Spectral Density Ratio Research Articles

A synthesis of machine learning and internet of things in developing autonomous fleets of heterogeneous unmanned aerial vehicles for enhancing the regenerative farming cycle

AbstractThe use of Unmanned Aerial Vehicles (UAVs) for agricultural monitoring and management offers additional advantages over traditional methods, ranging from cost reduction to environmental protection, especially when they utilize Machine Learning (ML) methods, and Internet of Things (IoT). This article presents an autonomous fleet of heterogeneous UAVs for use in regenerative farming the result of a synthesis of Deep Reinforcement Learning (DRL), Ant Colony Optimization (ACO) and IoT. The resulting aerial framework uses DRL for fleet autonomy and ACO for fleet synchronization and task scheduling inflight. A 5G Multiple Input Multiple Output-Long Range (MIMO-LoRa) antenna enhances data rate transmission and link reliability. The aerial framework, which has been originally prototyped as a simulation to test the concept, is now developed into a functional proof-of-concept of autonomous fleets of heterogeneous UAVs. For assessing performance, the paper uses Normalized Difference Vegetation Index (NDVI), Mean Squared Error (MSE) and Received Signal Strength Index (RSSI). The 5G MIMO-LoRa antenna produces improved results with four key performance indicators: Reflection Coefficient (S11), Cumulative Distribution Functions (CDF), Power Spectral Density Ratio (Eb/No), and Bit Error Rate (BER).

Comparative study of DCT-and DHT-based OFDM systems over doubly dispersive fading channels

Abstract In high-mobility operating scenarios orthogonal frequency division multiplexing (OFDM) system lacks its optimality, intercarrier interference (ICI) occurs and the Doppler shifts deteriorate the orthogonality of the subcarriers. However, this problem can be overcome by utilizing complicated equalizers at the receiver. Discrete cosine transform (DCT) and discrete Hartley transform (DHT) have been used instead of discrete Fourier transform (DFT) in the standard OFDM system. The performance results achieved enhancement over dispersive selective channels that cannot be accomplished with a standard OFDM system, even with utilizing complex equalizers. In this paper, the performance of the DCT-based OFDM system and DHT-based OFDM system is compared and analyzed with respect to DFT-based OFDM system performance over doubly dispersive fading channel at various Doppler shifts, the block diagrams of the proposed systems are provided to simplify the theoretical analysis by making it easier to follow. Simulation results emphasized that the performance of DCT and DHT-based OFDM systems under doubly dispersive fading conditions scenario outperforms DFT-based OFDM system of order 3 dB energy per bit to noise power spectral density ratio (Eb/N0).

A Subject-Specific Attention Index Based on the Weighted Spectral Power.

As an essential cognitive function, attention has been widely studied and various indices based on EEG have been proposed for its convenience and easy availability for real-time attention monitoring. Although existing indices based on spectral power of empirical frequency bands are able to describe the attentional state in some way, the reliability still needs to be improved. This paper proposed a subject-specific attention index based on the weighted spectral power. Unlike traditional indices, the ranges of frequency bands are not empirical but obtained from subject-specific change patterns of spectral power of electroencephalograph (EEG) to overcome the great inter-subject variance. In addition, the contribution of each frequency component in the frequency band is considered different. Specifically, the ratio of power spectral density (PSD) function in attentional and inattentional state is utilized to calculate the weight to enhance the effectiveness of the proposed index. The proposed subject-specific attention index based on the weighted spectral power is evaluated on two open datasets including EEG data of a total of 44 subjects. The results of the proposed index are compared with 3 traditional attention indices using various statistical analysis methods including significance tests and distribution variance measurements. According to the experimental results, the proposed index can describe the attentional state more accurately. The proposed index respectively achieves accuracies of 86.21% and 70.00% at the 1% significance level in both the t-test and Wilcoxon rank-sum test for two datasets, which obtains improvements of 41.38% and 20.00% compared to the best result of the traditional indices. These results indicate that the proposed index provides an efficient way to measure attentional state.

Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor.

Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems. We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type. 17 ET participants and 18 healthy controls were recruited for the study. Two movement disorder neurologists who were blinded to prior clinical information viewed video recordings and scored the FTM. Participants drew a guided Archimedes spiral while wearing an inertial measurement unit placed at the mid-point between the lateral epicondyle of the humerus and the anatomical snuff box. Acceleration and gyroscope recordings were analyzed. The ratio of the power spectral density between frequency bands 0.5-4 Hz and 4-12 Hz, and the sum of power spectrum density over the entire spectrum of 2-74 Hz, for both accelerometer and gyroscope data, were computed. FTM was estimated using regression model and classification using SVM was validated using the leave-one-out method. Regression analysis showed a moderate to good correlation when individual features were used, while correlation was high ([Formula: see text] = 0.818) when suitable features of the gyro and accelerometer were combined. The accuracy for two-class classification of the combined features using SVM was 91.42% while for four-class it was 68.57%. Potential applications of this novel wearable sensing method using a wearable Inertial Measurement Unit (IMU) include monitoring of ET and clinical trials of new treatments for the disorder.

Sensitive Properties of Power Spectral Density Transmissibility (PSDT) to Moving Vehicles and Structural States in Bridge Health Monitoring

Bridge health monitoring confronts a critical challenge in extracting meaningful features that are sensitive to structural damage while remaining nonsensitive to operational environments and loads. Most of structural response features such as power spectral density (PSD) in the long‐term monitored bridge are influenced by operational vehicle loads. The power spectral density transmissibility (PSDT), defined as the power spectral density ratio of two measured output responses at two different structural locations with the same reference output response, converges independently at the system poles of the applied excitations and transferring outputs. Capitalizing on such a unique property of PSDT around the system poles, the PSDT‐based spectral moment is proposed in the paper to establish a robust structural feature in bridge health monitoring taking into account the time‐varying characteristics under operational vehicle loads. Numerical simulations and comparisons with PSD‐based spectral moment analysis reveal that the PSDT‐based spectral moment exhibits an enhanced robustness to traffic flow excitations and heightened sensitivity to changes in structural parameters. Further laboratory experimental results on the beam under moving vehicle confirm that the PSDT‐based spectral moment is less affected by moving vehicle loads, but it demonstrates higher sensitivity to structural parameter changes. Given its robust properties of low sensitivity to operational vehicle loads and sensitivity to changes in structural parameters, the proposed PSDT‐based spectral moment emerges as an ideal structural feature suitable for the effective applications in the long‐term bridge health monitoring, such as structural damage identification, model updating, condition assessment, and safety warning.

Evaluating Performance: A Study of Encrypted vs. Unencoded Signals in SISO-OFDM with LS and MMSE Estimations

The continuous progress in digital communication has played a crucial role in meeting the increasing need for faster data rates. Orthogonal Frequency Division Multiplexing (OFDM), a pivotal methodology in this progression, attains improved data rates through the efficient utilisation of densely packed carriers within a specified channel bandwidth. This article focuses on the investigation of channel estimation in OFDM systems. the study of search lies in its examination of the performance consequences associated with the incorporation or lack thereof of a convolutional encoder in OFDM systems and looks at how well two well-known channel estimation algorithms, Least Square (LS) and Minimum Mean Square Error (MMSE), work in 4-Quadrature amplitude modulation (4-QAM) OFDM systems with and without a convolutional encoder with a comprehensive evaluation of the efficacy of the OFDM system across various channel conditions. It uses MATLAB implementations as its main tool. The findings of the study indicate that the MMSE algorithm, despite its higher complexity, exhibits superior performance in comparison to the LS algorithm when combined with a convolutional encoder. The gain in terms of bit error rate (BER) improvement approximately 12 dB. This represents the logarithmic scale improvement in BER from BER1(uncoded) to BER2(coded) at the same the energy per bit to noise power spectral density ratio (Eb/N0) of 40 dB.

