Frequency Domain Power Research Articles

Multi-UAV Cooperative Anti-Jamming for GNSS Signals Based on Frequency-Domain Power Inversion

Multi-UAV Cooperative Anti-Jamming for GNSS Signals Based on Frequency-Domain Power Inversion

Comparison and extension of advanced frequency domain non-active power decompositions

This research paper introduces new prospects in the field of power quality and energy transmission efficiency analysis and system design methodology through advanced frequency-domain power definitions. This proposal presents an improved extension of the power theory Currents’ Physical Components (CPC) by L.S. Czarnecki and compares its improvements with the CPC and an extension of the CPC by H. Lev-Ari. This proposal increases the resolution of physical phenomena reducing the power quality and hence contributes to its analysis and improvement. For this purpose, four additional non-active power components compared to the CPC are introduced. In contrast to the extension by H. Lev-Ari, these additional components are obtained from different definitions. In particular, the decomposition of the unbalance component into a spectral uniform and a spectral non-uniform component is novel to frequency-domain power definitions. As the CPC and its extension through H. Lev-Ari already decompose the collinear and shift components into spectral uniform and non-uniform components, the proposal for the unbalance components finally completes this structure. This yields a consistent and intuitive designation of physical phenomena related to spectral non-uniformity constituting as a concretization to the well-known distortion non-active power. Conclusive examples illustrate this improvement using the appertaining non-active power components.

Locating Two-Level Systems in a Superconducting Xmon Qubit

One significant source of decoherence in superconducting circuits is known as two-level systems (TLSs), found in amorphous oxide layers. These circuits can, however, also be utilized as spectral and temporal TLS probes. Comprehensive investigations on the physics of TLSs are now possible thanks to recent advancements in superconducting qubits. Here, we simultaneously measure the tunable Xmon qubit decoherence time as well as the resonance frequency for more than 3 days to investigate stochastic fluctuations. Time-domain Allan deviation and frequency-domain power spectral density analysis indicate that two TLSs in near resonance with the qubit are responsible for the fluctuations. From the extracted oscillation in T1 decay, we locate the two TLSs near the junctions.

QUANTITATIVE SIGNATURES OF BRAIN COGNITION IN YOUNG CHILDREN USING TASK-BASED EEG SIGNALS

Cognition is the practice of processing knowledge through mental processes. Understanding cognitive functions of the brain in young children helps in identifying neurodevelopmental disorders at early stages. Most studies used to diagnose neurodevelopmental disorders are based on behavioral scales. But most children with neurodevelopmental disorders are non-verbal. Thus, the goal of this research is to perform a task-based analysis of cognitive skills in children using electroencephalographic (EEG) signals. Spectral and fractal features of the EEG signals were acquired from pre-primary school children ([Formula: see text]) during arithmetic and path-finding cognitive tasks. The acquired signals were decomposed into various sub-bands. Investigations on the time-domain Higuchi Fractal Dimension (HFD) analysis and frequency domain Power Spectrum Analysis features demonstrated the dominance of the Theta band over the other subbands. The frontal electrode exhibited more workload in the arithmetic task, indicating the involvement of frontal regions and the temporal areas (T8) which were actively involved throughout the path-finding task, showing the activity related to spatial perception and navigation. Thus, the statistical information obtained from the spectral and fractal analyses seems to provide quantitative evidence of the variations in cognitive skills of young children thereby enhancing inferences about neurodevelopmental disorders.

Transforming Energy and Pedagogy

Transforming Energy and Pedagogy

Research on Vibration Data-Driven Fault Diagnosis for Iron Core Looseness of Saturable Reactor in UHVDC Thyristor Valve Based on CVAE-GAN and Multimodal Feature Integrated CNN

The imbalance of data samples and fluctuating operating conditions are the two main challenges faced by vibration data-driven fault diagnosis for the iron core looseness of saturable reactors in UHVDC thyristor valves. This paper proposes a vibration data-driven saturable reactor iron core looseness fault diagnosis strategy named CVG-MFICNN based on CVAE-GAN and MFICNN to overcome the two challenges. This strategy uses a novel 1-D CVAE-GAN model to produce generated samples and expand the training set based on imbalanced training samples. An MFICNN model structure is designed to allow the simultaneous processing of multimodal features such as the SST time-frequency spectrum, time-domain vibration sequence, frequency-domain power spectrum sequence, and time-domain statistics. Using these multimodal features and the MFICNN model, the hidden fault information in vibration data can be effectively mined. An experiment is conducted to collect vibration data of saturable reactors with different faults. Models based on the proposed strategy and other methods are trained and tested using the collected data. The comparison results show that the performance of the proposed CVG-MFICNN approach is significantly superior to that of single-feature CNNs, traditional machine learning methods, and classical image classification CNNs in the application of UHVDC thyristor valve saturable reactor iron core looseness fault diagnosis.

