterations of DRE.

Method for Controlling Full-Frequency Band Environment Vibration by Coordinating Metro Vibration Sources and Propagation Paths

Floating slab tracks (FSTs) are used to reduce the impact of vibration on precision instruments and historical relics along metro lines; however, ground vibration is universally amplified at the natural frequency of the tracks. In this study, a full-frequency control method that considers frequency matching for environmental vibrations, in combination with metro vibration sources and propagation paths, was developed based on the bandgap theory of the periodic structure. The effectiveness of this method was analysed by establishing a three-dimensional metro train–FST coupled model and a finite element analysis model of track bed–tunnel–soil–row piles. The results show that ground vibration can be reduced by approximately 3–5 dB at the natural frequency of the FST by adjusting the bandgap range of the periodic piles to 7–9 Hz, eliminating the adverse effect of vibration amplification at the natural frequency of the FSTs. The proposed control method shows good vibration control effects and can effectively minimise ground vibration in the full-frequency range.

A 40-50 GHz RF Front-End with Integrated Local Oscillator Leakage Calibration.

This article presents a transmitter (TX) front-end operating at frequencies covering 40-50 GHz, including a differential quadrature mixer with integrated amplitude and phase imbalance tuning, a power amplifier, and a detection mixer (DM) that supports local oscillator (LO) leakage signal or image signal calibration. Benefiting from the amplitude and phase imbalance tuning network of the in-phase quadrature (IQ) signal generator at the LO input, the TX exhibits more than 30 dBc image signal rejection over the full frequency band without any post-calibration. Based on the LO leakage signal fed back by the DM integrated at the RF output, the LO leakage of the TX has been improved by more than 10 dB through the LO leakage calibration module integrated in the quadrature mixer. When the intermediate frequency (IF) signal is fixed at 1 GHz, the TX's 1 dB compressed output power (OP1 dB) is higher than 13.5 dBm over the operating band. Thanks to the LO leakage signal calibration unit and the IQ signal generator, the TX is compliant with the error vector magnitude (EVM) requirement of the IEEE 802.11aj standard up to the 64-quadrature amplitude modulation (QAM) operating mode.

Active impedance control of a loudspeaker and its parallel combination with porous materials for broadband sound absorption

In this paper, a new hybrid passive-active sound absorption method is investigated, in which porous materials and a loudspeaker are parallelly combined so that sound absorption over the full frequency band could be expected. The target impedance of the loudspeaker’s diaphragm is theoretically derived under this parallel structure, and a novel active impedance control method is further proposed. In the proposed control method, dual microphones installed on the surface of the composite structure as well as in the back cavity of the loudspeaker are used to pick up the sound pressure and the velocity of the loudspeaker’s diaphragm, respectively. Modal expansion analyses are performed theoretically to find the optimal microphone placement on the structure surface. An error signal is then established with an auxiliary filter corresponding to the target impedance as well as an extra delay. With the help of this extra delay, a stable and causal design result of the auxiliary filter could be obtained by solving a weighted least square problem. Finally, the impedance control of the loudspeaker’s diaphragm is realized by minimizing the energy of the established error signal with the filtered-x least mean square algorithm. Both simulations and experiments are carried out based on a standing wave tube, through which it has been verified that the proposed active impedance control method can effectively enhance the low-frequency sound absorption performance of porous materials. The proposed composite structure with a thickness of 80 mm can exhibit a sound absorption coefficient greater than 0.9 for all the frequencies above 20 Hz.

Synchronous Vibration Force Suppression of Magnetically Suspended CMG Based on Modified Double SOGI-FLL

The mass imbalance of the rotor will produce the synchronous vibration force that is transmitted to the magnetically suspended control moment gyroscope (MSCMG) through the magnetic levitation stator, which affects the imaging performance of the satellite. The suppression of the synchronous vibration force needs the speed signal. In order to solve the synchronous vibration force of the MSCMG when the speed measurement sensor fails, this paper proposed an modified double second-order generalized integral frequency-locked loop (SOGI-FLL) method, which uses the frequency of the disturbance signal generated through the mass imbalance of the rotor to adaptively estimate the rotor speed and suppress the synchronous vibration force generated by the active magnetic bearing system. The phase compensation is introduced to ensure the stability of the system in full frequency band. The electromagnetic force is directly used as the input signal of the control algorithm to achieve zero magnetic force control. Simulation and experimental are carried out, and the results are given to prove that the algorithm proposed in this paper can accurately estimate the rotation speed and achieve the vibration force suppression in the full rotation speed range. It is of great significance for the high-precision control of the active magnetic bearing rotor system.

