Showing papers on "Signal-to-noise ratio published in 2018"
TL;DR: It is demonstrated that 2D metal carbide MXenes, which possess high metallic conductivity for low noise and a fully functionalized surface for a strong signal, greatly outperform the sensitivity of conventional semiconductor channel materials.
Abstract: Achieving high sensitivity in solid-state gas sensors can allow the precise detection of chemical agents. In particular, detection of volatile organic compounds (VOCs) at the parts per billion (ppb) level is critical for the early diagnosis of diseases. To obtain high sensitivity, two requirements need to be simultaneously satisfied: (i) low electrical noise and (ii) strong signal, which existing sensor materials cannot meet. Here, we demonstrate that 2D metal carbide MXenes, which possess high metallic conductivity for low noise and a fully functionalized surface for a strong signal, greatly outperform the sensitivity of conventional semiconductor channel materials. Ti3C2Tx MXene gas sensors exhibited a very low limit of detection of 50–100 ppb for VOC gases at room temperature. Also, the extremely low noise led to a signal-to-noise ratio 2 orders of magnitude higher than that of other 2D materials, surpassing the best sensors known. Our results provide insight in utilizing highly functionalized metallic...
979 citations
TL;DR: This letter is the first in literature that studies a novel 3-D UAV-BS placement that maximizes the number of covered users with different quality-of-service requirements and proposes a low-complexity algorithm, namely, maximal weighted area (MWA) algorithm to tackle the placement problem.
Abstract: The need for a rapid-to-deploy solution for providing wireless cellular services can be realized by unmanned aerial vehicle base stations (UAV-BSs). To the best of our knowledge, this letter is the first in literature that studies a novel 3-D UAV-BS placement that maximizes the number of covered users with different quality-of-service requirements. We model the placement problem as a multiple circles placement problem and propose an optimal placement algorithm that utilizes an exhaustive search (ES) over a 1-D parameter in a closed region. We also propose a low-complexity algorithm, namely, maximal weighted area (MWA) algorithm to tackle the placement problem. Numerical simulations are presented showing that the MWA algorithm performs very close to the ES algorithm with a significant complexity reduction.
403 citations
TL;DR: In this paper, the authors considered a frequency-selective mm-wave channel and proposed compressed sensing-based strategies to estimate the channel in the frequency domain, and evaluated different algorithms and computed their complexity to expose tradeoffs in complexity overhead performance as compared with those of previous approaches.
Abstract: Channel estimation is useful in millimeter wave (mm-wave) MIMO communication systems. Channel state information allows optimized designs of precoders and combiners under different metrics, such as mutual information or signal-to-interference noise ratio. At mm-wave, MIMO precoders and combiners are usually hybrid, since this architecture provides a means to trade-off power consumption and achievable rate. Channel estimation is challenging when using these architectures, however, since there is no direct access to the outputs of the different antenna elements in the array. The MIMO channel can only be observed through the analog combining network, which acts as a compression stage of the received signal. Most of the prior work on channel estimation for hybrid architectures assumes a frequency-flat mm-wave channel model. In this paper, we consider a frequency-selective mm-wave channel and propose compressed sensing-based strategies to estimate the channel in the frequency domain. We evaluate different algorithms and compute their complexity to expose tradeoffs in complexity overhead performance as compared with those of previous approaches.
233 citations
TL;DR: A new indicator, Combined Squared Envelope Spectrum, is employed to consider all the frequency bands with valuable diagnostic information and to improve the fault detectability of the Autogram, and a thresholding method is also proposed to enhance the quality of the frequency spectrum analysis.
224 citations
TL;DR: An overview of the techniques developed in the past decade for hyperspectral image noise reduction is provided, and the performance of these techniques by applying them as a preprocessing step to improve a hyperspectrals image analysis task, i.e., classification.
