Simultaneous Noise Research Articles

A CNN Based Software Gradiometer for Electromagnetic Background Noise Reduction in Low Field MRI Applications.

The shielding of electromagnetic noise is critical in obtaining magnetic resonance imaging measurements in the ultra-low magnetic field regime where the intrinsic signal-to-noise ratio is very small. The traditional approach of using an enclosure for electromagnetic shielding is expensive and hinders system portability. We describe here the use of a CNN-based software gradiometer to suppress the effect of electromagnetic ambient background noise sources that inductively couple into the signal detection coils. The system involves three ambient noise monitoring coils placed at a distance from the magnetic resonance signal detector. The three coils were used to synthesize the ambient noise captured by the signal detector; a convolutional neural network approach was used. Mathematical foundations are provided to justify the noise suppression framework. The results show that as much as 20-fold noise suppression can be achieved using an optimized convolutional neural network and simultaneous ambient noise measurements. The proposed approach has the potential to replace the requirement for magnetically shielded enclosures and make ultra-low field magnetic resonance imaging truly portable.

Influence of Seat Vibration Frequency on Total Annoyance and Interaction Effects Caused by Simultaneous Noise and Seat Vibrations in Commercial Vehicles

In vehicles, noise and seat vibrations can be perceived by the driver, depending on their signal properties, as annoying or unpleasant. Because perception is a complex process, it is necessary to consider both types of stimuli at the same time to assess annoyance in such situations. A perception experiment was carried out to investigate the interaction between simultaneous noise and seat vibrations, as well as the influence of seat vibration frequency in vehicle situations. For the experiment, acoustic and optical stimuli such as seat vibrations were recorded in a mini excavator and a refuse collection vehicle from the view of the driver. The recordings were prepared for plausible reproduction in a laboratory experiment. The participants of the experiment were presented with two different vehicle scenes with simultaneous noise, seat vibrations and visuals. The average total levels of vibration and noise, as well as the vibration frequency ranges of the various vehicle scenes, were varied. The results suggest that an interaction effect between noise and vibration should be accounted for when assessing total annoyance in such situations. Models for the prediction of the relative total annoyance causes of variations in noise and vibration levels were developed.

Position control for an autonomous underwater vehicle under noisy conditions

Position tracking of an autonomous underwater vehicle is not trivial as its accuracy is affected by process noise, measurement noise, and uncertain hydrodynamic parameters. Thus, in this article, we studied the position control of an autonomous underwater vehicle that operates under largely unbounded system uncertainties and large noise levels and proposed a method that reduces the interference and thereby enhances the overall autonomous underwater vehicle position estimation. Our technique extended the ensemble Kalman filter by combining it with a time-delay estimator to compensate against extended uncertainty of the system dynamics which cannot be solved by the traditional ensemble Kalman filter. Our synthetic simulations demonstrated the effectiveness of the proposed controller, highlighting its appealing position-control accuracy under simultaneous noise and uncertain hydrodynamics parameters. In addition, our simulation results showed that the proposed controller outperforms the conventional time-delay controller by a percentage range of approximately 30.8%–92.6% in terms of root-mean-square error and requires on average less than 88.2% calculation time than the conventional model predictive control.

An l ½ and Graph Regularized Subspace Clustering Method for Robust Image Segmentation

Segmenting meaningful visual structures from an image is a fundamental and most-addressed problem in image analysis algorithms. However, among factors such as diverse visual patterns, noise, complex backgrounds, and similar textures present in foreground and background, image segmentation still stands as a challenging research problem. In this article, the proposed method employs an unsupervised method that addresses image segmentation as subspace clustering of image feature vectors. Initially, an image is partitioned into a set of homogeneous regions called superpixels, from which Local Spectral Histogram features are computed. Subsequently, a feature data matrix is created whereupon subspace clustering methodology is applied. A single-stage optimization model is formulated with enhanced segmentation capabilities by the combined action of l ½ and l 2 norm minimization. Robustness of l ½ regularization toward both the noise and overestimation of sparsity provides simultaneous noise robustness and better subspace selection, respectively. While l 2 norm facilitates grouping effect. Hence, the designed optimization model ensures an improved sparse solution and a sparse representation matrix with an accurate block diagonal structure, which thereby favours getting properly segmented images. Then, experimental results of the proposed method are compared with the state-of-art algorithms. Results demonstrate the improved performance of our method over the state-of-art algorithms.

