Showing papers on "Noise measurement published in 2021"

Data-Driven Model Predictive Control With Stability and Robustness Guarantees

Abstract: We propose a robust data-driven model predictive control (MPC) scheme to control linear time-invariant systems. The scheme uses an implicit model description based on behavioral systems theory and past measured trajectories. In particular, it does not require any prior identification step, but only an initially measured input–output trajectory as well as an upper bound on the order of the unknown system. First, we prove exponential stability of a nominal data-driven MPC scheme with terminal equality constraints in the case of no measurement noise. For bounded additive output measurement noise, we propose a robust modification of the scheme, including a slack variable with regularization in the cost. We prove that the application of this robust MPC scheme in a multistep fashion leads to practical exponential stability of the closed loop w.r.t. the noise level. The presented results provide the first (theoretical) analysis of closed-loop properties, resulting from a simple, purely data-driven MPC scheme.

Noise-Immune Model Identification and State-of-Charge Estimation for Lithium-Ion Battery Using Bilinear Parameterization

Abstract: Accurate estimation of state of charge (SOC) is critical to the safe and efficient utilization of a battery system. Model-based SOC observers have been widely used due to their high accuracy and robustness, but they rely on a well-parameterized battery model. This article scrutinizes the effect of measurement noises on model parameter identification and SOC estimation. A novel parameterization method combining instrumental variable (IV) estimation and bilinear principle is proposed to compensate for the noise-induced biases of model identification. Specifically, the IV estimator is used to reformulate an overdetermined system so as to allow coestimating the model parameters and noise variances. The coestimation problem is then decoupled into two linear subproblems which are solved efficiently by a two-stage least squares algorithm in a recursive manner. The parameterization method is further combined with a Luenberger observer to estimate the SOC in real time. Simulations and experiments are performed to validate the proposed method. Results reveal that the proposed method is superior to existing method in terms of the immunity to noise corruption.

Dense CNN With Self-Attention for Time-Domain Speech Enhancement

Abstract: Speech enhancement in the time domain is becoming increasingly popular in recent years, due to its capability to jointly enhance both the magnitude and the phase of speech. In this work, we propose a dense convolutional network (DCN) with self-attention for speech enhancement in the time domain. DCN is an encoder and decoder based architecture with skip connections. Each layer in the encoder and the decoder comprises a dense block and an attention module. Dense blocks and attention modules help in feature extraction using a combination of feature reuse, increased network depth, and maximum context aggregation. Furthermore, we reveal previously unknown problems with a loss based on the spectral magnitude of enhanced speech. To alleviate these problems, we propose a novel loss based on magnitudes of enhanced speech and a predicted noise. Even though the proposed loss is based on magnitudes only, a constraint imposed by noise prediction ensures that the loss enhances both magnitude and phase. Experimental results demonstrate that DCN trained with the proposed loss substantially outperforms other state-of-the-art approaches to causal and non-causal speech enhancement.

Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement

Abstract: Remote photoplethysmography (rPPG) based physiological measurement has great application values in health monitoring, emotion analysis, etc. Existing methods mainly focus on how to enhance or extract the very weak blood volume pulse (BVP) signals from face videos, but seldom explicitly model the noises that dominate face video content. Thus, they may suffer from poor generalization ability in unseen scenarios. This paper proposes a novel adversarial learning approach for rPPG based physiological measurement by using Dual Generative Adversarial Networks (Dual-GAN) to model the BVP predictor and noise distribution jointly. The BVP-GAN aims to learn a noise-resistant mapping from input to ground-truth BVP, and the Noise-GAN aims to learn the noise distribution. The two GANs can promote each other’s capability, leading to improved feature disentanglement between BVP and noises. Besides, a plug-and-play block named ROI alignment and fusion (ROI-AF) block is proposed to alleviate the inconsistencies between different ROIs and exploit informative features from a wider receptive field in terms of ROIs. In comparison to state-of-the-art methods, our approach achieves better performance in heart rate, heart rate variability, and respiration frequency estimation from face videos.