Abstract: Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signals from the Earth’s surface emitted by the Sun. The received radiance at the sensor is usually degraded by atmospheric effects and instrumental (sensor) noises which include thermal (Johnson) noise, quantization noise, and shot (photon) noise. Noise reduction is often considered as a preprocessing step for hyperspectral imagery. In the past decade, hyperspectral noise reduction techniques have evolved substantially from two dimensional bandwise techniques to three dimensional ones, and varieties of low-rank methods have been forwarded to improve the signal to noise ratio of the observed data. Despite all the developments and advances, there is a lack of a comprehensive overview of these techniques and their impact on hyperspectral imagery applications. In this paper, we address the following two main issues; (1) Providing an overview of the techniques developed in the past decade for hyperspectral image noise reduction; (2) Discussing the performance of these techniques by applying them as a preprocessing step to improve a hyperspectral image analysis task, i.e., classification. Additionally, this paper discusses about the hyperspectral image modeling and denoising challenges. Furthermore, different noise types that exist in hyperspectral images have been described. The denoising experiments have confirmed the advantages of the use of low-rank denoising techniques compared to the other denoising techniques in terms of signal to noise ratio and spectral angle distance. In the classification experiments, classification accuracies have improved when denoising techniques have been applied as a preprocessing step.
208 citations
TL;DR: It is shown that the CNN-based information can be displayed in a novel artifact-free image format, enabling us to effectively remove reflection artifacts from photoacoustic images, which is not possible with traditional geometry-based beamforming.
Abstract: Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in the presence of highly echogenic structures, the resulting photoacoustic wave creates a reflection artifact that may appear as a true signal. We propose to use deep learning techniques to identify these types of noise artifacts for removal in experimental photoacoustic data. To achieve this goal, a convolutional neural network (CNN) was first trained to locate and classify sources and artifacts in pre-beamformed data simulated with $k$ -Wave. Simulations initially contained one source and one artifact with various medium sound speeds and 2-D target locations. Based on 3,468 test images, we achieved a 100% success rate in classifying both sources and artifacts. After adding noise to assess potential performance in more realistic imaging environments, we achieved at least 98% success rates for channel signal-to-noise ratios (SNRs) of −9dB or greater, with a severe decrease in performance below −21dB channel SNR. We then explored training with multiple sources and two types of acoustic receivers and achieved similar success with detecting point sources. Networks trained with simulated data were then transferred to experimental waterbath and phantom data with 100% and 96.67% source classification accuracy, respectively (particularly when networks were tested at depths that were included during training). The corresponding mean ± one standard deviation of the point source location error was 0.40 ± 0.22 mm and 0.38 ± 0.25 mm for waterbath and phantom experimental data, respectively, which provides some indication of the resolution limits of our new CNN-based imaging system. We finally show that the CNN-based information can be displayed in a novel artifact-free image format, enabling us to effectively remove reflection artifacts from photoacoustic images, which is not possible with traditional geometry-based beamforming.
179 citations
TL;DR: A general and flexible algorithm is proposed based on the majorization-minimization method with guaranteed monotonicity, lower computational complexity per iteration and/or convergence to a B-stationary point and many waveform constraints can be flexibly incorporated into the algorithm with only a few modifications.
Abstract: In this paper, we consider the joint design of both transmit waveforms and receive filters for a colocated multiple-input-multiple-output (MIMO) radar with the existence of signal-dependent interference and white noise. The design problem is formulated into a maximization of the signal-to-interference-plus-noise ratio (SINR), including various constraints on the transmit waveforms. Compared with the traditional alternating semidefinite relaxation approach, a general and flexible algorithm is proposed based on the majorization-minimization method with guaranteed monotonicity, lower computational complexity per iteration and/or convergence to a B-stationary point. Many waveform constraints can be flexibly incorporated into the algorithm with only a few modifications. Furthermore, the connection between the proposed algorithm and the alternating optimization approach is revealed. Finally, the proposed algorithm is evaluated via numerical experiments in terms of SINR performance, ambiguity function, computational time, and properties of the designed waveforms. The experiment results show that the proposed algorithms are faster in terms of running time and meanwhile achieve as good SINR performance as the the existing methods.
