Quadrature Signals Research Articles

Detecting and Localizing Wireless Spoofing Attacks on the Internet of Medical Things

This paper proposes a hybrid approach using design science research to identify rogue RF transmitters and locate their targets. We engineered a framework to identify masquerading attacks indicating the presence of multiple adversaries posing as a single node. We propose a methodology based on spatial correlation calculated from received signal strength (RSS). To detect and mitigate wireless spoofing attacks in IoMT environments effectively, the hybrid approach combines spatial correlation analysis, Deep CNN classification, Elliptic Curve Cryptography (ECC) encryption, and DSRM-powered attack detection enhanced (DADE) detection and localization (DAL) frameworks. A deep neural network (Deep CNN) was used to classify trusted transmitters based on Python Spyder3 V5 and ECC encrypted Hack RF Quadrature Signals (IQ). For localizing targets, this paper also presents DADE and DAL frameworks implemented on Eclipse Java platforms. The hybrid approach relies on spatial correlation based on signal strength. Using the training methods of Deep CNN1, Deep CNN2, and Long Short-Term Memory (LSTM), it was possible to achieve accuracies of 98.88%, 95.05%, and 96.60% respectively.

Dynamic range extension method for sinusoidal phase modulating interferometer based on in-phase component signal reconstruction

Abstract The continuous increase of the amplitude of the measured vibration will cause spectrum aliasing in the interference signal of the sinusoidal phase modulating interferometer (SPMI), thereby constraining its measurement dynamic range (DR). To address this issue, a DR extension method of SPMI based on the reconstruction of in-phase component signal is proposed. On the basis of conventional SPMI, an additional reference signal is introduced for estimating the phase modulation depth (PMD) and the major to minor axis ratio R e of the Lissajous ellipse formed by orthogonal interference signal pair and a phase demodulation algorithm named as phase generated carrier (PGC)- in-phase component signal reconstruction (ICR)- differential-and-cross-multiplying algorithm (DCM) which integrates PGC-DCM algorithm and the ICR is proposed. Initially, the PMD is regulated to approximately 1.8412 rad, ensuring that the quadrature component signal can be accurately extracted from the mixed signal of the interference and carrier signals, even in the presence of spectrum aliasing in the original interference signal. Then, the undistorted quadrature component signal is used to reconstruct an in-phase component signal with the estimated value of R e. Finally, the reconstructed in-phase component signal and quadrature component signal are normalized, and a DCM approach is applied to obtain the measured vibration. A SPMI measurement system based on Michelson interferometer structure is constructed to experimentally validate the proposed method. The experimental results demonstrate that the proposed method can effectively extend the DR of the SPMI system in the presence of spectrum aliasing. The DR of the system is increased from 97.52 dB @ 55 Hz to 102.6 dB @ 55 Hz, with the maximum measurable amplitude increasing from 7728.75 nm @ 55 Hz to 13 705.16 nm @ 55 Hz.

A lightweight deep learning architecture for automatic modulation classification of wireless internet of things

AbstractThe wireless Internet of Things (IoT) is widely used for data transmission in power systems. Wireless communication is an important part of the IoT. The existing modulation classification algorithms have low classification accuracy when facing strong electromagnetic interference, which causes decoding error link interruption and wastes wireless channel resources. Therefore, it is necessary to study signal modulation classification methods in a low signal‐to‐noise ratio (SNR) environment. In this paper, a lightweight Deep Neural Networks (DNNs) modulation classification method based on the Informer architecture classifier and two‐dimensional (2‐D) curves input of the spectral correlation function (SCF) is proposed, which uses in‐phase and quadrature (I/Q) signals to generate 2‐D cross‐section SCF curve first and then feeds the feature curve into the Informer network to classify the modulation method. This model can better learn the robustness characteristics in a long sequence. Through testing, the classification accuracy of the modulation signal is not much lower than that of the current good classification method when the SNR is 10 dB, and this method can still show higher accuracy when hardware resources are limited. It is a compact design of a modulation classification model and easy to deploy on low‐cost embedded platforms.