Augmentation Strategies for Learning with Noisy Labels

Abstract: Imperfect labels are ubiquitous in real-world datasets. Several recent successful methods for training deep neural networks (DNNs) robust to label noise have used two primary techniques: filtering samples based on loss during a warm-up phase to curate an initial set of cleanly labeled samples, and using the output of a network as a pseudo-label for subsequent loss calculations. In this paper, we evaluate different augmentation strategies for algorithms tackling the "learning with noisy labels" problem. We propose and examine multiple augmentation strategies and evaluate them using synthetic datasets based on CIFAR-10 and CIFAR-100, as well as on the real-world dataset Clothing1M. Due to several commonalities in these algorithms, we find that using one set of augmentations for loss modeling tasks and another set for learning is the most effective, improving results on the state-of-the-art and other previous methods. Furthermore, we find that applying augmentation during the warm-up period can negatively impact the loss convergence behavior of correctly versus incorrectly labeled samples. We introduce this augmentation strategy to the state-of-the-art technique and demonstrate that we can improve performance across all evaluated noise levels. In particular, we improve accuracy on the CIFAR-10 benchmark at 90% symmetric noise by more than 15% in absolute accuracy, and we also improve performance on the Clothing1M dataset.

UAV-Assisted Wireless Localization for Search and Rescue

Abstract: In the aftermath of disasters, localization of trapped victims is imperative to ensure their safety and rescue This article presents a novel localization and path planning approach that uses unmanned aerial vehicles (UAVs) The UAVs can extract one-hop neighbor information from the devices that may have run out of power by using directed wireless power transfer (WPT) The one-hop neighbor information corresponds to range measurements, which may or may not contain noise For the noiseless case, we present a customized online graph traversal approach that minimizes the search energy of the UAV and the number of unlocalized nodes The lower limits on the various performance aspects of this joint approach are presented For a noiseless case, the results of UAV travel distance and cells searched show a decreasing trend with an increase in the number of maximum neighbors These curves approximately approach their corresponding lower limits when the number of maximum neighbors is increased beyond 9 For the case of noisy range measurements, using the same objective function and graph traversal algorithm, the probabilistic region for search is determined that gives the least probability of flip errors To this end, we further optimize the UAV flight path and its search energy in the probabilistic region through clustering The proposed method is able to achieve linear scaling of the area searched with respect to the noise level For a given noise level and increasing number of nodes, the UAV search energy with clustering can reduce the energy cost to 70%

Power Transformer Fault Diagnosis Based on DGA Using a Convolutional Neural Network With Noise in Measurements

Abstract: Fault type diagnosis is a very important tool to maintain the continuity of power transformer operation. Dissolved gas analysis (DGA) is one of the most effective and widely used techniques for predicting the power transformer fault types. In this paper, a convolutional neural network (CNN) model is proposed based on the DGA approach to accurately predict transformer fault types under different noise levels in measurements. The proposed model is applied with three categories of input ratios: conventional ratios (Rogers’4 ratios, IEC 60599 ratios, Duval triangle ratios), new ratios (five gas percentage ratios and new form six ratios), and hybrid ratios (conventional and new ratios together). The proposed model is trained and tested based on 589 dataset samples collected from electrical utilities and literature with varying noise levels up to ±20%. The results indicate that the CNN model with hybrid input ratios has superior prediction accuracy. The high accuracy of the proposed model is validated in comparison with conventional and recently published AI approaches. The proposed model is implemented based on MATLAB/toolbox 2020b.

