Noise Consideration Research Articles

Coherent Noise (Ground roll) Consideration: Local Wave-field Decomposition (LWD) Based Approaches to Suppression

There is always a signal and a noise component in seismic data. Noise can be defined as any recorded energy that tampers with the desired signal of interest. Although it might be difficult to separate signal from noise due to the variety of noise kinds, effective noise suppression is crucial for high-quality imaging. Surface waves or ground roll are a prominent source-generated coherent noise in exploration seismology. Ground roll contains little to no information about the deeper subsurface as it moves laterally through the earth’s near surface. The low-frequency contents and strong amplitudes of these arrivals often mask recordings of body waves reflected by deeper subsurface targets. This discussion paper will describe ground rolls, a significant form of seismic coherent noise, as well as their unique characteristics, how they appear on acquired seismic data, the significance of suppressing them in seismic processing applications, and an examination of local wave-field decomposition (LWD) techniques and their applications.

Joint Design of Camera Spectral Sensitivity and Color Correction Matrix with Noise Consideration

Joint Design of Camera Spectral Sensitivity and Color Correction Matrix with Noise Consideration

Adaptive control concepts using radial basis functions and a Kalman filter for embedded control applications

Look-up tables or semi-physical models are used in engine control application to enhance the transient control performance. However, the calibration of these look-up tables and/or semi-physical models is highly data dependent, requires significant calibration effort, and does not account for effects such as hardware aging, drifts, fuel variations, etc. To address these challenges, this paper presents a generic adaptive control concept for real-time engine control applications. The boundary conditions for the operation on a state-of-the-art engine control unit (ECU) are considered and Radial Basis Function (RBF)-based neural networks are identified for the approximation of non-linear models. To ensure optimal convergence and un-susceptibility to signal noise, a Kalman filter (KF) is exploited for training the RBF network and a modified approximation is derived in this work. The derived KF approximation shows ∼30% better training performance in comparison to the approximation without variable noise consideration. The developed control concept is validated on an engine test bench and a demonstrator vehicle using a Rapid Control Prototyping (RCP) system. The potential of the developed control concept to ensure optimal control response and robustness has been demonstrated.

A Radio Frequency Region-of-Interest Convolutional Neural Network for Wideband Spectrum Sensing.

Wideband spectrum sensing plays a crucial role in various wireless communication applications. Traditional methods, such as energy detection with thresholding, have limitations like detecting signals with low signal-to-noise ratio (SNR). This article proposes a novel deep learning-based approach for RF signal detection in the wideband spectrum. The objective is to accurately estimate the noise distribution in a wideband radio spectrogram and improve the detection performance by substracting it. The proposed method utilizes convolutional neural networks to analyze radio spectrograms. Model evaluation demonstrates that the RFROI-CNN approach outperforms the traditional energy detection with thresholding method by achieving significantly better detection results, even up to 6 dB, and expanding the capabilities of wideband spectrum sensing systems. The proposed approach, with its precise estimation of noise distribution and consideration of neighboring signal power values, proves to be a promising solution for RF signal detection.

Analysis and Design of VCO-Based Neural Front-End With Mixed Domain Level-Crossing for Fast Artifact Recovery

Concurrent neural signal instrumentation withstanding neural stimulation artifacts is essential for bi-directional neural interfaces to guarantee signal integrity. In this work, different front-end structures and stimulation artifact mitigation techniques are firstly reviewed to benchmark their step response speed. Then, a mixed domain level-crossing scheme is proposed to achieve fast dynamic response with minimized hardware overhead. The benefit of extending the phase detection range of the phase detectors in VCO-based continuous time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rm \Delta \Sigma $ </tex-math></inline-formula> modulators is investigated with stability and noise consideration. Then a shift-register-based phase counter is proposed to extend the phase detectors’s detection range, thereby increase quantization resolution and stability margin for in-band noise optimization. The proposed VCO-based neural front-end was fabricated in a 180 nm CMOS process. The prototype achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.38~\mu $ </tex-math></inline-formula> Vrms input-referred noise over 0.5 Hz-10 kHz bandwidth. With a linear input range of 120 mVpp, it exhibits a SNDR of 71.6 dB and a DR of 77.0 dB, which could be further extended up to 100 dB in the artifact adaption mode. Measurements verify that the proposed neural front-end can recover from rail-to-rail differential mode or common mode artifacts within 10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> (minimum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.25~\mu \text{s}$ </tex-math></inline-formula> ) while the superposed small signal can be recorded uninterruptedly.

