Frequency Band Of Signal Research Articles

Social robots as effective language tutors for children: empirical evidence from neuroscience

The aim of the current study was to investigate children's brain responses to robot-assisted language learning. EEG brain signals were collected from 41 Japanese children who learned French vocabularies in two groups; half of the children learned new words from a social robot that narrated a story in French using animations on a computer screen (Robot group) and the other half watched the same animated story on the screen but only with a voiceover narration and without the robot (Display group). To examine brain activation during the learning phase, we extracted EEG functional connectivity (FC) which is defined as the rhythmic synchronization of signals recorded from different brain areas. The results indicated significantly higher global synchronization of brain signals in the theta frequency band in the Robot group during the learning phase. Closer inspection of intra-hemispheric and inter-hemispheric connections revealed that children who learned a new language from the robot experienced a stronger theta-band EEG synchronization in inter-hemispheric connections, which has been previously associated with success in second language learning in the neuroscientific literature. Additionally, using a multiple linear regression analysis, it was found that theta-band FC and group assignment were significant predictors of children's language learning with the Robot group scoring higher in the post-interaction word recognition test. These findings provide novel neuroscientific evidence for the effectiveness of social robots as second language tutors for children.

Operational modal identification of structures based on improved empirical wavelet transform

When empirical wavelet transform (EWT) is used to identify the modal parameters of civil engineering structures, the frequency band division is generally not accurate due to the noise effect on the Fourier spectrum. This phenomenon will lead to modal mixing and false modes in the analysis results. Therefore, this article establishes a signal frequency band division method by taking advantages of maximal spectrum technique and spectral skewness index. Combining the random decrement technique (RDT) and the least square method of single component modal parameter identification, an adaptive approach based on the improved EWT for operational modal parameter identification of civil engineering structures under stationary environmental excitations is proposed. The traditional frequency band division method based on EWT can not completely and effectively divide the meaningful frequency bands. While the proposed frequency band division technology based on the spectral skewness index can prevent the phenomenon of insufficient division and excessive division and realize adaptive frequency band division. The effectiveness of the proposed approach is validated by a numerical three-story frame and a field three-span concrete box girder bridge under ambient vibrations. The modal identification results from both numerical and experimental validations demonstrate that the proposed approach can effectively and accurately decompose the vibration responses and identify the structural modal parameters under operational conditions.

Reduced corticospinal drive and inflexible temporal adaptation during visually guided walking in older adults.

Corticospinal drive during walking is reduced in older adults compared with young adults, but it is not clear how this decrease might compromise one's ability to adjust stepping, particularly during visuomotor adaptation. We hypothesize that age-related changes in corticospinal drive could predict differences in older adults' step length and step time adjustments in response to visual perturbations compared with younger adults. Healthy young (n = 21; age 18-33 yr) and older adults (n = 20; age 68-80 yr) were tested with a treadmill task, incorporating visual feedback of the foot position and stepping targets in real-time. During adaptation, the visuomotor gain was reduced on one side, causing the foot cursor and step targets to move slower on that side of the screen (i.e., split-visuomotor adaptation). Corticospinal drive was quantified by coherence between electromyographic signals in the beta-gamma frequency band (15-45 Hz). The results showed that 1) older adults adapted to visuomotor perturbations during walking, with a similar reduction in error asymmetry compared with younger adults; 2) however, older adults showed reduced adaptation in step time symmetry, despite demonstrating similar adaptation in step length asymmetry compared with younger adults; and 3) smaller overall changes in step time asymmetry was associated with reduced corticospinal drive to the tibialis anterior in the slow leg during split-visuomotor adaptation. These findings suggest that changes in corticospinal drive may affect older adults' control of step timing in response to visual challenges. This could be important for safe navigation when walking in different environments or dealing with unexpected circumstances.NEW & NOTEWORTHY Corticospinal input is essential for visually guided walking, especially when the walking pattern must be modified to accurately step on safe locations. Age-related changes in corticospinal drive are associated with inflexible step time, which necessitates different locomotor adaptation strategies in older adults.

