In-phase Components Research Articles

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.

Mechanical wave velocities by clutter filter wave imaging detects myocardial dysfunction in acute coronary syndrome

Abstract Background/introduction Mechanical waves within the myocardium arising from physiologic events in the cardiac cycle such as the atrial kick (AK), mitral valve closure (MVC) and aortic valve closure (AVC) offer insight to tissue stiffness and can be measured by high frame rate echocardiography. Purpose To investigate the feasibility of measuring mechanical wave velocities in the left ventricle by clutter filter wave imaging (CFWI) and its potential for detecting regional myocardial dysfunction in patients with acute coronary syndrome. Methods We examined 60 patients (66±9 years of age, 72% male) with acute coronary syndrome by high frame rate echocardiography after revascularisation, imaging the left ventricle from the parasternal long axis view and three apical projections. Intrinsic mechanical waves generated by the AK, MVC and AVC were analysed off-line by CFWI of all walls using custom software (PyMWI) on 2000 frames of in-phase and quadrature component data per view extracted from a Vivid E95 scanner. Results We analysed a total number of 2372 waves recorded with a frame rate of 1180 (1134-1199) frames per second. The feasibility for measuring AK waves was high in all regions (mean 89%), but lower for the MVC and AVC waves with significant variability between regions (mean 44% and 46%, respectively; p<0.001). Feasibility declined from basal to apical segments for all waves (p=0.0001). Mean mechanical wave velocity was slowest for the AK wave at 2.5 (2.0-3.0) m/s, faster for the AVC wave at 5.1 (3.5-6.3) m/s and fastest for the MVC wave at 6.6 (4.9-8.6) m/s (p=0.0001). Basal AK wave velocity was faster in patients with basal wall motion abnormalities compared to those without (2.7 vs. 2.0 m/s, p=0.0004) and could detect the presence of wall motion abnormalities adjusting for age, gender and parameters of diastolic function (univariable OR 3.4, 95% CI 1.5-7.7, p<0.01; multivariable OR 4.4, 95% CI 1.4-14.1, p=0.01). Basal AK wave velocity detected wall motion abnormalities with an AUC of 0.78 with cut-off speeds of 2.3 m/s and 3.0 m/s, both correctly classifying 73% of cases (sensitivity/specificity 81%/68% and 43%/89%, respectively). Mechanical wave velocities of other waves and in other regions were not consistently and only weakly associated with echocardiographic parameters of systolic and diastolic function. Conclusion(s) Mechanical wave velocities by CFWI detects myocardial dysfunction in acute coronary syndrome. Elevated basal AK wave velocity was independently associated with myocardial dysfunction where AK wave velocity ≥2.3 m/s provided a good sensitivity and ≥3.0 m/s a good specificity for detecting regional wall motion abnormalities. The feasibility of measuring the AK wave was good, whereas MVC and AVC waves had lower feasibility and were inadequate for assessment of myocardial function. Feasibility variations between AK and MVC/AVC waves suggest differences in wave propagation dynamics or intrinsic wave properties.Illustration Clutter Filter Wave ImagingRisk of myocardial dysfunction

MIMO Volterra polynomial equalizer for PDM ultrahigh-order QAM signals.

In this work, we proposed and experimentally demonstrated a multiple-input multiple-output (MIMO) Volterra polynomial equalizer (MVPE), in a probabilistic shaping (PS) polarization-division multiplexed (PDM) 4096-quadrature amplitude modulation (QAM) coherent optical transmission system. In comparison with the conventional single-input single-output (SISO) Volterra nonlinear equalizers (VNLEs), the proposed MVPE can equalize the in-phase (I) and quadrature (Q) components in both the X and Y polarizations simultaneously and effectively alleviate nonlinear distortions. In the PS-PDM-4096-QAM experiment, the normalized generalized mutual information (NGMI) reached the threshold of DVB-S2 low-density parity-check (LDPC) codes with a 20% overhead. Compared to the SISO VNLE scheme, the MVPE scheme improves receiver sensitivity by approximately 2.8 dB while maintaining nearly similar computational complexity.

