Showing papers on "Signal published in 2021"
Proceedings Article•
03 May 2021TL;DR: WaveGrad offers a natural way to trade inference speed for sample quality by adjusting the number of refinement steps, and bridges the gap between non-autoregressive and autoregressive models in terms of audio quality.
Abstract: This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density. The model is built on prior work on score matching and diffusion probabilistic models. It starts from a Gaussian white noise signal and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram. WaveGrad offers a natural way to trade inference speed for sample quality by adjusting the number of refinement steps, and bridges the gap between non-autoregressive and autoregressive models in terms of audio quality. We find that it can generate high fidelity audio samples using as few as six iterations. Experiments reveal WaveGrad to generate high fidelity audio, outperforming adversarial non-autoregressive baselines and matching a strong likelihood-based autoregressive baseline using fewer sequential operations. Audio samples are available at https://wavegrad-iclr2021.github.io/.
351 citations
TL;DR: A comprehensive overview of the state-of-the-art on JCR systems from the signal processing perspective is provided in this article, where a balanced coverage on both transmitter and receiver is provided.
Abstract: Joint communication and radar sensing (JCR) represents an emerging research field aiming to integrate the above two functionalities into a single system, by sharing the majority of hardware, signal processing modules and, in a typical case, the transmitted signal. The close cooperation of the communication and sensing functions can enable significant improvement of spectrum efficiency, reduction of device size, cost and power consumption, and improvement of performance of both functions. Advanced signal processing techniques are critical for making the integration efficient, from transmission signal design to receiver processing. This paper provides a comprehensive overview of the state-of-the-art on JCR systems from the signal processing perspective. A balanced coverage on both transmitter and receiver is provided for three types of JCR systems, namely, communication-centric, radar-centric, and joint design and optimization.
334 citations
TL;DR: A new type of RIS is proposed, called active RIS, where each RE is assisted by active loads (negative resistance), that reflect and amplify the incident signal instead of only reflecting it with the adjustable phase shift as in the case of a passive RIS.
Abstract: Reconfigurable Intelligent Surface (RIS) is a promising solution to reconfigure the wireless environment in a controllable way. To compensate for the double-fading attenuation in the RIS-aided link, a large number of passive reflecting elements (REs) are conventionally deployed at the RIS, resulting in large surface size and considerable circuit power consumption. In this paper, we propose a new type of RIS, called active RIS, where each RE is assisted by active loads (negative resistance), that reflect and amplify the incident signal instead of only reflecting it with the adjustable phase shift as in the case of a passive RIS. Therefore, for a given power budget at the RIS, a strengthened RIS-aided link can be achieved by increasing the number of active REs as well as amplifying the incident signal. We consider the use of an active RIS to a single input multiple output (SIMO) system. However, it would unintentionally amplify the RIS-correlated noise, and thus the proposed system has to balance the conflict between the received signal power maximization and the RIS-correlated noise minimization at the receiver. To achieve this goal, it has to optimize the reflecting coefficient matrix at the RIS and the receive beamforming at the receiver. An alternating optimization algorithm is proposed to solve the problem. Specifically, the receive beamforming is obtained with a closed-form solution based on linear minimum-mean-square-error (MMSE) criterion, while the reflecting coefficient matrix is obtained by solving a series of sequential convex approximation (SCA) problems. Simulation results show that the proposed active RIS-aided system could achieve better performance over the conventional passive RIS-aided system with the same power budget.
223 citations
TL;DR: A new method is put forward that fuses multi-modal sensor signals, i.e. the data collected by an accelerometer and a microphone, to realize more accurate and robust bearing-fault diagnosis.
199 citations
TL;DR: It is shown that the recent NANOGrav result can be interpreted as a stochastic gravitational wave signal associated to formation of primordial black holes from high-amplitude curvature perturbations.