Non-stationary astrophysical stochastic gravitational-wave background: a new probe to the high-redshift population of binary black holes

ABSTRACT The astrophysical stochastic gravitational-wave background (SGWB) originates from the mergers of compact binary objects that are otherwise undetected as individual events, along with other sources such as supernovae, magnetars, etc. The individual gravitational-wave (GW) signal is time-varying over a time-scale that depends on the chirp mass of the coalescing binaries. Another time-scale that plays a role is the time-scale at which the sources repeat, which depends on the merger rate. The combined effect of these two leads to a breakdown of the time translation symmetry of the observed SGWB and a correlation between different frequency modes in the signal covariance matrix of the SGWB. Using an ensemble of SGWB due to binary black hole coalescence, calculated using simulations of different black hole mass distributions and merger rates, we show how the structure of the signal covariance matrix varies. This structure in the signal covariance matrix brings additional information about the sources on top of the power spectrum. We show that there is a significant improvement in the figure of merit by using this additional information in comparison to only power spectrum estimation for the LIGO–Virgo–KAGRA (LVK) network of detectors with the design sensitivity noise with 2 yr of observation. The inclusion of the off-diagonal correlation in the covariance of the SGWB in the data analysis pipelines will be beneficial in the quest for the SGWB signal in LVK frequency bands as well as in lower frequencies and in getting an insight into its origin.

An interpretable anti-noise convolutional neural network for online chatter detection in thin-walled parts milling

Chatter is a notoriously unstable phenomenon that can adversely affect both surface quality and machining efficiency. To achieve high-performance machining, the development of online chatter detection is of paramount importance. Nevertheless, changes in chatter frequency with cutting position and noise interference during the thin-walled parts milling process present significant challenges to chatter detection. To tackle this issue, an adaptive frequency band attention module (AFBAM) is designed, which is characterized by not relying on prior knowledge (namely modal parameters, frequency spectrum analysis, etc.), and adaptively enhances the frequency band containing abundant chatter information and reduces noise interference by learning time-frequency domain characteristics of signals. After AFBAM highlights the relevant frequency band of input signal, a discriminative feature attention module (DFAM) is constructed to adaptively recalibrate feature responses of each convolutional layer utilizing the global information. DFAM enhances relevant features and suppresses irrelevant features, thus improving the discriminative feature learning and redundant information suppression abilities of network. In addition, both AFBAM and DFAM exhibit clear physical interpretability, which improves the interpretability of network. Based on AFBAM and DFAM, an interpretable anti-noise convolutional neural network for online chatter detection, named AD-CNN, is established. Milling experiments with pocket-shaped thin-walled parts are conducted under different cutting parameters. The results show that the proposed method enables better detection accuracy and anti-noise ability than other state-of-the-art methods. Furthermore, visualization analysis of AFBAM and DFAM brings new insights into the interpretability of convolutional neural network in the field of chatter detection.

Predicting rock-paper-scissors choices based on single-trial EEG signals.

Decision prediction based on neurophysiological signals is of great application value in many real-life situations, especially in human-AI collaboration or counteraction. Single-trial analysis of electroencephalogram (EEG) signals is a very valuable step in the development of an online decision-prediction system. However, previous EEG-based decision-prediction methods focused mainly on averaged EEG signals of all decision-making trials to predict an individual's general decision tendency (e.g., risk seeking or aversion) over a period rather than on a specific decision response in a single trial. In the present study, we used a rock-paper-scissors game, which is a common multichoice decision-making task, to explore how to predict participants' single-trial choice with EEG signals. Forty participants, comprising 20 females and 20 males, played the game with a computer player for 330 trials. Considering that the decision-making process of this game involves multiple brain regions and neural networks, we proposed a new algorithm named common spatial pattern-attractor metagene (CSP-AM) to extract CSP features from different frequency bands of EEG signals that occurred during decision making. The results showed that a multilayer perceptron classifier achieved an accuracy significantly exceeding the chance level among 88.57% (31 of 35) of participants, verifying the classification ability of CSP features in multichoice decision-making prediction. We believe that the CSP-AM algorithm could be used in the development of proactive AI systems.

A New Dual-Input Deep Anomaly Detection Method for Early Faults Warning of Rolling Bearings.