A complex-valued convolutional fusion-type multi-stream spatiotemporal network for automatic modulation classification

Automatic Modulation Classification (AMC) is crucial in non-cooperative communication systems as it facilitates the identification of interference signals with minimal prior knowledge. Although there have been significant advancements in Deep Learning (DL) within the field of AMC, leveraging the inherent relationships between In-phase (I) and Quadrature-phase (Q) components, and enhance recognition accuracy under low signal-to-noise ratio (SNR) conditions remains a challenge. This study introduces a complex-valued convolutional fusion-type multi-stream spatiotemporal network (CC-MSNet) for AMC, which combines spatial and temporal feature extraction modules for modulation recognition. Experimental results demonstrate that the CC-MSNet performs well on three benchmark datasets, RML2016.10a, RML2016.10b, and RML2016.04c, with average recognition accuracy of 62.86%, 65.08%, and 71.12%. It also performs excellently in low SNR environments below 0dB, significantly outperforming other networks.

Transceiver impairment robust and joint monitoring of PDL, DGD, and CD using frequency domain pilot tones for digital subcarrier multiplexing systems

Optical performance monitoring is vital for enabling dynamically reconfigurable optical networks. This paper introduces a monitoring scheme that is robust to transceiver impairments for coherent digital subcarrier multiplexing (DSCM) systems that simultaneously monitors polarization-dependent loss (PDL), differential group delay (DGD), and chromatic dispersion (CD). In the proposed scheme, a pair of frequency domain pilot tones (FPTs) are inserted into the X and Y polarizations of the transmitted dual-polarization signal. Both the signal and the FPTs endure identical channel impairments during fiber transmission. At the receiver side, the transmitted FPTs are extracted to monitor the channel impairments. We begin by establishing a simplified system model using a single frequency tone to analyze the effects of system impairments on the frequency domain pilots. Based on this model, CD, PDL, and DGD are jointly and accurately estimated. In addition, the influences of temporal, amplitude, and phase mismatches between in-phase (I) and quadrature (Q) components of transceivers on channel impairment monitoring are completely eliminated. This achieves a channel impairment monitoring scheme that is robust to transceiver impairments. Experimental verification shows that the proposed scheme can jointly and accurately estimate a wide range of CD, PDL, and DGD. Additionally, the proposed monitoring scheme demonstrates strong robustness against transceiver impairments, rapid polarization fluctuations, and amplified spontaneous emission (ASE) noise.

Improvements to a novel learning algorithm for model-based wind turbine controllers

Wind turbine controllers are nowadays ever more advanced and rely on accurate internal controller model information. Therefore a calibrated model is needed for attaining predictable controller performance and ensuring stable operation. To calibrate the internal model information, a novel learning control scheme has recently been proposed that exploits the dynamics of the closed-loop controlled wind turbine system, without the need for wind speed measurements. The learning algorithm thereby periodically excites the generator power controller input signal. An extremum-seeking demodulation scheme was used to calibrate the internal model information. This paper improves the existing learning scheme in two ways: Firstly, it investigates how the frequency of the excitation signal influences the signal-to-noise ratio. Secondly, the problem was reformulated as a root-finding problem. This requires using the in-phase component of the phase-corrected learning signal. In addition, a precalculated lookup table relates the measured in-phase component directly to model uncertainty. It was found that an increased excitation frequency improves the signal-to-noise ratio (SNR) by an order of magnitude. Combined, these contributions improve the convergence speed more than twenty times, addressing the effect of aerodynamic degradation and its consequences on controller performance.

Understanding the Phase of Responsivity and Noise Sources in Frequency-Domain Multiplexed Readout of Transition Edge Sensor Bolometers