Learning Robust Graph-Convolutional Representations for Point Cloud Denoising

Abstract: Point clouds are an increasingly relevant geometric data type but they are often corrupted by noise and affected by the presence of outliers. We propose a deep learning method that can simultaneously denoise a point cloud and remove outliers in a single model. The core of the proposed method is a graph-convolutional neural network able to efficiently deal with the irregular domain and the permutation invariance problem typical of point clouds. The network is fully-convolutional and can build complex hierarchies of features by dynamically constructing neighborhood graphs from similarity among the high-dimensional feature representations of the points. The proposed approach outperforms state-of-the-art denoising methods showing robust performance in the challenging setup of high noise levels and in presence of structured noise.

A Novel Heavy-Tailed Mixture Distribution Based Robust Kalman Filter for Cooperative Localization

Abstract: In cooperative localization for autonomous underwater vehicles (AUVs), the practical stochastic noise may be heavy-tailed, and nonstationary distributed because of acoustic speed variation, multipath effect of acoustic channel, and changeable underwater environment. To address such noise, a novel heavy-tailed mixture (HTM) distribution is first proposed in this article, and then expressed as a hierarchical Gaussian form by employing a categorical distributed auxiliary vector. Based on that, a novel HTM distribution based robust Kalman filter is proposed, where the one-step prediction, and measurement likelihood probability density functions are, respectively, modeled as an HTM distribution, and a Normal-Gamma-inverse Wishart distribution. The proposed filter is verified by a lake experiment about cooperative localization for AUVs. Compared with the cutting-edge filter, the proposed filter has been improved by 50.27% in localization error but no more than twice computational time is required.

Robustness of Accelerated First-Order Algorithms for Strongly Convex Optimization Problems

Abstract: We study the robustness of accelerated first-order algorithms to stochastic uncertainties in gradient evaluation. Specifically, for unconstrained, smooth, strongly convex optimization problems, we examine the mean-squared error in the optimization variable when the iterates are perturbed by additive white noise. This type of uncertainty may arise in situations where an approximation of the gradient is sought through measurements of a real system or in a distributed computation over a network. Even though the underlying dynamics of first-order algorithms for this class of problems are nonlinear, we establish upper bounds on the mean-squared deviation from the optimal solution that are tight up to constant factors. Our analysis quantifies fundamental tradeoffs between noise amplification and convergence rates obtained via any acceleration scheme similar to Nesterov's or heavy-ball methods. To gain additional analytical insight, for strongly convex quadratic problems, we explicitly evaluate the steady-state variance of the optimization variable in terms of the eigenvalues of the Hessian of the objective function. We demonstrate that the entire spectrum of the Hessian, rather than just the extreme eigenvalues, influences robustness of noisy algorithms. We specialize this result to the problem of distributed averaging over undirected networks and examine the role of network size and topology on the robustness of noisy accelerated algorithms.

Variational Adaptive Kalman Filter With Gaussian-Inverse-Wishart Mixture Distribution

Abstract: In this article, a new variational adaptive Kalman filter with Gaussian-inverse-Wishart mixture distribution is proposed for a class of linear systems with both partially unknown state and measurement noise covariance matrices. The state transition and measurement likelihood probability density functions are described by a Gaussian-inverse-Wishart mixture distribution and a Gaussian-inverse-Wishart distribution, respectively. The system state vector together with the state noise covariance matrix and the measurement noise covariance matrix are jointly estimated based on the derived hierarchical Gaussian model. Examples are provided to demonstrate the effectiveness and potential of the developed new filtering design techniques.

Robust Minimum Disturbance Diffusion LMS for Distributed Estimation

Abstract: This brief proposes a robust distributed estimation algorithm in presence of impulsive noise Impulsive noises are present both in the measurements and in the communication links in a sensor network The proposed method is essentially a diffusion LMS algorithm with optimized variable coefficients in the adaptation and combination steps The optimized coefficients are obtained based on the minimum disturbance principle Moreover, it is shown that the optimized coefficients of the adaptation step are found by solving a linear system of equations, while the optimized coefficients of the combination step are calculated by an eigenvector of a particular matrix Moreover, the minimum disturbances calculated theoretically and their upper bounds are derived mathematically Simulation results show the better performance of the proposed minimum disturbance diffusion LMS algorithm over some state-of-the-art algorithms