Abstract: We show that the recent NANOGrav result can be interpreted as a stochastic gravitational wave signal associated to formation of primordial black holes from high-amplitude curvature perturbations. The indicated amplitude and power of the gravitational wave spectrum agrees well with formation of primordial seeds for supermassive black holes.
179 citations
CERN1
TL;DR: In this paper, peak-integrated sensitivity curves (PISC) are constructed for a cosmological first-order phase transition (SFOPT) with respect to the expected shape of the signal.
Abstract: Gravitational waves (GWs) from strong first-order phase transitions (SFOPTs) in the early Universe are a prime target for upcoming GW experiments. In this paper, I construct novel peak-integrated sensitivity curves (PISCs) for these experiments, which faithfully represent their projected sensitivities to the GW signal from a cosmological SFOPT by explicitly taking into account the expected shape of the signal. Designed to be a handy tool for phenomenologists and model builders, PISCs allow for a quick and systematic comparison of theoretical predictions with experimental sensitivities, as I illustrate by a large range of examples. PISCs also offer several advantages over the conventional power-law-integrated sensitivity curves (PLISCs); in particular, they directly encode information on the expected signal-to-noise ratio for the GW signal from a SFOPT. I provide semianalytical fit functions for the exact numerical PISCs of LISA, DECIGO, and BBO. In an appendix, I moreover present a detailed review of the strain noise power spectra of a large number of GW experiments. The numerical results for all PISCs, PLISCs, and strain noise power spectra presented in this paper can be downloaded from the Zenodo online repository [1]. In a companion paper [2], the concept of PISCs is used to perform an in-depth study of the GW signal from the cosmological phase transition in the real-scalar-singlet extension of the standard model. The PISCs presented in this paper will need to be updated whenever new theoretical results on the expected shape of the signal become available. The PISC approach is therefore suited to be used as a bookkeeping tool to keep track of the theoretical progress in the field.
165 citations
TL;DR: In this article, the NANOGrav collaboration for the pulsar timing array (PTA) observation recently announced evidence of an isotropic stochastic process, which may be the first detection of the Stochastic Gravitational Wave (GW) background.
153 citations
TL;DR: The investigation validates that CSI is a promising method to bridge the gap between signal recognition and DL, and develops a framework to transform complex-valued signal waveforms into images with statistical significance, termed contour stellar image (CSI), which can convey deep level statistical information from the raw wireless signal waves while being represented in an image data format.
Abstract: The rapid development of communication systems poses unprecedented challenges, e.g., handling exploding wireless signals in a real-time and fine-grained manner. Recent advances in data-driven machine learning algorithms, especially deep learning (DL), show great potential to address the challenges. However, waveforms in the physical layer may not be suitable for the prevalent classical DL models, such as convolution neural network (CNN) and recurrent neural network (RNN), which mainly accept formats of images, time series, and text data in the application layer. Therefore, it is of considerable interest to bridge the gap between signal waveforms to DL amenable data formats. In this article, we develop a framework to transform complex-valued signal waveforms into images with statistical significance, termed contour stellar image (CSI), which can convey deep level statistical information from the raw wireless signal waveforms while being represented in an image data format. In this article, we explore several potential application scenarios and present effective CSI-based solutions to address the signal recognition challenges. Our investigation validates that CSI is a promising method to bridge the gap between signal recognition and DL.
152 citations
TL;DR: A novelDenoising framework with deep convolutional neural networks (CNNs) of transforming the TEM signal denoising task into an image denoised task (namely, TEMDnet) is proposed in this article and can achieve much better performance compared with other state-of-the-art approaches on both simulated signals and real-world signals from a landfill leachate treatment plant in Chengdu, Sichuan, China.