Research on seat active noise control of driver's cab ground platform based on far-point secondary sound sources

Driver's cab noise has an impact on the long-term driving environment and physical health of drivers. The locomotive driver's cab has significant low-frequency noise due to structure-borne sound, which is suitable for effective suppression by the active noise control system. However, on the one hand, the seat headrest system hinders the driver's movement space, on the one hand, the active noise control for space zones in the driver's cab has limited noise reduction effect due to the large control area. Therefore, a ground research test platform of driver's cab for active noise control was designed, and its structure and control method were introduced in this study. Based on the near-point seat active noise reduction control system, the coherence of the reference acoustic signal of the chair to the acoustic signal at the driver's left ear is very close with the one on the floor, and both of their active noise reduction in the full frequency band from 50 Hz to 5000 Hz are very close (about 4.5 dB (A)). In order to alleviate the interference of the headrest system installed with secondary sound sources on the driver's operating space, a far-point active noise reduction control system was proposed. The results showed that when two reference microphone signals from the seat back and floor position are used at the same time, the active noise reduction next to the driver's left ear in the full frequency band of 50 Hz to 5000 Hz is 4.0 dB(A), which is still significant. This provides a useful reference for the engineering application of active noise control systems in rail driver's cabs.

Depth and frequency dependence of geoacoustic properties on the New England Mud Patch from reflection coefficient inversiona).

Muddy sediments cover significant portions of continental shelves, but their physical properties remain poorly understood compared to sandy sediments. This paper presents a generally applicable model for sediment-column structure and variability on the New England Mud Patch (NEMP), based on trans-dimensional Bayesian inversion of wide-angle, broadband reflection-coefficient data in this work and in two previously published reflection-coefficient inversions at different sites on the NEMP. The data considered here include higher frequencies and larger bandwidth and cover lower reflection grazing angles than the previous studies, hence, resulting in geoacoustic profiles with significantly better structural resolution and smaller uncertainties. The general sediment-column structure model includes an upper mud layer in which sediment properties change slightly with depth due to near-surface processes, an intermediate mud layer with nearly uniform properties, and a geoacoustic transition layer where properties change rapidly with depth (porosity decreases and sound speed, density, and attenuation increase) due to increasing sand content in the mud above a sand layer. Over the full frequency band considered in the new and two previous data sets (400-3125 Hz), there is no significant sound-speed dispersion in the mud, and attenuation follows an approximately linear frequency dependence.

Association between Opioid Dependence and Scale Free Fractal Brain Activity: An EEG Study

Self-similarities at different time scales embedded within a self-organizing neural manifold are well recognized. In this study, we hypothesize that the Hurst fractal dimension (HFD) of the scalp electroencephalographic (EEG) signal reveals statistical differences between chronic pain and opioid use. We test this hypothesis by using EEG resting state signals acquired from a total of 23 human subjects: 14 with chronic pain, 9 with chronic pain taking opioid medications, 5 with chronic pain and not taking opioid medications, and 9 healthy controls. Using the multifractal analysis algorithm, the HFD for full spectrum EEG and EEG frequency band time series was computed for all groups. Our results indicate the HFD varies spatially and temporally across all groups and is of lower magnitude in patients not taking opioids as compared to those taking opioids and healthy controls. A global decrease in HFD was observed with changes in gamma and beta power in the chronic pain group compared to controls and when paired to subject handedness and sex. Our results show the loss of complexity representative of brain wide dysfunction and reduced neural processing can be used as an EEG biomarker for chronic pain and subsequent opioid use.