First results of turbulence investigation on Globus-M2 using radial correlation Doppler reflectometry

The first results of investigation of the turbulence structure using Doppler backscattering (DBS) on the Globus-M2 tokamak are presented. A one-channel DBS system with a variable probing frequency within the 18–26 GHz range was installed to investigate the edge plasma at normalized minor radii 0.9–1.1. Radial correlation Doppler reflectometry was used to study the changes in turbulence eddies after the LH transition. Correlation analysis was applied to the phase derivative of complex in-phase and quadrature (IQ) signals of the DBS diagnostic as it contains information about the poloidal plasma rotation velocity. In L-mode, the radial correlation length L r is estimated to be 3 cm and after transition to H-mode reduces to approximately 2 cm. Gyrokinetic modelling in a linear local approximation using code GENE indicates that the instability with positive growth rate at the normalized minor radius 0.75 in L-mode and H-mode on Globus-M2 was the ion temperature gradient (ITG) mode.

Real-time in situ phase sensitivity calibration of interferometric fiber-optic ultrasonic sensors.

Output from a fiber-optic interferometric ultrasonic sensor can be affected by the operating point changes and/or the optical power fluctuations. We report a method for real-time and in situ calibration of the responses to ultrasound-induced phase delays for a low-finesse Fabry-Perot (FP) ultrasonic sensor demodulated using a phase-generated carrier. The carrier is produced from an electro-optic phase modulator, and its zero- and fundamental-carrier frequency components yield two quadrature signals. The same phase modulator is employed to generate controllable laser frequency modulations and calibration phase delay modulations. The response to the known calibration phase delays is used to gauge the sensor output in real time and in situ. The experiment has verified the effectiveness of the calibration method against the signal variations from both operating point changes and optical power variations.

An improved derivative‐based phase‐locked loop for single‐phase grid synchronization under abnormal grid conditions

SummaryGenerating a quadrature signal in a single‐phase system using a second‐order generalized integrator (SOGI) requires accurate frequency information. The standard SOGI phase‐locked loop (PLL) includes frequency feedback to the SOGI. However, during grid abnormalities such as voltage sag/swell and phase angle jumps, the SOGI‐PLL faces frequency disturbances that propagate to the phase detector (PD) and affects the quadrature signal generator (QSG). Furthermore, the SOGI‐PLL having two loops dependent on each other creates loop coupling phenomena; either a change in phase or frequency affects each other. SOGI‐PLL is tuning sensitive, as the SOGI block has a gain that needs to be adjusted, increases the complexity, and affects the performance of the system. There is a trade‐off between SOGI gain and PLL parameters that needs to be considered for adequate parameter design to provide accurate grid synchronization while maintaining the stability of the system. To attain better performance, researchers have proposed derivative‐based PLL (DPLL). The conventional DPLL faces challenges to noise and harmonics amplification. This paper presents an improved DPLL for single‐phase grid synchronization under adverse grid conditions. The improved derivative‐based PLL (IDPLL) comprises two improved derivative‐based quadrature signal generator (IDQSG) blocks to extract the phase‐error information for accurately estimating phase and frequency. The detailed mathematical modeling and bode plot for the IDQSG and IDPLL are presented. The proposed IDQSG eliminates the requirement of gain tuning, hence reducing complexity. Moreover, there is no interdependent loop in the IDPLL, which significantly improves the dynamic performance. A hardware setup is developed to evaluate the performance of the system in real‐time. The experimental results are obtained using an field programmable gate array (FPGA)‐based controller.

Overview of Open-Loop Frequency Estimation Techniques for Grid Synchronization of Single-Phase Power Converters

Open-loop synchronization techniques allow to overcome the stability issues to which closed-loop techniques are subject. Most open-loop techniques differ in their Quadratic Signal Generator and their frequency estimation method. In this paper, an overview of the main frequency estimation methods for single-phase power systems is presented. A description of each technique with their structure for numerical implementation is carried out. A comparative study of those frequency estimation methods is performed according to three test scenarios in order to evaluate the robustness of each method. Recommendations for the selection of the technique is given according to the simulation results.

