System Noise Research Articles

Spectroscopic performance of Low-Gain Avalanche Diodes for different types of radiation

LGADs (Low-Gain Avalanche Diodes or Detectors) are a type of silicon Avalanche Photo-Diodes originally developed for the fast detection of minimum ionizing particles in high-energy physics experiments. Thanks to their fast timing performance, the LGAD paradigm enables detectors to accurately measure minimum ionizing particles with a timing resolution of a few tens of picoseconds. Such a performance is due to a thin substrate and the presence of a moderate signal gain. This internal gain of a few tens is enough to compensate for the reduced charge deposition in the thinner substrate and the noise of fast read-out systems. While LGADs are optimized for the detection of minimum ionizing particles for high-energy particle detectors, it is critical to study their performance for the detection of different types of particle, such as X-rays, gamma-rays, or alphas. In this paper, we evaluate the gain of three types of LGADs: two devices with different geometries and doping profiles fabricated by Brookhaven National Laboratory, and one fabricated by Hamamatsu Photonics with a different process.Since the gain in LGADs depends on the bias voltage applied to the sensor, pulse-height spectra have been acquired for bias voltages spanning from the depletion voltage up to the breakdown voltage. The signal-to-noise ratio of the generated signals and the shape of their spectra allow us to probe the underlying physics of the multiplication process.

State estimation based on measurement from a part of nodes for a delayed Itô type of complex network with Markovian mode-dependent parameters

This article is devoted to coping with the state estimate problem for a class of Itô stochastic complex network. The target network under consideration is a hybrid system involving Markovian jumping parameters and mode-dependent distributed time-delays, and the system noise is characterized by the Brownian Motion. The problem addressed is to design a state estimator to track the states of target network under the assumption that the network outputs are from only a portion of network nodes. By utilizing the Lyapunov stability method and the Itô stochastic differential equation theory, sufficient conditions are educed so that the state estimation error of the estimator is mean-square exponentially ultimately bounded. In particular, in the case that the system is noise-free, the derived conditions can ensure the state estimation error is mean-square exponentially stable. Furthermore, the estimator design can be made by solving a set of matrix inequalities. Lastly, a numerical example is exploited to indicates the validity of the theoretical results.

Clipping Noise in Visible Light Communication Systems with OFDM and PAPR Reduction

This paper presents an analytical study of signal clipping that leads to the noise/distortion in the waveform of DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM)-based visible light communication (VLC) systems. The pilot-assisted (PA) technique is used to reduce the high peak-to-average power ratio (PAPR) of the time-domain waveform of the DCO-OFDM system. The bit error rate (BER) performance of the PA DCO-OFDM system is investigated analytically at three different clipping levels as well as without any clipping. The analytical BER performance is verified through simulation and then compared to that of the conventional DCO-OFDM without PAPR reduction at the selected clipping levels. The PA DCO-OFDM system shows improved BER performance at all three clipping levels.

Exploring the efficacy of GRU model in classifying the signal to noise ratio of microgrid model

Microgrids are small-scale energy system that supplies power to homes, businesses, and industries. Microgrids can be considered as a trending technology in energy fields due to their power to supply reliable and sustainable energy. Microgrids have a mode called the island, in this mode, microgrids are disconnected from the major grid and keep providing energy in the situation of an energy outage. Therefore, they help the main grid during peak energy demand times. The microgrids can be connected to the network, which is called networked microgrids. It is possible to have flexible energy resources by using their enhanced energy management systems. However, connection microgrid systems to the communication network introduces various challenges, including increased in systems complicity and noise interference. Integrating network communication into a microgrid system causes the system to be susceptible to noise, potentially disrupting the critical control signals that ensure smooth operation. Therefore, there is a need for predicting noise caused by communication network to ensure the operation stability of microgrids. In addition, there is a need for a simulation model that includes communication network and can generate noise to simulate real scenarios. This paper proposes a classifying model named Noise Classification Simulation Model (NCSM) that exploits the potential of deep learning to predict noise levels by classifying the values of signal-to-noise ratio (SNR) in real-time network traffic of microgrid system. This is accomplished by initially applying Gaussian white noise into the data that is generated by microgrid model. Then, the data has noise and data without noise is transmitted through serial communication to simulate real world scenario. At the end, a Gated Recurrent Unit (GRU) model is implemented to predict SNR values for the network traffic data. Our findings show that the proposed model produced promising results in predicting noise. In addition, the classification performance of the proposed model is compared with well-known machine learning models and according to the experimental results, our proposed model has noticeable performance, which achieved 99.96% classification accuracy.