To address the problem of low fault diagnosis accuracy caused by insufficient fault samples of rolling bearings, a dual-input deep anomaly detection method with zero fault samples is proposed for early fault warning of rolling bearings. First, the main framework of dual-input feature extraction based on a convolutional neural network (CNN) is established, and the two outputs of the main frame are subjected to the autoencoder structure. Then, the secondary feature extraction is performed. At the same time, the experience pool structure is introduced to improve the feature learning ability of the network. A new objective loss function is also proposed to learn the network parameters. Then, the vibration acceleration signal is preprocessed by wavelet to obtain multiple signals in different frequency bands, and the two signals in the high-frequency band are two-dimensionally encoded and used as the network input. Finally, the unsupervised learning of the model is completed on five sets of actual full-life rolling bearing fault data sets relying only on some samples in a normal state. The verification results show that the proposed method can realize earlier than the RMS, Kurtosis, and other features. The early fault warning and the accuracy rate of more than 98% show that the method is highly capable of early fault warning and anomaly detection.

Structural design of new mesh-type high-damping rail pad and comparative experimental study on the mechanical property with the traditional rail pads

Commonly used rail pads (RPs) in urban rail transit fastening systems comprise prismatic and grooved rail pads (PRP and GRP). This research introduced novel mesh-type and mesh-type high-damping rail pads (NMTRP and MTHDRP) to mitigate RP’s stress and enhance damping efficiency while optimizing elastic material utilization. A finite element model (FEM) of the fastening system was established and validated through stiffness and hammering tests of the RPs. The FEA results revealed that the mesh-type RP offered advantages in uniform stress distribution and efficient material usage. The MTHDRP has a significantly lower stress level than the PRP and GRP because of the damping block, it will prolong the MTHDRP’s service life. Both NMTRP and MTHDRP displayed reduced static and dynamic stiffness in comparison to PRP and GRP. Notably, MTHDRP demonstrated the lowest dynamic-to-static stiffness ratio at 1.45, indicating superior dynamic stiffness retention capacity relative to the other three RPs. The hammering test found that the MTHDRP exhibited the highest damping ratio among the four RPs and consistently lower vibration amplitudes in the time and frequency domains at the rail, clip, track bed, and ground when compared to the other RPs. Furthermore, analysis of the 1/3 octave frequency band of the acceleration signal underscored MTHDRP’s excellence, with vibration levels reduced by 4.31, 4.37, 4.21, and 1.43 dB at the rail, clip, track bed, and ground, respectively, in comparison to PRP. These findings affirm the superior vibration reduction performance of MTHDRP relative to the other three RPs and underscore its potential for enhanced foundation protection when incorporated into rail systems.

Methods of reducing antenna bandwidth requirements for envelope elimination and restoration transmitter

The task of radio transmitting path efficiency increasing is relevant both for stationary high-power applications and for autonomous low-power devices. The highest efficiency can be achieved in switching operating modes (classes D, E, F). For the amplification of modern spectrally efficient broadcast and telecommunication signals with variable amplitude, for example, with OFDM, the use of switching operating modes is possible only with the use of "synthetic" amplification methods. The most promising for application can be considered Envelope Elimination and Restoration (EER) method, which is successfully used in the field of high-power broadcasting in the LF, MF and HF bands, and is the object of a large number of studies aimed at expanding its scope. Transmitting devices of radio communication and broadcasting systems in some cases are forced to work with an electrically short antenna. However, when operating with OFDM signals to a narrow-band load, high-efficiency EER transmitters require matching with an antenna circuit with an SWR of at least 1.05 in the signal frequency band. Thus, the task of antenna bandwidth requirements reducing for EER transmitter is relevant. To solve it, a simulation model was previously developed and verified to study the operation of EER switching RF power amplifiers for a narrow-band load. With its help, the dependence of output signal distortion level on the envelope path LPF parameters was studied. This article proposes and analyzes hardware circuit solutions aimed at reducing the transmitter sensitivity to antenna bandwidth. The analysis of hardware methods for reducing the EER transmitter sensitivity to the resonant antenna SWR (passband) showed the following: 1. The damping circuit, together with a double-sided loaded envelope path LPF, allows you to reduce antenna SWR requirements to a maximum value of 1.22, while the allowable antenna bandwidth can be reduced to five signal bands. 2. The high-pass filter diplexer proposed in this paper, together with a double-sided loaded envelope path LPF, makes it possible to reduce antenna SWR requirements to a maximum value of 1.47, and the allowable antenna bandwidth can be reduced to 2.5 signal bands. The disadvantages of this method include the need, depending on the antenna used, for individual design and manufacture of high-pass filter diplexers installed in the low-frequency path of each amplifying cell of the transmitter. To expand the range of antennas used, it is possible to switch capacitors of the high-pass filter diplexer from a set of 2 ... 3 pieces. Data on the production of transmitters with the proposed HPF diplexer has not been found to date. 3. Multi-phase PWM when using a 2nd order unilaterally loaded low-pass filter and installing 2nd harmonic rejectors of the operating frequency in the cells of the RF path allows you to work on antennas with a bandwidth of up to half the signal bandwidth. Restrictions on the antenna bandwidth and its SWR are imposed only by the uneven antenna frequency response in the transmitted signal band. The disadvantages of this method include the need to install narrow-band rejectors that block the power supply circuit of each RF cell in terms of the 2nd harmonic, which makes such a transmitter a custom product for a fixed operating frequency. 4. The most promising solution should be considered the use of a high-pass filter diplexer, which allows relatively simple means to expand the permissible SWR of a narrow-band antenna in the signal band from the initial value of 1.05 to 1.47.