Optical signal denoising through temporal passive amplification

Mitigating the stochastic noise introduced during the generation, transmission, and detection of temporal optical waveforms remains a significant challenge across many applications, including radio-frequency photonics, light-based telecommunications, spectroscopy, etc. The problem is particularly difficult for the weak-intensity signals often found in practice. Active amplification worsens the signal-to-noise ratio, whereas noise mitigation based on optical bandpass filtering attenuates further the waveform of interest. Additionally, current optical filtering approaches are not optimal for signal bandwidths narrower than just a few GHz. We propose a versatile concept for simultaneous amplification and noise mitigation of temporal waveforms, here successfully demonstrated on optical signals with bandwidths spanning several orders of magnitude, from the kHz to GHz scale. The concept is based on lossless temporal sampling of the incoming coherent waveform through Talbot processing. By reaching high gain factors (>100), we show the recovery of ultra-weak optical signals, with power levels below the detector threshold, additionally buried under a much stronger noise background. The method is inherently self-tracking, a capability demonstrated by simultaneously denoising four data signals in a dense wavelength division multiplexing scheme.

A Robust Tensor-Based Submodule Clustering for Imaging Data Using l12 Regularization and Simultaneous Noise Recovery via Sparse and Low Rank Decomposition Approach.

The massive generation of data, which includes images and videos, has made data management, analysis, information extraction difficult in recent years. To gather relevant information, this large amount of data needs to be grouped. Real-life data may be noise corrupted during data collection or transmission, and the majority of them are unlabeled, allowing for the use of robust unsupervised clustering techniques. Traditional clustering techniques, which vectorize the images, are unable to keep the geometrical structure of the images. Hence, a robust tensor-based submodule clustering method based on regularization with improved clustering capability is formulated. The induced tensor nuclear norm (TNN), integrated into the proposed method, offers better low rankness while retaining the self-expressiveness property of submodules. Unlike existing methods, the proposed method employs a simultaneous noise removal technique by twisting the lateral image slices of the input data tensor into frontal slices and eliminates the noise content in each image, using the principles of the sparse and low rank decomposition technique. Experiments are carried out over three datasets with varying amounts of sparse, Gaussian and salt and pepper noise. The experimental results demonstrate the superior performance of the proposed method over the existing state-of-the-art methods.

A 20-44 GHz Wideband LNA Design Using the SiGe Technology for 5G Millimeter-Wave Applications.

This paper presents the design and implementation of a low-noise amplifier (LNA) for millimeter-wave (mm-Wave) 5G wireless applications. The LNA was based on a common-emitter configuration with cascode amplifier topology using an IHP’s 0.13 μm Silicon Germanium (SiGe) heterojunction bipolar transistor (HBT) whose f_T/f_MAX/gate-delay is 360/450 GHz/2.0 ps, utilizing transmission lines for simultaneous noise and input matching. A noise figure of 3.02–3.4 dB was obtained for the entire wide bandwidth from 20 to 44 GHz. The designed LNA exhibited a gain (S_21) greater than 20 dB across the 20–44 GHz frequency range and dissipated 9.6 mW power from a 1.2 V supply. The input reflection coefficient (S_11) and output reflection coefficient (S_22) were below −10 dB, and reverse isolation (S_12) was below −55 dB for the 20–44 GHz frequency band. The input 1 dB (P1dB) compression point of −18 dBm at 34.5 GHz was obtained. The proposed LNA occupies only a 0.715 mm2 area, with input and output RF (Radio Frequency) bond pads. To the authors’ knowledge, this work evidences the lowest noise figure, lowest power consumption with reasonable highest gain, and highest bandwidth attained so far at this frequency band in any silicon-based technology.

Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent.

A hallmark of electrophysiological brain activity is its 1/f-like spectrum - power decreases with increasing frequency. The steepness of this 'roll-off' is approximated by the spectral exponent, which in invasively recorded neural populations reflects the balance of excitatory to inhibitory neural activity (E:I balance). Here, we first establish that the spectral exponent of non-invasive electroencephalography (EEG) recordings is highly sensitive to general (i.e., anaesthesia-driven) changes in E:I balance. Building on the EEG spectral exponent as a viable marker of E:I, we then demonstrate its sensitivity to the focus of selective attention in an EEG experiment during which participants detected targets in simultaneous audio-visual noise. In addition to these endogenous changes in E:I balance, EEG spectral exponents over auditory and visual sensory cortices also tracked auditory and visual stimulus spectral exponents, respectively. Individuals' degree of this selective stimulus-brain coupling in spectral exponents predicted behavioural performance. Our results highlight the rich information contained in 1/f-like neural activity, providing a window into diverse neural processes previously thought to be inaccessible in non-invasive human recordings.

