Noise Rate Research Articles

A MATLAB Simulink Study on the Performance of QAM Modulation Scheme in AWGN, Rayleigh, and Rician Fading Channels: BER Analysis

Wireless communication is the fastest-growing segment in the communication industry, with mobile communication being the most widely used. However, it faces several technical challenges, such as Fading, Shadowing, Interference, and Propagation path loss. Meeting the higher demand for capacity with high-quality service is crucial. Orthogonal Frequency Division Multiplexing (OFDM) is a technique that converts wideband signals into narrowband signals for transmission, making it a suitable option for high bandwidth data transmission. The transmission of these narrowband signals is executed with an orthogonal carrier. This paper focuses on building a QAM model using MATLAB to simulate Bit Error Rate (BER) performance for real data communication under different communication channels, including AWGN and fading channels (Rayleigh and Rician). The aim is to investigate the reduction of noise and bit error rate in communication channels. The simulation model built for this research work demonstrates that QAM scheme performs better in AWGN channels than Rayleigh or Rician fading channels.

Optimization of capacitively coupled Low Gain Avalanche Diode (AC-LGAD) sensors for precise time and spatial resolution

Capacitively-coupled Low-Gain Avalanche Diode (AC-LGAD) sensors are being developed for high-energy particle physics experiments as a detector which provides fast time information with fine spatial resolution. This paper describes optimizations of AC-LGAD sensor fabrication parameters, such as doping concentrations of the gain and electrode layers as well as the AC insulator capacitance, to realize O(10) μm spatial resolution, small charge cross talk to the neighboring electrodes, detection efficiency higher than 99% at a 10−4 noise rate and time resolution of about 30 ps. The radiation tolerance of the sensor is presented. In addition, further application to a device capable of visible and infra-red light detection is discussed.

High Optical Feedback Tolerance of a Detuned DBR Laser for 10-Gbps Isolator-Free Operation

The optical feedback tolerance (OFT) of a distributed Bragg reflector (DBR) laser was investigated experimentally. The static and modulation performance of the DBR laser under optical feedback was examined by evaluating its relative intensity noise (RIN) and bit error rate (BER). It is shown that the OFT of the DBR laser is closely related to its peak wavelength detuning relative to the Bragg wavelength. A high tolerance to optical feedback was demonstrated, both in the continuous wave (CW) and the direct modulation (DM) states, when the DBR laser was red-detuned relative to the Bragg wavelength. The excess RIN induced by optical feedback was well suppressed to a level of −140 dB/Hz at a feedback level of −9 dB in the CW state. In a 10 Gbps direct modulation state, bit error rates (BER) below 1 × 10−9 and 3 × 10−7 were obtained under a feedback level of −15 dB and −9 dB, respectively, for the case of back-to-back transmission. After 20 km fiber transmission, the BER still maintained below 1 × 10−7 under a feedback level of −15 dB, with a power penalty of less than 1 dB.

Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach

Everyone has paid much attention to modulation-type recognition in the past few years. There are many ways to find the modulation type, but only a few good ways to deal with signals with a lot of noise. This study comes up with a way to test how well different machine learning algorithms can handle noise when detecting digital and analogue modulations. This study looks at the four most common digital and analogue modulations: Phase Shift Keying, Quadrature Phase Shift Keying, Amplitude Modulation, and Morse Code. A signal noise rate from -10dB to +25dB is used to find these modulations. We used machine learning algorithms to determine the modulation type like Decision Tree, Random Forest, Support Vectors Machine, and k-nearest neighbours. After the IQ samples had been converted to the amplitude of samples and radio frequency format, the accuracy of each method looked good. Still, in the format of the sample phase, each algorithm's accuracy was less. The results show that the proposed method works to find the signals that have noises. When there is less noise, the random forest algorithm gives better results than SVM, but SVM gives better results when there is more noise.