Abstract: The considerable prospecting depth and accurate subsurface characteristics can be obtained by the transient electromagnetic method (TEM) in geophysics. Nevertheless, the time-domain TEM signal received by the coil is easily disturbed by environmental background noise, artificial noise, and electronic noise of the equipment. Recently, deep neural networks (DNNs) have been used to solve the TEM denoising problem and have achieved better performance than traditional methods. However, the existing denoising method with DNN adopts fully connected neural networks and is therefore not flexible enough to deal with various signal scales. To address these issues, a novel denoising framework with deep convolutional neural networks (CNNs) of transforming the TEM signal denoising task into an image denoising task (namely, TEMDnet) is proposed in this article. Specifically, a novel signal-to-image transformation method is developed first to preserve the structural features of TEM signals. Then, a novel deep CNN-based denoiser is proposed to further perform feature learning, in which the residual learning mechanism is adopted to model the noise estimation image for different signal features. Extensive experiments demonstrate that the proposed framework can achieve much better performance compared with other state-of-the-art approaches on both simulated signals and real-world signals from a landfill leachate treatment plant in Chengdu, Sichuan, China. Models and code are available at https://github.com/tonyckc/TEMDnet_demo.
143 citations
TL;DR: The operation cost of an intelligent high-speed train system is greatly increased by the enormous energy demand of large-scale signal and sensor networks, however, the wind energy generated by high...
Abstract: The operation cost of an intelligent high-speed train system is greatly increased by the enormous energy demand of large-scale signal and sensor networks. However, the wind energy generated by high...
119 citations
TL;DR: In this article, the enantiomer-based organic light-emitting diodes (OLEDs) exhibit pure green emission with narrow fullwidth at half-maximums (FWHMs) of 30 and 33 nm, high maximum external quantum efficiencies (EQEs) of 29.4% and 24.5% and clear circularly polarized electroluminescence (CPEL) signals with electrolUMinescence dissymmetry factors (gEL ) of +1.43 × 10-3 /-1.27 × 10 -
Abstract: Purely organic fluorescent materials that concurrently exhibit high efficiency, narrowband emission, and circularly polarized luminescence (CPL) remain an unaddressed issue despite their promising applications in wide color gamut- and 3D-display. Herein, the CPL optical property and multiple resonance (MR) effect induced thermally activated delayed fluorescence (MR-TADF) emission are integrated with high color purity and luminous efficiency together. Two pairs of highly efficient green CP-MR-TADF enantiomers, namely, (R/S)-OBN-2CN-BN and (R/S)-OBN-4CN-BN, are developed. The enantiomer-based organic light-emitting diodes (OLEDs) exhibit pure green emission with narrow full-width at half-maximums (FWHMs) of 30 and 33 nm, high maximum external quantum efficiencies (EQEs) of 29.4% and 24.5%, and clear circularly polarized electroluminescence (CPEL) signals with electroluminescence dissymmetry factors (gEL ) of +1.43 × 10-3 /-1.27 × 10-3 and +4.60 × 10-4 /-4.76 × 10-4 , respectively. This is the first example of a highly efficient OLED that exhibits CPEL signal, narrowband emission, and TADF concurrently.
TL;DR: Wang et al. as mentioned in this paper designed an end-to-end signal prior-guided layer separation and data-driven mapping network with layer-specified constraints for single-image low-light enhancement.
Abstract: Due to the absence of a desirable objective for low-light image enhancement, previous data-driven methods may provide undesirable enhanced results including amplified noise, degraded contrast and biased colors. In this work, inspired by Retinex theory, we design an end-to-end signal prior-guided layer separation and data-driven mapping network with layer-specified constraints for single-image low-light enhancement. A Sparse Gradient Minimization sub-Network (SGM-Net) is constructed to remove the low-amplitude structures and preserve major edge information, which facilitates extracting paired illumination maps of low/normal-light images. After the learned decomposition, two sub-networks (Enhance-Net and Restore-Net) are utilized to predict the enhanced illumination and reflectance maps, respectively, which helps stretch the contrast of the illumination map and remove intensive noise in the reflectance map. The effects of all these configured constraints, including the signal structure regularization and losses, combine together reciprocally, which leads to good reconstruction results in overall visual quality. The evaluation on both synthetic and real images, particularly on those containing intensive noise, compression artifacts and their interleaved artifacts, shows the effectiveness of our novel models, which significantly outperforms the state-of-the-art methods.