Cosmic microwave background (CMB) experiments have deployed focal planes with O(104)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(10^{4})$$\\end{document} transition edge sensor (TES) bolometers cooled to sub-Kelvin temperatures by multiplexing the readout of many TES channels onto a single pair of wires. Digital Frequency-domain Multiplexing (DfMux) is a multiplexing technique used in many CMB polarization experiments, such as the Simons Array, SPT-3 G, and EBEX. The DfMux system studied here uses LC filters with resonant frequencies ranging from 1.5 to 4.5 MHz connected to an array of TESs. Each detector has an amplitude-modulated carrier tone at the resonant frequency of its accompanying LC resonator. The signal is recovered via quadrature demodulation where the in-phase (I) component of the demodulated current is in phase with the complex admittance of the circuit and the quadrature (Q) component is orthogonal to I. Observed excess current noise in the Q component is consistent with fluctuations in the resonant frequency. This noise has been shown to be non-orthogonal to the phase of the detector’s responsivity. We present a detailed analysis of the phase of responsivity of the TES and noise sources in our DfMux readout system. Further, we investigate how modifications to the TES operating resistance and bias frequency can affect the phase of noise relative to the phase of the detector responsivity, using data from Simons Array to evaluate our predictions. We find that both the phase of responsivity and phase of noise are functions of the two tuning parameters, which can be purposefully selected to maximize signal-to-noise (SNR) ratio.

Airborne GNSS reflectometry for coastal monitoring of sea state

Sea level rise and sea state variability, resulting from climate change and global warming, are critical research areas. However, current techniques for observing and monitoring these phenomena have limitations in terms of spatial and temporal resolution, particularly in dynamic coastal zones. GNSS Reflectometry (GNSS-R) is an emerging bistatic radar-based technique that utilizes the GNSS direct (transmitter-receiver) and reflected (transmitter-reflection point-receiver) signals to extract properties of the reflecting surface. This study explores the potential of airborne GNSS-R as a means to monitor sea state in coastal areas by using the Doppler spread and reflectivity as observables. The paper aims to derive a sea state factor from the reflected signal power and the Doppler shift distribution to analyze its correlation with wind speed and significant wave height data obtained from the ERA5 model. The experiment involved four flights conducted along the coast between Calais and Boulogne-sur-Mer, France, in July 2019. A GNSS software receiver processes the direct and reflected signals, tracking and re-tracking the reflected signals with the aid of a specular reflection model. The resulting in-phase and quadrature components are analyzed in the spectral domain every minute to estimate the power, the surface reflectivity, and the relative Doppler shift. The findings reveal that the sea state factor and Doppler spreading are sensitive to sea state conditions, correlated with the ERA5 parameters, and influenced by the elevation angle of GNSS satellites. At low elevations (E<10°), the sea state factor demonstrates an inverse relationship (anti-correlation) with the wind speed and significant wave height, while the Doppler distribution shows a correlation with these parameters. Both correlations decrease with increasing elevation angle. This research underscores the potential of airborne GNSS-R for monitoring sea state variability in coastal areas enhancing our understanding of the relationships between GNSS-R measurements and sea state parameters.

Theoretical and experimental study of modulation transfer spectroscopy for non-cycling transitions of Rb D1 lines

Modulation transfer spectroscopy (MTS) of the D1 transitions of 85Rb and 87Rb atoms with only non-cycling transitions have been investigated theoretically and experimentally using the density matrix equation for four different polarization configurations: linear orthogonal (lin⊥lin), linear parallel (lin ∥ lin), circular orthogonal (σ+−σ−), and circular parallel (σ+−σ+). MTS signals for the Fg=3→Fe=2 transition of 85Rb atom and Fg=2→Fe=1 transition of the 87Rb atom among D1 transition lines were found to be stronger than those for the other transitions. The two non-cycling transitions have strong MTS signals because of the branching ratios and populations of the relevant ground states. The observed line shapes of the in-phase and quadrature components of MTS signals agree well with the theoretical shapes obtained by solving the density matrix equation.

Nonlinear-tolerant two-dimensional distribution matcher scheme for probabilistic shaping.

This Letter proposes a nonlinear-tolerant two-dimensional distribution matcher (2D-DM) scheme. It removes the corner points of probabilistically shaped quadrature amplitude modulation (QAM) to obtain better nonlinear tolerance. Because the remaining number of points is not a power of 2, we propose to divide constellation points into different layers and symbols. Then, the proposed 2D-DM performs matching using one-dimensional shapers, which generates the in-phase and quadrature components of QAM together. In fact, it realizes two-dimensional shapers from one-dimensional shapers. Simulation results show that two-dimensional shapers generated by the proposed 2D-DM have higher tolerance to power amplifier nonlinearity and fiber nonlinearity compared to one-dimensional shapers.