Silicon Echoes: Non-Invasive Trojan and Tamper Detection using Frequency-Selective Impedance Analysis

The threat of chip-level tampering and its detection has been widely researched. Hardware Trojan insertions are prominent examples of such tamper events. Altering the placement and routing of a design or removing a part of a circuit for side-channel leakage/fault sensitivity amplification are other instances of such attacks. While semi- and fully-invasive physical verification methods can confidently detect such stealthy tamper events, they are costly, time-consuming, and destructive. On the other hand, virtually all proposed non-invasive side-channel methods suffer from noise and, therefore, have low confidence. Moreover, they require activating the tampered part of the circuit (e.g., the Trojan trigger) to compare and detect the modifications. In this work, we introduce a non-invasive post-silicon tamper detection technique applicable to different classes of tamper events at the chip level without requiring the activation of the malicious circuit. Our method relies on the fact that physical modifications (regardless of their physical, activation, or action characteristics) alter the impedance of the chip. Hence, characterizing the impedance can lead to the detection of the tamper events. To sense the changes in the impedance, we deploy known RF tools, namely, scattering parameters, in which we inject sine wave signals with high frequencies to the power distribution network (PDN) of the system and measure the “echo” of the signal. The reflected signals in various frequency bands reveal different tamper events based on their impact size on the die. To validate our claims, we performed measurements on several proof-ofconcept tampered hardware implementations realized on FPGAs manufactured with a 28 nm technology. We further show that deploying the Dynamic Time Warping (DTW) distance can distinguish between tamper events and noise resulting from manufacturing process variation of different chips/boards. Based on the acquired results, we demonstrate that stealthy hardware Trojans, as well as sophisticated modifications of P&R, can be detected.

Modeling and experimental analysis of high-efficiency fluid temperature fluctuation attenuator based on phase regulator

Temperature fluctuation attenuators are important passive components for disturbance rejection in precise temperature control system, but previous attenuator researches mainly focused on increasing effective heat capacity to achieve better attenuation. In this paper, the temperature phase regulator (TPR) model is proposed as a superior method to reduce fluid temperature disturbances with less capacity. It achieves output temperature fluctuation attenuation by shifting the phase of the delay side temperature signal and mixing it with the normal side. Similar to the principle that notch filters can highly attenuate signals in specific frequency bands, we constructed TPR theoretical models under equal flow ratios and non-equal flow ratios. Different bandwidths, attenuation depths and target center frequencies can be achieved by changing the two parameters of equivalent heat capacity and flow ratio, and the mechanism of parameters affecting the TPR amplitude-frequency characteristics was analyzed. In terms of effect evaluation, we verified the correlation between the frequency domain and the time domain data, and compared the TPR with the thermal capacity attenuator and other popular attenuators in terms of MSE, MAE and Max-min of temperature data. In a typical temperature attenuation test, TPR can achieve 40% and 35% of MAE and Max-min value attenuation rates, which are exceeding the ideal upper limit of other attenuators and far superior to typical attenuators. The experimental data confirmed the feasibility of the heat capacity of the pipe to support the TPR delay side as a pure delay. The experimental results of non-equal flow ratios verified the model reference-ability of changing flow ratio. In the equal flow ratio test, the fit of the actual data curve and the theoretical curve showed the accuracy of the theoretical model, and the MAE attenuation ratio better than 70% reflected the excellent attenuation performance.

