Noise Transfer Research Articles

Linking Room- and Low-Temperature Electrical Performance of MOS Gate Stacks for Cryogenic Applications

Based on MOSFETs with four different gate stacks, we extract the oxide trap density and transconductance from the low frequency noise and DC transfer characteristics at room temperature, respectively. With the same gate stacks, Hall mobility as a function of carrier density is and the critical density is extracted at low temperatures. These physical quantities are analyzed and correlated explicitly, offering a method to qualitatively compare the quality of the four gate stacks for cryogenic MOS devices, and providing further insight into the material physics at cryogenic temperatures.

Analysis and Design Optimization of a 2-Path Sigma Delta Modulator

This paper presents a new design for the optimization of analog to digital sigma delta-coupling noise annotations. The proposed technique increases the level of the signal-to quantization-noise-ratio (SQNR) by improving the zero of the noise transfer function, which leads to an increase in signal to noise. In the new SQNR design, the modulator rises up to 8.5 db. The proposed structure of the first-order dual-channel sigma-delta modulator is also presented.

Transfer Path Contribution to Floor Vibration of Metro Vehicles Based on Operational Transfer Path Analysis Method

Operational transfer path analysis (OTPA) is an advanced vibration and noise transfer path identification and contribution evaluation method. However, the application of OTPA to rail transit vehicles considers only the excitation amplitude and ignores the influence of the excitation phase. This study considers the influence of the excitation amplitude and phase, and analyzes the contribution of the secondary suspension path to the floor vibration when the metro vehicle runs at 60 km/h, using an analysis based on the OTPA method. The results show that the vertical direction of the anti-rolling torsion bar area provides the maximum contribution to the floor vibration, with a contribution of 22.1%, followed by the longitudinal vibration of the air spring area, with a contribution of 17.1%. Based on the contribution analysis, a transfer path optimization scheme is proposed, which may provide a reference for the optimization of the transfer path of metro vehicles in the future.

Supercontinuum intensity noise coupling in Fourier transform photoacoustic spectroscopy.

We investigate the noise transfer mechanism from the light source intensity fluctuations to the acoustic signal in Fourier transform photoacoustic spectroscopy (FT-PAS). This noise coupling is expected to be reduced in FT-PAS compared with conventional Fourier transform spectroscopy, as only the specific spectral components that are absorbed by the probed sample contribute to the noise level. We employ an incoherent supercontinuum (SC) light source in our experiments and observe a linear relation between the sample gas concentration and the detected noise level, which significantly reduces the influence of the SC noise on the detection limit. Based on our experimental results, we derive a model for the noise level, which establishes the foundation for practical sensitive implementation of FT-PAS.

A 20 MHz Bandwidth 79 dB SNDR SAR-Assisted Noise-Shaping Pipeline ADC With Gain and Offset Calibrations

This article presents a second-order successive approximation register (SAR)-assisted noise-shaping (NS) pipeline analog-to-digital converter (ADC) with optimized noise transfer function (NTF) zeros, background inter-stage gain, and offset calibrations. The NS, realized with an error-feedback (EF) structure, takes place in the second stage of the pipeline. A single amplifier, utilized for residue handover, realizes the EF and the optimized zeros. Copies of the EF capacitors allow their rotation role for residue saving, feedback, and sampling operations, thus omitting the extra phase for the EF operation. While further incorporating a coarse SAR ADC and partial interleaving techniques, the prototype can run at high speed with low power and strong in- band noise suppression. A split-over-time architecture with level-shift-based gain calibration is introduced to ensure the robustness of the ADC, whose hardware also facilitates the offset calibration. Besides, the split second-stage ADCs also share a single comparator, thus enabling a mismatch error insensitive split-ADC architecture with a fast convergence speed of 4000 cycles. Fabricated in 28 nm CMOS, the ADC prototype runs at a 260 MHz sampling rate and retains 20 MHz bandwidth (BW) with a signal-to-noise-and-distortion ratio (SNDR) of 79.1 dB under a small over-sampling ratio (OSR) of 6.5. It consumes 3.1 mW power at a 1 V supply and yields a Schreier figure-of-merit (FoM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sub> ) of 177.2 dB.

Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer.

MotivationCryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from the extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryo-EM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods.ResultsHere, we introduce the Noise-Transfer2Clean (NT2C), a denoising deep neural network (DNN) for cryo-EM to enhance image contrast and restore specimen signal, whose main idea is to improve the denoising performance by correctly learning the noise distribution of cryo-EM images and transferring the statistical nature of noise into the denoiser. Especially, to cope with the complex noise model in cryo-EM, we design a contrast-guided noise and signal re-weighted algorithm to achieve clean-noisy data synthesis and data augmentation, making our method authentically achieve signal restoration based on noise’s true properties. Our work verifies the feasibility of denoising based on mining the complex cryo-EM noise patterns directly from the noise patches. Comprehensive experimental results on simulated datasets and real datasets show that NT2C achieved a notable improvement in image denoising, especially in background noise removal, compared with the commonly used methods. Moreover, a case study on the real dataset demonstrates that NT2C can greatly alleviate the obstacles caused by the SNR to particle picking and simplify the identifying of particles.Availabilityand implementationThe code is available at https://github.com/Lihongjia-ict/NoiseTransfer2Clean/.Supplementary information Supplementary data are available at Bioinformatics online.

Correlation analysis between radiation exposure and the image quality of cone-beam computed tomography in the dental clinical environment.

PurposeThis study was conducted to measure the radiation exposure and image quality of various cone-beam computed tomography (CBCT) machines under common clinical conditions and to analyze the correlation between them.Materials and MethodsSeven CBCT machines used frequently in clinical practice were selected. Because each machine has various sizes of fields of view (FOVs), 1 large FOV and 1 small FOV were selected for each machine. Radiation exposure was measured using a dose-area product (DAP) meter. The quality of the CBCT images was analyzed using 8 image quality parameters obtained using a dental volume tomography phantom. For statistical analysis, regression analysis using a generalized linear model was used.ResultsPolymethyl-methacrylate (PMMA) noise and modulation transfer function (MTF) 10% showed statistically significant correlations with DAP values, presenting positive and negative correlations, respectively (P<0.05). Image quality parameters other than PMMA noise and MTF 10% did not demonstrate statistically significant correlations with DAP values.ConclusionAs radiation exposure and image quality are not proportionally related in clinically used equipment, it is necessary to evaluate and monitor radiation exposure and image quality separately.

Wideband 1-Bit Bandpass Delta Sigma Modulator Using Elliptic Filter in Noise Transfer Function

A 1-bit band-pass delta-sigma modulator (BP-DSM), which utilizes oversampling technology, allows a modulated signal to be directly output by using a high-speed 1-bit digital pulse train, thus realizing miniaturization of transmitters. For 1-bit BP-DSM, the noise transfer function (NTF) is used to suppress quantization noise power in the transmission band and a guideline of out-of-band gain of |NTF|&#x003C; 1.5 is used to prevent oscillation. However, in previous studies, such as Butterworth and inverse Chebyshev filters, the out-of-band gain was designed indirectly by tuning the zeros and poles in the transmission band and thus, was not stabilized sufficiently. Furthermore, even though the zeros of the NTF are identical to the poles of the loop filter, there are still widely used designs in which the zeros are set on the unit circle, making stabilization quite difficult. Therefore, in this paper, we propose a feasible implementation of the NTF for 1-bit BP-DSM with an elliptic filter that can be used to set not only in-band but also out-of-band gain, in which both the zeros and poles are set inside the unit circle. As a design result, a modulation bandwidth of 400 MHz as a relative bandwidth of 11%, a noise suppression bandwidth of 800 MHz, and an adjacent channel leakage power ratio of 50 dB were achieved at a center frequency of 3.6 GHz, enabling a wider bandwidth and higher SNR than before by improving the stability.