Geophysical characterization of lamproite fields in the Dharwar craton using VLF-EM and advanced filtering techniques: insights from conductivity analysis and analytical signal mapping

Abstract This study presents a geophysical investigation of the lamproite fields located in the Dharwar craton, aiming to map conductivity variations using contemporary techniques. The study employs very low-frequency electromagnetic (VLF-EM) methods, applying Hilbert transformations, and first-order vertical derivatives to the Fraser and Karous–Hjelt filtered contoured of VLF-EM data. The peninsular gneissic complex (PGC) granitic rocks in the study area experienced tectonic forces, resulting in fractures along specific WNW–ESE to NW–SE trends. Within these crustal weak zones, these lamproites are emplaced. The lamproite pipes are volcanic rocks. As a result, the top portions are weathered and tend to conductive, and the conductivity tends to decrease with depth. The volumetric size of lamproites ranges from centimetres to hundreds of metres, unlike kimberlites that are larger. Hence, the exploration of lamproites poses challenges. The contours of in-phase and quadrature components were used to identify the cluster of lamproite zones within the study area. From this study, the boundaries of the lamproite pipes were clearly identified using real component's analytical and first-order vertical derivative signal contour maps. The VLF-EM pseudo-depth current density section was used to identify anomalous lamproite, pipes, and their subsurface extensions, along with the surrounding formations. The current investigation findings specify that the lamproites exhibit weak conductive. These results provide valuable insights for exploration efforts within the Dharwar craton, and can aid in the identification and mapping of the lamproite fields.

EEG stress classification based on Doppler spectral features for ensemble 1D-CNN with LCL activation function

The paper proposes an induced stress classification algorithm that uses features from the Doppler spectrum. In this approach, a reference signal source is used to obtain the quadrature and in-phase components of the EEG signal. The higher frequency components from the in-phase and quadrature are then eliminated using a pair of low-pass filters. The Doppler spectrum was then constructed from which the Doppler frequency features are then estimated. The features that were thus obtained are trained using an ensemble 1D-CNN (one-dimensional Convolutional neural network) which uses two sections of 1D-CNN. The first section 1D-CNN trains the features based on the EEG signal classes namely Stroop test, arithmetic task, and mirror tasks, while the second 1D-CNN section trains the features based on the EEG signal intense classes namely high, low, and medium stress. We also propose a linear-cosine-linear (LCL) activation function for the ensemble 1D-CNN which was derived from the cosine signal. The proposed stress classification scheme was evaluated using the SAM-40 datasets with induced stress classes namely arithmetic task, Stroop color-word test, and mirror image recognition task with stress levels namely high, low, and medium with the evaluation metrics such as precision, F1-score, accuracy, specificity, and recall. The proposed stress classification scheme attains an average accuracy, precision, and recall of 95.25%, 95.22%, and 92.9% when evaluated in 9 classes of EEGs.

Graphene nanomechanical vibrations measured with a phase-coherent software-defined radio

Software-defined radios (SDRs) are radio frequency transceivers designed to facilitate digital signal processing through the use of vast libraries of open-source software. Here, we assemble a simple data acquisition system whose architecture, based on SDR, allows us to develop a comprehensive suite of tools to study the vibrations of a few-layer graphene nanomechanical resonator. Namely, we measure the cross-spectrum of vibrations in the frequency domain, we measure their energy decay rate in the time domain, we perform vector measurements of their in-phase and quadrature components, and we control their phase using a time-dependent strain field –all with a single measurement platform. Our approach allows us to tailor our experiments at will and gives us control over every stage of data processing. Overall, our versatile system enables measuring a wide range of nanomechanical properties of graphene by customizing the signal acquisition and replacing some analog electrical circuits, such as filters, mixers, and demodulators, by blocks of code.

Vector-based feedback of continuous wave radiofrequency compression cavity for ultrafast electron diffraction.

The temporal resolution of ultrafast electron diffraction at weakly relativistic beam energies (100 keV) suffers from space-charge induced electron pulse broadening. We describe the implementation of a radio frequency (RF) cavity operating in the continuous wave regime to compress high repetition rate electron bunches from a 40.4 kV DC photoinjector for ultrafast electron diffraction applications. Active stabilization of the RF amplitude and phase through a feedback loop based on the demodulated in-phase and quadrature components of the RF signal is demonstrated. This scheme yields 144 ± 19 fs RMS temporal resolution in pump-probe studies.