Examining the relationship between system noise and organisational performance in local government in Australia

Examining the relationship between system noise and organisational performance in local government in Australia

Contact Force Estimation of Robot Manipulators With Imperfect Dynamic Model: On Gaussian Process Adaptive Disturbance Kalman Filter

This paper is concerned with the contact force estimation problem of robot manipulators based on imperfect dynamic models of the manipulator and the contact force. To handle the imperfect dynamic information of the manipulator, a hybrid model, consisting of the nominal model and the residual dynamics, is established for the manipulator, and the Gaussian process regression (GPR) technique is employed to learn the mean and covariance of the residual dynamics. On this basis, a virtual measurement equation is established for contact force estimation and a Gaussian process adaptive disturbance Kalman filter (GPADKF) is developed where the variational Bayes technique is employed to achieve online identification of the noise statistics in the force dynamics. The GPADKF is capable of decoupling the contact force from residual dynamics and system noises, thereby reducing the dependence on accurate dynamic models of the manipulator and the contact force. Simulation and experimental results demonstrate that the proposed scheme outperforms the state-of-art methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Contact force estimation for robot manipulators can be achieved by fusing the dynamic models of the manipulator and the contact force. When both models are imprecise, the traditional inverse dynamics-based and disturbance Kalman filter-based approaches can no longer provide accurate force estimates. To handle this challenge, a computationally efficient hybrid dynamic model is established for the manipulator, which consists of the nominal model and a residual dynamics compensation term learned from offline data via the GPR. On this basis, an adaptive disturbance Kalman filter is constructed by using the variational Bayes technique to deal with the inaccurate noise covariance matrix in the force dynamic model. Compared with the existing approaches, the force estimate obtained via the proposed scheme is more accurate and reliable, as refined noise covariance matrices (provided by both the GPR and the variational Bayes procedure) have been adopted in the Kalman gain calculation. The proposed GPADKF method is the extension of the composite disturbance filtering (CDF) framework. With the proposed scheme, the dependency on the perfect dynamic models in contact force estimation can be significantly reduced, and this makes our approach especially suitable for contact force estimation problems under unfamiliar and complicated environments.

Research on signal equalization technology based on visible light communication

Abstract Visible Light Communication (VLC) transmits signals through visible light and realizes the two functions of lighting and communication at the same time without the need for other hardware equipment. There are still some shortcomings in the visible light communication system. The optical signal propagated indoors is affected by inter-symbol interference (ISI) and external light noise at the receiving end. The signal will be severely distorted. These factors will significantly reduce the accuracy of the received signal decision, thereby seriously affecting the performance of the visible light communication system. Given the problems of intersymbol interference and optical noise in indoor visible light systems, this article studies adaptive equalization technology and modulation and demodulation technology suitable for visible light communication systems and improves the signal-receiving end equalization. Based on the existing equalizer structure, we combined the advantages of two different equalizers and optimized their combined structure parameters. On this basis, we added the MAP algorithm and proposed a hyperbolic tangent function to improve the traditional error function. The defects speed up the convergence speed without changing the mean square error. Experimental results show that the algorithm has improved in convergence performance. The algorithm’s convergence speed is 53.6% higher than the traditional algorithm, the steady-state error is reduced by about 6 dB, and the steady-state error of the algorithm is reduced by about 5 dB respectively.

Noise suppression for an aeromagnetic measurement system on an unmanned helicopter.

An unmanned helicopter is one of the main platforms for conducting unmanned aerial vehicle aeromagnetic measurements and combines the advantages of rotary-wing and fixed-wing unmanned aerial vehicles. However, unmanned helicopter-based aeromagnetic measurement systems face complex static magnetic noise and maneuvering magnetic interference, which limit their practical performance. To address this issue, an improved multi-channel frequency measurement algorithm for the optically pumped magnetic sensor is proposed to suppress the static magnetic noise proportional to the frequency noise generated by the random quantization error and the airborne electromagnetic interference. A novel aeromagnetic compensation method for the maneuvering magnetic interference is then proposed to weaken the negative effects of the strong multicollinearity of the attitude parameters of the unmanned helicopter on the compensation accuracy and stability by introducing a regularization term and weight matrix. In addition, dedicated software is developed for the real-time calculation and compensation of magnetic interference fields. A dedicated unmanned-helicopter-based aeromagnetic measurement system is developed, and ground and flight experiments are carried out. The ground test results indicate that the static noise of the proposed system is only 0.000 82 nT. In the flight experiments, the system achieves an improvement ratio of 8.33, which is higher than the improvement ratio of 4.37 for a state-of-the-art commercial compensator. Furthermore, the dynamic noise after compensation decreases by 37.6% from 0.0157 to 0.0098 nT.