NRN-RSSEG: A Deep Neural Network Model for Combating Label Noise in Semantic Segmentation of Remote Sensing Images

The performance of deep neural networks depends on the accuracy of labeled samples, as they usually contain label noise. This study examines the semantic segmentation of remote sensing images that include label noise and proposes an anti-label-noise network framework, termed Labeled Noise Robust Network in Remote Sensing Image Semantic Segmentation (NRN-RSSEG), to combat label noise. The algorithm combines three main components: network, attention mechanism, and a noise-robust loss function. Three different noise rates (containing both symmetric and asymmetric noise) were simulated to test the noise resistance of the network. Validation was performed in the Vaihingen region of the ISPRS Vaihingen 2D semantic labeling dataset, and the performance of the network was evaluated by comparing the NRN-RSSEG with the original U-Net model. The results show that NRN-RSSEG maintains a high accuracy on both clean and noisy datasets. Specifically, NRN-RSSEG outperforms UNET in terms of PA, MPA, Kappa, Mean_F1, and FWIoU in the presence of noisy datasets, and as the noise rate increases, each performance of UNET shows a decreasing trend while the performance of NRN-RSSEG decreases slowly and some performances show an increasing trend. At a noise rate of 0.5, the PA (−6.14%), MPA (−4.27%) Kappa (−8.55%), Mean_F1 (−5.11%), and FWIOU (−9.75%) of UNET degrade faster; while the PA (−2.51%), Kappa (−3.33%), and FWIoU of NRN-RSSEG (−3.26) degraded more slowly, MPA (+1.41) and Mean_F1 (+2.69%) showed an increasing trend. Furthermore, comparing the proposed model with the baseline method, the results demonstrate that the proposed NRN-RSSEG anti-noise framework can effectively help the current segmentation model to overcome the adverse effects of noisy label training.

Transitions in Computational Complexity of Continuous-Time Local Open Quantum Dynamics.

We analyze the complexity of classically simulating continuous-time dynamics of locally interacting quantum spin systems with a constant rate of entanglement breaking noise. We prove that a polynomial time classical algorithm can be used to sample from the state of the spins when the rate of noise is higher than a threshold determined by the strength of the local interactions. Furthermore, by encoding a 1D fault tolerant quantum computation into the dynamics of spin systems arranged on two or higher dimensional grids, we show that for several noise channels, the problem of weakly simulating the output state of both purely Hamiltonian and purely dissipative dynamics is expected to be hard in the low-noise regime.

TelePix – A fast region of interest trigger and timing layer for the EUDET Telescopes

Test beam facilities are essential to study the response of novel detectors to particles. At the DESY II Test Beam facility, users can test their detectors with an electron beam with a momentum from 1–6 GeV. To track the beam particles, EUDET-style telescopes are provided in each beam area. They provide excellent spatial resolution, but the time resolution is limited by the rolling shutter architecture to a precision of approximately 230 μs. Since the demand on particle rates – and hence track multiplicities – is increasing timing is becoming more relevant. DESY foresees several upgrades of the telescopes. TelePix is an upgrade project to provide track timestamping with a precision of better than 5 ns and a configurable region of interest to trigger the telescope readout. Small scale prototypes have been characterized in laboratory and test beam measurements. Laboratory tests with an injection corresponding to 2300 electrons show a S/N of above 20. Test beam characterization shows efficiencies of above 99% over a threshold range of more than 100 mV and time resolutions of 2.4 ns at low noise rates.

Gaussian‐like measurement likelihood based particle filter for extended target tracking

AbstractExtended target tracking based on the star‐convex model is a multi‐dimensional nonlinear estimation problem. To approximate the shape of an extended target, the star‐convex model requires a higher‐order Fourier series expansion, which will increase the dimensionality of the extended state, so that the nonlinear filters under Gaussian assumptions cannot converge to the optimal state estimation. In this paper, a novel particle filter algorithm based on Gaussian‐like measurement likelihood (GL) is proposed. As latent variables with high uncertainty in the measurement model, the scattering centres are modelled as a Gaussian‐like probability distribution characterising the target's shape. Then the measurement noise is integrated into the Gaussian‐like probability distribution via Gaussian quadrature to obtain the accurate measurement likelihood. Moreover, particle swarm optimization is employed to reduce computational complexity. It enables particles to move into the vicinity of measurements, thereby increasing the diversity of the particles belonging to the target's shape. The simulation results show that the GL can improve the accuracy of shape estimation under high measurement noise and low measurement rates. Furthermore, the proposed particle filter can track the extended target effectively with an aeroplane‐like shape.