Parallel frequency-domain detection of molecular affinity kinetics by single nanoparticle plasmon sensors

Based on frequency-domain optical measurement using single nanoparticle plasmon sensors (NanoSPR), a versatile multiplex molecular affinity kinetics detection method is proposed. To improve the detection precision and throughput, a single-color imaging NanoSPR method (SI-NanoSPR) is used to obtain the light scattering signals of thousands of gold nanorod sensors over time under the configuration of a total internal reflection dark-field microscope. The frequency-domain power spectral density analysis of the fluctuation signal extracts the characteristic frequency fc, by which the molecular affinity kinetics manifest the identifiable measurand. By measuring the kinetics of two different aptameric affinity systems in the same microscope field of view, the obtained equilibrium dissociation constants (KD values) are demonstrated to be in agreement with previous studies, which were measured by traditional techniques. We expect that our NanoSPR method may pave the way for a deeper understanding of the physiological essence of biological affinity systems by accurately quantifying multiple affinity constants. The high-throughput biosensing potential is of great significance in further biomedical and pharmaceutical applications.

Constraining the stochastic gravitational wave background with photometric surveys

The detection of the Stochastic Gravitational Wave Background (SGWB) is essential for understanding black hole populations, especially for supermassive black hole binaries. The recent promising results from various Pulsar Timing Array (PTA) collaborations allude to an imminent detection. In this paper, we investigate the relative astrometric gravitational wave detection method, which can contribute to SGWB studies in the microhertz range. We consider the Roman Space Telescope and Gaia as candidates and quantitatively discuss the survey sensitivity in both the frequency and spatial domains. We emphasize the importance of survey specific constraints on performance estimates by considering mean field of view (FoV) signal subtraction and angular power spectrum binning. We conclude that if the SGWB is at a similar level as in PTA estimates, both Roman and Gaia have the potential to detect this frequency-domain power excess. However, both Roman and Gaia are subject to FoV limitations, and are unlikely to be sensitive to the spatial pattern of the SGWB.

Assessing the microarchitecture and microenvironment of cancer in vivo by photoacoustic imaging techniques

Majority of current studies on photoacoustic (PA) imaging are focused on the total signal magnitudes as the reflection of the macroscopic optical absorption by specific chemical contents at single or multiple optical wavelengths. Our recent research has demonstrated that the frequency domain power distribution of radio-frequency PA signals contains the microscopic information of the optically absorbing materials in biological samples. In this research, I will present our recent development of quantitative PA methods for in vivo evaluation of histological microarchitectures of biological tissues as well as potential clinical applications of these methods such as the assessment of prostate cancer aggressiveness. In addition, powered by our recently developed polyacrylamide hydrogel nanoparticles containing a variety of functional contrast agents, multi-wavelength PA imaging plus spectral unmixing technique can enable quantitative mapping of the chemical and metabolic properties in cancer microenvironments, such as tissue oxygenation, pH level, and ion concentrations. Imaging of these chemical makeups of an entire tumor in vivo, non-invasively, with high sensitivity and high spatial resolution can shed new light to the understanding of cancer onset, progress, and responses to therapies.

C-band 120-Gb/s PAM-4 transmissions over a 100-km dispersion-uncompensated SSMF using joint combined pulse shaping and low-complexity nonlinear equalization.

In C-band intensity modulation and direct detection (IM/DD) systems, the frequency-dependent power fading induced by chromatic dispersion (CD) and square-law detection limits the transmission capacity and distance, especially for beyond 100-Gb/s transmissions over a 100-km dispersion-uncompensated link. To reach this goal, we propose a scheme of nonlinear pre-distortion, novel, to the best of our knowledge, combined pulse shaping, and post nonlinear equalization for four-level pulse amplitude modulation (PAM-4)-based IM/DD systems. At the transmitter, the nonlinear pre-distortion is used to generate unequally spaced PAM-4 symbols for pre-compensating the nonlinearities. While the novel pulse shaping, simply shaped by the linear combination of two inter-symbol interference (ISI)-free pulses, alters the frequency-domain power distribution of the PAM-4 signal and results in performance improvement. At the receiver, low-complexity post nonlinear equalization using an absolute-term based nonlinear equalizer with weight sharing (AT-NLE-WS) is performed to eliminate CD-induced power fading and residual nonlinear impairments. With the cooperation of these techniques, record 120-Gb/s PAM-4 signals are successfully transmitted over a 100-km standard single-mode fiber (SSMF) with the measured bit error ratio (BER) below 3.8 × 10-3, achieving >9% improvement of system capacity in comparison with the conventional pulse shaping schemes.