Integrated Neural Network Approach for Enhanced Vital Signal Analysis Using CW Radar

This study introduces a novel approach for analyzing vital signals using continuous-wave (CW) radar, employing an integrated neural network model to overcome the limitations associated with traditional step-by-step signal processing methods. Conventional methods for vital signal monitoring, such as electrocardiograms (ECGs) and sphygmomanometers, require direct contact and impose constraints on specific scenarios. Conversely, our study primarily focused on non-contact measurement techniques, particularly those using CW radar, which is known for its simplicity but faces challenges such as noise interference and complex signal processing. To address these issues, we propose a temporal convolutional network (TCN)-based framework that seamlessly integrates noise removal, demodulation, and fast Fourier transform (FFT) processes into a single neural network. This integration minimizes cumulative errors and processing time, which are common drawbacks of conventional methods. The TCN was trained using a dataset comprising preprocessed in-phase and quadrature (I/Q) signals from the CW radar and corresponding heart rates measured via ECG. The performance of the proposed method was evaluated based on the L1 loss and accuracy against the moving average of the estimated heart rates. The results indicate that the proposed approach has the potential for efficient and accurate non-contact vital signal analysis, opening new avenues in health monitoring and medical research. Additionally, the integration of CW radar and neural networks in our framework offers a robust and scalable solution, enhancing the practicality of non-contact health monitoring systems in diverse environments. This technology can be leveraged in healthcare robots to provide continuous and unobtrusive monitoring of patients’ vital signs, enabling timely interventions and improving overall patient care.

Характеристики обнаружения когерентной системы при рассогласовании квадратурных каналов

Introduction – The operation of an adaptive coherent detection system is considered. The structure of a processing device is presented, containing a series-connected shaper of quadrature signal components that convert high-frequency input radio signals to video frequency, an adaptive multi-channel coherent storage device with threshold devices. Goal – To study the influence of amplitude and phase mismatches of quadrature channel parameters, which are inevitable in practice, on the operation of an adaptive coherent detection system. Formulation of the problem – Determine the spectral-correlation characteristics of the processed signals when the parameters of the quadrature channels are mismatched. Assess the influence of amplitude and phase deviations of the parameters of quadrature channels on the characteristics of a coherent detection system. Method – The complex variable method is used, in which the input high-frequency signals are represented at the video frequency by a pair of complex conjugate real components, shifted in phase by ninety degrees. Results – A model of the processed signal is presented at the video frequency, taking into account possible mismatches in the parameters of quadrature channels in practice. Expressions are obtained and graphs of the spectral density of the complex signal are plotted separately for the amplitude and phase mismatch of the quadrature channels. It is shown that the mismatch of quadrature channels leads to the appearance in the spectrum of the output signal of phase detectors of a component at a frequency that is the mirror frequency of the signal, which leads to an increase in the probability of false detection. The results of calculations of the probability of false detection of a signal depending on the deviation of the transmission coefficients of the quadrature channels and the phase mismatch of the reference voltages of the quadrature channels are presented. Conclusion – The significant influence of amplitude and phase mismatches of the parameters of quadrature channels on the spectral-correlation characteristics of the complex signal is shown. The mismatch of the parameters of the quadrature channels leads to deterioration in the detection characteristics of the coherent processing system. The research methodology used allows us to develop requirements for the quality of tuning quadrature channels.

Dual-sensitized heterojunction Ag2S/ZnS/NiS composites with entire visible-light region absorption for ultrasensitive photoelectrochemical detection of tobramycin

In this study, an ultrasensitive photoelectrochemical (PEC) aptasensor based on dual-sensitized heterojunction Ag2S/ZnS/NiS composites as a signal probe was proposed for the detection of tobramycin (TOB) by combining a cascaded quadratic signal amplification strategy. Specifically, compared to the limited visible light-harvesting capability of single sensitized composites, Ag2S/ZnS/NiS composites with p-n and n-n heterojunction could greatly improve the light energy utilization to tremendously strengthen the optical absorption in the entire visible-light region. Moreover, dual-sensitized heterojunction could effectively hinder the rapid recombination of photoelectrons and holes (carriers) to obtain a good photocurrent for improving the sensitivity of the aptasensor. Furthermore, a cascaded quadratic signal amplification strategy was applied to convert trace target TOB into plentiful gold nanoclusters (Au NCs) labelled double-stranded DNA for the construction of PEC aptasensor, with a broad linear detection range from 0.01 to 100 ng mL−1 and a low detection limit of 3.38 pg mL−1. Importantly, this study provided a versatile and sensitive PEC biosensing platform for TOB analysis, and demonstrated its successful application for TOB detection in milk samples. This protocol provides a novel dual-sensitized heterojunction composites to develop a highly efficient and harmfulless PEC aptasensor, which is expected to be used in food safety, environmental monitoring and other areas.