Improving the Pulse-Limited Footprint Resolution of GNSS-R Based on the Novel Joint Bandwidth Method

The bistatic global navigation satellite system’s (GNSS) signal reflection technology has become an effective means of space-based sea surface wind field retrieval and height retrieval. By adopting a wider signal bandwidth, a higher pulse-limited footprint resolution can be achieved. However, for the GNSS-Reflectometry (GNSS-R) system, its signal bandwidth is affected by the signal bandwidth of the GNSS satellite, which limits the further improvement of the pulse-limited footprint resolution. This article proposes a method based on the novel signal bandwidth joint principle to improve the resolution of GNSS-R pulse-limited footprints. Firstly, currently in-orbit GNSS-R satellites use the traditional single frequency band (TSFB) method, which is limited by the GNSS satellite’s signals and has a theoretical upper limit on its signal bandwidth. In response to this issue, this article proposes the novel joint bandwidth (NJBW) method (Galileo E5a and E5b signals) based on the auto-correlation function (ACF) signal ambiguity theory. The NJBW method reduces the main lobe width of the ACF of the GNSS-R signal by jointly processing the signals of E5a and E5b frequency bands, thus improving the pulse-limit footprint resolution of GNSS-R. Secondly, in order to verify the improvement effect of the novel joint bandwidth method on the pulse-limited footprint resolution of GNSS-R, this paper designs and fabricates an NJBW antenna verification prototype for the joint Galileo E5a and E5b frequency band and tests it in a microwave anechoic chamber. The test results indicate that the radio frequency (RF) bandwidth of the NJBW antenna validation prototype can cover both the frequency bands of E5a and E5b, making it suitable for use as the NJBW method for the GNSS-R receiving antenna. The bandwidth test values of the NJBW antenna validation prototype are consistent with the design values, which verifies the correctness of the NJBW antenna design model and further proves the feasibility of the NJBW method. Thirdly, based on the joint Galileo E5a and E5b frequency band signals, the NJBW method was applied to analyze the improvement effect of the pulse-limited footprint resolution. Compared to the TSFB method, the application of the NJBW method can increase the resolution of the GNSS-R pulse-limiting footprint by 1.73 times, which effectively improves the performance of the GNSS-R system. The NJBW method proposed in this article provides the theoretical method foundation and key technical support for sea surface wind field retrieval and height retrieval and the antenna design for the future high-precision and high pulse-limited footprint resolution GNSS-R sea surface wind field retrieval and height retrieval verification satellite.

Double-Scale Convolutional Autoencoder and Extreme Learning Machine for Parameter Identification of DC Bus Capacitor in Power Electronic Transformer

Aluminum electrolytic capacitors (AECs) are utilized as the key components in power electronic transformers (PETs). The AEC degradation monitoring is crucial for the maintenance of PET. Degradation of AEC performance is often reflected by changes of capacitance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> ) and equivalent series resistance (ESR). Since <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> and ESR dominate the low- and mid-frequency impedance characteristics of the AEC, respectively, the features of the corresponding frequency bands of the respective signals need to be simultaneously extracted. However, the current studies on parameter identification of AECs have not focused on this issue. In this article, the double-scale convolutional autoencoder and extreme learning machine (DCAE-ELM) framework is proposed to identify <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> and ESR based on AEC voltage. Specifically, DCAE extracts the low- and mid-frequency features of AEC voltages with large- and small-scale convolutional kernels, respectively. Then ELM is employed to identify <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> and ESR based on the features extracted by DCAE. Moreover, the mathematical mechanisms between the gradients and reconstructed data of DCAE with data concatenated in columns (cDCAE) and rows (rDCAE) are analyzed. Validation results of both simulation and experimental data have verified the data reconfiguration performance of rDCAE and the parameter identification capability of the proposed DCAE-ELM framework.