TL;DR: Recommendations are put forward for efficient and accurate deformation-signal retrieval from large stacks of multilooked interferograms based on the presence of a new interferometric signal in multilook synthetic aperture radar (SAR) interferogram that cannot be attributed to the atmospheric or Earth-surface topography changes.
Abstract: This article investigates the presence of a new interferometric signal in multilooked synthetic aperture radar (SAR) interferograms that cannot be attributed to the atmospheric or Earth-surface topography changes. The observed signal is short-lived and decays with the temporal baseline; however, it is distinct from the stochastic noise attributed to temporal decorrelation. The presence of such a fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. Here, the contribution of the mentioned phase component is quantitatively assessed. The biasing impact on the deformation-signal retrieval is further evaluated. A quality measure is introduced to allow the prediction of the associated error with the fading signals. Moreover, a practical solution for the mitigation of this physical signal is discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease significantly. Based on these analyses, we put forward our recommendations for efficient and accurate deformation-signal retrieval from large stacks of multilooked interferograms.
TL;DR: Huang et al. as discussed by the authors used mice that had been genetically modified to produce a calcium indicator in neurons of the visual cortex and simultaneously obtained both fluorescence measurements and electrical recordings from these neurons.
Abstract: Neurons, the cells that make up the nervous system, transmit information using electrical signals known as action potentials or spikes. Studying the spiking patterns of neurons in the brain is essential to understand perception, memory, thought, and behaviour. One way to do that is by recording electrical activity with microelectrodes. Another way to study neuronal activity is by using molecules that change how they interact with light when calcium binds to them, since changes in calcium concentration can be indicative of neuronal spiking. That change can be observed with specialized microscopes know as two-photon fluorescence microscopes. Using calcium indicators, it is possible to simultaneously record hundreds or even thousands of neurons. However, calcium fluorescence and spikes do not translate one-to-one. In order to interpret fluorescence data, it is important to understand the relationship between the fluorescence signals and the spikes associated with individual neurons. The only way to directly measure this relationship is by using calcium imaging and electrical recording simultaneously to record activity from the same neuron. However, this is extremely challenging experimentally, so this type of data is rare. To shed some light on this, Huang, Ledochowitsch et al. used mice that had been genetically modified to produce a calcium indicator in neurons of the visual cortex and simultaneously obtained both fluorescence measurements and electrical recordings from these neurons. These experiments revealed that, while the majority of time periods containing multi-spike neural activity could be identified using calcium imaging microscopy, on average, less than 10% of isolated single spikes were detectable. This is an important caveat that researchers need to take into consideration when interpreting calcium imaging results. These findings are intended to serve as a guide for interpreting calcium imaging studies that look at neurons in the mammalian brain at the population level. In addition, the data provided will be useful as a reference for the development of activity sensors, and to benchmark and improve computational approaches for detecting and predicting spikes.
TL;DR: In this paper, the energy function model of the grid-forming and grid-following controlled converters is established and compared in detail, and the large-signal stability boundaries are derived through a general method.
Abstract: The grid-forming and grid-following controls are adopted in three-phase voltage source converters according to different grid conditions. However, the basic operation principle of the two kinds of control is different and will lead to the instability of the grid-connected system through various paths. In this article, the energy function model of the grid-forming and grid-following controlled converters are established and compared in detail. The influence of system parameters is studied, and the large-signal stability boundaries are derived through a general method. Moreover, compared to the grid-forming controlled converter, the grid-following converter will lose stability by exhibiting a varying damping coefficient transient. The equivalent damping coefficient can be used as a criterion of whether the grid-forming/following control is suitable for the weak/strong grid condition. The simulation and experiment results show the difference of the transient process and validate the control criterion for different grid strength under large-signal disturbance.