Dynamic Expectation Maximization Algorithm for Estimation of Linear Systems with Colored Noise

The free energy principle from neuroscience has recently gained traction as one of the most prominent brain theories that can emulate the brain’s perception and action in a bio-inspired manner. This renders the theory with the potential to hold the key for general artificial intelligence. Leveraging this potential, this paper aims to bridge the gap between neuroscience and robotics by reformulating an FEP-based inference scheme—Dynamic Expectation Maximization—into an algorithm that can perform simultaneous state, input, parameter, and noise hyperparameter estimation of any stable linear state space system subjected to colored noises. The resulting estimator was proved to be of the form of an augmented coupled linear estimator. Using this mathematical formulation, we proved that the estimation steps have theoretical guarantees of convergence. The algorithm was rigorously tested in simulation on a wide variety of linear systems with colored noises. The paper concludes by demonstrating the superior performance of DEM for parameter estimation under colored noise in simulation, when compared to the state-of-the-art estimators like Sub Space method, Prediction Error Minimization (PEM), and Expectation Maximization (EM) algorithm. These results contribute to the applicability of DEM as a robust learning algorithm for safe robotic applications.

Simultaneous model construction and noise reduction for hierarchical time series via Support Vector Regression

In several applications, there are hierarchically-organized time series that can be aggregated at various levels. In this paper, a novel Support Vector Regression approach is proposed for dealing with hierarchical time series forecasting. The main idea is to pool information across levels of hierarchy, preventing bottom-level series from deviate much with respect to the series at the upper levels. The reasoning behind this approach is to estimate robust bottom-level models that can deal with the intrinsic noise present at this level due to the lack of information. Two variants are presented: First, we solve a single optimization problem that constructs all the related regression functions together, relating the bottom level series with the root node, while the second variant pools relates the leaf nodes with their respective parent nodes. The proposed approach showed best performance when compared with the state of the art on hierarchical time series forecasting using well-known benchmark datasets.

Data-driven framework for high-accuracy color restoration of RGBN multispectral filter array sensors under extremely low-light conditions.

RGBN multispectral filter array provides a cost-effective and one-shot acquisition solution to capture well-aligned RGB and near-infrared (NIR) images which are useful for various optical applications. However, signal responses of the R, G, B channels are inevitably distorted by the undesirable spectral crosstalk of the NIR bands, thus the captured RGB images are adversely desaturated. In this paper, we present a data-driven framework for effective spectral crosstalk compensation of RGBN multispectral filter array sensors. We set up a multispectral image acquisition system to capture RGB and NIR image pairs under various illuminations which are subsequently utilized to train a multi-task convolutional neural network (CNN) architecture to perform simultaneous noise reduction and color restoration. Moreover, we present a technique for generating high-quality reference images and a task-specific joint loss function to facilitate the training of the proposed CNN model. Experimental results demonstrate the effectiveness of the proposed method, outperforming the state-of-the-art color restoration solutions and achieving more accurate color restoration results for desaturated and noisy RGB images captured under extremely low-light conditions.

Time‐domain signal and noise modelling and analysis of millimetre‐wave and THz high electron mobility transistors

This study presents the simultaneous signal and noise modelling and analysis of mm-wave/THz high electron mobility transistors (HEMTs) in the linear regime as stochastic active multi-conductor transmission lines (SAMTLs). For such devices, stochastic inhomogeneous telegrapher equations of SAMTLs are extracted assuming the domination of the quasi-transverse electromagnetic mode. Analytical solutions for these equations do not exist. So a numerical analysis of such equations is introduced here at millimetre-wave and terahertz frequencies. These equations are analysed with a numerical method in the time domain, namely the finite-integration technique. A semi-distributed model in the advanced design system commercial software is used to validate the numerical results. Also, the results of noise figure and scattering parameters (S-parameters) for the metal–semiconductor field-effect transistor transistor, which has the same SAMTLs model as the HEMT transistor, are compared with the measurement reports at microwave frequencies.

Findings on the Central Auditory Functions of Endemic Disease Control Agents.