166 citations
19 Apr 2018
TL;DR: This paper proposes a data-driven pitch tracking algorithm, CREPE, which is based on a deep convolutional neural network that operates directly on the time-domain waveform, and evaluates the model's generalizability in terms of noise robustness.
Abstract: The task of estimating the fundamental frequency of a monophonic sound recording, also known as pitch tracking, is fundamental to audio processing with multiple applications in speech processing and music information retrieval. To date, the best performing techniques, such as the pYIN algorithm, are based on a combination of DSP pipelines and heuristics. While such techniques perform very well on average, there remain many cases in which they fail to correctly estimate the pitch. In this paper, we propose a data-driven pitch tracking algorithm, CREPE, which is based on a deep convolutional neural network that operates directly on the time-domain waveform. We show that the proposed model produces state-of-the-art results, performing equally or better than pYIN. Furthermore, we evaluate the model's generalizability in terms of noise robustness. A pre-trained version of CREPE is made freely available as an open-source Python module for easy application.
164 citations
TL;DR: Numerical results show that the proposed method achieves a significant power saving compared to conventional approaches, while obtaining a favorable performance-complexity tradeoff.
Abstract: We propose a novel approach to enable the coexistence between Multi-Input-Multi-Output (MIMO) radar and downlink multiuser multi-input single-output communication system. By exploiting the constructive multiuser interference (MUI), the proposed approach tradeoff useful MUI power for reducing the transmit power, to obtain a power efficient transmission. This paper focuses on two optimization problems: a) Transmit power minimization at the base station (BS), while guaranteeing the receive signal-to-interference-plus-noise ratio (SINR) level of downlink users and the interference-to-noise ratio level to radar; b) Minimization of the interference from BS to radar for a given requirement of downlink SINR and transmit power budget. To reduce the computational overhead of the proposed scheme in practice, an algorithm based on gradient projection is designed to solve the power minimization problem. In addition, we investigate the tradeoff between the performance of radar and communication, and analytically derive the key metrics for MIMO radar in the presence of the interference from the BS. Finally, a robust power minimization problem is formulated to ensure the effectiveness of the proposed method in the case of imperfect channel state information. Numerical results show that the proposed method achieves a significant power saving compared to conventional approaches, while obtaining a favorable performance-complexity tradeoff.
158 citations
TL;DR: The CNN output noise level is lower than the ground truth and equivalent to the iterative image reconstruction result, and the proposed deep learning method is useful for both super-resolution and de-noising.
Abstract: The objective of this study is to develop a convolutional neural network (CNN) for computed tomography (CT) image super-resolution. The network learns an end-to-end mapping between low (thick-slice thickness) and high (thin-slice thickness) resolution images using the modified U-Net. To verify the proposed method, we train and test the CNN using axially averaged data of existing thin-slice CT images as input and their middle slice as the label. Fifty-two CT studies are used as the CNN training set, and 13 CT studies are used as the test set. We perform five-fold cross-validation to confirm the performance consistency. Because all input and output images are used in two-dimensional slice format, the total number of slices for training the CNN is 7670. We assess the performance of the proposed method with respect to the resolution and contrast, as well as the noise properties. The CNN generates output images that are virtually equivalent to the ground truth. The most remarkable image-recovery improvement by the CNN is deblurring of boundaries of bone structures and air cavities. The CNN output yields an approximately 10% higher peak signal-to-noise ratio and lower normalized root mean square error than the input (thicker slices). The CNN output noise level is lower than the ground truth and equivalent to the iterative image reconstruction result. The proposed deep learning method is useful for both super-resolution and de-noising.
152 citations
Proceedings Article•
15 Feb 2018TL;DR: It is shown that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel.
Abstract: Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the discovery of decoding algorithms via deep learning. We study a family of sequential codes parameterized by recurrent neural network (RNN) architectures. We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms). We show strong generalizations, i.e., we train at a specific signal to noise ratio and block length but test at a wide range of these quantities, as well as robustness and adaptivity to deviations from the AWGN setting.