TL;DR: In this paper, a self-powered photoelectrochemical (PEC) sensing platform was developed for rapid detection of prostate-specific antigen (PSA) as a model disease-related protein by integrating a selfpowered photoelectric signal output system catalyzed with chemiluminescence-functionalized Au nanoparticles (AuNPs) and a phosphomolybdic acid (PMA)-based photochromic visualization platform.
Abstract: Early diagnosis of cancers relies on the sensitive detection of specific biomarkers, but most of the current testing methods are inaccessible to home healthcare due to cumbersome steps, prolonged testing time, and utilization of toxic and hazardous substances. Herein, we developed a portable self-powered photoelectrochemical (PEC) sensing platform for rapid detection of prostate-specific antigen (PSA, as a model disease-related protein) by integrating a self-powered photoelectric signal output system catalyzed with chemiluminescence-functionalized Au nanoparticles (AuNPs) and a phosphomolybdic acid (PMA)-based photochromic visualization platform. TiO2-g-C3N4-PMA photosensitive materials were first synthesized and functionalized on a sensor chip. The sensor consisted of filter paper modified with a photocatalytic material and a regional laser-etched FTO electrode as an alternative to a conventional PEC sensor with a glass-based electrode. The targeting system involved a monoclonal anti-PSA capture antibody-functionalized Fe3O4 magnetic bead (mAb1-MB) and a polyclonal anti-PSA antibody (pAb2)-N-(4-aminobutyl)-N-ethylisoluminol-AuNP (ABEI-AuNP). Based on the signal intensity of the chemiluminescent system, the photochromic device color changed from light yellow to heteropoly blue through the PMA photoelectric materials integrated into the electrode for visualization of the signal output. In addition, the electrical signal in the PEC system was amplified by a sandwich-type capacitor and readout on a handheld digital multimeter. Under optimum conditions, the sensor exhibited high sensitivity relative to PSA in the range of 0.01-50 ng mL-1 with a low detection limit of 6.25 pg mL-1. The flow-through chemiluminescence reactor with a semiautomatic injection device and magnetic separation was avoid of unstable light source intensity inherent in the chemiluminescence process. Therefore, our strategy provides a new horizon for point-of-care analysis and rapid cost-effective clinical diagnosis.
TL;DR: Methods for raw signal improvement including sample preparation, system optimization, and especially plasma modulation, which modulates the laser-induced plasma evolution process for higher signal repeatability and signal-to-noise ratio, were reviewed and discussed.
Abstract: Laser-induced breakdown spectroscopy (LIBS) is regarded as the future superstar for chemical analysis, but the relatively high measurement uncertainty and error remain the persistent challenges for its technological development as well as wide applications. In the present work, mechanisms of measurement uncertainty generation and basic principle of signal uncertainty and matrix effects impacting quantification performance were explained. Furthermore, methods for raw signal improvement including sample preparation, system optimization, and especially plasma modulation, which modulates the laser-induced plasma evolution process for higher signal repeatability and signal-to-noise ratio, were reviewed and discussed. Different LIBS mathematical quantification methods including calibration-free methods and calibration methods, which were classified into physical-principle based calibration model, data-driven based calibration model, and hybrid model, were discussed and compared. Overall, a framework of quantification improvement strategy including key steps and main way-out was summarized and recommended for LIBS future development.
TL;DR: The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection, and this method also has practical application value for engineering rotating machinery.
Abstract: In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the SNR is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery.
17 Aug 2021
TL;DR: In this article, the authors provide a comprehensive overview of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection for terahertz (THz)-band communications.