A 320-MS/s 2-b/cycle Second-order Noise-shaping SAR ADC

A 2<SUP>nd</SUP>-order Noise-Shaping ADC using a 2-bit/cycle SAR ADC is proposed. With a designated reference DAC and a signal DAC, three comparators in the SAR ADC enable 2-bit conversion in each comparison cycle. The noise transfer function (NTF) of the ADC is implemented in an error-feedback structure to bypass the need for power-consuming integrator. A low-gain switched input/output openloop residue amplifier and a switched-capacitor FIR filter realizes the NTF coefficients. The proposed ADC was designed with a 28-nm CMOS process with 1-V power supply. The SPICE simulation results show that the designed ADC has SNDR of 69.9 dB and power consumption of 4.08 mW, when operated with a sampling rate of 320-MS/s and OSR of 8 achieving a Walden figure-of-merit (FoM) of 39.9-fJ/conv.-step.

Amplification and phase noise transfer of a Kerr microresonator soliton comb for low phase noise THz generation with a high signal-to-noise ratio.

Optical injection locking is implemented to faithfully transfer the phase noise of a dissipative Kerr microresonator soliton comb in addition to the amplification of the Kerr comb. Unlike Er-doped fiber and semiconductor optical amplifiers, the optical injection locking amplifies the comb mode without degrading the optical signal-to-noise ratio. In addition, we show that the residual phase noise of the optical injection locking is sufficiently small to transfer the relative phase noise of comb modes (equivalent to the repetition frequency) of low phase noise Kerr combs, concluding that the optical injection locking of a Kerr comb can be an effective way to generate low phase noise terahertz (THz) waves with a high signal-to-noise ratio through an optical-to-electronic conversion of the Kerr comb.

Estimation of Phase Noise Transfer Function

Quantifying frequency converters’ residual phase noise is essential in various applications, including radar systems, high-speed digital communication, or particle accelerators. Multi-input signal source analyzers can perform such measurements out of the box, but the high cost limits their accessibility. Based on an analysis of phase noise transmission theory and the capabilities of popular instrumentation, we propose a technique extending the functionality of single-input devices. The method supplements absolute noise measurements with estimates of the phase noise transfer function (also called the jitter transfer function), allowing the calculation of residual noise. The details of the hardware setup used for the method verification are presented. The injection of single-tone and pseudo-random modulations to the test signal is examined. Optional employment of a spectrum analyzer can reduce the time and number of data needed for characterization. A wideband synthesizer with an integrated voltage-controlled oscillator was investigated using the method. The estimated transfer function matches a white-box model based on synthesizer’s structure and values of loop components. The first results confirm the validity of the proposed technique.

Optimization of Noise Transfer Path Based on the Composite Panel Acoustic and Modal Contribution Analysis

The noise of a cab directly affects the comfort and labor efficiency of the operators. The optimization of the structure-borne transmission path can obviously reduce the cab noise. The method of panel acoustic contribution analysis (PACA) is used to reduce structure noise. However, most studies only consider the panel acoustic contribution of a single frequency, without considering the contribution of major frequencies synthesis to confirm the optimized panels. In this paper, a novel method is proposed based on composite panel acoustic and modal contribution analysis and noise transfer path optimization in a vibro-acoustic model. First, the finite element model (FEM) and the acoustic model are established. Based on the acoustic transfer vector (ATV) method, a composite panel acoustic contribution analysis method is proposed to identify the panels affecting the noise of the field point. Combined with the modal acoustic contribution of the modal acoustic transfer vector (MATV) method, the noise field point is confirmed in the area which has the most significant influence. Second, the optimization algorithm NLOPT which is a nonlinear optimization is applied to design the areas. The noise transfer path optimization with vibroacoustic coupling response can quickly determine the optimal thickness of the panels and reduce low-frequency noise. The effectiveness of the proposed method is applied and verified in an excavator cab. The sound pressure level (SPL) the driver’s right ear (DRE) decreased obviously. The acoustic analysis of the composite panel acoustic contribution and modal acoustic contribution can more accurately recognize an optimized area than the traditional PACA. This method can be applied in the optimization of the structure-borne transmission path for construction machinery cab and vehicle body.

Analysis of pump-to-signal noise transfer in two-stage ultra-broadband optical parametric chirped-pulse amplification.