Broadband and strong microwave absorption combining excellent EMI shielding of VGCF/carbonyl iron composites derived from synergistic magnetic and dielectric losses

As one of the most effective approaches to reduce microwave radiation in electromagnetic stealth and compatibility, broadband microwave absorbers have attracted widespread attention. However, it is challenging to maintain broadband absorption performance and maintain thin thickness simultaneously. Herein, by cooperating electromagnetic characteristics of vapor-grown carbon fiber (VGCF) and flaky carbonyl iron particles (CIP) to manipulate complex permittivity and complex permeability, a broadband metastructure absorber is proposed. The local conducting network composed of VGCF enriches the transmission path and greatly boosts dielectric loss capability of VGCF/CIP composites. The constructed VGCF/CIP reinforced metastructure absorber covered the 95% (−13 dB) absorption bandwidth in the full frequency band of 2–18 GHz at 8.4 mm thickness. The outstanding microwave absorption performance is benefit from the synergistic effect of magnetic-dielectric characteristics of the VGCF/CIP composite. The result provides a multi-resonant coupling mode to improve the overall impedance matching characteristics, which opens up a broad prospect for the development of ultra-wideband microwave absorbing materials. Moreover, VGCF/CIP composite shows a high electromagnetic interference (EMI) shielding effectiveness, and the excellent green shielding index (gs) of more than 10 indicates its potential application as an EMI shielding material.

Abnormal interaction between cortical regions of obstructive sleep apnea hypopnea syndrome children.

Obstructive sleep apnea hypopnea syndrome negatively affects the cognitive function of children. This study aims to find potential biomarkers for obstructive sleep apnea hypopnea syndrome in children by investigating the patterns of sleep electroencephalography networks. The participants included 16 mild obstructive sleep apnea hypopnea syndrome children, 12 severe obstructive sleep apnea hypopnea syndrome children, and 13 healthy controls. Effective brain networks were constructed using symbolic transfer entropy to assess cortical information interaction. The information flow pattern in the participants was evaluated using the parameters cross-within variation and the ratio of posterior-anterior information flow. Obstructive sleep apnea hypopnea syndrome children had a considerably higher symbolic transfer entropy in the full frequency band of N1, N2, and rapid eye movement (REM) stages (P < 0.05), and a significantly lower symbolic transfer entropy in full frequency band of N3 stage (P < 0.005), in comparison with the healthy controls. In addition, the cross-within variation of the β frequency band across all sleep stages were significantly lower in the obstructive sleep apnea hypopnea syndrome group than in the healthy controls (P < 0.05). What is more, the posterior-anterior information flowin the β frequency band of REM stage was significantly higher in mild obstructive sleep apnea hypopnea syndrome children than in the healthy controls (P < 0.05). These findings may serve as potential biomarkers for obstructive sleep apnea hypopnea syndrome in children and provide new insights into the pathophysiological mechanisms.

A composite acoustic black hole for ultra-low-frequency and ultra-broad-band sound wave control

Achieving ultra-low and ultra-broad-band sound absorption and full-band sound insulation is a major challenge. Here, we propose a composite structure of a multilayer micro-perforated plate and acoustic black holes to achieve this purpose. Combining the stable sound absorption effect of the multilayer micro-perforated plate in the full frequency band and the sound insulation effect of the acoustic black hole in the low frequency and the excellent sound absorption effect in the high frequency, the excellent sound control effect of 600–3150 Hz absorption coefficient greater than 0.8 and 100–3150 Hz sound transmission loss greater than 50 dB is achieved. The acoustic properties of different components and different acoustic black hole outlet were evaluated by finite element method, and the principles of sound absorption and insulation of the composite structure were elaborated. Finally, the results of finite element method are verified by impedance tube experiments. This work can make further progress in elucidating the acoustic properties of the ABH and open up new avenues in the control of ultra-low and ultra-wide frequency acoustic waves.

A highly robust RF 65 nm CMOS power amplifier design using Quasi-Newton control algorithm for wireless system

This article reports a novel robust approach towards CMOS power amplifier (PA) using Quasi-newton (QN) control algorithm in 65 nm CMOS process which provides best performance parameters over redundant wide bandwidth ranging from 2.4 to 16.4 GHz frequency band. Each stage are designed and optimized using QN algorithm to get desired goals such as high linearity, small group delay variations and high PAE across the entire frequency band of interest. Moreover, pole-zeros compensation technique is adopted and derived to get better stability of the proposed PA. The simulation and measurement results of PA achieved a small signal power gain of 10.5–16.8 dB with input return loss of better than 10 dB over the frequency band of 2.4 GHz to 16.4 GHz. A small group delay variation of ±58 ps over full frequency band of operation is achieved by optimizing the design parametric analysis. It is also observed that within the frequency of 6.5 to 14.6 GHz, an excellent small group delay variation of only ±11 ps is achieved and this is due to stage-2 tuning compensation technique. It also demonstrates the achieved input power in 1 dB compression points are −3.1 to 4.3 dBm, leading to maximum power added efficiency of 36.3%, respectively. The proposed PA consumes a lower DC power of 20.5 mW under supply voltage of 1.5. In addition, Process, voltage and temperature (PVT) analysis is executed at different conditions in order to achieve a robustness of the proposed PA over the entire band of operation.

