Signal Model Research Articles

Integrated signal mapping and model updating in iterative hybrid testing for seismic fluid–structure interaction

Integrated signal mapping and model updating in iterative hybrid testing for seismic fluid–structure interaction

Distinguishing microgliosis and tau deposition in the mouse brain using paramagnetic and diamagnetic susceptibility source separation

Abstract Tauopathies, including Alzheimer’s disease (AD), are neurodegenerative disorders characterized by hyperphosphorylated tau protein aggregates in the brain. In addition to protein aggregates, microglia-mediated inflammation and iron dyshomeostasis are other pathological features observed in AD and other tauopathies. It is known that these alterations at the subcellular level occur much before the onset of macroscopic tissue atrophy or cognitive deficits. The ability to detect these microstructural changes with MRI, therefore, has substantive importance for improved characterization of disease pathogenesis. In this study, we demonstrate that quantitative susceptibility mapping (QSM) with paramagnetic and diamagnetic susceptibility source separation has the potential to distinguish neuropathological alterations in a transgenic mouse model of tauopathy. 3D multi-echo gradient echo data were acquired from fixed brains of PS19 (Tau) transgenic mice and age-matched wild-type (WT) mice (n = 5 each) at 11.7 T. The multi-echo data were fit to a 3-pool complex signal model to derive maps of paramagnetic component susceptibility (PCS) and diamagnetic component susceptibility (DCS). Group-averaged signal fraction and composite susceptibility maps showed significant region-specific differences between the WT and Tau mouse brains. Significant bilateral increases in PCS and |DCS| were observed in specific hippocampal and cortical sub-regions of the Tau mice relative to WT controls. Comparison with immunohistological staining for microglia (Iba1) and phosphorylated-tau (AT8) further indicated that the PCS and DCS differences corresponded to regional microgliosis and tau deposition in the PS19 mouse brains, respectively. The results demonstrate that quantitative susceptibility source separation may provide sensitive imaging markers to detect distinct pathological alterations in tauopathies.

Traffic Signal and Autonomous Vehicle Control Model: An Integrated Control Model for Connected Autonomous Vehicles at Traffic-Conflicting Intersections Based on Deep Reinforcement Learning

Traffic Signal and Autonomous Vehicle Control Model: An Integrated Control Model for Connected Autonomous Vehicles at Traffic-Conflicting Intersections Based on Deep Reinforcement Learning

Accelerated model-based T1, T2* and proton density mapping using a Bayesian approach with automatic hyperparameter estimation.

To achieve automatic hyperparameter estimation for the model-based recovery of quantitative MR maps from undersampled data, we propose a Bayesian formulation that incorporates the signal model and sparse priors among multiple image contrasts. We introduce a novel approximate message passing framework "AMP-PE" that enables the automatic and simultaneous recovery of hyperparameters and quantitative maps. We employed the variable-flip-angle method to acquire multi-echo measurements using gradient echo sequence. We explored undersampling schemes to incorporate complementary sampling patterns across different flip angles and echo times. We further compared AMP-PE with conventional compressed sensing approaches such as the -norm minimization, PICS and other model-based approaches such as GraSP, MOBA. Compared to conventional compressed sensing approaches such as the -norm minimization and PICS, AMP-PE achieved superior reconstruction performance with lower errors in mapping and comparable performance in and proton density mappings. When compared to other model-based approaches including GraSP and MOBA, AMP-PE exhibited greater robustness and outperformed GraSP in reconstruction error. AMP-PE offers faster speed than MOBA. AMP-PE performed better than MOBA at higher sampling rates and worse than MOBA at a lower sampling rate. Notably, AMP-PE eliminates the need for hyperparameter tuning, which is a requisite for all the other approaches. AMP-PE offers the benefits of model-based recovery with the additional key advantage of automatic hyperparameter estimation. It works adeptly in situations where ground-truth is difficult to obtain and in clinical environments where it is desirable to automatically adapt hyperparameters to individual protocol, scanner and patient.

