Power Spectral Density Of Signal Research Articles

Adaptive Ramanujan package decomposition method: a novel precise health indicator of rolling bearings

ABSTRACT In this paper, an adaptive Ramanujan packet decomposition (ARPD) method based on precise envelope segmentation is proposed to improve the accuracy of weak fault feature extraction and realise early non-destructive testing and condition evaluation of rolling bearings. Firstly, the power spectral density of the original signal is envelope-processed by accurate envelope segmentation method, so as to reduce the extreme points produced by interference frequency, and then realise the optimal frequency band division and reduce the influence of noise components. Secondly, the Ramanujan spectrum signal-to-noise ratio (RSSNR) index is introduced to quantify the fault characteristic information of the corresponding component in each frequency band. The largest component of the index is selected as the optimal mode, and the current number of layers is defined as the optimal number of modes. Finally, through the idea of iterative decomposition and RSSNR index, the problem that the optimal mode number is difficult to determine accurately is eliminated, and the optimal mode number is determined adaptively. The results of simulation and experimental signal analysis show that ARPD method can accurately divide the frequency bands and has excellent weak fault feature extraction ability, so it is an effective early non-destructive testing and condition evaluation method for rolling bearings.

Experimental investigation of the relation between operating conditions and offshore wind turbine drivetrain dynamics

Abstract This study investigates the impact of operating and environmental conditions on the vibration induced on an offshore wind turbine drivetrain. Furthermore, it explores the role of the dynamic response of the global wind turbine structure to vibration on the drivetrain. A prolonged experimental campaign dedicated to condition monitoring the drivetrain provides experimental vibration signals. These vibration signals are acquired under varying operating conditions across multiple locations of an offshore wind turbine drivetrain of a Belgian Offshore Wind farm and serve as the basis for analysis. The signals’ statistics facilitate establishing connections between dynamic behaviour and operational variables, such as active power, rotor speed, blade pitch angle, and turbulence intensity, sampled at every second and provided by Fast-SCADA. The initial phase of the analysis involves a comprehensive examination of various statistical properties of the vibration signals to link the drivetrain’s dynamic response to operational conditions. This exploration includes both active and standstill periods of the wind turbine. A comprehensive summary that relates the global vibration characteristics with the operating conditions is formed by scrutinizing the overall vibration signals. In the study’s second phase, a detailed investigation is conducted on the vibration signals recorded during standstill conditions. Investigation of standstill data ensures that the structural modes’ spectral content does not interfere with the machines’ kinematic content. The analysis of standstill data proceeds from determining the most energetic resonance frequencies through the average power spectral density of the vibration signals. These identified frequency bands enable a thorough exploration, revealing the interlink between the offshore wind turbine drivetrain’s dynamic response and the operating/environmental conditions.

Improving adaptive noise reduction performance of body sound auscultation through linear preprocessing

Adaptive filtering methods based on least-mean-square (LMS) error criterion have been commonly used in auscultation to reduce ambient noise. For non-Gaussian signals containing pulse components, such methods are prone to weights misalignment. Unlike the commonly used variable step-size methods, this paper introduced linear preprocessing to address this issue. The role of linear preprocessing in improving the denoising performance of the normalized least-mean-square (NLMS) adaptive filtering algorithm was analyzed. It was shown that, the steady-state mean square weight deviation of the NLMS adaptive filter was proportional to the variance of the body sounds and inversely proportional to the variance of the ambient noise signals in the secondary channel. Preprocessing with properly set parameters could suppress the spikes of body sounds, and decrease the variance and the power spectral density of the body sounds, without significantly reducing or even with increasing the variance and the power spectral density of the ambient noise signals in the secondary channel. As a result, the preprocessing could reduce weights misalignment, and correspondingly, significantly improve the performance of ambient-noise reduction. Finally, a case of heart-sound auscultation was given to demonstrate how to design the preprocessing and how the preprocessing improved the ambient-noise reduction performance. The results can guide the design of adaptive denoising algorithms for body sound auscultation.

Temporal complexity of the BOLD-signal in preterm versus term infants.