Neural Network Quadrature Signal Generator-Based Virtual Flux Estimation for Predictive Control of PWM Converters

In this paper, a frequency-adaptive neural network-based virtual flux (FANN-VF) estimator is developed for sensorless control of a pulse-width modulation converter under unbalanced and distorted grid conditions. This method exploits two parallel FANNs configured as quadrature signal generators. The VF fundamental positive and negative sequence components (PNSCs) are inherently separated in the estimator without employing any cascaded filters. A frequency-locked loop is introduced to accurately track the frequency variations. The estimated VF PNSCs are directly exploited to compute the power references in VF-based predictive direct power control scheme. The developed strategy ensures constant active power and sinusoidal current waveforms under nonideal grid conditions. Simulation and experimental tests are performed to verify the effectiveness of the proposed method.

Nonlinear error elimination using the fusion of PGC-DCM, geometric fitting, and Atan algorithms.

A phase generated carrier (PGC) demodulation scheme is always accompanied by nonlinear errors. We propose a fusion of PGC differential and cross multiplying (PGC-DCM), geometric fitting, and arctangent (Atan) algorithms for fiber optic interferometric sensors to eliminate nonlinear errors. The output amplitude of the PGC-DCM algorithm is used to judge whether the Lissajous figure of quadrature signals is larger than 1/2 ellipse arc. When the Lissajous figure exceeds 1/2 ellipse arc, the contaminated quadrature signals are corrected by the ellipse correction parameters calculated from the geometric fitting; otherwise, the previous fitting parameters are employed for correction. Geometric fitting is realized by minimizing the Sampson error, and its failure problem under small signals is solved by using the temporary stability of fitting results. Finally, desired signals are extracted from the corrected quadrature signals by the Atan algorithm. Experimental results show that the fusion combines the merits of the three algorithms and expands the application of the geometric fitting in PGC demodulation schemes.

Four-wavelength laser interferometry for the demodulation of dual-cavity fiber-optic extrinsic Fabry-Perot interferometric sensors

A four-wavelength passive demodulation algorithm is proposed and experimentally demonstrated for the interrogation of the one cavity in a dual-cavity extrinsic Fabry-Perot interferometric (EFPI) sensor. The lengths of two cavities are adjusted to generate four quadrature signals for each individual cavity. Both simulation and experimental results are presented to validate the performance of this technique. The experimental results demonstrate that dynamic signals at frequencies of 100 Hz, 200 Hz, and 300 Hz with varying amplitude are successfully extracted from a dual-cavity EFPI sensor with initial lengths of 93.4803 µm and 94.0091 µm. The technique shows the potential application to measure dynamic signals in dual-cavity fiber-optic EFPI sensors.

A PGC demodulation scheme based on the Lissajous ellipse fitting for homodyne interferometer without using second harmonic carrier signal

This paper proposes a phase generated carrier (PGC) demodulation scheme for sinusoidal phase modulation interferometer (SPMI) that does not require a second harmonic carrier signal. In this scheme, an in-phase interference signal component with direct current (DC) bias is obtained by low-pass filtering the original interference signal, and a quadrature interference signal component is obtained by conventional means. The Lissajous figure formed by these two quadrature interference signal components is an ellipse whose center deviates from the origin of the I-Q coordinates. By fitting this ellipse, the major and minor axes and the center coordinates can be obtained. These elliptic parameters are then used to eliminate DC bias and normalize the amplitude of the quadrature signal components. Finally, the phase demodulation of the interference signal can be realized by using arctangent algorithm or differential cross multiplication (DCM) algorithm. The results of simulation and experiment have verified the validity and accuracy of the proposed PGC demodulation scheme, and show that it is insensitive to the phase modulation depth C. Compared with conventional PGC demodulation, this scheme requires lower depth of carrier phase modulation. The vibration measurement results indicate that the average signal-to-noise and distortion Ratio (SINAD) and total harmonic distortion (THD) values of demodulation results in the frequency range of 130 Hz to 250 Hz are 34 dB and -33 dB respectively. The proposed PGC demodulation scheme shows good performance in displacement measurement using SPMI.