Chain-structure time-delay reservoir computing for synchronizing chaotic signal and an application to secure communication

In this work, a chain-structure time-delay reservoir (CSTDR) computing, as a new kind of machine learning-based recurrent neural network, is proposed for synchronizing chaotic signals. Compared with the single time-delay reservoir, our proposed CSTDR computing shows excellent performance in synchronizing chaotic signal achieving an order of magnitude higher accuracy. Noise consideration and optimal parameter setting of the model are discussed. Taking the CSTDR computing as the core, a novel scheme of secure communication is further designed, in which the “smart” receiver is different from the traditional in that it can synchronize to the chaotic signal used for encryption in an adaptive manner. The scheme can solve the issues such as design constrains for identical dynamical systems and couplings between transmitter and receiver in conventional settings. To further manifest the practical significance of the scheme, the digital implementation using field-programmable gate array is conducted and tested experimentally with real-world examples including image and video transmission. The work sheds light on developing machine learning-based signal processing and communication applications.

Static Analysis and Health Monitoring of Pipelines in Nuclear Power Plants With Noise Consideration

Abstract This study proposes a numerical methodology for the assessment of physical parameters which are critical to damage detection and structural health monitoring of pipelines propagating fluids under high temperatures. The thermal gradient of the fluid, self-weight of the piping system, and the pressure exerted on the pipe walls are taken into consideration for static analysis. The displacement, stresses, and strains are computed for the imposed loads. The boundary conditions for a pipeline element are assigned through translational and rotational springs whose stiffnesses are unknown. The expression for the boundary stiffnesses is derived by coupling the internal pressure due to fluid flow with thermal loading and weight. The dependent and independent parameters for deciphering the degradation of the pipe element are identified to minimize instrumentation for health monitoring. The primary aspects of damage being investigated are wall thinning due to corrosion resulting in stiffness degradation. In practical scenarios, it has been observed that excessive instrumentation of a structural component for health monitoring might result in measurement noise. Hence, artificial noise is introduced to the measurement stresses and strains in a Gaussian distribution form, which is corrected through a numerical algorithm.

Lossless Snubber for GaN-Based Flyback Converter With Common Mode Noise Consideration

The previous lossless snubber for flyback converters, although with a small number of components, simple control and high power conversion efficiency, has no consideration for electromagnetic interference (EMI). This paper proposes a lossless snubber converter with a novel drain voltage rising slew rate control for the main switch of the flyback converter. Compared to traditional topologies, the proposed snubber topology has two advantages: first, only an additional inductor is used. Second, the proposed method can reduce EMI emissions significantly. Finally, a prototype of the proposed snubber is built and verified with two existing similar snubbers.

Hub and Spoke allocation to minimize the distance and frequency of transportation with Lower Noise Consideration

City logistics is a concept that has developed lately. One method that can be applied is the application of Hub and Spoke in the Urban Logistics system. Hubs and spoke are commonly used for logistics systems in general. However, for hub and spoke in urban logistics systems certainly have different considerations. Using the system methodology, this paper proposes a mathematical model of the application of hub and spoke in urban logistics systems by considering several constraints, one of which is the minimization of noise caused by the transportation process.

Expanding Bias-instability of MEMS Silicon Oscillating Accelerometer Utilizing AC Polarization and Self-Compensation.

This paper presents a MEMS (Micro-Electro-Mechanical System) Silicon Oscillating Accelerometer (SOA) with AC (alternating current) polarization to expand its bias-instability limited by the up-converted 1/f noise from front-end transimpedance amplifier (TIA). In contrast to the conventional DC (direct current) scheme, AC polarization breaks the trade-off between input transistor gate size and white noise floor of TIA, a relative low input loading capacitance can be implemented for low noise consideration. Besides, a self-compensation technique combining polarization source and reference in automatic-gain-control (AGC) is put forward. It cancels the 1/f noise and drift introduced by the polarization source itself, which applies to both DC and AC polarization cases. The experimental result indicates the proposed AC polarization and self-compensation strategy expand the bias-instability of studied SOA from 2.58 μg to 0.51 μg with a full scale of ± 30 g, a 155.6 dB dynamic range is realized in this work.

Genetic Algorithm Approach to the 3D Node Localization in TDOA Systems.