Preterm birth causes alterations in structural and functional cerebral development that are not fully understood. Here, we investigate whether basic characteristics of BOLD signal itself might differ across preterm, term equivalent, and term infants. Anatomical, fMRI, and diffusion weighted imaging data from 716 neonates born at 23-43 weeks gestational age were obtained from the Developing Human Connectome Project. Hurst exponent (H; a measure of temporal complexity of a time-series) was computed from the power spectral density of the BOLD signal within 13 resting state networks. Using linear mixed effects models to account for scan age and birth age, we found that H increased with age, that earlier birth age contributed to lower H values, and that H increased most in motor and sensory networks. We then tested for a relationship between temporal complexity and structural development using H and DTI-based estimates of myelination and found moderate but significant correlations. These findings suggest that the temporal complexity of BOLD signal in neonates relates to age and tracks with known developmental trajectories in the brain. Elucidating how these signal-based differences might relate to maturing hemodynamics in the preterm brain could yield new information about neurophysiological vulnerabilities during this crucial developmental period.

Characterization of Seismic Signal Patterns and Dynamic Pore Pressure Fluctuations Due to Wave-Induced Erosion on Non-Cohesive Slopes

Wave erosion of slopes can easily trigger landslides into marine environments and pose severe threats to both the ecological environment and human activities. Therefore, near-shore slope monitoring becomes crucial for preventing and alerting people to these potential disasters. To achieve a comprehensive understanding, it is imperative to conduct a detailed investigation into the dynamics of wave erosion processes acting on slopes. This research is conducted through flume tests, using a wave maker to create waves of various heights and frequencies to erode the slope models. During the tests, seismic signals, acoustic signals, and pore pressure generated by wave erosion and slope failure are recorded. Seismic and acoustic signals are analyzed, and time-frequency spectra are calculated using the Hilbert–Huang Transform to identify the erosion events and signal frequency ranges. Arias Intensity is used to assess seismic energy and explore the relationship between the amount of erosion and energy. The results show that wave height has a more decisive influence on erosion behavior and retreat than wave frequency. Rapid drawdown may potentially cause the slope to slide during cyclic swash and backwash wave action. As wave erosion changes from swash to impact, there is a significant increase in the spectral magnitude and Power Spectral Density (PSD) of both seismic and acoustic signals. An increase in pore pressure is observed due to the rise in the run-up height of waves. The amplitude of pore pressure will increase as the slope undergoes further erosion. Understanding the results of this study can aid in predicting erosion and in planning effective management strategies for slopes subject to wave action.

Hypospadias Reconstruction Training: Development of an Ex-Vivo Model for Objective Evaluation of Surgical Skills

To objectively evaluate technical skill acquisition in hypospadias repair procedures during surgical training using noninvasive wearable sensor technology. We combined subjective video evaluations with objective electromyography (EMG) measurements in a hands-on hypospadias training course. Surgeons wore wireless EMG and accelerometer sensors on their dominant hand while performing tasks on ex-vivo cadaveric calf penises. The study focused on 4 skills as follows: urethral mobilization, dorsal inlay graft harvest/implantation, meatal-based flap urethroplasty, and dorsal plication. Machine learning techniques analyzed muscle activation patterns and attributes for assessing surgical precision. The course included 18 participants (10 female, 8 males; average age 40.18±8.46 years) categorized as novice (n=10, <3 years' experience), intermediate (n=5, 3-5 years), and expert (n=3, >5 years). Video evaluations did not reveal significant differences due to short-term training. However, EMG measurements showed significant reductions in average EMG power, total time, dominant frequency, and cumulative muscle workload after training. Additionally, the mean power spectral density of the EMG signal decreased notably post-training. This study presents a structured approach for hypospadias training and highlights the effectiveness of wearable sensor technology for objective skill assessment. While video evaluations did not detect significant changes, EMG data provided measurable differences in skill acquisition, suggesting that wearable sensors could enhance objective evaluations of surgical proficiency in residency programs.