Metrological Evaluation of Software-Defined Radios (Adalm-Pluto and LimeSDR usb) in Radio Frequency Signal Generation

In the automotive industry, products must be tested and verified before being launched; currently, products such as automotive radio are usually tested with expensive metrology equipment, such as the Rohde & Schwarz SFE100, which produces radio frequency signals. This study assessed the feasibility of replacing the SFE100 with two inexpensive devices, Adalm-Pluto and LimeSDR usb, which are software-defined radios. For this evaluation, a study was carried out to determine specific metrological characteristics and then to compare with those of metrological equipment. Based on the results and tests of the study, we conclude that SDRs can generate test signals (FM, DAB, DVB, ISDB and SXM) with uncertainties of 0.5dBm and a spectral density ratio comparable to SFE100, thereby enabling their replacement. Furthermore, the evaluation datasets of all equipment have been publicly available [1], enabling the scientific community to extract the metrological uncertainties for all signal carriers generated by the device. The result of this study proposes a methodology for testing auto radios that is innovative and can be applied to a production line based on the principles of Industry 4.0.

Calming effect of Clinically Designed Improvisatory Music for patients admitted to the epilepsy monitoring unit during the COVID-19 pandemic: a pilot study.

Epilepsy monitoring requires simulating seizure-inducing conditions which frequently causes discomfort to epilepsy monitoring unit (EMU) patients. COVID-19 hospital restrictions added another layer of stress during hospital admissions. The purpose of this pilot study was to provide evidence that live virtual Clinically Designed Improvisatory Music (CDIM) brings relief to EMU patients for their psychological distress. Five persons with epilepsy (PWEs) in the EMU during the COVID-19 lockdown participated in the study (average age ± SD = 30.2 ± 6 years). Continuous electroencephalogram (EEG) and electrocardiogram (EKG) were obtained before, during, and after live virtual CDIM. CDIM consisted of 40 minutes of calming music played by a certified clinical music practitioner (CMP) on viola. Post-intervention surveys assessed patients' emotional state on a 1-10 Likert scale. Alpha/beta power spectral density ratio was calculated for each subject across the brain and was evaluated using one-way repeated analysis of variance, comparing 20 minutes before, during, and 20 minutes after CDIM. Post-hoc analysis was performed using paired t-test at the whole brain level and regions with peak changes. Patients reported enhanced emotional state (9 ± 1.26), decrease in tension (9.6 ± 0.49), decreased restlessness (8.6 ± 0.80), increased pleasure (9.2 ± 0.98), and likelihood to recommend (10 ± 0) on a 10-point Likert scale. Based on one-way repeated analysis of variance, alpha/beta ratio increased at whole-brain analysis (F3,12 = 5.01, P = 0.018) with a peak in midline (F3,12 = 6.63, P = 0.0068 for Cz) and anterior medial frontal region (F3,12 = 6.45, P = 0.0076 for Fz) during CDIM and showed a trend to remain increased post-intervention. In this pilot study, we found positive effects of CDIM as reported by patients, and an increased alpha/beta ratio with meaningful electroencephalographic correlates due to the calming effects in response to CDIM. Our study provides proof of concept that live virtual CDIM offered demonstrable comfort with biologic correlations for patients admitted in the EMU during the COVID-19 pandemic.

Performance analysis of satellite link using Gaussian mixture model under rain

SummaryThe evolution of communication systems to the next generation, for example, B5G and 6G, demands an ultrareliable performance regardless of weather conditions. Such ultrareliable system design will require that the effects of adverse weather events on the communication system have to be computed more accurately so that physical layer compensation should be optimally and dynamically adaptive to such events. The performance of satellite links is severely affected by dynamic rain attenuation, and thus, accurate and reliable modeling of performance parameters is essential for dynamic fade countermeasures, especially above 10 GHz. In this work, we model the energy per bit to noise spectral density ratio ( ) using Gaussian mixture (GM) model during rainy events. The developed mathematical expression is used to accurately model the average , bit error rate (BER), outage probability, and ergodic channel capacity of the link. The average BER, upper bound on BER, and average ergodic capacity of an M‐ary phase shift keying scheme (MPSK) using the GM model of are derived to evaluate the performance of the link under such weather impairments. We then show the numerical results and analysis using the GM model of the measured data obtained with the AMoS‐7 satellite at a site located in Israel.

EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer's Disease.