DCNNs-Based Denoising With a Novel Data Generation for Multidimensional Geological Structures Learning

Abstract: Noise attenuation has been a long-standing but still active topic in seismic data processing. The deep convolutional neural networks (CNNs) have been recently adopted to remove the learned random noise from noisy seismic data, but it is still difficult to improve the generalization ability of learned denoisers due to the limited diversity of training data sets. In this letter, we investigate an end-to-end deep denoising CNNs (DCNNs) with a novel data generation method involving multidimensional geological structure features for seismic denoising. To learn an optimized network denoiser, seismic amplitude data are extracted from 3-D synthetic seismic data along three directions (i.e., two spatial directions and one temporal direction) to prepare a training data set. Compared with using seismic data from only a certain single direction to generate all training samples, this strategy enables DCNNs to learn abundant geological structural information from three directions, and helps DCNNs have a better performance on noise reduction. Another 3-D synthetic seismic data and 3-D real land data examples with plentiful faults and fluvial channels are used to illustrate that the optimized network denoiser can be directly extended to attenuate random noise. The denoising results demonstrate that DCNNs learned from the multidimensional geological structures can accomplish the self-adaptive random noise attenuation, and meanwhile preserve spatial geological structures.

Maximum Correntropy Rauch–Tung–Striebel Smoother for Nonlinear and Non-Gaussian Systems

Abstract: We propose a new robust recursive fixed-interval smoother for nonlinear systems under non-Gaussian process and measurement noises, i.e., the nominal Gaussian noise is polluted by large noise from unknown distributions. Taking advantage of correntropy in handling non-Gaussian noise, a robust Rauch–Tung–Striebel smoother is derived according to the maximum-correntropy-criterion-based cost functions with nonlinear functions linearized by their first-order Taylor series expansions, where two weights are utilized to adjust the estimation gains of forward filtering and backward smoothing, respectively. Simulation results demonstrate the effectiveness of the proposed smoother in the presence of various non-Gaussian process and measurement noises, especially the shot sequences and multimodal noise.

Nonlinear Stochastic Estimators on the Special Euclidean Group SE(3) Using Uncertain IMU and Vision Measurements

Abstract: Two novel robust nonlinear stochastic full pose (i.e., attitude and position) estimators on the Special Euclidean Group $\mathbb {SE}(3)$ are proposed using the available uncertain measurements. The resulting estimators utilize the basic structure of the deterministic pose estimators adopting it to the stochastic sense. The proposed estimators for six degrees of freedom (DOF) pose estimations consider the group velocity vectors to be contaminated with constant bias and Gaussian random noise, unlike nonlinear deterministic pose estimators which disregard the noise component in the estimator derivations. The proposed estimators ensure that the closed-loop error signals are semi-globally uniformly ultimately bounded in mean square. The efficiency and robustness of the proposed estimators are demonstrated by the numerical results which test the estimators against high levels of noise and bias associated with the group velocity and body-frame measurements and large initialization error.

Variational Bayesian-Based Maximum Correntropy Cubature Kalman Filter With Both Adaptivity and Robustness

Abstract: This paper focuses on solving the problems of unknown measurement noise covariance and measurement outliers, which occurs in the vision/dual-IMU integrated attitude determination system. Although many adaptive filters and robust filters have been proposed to deal with the unknown measurement noise covariance or measurement outliers, most of them cannot handle both the unknown noise covariance and outliers. The adaptive filters assume no outliers in measurements and the robust filters assume accurate measurement noise covariance matrices. In this paper, we propose an adaptive and robust cubature Kalman filter, which achieves the adaptivity by estimating the measurement noise covariance through the variational Bayesian (VB) method, and achieves the robustness by suppressing the outliers based on the maximum correntropy criterion (MCC). The robustness and adaptivity of the proposed filter are verified through a typical tracking simulation example. Furthermore, the experimental results show that the proposed filter can obtain higher estimation accuracy than other filters in the vision/dual-IMU integrated system.