A wavelet packet deep learning model for Energy-Based structural collapse assessment under Earthquake-Fire Scenarios: Framework and hybrid simulation

An energy-based framework is proposed for the dynamic stability assessment of structures subjected to earthquake-fire scenarios and verified through earthquake-fire hybrid simulation. In this framework, the wavelet packet Long Short-Term Memory (LSTM) model is used to separate the noise and residue caused by earthquake and fire within the structural response signals, guided by wavelet packet energy and power spectral density of signals. Moreover, a comparative analysis is performed with 4 previous signal processing models (empirical mode decomposition, variational mode decomposition, wavelet packet transform, and LSTM). Additionally, Latin hypercube sampling is employed to account for uncertainties in structural characteristics and hazards for dataset establishment and fragility analysis. The experimental findings suggest a decline in the structural dominant frequency with the emergence of high-frequency noise and residuals in structural responses due to the multi-hazard effect. The wavelet packet transform eliminates the high-frequency noise in the signal and avoids the oscillation of the LSTM prediction results. Post-earthquake fire increases the structural collapse possibility even under a moderate earthquake excitation. The proposed framework proves to be rational and has the potential to be further applied to other multi-hazard scenarios.

Multi-shaker time-varying root mean square non-stationary random vibration environmental control test

The current multi-shaker random control methods concentrate on the simulation of stationary vibrations. This work presents an innovative technique for multi-shaker non-stationary random vibration environmental control test. Firstly, an amplitude modulation method is introduced to generate multi-input non-stationary random signals with time-varying root mean square values, whose kurtoses and moving RMS values are specified by amplitude modulation functions. Then, the generated signals are applied as excitation signals for the non-stationary random vibration environmental control test. The response signals of a linear vibration system under non-stationary excitations are derived in the frequency domain. The derived formulas for kurtoses and moving RMS values are also applicable to non-stationary response signals. Meanwhile, the stationary power spectral densities of response signals are put forward. When the amplitude modulation functions are zero mean Gaussian distributions, the power spectral densities of the response signals can be directly calculated. Ultimately, the control of kurtoses, moving RMS values and stationary power spectral densities of response non-stationary random signals is realized. Simulation and experimental examples both adopt a two-input two-output beam vibration system and the results prove the correctness and practicability of the proposed method. The presented control procedure is concise and efficient in simulating multi-shaker non-stationary random vibration environments.

CFAR Target Detector in Synthetic Aperture Radar

Introduction. Constant false alarm rate (CFAR) detectors have found application in synthetic aperture radar (SAR) systems. The operating principle of a classic cell averaging detector (CA-CFAR detector) is based on comparing the decision statistics in the test resolution element with an adaptive threshold, which is calculated from signals in the reference cells. The decision statistic is an estimate of the signal power. Therefore, target signal detection is based on the brightness contrast of the test and reference resolution cells. Such a detector is optimal provided that the noise background is homogeneous. In cases where the background homogeneity is violated, the quality of detection deteriorates. There are several known methods for improving the quality of detection (GO-CFAR, SO-CFAR, OS-CFAR, etc.). However, the precise principle of detection by brightness contrast in such CFAR detectors remains unchanged.Aim. To synthesize a CFAR detector that uses not only the brightness contrast between the test and reference resolution cells, but also the spectral differences of the signals.Materials and methods. The proposed CFAR detector uses estimates of the algebraic moments of the power spectral density of signals in range cells, based on which three decision statistics are calculated containing information about the power, the position of the energy center, and the width of the signal spectrum. The decision about the presence of a target in the test cell is carried out according to the 2/3 rule (2 threshold overshoots out of 3 comparisons).Results. A comparison of the proposed detector with the SO-CFAR detector, performed by computer simulation, showed that, under a signal-to-clutter ratio of -6 dB and a false alarm probability of 10-4, the detection probability of the proposed detector was 0.933 versus 0.708 for the SO-CFAR detector.Conclusion. The article proposes a three-parameter CFAR detector for a synthetic aperture radar system, in which the decision on the presence of a target in the test cell is made via estimation of the first three algebraic moments of the signal spectrum. The synthesized detection algorithm can also be used when detecting moving targets in SAR.

K-wire is more damaging than standard or acrylic drill bits when evaluating torsional properties of rabbit (Oryctolagus cuniculi) femurs.