Alzheimer's disease (AD) is associated with EEG changes across the sleep-wake cycle. As the brain is a non-linear system, non-linear EEG features across behavioral states may provide an informative physiologic biomarker of AD. Multiscale fluctuation dispersion entropy (MFDE) provides a sensitive non-linear measure of EEG information content across a range of biologically relevant time-scales. To evaluate MFDE in awake and sleep EEGs as a potential biomarker for AD. We analyzed overnight scalp EEGs from 35 cognitively normal healthy controls, 23 participants with mild cognitive impairment (MCI), and 19 participants with mild dementia due to AD. We examined measures of entropy in wake and sleep states, including a slow-to-fast-activity ratio of entropy (SFAR-entropy). We compared SFAR-entropy to linear EEG measures including a slow-to-fast-activity ratio of power spectral density (SFAR-PSD) and relative alpha power, as well as to cognitive function. SFAR-entropy differentiated dementia from MCI and controls. This effect was greatest in REM sleep, a state associated with high cholinergic activity. Differentiation was evident in the whole brain EEG and was most prominent in temporal and occipital regions. Five minutes of REM sleep was sufficient to distinguish dementia from MCI and controls. Higher SFAR-entropy during REM sleep was associated with worse performance on the Montreal Cognitive Assessment. Classifiers based on REM sleep SFAR-entropy distinguished dementia from MCI and controls with high accuracy, and outperformed classifiers based on SFAR-PSD and relative alpha power. SFAR-entropy measured in REM sleep robustly discriminates dementia in AD from MCI and healthy controls.

Association between Beta Oscillations from Subthalamic Nucleus and Quantitative Susceptibility Mapping in Deep Gray Matter Structures in Parkinson's Disease.

This study aimed to investigate the association between beta oscillations and brain iron deposition. Beta oscillations were filtered from the microelectrode recordings of local field potentials (LFP) in the subthalamic nucleus (STN), and the ratio of the power spectral density of beta oscillations (PSDXb) to that of the LFP signals was calculated. Iron deposition in the deep gray matter (DGM) structures was indirectly assessed using quantitative susceptibility mapping (QSM). The Unified Parkinson's Disease Rating Scale (UPDRS), part III, was used to assess the severity of symptoms. Spearman correlation coefficients were applied to assess the associations of PSDXb with QSM values in the DGM structures and the severity of symptoms. PSDXb showed a significant positive correlation with the average QSM values in DGM structures, including caudate and substantia nigra (SN) (p = 0.008 and 0.044). Similarly, the PSDXb showed significant negative correlations with the severity of symptoms, including axial symptoms and the gait in the medicine-off state (p = 0.006 for both). The abnormal iron metabolism in the SN and striatum pathways may be one of the underlying mechanisms for the occurrence of abnormal beta oscillations in the STN, and beta oscillations may serve as important pathophysiological biomarkers of PD.

Introduction to the statistical theory of differential communication based on chaotic signals

The purpose of this paper is to analyse the statistical characteristics of a Direct Chaotic Differentially Coherent communication scheme based on chaotic radio pulses in a communication channel with additive white Gaussian noise, where the chaotic signal is given by different instantaneous distributions. Methods. To achieve this goal, numerical modelling of the noise immunity of Direct Chaotic Differentially Coherent communication is conducted and compared with the results of analytical research. Results. The regularities associated with the use of chaotic signals with various statistical distributions of instantaneous values were studied. The minimum values of energy per bit to white Gaussian noise power spectral density ratio were obtained, providing the required error probabilities. Conclusion. It is shown that the proposed system works efficiently at high values of processing gain, and as the processing gain increases, the dependence of noise immunity on the specific statistical distribution of the chaotic signal is levelled out.

Noise-to-signal ratio of single-trajectory spectral densities in centered Gaussian processes

We discuss the statistical properties of a single-trajectory power spectral density of an arbitrary one-dimensional real-valued centered Gaussian process X(t), where ω is the angular frequency and the observation time. We derive a double-sided inequality for its noise-to-signal ratio and obtain the full probability density function of . Our findings imply that the fluctuations of exceed its average value . This implies that using to describe the behavior of these processes can be problematic. We finally evaluate the typical behavior of and find that it deviates markedly from the average in most cases.