Abstract: Terahertz (THz)-band communications are a key enabler for future-generation wireless communication systems that promise to integrate a wide range of data-demanding applications. Recent advances in photonic, electronic, and plasmonic technologies are closing the gap in THz transceiver design. Consequently, prospect THz signal generation, modulation, and radiation methods are converging, and corresponding channel model, noise, and hardware-impairment notions are emerging. Such progress establishes a foundation for well-grounded research into THz-specific signal processing techniques for wireless communications. This tutorial overviews these techniques, emphasizing ultramassive multiple-input–multiple-output (UM-MIMO) systems and reconfigurable intelligent surfaces, vital for overcoming the distance problem at very high frequencies. We focus on the classical problems of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection. We also motivate signal processing techniques for THz sensing and localization.
TL;DR: In this article, a kinematic model-based VSA estimation method is proposed by fusing information from a global navigation satellite system (GNSS) and an inertial measurement unit (IMU).
Abstract: Vehicle slip angle (VSA) estimation is of paramount importance for connected automated vehicle dynamic control, especially in critical lateral driving scenarios. In this paper, a novel kinematic-model-based VSA estimation method is proposed by fusing information from a global navigation satellite system (GNSS) and an inertial measurement unit (IMU). First, to reject the gravity components induced by the vehicle roll and pitch, a vehicle attitude angle observer based on the square-root cubature Kalman filter (SCKF) is designed to estimate the roll and pitch. A novel feedback mechanism based on the vehicle intrinsic information (the steering angle and wheel speed) for the pitch and roll is designed. Then, the integration of the reverse smoothing and grey prediction is adopted to compensate for the cumulative velocity errors during the relatively low sampling interval of the GNSS. Moreover, the GNSS signal delay has been addressed by an estimation-prediction integrated framework. Finally, the results confirm that the proposed method can estimate the VSA under both the slalom and double lane change (DLC) scenarios.
01 Jun 2021
TL;DR: In this article, the authors apply standard meta-learning algorithms to learn the initial weight parameters for these fully-connected networks based on the underlying class of signals being represented (e.g., images of faces or 3D models of chairs).
Abstract: Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals. However, optimizing a coordinate-based network from randomly initialized weights for each new signal is inefficient. We propose applying standard meta-learning algorithms to learn the initial weight parameters for these fully-connected networks based on the underlying class of signals being represented (e.g., images of faces or 3D models of chairs). Despite requiring only a minor change in implementation, using these learned initial weights enables faster convergence during optimization and can serve as a strong prior over the signal class being modeled, resulting in better generalization when only partial observations of a given signal are available. We explore these benefits across a variety of tasks, including representing 2D images, reconstructing CT scans, and recovering 3D shapes and scenes from 2D image observations.
TL;DR: In this article, a separable modeling scheme is presented for estimating the signal parameters in terms of different characteristics between the signal output and signal parameters, in order to seize the real-time information of the signals to be modeled, a sliding measurement window is designed for using the observations dynamically and implementing accurate parameter estimates.
Abstract: Signal modeling is an important technique in many engineering applications. This paper is concerned about signal modeling problem for the sine multi-frequency signals or periodic signals. In terms of different characteristics between the signal output and the signal parameters, a separable modeling scheme is presented for estimating the signal parameters. In order to seize the real-time information of the signals to be modeled, a sliding measurement window is designed for using the observations dynamically and implementing accurate parameter estimates. Because the amplitude parameters are linear with respect to the signal output and the angular frequency parameters are nonlinear with respect to the signal output, the signal parameters are separated into a linear parameter set and a nonlinear parameter set. Based on these separable parameter sets, a nonlinear optimization problem is converted into a combination of the optimization quadric and the nonlinear optimization. Then, a separable multi-innovation Newton iterative signal modeling method is derived and implemented to estimate sine multi-frequency signals and periodic signals. The simulation results are found to be effective of modeling dynamic signals. For the reason that the proposed method is based on dynamic sliding measurement window, it can be used for online estimation applications.
TL;DR: A VR train monitoring demonstration has been conducted showing the great prospect of the self-sustainable WSN in harsh environments.