TL;DR: Qualitative and quantitative study and analysis indicate that the proposed technique can be used as an effective tool for denoising of ECG signals and hence can serve for better diagnostic in computer-based automated medical system.
TL;DR: Simulation and analysis show that the proposed scheme actually can achieve a secure and precise wireless transmission of confidential messages in line-of-propagation channel, and the derived theoretical formula of average secrecy rate is verified to coincide with the exact results well for medium and large scale transmit antenna array or in the low and medium SNR regions.
Abstract: In this paper, a practical wireless transmission scheme is proposed to transmit confidential messages to the desired user securely and precisely by the joint use of multiple techniques, including artificial noise (AN) projection, phase alignment/beamforming, and random subcarrier selection (RSCS) based on orthogonal frequency division multiplexing (OFDM), and directional modulation (DM), namely RSCS-OFDM-DM. This RSCS-OFDM-DM scheme provides an extremely low-complexity structure for the desired receiver and makes the secure and precise wireless transmission realizable in practice. For illegal eavesdroppers, the receive power of confidential messages is so weak that their receivers cannot intercept these confidential messages successfully once it is corrupted by AN. In such a scheme, the design of phase alignment/beamforming vector and AN projection matrix depends intimately on the desired direction angle and distance. It is particularly noted that the use of RSCS leads to a significant outcome that the receive power of confidential messages mainly concentrates on the small neighboring region around the desired receiver and only small fraction of its power leaks out to the remaining large broad regions. This concept is called secure precise transmission. The probability density function of real-time receive signal-to-interference-and-noise ratio (SINR) is derived. Also, the average SINR and its tight upper bound are attained. The approximate closed-form expression for average secrecy rate is derived by analyzing the first-null positions of the SINR and clarifying the wiretap region. Simulation and analysis show that the proposed scheme actually can achieve a secure and precise wireless transmission of confidential messages in line-of-propagation channel, and the derived theoretical formula of average secrecy rate is verified to coincide with the exact results well for medium and large scale transmit antenna array or in the low and medium SNR regions.
TL;DR: A primal-dual algorithm based on the block successive upper-bound minimization method of multipliers (BSUM-M) is developed to deal with the joint design of transmit waveform and receive filter for multiple-input multiple-output radar in the presence of signal-dependent interference.
Abstract: The paper investigates the joint design of transmit waveform and receive filter for multiple-input multiple-output radar in the presence of signal-dependent interference, subject to a peak-to-average-power ratio constraint as well as a waveform similarity constraint. Owing to this kind of signal dependence and constraints, the formulated optimization problem of the output signal-to-interference-plus-noise ratio (SINR) maximization is NP-hard. To this end, an auxiliary variable is first introduced to modify the original problem, and then a primal-dual algorithm based on the block successive upper-bound minimization method of multipliers (BSUM-M) is developed to deal with the resulting problem. Moreover, an active set method is exploited to solve the quadratic programming problem involved in each update procedure of the proposed BSUM-M algorithm. Finally, numerical simulations are performed to demonstrate the superiority of the proposed algorithm over state-of-the-art methods in terms of the output SINR, beampattern, computational complexity, pulse compression, and ambiguity properties.
TL;DR: This work considers the problem of estimating a signal from noisy circularly translated versions of itself, called multireference alignment, and proposes and analyzes a method based on estimating the signal directly, using features of the signal that are invariant under translations.