Positioning asynchronous architectures based on time measurements are reaching growing importance in Local Positioning Systems (LPS). These architectures have special relevance in precision applications and indoor/outdoor navigation of automatic vehicles such as Automatic Ground Vehicles (AGVs) and Unmanned Aerial Vehicles (UAVs). The positioning error of these systems is conditioned by the algorithms used in the position calculation, the quality of the time measurements, and the sensor deployment of the signal receivers. Once the algorithms have been defined and the method to compute the time measurements has been selected, the only design criteria of the LPS is the distribution of the sensors in the three-dimensional space. This problem has proved to be NP-hard, and therefore a heuristic solution to the problem is recommended. In this paper, a genetic algorithm with the flexibility to be adapted to different scenarios and ground modelings is proposed. This algorithm is used to determine the best node localization in order to reduce the Cramér-Rao Lower Bound (CRLB) with a heteroscedastic noise consideration in each sensor of an Asynchronous Time Difference of Arrival (A-TDOA) architecture. The methodology proposed allows for the optimization of the 3D sensor deployment of a passive A-TDOA architecture, including ground modeling flexibility and heteroscedastic noise consideration with sequential iterations, and reducing the spatial discretization to achieve better results. Results show that optimization with 15% of elitism and a Tournament 3 selection strategy offers the best maximization for the algorithm.

Robust Power System State Estimator using Projection Statistics for IEEE systems

In Real-time applications like measurement noise in distributed power system. From model of Gaussian frequently deviates or unknown are obtained and estimated in terms of impulse pulse of noise. Under such situations, the efficiency behavior changes. State estimation (SE) methods which are conventional which have both measured and estimated values with the noise consideration is greatly depleted. In this paper, state estimation methods are used in power systems to reduce the error and noise either in two stages of operation. To achieve a linear property of the estimated output by using adjustment of weights and its measurements. The estimator considers the limits and statistical data with a good efficiency even with and without noise effects and disturbances. Tu understand the behavior of SE methods, IEEE test bench of 14-bus and 30- buses are considered. the results are simulated in MTALAB-SIMULINK environment. Index Terms: Load flow study, IEEE 14 and 30 Bus systems, Statistics, GM-estimator, MATLAB, M file.

Analysis of impacting factors on polarimetric SAR oil spill detection

Polarimetric synthetic aperture radar (SAR) oil spill detection parameters conformity coefficient (μ), Muller matrix parameters (|C|, B0), the eigenvalues of simplified coherency matrix (λnos) and the influence of SAR observing parameters, ocean environment and noise level are investigated. Radarsat-2 data are used to make systematic analysis of polarimetric parameters for different incidences, wind speeds, noise levels and the ocean phenomena (oil slick and look likes). The influence of the SAR observing parameters, the ocean environment and the noise level on the typical polarimetric SAR parameter conformity coefficient has been analyzed. The results indicate that conformity coefficient cannot be simply used for oil spill detection, which represents the image signal to the noise level to some extent. When the signals are below the noise level for the oil slick and the look likes, the conformity coefficients are negative; while the signals above the noise level corresponds to positive conformity coefficients. For dark patches (low wind and biogenic slick) with the signal below the noise, polarization features such as conformity coefficient cannot separate them with oil slick. For the signal above the noise, the oil slick, the look likes (low wind and biogenic slick) and clean sea all have positive conformity coefficients, among which, the oil slick has the smallest conformity coefficient, the look likes the second, and the clean sea the largest value. For polarimetric SAR data oil spill detection, the noise plays a significant role. So the polarimetric SAR data oil spill detection should be carried out on the basis of noise consideration.

LCL Filter Design with EMI Noise Consideration for Grid-Connected Inverter

The grid-injected current total harmonics distortion (THD) and electromagnetic interference (EMI) noise must be considered in a transformer-less grid-connected inverter for a more electric aircraft (MEA) system. This paper develops a procedure for the design of the magnetic components of the LCL filter used in harmonic control and EMI reduction of the silicon carbide (SiC) grid-connected inverter in the MEA. The LCL filter is designed with effective harmonic attenuation and a compact size. To meet the space constraint requirement, specific design attention is focused on a customized amorphous cored inductor, with a comprehensive study of the relationships of inductor winding with respect to EMI noise. Reduced THD and EMI are verified through experiments performed on a 30 kW, 230 V/400 Hz three-phase grid-connected inverter prototype with the LCL filter.

