Signal Modeling Research Articles

Block-Adaptive Point Cloud Attribute Coding With Region-Aware Optimized Transform

Block-based compression scheme shows remarkable success in image and video coding. However, existing tree-type block partition methods usually divide point clouds into clusters with few or disjoint points due to the irregular sampling, which is adverse for the subsequent transform to exploit the local region correlation. Moreover, the widely-used optimal transform, e.g., Discrete Cosine Transform (DCT), is deduced with the assumption of the specified probability model. Thus these transforms cannot adequately conform to diverse statistical characteristics of point cloud attributes. To address the above two problems, we propose a block-adaptive codec with the optimized transform for point cloud attribute compression, including Progressive Clustering (PC) for block partition and Region-Aware Signal Modeling (RASM) for transform. PC is designed via the split-and-merge strategy. In the splitting phase, over-segmented clusters are obtained by iteratively searching the nearest neighbors to maintain the spatial continuity of points in one cluster. In the merging step, blocks are determined by merging over-segmented clusters under the guidance of minimizing texture complexity, which adjusts block sizes to adapt to texture variation. RASM adaptively captures the color correlations to respond to diverse statistical characteristics by optimizing the overall Rate-Distortion (RD) cost. Then, the optimal transform bases are obtained via the eigen-decomposition of the color correlation representation with respect to the Gaussian Markov Random Field, where input colors are obtained considering the linear relationship between geometry and color variations. The linear coefficients also need to be encoded to combine geometry to reconstruct the transform bases at the decoder in the same way as the encoder. Particularly, we accelerate the RASM by constraining the independent relationship between signals in the local region, which hardly influences RD performance. Extensive experiments not only indicate the effectiveness of PC and RASM but also show the significant RD gains achieved by our approach compared with several state-of-the-art platforms.

The Influence of Narrowband Interference on DME System Operation

Within the new air traffic management concept, using the Global Navigation Satellite System (GNSS), it is assumed that distance measurement equipment (DME) will be retained. The results of research carried out by several authors have confirmed that global navigation satellite systems (GNSS) can be put out of operation in cases of interference. Therefore, it is very timely to investigate the accuracy and resistance to interference of DME systems. The presented work contains the results of research in the field of assessment of the accuracy and resistance of a DME system, which works in conditions of narrowband interference, using modeling and simulation. Based on the derived model of the DME measurement signal, its parameters, and narrowband interference, algorithms for processing the measurement signals of a DME system in conditions of narrowband interference were derived. A quasi-optimal nonlinear filtering method was used in the derivation of the measurement signal-processing algorithms. A quadratic loss function was used as an optimality criterion, which allows us to obtain the results of measuring the parameters of the DME measurement signal as a minimum of the a posteriori mean error. Within this method, Gaussian approximation and large and small parameter methods were used. Simulation of the operation of the DME system confirmed that the measurement accuracy of this system depends on the stability of the frequency of the DME support generator, and also depends on the signal-to-noise ratio and the signal-to-interference ratio of the DME receiver input. Comparing the results of the DME system receiver designed by us with the parameters listed in in published works that discuss the accuracy of this system, we can conclude that its accuracy is much better. The simulation results confirmed that the potential accuracy of the distance measurement is equal to 2.2 m. However, the mentioned algorithms require substantial simplification to be used for real-time signal processing. This will be our next research direction.

Searches for continuous gravitational waves from neutron stars: A twenty-year retrospective

Seven years after the first direct detection of gravitational waves, from the collision of two black holes, the field of gravitational wave astronomy is firmly established. A first detection of continuous gravitational waves from rapidly-spinning neutron stars could be the field’s next big discovery. I review the last twenty years of efforts to detect continuous gravitational waves using the LIGO and Virgo gravitational wave detectors. I summarise the model of a continuous gravitational wave signal, the challenges to finding such signals in noisy data, and the data analysis algorithms that have been developed to address those challenges. I present a quantitative analysis of 297 continuous wave searches from 80 papers, published from 2003 to 2022, and compare their sensitivities and coverage of the signal model parameter space.