Comprehensive demonstration of link fiber-induced spurious signals in interferometric FBG hydrophones

The interferometric Fiber Bragg Grating (FBG) hydrophone has wide applications in ocean acoustic detection and seismic wave monitoring. The interference signal from external sources is routed to the elements through the link fiber connecting the dry and wet ends, which is a critical bottleneck that limits system performance. This paper presents a comprehensive model for link fiber-induced spurious signals, which demonstrates the physical processes and signal characteristics of the Doppler effect and the Rayleigh scattering effect, illustrating how they interact to generate the link interference signal. It is found that the dominant physical mechanism depends on the location of the link interference signal source. The interference signal source closer to the dry end mainly transmits signals to hydrophone elements through the Doppler effect, and the amplitude of the induced spurious signal depends on the frequency. When the link fiber is longer, the interference signal from the source near the wet end is simultaneously transmitted to hydrophone elements through the Doppler effect and Rayleigh scattering effect, during which the Rayleigh scattering effect leads to a spurious signal intensity increase of up to 10 dB. Additionally, the correlation with signal frequency decreases as instability rises. This thorough analysis of the physical process of link fiber-induced spurious signals will advance the development of interference suppression techniques, substantially boosting the sensitivity and reliability of interferometric FBG hydrophones for applications in harsh environments with disturbances.

Time–Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks

Background: Transforming one-dimensional (1D) biomedical signals into two-dimensional (2D) images enables the application of convolutional neural networks (CNNs) for classification tasks. In this study, we investigated the effectiveness of different 1D-to-2D transformation methods to classify electrocardiogram (ECG) and electroencephalogram (EEG) signals. Methods: We select five transformation methods: Continuous Wavelet Transform (CWT), Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Signal Reshaping (SR), and Recurrence Plots (RPs). We used the MIT-BIH Arrhythmia Database for ECG signals and the Epilepsy EEG Dataset from the University of Bonn for EEG signals. After converting the signals from 1D to 2D, using the aforementioned methods, we employed two types of 2D CNNs: a minimal CNN and the LeNet-5 model. Our results indicate that RPs, CWT, and STFT are the methods to achieve the highest accuracy across both CNN architectures. Results: These top-performing methods achieved accuracies of 99%, 98%, and 95%, respectively, on the minimal 2D CNN and accuracies of 99%, 99%, and 99%, respectively, on the LeNet-5 model for the ECG signals. For the EEG signals, all three methods achieved accuracies of 100% on the minimal 2D CNN and accuracies of 100%, 99%, and 99% on the LeNet-5 2D CNN model, respectively. Conclusions: This superior performance is most likely related to the methods’ capacity to capture time–frequency information and nonlinear dynamics inherent in time-dependent signals such as ECGs and EEGs. These findings underline the significance of using appropriate transformation methods, suggesting that the incorporation of time–frequency analysis and nonlinear feature extraction in the transformation process improves the effectiveness of CNN-based classification for biological data.

Analysis of high speed dynamic current mode D-FlipFlop in nanometer CMOS

ABSTRACT The advent of digital IC has increased rapidly to sustain the end-user’s requirement of high speed computation with least possible power burn to enhance the battery lifetime in portable electronic gadgets. Although CMOS serves to configure such digital logic, the switching power is found to be proportional to the operating frequency due to which MOS current mode logic (MCML) came into existence. Accordingly, this article presents a design of novel high-speed dynamic CML-based D-latch and D-FF for 90 nm process with a layout area requirement of 108.624 μ m 2 . The small signal model of proposed design is used to compute the propagation delay equation and is compared with conventional CML D-Latch and triple tail D-latch circuit to prove worth of this new architecture. The simulation using Cadence® Virtuoso with a clock of 10 GHz and V dd = 1.2 V record the power burn of 219.05 μW , delay of 31.30ps, set-up time of 12.80ps, hold time of −27.40ps and latency of 43.80ps while driven by a 2.5 GHz input datastream. Besides, the validation of proposed design is also carried out in lower technology nodes like 65 nm and 40 nm UMC where the reported parametric values could depict the design’s stable performance.