Fundamental Study for Increasing Multiplicity of Frequency Division Multiplex HPLC-MS

This fundamental study was conducted to increase the multiplicity of frequency division in multiplex high-performance liquid chromatography-mass spectrometry (HPLC-MS). Virtual chromatograms were used to investigate the high efficiency separation, i.e., the peak with a narrow band in time-domain chromatograms. They revealed that the peak induces the broad signals in the frequency-domain power spectrum obtained from the Fourier transform (FT) of the chromatogram. For a high performance separation of N = 50 000 and 200 000, a minimum modulation frequency of 22.5 and 45 Hz, respectively, are required for 4HPLC-1MS. The high-frequency modulation was successfully achieved by increasing discharge rate with modifying the electric circuit in the modulation system. In addition, a higher sampling rate in MS is required for a higher modulation frequency to achieve successful FT analytical results. In other words, the sampling frequency must be increased by a factor of five for a given modulation frequency. Using a laboratory-made four channel interface, the simultaneous sample introductions in infusion mode was achieved with various modulation frequencies. The signal separation in the frequency region was successfully achieved for modulation frequencies up to 30 Hz with a sampling rate of 160 Hz.

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

Electronic Mode Stirring for Improved Backscatter Communication Link Margin in a Reverberant Cavity Animal Cage Environment

Neuroscience research in nonhuman primates (NHPs) often requires multiday neural recordings from freely moving animals inside their home cages, making ultralow-power uplinks using wireless backscatter communication highly desirable. Previous work reveals that the channel transfer function (CTF) of a standard NHP home cage in the 915 MHz and 2.4 GHz industrial, scientific, and medical (ISM) bands resembles a resonant cavity exhibiting deep nulls throughout the cage volume, which are particularly acute for round-trip backscatter paths. In this work, we investigate a novel application of passive antenna mode stirring via switched parasitic antennas (SPAs) to reduce the magnitude and prevalence of deep nulls in the cage CTF. We present a system leveraging four cage-ceiling-mounted SPAs with two dynamically controlled impedance states each, yielding 16 total mode stirring configurations. We compare the frequency-domain power ratio measurements at 126 positions throughout the cage taken with and without passive antenna mode stirring. In the 915 MHz ISM band, the optimized SPA configuration improved the maximum two-way insertion loss in 68% of testing locations, reducing the worst case two-way insertion loss by 60.2 dB. In the 2.4 GHz ISM band, the maximum two-way insertion loss was improved in 53% of testing locations, reducing the worst case two-way insertion loss by 35.6 dB. This approach eliminates the deepest nulls in the cage volume and leads to significantly improved link margin for a backscatter-based wireless brain–computer interface (BCI).

Preparation, Characterization, and Terahertz Spectroscopy Characteristics of Reduced Graphene Oxide-Doped Epoxy Resin Coating

Reduced graphene oxide has attracted numerous interests due to its unique, superior electronic, optical, mechanical, and chemical properties. An epoxy resin with excellent mechanical and electrical properties can be obtained by doping with reduced graphene oxide to enhance the function of the polymer. Here, we prepared a uniform reduced graphene oxide/epoxy resin coating with a different reduced graphene oxide content and characterized it using a field-emission scanning electron microscope (FE-SEM), X-ray diffractometer (XRD), Raman, and Fourier transform infrared spectrometer (FTIR). Furthermore, the spectral characteristics of the composite coating in the terahertz band were discussed. The cross-sectional SEM results show that a fold structure with ductile failure was intensively formed due to the compatibility of graphene and polymer materials. Both the Raman G and Raman 2D peaks of reduced graphene oxide were confirmed using Raman spectrum testing. The diffraction peak of reduced graphene oxide at 24° disappeared within the reduced graphene oxide/epoxy resin coating, and a wide diffraction peak of the amorphous structure was formed together. Additionally, the intensity of the Raman spectrum increased significantly with increased reduced graphene oxide content, thereby making the surface electrical resistance of the coatings decrease exponentially. Additionally, the intensity of the terahertz time-domain signal and frequency-domain power spectrum linearly reduced with increased reduced graphene oxide concentration. However, the terahertz absorption coefficient and refractive index both increased gradually with increased reduced graphene oxide doping due to increased orientation polarization in the composite coating.