Factor analysis for signal modeling and noise characterization in spectro-kinetic data

Measurement signals and noise (heteroscedastic and homoscedastic) in different evolutionary spectral kinetic data sets are modeled and characterized by applying maximum likelihood common factor analysis (MLFA) and principal axis factoring (PAF), as factor-based techniques. They decompose data into common and specific factors, that are assumed to be constant heteroscedastic. The digital filter is also used for characterization of noise in data and to confirm the obtained results from factor analysis techniques. No one of the applied methods requires the use of replicated measurements. Inverted Blackman windowed sinc (BWS) smoothing coefficients as high-pass digital filters are employed to provide point and bin estimates of noise from measurement vectors without any assumption about noise structure. The proportionality of noise to signals and their characteristics are also reported and are comparable when applying different methods.The proposed methods are compared using both simulated and experimental kinetic data sets. Experimental data includes fluorescence and UV–vis absorbance kinetic spectra. A simple solution for problem of MLFA application to fat data is proposed and successfully applied. Different types of independent noise including identical independent distribution (iid), constant heteroscedastic, and general heteroscedastic are added to the simulated data, in various levels.In the case of iid noise results from all methods are almost the same. In the presence of heteroscedastic noise results from MLFA and PAF are preferred to PCA regarding subspace angles and discrepancy function values. Employment of methods for highly overlapped, skinny, and fat data shows the superiority of MLFA to PAF and PCA. The independent noise characteristics obtained from factor analysis methods are comparable to that from digital filters.

Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy

Raman spectroscopy is a kind of vibrational method that can rapidly and non-invasively gives chemical structural information with the Raman spectrometer. Despite its technical advantages, in practical application scenarios, Raman spectroscopy often suffers from interference, such as noises and baseline drifts, resulting in the inability to acquire high-quality Raman spectroscopy signals, which brings challenges to subsequent spectral analysis. The commonly applied spectral preprocessing methods, such as Savitzky–Golay smooth and wavelet transform, can only perform corresponding single-item processing and require manual intervention to carry out a series of tedious trial parameters. Especially, each scheme can only be used for a specific data set. In recent years, the development of deep neural networks has provided new solutions for intelligent preprocessing of spectral data. In this paper, we first creatively started from the basic mechanism of spectral signal generation and constructed a mathematical model of the Raman spectral signal. By counting the noise parameters of the real system, we generated a simulation dataset close to the output of the real system, which alleviated the dependence on data during deep learning training. Due to the powerful nonlinear fitting ability of the neural network, fully connected network model is constructed to complete the baseline estimation task simply and quickly. Then building the Unet model can effectively achieve spectral denoising, and combining it with baseline estimation can realize intelligent joint processing. Through the simulation dataset experiment, it is proved that compared with the classic method, the method proposed in this paper has obvious advantages, which can effectively improve the signal quality and further ensure the accuracy of the peak intensity. At the same time, when the proposed method is applied to the actual system, it also achieves excellent performance compared with the common method, which indirectly indicates the effectiveness of the Raman signal simulation model. The research presented in this paper offers a variety of efficient pipelines for the intelligent processing of Raman spectroscopy, which can adapt to the requirements of different tasks while providing a new idea for enhancing the quality of Raman spectroscopy signals.

Damage Rule of Marine Isolator under Secondary Shock Vibration

To study the damage of the shock vibration generated by the ship hull to the internal equipment of the ship under the secondary explosion impact, a three-dimensional simulation model of the BE rubber isolator was established. The signal of the impact vibration load was loaded by the AUTODYN software. And the relationship between the damage of the isolator under the primary vibration impact and the frequency, amplitude, and attenuation of the loading signal was found. It is revealed that the damage law curve of the isolator under the secondary shock vibration conforms to a rational function. Based on this discovery, a mathematical model of the secondary shock vibration signal that can predict the isolator damage through the primary shock vibration is established.

Physiological modeling of the BOLD signal and implications for effective connectivity: A primer

In this primer, I provide an overview of the physiological processes that contribute to the observed BOLD signal (i.e., the generative biophysical model), including their time course properties within the framework of the physiologically-informed dynamic causal modeling (P-DCM). The BOLD signal is primarily determined by the change in paramagnetic deoxygenated hemoglobin, which results from combination of changes in oxygen metabolism, and cerebral blood flow and volume. Specifically, the physiological origin of the so-called BOLD signal “transients” will be discussed, including the initial overshoot, steady-state activation and the post-stimulus undershoot. I argue that incorrect physiological assumptions in the generative model of the BOLD signal can lead to incorrect inferences pertaining to both local neuronal activity and effective connectivity between brain regions. In addition, I introduce the recent laminar BOLD signal model, which extends P-DCM to cortical depths-resolved BOLD signals, allowing for laminar neuronal activity to be determined using high-resolution fMRI data.