Abstract: We consider the problem of estimating a signal from noisy circularly translated versions of itself, called multireference alignment (MRA). One natural approach to MRA could be to estimate the shifts of the observations first, and infer the signal by aligning and averaging the data. In contrast, we consider a method based on estimating the signal directly, using features of the signal that are invariant under translations. Specifically, we estimate the power spectrum and the bispectrum of the signal from the observations. Under mild assumptions, these invariant features contain enough information to infer the signal. In particular, the bispectrum can be used to estimate the Fourier phases. To this end, we propose and analyze a few algorithms. Our main methods consist of nonconvex optimization over the smooth manifold of phases. Empirically, in the absence of noise, these nonconvex algorithms appear to converge to the target signal with random initialization. The algorithms are also robust to noise. We then suggest three additional methods. These methods are based on frequency marching, semidefinite relaxation, and integer programming. The first two methods provably recover the phases exactly in the absence of noise. In the high noise level regime, the invariant features approach for MRA results in stable estimation if the number of measurements scales like the cube of the noise variance, which is the information-theoretic rate. Additionally, it requires only one pass over the data, which is important at low signal-to-noise ratio when the number of observations must be large.
TL;DR: The simulation results have shown that the proposed multi-modal cooperative spectrum sensing can achieve better sensing performance in fading channel.
Abstract: In 5G-based cognitive radio, the primary user signal is more active due to the broad frequency band. The traditional cooperative spectrum sensing only detects one characteristic of PU using one kind of detector, which may decrease the sensing performance when the wideband PU is in severe fading channel. In this paper, a multi-modal cooperative spectrum sensing is proposed to make an accurate decision through combining multi-modal sensing data of the PU signal, such as energy, power spectrum, and signal waveform. Each secondary user (SU) deploys multiple kinds of detectors, such as energy detector, spectral detector and waveform detector. The multi-modal sensing data from different detectors are sent to a fusion center. In the fusion center, the local decision is achieved through the Bayesian fusion, while the global decision is determined by the DS fusion. The sensing credibility of each detector can be fully considered in the DS fusion, in order to avoid the performance difference of different detectors. Weight DS fusion is also proposed to improve the decision performance through decreasing the sensing impact of malicious SU while increasing the fusion proportion of dominant SU. The simulation results have shown that the proposed multi-modal cooperative spectrum sensing can achieve better sensing performance in fading channel.
TL;DR: Measurements show that the dynamic bias comparator can reduce the average energy consumption by about a factor 2.5 for the same input-equivalent noise at an input common-mode level of half the supply voltage.
Abstract: A latch-type comparator with a dynamic bias pre-amplifier is implemented in a 65-nm CMOS process. The dynamic bias with a tail capacitor is simple to implement and ensures that the pre-amplifier output nodes are only partially discharged to reduce the energy consumption. The comparator is analyzed and compared to its prior art in terms of energy consumption and input referred noise voltage. First-order equations are presented that show how to optimize the pre-amplifier for low noise and high gain. Both the dynamic bias comparator and the prior art are implemented on the same die and measurements show that the dynamic bias can reduce the average energy consumption by about a factor 2.5 for the same input-equivalent noise at an input common-mode level of half the supply voltage.
TL;DR: Simulations demonstrate that the ISI effect is significantly reduced and the adaptive detection schemes are reliable and robust for mobile molecular communication.
Abstract: Current studies on modulation and detection schemes in molecular communication mainly focus on the scenarios with static transmitters and receivers. However, mobile molecular communication is needed in many envisioned applications, such as target tracking and drug delivery. Until now, investigations about mobile molecular communication have been limited. In this paper, a static transmitter and a mobile bacterium-based receiver performing random walk are considered. In this mobile scenario, the channel impulse response changes due to the dynamic change of the distance between the transmitter and the receiver. Detection schemes based on fixed distance fail in signal detection in such a scenario. Furthermore, the intersymbol interference (ISI) effect becomes more complex due to the dynamic character of the signal which makes the estimation and mitigation of the ISI even more difficult. In this paper, an adaptive ISI mitigation method and two adaptive detection schemes are proposed for this mobile scenario. In the proposed scheme, adaptive ISI mitigation, estimation of dynamic distance, and the corresponding impulse response reconstruction are performed in each symbol interval. Based on the dynamic channel impulse response in each interval, two adaptive detection schemes, concentration-based adaptive threshold detection and peak-time-based adaptive detection, are proposed for signal detection. Simulations demonstrate that the ISI effect is significantly reduced and the adaptive detection schemes are reliable and robust for mobile molecular communication.