2.4-GHz Highly Selective IoT Receiver Front End With Power Optimized LNTA, Frequency Divider, and Baseband Analog FIR Filter

Abstract: High selectivity becomes increasingly important with an increasing number of devices that compete in the congested 2.4-GHz industrial, scientific, and medical (ISM)-band. In addition, low power consumption is very important for Internet-of-Things (IoT) receivers. We propose a 2.4-GHz zero-intermediate frequency (IF) receiver front-end architecture that reduces power consumption by 2 $\times $ compared with state-of-the-art and improves selectivity by >20-dB without compromising on other receiver metrics. To achieve this, the entire receive chain is optimized. The low-noise transconductance amplifier (LNTA) is optimized to combine low noise with low power consumption. State-of-the-art sub-30-nm complementary metal–oxide–semiconductor (CMOS) processes have almost equal strength complementary field-effect transistors (FETs) that result in altered design tradeoffs. A Windmill 25%-duty cycle frequency divider architecture is proposed, which uses only a single NOR-gate buffer per phase to minimize power consumption and phase noise. The proposed divider requires half the power consumption and has 2 dB or more reduced phase noise when benchmarked against state-of-the-art designs. An analog finite impulse response (FIR) filter is implemented to provide very high receiver selectivity with ultralow power consumption. The receiver front end is fabricated in a 22-nm fully depleted silicon-on-insulator (FDSOI) technology and has an active area of 0.5 mm2. It consumes 370 $\mu \text{W}$ from a 700-mV supply voltage. This low power consumption is combined with a 5.5-dB noise figure. The receiver front end has −7.5-dBm input-referred third-order-intercept point (IIP3) and 1-dB gain compression for a −22-dBm blocker, both at maximum gain of 61 dB. From three channels offset onward, the adjacent channel rejection (ACR) is ≥63 dB for Bluetooth Low-Energy (BLE), BT5.0, and IEEE802.15.4.

Unsupervised Denoising for Satellite Imagery Using Wavelet Directional CycleGAN

Abstract: Multispectral satellite imaging sensors acquire various spectral band images and have a unique spectroscopic property in each band. Unfortunately, image artifacts from imaging sensor noise often affect the quality of scenes and have a negative impact on applications for satellite imagery. Recently, deep learning approaches have been extensively explored to remove noise in satellite imagery. Most deep learning denoising methods, however, follow a supervised learning scheme, which requires matched noisy image and clean image pairs that are difficult to collect in real situations. In this article, we propose a novel unsupervised multispectral denoising method for satellite imagery using a wavelet directional cycle-consistent adversarial network (WavCycleGAN). The proposed method is based on an unsupervised learning scheme using adversarial loss and cycle-consistency loss to overcome the lack of paired data. Moreover, in contrast to the standard image-domain cycleGAN, we introduce a wavelet directional learning scheme for effective denoising without sacrificing high-frequency components such as edges and detailed information. Experimental results for the removal of vertical stripes and wave noise in satellite imaging sensors demonstrate that the proposed method effectively removes noise and preserves important high-frequency features of satellite images.

Joint Denoising and Demosaicking With Green Channel Prior for Real-World Burst Images

Abstract: Denoising and demosaicking are essential yet correlated steps to reconstruct a full color image from the raw color filter array (CFA) data. By learning a deep convolutional neural network (CNN), significant progress has been achieved to perform denoising and demosaicking jointly. However, most existing CNN-based joint denoising and demosaicking (JDD) methods work on a single image while assuming additive white Gaussian noise, which limits their performance on real-world applications. In this work, we study the JDD problem for real-world burst images, namely JDD-B. Considering the fact that the green channel has twice the sampling rate and better quality than the red and blue channels in CFA raw data, we propose to use this green channel prior (GCP) to build a GCP-Net for the JDD-B task. In GCP-Net, the GCP features extracted from green channels are utilized to guide the feature extraction and feature upsampling of the whole image. To compensate for the shift between frames, the offset is also estimated from GCP features to reduce the impact of noise. Our GCP-Net can preserve more image structures and details than other JDD methods while removing noise. Experiments on synthetic and real-world noisy images demonstrate the effectiveness of GCP-Net quantitatively and qualitatively.