Pulse wave signal modelling and feature extraction based on Lognormal function from photoplethysmography in wireless body area networks

Among the various physiological signals of the human body, the pulse wave signal from photoplethysmography is widely used for commercial products in Wireless Body Area Networks. The comfortable wearing of a product and the richness of physiological information are two advantages of Wireless Body Area Networks. To address the shortcomings of the existing modelling pulse wave signal in lacking characteristics that are consistent with a human physiological mechanism in medicine, we proposed a Lognormal function model of the pulse wave signal and a novel model to extract physiological parameters. The proposed Lognormal function model consists of four successively spaced single-peaked pulses with long trailing features. To define the parameters in the proposed Lognormal function model, we proposed a method that took advantage of the positivity and negativity of the first-order derivatives and the trans-zero point of the second-order derivatives. Based on the determined parameters, we introduced a four-shot staged curve fitting approach that can displace the sum of the four fits at iterative and different time scales. Finally, a parameter vector with 12 elements, which is known as a physiological feature to determine the health status of the human body in Wireless Body Area Networks. Experimental results show that the proposed Lognormal function model is superior to the conventional Gaussian function model in terms of physiological importance and waveform fitting accuracy.

Augmented Reality for Realtime Speech Recognition

Choosing the proper word out of samples with similar acoustic structures is a significant difficulty in speech recognition research, according to a case study of a basic domain-based speech recognition solution in speech recognition augmented reality products for hearing impaired persons. Our goal is to increase transcription accuracy in local vocabulary situations like meetings and lectures. To construct real-time ”live subtitles” utilising a unique voice recognition mechanism by combining our approach in an augmented reality environment. This programme allows hearing impaired and deaf persons to view real-time augmented reality subtitles while listening to a talk/speech on a certain topic. In the model of a tree-structured Hidden Markov Model (HMM) for speech recognition, there are different types of networks and graphs involved These networks and graphs play a crucial role in modeling and representing the relationships, transitions, and probabilities involved in speech recognition using tree-structured HMMs. They enable efficient modeling, adaptation, and recognition of speech signals in real- world noisy environments.

Growth of κ-([Al,In]xGa1-x)2O3 Quantum Wells and Their Potential for Quantum-Well Infrared Photodetectors.

The wide band gap semiconductor κ-Ga2O3 and its aluminum and indium alloys have been proposed as promising materials for many applications. One of them is the use of inter-sub-band transitions in quantum-well (QW) systems for infrared detectors. Our simulations show that the detection wavelength range of nowadays state of the art GaAs/AlxGa1-xAs quantum-well infrared photodetectors (QWIPs) could be substantially excelled with about 1-100 μm using κ-([Al,In]xGa1-x)2O3, while at the same time being transparent to visible light and therefore insensitive to photon noise due to its wide band gap, demonstrating the application potential of this material system. Our simulations further show that the QWIPs efficiency critically depends on the QW thickness, making a precise control over the thickness during growth and a reliable thickness determination essential. We demonstrate that pulsed laser deposition yields the needed accuracy, by analyzing a series of (InxGa1-x)2O3 QWs with (AlyGa1-y)2O3 barriers with high-resolution X-ray diffraction, X-ray photoelectron spectroscopy (XPS) depth profiling, and transmission electron microscopy (TEM). While the superlattice fringes of high-resolution X-ray diffraction only yield an average combined thickness of the QWs and the barrier and X-ray spectroscopy depth profiling requires elaborated modeling of the XPS signal to accurately determine the thickness of such QWs, TEM is the method of choice when it comes to the determination of QW thicknesses.