Speech Enhancement Using Sliding Window Empirical Mode Decomposition and Hurst-based Technique

The most challenging in speech enhancement technique is tracking non-stationary noises for long speech segments and low Signal-to-Noise Ratio (SNR). Different speech enhancement techniques have been proposed but, those techniques were inaccurate in tracking highly non-stationary noises. As a result, Empirical Mode Decomposition and Hurst-based (EMDH) approach is proposed to enhance the signals corrupted by non-stationary acoustic noises. Hurst exponent statistics was adopted for identifying and selecting the set of Intrinsic Mode Functions (IMF) that are most affected by the noise components. Moreover, the speech signal was reconstructed by considering the least corrupted IMF. Though it increases SNR, the time and resource consumption were high. Also, it requires a significant improvement under nonstationary noise scenario. Hence, in this article, EMDH approach is enhanced by using Sliding Window (SW) technique. In this SWEMDH approach, the computation of EMD is performed based on the small and sliding window along with the time axis. The sliding window depends on the signal frequency band. The possible discontinuities in IMF between windows are prevented by the total number of modes and the number of sifting iterations that should be set a priori. For each module, the number of sifting iterations is determined by decomposition of many signal windows by standard algorithm and calculating the average number of sifting steps for each module. Based on this approach, the time complexity is reduced significantly with suitable quality of decomposition. Finally, the experimental results show the considerable improvements in speech enhancement under non-stationary noise environments.

Axial stress measurement method for hollow cylindrical steel structures based on non-dispersive characteristics of T (0,1) guided waves

This article presents an equivalent sound propagation model for hollow cylindrical structures subjected to axial stress, which yields theoretical solutions for sound velocity changes in steel pipes under varying stress levels. To enable the excitation and detection of T (0,1) mode guided waves in hollow steel pipes, an electromagnetic ultrasonic transducer (EMAT) array based on the principle of magnetostriction is designed and tested. To accurately extract nanosecond-level sound time changes due to stress, this study devises the LMS-Gabor algorithm, which exploits the non-dispersive properties of T (0,1) mode guided waves to process the echo signal. Experimental results demonstrate that this stress measurement approach can achieve high accuracy, even in the presence of noise and signals in the same frequency band.

Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model

The hippocampus plays a vital role in navigation, learning, and memory, and is affected in Alzheimer's disease (AD). This study investigated the classification of AD-transgenic rats versus wild-type littermates using electrophysiological activity recorded from the hippocampus at an early, presymptomatic stage of the disease (6months old) in the TgF344-AD rat model. The recorded signals were filtered into low frequency (LFP) and high frequency (spiking activity) signals, and machine learning classifiers were employed to identify the rat genotype (TG vs. WT). By analyzing specific frequency bands in the low frequency signals and calculating distance metrics between spike trains in the high frequency signals, accurate classification was achieved. Gamma band power emerged as a valuable signal for classification, and combining information from both low and high frequency signals improved the accuracy further. These findings provide valuable insights into the early stage effects of AD on different regions of the hippocampus.

Beyond Low-Pass Filtering: Graph Convolutional Networks With Automatic Filtering

Graph convolutional networks are becoming indispensable for deep learning from graph-structured data. Most of the existing graph convolutional networks share two big shortcomings. First, they are essentially low-pass filters, thus the potentially useful middle and high frequency band of graph signals are ignored. Second, the bandwidth of existing graph convolutional filters is fixed. Parameters of a graph convolutional filter only transform the graph inputs without changing the curvature of a graph convolutional filter function. In reality, we are uncertain about whether we should retain or cut off the frequency at a certain point unless we have expert domain knowledge. In this paper, we propose Automatic Graph Convolutional Networks (AutoGCN) to capture the full spectrum of graph signals and automatically update the bandwidth of graph convolutional filters. While it is based on graph spectral theory, our AutoGCN is also localized in space and has a spatial form. Experimental results show that AutoGCN achieves significant improvement over baseline methods which only work as low-pass filters.