A Hybrid Quantum-Classical Approach to Mitigating Measurement Errors in Quantum Algorithms

Abstract: When noisy intermediate scalable quantum (NISQ) devices are applied in information processing, all of the stages through preparation, manipulation, and measurement of multipartite qubit states contain various types of noise that are generally hard to be verified in practice. In this article, we present a scheme to deal with unknown quantum noise and show that it can be used to mitigate errors in measurement readout with NISQ devices. Quantum detector tomography that identifies a type of noise in a measurement can be circumvented. The scheme applies single-qubit operations only, that are with relatively higher precision than measurement readout or two-qubit gates. A classical post-processing is then performed with measurement outcomes. The scheme is implemented in quantum algorithms with NISQ devices: the Bernstein-Vazirani algorithm and a quantum amplitude estimation algorithm in $\mathrm{IBMQ\_yorktown}$ IBMQ _ yorktown and $\mathrm{IBMQ\_essex}$ IBMQ _ essex . The enhancement in the statistics of the measurement outcomes is presented for both of the algorithms with NISQ devices.

The Denoising of Desert Seismic Data Based on Cycle-GAN With Unpaired Data Training

Abstract: The seismic data with high quality are the essential foundation of imaging and interpretation. However, the real seismic data are inevitably contaminated by noise, which affects the subsequent processing and interpretation of seismic data. In desert seismic data, the energy of noise is stronger. Also, the frequency-band overlap between noise and effective signals is more serious. Recently, some methods based on supervised learning can suppress the desert seismic noise to some extent. Generally, supervised learning-based methods use synthetic noisy data and paired pure data as training sets to train model. However, the difference between synthetic noisy data of training and real seismic data of testing leads to the degradation of the model, and the denoising results often have many false seismic events when dealing with field seismic data. To solve the above problem, we introduce Cycle-generative adversarial networks (GANs) into the denoising of desert seismic records. Cycle-GAN is an unsupervised learning-based method. It can learn the domain mapping from noisy data domain to effective signal data domain through unpaired data training. So we use unpaired real desert common-shot-point data and synthetic pure data to train Cycle-GAN, so as to effectively improve the denoising ability of the method for real seismic data. Finally, the denoising of desert seismic data is realized. The experiment shows that the Cycle-GAN with unpaired data training can effectively suppress desert seismic noise and retain the effective signal amplitude. Also, the denoising result has less false seismic reflection.

A Local Consensus Index Scheme for Random-Valued Impulse Noise Detection Systems

Abstract: The issue of impulse noise detection and reduction is a critical problem for image processing application systems. In order to detect impulse noises in corrupted images, a statistic named local consensus index (LCI) is proposed for quantitatively evaluating how noise free a pixel is, and then an impulse noise detection scheme based on LCI is introduced. First, the similarity between arbitrary two pixels in an image is quantified based on both their geometric distance and intensity difference, and the LCI of arbitrary pixel is calculated by summing all the similarity values of pixels in its neighborhood. As a new statistic, the value of LCI indicates the local consensus of the concerned pixel regarding its neighbors and could also tell whether a pixel is noise free or impulsive. Therefore, LCI can be directly used as an efficient indicator of impulse noise. Furthermore, to improve the performance of impulse noise detection, different strategies are applied to the pixels at flat regions and the ones with complex textures, since distributions of LCI value within those regions are totally different. As for impulse noise filtering, a hybrid graph Laplacian regularization (HGLR) method is introduced to restore the intensities of those pixels degraded by impulse noise. We conduct extensive experiments to verify the effectiveness of our impulsive noise detection and reduction method, and the results show that the proposed method outperforms the state-of-the-art techniques in terms of impulse detection and noise removal.