The overlapped latent fingerprint separation by using Fourier Optics (FO)

Latent fingerprints are one of the most important pieces of evidence left at a crime scene and can be linked to all individuals involved. Each person’s fingerprints are unique and permanent, becoming an ideal biometric trait for the personal identification by extracting and comparing characteristic points (minutiae) of ridges. The overlapping fingerprint cases are frequently encountered in a crime scene and causing a difficult interpretation for an investigator. The problem has been challenging for forensic scientists over a decade. The method proposed in this study for the separation of overlapped latent fingerprints is based on the well-known spatial filtering method in the Fourier Optics (FO). Instead of tackling the problem by experiment, an alternative and simple means of image processing was proposed and conducted. The working principles start form converting spatial domain patterns (an image of overlapped fingerprints) to spatial frequency domain patterns or power spectrum, filtering out unwanted components (unwanted fingerprint) by appropriate spatial filters, and finally converting the modified pattern back to spatial domain patterns (an image of suspect fingerprint). As a result, the final image is improved from its original state. The periodic pattern of ridges is the key that allows FO to be used in the separation of the overlapped fingerprints. In this work, the procedures described are simply performed by an open source software: ImageJ. The FO-based image processing technique satisfactorily demonstrated its ability to recover an individual fingerprint from overlapping fingerprints.

Technical note: On the development of an outcome-driven frequency filter for improving radiomics-based modeling of human papillomavirus (HPV) in patients with oropharyngeal squamous cell carcinoma.

To implement an outcome-driven frequency filter for improving radiomics-based modeling of human papillomavirus (HPV) for patients with oropharyngeal squamous cell carcinoma (OPSCC). One hundred twenty-eight OPSCC patients with known HPV status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16 protein testing) were retrospectively studied. A 3D Discrete Fourier Transform was applied on contrast-enhanced computed tomography (CE-CT) images of patient gross tumor volumes (GTVs) to transform intensity distributions to the frequency domain and estimate frequency power spectrums of HPV- and HPV+ patient cohorts. Statistical analyses were performed to rank frequency bands contributing toward the prediction of HPV status. An outcome-driven frequency filter was designed accordingly and applied to GTV frequency information. A 3D inverse discrete Fourier transform was applied to reconstruct HPV-related frequency-filtered images. Radiomics features (11 feature-categories) were extracted from pre- and post-frequency filtered images using our previously published "ROdiomiX" software. Least-absolute-shrinkage-and-selection-operation (Lasso) combined with a generalized linear model (Lasso-GLM) was developed to identify and rank feature subsets with the optimal information for prediction of HPV+/-. Radiomics-based Lasso-GLM classifiers (pre- and post-frequency filtered) were constructed and validated using random permutation sampling and nested cross-validation (CV) techniques. Average area under the receiver operating characteristic (AUC), and positive and negative predictive values (PPV and NPV) were computed to estimate generalization error and prediction performance. Among 192 radiomic features, 15 features were found to be statistically significant discriminators between HPV+/- cohorts on post-frequency filtered CE-CT images. Fourteen such radiomic features were observed on pre-frequency filtered datasets. Discriminant features included tumor morphology and intensity contrast. Performances for prediction of HPV for the pre- and post-frequency filtered Lasso-GLM classifiers were as follows: AUC/PPV/NPV=0.789/0.755/0.805 and 0.850/0.808/0.877, respectively. Nested CV performances for prediction of HPV for the pre- and post-frequency filtered Lasso-GLM classifiers were as follows: AUC/PPV/NPV=0.814/0.725/0.877 and 0.890/0.820/0.911, respectively. Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results on the importance of frequency analysis prior to radiomic feature extraction toward enhancement of model performance for characterizing HPV in patients with OPSCC.

Earth\u2019s free oscillations excited by the 2011 Tohoku earthquake recorded in multiple GPS networks

We search in the continuous GPS 3-D displacement data for the signals of the normal modes of Earth’s free oscillation that were excited by the 2011 Mw 9.0 Tohoku earthquake. A previous study has reported such a detection; we here conduct a more comprehensive and detailed study. We use GPS data from three separate networks: (i) about 1000 stations from the Japan GEONET; (ii) about 600 stations from the western USA PBO; and (iii) about 140 stations of the global IGS, and solve and form records of 21 h length at 30-s sampling rate. We conduct various multiple-record stacking methods: the frequency-domain power spectrum stacking that reduces the variance of the noises, and the time-domain stackings that boost the SNR of target modes while suppressing the non-target modes. We find the time-domain stacking method of optimal sequence estimation (OSE) to be the most effective, which show clearly high sensitivity and detectability of the modes in the spectrum. For the near-field GEONET where all excited modes have anti-nodes, all the spheroidal fundamental modes 0S9–0S43 below 5 MHz and some of the lower-degree overtones as well as most of the low-degree toroidal fundamental modes show up as prominent spectral peaks against the PREM model eigenfrequencies. The PBO sees less strong (being far-field and generally off-antinodes), but still clearly identifiable spectral peaks of the fundamental modes. The global IGS network data detect barely a handful of these modes because of its sparsity and small numbers of stations. We thus demonstrate that GPS does actually record the tiny seismic signals that can be revealed by means of multiple-record stacking methods, potentially useful for studying earthquake source mechanisms exciting the normal modes.