Method of assessment of energy intensity of public communication radio equipment with software adjustable working frequency for determination of interruption protection indicators

The ability of modern departmental communication systems to perform tasks in conditions of radio-electronic suppression by the enemy characterizes their immunity to interference. It is known that the interference protection of radio equipment in conditions of enemy radio reconnaissance and electronic suppression can be described by a set of probability indicators that characterize its secrecy (energy) and interference resistance. Secrecy (energy) is the ability of departmental communication systems to counteract the enemy's radio reconnaissance means, which are aimed at detecting the fact of the departmental communication system's operation, determining the parameters of its radio emissions, intercepting information for further deliberate radio-electronic and electromagnetic interference. One of the methods that significantly increases the secrecy of departmental communication systems is the use of signals with software-defined frequency conversion. These signals are a set of radio pulses (signal elements) whose frequencies change over time according to the law of pseudorandom sequence. The enemy searches for and detects the elements of these signals in a wide frequency band. This search is carried out under conditions of partial a priori uncertainty regarding their spectral-time structure using panoramic receivers. It should be noted that the basis of the radio reconnaissance means of the world's leading states is Fourier processors, which perform Fourier transforms from the implementation of a set of input signals. When designing and developing methods for interference protection, the problem of evaluating their energy stealth arises. Known research results and existing methodologies allow for a simplified assessment of the energy stealth of radio equipment with software frequency hopping under time constraints for decision making and do not take into account the peculiarities of the construction of modern panoramic analysis tools. In particular, the known methodologies do not take into account the peculiarities of the process of frequency-time searching for elements of such signals in a wide frequency range using panoramic receiving devices based on the complex application of different types of Fourier processors, the order of spectral-time processing of signal elements at the output of Fourier processors, requirements for their threshold sensitivity and dynamic signal range. Therefore, the article proposes a methodology for evaluating the stealthiness of communication systems with software-defined frequency tuning to determine the indicators of interference protection based on Fourier processors in the conditions of the opponent's use of modern radio-electronic intelligence tools.

Connectivity gradients in spontaneous brain activity at multiple frequency bands.

The intrinsic organizational structure of the brain is reflected in spontaneous brain oscillations. Its functional integration and segregation hierarchy have been discovered in space by leveraging gradient approaches to low-frequency functional connectivity. This hierarchy of brain oscillations has not yet been fully understood, since previous studies have mainly concentrated on the brain oscillations from a single limited frequency range (~ 0.01-0.1Hz). In this work, we extended the frequency range and performed gradient analysis across multiple frequency bands of fast resting-state fMRI signals from the Human Connectome Project and condensed a frequency-rank cortical map of the highest gradient. We found that the coarse skeletons of the functional organization hierarchy are generalizable across the multiple frequency bands. Beyond that, the highest integration levels of connectivity vary in the frequency domain across different large-scale brain networks. These findings are replicated in another independent dataset and demonstrated that different brain networks can integrate information at varying rates, indicating the significance of examining the intrinsic architecture of spontaneous brain activity from the perspective of multiple frequency bands.

FCAN–XGBoost: A Novel Hybrid Model for EEG Emotion Recognition

In recent years, artificial intelligence (AI) technology has promoted the development of electroencephalogram (EEG) emotion recognition. However, existing methods often overlook the computational cost of EEG emotion recognition, and there is still room for improvement in the accuracy of EEG emotion recognition. In this study, we propose a novel EEG emotion recognition algorithm called FCAN–XGBoost, which is a fusion of two algorithms, FCAN and XGBoost. The FCAN module is a feature attention network (FANet) that we have proposed for the first time, which processes the differential entropy (DE) and power spectral density (PSD) features extracted from the four frequency bands of the EEG signal and performs feature fusion and deep feature extraction. Finally, the deep features are fed into the eXtreme Gradient Boosting (XGBoost) algorithm to classify the four emotions. We evaluated the proposed method on the DEAP and DREAMER datasets and achieved a four-category emotion recognition accuracy of 95.26% and 94.05%, respectively. Additionally, our proposed method reduces the computational cost of EEG emotion recognition by at least 75.45% for computation time and 67.51% for memory occupation. The performance of FCAN–XGBoost outperforms the state-of-the-art four-category model and reduces computational costs without losing classification performance compared with other models.