Abstract PR012: Ultra-sensitive detection of circulating tumour DNA enriches for patients with higher risk disease in clinically localised prostate cancer

Abstract Purpose Circulating tumour DNA (ctDNA) analysis has demonstrated utility for diagnostic and prognostic applications in many cancer types, including metastatic prostate cancer. However, localised prostate cancer yields relatively low levels of ctDNA and therefore it has been difficult to detect in this context using conventional methods. We aimed to assess the limits of detection of ctDNA in localised prostate cancer by leveraging thousands of patient-specific mutations per case with the high-sensitivity INVAR method, and to test the hypothesis that ctDNA detection is associated with high risk disease. Experimental Procedures A total of 128 individuals with clinically localised prostate cancer at the time of sample collection were selected from cohorts in Australia (n=48) and the UK (n=80). Additionally, 27 healthy individuals without prostate cancer were included as negative controls. Primary tumour tissue and matched bloods from all cases were whole genome sequenced (WGS) and somatic variants were called using pipelines from the Pan Prostate Cancer Group. Plasma cell-free DNA (cfDNA) samples from cases and controls were profiled using custom targeted sequencing panels, with saturating coverage of patient-specific mutations identified by WGS. We assessed ctDNA detection in cases using the highly sensitive INVAR pipeline, that leverages consensus read sequencing alignments, background error modelling and integration of signals across thousands of patient-specific variants. Biochemical recurrence and metastasis-free survival curves were used to assess the relationship between ctDNA detection and disease progression. Results We analysed pre-treatment blood plasma ctDNA in a large cohort of localised prostate cancer patients, using error-suppressed targeted sequencing of over 280k patient-specific mutations. To comprehensively assess ctDNA mutation analysis in this context, we combined signals across the maximum number of genome-wide patient specific mutations and leveraged an established analysis pipeline (INVAR) that corrects for background error rates and calculates a global integrated mutant allele fraction. ctDNA was detected in 9.3% of localised prostate cancer patients. In cases where ctDNA was detected we found significant associations with biochemical recurrence (p=0.01) and shorter metastasis-free survival (p < 0.0001). Conclusions Our study provides clear insights into the required analytical sensitivity and potential utility of ctDNA mutation analysis in localised prostate cancer. We found that mutation-based ctDNA detection rates were low in localised prostate cancer (<10% cases), but in ctDNA positive cases there was a significant association with relapse after surgical intervention alone. This raises the potential for including ctDNA detection as an additional tool for patient stratification in future neo/adjuvant treatment trials aiming to assess the impact of treatment escalation in men at high risk of relapse with current standard of care treatment alone. Citation Format: Bernard J. Pope, Gahee Park, Edmund Lau, Jelena Belic, Radoslaw Lach, Anne George, Patrick McCoy, Anne Nguyen, Corrina Grima, Chol-hee Jung, Emma-Jane Ditter, Hui Zhao, David Wedge, Rosalind A Eeles, Daniel Brewer, Andy G. Lynch, Harveer Dev, Christopher M Hovens, Vincent J. Gnanpragasam, Nitzan Rosenfeld, Niall M. Corcoran, Charles E. Massie. Ultra-sensitive detection of circulating tumour DNA enriches for patients with higher risk disease in clinically localised prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr PR012.

Heart Sound Classification using the Nonlinear Dynamic Feature Approach along with Conventional Classifiers

Heart sounds show chaotic and complex behavior when murmurs are present, containing nonlinear and non-Gaussian information. This paper studies ways to extract features from nonlinear dynamic models. The features frequently used to describe the underlying dynamics of the heart are derived from nonlinear dynamical modeling of heart sound signals. This study incorporates nonlinear dynamic features alongside conventional classifiers in the analysis of phonocardiograms (PCGs), achieving a significant improvement in the classification performance with 0.90 sensitivity and 0.92 specificity.

Transmission model of transient flow wave signal in intelligent layered water injection system

Fluid wave code communication is used in layered water injection intelligent monitoring system, but model of fluid transient flow wave signal transmission is still unknown. Based on the fluid energy equation of steady flow, a transmission mathematical model of fluid transient flow wave signal in intelligent layered water injection system was established. The model can accurately describe the transient flow wave transmission characteristics in the tubular string of water injection wells. The transient flow wave signals and influencing factors generated by the ground electric control valve and the downhole water distributor were studied, and the transmission mechanism of the signal in the water injection tubular string was revealed. Studies show that ground and downhole transient flow wave signals are generated by discharge changes caused by changes in the opening degree of the ground valve and the downhole water distributor. The length of the water injection tube has no effect on the downlink transmission of the wellhead signal, but has a certain influence on the uploading of the downhole signal. Numerical calculations show that the flow rate of the water injection tube has a great influence on the amplitude of the pressure signal. The larger flow rate can generate larger signal amplitude, which is beneficial to the signal transmission, signal detection and processing. It was verified by the experiments and simulations that the pressure and flow changes in the downhole and wellhead had good consistency during the transmission of transient flow waves. It is found that the greater the variation of opening degree, the greater the amplitude of transient flow wave signal, which is beneficial to the wave signal transmission. The optimal settings for the valve opening are selected as 100% rightleftarrows 0%. This study has theoretical guiding significance for the design and performance improvement of fluid wave code communication systems.