The objective of this study is to compare drilling variables and torsional mechanical properties of rabbit femora after bicortical drilling with a 1.5-mm standard surgical drill bit, acrylic drill bit, and K-wire. 24 pairs of rabbit femora. After drilling under controlled axial displacement rate, each bone was biaxially loaded in compression followed by rapid external torsion to failure. Maximum axial thrust force, maximum drill torque, integral of force and displacement, change in temperature, maximum power spectral density of the torque signal, torque vibration, and torque and angle at the yield and failure points were collected. Pre- and postyield stiffness, yield and failure energies, and postyield energy were calculated. The work required to drill through the cis- and transcortices (integral of force and displacement) was greater for the K-wire, followed by the acrylic and then standard drill bits, respectively. The K-wire demonstrated higher maximum torque than the drill bits at the ciscortex, and the force of drilling was significantly greater. The vibration data was greater with the acrylic and standard drill bits than the K-wire. There was no difference in torsional strength between drilling types. Mechanical differences exist between different drill bits and K-wire and demonstrate that the K-wire is overall more damaging than the surgical drill bit.

Lavender Essential Oil Inhalation Improves Attentional Shifting and Accuracy: Evidence from Dynamic Changes of Cognitive Flexibility and Power Spectral Density of Electroencephalogram Signals.

Cognitive flexibility, a vital component of executive function, entails the utilization of extended brain networks. Olfactory stimulation has been shown to influence various brain functions, particularly cognitive performance. To investigate aroma inhalation's effects on brain activity dynamics associated with cognitive flexibility, 20 healthy adults were recruited to complete a set-shifting task during two experimental conditions: no aroma stimuli vs. lavender essential oil inhalation. Using Thomson's multitaper approach, the normalized power spectral density (NPSD) was assessed for five frequency bands. Findings confirm that aroma inhalation significantly affects behavioral indices (i.e., reaction time (RT) and response accuracy) and electroencephalogram (EEG) signatures, especially in the frontal lobe. Participants showed a tremendous increase in theta and alpha NPSD, associated with relaxation, along with beta NPSD, associated with clear and fast thinking after inhaling the aroma. NPSD of the delta band, an indicator of the unconscious mind, significantly decreased when stimulated with lavender essential oil. Further, participants exhibited shorter RT and more accurate responses following aroma inhalation. Our findings revealed significant changes in oscillatory power and behavioral performance after aroma inhalation, providing neural evidence that olfactory stimulation with lavender essential oil may facilitate cognitive flexibility.

Application of modulated vibration to restore driver alertness

Since driving while drowsy is a significant cause of vehicle accidents, road safety could be improved if more effective methods were available for improving driver alertness. The present paper investigated whether it is possible to improve alertness via a wearable device that applied somatosensory vibration to the driver's wrist. The vibration used modulation frequencies ranging from 12 Hz to 50 Hz and a carrier frequency of 250 Hz. Random on-and-off intervals and variable vibration amplitudes were used to minimise sensory adaptation. Fifteen participants undertook a sixty-minute simulated driving task that caused drowsiness. Karolinska Sleepiness Scale (KSS), Power Spectral Density (PSD) and Higuchi's Fractal Dimension (HFD) analyses of brainwave signals were used to identify variations in driver alertness. The vibration stimulus was found to significantly improve alertness when compared to a no-vibration condition. Participants experienced an immediate improvement in alertness that reached significance within 9 min and was then sustained at the level seen at the beginning of the experiment, indicating a full restoration of alertness. This result demonstrates that wearable vibration devices have the potential to improve alertness in drivers.

Structural EEG signal analysis for sleep apnea classification.

Diagnosing the sleep apnea can be critical in preventing the person having sleep disorder from unhealthy results. The aim of this study is to obtain a sleep apnea scoring approach by comparing parametric and non-parametric power spectral density (PSD) estimation methods from EEGsignals recorded from different brain regions (C4-M1 and O2-M1) for transient signal analysis of sleep apnea patients. Power Spectral Density (PSD) methods (Burg, Yule-Walker, periodogram, Welch and multi-taper) are examined for the detection of apnea transition states including pre-apnea, intra-apnea and post-apnea together with statistical methods. In the experimental studies, EEG recordings available in the database were analyzed with PSD methods. Results showed that there are statistically significant differences between parametric and non-parametric methods applied for PSD analysis of apnea transition states in delta, theta, alpha and beta bands. Moreover, it was also revealed that PSD of EEG signals obtained from C4-M1 and O2-M1 channels were also found statistically different as proved by classification using the K-nearest neighbour (KNN) method. It was concluded that not only applying different PSD methods, but also EEG signals from different brain regions provided different statistical results in terms of apnea transition states as obtained from KNN classification.

