Noise Rate Research Articles

Optical design and intensity interferometry simulations in support of the Kilometer Space Telescope.

Very large telescopes will be needed for the future of space science, space laser-com, and other interstellar or interplanetary applications. However, as telescopes' diameters increase, their weight and cost increase dramatically. An approximation for ground-based observatories is that their cost increases to the power of 2.7 of its diameter [SAO, Special Report #385 (1979), p. 9]. Large space-based telescopes become limited by the rocket size and power. Multiple telescopes are also needed for very long baseline interferometry (VLBI), which further increases cost. A solution to overcome these issues is the use of inflatable telescopes. A thin mirror material clearly has much lower mass; however, controlling its surface error or wavefront can be a challenge. Intensity interferometry is an imaging method that has a much looser sensitivity to wavefront error (WFE), and thus is an ideal match for very large inflatable telescopes. A spherical inflatable mirror is the most practical; however, it suffers from spherical aberration. This paper presents new optical designs and simulations for intensity interferometry in support of large inflatable spherical telescopes. The optical system design includes a novel five-mirror off-axis, free-form, spherical aberration corrector. The system design shown is a 10 m diameter f/1 spherical primary mirror with 1.2 arcmin (0.02°) field of view (FOV). MATLAB simulations of intensity interferometry combine signal with noise and WFE. Visibility-based VLBI image simulations are shown based on various telescope arrays. Simulations show that with a 1 GHz detector, a 1 cm RMS WFE is tolerable. So the challenge of the optical design is then more about gathering and concentrating the light down to a reasonable size detector. Further simulations of signal combined with noise indicate that the signal rate must be about 100 times higher than the noise rate for an adequate intensity interferometry measurement. Visibility-based image simulations reveal that many telescopes (5-20 per axis) are needed in a 2D array out to the first visibility minimum to adequately resolve unknown features of a distant object.

High frequency hearing loss detection rate in occupational noise exposed workers in China: a Meta-analysis

Objective: A Meta-analysis was used to evaluate the high frequency hearing loss detection rate, its area and time distributions in occupational noise exposed workers in China to provide evidence for the prevention and control of occupational noise-induced deafness. Methods: From February to April 2020, the number of hearing tests and the number of binaural high frequency average hearing threshold ≥40 dB of workers exposed to noise in key occupational disease monitoring and occupational health risk assessment project in China was collected from China National Knowledge Infrastructure (CNKI) , VIP database and PubMed database. The retrieval time was set from the establishment of the database to February 28, 2020. The R 3.6.2 software was used to calculate the pooled high frequency hearing loss detection rate. Results: A total of 28 studies, included 34 data and 1259571 occupational noise exposed workers, were enrolled. The articles covered 16 provinces in 2015-2018. The pooled high frequency hearing loss detection rate in occupational noise exposed workers was 9.45% (95%CI: 7.64%-11.64%) . The high frequency hearing loss detection rate (10.54%) was higher in 2018 than other years (2017: 10.17%, 2016: 9.21%, 2015: 7.88%) . The high frequency hearing loss detection rate (12.23%) was higher in central China than in eastern (10.21%) , western (7.91%) and north-eastern (5.21%) China. Conclusion: The overall high frequency hearing loss detection rate in occupational noise exposed workers is generally high in China. It is important to strengthen the hearing protection in occupational noise exposed workers to facilitate the occupational noise-induced deafness prevention and control in China.

Optimization of Mouse-on-Mouse Immunohistochemistry by Utilizing Fluorescent-dye Conjugated Secondary Anti-Mouse Antibody.

The application of mouse monoclonal antibody for immunostaining the mouse tissues results in a high rate of background noise because of the interaction of the secondary antibody with endogenous immunoglobulins and other immune components. The most advised blocking strategy for the mouse-on-mouse immunostaining is the use of anti-mouse Fab fragments. Nevertheless, the commercial kits containing Fab fragment are costly and unavailable in many research laboratories. In this study, we provide evidence showing the potential of the fluorescent-dye conjugated secondary anti-mouse antibody for reducing the background noise in the mouse-on-mouse immunohistochemistry. Furthermore, our findings demonstrate the inadequacy of goat serum/protein-blocking solution alone as an immunohistochemistry blocking system for reducing the background noise.