Modeling and analysis of cardioimpedance signals using polynomial models and fuzzy rule-based models

The determination of characteristic points of bioimpedance curves is crucial to the analysis of bioimpedance signals. Traditional methods need to calculate the derivative of the bioimpedance curve and rely on the help of other simultaneously recorded signals. However, these methods are sensitive to noise while some auxiliary signal may be unrecorded. A novel approach is proposed in this study to automatically determine the positions of characteristic points. The overall development process is realized as a two-phase construct. The first stage of the process involves a fitting of the impedance change curves using nonlinear regression models. Then, a prediction model is trained to predict the positions of characteristic points through a linear combination of the parameters of the nonlinear regression model. Experimental studies constructed on a collection of real-world bioimpedance signals help quantify the performance of the algorithm and gain a deep insight into the superiority of the proposed methodology. It is shown that the proposed method offers a substantial improvement in the prediction accuracy in comparison with the baseline method. Moreover, the proposed characteristic point determination method is robust to noise since its performance is not affected in the presence of noise.

Cerebral oxygen metabolism from MRI susceptibility

This article provides an overview of MRI methods exploiting magnetic susceptibility properties of blood to assess cerebral oxygen metabolism, including the tissue oxygen extraction fraction (OEF) and the cerebral metabolic rate of oxygen (CMRO2). The first section is devoted to describing blood magnetic susceptibility and its effect on the MRI signal. Blood circulating in the vasculature can have diamagnetic (oxyhemoglobin) or paramagnetic properties (deoxyhemoglobin). The overall balance between oxygenated and deoxygenated hemoglobin determines the induced magnetic field which, in turn, modulates the transverse relaxation decay of the MRI signal via additional phase accumulation. The following sections of this review then illustrate the principles underpinning susceptibility-based techniques for quantifying OEF and CMRO2. Here, it is detailed whether these techniques provide global (OxFlow) or local (Quantitative Susceptibility Mapping - QSM, calibrated BOLD - cBOLD, quantitative BOLD - qBOLD, QSM+qBOLD) measurements of OEF or CMRO2, and what signal components (magnitude or phase) and tissue pools they consider (intravascular or extravascular). Validations studies and potential limitations of each method are also described. The latter include (but are not limited to) challenges in the experimental setup, the accuracy of signal modeling, and assumptions on the measured signal. The last section outlines the clinical uses of these techniques in healthy aging and neurodegenerative diseases and contextualizes these reports relative to results from gold-standard PET.

Model of an automated biotechnical system for analyzing pulseograms as a kind of edge devices

The rapid development of computer biometrics over the past 2-3 decades is largely due to the development and widespread introduction into clinical practice of new methods of studying human body health, including pulse methods. It is possible to judge changes in hemodynamic characteristics, heart rate, and blood flow rate in the studied part of the body based on the parameters of the pulse wave signal. At the same time, the physical processes of the formation of the pulse wave shape have not been fully studied, although the number of biophysical models of blood circulation is quite significant. The development of such a model will allow us to effectively apply modern developments in digital signal processing to the pulse wave and increase its diagnostic value. A qualitative model of pulse signal can be entrusted to the development of the base unit of the biotechnical system as a type of edge device. The work is devoted to the improvement of methods of rapid diagnosis of the cardiovascular system based on the analysis of model pulse-grams. An adequate mathematical model of the pulse wave, which corresponds to real pulse signals in different states of the human body and contains mathematical relationships between the main parameters of pulse-grams, has been refined. The algorithm of express diagnostics with the established criteria of the analysis of pulsograms is offered.