Heart Rate Variability Estimation Based on RFID Tag-Pair in Dynamic Environments

With the rapid development of smart health care, accurate heart rate variability (HRV) estimation for the early detection of diseases has become a hot research topic. Advanced work uses the wireless signal to estimate the heartbeat in a contact-free way, which usually cannot separate multiple users or work in a dynamic environment. In this article, we propose a lightweight heartbeat-sensing method based on RFID tag pairs, which focuses on HRV extraction in a more general sensing scenario. Based on the tag-pair design, we build a novel heartbeat and respiration model to describe the signal relationship between the two tags from the time and space domains. Based on the model, we propose a Calibrated Temporal-Spatial IQ-Shaping-based signal cancellation algorithm to cancel the respiration and extract the heartbeat. To remove the interference in dynamic measurement, we build an IQ-based signal model via a Principal Component Analysis-based interference estimation. To reduce the statistical error in HRV extraction, we further design a neural network to predict the HRV index. We have implemented a system prototype in a real environment with COTS RFID devices. Extensive experiments show that our system can achieve a median RMSSD error of 7.51 ms, which satisfies the medical demand in HRV measurement.

Multi-Baseline Bistatic SAR Three-Dimensional Imaging Method Based on Phase Error Calibration Combining PGA and EB-ISOA

Tomographic synthetic aperture radar (TomoSAR) is an advanced three-dimensional (3D) synthetic aperture radar (SAR) imaging technology that can obtain multiple SAR images through multi-track observations, thereby reconstructing the 3D spatial structure of targets. However, due to system limitations, the multi-baseline (MB) monostatic SAR (MonoSAR) encounters temporal decorrelation issues when observing the scene such as forests, affecting the accuracy of the 3D reconstruction. Additionally, during TomoSAR observations, the platform jitter and inaccurate position measurement will contaminate the MB SAR data, which may result in the multiplicative noise with phase errors, thereby leading to the decrease in the imaging quality. To address the above issues, this paper proposes a MB bistatic SAR (BiSAR) 3D imaging method based on the phase error calibration that combines the phase gradient autofocus (PGA) and energy balance intensity-squared optimization autofocus (EB-ISOA). Firstly, the signal model of the MB one-stationary (OS) BiSAR is established and the 3D imaging principle is presented, and then the phase error caused by platform jitter and inaccurate position measurement is analyzed. Moreover, combining the PGA and EB-ISOA methods, a 3D imaging method based on the phase error calibration is proposed. This method can improve the accuracy of phase error calibration, avoid the vertical displacement, and has the noise robustness, which can obtain the high-precision 3D BiSAR imaging results. The experimental results are shown to verify the effectiveness and practicality of the proposed MB BiSAR 3D imaging method.

An extended state repetitive observer formulation for periodic/polynomial signal rejection in feedback control schemes

Repetitive control is a renowned method for managing periodic disturbances and references in control systems. Despite its effectiveness, performance issues arise when confronting disturbances like polynomial or sinusoidal signals. To enhance its capability, we introduce an extended state repetitive observer by incorporating internal models of periodic, sinusoidal, and polynomial signals into a novel observer framework. This design empowers the observer to adeptly reject and track both periodic and combined sinusoidal+polynomial signals. Beyond addressing the limitations of traditional repetitive control, our observer offers precise disturbance estimation for further analysis and feedback implementation. The observer’s performance is demonstrated through experimental validation within a control setup across various scenarios, focusing on its ability to reject periodic disturbances and track periodic signals effectively.

Height Measurement Method for Meter-Wave Multiple Input Multiple Output Radar Based on Transmitted Signals and Receive Filter Design.

To address the issue of low-elevation target height measurement in the Multiple Input Multiple Output (MIMO) radar, this paper proposes a height measurement method for meter-wave MIMO radar based on transmitted signals and receive filter design, integrating beamforming technology and cognitive processing methods. According to the characteristics of beamforming technology forming nulls at interference locations, we assume that the direct wave and reflected wave act as interference signals and hypothesize a direction for a hypothetical target. Then, the data received are processed to obtain the height of low-elevation-angle targets using a cognitive approach that jointly optimizes the transmitted signal and receive filter. Firstly, a signal model for the meter-wave MIMO radar based on the transmit weight matrix is established under low-elevation scenarios. Secondly, the signal model is analyzed and transformed. Thirdly, the beamforming algorithm that jointly optimizes the transmitted signals and receive filter is derived and analyzed. The algorithm maximizes the output Signal-to-Interference-plus-Noise ratio (SINR) of the receiver by designing the transmit weight matrix and receive filter. The optimization problem based on the SINR criterion is non-convex and difficult to solve. We transformed it into two sub-optimization problems and approximated the optimal solution through an alternating iteration algorithm. Finally, the proposed height measurement algorithm is compared with the Generalized Multiple Signal Classification (GMUSIC) and Maximum Likelihood (ML) height measurement algorithms. Simulation results show that the proposed algorithm can realize the height measurement of low-elevation targets. Compared to the GMUSIC and ML algorithms, it demonstrates superior performance in terms of computational complexity and multi-target elevation estimation.