Microstructure and Corrosion Resistance of AZ91 Magnesium Alloy after Surface Remelting Treatment.

The effect of surface remelting treatment on the microstructure and corrosion resistance of the AZ91 magnesium alloy was studied. The surface layer was remelted by GTAW (gas tungsten arc welding). An original two-burner system with welding torches operating in a tandem configuration was used, allowing the combination of cleaning the surface from oxides with the remelting process. The studies of the corrosion resistance of the alloy included electrochemical tests and measurements of the rate of hydrogen evolution. The results showed that surface remelting treatment leads to favorable microstructural changes, manifested in strong grain refinement and a more uniform arrangement of the β-Mg17Al12 phase. The changes in the microstructure caused by remelting and the accompanying fast crystallization contributed to an increase in the corrosion resistance of the remelted samples in comparison to their non-remelted equivalents. The results obtained on the basis of the polarization curves showed three-fold lower values of the corrosion current density in the case of the remelted material than the value of the corrosion current density determined for the starting material. In turn, in the case of measurements of the electrochemical noise and corrosion rate determined by the method of measuring the rate of hydrogen evolution, this value for the remelted alloy was two times lower. The research also showed that GTAW technology is highly effective and can be a valuable alternative to laser techniques. The complete experimental details, obtained results and their analyses are presented in this paper.

KNN Based Denoising Algorithm for Photon-Counting LiDAR: Numerical Simulation and Parameter Optimization Design

Photon-counting LiDAR can obtain long-distance, high-precision target3D geographic information, but extracting high-precision signal photons from background noise photons is the key premise of photon-counting LiDAR data processing and application. This study proposes an adaptive noise filtering algorithm that adjusts parameters according to the background photon count rate and removes noise photons based on the local mean Euclidean distance. A simulated photon library that provides different background photon count rates and detection probabilities was constructed. It was then used to fit the distribution relationship between the background photon count rate and the average KNN (K-Nearest Neighbor) distance (k = 2–6) and to obtain the optimal denoising threshold under different background photon count rates. Finally, the proposed method was evaluated by comparing it with the modified density-based spatial clustering (mDBSCAN) and local distance-based statistical methods. The experimental results show that various methods are similar when the background noise rate is high. However, at most non-extreme background photon count rate levels, the F of this algorithm was maintained between 0.97–0.99, which is an improvement over other classical algorithms. The new strategy eliminated the artificial introduction of errors. Due to its low error rates, the proposed method can be widely applied in photon-counting LiDAR signal extraction under various conditions.

Error Propagation in NISQ Devices for Solving Classical Optimization Problems

We propose a random circuit model to analyze the impact of noise on the performance of variational quantum circuits for classical optimization problems. Our model accounts for the propagation of arbitrary single qubit errors through the circuit. We find that even with a small noise rate, the quality of the obtained classical optima is low on average and a single-qubit error rate of $1 / nD$, where $n$ is the number of qubits and $D$ is the circuit depth, is needed for the possibility of a quantum advantage. We estimate that this translates to an error rate lower than $10^{-6}$ using QAOA for classical optimization problems with 2D circuits.