TL;DR: In this paper, a spatial optical transmitter based on on-off-key line coding modulation scheme for the optimum performance of telecommunication systems is presented and discussed in detail, where the total power measured in W and dBm as well as the signal power amplitude level obtained through the infinite impulse response (IIR) filter based on both Z domain and pole/zero coefficient filter types are illustrated clearly.
Abstract: This study has presented a spatial optical transmitter based on on off keying line coding modulation scheme for the optimum performance of telecommunication systems. The encircled flux versus fiber core radius, the 3D graph for fiber mode versus core radius, and the signal power level in dBm versus wavelength through coarse wavelength division multiplexing with a fiber length of 20 km are presented and discussed in detail. The total power measured in W and dBm as well as the signal power amplitude level obtained through the infinite impulse response (IIR) filter based on both Z domain and pole/zero coefficient filter types are illustrated clearly. Signal gain, noise figure, maximum Q factor, and received power are also clarified against bit rates for various modulation line coding schemes.
TL;DR: In this paper, a vibration-based wear mechanism identification procedure is proposed, and then the wear evolution is tracked using an indicator of vibration cyclostationarity (CS), with consideration of the underlying physics of the gear meshing process and the unique surface features induced by fatigue pitting and abrasive wear.
TL;DR: A comprehensive survey of the state-of-the-art DL-based modulation classification algorithms, especially the techniques of signal representation and data preprocessing utilized in these algorithms, is provided in this article.
Abstract: Modulation classification is one of the key tasks for communications systems monitoring, management, and control for addressing technical issues, including spectrum awareness, adaptive transmissions, and interference avoidance. Recently, deep learning (DL)-based modulation classification has attracted significant attention due to its superiority in feature extraction and classification accuracy. In DL-based modulation classification, one major challenge is to preprocess a received signal and represent it in a proper format before feeding the signal into deep neural networks. This article provides a comprehensive survey of the state-of-the-art DL-based modulation classification algorithms, especially the techniques of signal representation and data preprocessing utilized in these algorithms. Since a received signal can be represented by either features, images, sequences, or a combination of them, existing algorithms of DL-based modulation classification can be categorized into four groups and are reviewed accordingly in this article. Furthermore, the advantages as well as disadvantages of each signal representation method are summarized and discussed.
TL;DR: The physiology of the AP waveform, basic principles of PWA algorithms for CO estimation, and PWA technologies available for clinical practice are described, including Windkessel models, long time interval or multi-beat analysis, pulse power analysis, or the pressure recording analytical method.
Abstract: Pulse wave analysis (PWA) allows estimation of cardiac output (CO) based on continuous analysis of the arterial blood pressure (AP) waveform. We describe the physiology of the AP waveform, basic principles of PWA algorithms for CO estimation, and PWA technologies available for clinical practice. The AP waveform is a complex physiological signal that is determined by interplay of left ventricular stroke volume, systemic vascular resistance, and vascular compliance. Numerous PWA algorithms are available to estimate CO, including Windkessel models, long time interval or multi-beat analysis, pulse power analysis, or the pressure recording analytical method. Invasive, minimally-invasive, and noninvasive PWA monitoring systems can be classified according to the method they use to calibrate estimated CO values in externally calibrated systems, internally calibrated systems, and uncalibrated systems.
TL;DR: An alternative paradigm for sensing and recovery, called the Unlimited Sampling Framework, which derives conditions when perfect recovery is possible and complement them with a stable recovery algorithm and guarantees extend to measurements affected by bounded noise, which includes round-off quantization.