Noise intensity of a Markov chain.

Stochastic transitions between discrete microscopic states play an important role in many physical and biological systems. Often these transitions lead to fluctuations on a macroscopic scale. A classic example from neuroscience is the stochastic opening and closing of ion channels and the resulting fluctuations in membrane current. When the microscopic transitions are fast, the macroscopic fluctuations are nearly uncorrelated and can be fully characterized by their mean and noise intensity. We show how, for an arbitrary Markov chain, the noise intensity can be determined from an algebraic equation, based on the transition rate matrix; these results are in agreement with earlier results from the theory of zero-frequency noise in quantum mechanical and classical systems. We demonstrate the validity of the theory using an analytically tractable two-state Markovian dichotomous noise, an eight-state model for a calcium channel subunit (De Young-Keizer model), and Markov models of the voltage-gated sodium and potassium channels as they appear in a stochastic version of the Hodgkin-Huxley model.

Reducción del Sobrepico de un Elevador de tijeras Mediante Observador de Estado, LQR y LQG

Scissor lifts are crucial in various industries, known for their robust design and ability to lift heavy loads to significant heights. Their interlinked scissor mechanism allows controlled elevation and descent of the work platform. Precision and safety in their operation are essential to prevent accidents and ensure efficiency. A critical issue is the "overshoot," where the lift exceeds the desired height before stabilizing, causing instability and safety risks. This phenomenon is related to the system's dynamics and control, which typically employ hydraulic or electric systems to regulate the extension and retraction of the scissors. Precision in these systems is vital to stop the platform at the correct height smoothly and accurately. This paper presents a solution based on MATLAB/Simulink to improve the overshoot in scissor lifts using advanced control techniques. Different control strategies are implemented and evaluated, including the use of a State Observer, LQR (Linear Quadratic Regulator), and LQG (Linear Quadratic Gaussian Controller). The State Observer is used to estimate the system's internal variables, allowing for more precise feedback. The LQR is employed to design a controller that minimizes a cost function, optimizing the balance between control effort and state error. Finally, the LQG incorporates a Kalman filter to handle system uncertainty and noise, providing robust and efficient control. This demonstrates a significant improvement in the precision and stability of the scissor lift, reducing overshoot and enhancing operational safety. This research contributes to the development of more advanced and safer control systems for industrial applications, optimizing the performance and reliability of scissor lifts.

A study on multi-channel active sound profiling algorithm for hybrid control of broadband and narrowband noise inside vehicles

The vehicle interior noise consists of both broadband road noise and narrowband engine order noise. Currently, the road noise control (RNC) system and engine noise control (EOC) system operate independently in engineering applications. This paper introduces a novel multi-channel hybrid broadband and narrowband active noise control (HBNANC) algorithm, designed to simultaneously attenuate road noise and engine order noise inside vehicles. On this basis, by integrating an error signal design (ESD) subsystem and a sinusoidal error separation (SES) subsystem, a multi-channel hybrid broadband and narrowband active sound profiling (HBNASP) algorithm is further proposed, enabling specific attenuation or amplification of individual narrowband components to meet subjective acoustic comfort requirements. The paper validates the algorithms’ effectiveness and robustness through simulations, and real vehicle experimental measurements under various conditions. The research offers a comprehensive approach to enhancing the auditory experience inside vehicles, contributing to the advancement of active noise control technology.

On-chip silicon photonic micro-ring processor lights up optical image encryption.

Optical image encryption has long been an important concept in the fields of photonic network processing and communication. Here, we propose a convolution-like operation-based optical image encryption algorithm exploiting a silicon photonic multiplexing architecture to achieve content security. Particularly, the encryption process is completed in a 3 × 3 cross-shaped photonic micro-ring resonator (MRR) array on chip. For the first time, to the best of our knowledge, this algorithm encodes information in an integrated intensity modulation, effectively reducing the encoding difficulty. Moreover, the high reliability and scalability of optical encryption are ensured using both linear and nonlinear operations on photonic chips according to characteristics of MRRs. As the encryption and decryption experiments show, the image restoration accuracy of our optical encryption algorithm exceeds 99% under real system noise at the pixel level, indicating its noise-robust property. Meanwhile, the peak signal-to-noise ratios of the restored and encrypted images are >60 and <15 dB, respectively, revealing both the high accuracy of the restored image and the small correlation between the encrypted and original images. This work adds to the rapidly expanding field of optical image encryption on photonic chips.