Experimental Evaluation of Adaptive Step-Size Based FxLMS Approach for Active Noise Control in Magnetic Resonance Imaging Systems

Magnetic Resonance Imaging (MRI) scanners emit up to 135 decibels of acoustic noise, which is a major source of discomfort for patients and personnel evaluating them during routine medical scans, necessitating the development of a method to reduce the acoustic noise generated during MRI testing. The goal of this study is to propose a frequency-domain Active Noise Control (ANC) method for acoustic noise reduction in MRI and to demonstrate its ANC effectiveness on an experimental MRI scanner model specifically built for this purpose. In comparison to the standard Least Mean Square (LMS) algorithm, we used the Filtered-x Least Mean Square (FxLMS) approach with an adaptive variable step-size approach to adjust the filter coefficients dynamically, which considerably enhances the ANC system's convergence and reduces acoustic noise. The simulation results obtained from the MATLAB Simulink model on a pre-recorded 30-sec MRI noise signal represented by the step-size variation over time, error and noise convergence plots reveal that the adaptive step-size FxLMS (ASFxLMS) technique increases noise and error convergence rate significantly more than existing ANC algorithms to facilitate its use during MRI scans. Experimental results with our functional MRI (fMRI) testbed show approximately 25-dB overall noise reduction relative to the noise levels without ANC.

Efficient detection of faults and false data injection attacks in smart grid using a reconfigurable Kalman filter

The distribution denial of service (DDoS) attack, fault data injection attack (FDIA) and random attack is reduced. The monitoring and security of smart grid systems are improved using reconfigurable Kalman filter. Methods: A sinusoidal voltage signal with random Gaussian noise is applied to the Reconfigurable Euclidean detector (RED) evaluator. The MATLAB function randn() has been used to produce sequence distribution channel noise with mean value zero to analysed the amplitude variation with respect to evolution state variable. The detector noise rate is analysed with respect to threshold. The detection rate of various attacks such as DDOS, Random and false data injection attacks is also analysed. The proposed mathematical model is effectively reconstructed to frame the original sinusoidal signal from the evaluator state variable using reconfigurable Euclidean detectors.

Optimal Signal Shaping in Solid State Detector Spectrometers

The performance of the most widely used shapers in solid state detector spectrometers is investigated in respect to noise suppression and count rate ability. Three shaper types have been experimentally checked with two detector types – X-ray Silicon Drift Detector and HPGe gamma detector. The results show small difference in noise line width and negligible difference in obtainable energy resolution while the high count rate performance depends noticeably on shaper type selected. This allows for simultaneous optimization of noise and count rate performance.

Energy efficiency optimization for MM-NOMA heterogeneous network with wireless backhauling and energy harvesting

A novel architecture of massive MIMO-NOMA (MM-NOMA) based two-tier heterogeneous network is proposed to accommodate huge traffic while considering wireless backhaul transmission is presented in this paper. In addition, full-duplex transmission and energy harvesting are also explored to enhance system performance. Simultaneous wireless information and power transfer (SWIPT) technique has been applied at small cell base station for harvesting energy. First, signal to interference plus noise ratio (SINR) and rate equations are modeled for the proposed network where harvested energy and energy efficiency (EE) are also defined. EE is then optimized by determining the time splitting factor assignment, bandwidth allocation, and power allocation subject to constraints of backhauling, minimum harvested power and quality of service. The formulated optimization problem is non-linear and non-convex. It is difficult to obtain the optimal solution, hence a sub-optimal algorithm is proposed. Furthermore, the complexity of the proposed algorithm is examined and simulation results demonstrate the effectiveness of the proposed algorithm by comparing with existing benchmark schemes.

Entanglement entropy scaling of noisy random quantum circuits in two dimensions

Whether noisy quantum devices without error correction can provide quantum advantage over classical computers is a critical issue of current quantum computation. In this work, the random quantum circuits, which are used as the paradigm model to demonstrate quantum advantage, are simulated with depolarizing noise on experiment relevant two-dimensional architecture. With comprehensive numerical simulation and theoretical analysis, we find that the maximum achievable operator entanglement entropy, which indicates maximal simulation cost, has area law scaling with the system size for constant noise rate. On the other hand, we also find that the maximum achievable operator entanglement entropy has power law scaling with the noise rate for fixed system size, and the volume law scaling can be obtained only if the noise rate decreases when system size increase.

Robust co-teaching learning with consistency-based noisy label correction for medical image classification.