An Optimization Coverage Strategy for Wireless Sensor Network Nodes Based on Path Loss and False Alarm Probability.

In existing coverage challenges within wireless sensor networks, traditional sensor perception models often fail to accurately represent the true transmission characteristics of wireless signals. In more complex application scenarios such as warehousing, residential areas, etc., this may lead to a large gap between the expected effect of actual coverage and simulated coverage. Additionally, these models frequently neglect critical factors such as sensor failures and malfunctions, which can significantly affect signal detection. To address these limitations and enhance both network performance and longevity, this study introduces a perception model that incorporates path loss and false alarm probability. Based on this perception model, the optimization objective function of the WSN node optimization coverage problem is established, and then the intelligent optimization algorithm is used to solve the objective function and finally achieve the optimization coverage of sensor nodes. The study begins by deriving a logarithmic-based path loss model for wireless signals. It then employs the Neyman-Pearson criterion to formulate a maximum detection probability model under conditions where the cost function and prior probability are unknown, constraining the false alarm rate. Simulated experiments are conducted to assess the influence of various model parameters on detection probability, providing comparative analysis against traditional perception models. Ultimately, an optimization model for WSN coverage, based on combined detection probability, is developed and solved using an intelligent optimization algorithm. The experimental results indicate that the proposed model more accurately captures the signal transmission and detection characteristics of sensor nodes in WSNs. In the coverage area of the same size, the coverage of the model constructed in this paper is compared with the traditional 0/1 perception model and exponential decay perception model. The model can achieve full coverage of the area with only 50 nodes, while the exponential decay model requires 54 nodes, and the coverage of the 0/1 model is still less than 70% at 60 nodes. According to the simulation experiments, it can be basically proved that the WSN node optimization coverage strategy based on the proposed model provides an effective solution for improving network performance and extending network lifespan.

Digitizing Low-Frequency Analog Control Circuit Using Bilinear Function Algorithm

In the past, low-frequency analog control circuits were largely used in analog control systems. With the rapid development of modern digital electronic technology, the digitization of traditional low-frequency analog control circuits while retaining the same functions as the original analog control systems has become an important trend in the field of electronic design. For this reason, this study aimed to develop an analog signal digitization model for control signals at a low frequency below 10 Hz. In terms of signal receiving and transmission requirements, the proposed model is configured with analog-to-digital converter (ADC), digital-to-analog converter (DAC), and pulse width modulation (PWM) functions. In the development environment, the input and output signals are first normalized and processed with PWM, enabling the digital signal processor to deal with analog signal receiving, processing, and external transmission. To replace the existing compensator in analog circuits, a digital compensator is used in the digital signal processor. Based on the bilinear function in MATLAB software, the parameter values demanded for the digital compensator can thus be obtained, achieving the automatic calculation of bilinear transformation in the system. Multisim simulation is then used to simulate analog circuit systems for comparison with the digitized outcomes. The experimental results reveal that the performance of the designed digital control circuit matches the simulation outcomes in both Bode gain (dB) and phase responses when the signal frequency is below 10 Hz. The effectiveness of the digitized analog control circuit for low-frequency control signals is therefore confirmed.