Reconstruction methods for super-resolution imaging with PSF modulation

Recently, a super-resolution imaging method called the PSF (point spread function) modulation method was developed (Lu, IEEE TUFFC 2024). In this method, the amplitude, phase, or both of the PSF of a linear shift-invariant (LSI) imaging system is modulated so that the modulated PSF has a higher spatial frequency than that of the original PSF to reconstruct super-resolution images. The modulator can be produced and manipulated remotely by methods such as radiation force or it can be a physical particle such as micro- or nano-particle manipulated by an external force such as electrical and electromagnetic force. In principle, the super-resolution imaging method can be applied to any LSI imaging system, such as ultrasound, optical, photoacoustic, electromagnetic, underwater, nondestructive evaluation (NDE), and magnetic resonance imaging (MRI) system. These include pulse-echo ultrasound imaging, transmission imaging, wave source/field imaging, acoustical camera, and optical bright-field microscope. To optimize the quality of the images, methods for the reconstruction of pulse-echo and wave source/field super-resolution images are studied and the results will be presented. These methods include the uses of an analytic envelope of radio-frequency (RF) signals with and without windowing, “I” (in-phase) and “Q” (quadrature) signals, and the DC (direct current) component removal.

Detection of Water Surface Acoustic Waves Using Sinusoidal Phase Modulation Interferometer and Prenormalized PGC-Arctan Algorithm

A sinusoidal phase modulation laser interferometer is proposed to detect water surface acoustic waves excited by underwater acoustic radiation, and an improved PGC-Arctan demodulation algorithm that combines prenormalization and Lissajous ellipse fitting is proposed to demodulate detection signals. In this paper, the effects of phase modulation depth, carrier phase delay, and interference signal visibility on the Lissajous figure formed by quadrature interference components are analyzed. The demodulation algorithm first uses the amplitudes of multiple Fourier spectral components of an interference signal to calculate the phase modulation depth C, and calculation of the carrier phase delay Vc is achieved through the introduction of a quadrature carrier signal. Then, certain coefficients regarding C and Vc are constructed for prenormalization of the two quadrature interference signal components to eliminate the local nonuniform widening phenomenon of Lissajous ellipse. Next, the outer and the inner contours are extracted from a uniformly widened Lissajous ellipse resulting from light intensity disturbance, and the axial ratio of the ellipse is obtained, which is used to correct the ratio of the quadrature interference signal to eliminate the effect of filter gain coefficients. At last, through the combination of an Arctan algorithm and a phase-unwrapping algorithm, high-precision demodulation of the interference signal is realized. A sinusoidal phase modulation interferometer was set up to detect water surface acoustic waves, and a series of detection experiments were carried out. The experiment results show that the detection method and demodulation algorithm described in this paper can accurately realize the measurement of weak water surface acoustic waves. The proposed algorithm shows less distortion in demodulation results, and its signal-to-noise distortion ratio is less than 20 dB at 500 Hz, which is significantly better than traditional algorithms. The experimental results demonstrate the effectiveness and accuracy of water surface acoustic wave detection using sinusoidal phase modulation interferometer.

Extrema-Triggered Conversion for Non-Stationary Signal Acquisition in Wireless Sensor Nodes