Noise-Powered Disentangled Representation for Unsupervised Speckle Reduction of Optical Coherence Tomography Images

Abstract: Due to its noninvasive character, optical coherence tomography (OCT) has become a popular diagnostic method in clinical settings. However, the low-coherence interferometric imaging procedure is inevitably contaminated by heavy speckle noise, which impairs both visual quality and diagnosis of various ocular diseases. Although deep learning has been applied for image denoising and achieved promising results, the lack of well-registered clean and noisy image pairs makes it impractical for supervised learning-based approaches to achieve satisfactory OCT image denoising results. In this paper, we propose an unsupervised OCT image speckle reduction algorithm that does not rely on well-registered image pairs. Specifically, by employing the ideas of disentangled representation and generative adversarial network, the proposed method first disentangles the noisy image into content and noise spaces by corresponding encoders. Then, the generator is used to predict the denoised OCT image with the extracted content features. In addition, the noise patches cropped from the noisy image are utilized to facilitate more accurate disentanglement. Extensive experiments have been conducted, and the results suggest that our proposed method is superior to the classic methods and demonstrates competitive performance to several recently proposed learning-based approaches in both quantitative and qualitative aspects. Code is available at: https://github.com/tsmotlp/DRGAN-OCT .

A 22.9–38.2-GHz Dual-Path Noise-Canceling LNA With 2.65–4.62-dB NF in 28-nm CMOS

Abstract: In this article, a 22.9–38.2-GHz dual-path noise-canceling low noise amplifier (LNA) is proposed, which can achieve a low noise figure (NF) by reducing the noise of both paths. Such LNA consists of one common gate (CG) amplifier with one three-stage transformer, one resistive feedback common-source (CS) amplifier, and two amplitude-adjusting amplifiers. The three-stage transformer is used in the CG amplifier to provide gain-boosting, noise-reducing, and wideband inter-stage matching operation, simultaneously. Meanwhile, amplitude-adjusting amplifiers with reconfigurable phase-tuning lines are utilized in both paths to optimize the noise-canceling performance. To verify the aforementioned principle, a dual-path noise-canceling LNA is implemented and fabricated using a conventional 28-nm CMOS technology. The proposed LNA consumed 18.9 mW under a 0.9-V supply. The measured NF is 2.65–4.62 dB within the operating frequency range of 22.9–38.2 GHz, while the peak gain is 14.5 dB. The in-band input 1-dB compression point (IP1 dB) and input third-order intercept point (IIP3) are −13.2 to −6.6 and −3.6 to 3.2 dBm, respectively.

Stress Detection With Single PPG Sensor by Orchestrating Multiple Denoising and Peak-Detecting Methods

Abstract: Stress is one of the major causes of diseases in modern society. Therefore, measuring and managing the degree of stress is crucial to maintain a healthy life. The goal of this paper is to improve stress-detection performance using precise signal processing based on photoplethysmogram (PPG) data. PPG signals can be collected through wearable devices, but are affected by many internal and external noises. To solve this problem, we propose a two-step denoising method, to filter the noise in terms of frequency and remove the remaining noise in terms of time. We also propose an ensemble-based multiple peak-detecting method to extract accurate features through refined signals. We used a typical public dataset, namely, wearable stress and affect detection dataset (WESAD) and measured the performance of the proposed PPG denoising and peak-detecting methods by lightweight multiple classifiers. By measuring the stress-detection performance using the proposed method, we demonstrate an improved result compared with the existing methods: accuracy is 96.50 and the F1 score is 93.36%. Our code is available at https://github.com/seongsilheo/stress_classification_with_PPG .