Action potential-like modes as modulated waves in an extended soliton model for biomembranes and nerves

By extending the Heimburg–Jackson soliton model for neural signals that considers the effects of higher-order nonlinearities, the dynamics of modulated waves characterizing electromechanical density pulses is described in the form of soliton-like pulse signals representing nerve impulses, well-known as action potential pulses (Appulses). The investigation is performed both analytically and numerically, where a comprehensive picture of higher-order nonlinearities effects on the generation and evolution of nerve impulses is provided. Within the framework of a multiple-scale-expansion analysis and the reductive perturbation method, while considering third- and fourth-order nonlinearities, the electromechanical area-density pulse propagation is investigated, leading to the generation of a localized Appulse. Accordingly, the analytical theory uses a perturbative technique, and a damped cubic–quintic nonlinear Schrödinger equation is derived, which admits a single-pulse-type solitary solution that possesses different phase characteristics of the typical neuronal Appulse structure, representative of nerve impulse profiles. A modulational instability (MI) analysis demonstrates the increase of the modulation gain in the system with increasing fourth-order nonlinearity, indicating that the higher-order nonlinearities influence the MI in the proposed extended soliton model. Furthermore, a numerical analysis is performed, and consistent agreement with the analytical prediction is achieved, confirming a localized typical longitudinal single pulse-like solitary wave solution for the extended soliton model. Importantly, the appearance of a typical longitudinal single-solitary pulse-type structure can evolve uniformly with increasing fourth-order nonlinearity, leading to the splitting of the single-pulse-soliton signal and resulting in the appearance of a double asymmetric localized pulse-like mode or bisoliton-pulse structure, characteristic of a coupled Appulse.

Cost-effective fabrication of RF AlGaN/GaN HEMTs on surface activated bonding-bonded SiC-SiC substrate

High cost of high-purity semi-insulating (HPSI) SiC is a significant barrier to its widespread industrial use as a substrate for RF GaN transistors. This study presents a cost-effective 6 in. SiC-on-SiC (SOS) composite substrate. This novel substrate is created by bonding an HPSI-SiC film onto a more affordable supporting SiC wafer using smart cut and surface activated bonding technologies. This approach not only allows multiple transfers of HPSI-SiC films but also enables the reuse of the supporting substrate, potentially reducing costs by over 60%. The epitaxial AlGaN/GaN heterostructure grown on the SOS substrate exhibited excellent high-resistance properties with minimal RF loss, measuring less than 0.04 dB/mm across frequencies from 30 MHz to 40 GHz. The RF loss characteristics were analyzed using a small signal model, revealing detailed capacitance and resistance behavior. Additionally, an AlGaN/GaN RF device, developed using a 0.12 μm process on the SOS substrate, achieved good electric performance with an output current of 1.3 A/mm and ft/fmax values of 63.3/173.4 GHz. The SOS substrate offers a cost-effective alternative to traditional GaN substrates while maintaining the high standards required for advanced RF electronic devices.

A Mathematical Approach to the Stability of Electroencephalography Signals Model Using Integral Equations During Epileptic Seizures

In this study, the mathematical stability of EEG signal models represented by the integral equation during an epileptic seizure is investigated. The modelling of the EEG signals should be done with utmost accuracy to understand the actual nature of the brain dynamics and to enhance seizure prediction approaches. We investigate the stability of such models by applying Lyapunov's stability theorem and show that stability is powerfully dependent on parameters such as seizure duration and intensity, along with neural connectivity. We demonstrate that some of the parameter regimes yield stable behaviour, while others give rise to instability, making EEG interpretation complicated during epileptic seizures. By comparing integral equation-based models with current approaches, their advantages are highlighted in capturing the transient dynamics of epileptic activity. This work focuses on developing solid mathematical frameworks that will allow for real-time EEG monitoring. It contributes to the wider knowledge on the dynamics of seizures in the brain and will have practical implications for the development of neurological diagnostic and therapeutic tools.