Optimal Massive-MIMO-Aided Clustered Base-Station Coordination

A large-scale clustered massive MIMO network is proposed for improving the spectral efficiency of the next-generation wireless infrastructure by maximizing its sum-rate. Our solution combines the advantages of the centralized processing architecture and massive MIMO. Explicitly, the network is divided into multiple clusters; each cluster is handled by a centralized processing unit, which connects to a certain number of massive MIMO-aided BSs, where only limited information is exchanged among the clusters; each user of a cluster can be served by several nearby BSs in a user-centric way. We analyze the maximum sum-rate of the network with multiple antennas at BSs and UEs, relying on the optimal transmit precoder matrix of each BS configured for each user, and on the optimal frequency-domain power sharing scheme of each cluster. The optimizations are conceived for multiple coordination schemes that were widely studied in literature, namely the coherent-joint-transmission (CJT) scheme, the non-coherent-joint-transmission (NCJT) scheme and the coordinated-beamfoming/scheduling (CBF/CS) scheme. Our simulations show that the optimal CJT achieves 2.2 – 4.5 times higher average sum-rate than its non-cooperative massive MIMO network counterpart, while the optimal NCJT and the optimal CBF/CS achieve at most a factor 1.3 average sum-rate gain. The popular signal-to-leakage-and-noise-ratio (SLNR) scheme is also extended to the multi-antenna UE scenario and achieves a factor 1.1 – 1.2 gain.

Correlation of obesity indices with heart rate variability in young male adults

Background: Due to its well-known risk for hypertension, diabetes mellitus, and cardiovascular diseases, obesity has become a major public health issue. The sympathetic nervous system can be triggered by body fat resulting in an autonomic impairment. The aim of the present study was to analyse the relationship between indicators of obesity and heart rate variability (HRV) in young adult males. Aims and Objectives: The aims of the study were to compare obesity indicators with HRV in young adult males and to examine whether there is any link between obesity and HR variability. Materials and Methods: This research was directed on 110 healthy college MBBS male students between 18 and 22 years of age. The study population (n = 110) was classified using the body mass index into a control group (BMI = 18.5–22.9 kg/m²) and a study group (BMI = 23.0–29.9 kg/m²). Approval from the Institutional Ethical Committee was obtained before beginning the study. The central obesity assessment was performed using waist circumference and waist to hip ratio. All HRV fluctuations were determined by a 5-min time domain and frequency domain power spectral density analysis using the ML4818 Power Lab 15T software. Results: Unpaired t-test was used to analyse all parameters of the study (control and study group). The results implied a lower level of parasympathetic and a higher sympathetic activity in the study group when matched against the control group. Conclusion: Due to an imbalance of the sympathovagal action of the autonomic nervous system, overweight or pre-obese individuals are more likely to suffer from cardiometabolic disorders in the future.

SSVEP signal classification and recognition based on individual signal mixing template multivariate synchronization index algorithm

With the development of automation technology, Brain Computer Interface (BCI) has been increasingly integrated into people's daily life, among which Steady State Visual Evoked Potential (SSVEP) has attracted much attention due to its high signal-to-noise ratio (SNR) and wide application scenarios. To improve the classification accuracy of SSVEP signals, a novel individual signal mixing template multivariate synchronization index algorithm (IST-MSI) was proposed in this paper, which incorporated individual training template and individual harmonic sensitivity coefficient into the standard MSI algorithm. Specifically, the proposed method first enlarged the frequency-domain power spectrum of the fundamental frequency and its harmonics to reduce the redundant information in the individual training template. The synchronization index values at non-target frequency identified by MSI algorithm are significantly reduced through unequal ratio scaling of harmonic sensitivity coefficient, thereby improving the SSVEP recognition. The experimental results showed that under the signal length of 1.2 s, the average classification accuracy of IST-MSI algorithm reached 84.3 % in six target frequencies, which was 5.8 % higher than that of standard MSI algorithm. This study confirmed the efficacy of the proposed IST-MSI algorithm for SSVEP recognition, demonstrating its promise in developing an improved BCI system.