Limited Memory-Based Random-Weighted Kalman Filter.

The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent filtering solutions. To address this issue, this paper presents a new method by combining the random weighting concept with the limited memory technique to accurately estimate system noise statistics. To avoid the influence of excessive historical information on state estimation, random weighting theories are established based on the limited memory technique to estimate both process noise and measurement noise statistics within a limited memory. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. The proposed method improves the Kalman filtering accuracy by adaptively adjusting the weights of system noise statistics within a limited memory to suppress the interference of system noise on system state estimation. Simulations and experiments as well as comparison analysis were conducted, demonstrating that the proposed method can overcome the disadvantage of the traditional limited memory filter, leading to im-proved accuracy for system state estimation.

Noise and fading reduction in a phase-OTDR system using a multi-strand optical fiber sensing cable

This paper introduces an innovative architecture, to the best of our knowledge, for a phase-OTDR sensing system, employing a multi-strand optical fiber cable as the sensing element. Within the cable, the fiber strands are interconnected to form a single serpentine-shaped fiber. The key advantage of this architecture lies in its ability to enhance the detection performance of a phase-OTDR system by reducing missing alarms caused by interference fading. Additionally, noise reduction can be achieved by aggregating the sub-traces corresponding to all fiber strands. To further enhance performance, we associate the use of a multi-pulse averaging method, which involves aggregating traces obtained using multiple probe pulses with varying temporal widths. The results obtained indicate that the performance of this architecture is closely related to the number of fiber strands and the number of probe pulses used. We expect that the proposed phase-OTDR architecture is particularly well-suited for effective distributed intrusion monitoring applications in short- to medium-range sensitive infrastructures where high security is paramount.

Optical frequency dissemination via 103-km urban fiber link with remote passive phase stabilization

In this paper, we propose a technique for a fiber-based optical frequency dissemination system with remote passive phase noise cancellation. At the remote site, a 1×2 fiber pigtailed acousto-optic modulator (AOM) with two diffraction order outputs (0 and −1 order) is employed as the phase-compensated device, the undesired phase noise of fiber link introduced by environmental perturbations are passively canceled at remote sites. Different from other existing schemes, the proposed technique harnesses the benefits of remote radio frequency (RF) independence and low-temperature sensitivity in this noise-suppression configuration. Consequently, the system noise floor of the proposed optical frequency dissemination system achieves 9.44 × 10−21 without requiring a precise remote RF reference, and the phase-temperature coefficient is reduced to about 2 fs/K. A real-world experiment is conducted over a noisy round-trip 103 km urban fiber link. After being passively compensated, we demonstrate a fractional frequency instability of 1.57 × 10−14 at the integration time of 1 s and scales down to 3.96 × 10−20 at 10,000 s in terms of modified Allan deviation. The frequency uncertainty of the retrieved light after transferring through this noise-compensated fiber link relative to that of the input light achieves 1.80 × 10−18. This work demonstrates the system’s capability to disseminate the ultra-stable optical frequency standards and is a significant step towards realizing multi-node dissemination of the state-of-the-art optical clock signal with remote noise compensation via a tree-like topology fiber network.

Analysis and denoising of electric noise in phased array ultrasonic testing system

Phased array ultrasonic testing (PAUT) is an advanced technique used for non-destructive testing (NDT) to detect and assess the integrity of materials or structures. In composite materials with significant acoustic attenuation, electrical noise significantly impacts PAUT detection accuracy, highlighting a crucial issue. This paper presents a comprehensive analysis of electrical noise, explains the relationship between electrical noise and inspection parameters, and proposes recommendations to reduce it. A time-varying filter empirical mode decomposition (TVF-EMD) joint wavelet thresholding method is proposed for denoising electrical noise after time-corrected gain (TCG). The ultrasonic echo signal goes through TVF-EMD decomposition, resulting in intrinsic mode functions (IMFs). Energy entropy is utilized to identify the signal-dominant IMFs, and wavelet thresholding is applied to further reduce noise within these selected IMFs. The proposed method effectively removes noise and ensures signal integrity by validating simulated and experimental signals.

Semiconductor laser systems excited by multiplicative Ornstein-Uhlenbeck noise and additive sine-Wiener noise in relation to real and imaginary parts.