Spin glass state in Co doped NdMnO3

The temperature-dependent ac susceptibility studies of in-phase component (χ′ac) and out of phase component (χac″) was done on a single phase polycrystalline NdMn0.6Co0.4O3 sample. The measurements were conducted in 4 Oe ac field and 0 Oe dc field at frequencies of 111, 311, 1011, and 1311 Hz. As the frequency increases, the magnitude of χ′ac decreases, and its peak position, corresponding to Tf, moves towards higher temperatures. This strongly suggests the presence of a spin glass (SG) state and the absence of long-range ferromagnetic ordering. The value of relative shift in freezing temperature (δTf) is found to be δTf = 0.0089, which suggests the existence of canonical SG state in NdMn0.6Co0.4O3 sample. To confirm it the ac susceptibility data was further analyzed using Arrhenius, Vogel Fulcher, and Power–law.

IMPLEMENTATION OF DISTRIBUTED COMPUTING SYSTEM FOR SIMULATING OF THE RADIO COMMUNICATION PROCESSES

Today, the most important component of the mathematical modeling method, which is effectively used to solve modern application problems, is modern information technology, which allows not only to accumulate practically unlimited amounts of experimental information, but also to analyze this data using visualization methods. The article discusses issues related to the development and implementation of a distributed computing system that makes it possible to simulate and analyze multiple experiments for data transmission in the case of using multi-frequency communication sys-tems. Signal models with maximum entropy are used as message source models. As a model of a communication channel with noise, a channel with additive Gaussian noise which has a uniform spectrum in a given frequency band is used. The implemented computing system allows tabular and visual analysis of such parameters of the multiple experiments as the number of signal subcarriers, the range of time interval for signal restoration, the signal-to-noise ratio, the quadrature and in-phase components of the QAM signal, the energy and information characteristics of the signal and the speed of data transmission of the communication channel. The results of the serial experiments are illustrated in form of interactive graphs with animated effects.

The Reexamination of the Moisture–Vortex and Baroclinic Instabilities in the South Asian Monsoon

Observational analyses reveal that a dominant mode in the South Asian Monsoon region in boreal summer is a westward-propagating synoptic-scale disturbance with a typical wavelength of 4000 km that is coupled with moistening and precipitation processes. The disturbances exhibit an eastward tilt during their development before reaching their maximum activity center. A 2.5-layer model that extends a classic 2-level quasi-geostrophic model by including a prognostic lower-tropospheric moisture tendency equation and an interactive planetary boundary layer was constructed. The eigenvalue analysis of this model shows that the most unstable mode has a preferred zonal wavelength of 4000 km, a westward phase speed of 6 m s−1, an eastward tilt vertical structure, and a westward shift of maximum moisture/precipitation center relative to the lower-tropospheric vorticity center, all of which agree with the observations. Sensitivity experiments show that the moisture–vortex instability determines, to a large extent, the growth rate, while the baroclinic instability helps set up the preferred zonal scale. Ekman-pumping-induced vertical moisture advection prompts an in-phase component of perturbation moisture relative to the low-level cyclonic center, allowing the generation of available potential energy and perturbation growth, regardless of whether or not a low-level mean westerly is presented. In contrast to a previous study, the growth rate is reversely proportional to the convective adjustment time. The current work sheds light on understanding the moisture–vortex and the baroclinic instability in a monsoonal environment with a pronounced easterly vertical shear.

Smart meter for the estimation of power quality indices based on second-order generalized integrator.