Response of silicon solar cells to neutrons in post-detonation monitoring

In this study, we evaluated the feasibility of using solar photovoltaic (PV) panels for the detection of prompt neutrons from a nuclear denotation. Monte Carlo (MC) simulations were performed to evaluate the response of silicon (Si) solar cells (SCs) to neutrons and support the experiments. The response of Si SCs to fission spectrum neutrons was measured experimentally using the fast neutron beam of a research reactor. Steady-state neutron measurements indicated a clear response of the Si SCs, which correlated with changes in the neutron intensity. Time-modulated neutron measurements performed using a neutron chopper demonstrated a clear transient response of Si SCs to neutrons, which was observed in the signal power spectral density plots containing peaks corresponding to frequencies of neutron pulses produced by the chopper. The resulting signal was found to be small, which was attributed to the extremely small active neutron interaction volume inside the SCs. In a real detonation scenario, the transient neutron flux is expected to be higher, and the effective neutron interaction volume of large area solar PV panels is much larger, which could significantly boost the signal. The study, overall, demonstrated the potential of large area solar PV panels or a solar farm for prompt detection and identification of a nuclear detonation.

Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals.

Mild cognitive impairment (MCI) is a neuropsychological syndrome that is characterized by cognitive impairments. It typically affects adults 60years of age and older. It is a noticeable decline in the cognitive function of the patient, and if left untreated it gets converted to Alzheimer's disease (AD). For that reason, early diagnosis of MCI is important as it slows down the conversion of the disease to AD. Early and accurate diagnosis of MCI requires recognition of the clinical characteristics of the disease, extensive testing, and long-term observations. These observations and tests can be subjective, expensive, incomplete, or inaccurate. Electroencephalography (EEG) is a powerful choice for the diagnosis of diseases with its advantages such as being non-invasive, based on findings, less costly, and getting results in a short time. In this study, a new EEG-based model is developed which can effectively detect MCI patients with higher accuracy. For this purpose, a dataset consisting of EEG signals recorded from a total of 34 subjects, 18 of whom were MCI and 16 control groups was used, and their ages ranged from 40 to 77. To conduct the experiment, the EEG signals were denoised using Multiscale Principal Component Analysis (MSPCA), and to increase the size of the dataset Data Augmentation (DA) method was performed. The tenfold cross-validation method was used to validate the model, moreover, the power spectral density (PSD) of the EEG signals was extracted from the EEG signals using three spectral analysis methods, the periodogram, welch, and multitaper. The PSD graphs of the EEG signals showed signal differences between the subjects of control and the MCI group, indicating that the signal power of MCI patients is lower compared to control groups. To classify the subjects, one of the best classifiers of deep learning algorithms called the Bi-directional long-short-term-memory (Bi-LSTM) was used, and several machine learning algorithms, such as decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN). These algorithms were trained and tested using the extracted feature vectors from the control and the MCI groups. Additionally, the values of the coefficient matrix of those algorithms were compared and evaluated with the performance evaluation matrix to determine which one performed the best overall. According to the experimental results, the proposed deep learning model of multitaper spectral analysis approach with Bi-LSTM deep learning algorithm attained the highest number of correctly classified samples for diagnosing MCI patients and achieved a remarkable accuracy compared to the other proposed models. The achieved classification results of the deep learning model are reported to be 98.97% accuracy, 98.34% sensitivity, 99.67% specificity, 99.70% precision, 99.02% f1 score, and 97.94% Matthews correlation coefficient (MCC).

Assessing focus through ear-EEG: a comparative study between conventional cap EEG and mobile in- and around-the-ear EEG systems.