In this paper we study a semiconductor laser system excited by multiplicative Ornstein-Uhlenbeck (OU) noise and additive sine-Wiener (SW) noise with a related real part and imaginary part. The two-dimensional Langevin equation(LE) with cross-correlating complex OU noise and complex SW noise is equivalent to the six-dimensional LE. Based on functional methods and spatial dimension extension methods, the six-dimensional Fokker-Planck equation(FPE) is achieved. Through Taylor expansion approximation and linear transformation, the dimensionality of FPE is reduced, and the exact expression of the probability distribution (SPD) in steady state is obtained. The impacts of noise parameters on laser intensity on SPD are further analyzed. Moreover, based on the reduced FPE, the information entropy of the system is obtained. This calculation is useful for us to understand further the influence of system parameters and noise correlation coefficient on the system. The main work lies in obtaining an alternative method for deriving a FPE corresponding to the two-dimensional stochastic semiconductor laser model, which is more applicable than methods in previous literature. An equivalent expression for cross-correlated OU noise and cross-correlated SW noise as well as a method for approximate solutions of a high-dimensional FPE are also provided.

Maximum correntropy unscented filter based on unbiased minimum-variance estimation for a class of nonlinear systems

Introduction: The unscented Kalman filter based on unbiased minimum-variance (UKF-UMV) estimation is usually used to handle the state estimation problem of nonlinear systems with an unknown input. When the nonlinear system is disturbed by non-Gaussian noise, the performance of UKF-UMV will seriously deteriorate.Methods: A maximum correntropy unscented filter based on the unbiased minimum variance (MCUF-UMV) estimation method is proposed on the basis of the UKF-UMV without the need for estimation of an unknown input and uses the maximum correntropy criterion (MCC) and fixed-point iterative algorithm for state estimation.Results: When the measurement noise of the nonlinear system is non-Gaussian noise, the algorithm performs well.Discussion: Our proposed algorithm also does not require estimation of an unknown input, and there is no prior knowledge available about the unknown input or any prior assumptions. The unknown input can be any signal. Finally, a simulation example is used to demonstrate the effectiveness and reliability of the algorithm.

ALGORITHM FOR DETERMINING THE PROBABILITY OF TARGET DETECTION AND RECOGNITION BY AN AVIATION THERMAL VISION SYSTEM IN NOISE CONDITIONS

Thermal imaging equipment is widely used in modern military operations due to its ability to observe targets even in adverse weather conditions continuously. Thermal imaging surveillance systems (TISS) are primarily designed to detect, recognize, and identify targets. Despite the progress, existing methods do not allow quickly calculating the probability of detecting, recogniz- ing, or identifying a target at user-defined distances from the TISS to the target. The article aims to develop a more advanced method and algorithm for calculating the probability of detection, recognition, and identification of a target by a noise-limited thermal imaging surveillance system. A more advanced (convenient) algorithm and method for calculating the probability of detection, recognition, and identi- fication of an object (target) by a thermal imaging surveillance system at a given range have been developed. This is based on the minimum resolution temperature difference, Johnson’s criterion, as well as the TTPF and TRTPF functions. The proposed algorithm makes it relatively easy to calculate the probability of detecting, recognizing, and identifying a target by a thermal imaging system, which is limited by system noise.

Privacy-aware quantum convolutional neural network for blockchain-based IoT health care data

Nowadays, the Internet of Things (IoT) is widely used in many applications, including healthcare to monitor the health condition of patients. However, it faces privacy and security issues due to the massive growth of hacking systems that provide illegal access to confidential health information. Blockchain (BC) is applied in IoT healthcare systems to manage healthcare data securely by using transparency features. In this research, a novel CAViaR Jellyfish Swarm Optimization enabled Quantum Convolutional Neural Network (CJSO-QCNN) is developed for the removal of noise in a privacy-aware BC-based IoT healthcare system. The CJSO is the combination of Conditional Autoregressive Value at Risk (CAViaR) and Jellyfish Search Optimizer (JSO). The data privacy is ensured according to the user preference by the service provider. The data is classified initially for identifying the sensitive data and is allowed for the treatment of noise, which is then stored in BC. Later, the user accesses the data by removing the noise using the CJSO-QCNN model. The valid data credentials are stored at the service provider according to user preferences. In addition, the superiority of the designed model is computed by comparing the performance with other prevailing approaches. The experimental results revealed that the CJSO-QCNN attained a maximum accuracy of 88.79%, a True Positive Rate (TPR) of 88.20%, and a True Negative Rate (TNR) of 88.61%.