СТАТИСТИЧЕСКИЕ ХАРАКТЕРИСТИКИ СИСТЕМЫ ОТНОСИТЕЛЬНОЙ ПЕРЕДАЧИ ИНФОРМАЦИИ НА ОСНОВЕ ХАОТИЧЕСКИХ ИМПУЛЬСОВ В КАНАЛЕ С ГАУССОВСКИМ ШУМОМ

The purpose of this paper is to analyze the statistical characteristics of a differentially coherent communication scheme based on the use of chaotic pulses in the presence and absence of thermal noise for various distributions of chaotic signal instantaneous values. To achieve this goal, a numerical simulation for the performance of direct chaotic differentially coherent communication scheme is carried out in the work by a computer simulation. A numerical simulation was carried out, confirming the previously obtained analytical bit error probability estimates vs energy per bit to gaussian noise power spectral density ratio. The minimum energy per bit to gaussian noise power spectral density ratio values, which provide the given error probabilities, are obtained. It is shown that the proposed communication system works effectively at high values of processing gain(the behavior of the communication system was studied for various values of processing gain), and in the case of high values of processing gain, the results obtained do not depend on the choice of a specific discrete chaotic signal distribution.

Análisis de la técnica NOMA-OFDM en un canal multitrayecto y usando estimación de canal

NOMA (Non-Orthogonal Multiple Access) is a non-orthogonal access technique that can increase spectral efficiency and is considered a candidate technology for 5G. NOMA-OFDM combines the non-orthogonal access of NOMA with OFDM, which is widely used due to several advantages such as its high spectral efficiency. Channel estimation is an essential process in a NOMA-OFDM system, so in this paper a channel estimation scheme with preamble for the downlink is proposed considering a multipath channel and the LS (Least Square) technique. The analysis of the results is performed based on graphs of BER (Bit Error Rate) vs Eb/N0 (Energy per bit to noise power spectral density ratio) of the simulation implemented in Matlab and shows that channel estimation is possible with the LS method with the advantage that it is a simple technique although it produces a degradation of approximately 3 dB in the BER compared to the ideal case of perfect estimation. In addition, the results show that the selection of the injection factor is very important for the performance of the users to be adequate.

A novel out‐of‐band emission suppression method in future wireless communications employing filtered orthogonal frequency division multiplexing waveform

SummaryIn order to achieve flexible configuration and asynchronous transmission of different subbands (SBs), SB filters are utilized in filtered orthogonal frequency division multiplexing (f‐OFDM) systems to isolate different SBs efficiently. Nevertheless, the price is the insertion of some intrinsic interference.Therefore, the suppression of the out‐of‐band emission (OOBE) between SBs is essential for the system performance improvement of f‐OFDM. According to the system structure of f‐OFDM, an optimization design model based on active interference cancelation (AIC) is proposed in this study, and the optimization design model is solved through second order cone programming. Furthermore, the effectiveness of the proposed AIC algorithm in OOBE suppression is verified by computer simulations. To the whole system, the OOBE suppression also means the decrease of inter‐SB interference (ITBI). In addition, the influences of main parameters, such as the number of the cancelation carriers (CCs), on the OOBE suppression performance of the proposed AIC algorithm, are simulated and analyzed. Moreover, some intrinsic characteristics of the proposed algorithm in parameter configuration are revealed in terms of power spectral density (PSD) and bit error ratio (BER).

Federated Edge Learning With Misaligned Over-the-Air Computation

Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel-gain precoding and strict synchronization among edge devices, which are challenging in practice. As such, how to design the maximum likelihood (ML) estimator in the presence of residual channel-gain mismatch and asynchronies is an open problem. To fill this gap, this paper formulates the problem of misaligned OAC for FEEL and puts forth a whitened matched filtering and sampling scheme to obtain oversampled, but independent samples from the misaligned and overlapped signals. Given the whitened samples, a sum-product ML (SP-ML) estimator and an aligned-sample estimator are devised to estimate the arithmetic sum of the transmitted symbols. In particular, the computational complexity of our SP-ML estimator is linear in the packet length, and hence is significantly lower than the conventional ML estimator. Extensive simulations on the test accuracy versus the average received energy per symbol to noise power spectral density ratio (EsN0) yield two main results: 1) In the low EsN0 regime, the aligned-sample estimator can achieve superior test accuracy provided that the phase misalignment is not severe. In contrast, the ML estimator does not work well due to the error propagation and noise enhancement in the estimation process. 2) In the high EsN0 regime, the ML estimator attains the optimal learning performance regardless of the severity of phase misalignment. On the other hand, the aligned-sample estimator suffers from a test-accuracy loss caused by phase misalignment.