TL;DR: The proposed D2D aided CRS using non-orthogonal multiple access (NOMA) with the power allocation is shown to improve the achievable rate greatly compared to conventional CRSs with and without NOMA, and it is proved that the sum-capacity scaling is log SNR for the proposed one, whereas (2/3) log SNr for the conventional ones.
Abstract: This letter proposes a device-to-device (D2D) aided cooperative relaying system (CRS) using non-orthogonal multiple access (NOMA) to enhance the spectral efficiency. In addition, a power allocation strategy is proposed to achieve the maximum capacity scaling according to signal-to-noise ratio (SNR). The proposed D2D aided CRS using NOMA with the power allocation is shown to improve the achievable rate greatly compared to conventional CRSs with and without NOMA. In particular, it is proved that the sum-capacity scaling is log SNR for the proposed one, whereas (2/3) log SNR for the conventional ones.
TL;DR: A blind channel estimator based on the expectation maximization algorithm to acquire the modulus values of channel parameters is proposed and the ranges of the initial values of the suggested estimator are obtained and the modified Bayesian Cramér–Rao bound is derived.
Abstract: The availability of perfect channel state information is assumed in current ambient-backscatter studies. However, the channel estimation problem for ambient backscatter is radically different from that for traditional wireless systems, where it is common to transmit training (pilot) symbols for this purpose. In this letter, we thus propose a blind channel estimator based on the expectation maximization algorithm to acquire the modulus values of channel parameters. We also obtain the ranges of the initial values of the suggested estimator and derive the modified Bayesian Cramer–Rao bound of the proposed estimator. Finally, simulation results are provided to corroborate our theoretical studies.
TL;DR: This work analyzes the secrecy outage performance of a single-input multiple-output wiretap model, where a base station forwards the signal transmitted from a user to a data center, while an eavesdropper wiretaps the confidential information by decoding the received signal.
Abstract: We analyze the secrecy outage performance of a mixed radio frequency-free space optical (RF-FSO) transmission system with imperfect channel state information (CSI). We deal with a single-input multiple-output wiretap model, where a base station (works as the relay) forwards the signal transmitted from a user (source) to a data center (works as the destination), while an eavesdropper wiretaps the confidential information by decoding the received signal. Both the relay and the eavesdropper are armed with multiple antennas, and maximal ratio combining scheme is utilized to improve the received signal-to-noise ratio (SNR). The effects of imperfect CSI of the RF link and the FSO link, misalignment, detection schemes, and relaying schemes on the secrecy outage performance of mixed RF-FSO systems are studied. First, the cumulative distribution function and probability density function of FSO links with pointing error and two different detection technologies are derived. Then, we derive the closed-form expressions for the lower bound of the secrecy outage probability (SOP) with fixed-gain relaying and variable-gain relaying schemes. Furthermore, asymptotic results for the SOP are investigated by exploiting the unfolding of Meijer's $G$ -function when the electrical SNR of FSO link approaches infinity. Finally, Monte Carlo simulation results are presented to corroborate the correctness of the analysis.
TL;DR: A coherent pulsed-FDA radar signal model to deal with the angle-range-dependent and time-variance problem is devised under additive colored Gaussian noise scenarios, followed by the corresponding waveform design principle.
Abstract: Different from conventional phased-array providing only angle-dependent beampattern, frequency diverse array (FDA) produces angle-range-dependent and time-variant transmit beampattern. Existing investigations show that FDA offers improved performance in interference suppression and target localization, but the time-variant beampattern will bring interferences to subsequent matched filtering. More seriously, the range-dependent signal phase may be canceled out in the filtering process. In fact, traditional single-channel receiver does not fully exploit the multicarrier feature in FDA signals. In this paper, we propose a multichannel matched filtering structure with considering the time-variance property for receiving pulsed-FDA signals. A coherent pulsed-FDA radar signal model to deal with the angle-range-dependent and time-variance problem is devised under additive colored Gaussian noise scenarios, followed by the corresponding waveform design principle. Moreover, closed-form expressions of the output signal-to-interference-plus-noise ratio and Cramer–Rao bounds for angle and range are derived. The proposed receiver design approach and corresponding theoretical performance derivations are verified by extensive numerical results.