As our attention is becoming a commodity that an ever-increasing number of applications are competing for, investing in modern day tools and devices that can detect our mental states and protect them from outside interruptions holds great value. Mental fatigue and distractions are impacting our ability to focus and can cause workplace injuries. Electroencephalography (EEG) may reflect concentration, and if EEG equipment became wearable and inconspicuous, innovative brain-computer interfaces (BCI) could be developed to monitor mental load in daily life situations. The purpose of this study is to investigate the potential of EEG recorded inside and around the human ear to determine levels of attention and focus. In this study, mobile and wireless ear-EEG were concurrently recorded with conventional EEG (cap) systems to collect data during tasks related to focus: an N-back task to assess working memory and a mental arithmetic task to assess cognitive workload. The power spectral density (PSD) of the EEG signal was analyzed to isolate consistent differences between mental load conditions and classify epochs using step-wise linear discriminant analysis (swLDA). Results revealed that spectral features differed statistically between levels of cognitive load for both tasks. Classification algorithms were tested on spectral features from twelve and two selected channels, for the cap and the ear-EEG. A two-channel ear-EEG model evaluated the performance of two dry in-ear electrodes specifically. Single-trial classification for both tasks revealed above chance-level accuracies for all subjects, with mean accuracies of: 96% (cap-EEG) and 95% (ear-EEG) for the twelve-channel models, 76% (cap-EEG) and 74% (in-ear-EEG) for the two-channel model for the N-back task; and 82% (cap-EEG) and 85% (ear-EEG) for the twelve-channel, 70% (cap-EEG) and 69% (in-ear-EEG) for the two-channel model for the arithmetic task. These results suggest that neural oscillations recorded with ear-EEG can be used to reliably differentiate between levels of cognitive workload and working memory, in particular when multi-channel recordings are available, and could, in the near future, be integrated into wearable devices.

The flow and heat transfer behavior of synthetic jets with star shaped orifice of different lobes

In the present experimental investigation, the flow and heat transfer characteristics of synthetic jet (SJ) issuing from different lobed (3–10) star-shaped orifices are studied. Tests are performed by actuating the SJ actuator at constant input power of 4Vrms, with varied range of orifice to surface spacings (1–16) and numerous values of Reynolds number (8692–10746). The synthetic jet's flow characteristics are studied using hot-wire anemometry, while the thermal characteristics are analyzed using an IR camera. Distinct peaks in turbulence intensity are observed in major and minor planes for different lobed (3–6) star orifices; this may be due to the development of two shear layers in such orifices, which consequently merges into a single shear layer for higher lobed (8–10) star orifice. The 6-lobed star orifice is found to exhibit maximum heat transfer performance at z/d varying between 2 and 14. At z/d = 2, the 6-lobed star orifice exhibit 7.6% and 10.1% improvement in average Nusselt number compared to circular and 10-lobed star orifice respectively, which may be due to formation of vortical structures along the lobed edges and subsequent switching of axes between the major and minor planes. To comprehend the energy associated with the issuing vortices, the power spectral density of the velocity signal at the orifice exit is examined. A unified correlation for the average Nusselt number is also derived for various star-shaped orifices.

Epileptic seizure detection combining power spectral density and high-frequency oscillations

Detection of pre-seizure signs in epileptic signals may help patients to survive the seizure with minimal damage. This study aims to detect epileptic seizure patterns using EEG datasets of five patients. The signals' maximum power spectral density (PSD) and high-frequency oscillations (HFOs) signals are investigated. The PSDs of all patients' signals are calculated, and the subbands of the maximum PSD are examined. It is observed that 95%, 85%, 85%, 75%, and 80% of the channels of the five patients are in the sum of delta and theta subbands of the maximum PSD, respectively. All patients' maximum power frequency subbands of F4 and T3 channels included only delta and theta subbands. Furthermore, frequency increase rates of pre-ictal and ictal signals are investigated, and increasing PSDs of ripples and fast ripples are then calculated. A much higher-frequency ripple follows the low-frequency ripple in 75%, 75%, 65%, 85%, and 75% of the channels of the first, second, third, fourth, and fifth patients, respectively. For the pre-ictal data, a much higher frequency ripple is not seen, followed by a low-frequency ripple in 90%, 85%, 75%, 90%, and 90% of all channels of five patients, respectively. In addition, in this study, the frequency of signals is observed to be 80 Hz and above in the Fp2, C4, P4, O2, and Pz channels, which are common to all patients. Consequently, examining PSD and HFO signals ensures the detection of the differences between the data sets and detects the epileptic seizure patterns in all five patients.