Deep neural networks (DNNs) have made great achievements in computer-aided diagnostic systems, but the success highly depends on massive data with high-quality labels. However, for many medical image datasets, a considerable number of noisy labels are introduced by inter- and intra-observer variability, thus hampering DNNs' performance. To address this problem, a robust noisy label correction method with the co-teaching learning paradigm is proposed. The proposed method aims to reduce the effect of noisy labels by correcting or removing them. It consists of two modules. An adaptive noise rate estimation module is employed to calculate the dataset's noise rate, which is helpful to detect noisy labels but is usually unavailable in clinical applications. A consistency-based noisy label correction module aims to detect noisy labels and correct them to reduce the disturbance from noisy labels and exploit useful information in data. Experiments are conducted on the public skin lesion dataset ISIC-2017, ISIC-2019, and our constructed thyroid ultrasound image dataset. The results demonstrate that the proposed method outperforms other noisy label learning methods in medical image classification tasks. It is also evaluated on the natural image dataset CIFAR-10 to show its generalization. This paper proposes a noisy label correction method to handle noisy labels in medical image datasets. Experimental results show that it can self-adapt to different datasets and efficiently correct noisy labels, which is suitable for medical image classification.

Incorporating information from LIGO data quality streams into the PyCBC search for gravitational waves

We present a new method which accounts for changes in the properties of gravitational-wave detector noise over time in the PyCBC search for gravitational waves from compact binary coalescences. We use information from LIGO data quality streams that monitor the status of each detector and its environment to model changes in the rate of noise in each detector. These data quality streams allow candidates identified in the data during periods of detector malfunctions to be more efficiently rejected as noise. This method allows data from machine learning predictions of the detector state to be included as part of the PyCBC search, increasing the the total number of detectable gravitational-wave signals by up to 5%. When both machine learning classifications and manually-generated flags are used to search data from LIGO-Virgo's third observing run, the total number of detectable gravitational-wave signals is increased by up to 20% compared to not using any data quality streams. We also show how this method is flexible enough to include information from large numbers of additional arbitrary data streams that may be able to further increase the sensitivity of the search.

Characterisation of Hamamatsu SiPM for cosmic muon veto detector at IICHEP

A test setup is built to characterise the SiPM and to measure muon detection efficiency of an extruded plastic scintillator detector. In the setup, light from the scintillator is readout by SiPMs using two embedded Wavelength Shifting Fibres (WLS) fibres. The SiPM was calibrated using LED source, but alternate calibration procedures using radio-active source as well as noise data were also established. The SiPM is studied at various over voltages (V ov ) and determined the operating V ov by optimising the muon detection efficiency and noise rate. The muon position along the length of the scintillator is measured using timing information from both sides of the fibres. Results from our characterisation studies of SiPM (2mm × 2mm, Model S1330-2050VE), e.g. after pulse, cross-talk, recovery time etc. will be discussed in this paper.

RESIDUAL LEARNING BASED IMAGE DENOISING AND COMPRESSION USING DNCNN

Image compression has become an essential subfield in image processing for many generations. This should be an effective process with decreasing this amount about a file format through frames unless significantly lowering from an exceptional standard. Image quality endures outcome with image compression or image visibility experiences as leading with maximum noise rate increases. In order to be accurate whole, developers are using a technology called denoising, which increases image quality, decreases effects of noise, and restores compression to its original condition. Image denoising has been an effective process with manipulating image datasets with just one graphically premium quality image. Those who start to move ahead included paper besides analyzing this same development with produce denoising convolutional neural networks (DnCNNs) for incorporate advances in rather a classification model, machine learning, but rather maximum likelihood methods into other image denoising. Need to remain further unique, residual learning, along with batch normalization, ought to be utilized to speed up the training stage of evolution even while enhancing denoising effectiveness. To begin, images encrypted with block-based optimization techniques display blockages, which was among the particular majority perplexing artifacts throughout compressed images and video. Furthermore, the DnCNN substructure is used to handle a variety of different image denoising functions, including singular attribute extremely but rather JPEG appearance deblocking with possibly enforced successfully through exploiting computations.