A Wasserstein GAN Autoencoder for SCMA Networks

In the view of the exponential growth of mobile applications, the Sparse Code Multiple Access (SCMA) is one promising code-domain Non-Orthogonal Multiple Access (NOMA) technique. The challenge in the adoption of SCMA in practical networks is twofold: designing optimal codebooks at the transmitter and decoding the data at the receiver. However, most of the works available in the literature address only one aspect. In this letter, we design an end-to-end SCMA en/deconding structure based on the integration between a state-of-the-art autoencoder architecture and a novel Wasserstein Generative Adversarial Network (WGAN) that improves the robustness to the channel noise. We compare the performance obtained by the proposed network with conventional computationally intensive solutions and a deep learning based autoencoder. The performance achieved show better performances in terms of Symbol Error Rate (SER), especially at low energy per bit to noise power spectral density ratio.

Wearable sensors during drawing tasks to measure the severity of essential tremor

Commonly used methods to assess the severity of essential tremor (ET) are based on clinical observation and lack objectivity. This study proposes the use of wearable accelerometer sensors for the quantitative assessment of ET. Acceleration data was recorded by inertial measurement unit (IMU) sensors during sketching of Archimedes spirals in 17 ET participants and 18 healthy controls. IMUs were placed at three points (dorsum of hand, posterior forearm, posterior upper arm) of each participant’s dominant arm. Movement disorder neurologists who were blinded to clinical information scored ET patients on the Fahn–Tolosa–Marin rating scale (FTM) and conducted phenotyping according to the recent Consensus Statement on the Classification of Tremors. The ratio of power spectral density of acceleration data in 4–12 Hz to 0.5–4 Hz bands and the total duration of the action were inputs to a support vector machine that was trained to classify the ET subtype. Regression analysis was performed to determine the relationship of acceleration and temporal data with the FTM scores. The results show that the sensor located on the forearm had the best classification and regression results, with accuracy of 85.71% for binary classification of ET versus control. There was a moderate to good correlation (r2 = 0.561) between FTM and a combination of power spectral density ratio and task time. However, the system could not accurately differentiate ET phenotypes according to the Consensus classification scheme. Potential applications of machine-based assessment of ET using wearable sensors include clinical trials and remote monitoring of patients.

Ultrasonic horn contact-induced transient anharmonic resonance effect on vibro-thermography

We present some new insights into the impact-separation contact dynamics induced spatial localization of anharmonic in a cavity-like feature. Origin of different nonlinear anharmonics is identified with the help of an inelastic/plastic collision signal model, a nonlinear Duffing oscillator model, and a detailed 3D finite element simulation, with increasing granularity and physical assumptions regarding contact dynamics. A rigid contact on the target elastic structure generates super-harmonics when the target structure resonates precisely with the excitation frequency. It generates sub-super harmonics when there is a slight mistuning in the motion of the target structure. The response amplitude and the energy in the primary excitation frequency reduce due to the energy partitioning over the different anharmonics generated. The ratio of Power Spectral Density (PSD) of primary harmonics to input excitation decreases almost linearly with decreasing separation distance between the actuator horn tip and the stationary target. The ratios of PSD of all anharmonics to that of primary excitation decreases quadratically with decreasing the relative amplitude of an elastic target structure moving in phase with the actuator. Different anharmonics further evolve as elastic waves in the target structure. The non-stationary vibration at the beginning of the excitation governs the evolving patterns of heat generation, which is of great interest in applications such as vibro-thermographic inspection. The effect of the initial transient state on time required to reach the steady-state excitation is analyzed, and related computational aspects are discussed. Spatio-temporal anharmonic modal distribution and the thermal signature obtained from numerical simulations are compared with the experimental results. The results strongly correlate the anharmonic modal contribution with the transient thermal signature due to a cavity-like feature in the target structure.