Abstract: Shannon's sampling theorem, at the heart of digital signal processing, is well understood and explored. However, its practical realization still suffers from a fundamental bottleneck due to dynamic range limitations of the underlying analog–to–digital converters (ADCs). This results in clipping or saturation for signal amplitudes exceeding their maximum recordable voltage thus leading to a significant information loss. In this paper, we develop an alternative paradigm for sensing and recovery, called the Unlimited Sampling Framework . The key observation is that applying a modulo operation to the signal before the ADC prevents saturation; instead, one encounters a different type of information loss. Such a setup can be implemented, for example, via so-called folding or self-reset ADCs, as proposed in various contexts in the circuit design literature. The key challenge for this new type of information loss is to recover a bandlimited signal from its modulo samples. We derive conditions when perfect recovery is possible and complement them with a stable recovery algorithm. The required sampling density is independent of the maximum recordable ADC voltage and depends on the signal bandwidth only. Our guarantees extend to measurements affected by bounded noise, which includes round-off quantization. Numerical experiments validate our approach. For example, it is possible to recover functions with amplitudes orders of magnitude higher than the ADC's threshold from quantized modulo samples up to the unavoidable quantization error. Applications of the unlimited sampling paradigm can be found in a number of fields such as signal processing, communication and imaging.
TL;DR: A Bayesian-network-based data-driven early fault diagnostic methodology of PMSM is proposed with vibration and acoustic emission data and shows that the accuracy for early faults is more than 90% when acoustic emission signal is used, and it is higher than the accuracy with vibration signal.
Abstract: Permanent magnet synchronous motor (PMSM) is one of the common core power components in modern industrial systems. Early fault diagnosis can avoid major accidents and plan maintenance in advance. However, the features of early faults are weak, and the symptoms are not obvious. Meanwhile, the fault signal is often overwhelmed by noise. Accordingly, fault diagnosis for early faults is difficult, and the diagnostic accuracy is generally low. A Bayesian-network-based data-driven early fault diagnostic methodology of PMSM is proposed with vibration and acoustic emission data. The wavelet threshold denoising and minimum entropy deconvolution methods are used to improve the signal-to-noise ratio. The complementary ensemble empirical mode decomposition method is used to extract signal eigenvalues, and Bayesian networks are applied to identify the early, middle, and permanent faults. Experimental data carried out with Tyco ST8N80P100V22E medium PMSM are used to train the fault diagnostic model and validate the proposed fault diagnostic methodology. Result shows that the accuracy for early faults is more than 90% when acoustic emission signal is used, and it is higher than the accuracy with vibration signal. The influence of load on diagnostic accuracy is also investigated, and it indicates that the accuracy with acoustic emission signal is higher than that with vibration signal under different loads.
TL;DR: In this article, the bias and modulation peak currents based laser rate equations are optimized to achieve max Q factor and min bit error rate (BER) using first proposed model and optical/electrical signal power, optical and electrical signal to noise ratio are also enhanced using second proposed model.
Abstract: This study outlines the management of either direct or external modulation semiconductor laser systems for the key solution of bit rate up to 25 Gb/s under relative intensity noise (RIN) control. The bias and modulation peak currents based laser rate equations are optimized to achieve max Q factor and min bit error rate (BER) using first proposed model and optical/electrical signal power, optical/electrical signal to noise ratio are also enhanced using second proposed model. The percentage enhancement ratio in max. Q-factor and min. BER using first proposed model ranges from 53.25 % to 71.63 % in compared to the previous model. In the same way, by using second proposed model, the electrical signal power at optical receiver is enhanced within the range of 48.66 % to 68.88 % in compared to the previous model. Optical signal/noise ratio (OSNR) after optical fiber cable (OFC), signal/noise ratio (SNR) after electrical filter are measured with using different electrical pulse generators and electrical modulators at the optimization stage. The first proposed model reported better max. Q and min. BER values than the previous model. In addition to the second proposed model (direct modulation) has outlined better optical/electrical signal power than the previous model, while max. Q, min. BER values are kept constant. It is found that non return to zero pulse generator has presented better signal power than other pulse generators by using second proposed model. As well as the mixed of raised cosine pulse generator with external modulator reported max. Q, min. BER with other pulse generators by using first proposed model. OSNR at OFC is optimized by using continuous phase frequency shift keying (CPFSK) electrical modulator, While SNR at optical receiver is optimized by using phase shift keying (PSK) electrical modulator.