МАТЕМАТИЧНА МОДЕЛЬ СИГНАЛУ ІЗ ЗАДАНИМИ ПОЛЯРИЗАЦІЙНИМИ ПАРАМЕТРАМИ В ОРТОГОНАЛЬНОМУ ПОЛЯРИЗАЦІЙНОМУ БАЗИСІ

The experience of combat operations in the context of the large-scale invasion of Ukraine by the russian federation shows that the current level of development of electronic weapons is characterised by a wide variety of types and types of electronic means, the use of multifunctional means with widely variable signal parameters and operating modes. For example, modern (state-of-the-art) ground, air and sea radar stations use deliberate changes in the frequency, time and polarisation parameters of emitted signals in different modes of operation, depending on the tasks and conditions of a complex electronic environment. The presence of differences in the polarisation parameters of signals objectively creates prerequisites for their consideration in processing in order to improve the detection, recognition and direction finding performance of radio monitoring systems in a complex electronic environment. The approach is based on the use of antennas with orthogonal polarisation antenna elements, which allows signals to be represented as polarisation vectors. During the processing, it is necessary to take into account the influence of the polarisation characteristics of the antenna system on the parameters of the polarisation vector (change of the polarisation basis), which is relevant since the signals are received by the antenna system from different angular directions. An important condition for obtaining objective results when studying the efficiency of polarisation processing algorithms is the use of a correct mathematical model of signals with given polarisation parameters in a certain orthogonal polarisation basis, as well as the use of signals from real radio equipment whose polarisation parameters can be determined in a polarisation basis other than the specified one. The article presents a mathematical model that allows expressing the field components through the parameters of the polarisation ellipse, and also gives expressions describing the relationship between polarisation vectors and their covariance matrices in different polarisation bases of the antenna system.

An FDA-MIMO radar 2-D parameter estimation algorithm based on graph signal processing

ABSTRACT To improve the range-angle estimation accuracy of frequency diverse array multiple Input multiple output (FDA-MIMO) radar at low SNR, this letter proposes a joint estimation algorithm of FDA-MIMO radar target range-angle based on graph signal processing (GSP). Firstly, the adjacency matrix is constructed according to the FDA-MIMO radar signal model, and then the eigenvector of the target is extracted by the eigen-decomposition of the adjacency matrix. Secondly, the echo signal is processed by graph Fourier transform (GFT). Finally, the spectral peak search response function is established to obtain the range-angle information of the target. The experimental results show that the proposed algorithm has higher estimation accuracy than the MUSIC algorithm when the SNR is lower than 0 dB, and it has certain advantages and application potential for the parameter estimation of weak targets.

Converters with Reduced Duty Cycle Range and Ground Connected to the Positive Input Voltage Terminal

A family of converters with reduced duty cycle range is treated here. Two-step-down, two step-up, and four step-up-down converters are investigated. Four different voltage transformation ratios can be found. For all converters, the large signal and the small signal models of the ideal case are given. Furthermore, the connections of the operating point values and the voltage stress across the semiconductors are indicated. Simulations for the steady-state are shown. A method to calculate the transfer functions is proposed and one transfer function for type 1 is shown as an example. The inrush current, when the converter is connected to a stiff input voltage source, is studied with the help of calculations, and time- and phase-diagrams. Several variations and combinations are also shown.

Emotion Recognition Model of EEG Signals Based on Double Attention Mechanism.

Emotions play a crucial role in people's lives, profoundly affecting their cognition, decision-making, and interpersonal communication. Emotion recognition based on brain signals has become a significant challenge in the fields of affective computing and human-computer interaction. Addressing the issue of inaccurate feature extraction and low accuracy of existing deep learning models in emotion recognition, this paper proposes a multi-channel automatic classification model for emotion EEG signals named DACB, which is based on dual attention mechanisms, convolutional neural networks, and bidirectional long short-term memory networks. DACB extracts features in both temporal and spatial dimensions, incorporating not only convolutional neural networks but also SE attention mechanism modules for learning the importance of different channel features, thereby enhancing the network's performance. DACB also introduces dot product attention mechanisms to learn the importance of spatial and temporal features, effectively improving the model's accuracy. The accuracy of this method in single-shot validation tests on the SEED-IV and DREAMER (Valence-Arousal-Dominance three-classification) datasets is 99.96% and 87.52%, 90.06%, and 89.05%, respectively. In 10-fold cross-validation tests, the accuracy is 99.73% and 84.26%, 85.40%, and 85.02%, outperforming other models. This demonstrates that the DACB model achieves high accuracy in emotion classification tasks, demonstrating outstanding performance and generalization ability and providing new directions for future research in EEG signal recognition.