This study aimed to assess the central auditory functions of endemic disease control agents. This cross-sectional cohort study comprised two groups: the exposed group, with 38 male endemic disease control agents with simultaneous occupational noise and pesticide exposure; and the control group, with 18 age- and sex-matched workers without occupational noise and/or pesticide exposure. All participants underwent pure-tone audiometry, brainstem auditory evoked potentials, dichotic digits test, and transient-evoked otoacoustic emissions suppression effect. There was a significant inter-group difference in waves III and V absolute latencies, and interpeak I–III and I–V latencies bilaterally, with worse results found in the exposed group. Abnormal dichotic digits test results occurred more often in the exposed group, with a significant association between pesticide- and noise-exposure and the abnormal results (p = 0.0099). The transient-evoked otoacoustic emissions with suppression effect did not yield significant inter-group differences. It was concluded that pesticide and noise exposure induce harmful effects on the central auditory functions, particularly on the brainstem and figure-ground speech-sound auditory skills.

A 0.061-mm² 1–11-GHz Noise-Canceling Low-Noise Amplifier Employing Active Feedforward With Simultaneous Current and Noise Reduction

This article proposes a novel wideband common-gate (CG) common-source (CS) low-noise amplifier (LNA) with active feedforward for simultaneous current and noise reduction. By employing an active feedforward stage in the main path, the current dissipation and the thermal noise are effectively suppressed, while the noise and distortion cancelation properties of CG-CS topology are preserved. Moreover, no extra noise is introduced since the additional g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> -boost amplifier follows the same noise-canceling (NC) principle as the CG amplifier, and its noise can be fully canceled at the output theoretically. Thus, the proposed LNA benefits from the g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> -boosting and NC technique simultaneously to obtain a good tradeoff between gain, noise figure (NF), and power consumption. Fabricated in standard 40-nm CMOS technology, the measured results show the proposed LNA exhibits a peak gain of 17 dB, NF of 3.5-5.5 dB from 1 to 11 GHz and an IIP3 of -2.8 dBm at 6 GHz. It consumes 9 mW from a 1.2-V supply and occupies a very compact die area of 0.061 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

A D-Band High-Gain and Low-Power LNA in 65-nm CMOS by Adopting Simultaneous Noise- and Input-Matched G max-Core

This article proposes a high-gain and low-power low-noise amplifier (LNA) by adopting a simultaneous noise- and input-matched (SNIM) maximum achievable gain ( G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> ) core. The G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> -core is implemented by adjusting the infinite combinations of embedding networks with three passive elements. Based on the proposed two-port noise analysis for implementing the G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> -core, the input stage G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> -core can achieve a simultaneous power gain and noise matching. The adoption of the G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> -core in the input stage can maximize the amount of gain per stage, leading to higher total power gain and lower noise figure (NF). The two-stage 150-GHz LNA adopting the SNIM G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> -core is implemented in a 65-nm CMOS process. The measurement results show a peak gain of 17.9 dB at 152.2 GHz, 3-dB bandwidth of 11 GHz, NF of 4.7 and 6.2 dB at 148 and 150 GHz, respectively, and a peak power added efficiency (PAE) of 7.7% while dissipating only 13.73 mW. This work shows the highest gain per stage and the lowest NF with the lowest dc power consumption among other reported CMOS D-band amplifiers.

Novel phase unwrapping technique based on extended information filter

A general framework of a phase unwrapping procedure based on extended information filter is built. Two phase unwrapping algorithms with different observed models, are proposed by combining an extended information filter with an efficient phase gradient estimator, and an efficient quality-guided strategy based on heap-sort. The extended information filter that is a new type of filter with high efficiency and high robustness, is applied to acquire the unambiguous unwrapped phase of wrapped phase images for the first time to our knowledge. Through the application of the extended information filter, simultaneous phase unwrapping and noise suppression have been performed. The efficient phase gradient estimator that has been demonstrated well, is used to efficiently acquire phase gradient information required for the proposed algorithms. The efficient quality-guided strategy based on heap-sort is used to define the path of the proposed algorithms and to guarantee that they efficiently unwrap wrapped pixels along the path, from high-reliance to low-reliance regions. Synthetic and experimental data is used to demonstrate that our proposed algorithms can obtain robust solutions with reasonable time consumption, compared with some of the more traditional algorithms.