Noise and Bandwidth Consideration in Designing Op-Amp Based Transimpedance Amplifier for VLC

In a visible light communication (VLC) system, there are many modules involved. One of the important modules is Transimpedance Amplifier (TIA) that resides in the analog front-end receiver (Rx-AFE). TIA is responsible for performing signal conversion from current signal, which is provided from the photodiode (PD) to voltage signal. It is the reason why the TIA should be operating in low noise condition and wide bandwidth of frequency. These will enable a flexible coverage of the VLC system in performing its signal processing. Hence, in this research, we provide considerations of the noise and frequency bandwidth analysis in designing TIA to cope with the required design specification of a VLC system.

Low noise consideration of cabin less tractor by using sound source model of internal combustion engine

Low noise consideration of cabin less tractor by using sound source model of internal combustion engine

A 12-bit 210-MS/s 2-Times Interleaved Pipelined-SAR ADC With a Passive Residue Transfer Technique

A 12-bit 210-MS/s 2-channel time-interleaved analog-to-digital converter (ADC) employing a pipelined-SAR architecture for low-power and high-speed application is presented. The proposed ADC is partitioned into 3 stages with a passive residue transfer technique between the 1st and 2nd stages for power saving and active residue amplification between the 2nd and 3rd stages for noise consideration. Furthermore, a 2.8-bit SAR conversion embedded in the 1st stage also improves the speed and downsizes the required decoupling capacitance of reference voltage. With the foreground calibration, the inter-stage DAC error and gain/offset errors between interleaving channels are resolved. The proposed ADC, fabricated in a 65-nm CMOS technology, consumes 5.3 mW from a 1-V supply and achieves an SNDR of 63.5 dB at low input frequencies and 60.1 dB near Nyquist rate. The prototype occupies an active area of 0.48 $\text{mm}^{2}$ and the corresponding FoM is 30.3 fJ/conversion-step.

Quantization noise consideration and characterization in Sigma-Delta MEMS accelerometer

In order to predict the noise performance of a digital capacitive MEMS accelerometer and optimize the parameters of circuits, an improved quantization noise model is presented in this paper. Considering the distortion produced by the nonlinear characteristic of a quantizer, a system model of the Sigma-Delta (ΣΔ) accelerometer is established on the basis of describing function method model of a 1-bit quantizer. On the basis of this model, the formula of quantization noise before noise shaping, the transfer function of quantization noise and its expression in the signal band are presented. The model of quantization noise proposed in this paper includes the influence of sensing element parameters and the high non-linearity of a 1-bit quantizer. DC and AC simulation results show that the model can forecast the quantization noise in the signal band more accurately, compared with the models based on linear and quasi-linear model of a quantizer. The influences of electronic noise and sampling frequency on quantization noise are also analyzed. The results show that in ΣΔ MEMS accelerometer, electronic noise will lead to a reduction of the quantization gain and impact the noise shaping ability seriously. Increasing sampling frequency cannot effectively reduce the output quantization noise, but it will decline with sampling frequency at the slope of −3dB/oct.

Induction Machine Design and Analysis for General Motors e-Assist Electrification Technology

The integrated starter generator replaces the conventional starter and alternator with one electrical machine handling both functions. Start/Stop functionality, vehicle launch assistance, and higher speed transient power supplementing enhance the vehicle performance at the lower fuel consumption rate. This functionality requires the electrical machine to provide high starting and launch assistant torque in motoring mode and relatively high power capability over the wide speed range for battery charging. The overall cost of the system is the underlining concern and crucial part of the design optimization. This paper focuses on advantages of induction machines (IMs) in automotive industry and an approach to design a cost-effective electrical machine for belted starter–alternator applications. Design optimization of the IM is described to achieve desired performance, including rotor bar count, solid conductor (bar winding) versus stranded winding design, rotor bar shape optimization, and finally performance maps for the electrical machine, including both predicted and measured results. A thermal study of the machine is also presented, as well as the noise, vibration, and harshness (NVH) consideration in the design selection.

High accuracy localization of long term evolution based on a new multiple carrier noise model.

A high accuracy localization technique using Long Term Evolution (LTE) based on a new and accurate multiple carrier noise model has been developed. In the noise consideration, the LTE multiple carriers phase noise has been incorporated so that a new and accurate noise model is achieved. An experiment was performed to characterize the phase noise of carriers at 2 GHz. The developed noise model was incorporated into LTE localization analysis in a high traffic area in Hong Kong to evaluate the accuracy of localization. The evaluation and analysis reveals that the new localization method achieves an improvement of about 10% accuracy comparing to existing widely adopted schemes.