With the increasing integration of renewable energy sources and nonlinear loads in the grid, it is necessary to compute energy and power quality parameters for monitoring as well as analyzing the power consumption indices (PCI) and power quality indices (PQI). Smart meters (SMs) can monitor the PCI and PQI at the consumer end. SM necessitates the measurement of voltage and current, which can be further processed by an algorithm for estimating different parameters through signal processing and arithmetic operations. This paper proposes the combined dual second-order generalized integrator (SOGI) and proportionate least mean square (PLMS) algorithm-based PCI and PQI estimation for smart metering. Dual SOGIs individually process voltage and current signals to extract the respective fundamental in-phase and quadrature components. To account the variations in grid frequency, the SOGI processing the voltage also incorporates a frequency locked loop. This prevents performance degradation on account of frequency variations. The PLMS algorithm then process the output of SOGI processing the current for extracting the fundamental active and reactive components of current. Moreover, the PLMS algorithm results in further attenuation of harmonics. Also, only two separate values of learning rate are required, which is easy to tune for better dynamic performance. With only two quantities to be determined, this results in largely reduced computation. These computations facilitate in PCI and PQI computations. The proposed smart meter calculates peak value, RMS value, and phase angle for the fundamental components of voltage and current for each phase. Additionally, it assesses fundamental power factor, total harmonic distortion (THD), distortion factor, true power factor, active power, reactive power, and apparent power for each phase. The performance validation of the proposed combined dual-SOGI-PLMS algorithm-based PCI and PQI estimation for smart metering is carried out in MATLAB/SIMULINK for fifteen different operating scenarios. Further, the real-time implementation of the proposed methodology is carried out on dSPACE MicroLab Box 1202. Comparative analysis is also presented, which reveals the computational simplicity and other merits of the proposed scheme over the earlier reported scheme.

Hardware-Independent Deep Signal Processing: A Feasibility Study in Echocardiography.

Deep learning (DL) models have emerged as alternative methods to conventional ultrasound (US) signal processing, offering the potential to mimic signal processing chains, reduce inference time, and enable the portability of processing chains across hardware. This paper proposes a DL model that replicates the fine-tuned BMode signal processing chain of a high-end US system and explores the potential of using it with a different probe and a lower-end system. A deep neural network was trained in a supervised manner to map raw beamformed in-phase and quadrature component data into processed images. The dataset consisted of 30,000 cardiac image frames acquired using the GE HealthCare Vivid E95 system with the 4Vc-D matrix array probe. The signal processing chain includes depth-dependent bandpass filtering, elevation compounding, frequency compounding, and image compression and filtering. The results indicate that a lightweight DL model can accurately replicate the signal processing chain of a commercial scanner for a given application. Evaluation on a 15 patient test dataset of about three thousand image frames gave a structural similarity index measure of 98.56 ± 0.49. Applying the DL model to data from another probe showed equivalent or improved image quality. This indicates that a single DL model may be used for a set of probes on a given system that targets the same application, which could be a cost-effective tuning and implementation strategy for vendors. Further, the DL model enhanced image quality on a Verasonics dataset, suggesting the potential to port features from high-end US systems to lower-end counterparts.

A Novel OFDM-Based Time Domain Quadrature GSM for Visible Light Communication System

In order to improve the spectral efficiency (SE) as well as the receiver performance of band-limited visible light communications (VLCs), two orthogonal frequency division multiplexing (OFDM)-based quadrature generalized multiple-input multiple-output (QG-MIMO) transmission schemes, including time domain (TD) quadrature generalized spatial modulation (TD-QGSM) and TD quadrature generalized spatial multiplexing (TD-QGSMP), are proposed in this paper. Firstly, the constellation symbols in the frequency domain are split into in-phase and quadrature components to perform the OFDM modulation separately. Then, the corresponding time domain signal is spatially mapped on different light emitting diodes (LEDs) for achieving the diversity or multiplexing. In addition, we also propose an illegal vector correction (IVC)-based orthogonal matching pursuit (OMP) detection algorithm to deal with the error propagation and noise amplification effect, where a novel correction criterion is involved for assisting the index vectors estimation and thus for improving the demodulation performance. The simulation results demonstrate that the SE can be significantly improved by the proposed schemes as compared with the existing OFDM-based generalized MIMO schemes, with the TD-QGSM increasing by at least 56.5% and the TD-QGSMP increasing by at least 72.3%. Moreover, the bit error rate (BER) performance can be further improved when applying the proposed IVC-OMP detection method, which outperforms the traditional maximum-likelihood and maximum ratio combining (ML-MRC) detection by at least 62.5%.