TL;DR: Simulation results validate the better performance of the proposed method for baseline wander (BW) and power line interference (PLI) removal from electrocardiogram (ECG) signals than compared methods at different noise levels.
TL;DR: An improved incipient fault detection method based on Kullback-Leibler (KL) divergence under multivariate statistical analysis frame that can detect slight anomalous behaviors by comparing the online probability density function online with the reference PDF obtained from large scale off-line data set is presented.
Abstract: This paper presents an improved incipient fault detection method based on Kullback-Leibler (KL) divergence under multivariate statistical analysis frame. Different from the traditional multivariate fault detection methods, this methodology can detect slight anomalous behaviors by comparing the online probability density function (PDF) online with the reference PDF obtained from large scale off-line data set. In the principal and residual subspaces obtained via PCA, a symmetric evaluation function is defined for both single variate and multivariate cases. The uniform form of probability distribution and fault detection thresholds associated with all eigenvalues are given. In addition, the robust performance is analyzed with respect to a wide range of Signal to Noise Ratio (SNR). Case studies are conducted with three types of incipient faults on a numerical example; combining with two nonlinear projections, the proposed scheme is successfully used for incipient fault detection in non-Gaussian electrical drive system. The results can demonstrate the superiority of the proposed method than several other methods.
TL;DR: This paper proposed a de-noising algorithm for RFF as the present performance of RFF is seriously affected by the noise and shows that the optimized classification algorithm achieves a high accuracy at a relatively low SNR (0 dB), which is the best result compared with other existing methods.
Abstract: This paper focuses on the security risks in the access authentication of Internet of Things, to provide an optimized classification algorithm of radio frequency fingerprinting (RFF). The novel method is based on coherent integration, multiresolution analysis, and Gaussian support vector machine (SVM). First, we proposed a de-noising algorithm for RFF as the present performance of RFF is seriously affected by the noise. The optimized coherent integration first developed in this paper effectively improves the signal-to-noise ratio (SNR) of the waveform without increasing the number of required signals, by which a de-noising processing is performed and the identification accuracy is improved. Then a wavelet-based multiresolution analysis is applied to extract feature points in the waveform that has passed the de-noising optimizer, because the less sample points are needed for the SVM classification processing, which reduces the computational complexity of SVM compared to the classical SVM classification methods where massive sample points are necessary. Extensive experiments are performed. Simulation result shows that the optimized classification algorithm achieves a high accuracy (exceed 99%) at a relatively low SNR (0 dB), which is the best result compared with other existing methods.
TL;DR: A new RAB algorithm based on interference-plus-noise covariance (INC) matrix reconstruction and steering vector (SV) estimation is proposed, which outperforms the existing RAB techniques in terms of the overall performance in cases of various mismatches.
Abstract: To ensure link reliability and signal receiving quality, robust adaptive beamforming (RAB) is vital important in mobile communications. In this paper, we propose a new RAB algorithm based on interference-plus-noise covariance (INC) matrix reconstruction and steering vector (SV) estimation. In this method, the INC matrix is reconstructed by estimating all interferences SVs and powers, as well as the noise power. The interference SVs are estimated by using the Capon spatial spectrum together with robust Capon beamforming principle, subsequently the interference powers are estimated based on the orthogonality between different signal SVs. On the other hand, the desired signal SV is estimated via maximizing the beamformer output power by solving a quadratic convex optimization problem. The proposed algorithm only needs to know in advance the array geometry and angular sector, in which the desired signal lies. Simulation results indicate that the proposed algorithm outperforms the existing RAB techniques in terms of the overall performance in cases of various mismatches.
TL;DR: In this paper, a constructive artificial noise (AN) precoding scheme was proposed to improve the receive signal-to-interference and noise ratio at the intended receiver (IR) through exploiting the AN power in an attempt to minimize the total transmit power, while hindering detection at the Eves.