A 24 GHz Direct Conversion Receiver for FMCW Ranging Radar Based on Low Flicker Noise Mixer

In this paper, a 24 GHz direct conversion receiver (DCR) for frequency-modulated continuous-wave (FMCW) ranging radar based on low flicker noise mixer in 90 nm silicon-on-insulator (SOI) CMOS technology is presented. A low-noise and low-power low-noise-amplifier (LNA) adopting simultaneous noise and input matching (SNIM) method is designed. Neutralized technology and boost inductor are introduced to improve performance. The measurement results of standalone LNA show that the peak gain is 17.2 dB at 23.8 GHz and the −3 dB bandwidth is around 2.2 GHz from 22.8 GHz to 25 GHz. The LNA achieves an average 3 dB NF within the 24 GHz band. A current-bleeding mixer is used to lower noise and the factors influencing flicker noise have been discussed. Proper element values and local oscillator (LO) power have been chosen to make the mixer low-noise. Measurement results illustrate that the receiver exhibits 20.3 dB peak gain, 7 dB SSB noise figure (NF) and −22 dBm IP1dB. Flicker noise of the mixer and the receiver are measured respectively and the noise knee-point of receiver is observed 60 kHz. The receiver consumes only 16 mW with chip area of 0.65 mm2 including pads. The results demonstrate that the proposed receiver can be a promising candidate for FMCW ranging radar.

Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning.

A machine learning framework predicting pump powers and noise figure profile for a target distributed Raman amplifier gain profile is experimentally demonstrated. We employ a single-layer neural network to learn the mapping from the gain profiles to the pump powers and noise figures. The obtained results show highly accurate gain profile designs and noise figure predictions, with a maximum error on average of ∼0.3dB. This framework provides a comprehensive characterization of the Raman amplifier and thus is a valuable tool for predicting the performance of next-generation optical communication systems, expected to employ Raman amplification.

Decreased Reemerging Auditory Brainstem Responses Under Ipsilateral Broadband Masking as a Marker of Noise-Induced Cochlear Synaptopathy.

In mammals, a 2-hr exposure to an octave-band noise (OBN) at 100 to 108 dB SPL induces loss of synaptic ribbons between inner hair cells and auditory nerve fibers with high thresholds of response (hiT neurons), that encode high-intensity sounds. Here, we tackle the challenge of diagnosing this synaptopathy by a noninvasive functional audiological test, ultimately in humans, despite the expected absence of auditory-threshold elevation and of clear electrophysiological abnormality, hiT neuron contributions being hidden by those of more sensitive and robust neurons. The noise-induced synaptopathy was replicated in mice (at 94, 97, and 100 dB SPL; n = 7, 7, and 8, respectively, against 8 unexposed controls), without long-lasting auditory-threshold elevation despite a twofold decrease in ribbon-synapse number for the 100-dB OBN exposure. Auditory brainstem responses (ABRs) were collected using a simultaneous broadband noise masker just able to erase the ABR response to a 60-dB tone burst. Tone burst intensity was then increased up to 100 dB SPL for eliciting reemerging ABRs (R-ABRs), dependent on hiT neurons as more sensitive neurons are masked. In most ears exposed to 97-dB-SPL and all ears exposed to 100-dB-SPL OBN, contrary to controls, R-ABRs from the overexposed region have vanished, whereas standard ABR distributions widely overlap. R-ABRs afford an individual noninvasive marker of normal-auditory-threshold cochlear synaptopathy. A simple modification of standard ABRs would allow hidden auditory synaptopathy to be searched in a patient. ABR: auditory brainstem response; dB SPL: decibel sound pressure level; DPOAE: distortion-product otoacoustic emission; hiT neuron: high-threshold neuron; IHC: inner hair cell; loT neuron: low-threshold neuron; OBN: octave-band noise; OHC: outer hair cell; PBS: phosphate buffer saline; R-ABR: reemerging ABR.

Relationship between Na layer and CNA variations observed at Syowa, Antarctic

We have performed a statistical data analysis on relationship between simultaneous Na density data and cosmic noise absorption (CNA) data, which is an indicator for energetic particle precipitation, obtained at Syowa, Antarctic in 2000–2002. It is found that the Na densities around the topside of Na layers (above sim 95 ,hbox {km} height) tended to be smaller when the CNA was larger. The amounts of Na density responses, i.e., Na density decrease or Na loss, were increasing with magnetic local time (MLT) from dusk hours to dawn hours, and those of CNA responses, i.e., CNA increase, were also increasing with MLT. Thus, there were clear negative correlations between the Na density and CNA variations. These results indicate that the Na loss around the topside of Na layer would be induced by the energetic particle precipitation, and its effect would be more severe in dawn hours.