While wireless sensor node (WSNs) have proliferated with the rise of the Internet of Things (IoT), uniformly sampled analog–digital converters (ADCs) have traditionally reigned paramount in the signal processing pipeline. The large volume of data generated by uniformly sampled ADCs while capturing most real-world signals, which are highly non-stationary and sparse in information content, considerably strains the power budget of WSNs during data transmission. Given the pressing need for intelligent sampling, this work proposes an extrema pulse generator devised to trigger ADCs at significant signal extrema, thereby curbing the volume of data points collected and transmitted, and mitigating transmission power draw. After providing a comprehensive signal-theoretic rationale, we construct and experimentally validate these circuits on a system-on-chip field-programmable analog array in a 350 nm complementary metal-oxide-semiconductor (MOS) process. Operating within a power range of 4.3–12.3 µW (contingent on the input bandwidth requirements), the extrema pulse generator has proven to be capable of effectively sampling both synthetic and natural signals, achieving significant reductions in data volume and signal reconstruction error. Using a nonideality-resilient reconstruction algorithm, that we develop in this work, experimental comparisons between extrema and uniform sampling show that extrema sampling achieves an 18-fold lower normalized root mean square reconstruction error for a quadratic chirp signal, despite requiring 5-fold fewer sample points. Similar improvements in both the reconstruction error and effective sampling rate objectives are found experimentally for an electrocardiogram signal. Using both theoretical and experimental methods, this work demonstrates the potential of extrema-triggered systems for extending Pareto frontiers in modern, resource-constrained sensing scenarios.

Spectral-based Quantitative Ultrasound Imaging Processing Techniques: Comparisons of RF Versus IQ Approaches.

Quantitative ultrasound (QUS) is an imaging technique which includes spectral-based parameterization. Typical spectral-based parameters include the backscatter coefficient (BSC) and attenuation coefficient slope (ACS). Traditionally, spectral-based QUS relies on the radio frequency (RF) signal to calculate the spectral-based parameters. Many clinical and research scanners only provide the in-phase and quadrature (IQ) signal. To acquire the RF data, the common approach is to convert IQ signal back into RF signal via mixing with a carrier frequency. In this study, we hypothesize that the performance, that is, accuracy and precision, of spectral-based parameters calculated directly from IQ data is as good as or better than using converted RF data. To test this hypothesis, estimation of the BSC and ACS using RF and IQ data from software, physical phantoms and in vivo rabbit data were analyzed and compared. The results indicated that there were only small differences in estimates of the BSC between when using the original RF, the IQ derived from the original RF and the RF reconverted from the IQ, that is, root mean square errors (RMSEs) were less than 0.04. Furthermore, the structural similarity index measure (SSIM) was calculated for ACS maps with a value greater than 0.96 for maps created using the original RF, IQ data and reconverted RF. On the other hand, the processing time using the IQ data compared to RF data were substantially less, that is, reduced by more than a factor of two. Therefore, this study confirms two things: (1) there is no need to convert IQ data back to RF data for conducting spectral-based QUS analysis, because the conversion from IQ back into RF data can introduce artifacts. (2) For the implementation of real-time QUS, there is an advantage to convert the original RF data into IQ data to conduct spectral-based QUS analysis because IQ data-based QUS can improve processing speed.

Independent Control of Active and Reactive Power Flow for a Single-Phase, Unidirectional Onboard Power Converter Connecting the DC Power Bus to the AC Bus

The paper presents a proposed system that supplies a 400 Hz single-phase onboard grid from the DC onboard bus. This system enables independent compensation of reactive power in the AC grid. Independent control of active and reactive power flow requires the decomposition of current in the grid into active and reactive components. Independent control of active and reactive power requires the use of synchronizers that operate in the dq frame system. If synchronization is performed with a single-phase grid, the transformation of dq requires the virtual quadrature signals. Standard quadrature signal generation systems use a second-order generalized integrator. To improve the dynamics of the system, the paper proposes a new quadrature generator that operates on the basis of trigonometric calculations instead of a second-order integration system. The developed system was implemented in a proportional-resonant current control system. Tests carried out in steady state and in dynamic states related to typical grid disturbances proved significantly better dynamic properties than those of a standard integrator-based system.

A behavioral model for RF vector‐sum phase shifter

AbstractIn this article, a behavioral model for RF vector‐sum phase shifters is developed to estimate their behavioral and guide the phased‐array front‐end system design. According to their building blocks, the model consists of two typical parts, quadrature signal generator and variable gain amplifiers (VGAs). The modeling process for the quadrature signal generator is based on a complex curve fitting method. For the linear VGA, the polynomial fitting method is used to fit the gain and phase responses of the VGA, the first variable is frequency, and the second variable is the control voltage. Then the complex curve fitting method is used to achieve the final frequency response. Finally, the layout simulation results of an actual phase shifter circuit are used as input, and the two modules are modeled and compared with the measured results to verify the usability of the model.