Variational Bayesian-Based Filter for Inaccurate Input in Underwater Navigation

Abstract: Autonomous underwater vehicle (AUV) has been employed in oceanography applications based on a reliable navigation. The complex underwater environment leads to more velocity measurement errors of AUV, so it is difficult to determine the accurate navigation and positioning information. To solve the problem, a novel variational Bayesian-based filter for inaccurate input (VBFII) is proposed to determine the state information under the complex marine condition of inaccurate input. Firstly, the velocities are assumed to follow the Gaussian distribution, which can better describe the model of input information. Secondly, the augmentation method is used to augment the state vector and error covariance matrix to simplify calculation. The augmented state vector, the augmented predicted error covariance and measurement error noise matrices are derived more accurately based on the variational Bayesian (VB) approach. The experiment results show that the proposed VBFII has better estimation accuracy and robustness than other comparison algorithms.

Self-supervised training for blind multi-frame video denoising

Abstract: We propose a self-supervised approach for training multi-frame video denoising networks. These networks predict each frame from a stack of frames around it. Our self-supervised approach benefits from the temporal consistency in the video by minimizing a loss that penalizes the difference between the predicted frame and a neighboring one, after aligning them using an optical flow. We use the proposed strategy to denoise a video contaminated with an unknown noise type, by fine-tuning a pre-trained denoising network on the noisy video. The proposed fine-tuning reaches and sometimes surpasses the performance of state-of-the-art networks trained with supervision. We demonstrate this by showing extensive results on video blind denoising of different synthetic and real noises. In addition, the proposed fine-tuning can be applied to any parameter that controls the denoising performance of the network. We show how this can be expoited to perform joint denoising and noise level estimation for heteroscedastic noise.

PID Control With Higher Order Derivative Degrees for IPDT Plant Models

Abstract: In this paper, we discuss the main features of the generalized higher-order proportional-integrative-derivative control (HO-PID) based on the integral-plus-dead-time (IPDT) plant models. It was developed by extending the traditional PI-control to include $m$ th-order derivatives and $n\geq m$ th-order binomial series filters. The HO-PID control provides two additional degrees of freedom, which allow to appropriately modify the speed of the transients and the attenuation of the measurement noise, together with the closed-loop robustness. In this way, it pursues similar goals as an alternative fractional-order PID control. A broad family of the HO-PID controllers with the included low-pass filters is employed to solve a number of new problems. Their integrated suboptimal tuning, based on explicit formulas derived by the multiple real dominant pole (MRDP) method and evaluated by a novel approach that relates the speed of transients to the excessive input and output increments, has been simplified by introducing two integrated tuning procedures (ITPs). The main new finding is that HO-PID control enables faster transients by simultaneously reducing the negative effects of measurement noise and increasing the closed-loop robustness. A brief experimental evaluation using new sensitivity measures fully confirms the excellent HO-PID characteristics and shows that commissioning remains almost as simple as with the filtered PI-control.

Fifty Years of Noise Modeling and Mitigation in Power-Line Communications

Abstract: Building on the ubiquity of electric power infrastructure, power line communications (PLC) has been successfully used in diverse application scenarios, including the smart grid and in-home broadband communications systems as well as industrial and home automation. However, the power line channel exhibits deleterious properties, one of which is its hostile noise environment. This article aims for providing a review of noise modeling and mitigation techniques in PLC. Specifically, a comprehensive review of representative noise models developed over the past fifty years is presented, including both the empirical models based on measurement campaigns and simplified mathematical models. Following this, we provide an extensive survey of the suite of noise mitigation schemes, categorizing them into mitigation at the transmitter as well as parametric and non-parametric techniques employed at the receiver. Furthermore, since the accuracy of channel estimation in PLC is affected by noise, we review the literature of joint noise mitigation and channel estimation solutions. Finally, a number of directions are outlined for future research on both noise modeling and mitigation in PLC.