In optical parametric chirped-pulse amplification (OPCPA), pump temporal intensity modulation is transferred to the chirped-signal spectrum via instantaneous parametric gain and results in contrast degradation of the recompressed signal. We investigate, for the first time to our knowledge, the pump-to-signal noise transfer in a two-stage ultra-broadband OPCPA pumped by a single laser and show the dependence of pump-induced signal noise, characterized both before and after pulse compression, on the difference in pump-seed delay in the two stages. We demonstrate an up-to-15-dB reduction of the pump-induced contrast degradation via pump-seed delay optimization. Experiments and simulations show that, even when parametric amplifiers are operated in saturation, the pump-seed delay can be used to minimize the pump-induced contrast degradation that is attributed largely to the noises from the unsaturated edges of the pulse and that of the beam.

Detection of invisible cracks in ceramic materials using by pre-trained deep convolutional neural network

Ceramic materials are an indispensable part of our lives. Today, ceramic materials are mainly used in construction and kitchenware production. The fact that some deformations cannot be seen with the naked eye in the ceramic industry leads to a loss of time in the detection of deformations in the products. Delays that may occur in the elimination of deformations and in the planning of the production process cause the products with deformation to be excessive, which adversely affects the quality. In this study, a deep learning model based on acoustic noise data and transfer learning techniques was designed to detect cracks in ceramic plates. In order to create a data set, noise curves were obtained by applying the same magnitude impact to the ceramic experiment plates by impact pendulum. For experimental application, ceramic plates with three invisible cracks and one undamaged ceramic plate were used. The deep learning model was trained and tested for crack detection in ceramic plates by the data set obtained from the noise graphs. As a result, 99.50% accuracy was achieved with the deep learning model based on acoustic noise.

A 12.1bit-ENOB noise shaping SAR ADC for biosensor applications

This paper proposes a low power fully-passive noise shaping successive approximation register analog-to-digital converter (SAR ADC) for biosensor applications. The proposed ADC includes a second-order fully-passive integrator achieves a second-order noise transfer function. With an independent residue voltage generator to adjust common voltage between the main and residue path, the power efficiency of three differential inputs comparator is improved. The 0.18μm CMOS prototype chip occupies a total area of 1.37 mm2 with a core area of 0.58 mm2 and consumes 131 μW from a 1.3 V supply. The proposed SAR ADC achieves a peak signal-to-noise-and-distortion ratio (SNDR) of 74.8 dB and a peak spurious-free dynamic range (SFDR) of 89.7 dB at a sampling rate of 8 MSPS and oversampling rate of 16, leading to the Schreier figure of merit (FoMs) of the proposed ADC of 167.4 dB.

Maximum Likelihood estimation for non-minimum-phase noise transfer function with Gaussian mixture noise distribution

In this paper a Maximum Likelihood estimation algorithm for a linear dynamic system driven by an exogenous input signal, with non-minimum-phase noise transfer function and a Gaussian mixture noise is developed. We propose a flexible identification technique to estimate the system model parameters and the Gaussian mixture parameters based on the Expectation–Maximization algorithm. The benefits of our proposal are illustrated via numerical simulations.

Multi-scale noise transfer and feature frequency detection in SSVEP based on FitzHugh–Nagumo neuron system

Objective. The steady-state visual evoked potential (SSVEP) is one of the most commonly used control signals for brain–computer interfaces (BCIs) due to its excellent interactive potential, such as high tolerance to noises and robust performance across users. In addition, it has a stable cycle, obvious characteristics and minimal training requirements. However, the SSVEP is extremely weak and companied with strong and multi-scale noise, resulting in a poor signal-to-noise ratio in practice. Common algorithms for classification are based on the principle of template matching and spatial filtering, which cannot obtain satisfied performance of SSVEP detection under the multi-scale noise. Therefore, using linear methods to extract SSVEP with obvious nonlinear and non-stationary characteristics, the useful signal will be attenuated or lost. Approach. To address this issue, two novel frameworks based on a two-dimensional nonlinear FitzHugh–Nagumo (FHN) neuron system are proposed to extract feature frequency of SSVEP. Results. In order to evaluate the effectiveness of the proposed methods, this research recruit 22 subjects to participate the experiment. Experimental results show that nonlinear FHN neuron model can force the energy of noise to be transferred into SSVEP and hence amplifying the amplitude of the target frequency. Compared with the traditional methods, the FHN and FHNCCA methods can achieve higher classification accuracy and faster processing speed, which effectively improves the information transmission rate of SSVEP-based BCI.