Multi‐channel response reconstruction using transformer based generative adversarial network

AbstractAccurate measurement data are a basic prerequisite for effective structural health monitoring (SHM). However, data loss are inevitable in the long‐term monitoring of large‐scale structures. To solve this problem, this research proposes a transformer‐based generative adversarial network (GAN) to reconstruct lost measurements from observed measurements. The generator of GAN is an encoder‐decoder structure using transformer as the backbone combined with discrete wavelet transform. Skip connections are used between the encoder part and decoder part to promote multi‐scale information flow. A novel discriminator is designed to assess the reality of wavelet spectra of reconstructed samples. To deceive the discriminator, the generator must generate samples that are accurate over the full frequency band. The developed model is used to reconstruct linear responses of a footbridge under pedestrian excitations and nonlinear responses of a suspension bridge under typhoon events. Experimental results demonstrate that lost responses can be reconstructed accurately, even when a large proportion of data are lost. The effectiveness of the proposed method is further verified by comparing the reconstruction accuracy of the proposed model with those of other three state‐of‐the‐art models. The results demonstrate that an improved performance of applying the proposed approach for dynamic structural response reconstruction is achieved and validated with in‐field testing data under ambient and extreme excitation conditions.

Improved HHT-microstate analysis of EEG in nicotine addicts.

Substance addiction is a chronic disease which causes great harm to modern society and individuals. At present, many studies have applied EEG analysis methods to the substance addiction detection and treatment. As a tool to describe the spatio-temporal dynamic characteristics of large-scale electrophysiological data, EEG microstate analysis has been widely used, which is an effective method to study the relationship between EEG electrodynamics and cognition or disease. To study the difference of EEG microstate parameters of nicotine addicts at each frequency band, we combine an improved Hilbert Huang Transformation (HHT) decomposition with microstate analysis, which is applied to the EEG of nicotine addicts. After using improved HHT-Microstate method, we notice that there is significant difference in EEG microstates of nicotine addicts between viewing smoke pictures group (smoke) and viewing neutral pictures group (neutral). Firstly, there is a significant difference in EEG microstates at full-frequency band between smoke and neutral group. Compared with the FIR-Microstate method, the similarity index of microstate topographic maps at alpha and beta bands had significant differences between smoke and neutral group. Secondly, we find significant class × group interactions for microstate parameters at delta, alpha and beta bands. Finally, the microstate parameters at delta, alpha and beta bands obtained by the improved HHT-microstate analysis method are selected as features for classification and detection under the Gaussian kernel support vector machine. The highest accuracy is 92% sensitivity is 94% and specificity is 91%, which can more effectively detect and identify addiction diseases than FIR-Microstate and FIR-Riemann methods. Thus, the improved HHT-Microstate analysis method can effectively identify substance addiction diseases and provide new ideas and insights for the brain research of nicotine addiction.

Study on excitation threshold of strong modulation response and vibration suppression performance of bistable nonlinear energy sink

The dynamics and vibration reduction performance of bistable nonlinear energy sink (BNES) are studied in this paper. First, the negative stiffness of BNES is realized by geometric nonlinearity, and the dynamics model of the system is established. The slow flow equation of the system under 1:1 main resonance is analyzed based on the complexification-averaging (CX-A) method, and the boundary conditions of saddle node bifurcation and Hopf bifurcation are analyzed. Second, the slow invariant manifold (SIM) of the BNES is studied based on multiscale analysis, and the excitation threshold of strongly modulated response (SMR) is analyzed; the analysis results are verified by the numerical method. The results show that the analytical solution is highly consistent with the numerical solution, and the error is less than 1%. Finally, the influence of structural parameters on the vibration reduction performance is analyzed and optimized. The vibration reduction performance of BNES and CNES is compared, and the results show that the BNES has better vibration reduction performance in the full frequency band.