Abstract: Conventionally, interference and noise are treated as catastrophic elements in wireless communications. However, it has been shown recently that exploiting known interference constructively can contribute to signal detection ability at the receiving end. This paper exploits this concept to design artificial noise (AN) beamformers constructive to the intended receiver (IR) yet keeping AN disruptive to possible eavesdroppers (Eves). The scenario considered here is a multiple-input single-output wiretap channel with multiple Eves. This paper starts from AN design without any knowledge of Eve’s CSI, builds with solutions with statistical CSI up to full CSI. Both perfect and imperfect channel information have been considered, in particular, with different extent of Eves’ channel responses. The main objective is to improve the receive signal-to-interference and noise ratio at IR through exploitation of AN power in an attempt to minimize the total transmit power, while hindering detection at the Eves. Numerical simulations demonstrate that the proposed constructive AN precoding approach yields superior performance over conventional AN schemes in terms of transmit power. Critically, they show that, while the statistical constraints of conventional approaches may lead to instantaneous IR outages and security breaches from the Eves, the instantaneous constraints of our approach guarantee both IR performance and secrecy at every symbol period.
Posted Content•
TL;DR: In this article, a patch-based singular value shrinkage method for diffusion magnetic resonance image estimation targeted at low signal to noise ratio and accelerated acquisitions is proposed, where asymptotically optimal signal recovery guarantees can be attained by modeling the noise propagation in the reconstruction and subsequently simulating or calculating the limit singular value spectrum.
Abstract: We propose a patch-based singular value shrinkage method for diffusion magnetic resonance image estimation targeted at low signal to noise ratio and accelerated acquisitions. It operates on the complex data resulting from a sensitivity encoding reconstruction, where asymptotically optimal signal recovery guarantees can be attained by modeling the noise propagation in the reconstruction and subsequently simulating or calculating the limit singular value spectrum. Simple strategies are presented to deal with phase inconsistencies and optimize patch construction. The pertinence of our contributions is quantitatively validated on synthetic data, an in vivo adult example, and challenging neonatal and fetal cohorts. Our methodology is compared with related approaches, which generally operate on magnitude-only data and use data-based noise level estimation and singular value truncation. Visual examples are provided to illustrate effectiveness in generating denoised and debiased diffusion estimates with well preserved spatial and diffusion detail.
TL;DR: In this paper, the authors proposed a blind detection scheme that simultaneously estimates the channel and data by factorizing the received signal matrix, which can achieve a degree of freedom very close to the ideal case, provided that the channel is sufficiently sparse.
Abstract: In practical massive MIMO systems, a substantial portion of system resources are consumed to acquire channel state information (CSI), leading to a drastically lower system capacity compared with the ideal case, where perfect CSI is available. In this paper, we show that the overhead for CSI acquisition can be largely compensated by the potential gain due to the sparsity of the massive MIMO channel in a certain transformed domain. To this end, we propose a novel blind detection scheme that simultaneously estimates the channel and data by factorizing the received signal matrix. We show that by exploiting the channel sparsity, our proposed scheme can achieve a degree of freedom (DoF) very close to the ideal case, provided that the channel is sufficiently sparse. Specifically, the achievable DoF has a fractional gap of only $1/T$ from the ideal DoF, where $T$ is the channel coherence time. This is a remarkable advance for understanding the performance limit of the massive MIMO system. We further show that the performance advantage of our proposed scheme in the asymptotic SNR regime carries over to the practical SNR regime. Numerical results demonstrate that our proposed scheme significantly outperforms its counterpart schemes in the practical SNR regime under various system configurations.
TL;DR: The investigation on effectiveness of the empirical mode decomposition (EMD) with non-local mean (NLM) technique by using the value of differential standard deviation for denoising of ECG signal is performed and shows lesser standard deviation PRD, MSE, and SNR improvement compared to other well-known methods at different input SNR.