Measurement and Analysis of Through Glass Via Noise Coupling and Shielding Structures in a Glass Interposer

In this article, we first measured through glass via (TGV) noise coupling and the effectiveness of shielding structures in a glass interposer. To analyze the noise coupling between signal TGVs, an open-ended structure is adopted. Glass interposer test vehicles were fabricated to verify the noise coupling between signal TGVs. With these test vehicles, noise transfer functions between signal TGVs were measured. Based on these measurement results and the equivalent circuit model, the noise coupling between signal TGVs was analyzed. To suppress this TGV noise coupling, shielding structures for the TGV noise coupling were proposed and verified. The proposed shielding structures include the variation of signal TGV pitches and the number of grounded shield TGVs, ground pads, and guard rings, respectively. The effectiveness of the proposed shielding structures was verified up to 20 GHz in frequency-domain measurements. Using the proposed shielding structures, the noise transfer function decreased by 9.4 dB at 5 GHz. Also, the effectiveness of the proposed guard ring structure was verified by a time-domain coupling noise simulation with clock signals at frequencies of 1 GHz. The proposed guard ring successfully suppressed the clock noise coupling between signal TGVs by 60.5% and 69.2% when a signal TGV pitchis 300 and 900 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu {\text{m}}$</tex-math></inline-formula> , respectively.

Frequency domain analysis of optoelectronic oscillators utilizing optical and RF resonators with arbitrary transfer functions

Optoelectronic oscillators (OEOs) have attracted much attention for producing ultra-low phase-noise microwave/millimeter-wave oscillations. Traditional delay-based OEOs usually suffer from strong spurious peaks in their phase noise power spectral densities and possible mode-hopping phenomena. Some methods have been proposed in the literature such as using multi-loop architectures or injection locking to other OEOs or radio frequency (RF) oscillators to reduce these spurious peaks. In other approaches, optical filters/resonators other than optical fibers have been proposed to reduce or suppress these peaks and prevent the mode-hopping phenomenon, such as whispering gallery mode resonators (WGMRs), fiber Bragg gratings, and other forms of microwave photonic filters. Usually, approximate single-purpose approaches have been presented to analyze OEOs utilizing such resonators. Here a general framework for analyzing the performance of OEOs implementing RF and optical filters/resonators with arbitrary linear transfer functions is presented. Consequently, it can consider, for example, the most general dispersion models of the fibers as well as any OEO architecture using a combination of different optical resonators. It can also consider the noise transfer between any sidebands of the RF or optical signals and any kind of amplitude noise to phase noise transfers and vice versa. The non-idealities of the electro-optic modulators such as the chirping and finite extinction ratios can also be taken into account. The validity of the new approach is verified by comparing its results with those previously published in the literature. In particular, the case of a WGMR plus delay line OEO is considered for comparisons.

Using rectified linear unit and swish based artificial neural networks to describe noise transfer in a full vehicle context.

Vehicle interior noise is a quality criterion of passenger cars. A considerable amount of resources is used to evaluate and design the acoustic environment with respect to given requirements. The customer's perception in the end-of-line vehicle is the main criterion. Therefore, full vehicle testing is a large part of today's sound comfort development. To increase efficiency, it is desirable to limit the hardware testing to a specific component. A later reassembly of the full vehicle is done virtually using transfer functions. These transfer functions of the substructures can be derived numerically or through measurements. However, full vehicle simulations are still challenging. Hence, transfer functions are typically measured but come with the burden of complex procedures. In this work, the authors propose a machine learning algorithm to reduce the effort for finding suitable transfer models in the automotive context. Artificial neural networks with rectified linear unit and swish activation functions are trained on full vehicle measurements. Multiple operation conditions are used for training. The networks compute spectral system responses and relative sensitivities for the input features. The performance is discussed with respect to the full vehicle validation data. The results indicate an effective procedure to reduce the costs of full-size vehicle measurements.