Slope of the power spectral density flattens at low frequencies (200Hz) in human electroencephalogram.

The power spectral density (PSD) of the brain signals is characterized by two distinct features: oscillations, which are represented as distinct "bumps," and broadband aperiodic activity, that reduces in power with increasing frequency and is characterized by the slope of the power falloff. Recent studies have shown a change in the slope of the aperiodic activity with healthy aging and mental disorders. However, these studies analyzed slopes over a limited frequency range (<100Hz). To test whether the PSD slope is affected over a wider frequency range with aging and mental disorder, we analyzed the slope till 800Hz in electroencephalogram data recorded from elderly subjects (>49years) who were healthy (n = 217) or had mild cognitive impairment (MCI; n = 11) or Alzheimer's Disease (AD; n = 5). Although the slope reduced up to ~ 150Hz with healthy aging (as shown previously), surprisingly, at higher frequencies (>200Hz), it increased with age. These results were observed in all electrodes, for both eyes open and eyes closed conditions, and for different reference schemes. However, slopes were not significantly different in MCI/AD subjects compared with healthy controls. Overall, our results constrain the biophysical mechanisms that are reflected in the PSD slopes in healthy and pathological aging.

Gaussian-Distributed Spread-Spectrum for Covert Communications.

Covert communication techniques play a crucial role in military and commercial applications to maintain the privacy and security of wireless transmissions from prying eyes. These techniques ensure that adversaries cannot detect or exploit the existence of such transmissions. Covert communications, also known as low probability of detection (LPD) communication, are instrumental in preventing attacks such as eavesdropping, jamming, or interference that could compromise the confidentiality, integrity, and availability of wireless communication. Direct-sequence spread-spectrum (DSSS) is a widely used covert communication scheme that expands the bandwidth to mitigate interference and hostile detection effects, reducing the signal power spectral density (PSD) to a low level. However, DSSS signals possess cyclostationary random properties that an adversary can exploit using cyclic spectral analysis to extract useful features from the transmitted signal. These features can then be used to detect and analyse the signal, making it more susceptible to electronic attacks such as jamming. To overcome this problem, a method to randomise the transmitted signal and reduce its cyclic features is proposed in this paper. This method produces a signal with a probability density function (PDF) similar to thermal noise, which masks the signal constellation to appear as thermal white noise to unintended receivers. This proposed scheme, called Gaussian distributed spread-spectrum (GDSS), is designed such that the receiver does not need to know any information about the thermal white noise used to mask the transmit signal to recover the message. The paper presents the details of the proposed scheme and investigates its performance in comparison to the standard DSSS system. This study used three detectors, namely, a high-order moments based detector, a modulation stripping detector, and a spectral correlation detector, to evaluate the detectability of the proposed scheme. The detectors were applied to noisy signals, and the results revealed that the moment-based detector failed to detect the GDSS signal with a spreading factor, N = 256 at all signal-to-noise ratios (SNRs), whereas it could detect the DSSS signals up to an SNR of -12 dB. The results obtained using the modulation stripping detector showed no significant phase distribution convergence for the GDSS signals, similar to the noise-only case, whereas the DSSS signals generated a phase distribution with a distinct shape, indicating the presence of a valid signal. Additionally, the spectral correlation detector applied to the GDSS signal at an SNR of -12 dB showed no identifiable peaks on the spectrum, providing further evidence of the effectiveness of the GDSS scheme and making it a favourable choice for covert communication applications. A semi-analytical calculation of the bit error rate is also presented for the uncoded system. The investigation results show that the GDSS scheme can generate a noise-like signal with reduced identifiable features, making it a superior solution for covert communication. However, achieving this comes at a cost of approximately 2 dB on the signal-to-noise ratio.