Transmission pulse photoacoustic response of thin semiconductor plate

In modern high-speed semiconductor electronics, there is a high-rate heating of components that is affected by very fast relaxation processes. The relaxation times of these processes should be known for thermal management of these devices. For almost half a century, photoacoustic techniques have been successfully developed and used to investigate physical semiconductor properties. In order to enable observation of fast relaxation processes, the model of pulse photoacoustic signal is proposed that includes influence of thermal relaxations. It is shown that these processes can be observed in a thin semiconductor layer by choosing the frequency of short pulse train and their duty cycle.

A novel approach for modeling of the ultrasonic signal backscattered in immersed multilayer structures at normal incidence: Time, Frequency, and Velocity dispersion representation

This study proposes a theoretical procedure for modeling ultrasonic signal backscattered in normal incidence by a multilayer structure (MS) immersed in water. The propagation of a longitudinal ultrasonic wave in a MS is studied analytically by using the transfer matrix method (TMM), in which each layer is established as a quadrupole formalism combining the stresses and velocities. The transfer matrices allow us to determine the reflection coefficient of the multilayer structure. The MS solid/liquid/solid case is studied in both insonation configurations (direct and reverse). The modeled ultrasonic response is represented in both time and frequency domains and validated by comparison with experimental data published previously. The time–frequency methods such as spectrogram (SP) and the smoothed pseudo-Wigner–Ville (SPWV) are also used to investigate the character and the value of the non-dispersive of the longitudinal wave. Moreover, the TMM method is used under different conditions (different layer thicknesses, non-uniform distribution of layers, and any frequency range), which are among the causes of MS dispersion. TMM method is able to show this behavior and is exploited to produce (Distance versus Time) and (Distance versus Frequency) representations of wave propagation for each studied MS. The perfect agreement of the comparison of TMM dispersion results with the results is achieved by using time–frequency methods in the case of a 5 MHz central frequency.

A cost‐effective switched inductor‐based bridgeless Sepic converter for PQ enrichment in power solutions

SummaryPower solutions contribute a remarkable role in all the sectors. In order to develop the sustainable and affodable power solution, the bettered power quality (PQ) based modified bridgeless switched inductor (MBSL) Sepic power factor correction (PFC) converter is introduced in this article. An added feature to an already existing traditional Sepic converter resulted in switched inductor (SL) network that reasonably improves the step‐down voltage gain with low current stress across the output inductors and diodes which restricts the utilization of transformer that ultimately reduces the overall cost of power solution. This introduced structure acts as a good choice of low‐cost power conversion solution in the perspective of improved inline PQ indices, high efficiency and good reliability. Furthermore, the discontinuous conduction mode (DCM) functioning of SL network reveals the innate zero current switching (ZCS) capability with enhanced efficiency. Besides, the effective functionality of the one‐stage PFC converter is tested on hardware prototype of 250 W/48 V, and it also adopts an appropriate control approach to stabilize the output voltage. This study also exposes small signal modeling to prove the stability of the power solution. In addition, the test outcomes reveal that the preferred PFC procure sinusoidal current from the AC mains that confirms the good enshaping attribute and low current harmonic distortions within the stipulated IEC 61000‐3‐2 standard guidelines for both standstill and fluctuating conditions.

Review on Modeling and Control Strategies of DC–DC LLC Converters for Bidirectional Electric Vehicle Charger Applications

Bidirectional DC–DC converters are frequently chosen for applications requiring high power density such as in bidirectional electric vehicle (EV) chargers. Vehicle to Everything (V2X) technology makes the EV battery an electrical energy source. In this article, the use of a DC–DC LLC converter used in a bidirectional EV charger is reviewed. Different modeling approaches of the DC–DC LLC converter, such as small and large signal modeling, are discussed. Common modulation strategies applied to the DC–DC LLC converter in V2X mode, such as Pulse Frequency Modulation (PFM), Pulse Width Modulation (PWM) and Phase-Shift Modulation (PSM), are presented. The new challenge is to present the main characteristics and limitations of each modulation strategy in order to cover the whole operating zone of the EV charger in V2X mode. Furthermore, different control strategies based on a small or large signal model combined with different modulation strategies are highlighted. Linear and nonlinear controllers applied to the DC–DC LLC converter are discussed. Robust controllers are mainly highlighted regarding their advantage in ensuring the control robustness with respect to unexpected disturbances. A comparative study among modulation strategies as well as different control algorithms is conducted in terms of control performance and converter efficiency in V2X mode.