Radiation dose, contrast enhancement, image noise and heart rate variability of ECG-gated CT volumetry using 3D threshold-based segmentation: Comparison between conventional single scan and dual focused scan methods

ObjectiveTo compare radiation dose, contrast enhancement, image noise and heart rate variability in electrocardiography (ECG)-gated computed tomography (CT) ventricular volumetry using a three-dimensional (3D) threshold-based segmentation between the conventional single scan and dual focused scan methods in patients with congenital heart disease. MethodsAfter matching for age, sex, heart rate during the CT examination, and tube voltage, 96 patients (age range, 7 − 36 years; male:female = 63:33) who underwent ECG-gated CT volumetry using a 3D threshold-based segmentation, were divided into 32 patients who underwent a conventional single scan (group 1) and 64 who underwent dual focused scans (group 2). CT radiation dose, contrast enhancement, image noise, and heart rate variability were compared between the two groups. ResultsVolume CT dose index, dose-length product, and effective dose estimates, in group 1 were significantly higher than those in group 2 (28.4 ± 24.6 mGy vs. 9.7 ± 4.5 mGy, 636.5 ± 572.9 mGy cm vs. 379.5 ± 192.4 mGy cm, 8.9 ± 8.0 mSv vs. 5.3 ± 2.7 mSv, 8.2 ± 6.4 mSv vs. 5.0 ± 2.2 mSv, respectively; p values < 0.019). No significant differences in right and left ventricular contrast enhancement were found between the two groups (470.2 ± 152.4 HU vs. 481.0 ± 139.2 HU and 428.6 ± 97.8 HU vs. 429.3 ± 106.7 HU, respectively; p values > 0.05). Image noise measured in the air showed no significant differences between groups 1 and 2 (5.6 ± 1.9 HU vs. 5.5 ± 1.1 HU; p > 0.05). The proportion of heart rate variability ≥ 20 beats per minute in group 1 (15.6 %, 5/32) was significantly higher than that in group 2 (3.1 %, 2/64; p < 0.040). ConclusionCompared with the conventional single scan method, the dual focused scan method can provide a lower radiation dose with comparable contrast enhancement and image noise for ECG-gated CT ventricular volumetry using a 3D threshold-based segmentation in patients with congenital heart disease.

Jitter Modeling in Digital CDR with Quantization Noise Analysis

Phase rotator-based digital clock and data recovery (CDR) using multi-level bang-bang phase detector (ML-BBPD) and time to digital converter (TDC) is analyzed at system and circuit level. A model is proposed for calculating the quantization noise and bit error rate (BER), in order to evaluate the important parameters in CDR design. The jitter analysis is done based on the probability density function achieved from the quantization noise error of the BBPD and TDC. The analysis of ML-BBPD is shown that by increasing the number of sampling clocks, the quantization noise and consequently the jitter and BER are significantly reduced. Also, it is shown that by improving the resolution of the TDC and increasing number of delay cells for the purpose of keeping fixed the dynamic range, the output jitter of TDC is decreased. In the proposed model and also simulation, it is approved that by increasing the ratio of RMS input Gaussian jitter to the quantization step, the output jitter reaches to its saturated value. To prove the jitter model, the goodness of fit test based on Kolmogorov–Smirnov test is used and in addition, the simulation is provided for circuit level CDR. The circuit level simulation is done in TSMC 65 nm CMOS technology. The CDR is worked under 1 V supply voltage at 480Mbit/s bit rate useful for USB2 applications. The CDR dissipates 913 µW power and generates 0.258 ps RMS jitter, while it occupies 166 µm × 104 µm chip area.

A divergence-dependent transmission model for wearable antenna communication in medical application

Wearable sensor technology in medical systems has led to the design and fabrication of body-worn tiny sensing devices. These devices are capable of sensing and transmitting the physiological vitals for clinical analysis and diagnosis. The perplexing factor in transmitting the signals is the occurrence of noise and distortion in the channel. This article introduces a Divergence-Dependent Transmission Scheme to improve the output of the delivering signals. In this transmission scheme, the convergence-free channels are identified based on recurrent feedback analysis. The channel feedback from the previous state of transmission is obtained and is analyzed for distortion and noise. A distortion-less non-overlapping channel is selected for transmitting the sensed signals. The noise mitigation is performed through diagonal analysis of the assessed feedback. This helps to improve the communication efficiency and reduce the noise rate and distortion ratio.

A firefly algorithm for power management in wireless sensor networks (WSNs)

In wireless sensor networks (WSNs), designing a stable, low-power routing protocol is a major challenge because successive changes in links or breakdowns destabilize the network topology. Therefore, choosing the right route in this type of network due to resource constraints and their operating environment is one of the most important challenges in these networks. Therefore, the main purpose of these networks is to collect appropriate routing information about the environment around the network sensors while observing the energy consumption of the sensors. One of the important approaches to reduce energy consumption in sensor networks is the use of the clustering technique, but in most clustering methods, only the criterion of the amount of energy of the cluster or the distance of members to the cluster has been considered. Therefore, in this paper, a method is presented using the firefly algorithm and using the four criteria of residual energy, noise rate, number of hops, and distance. The proposed method called EM-FIREFLY is introduced which selects the best cluster head with high attractiveness and based on the fitness function and transfers the data packets through these cluster head to the sink. The proposed method is evaluated with NS-2 simulator and compared with the algorithm-PSO and optimal clustering methods. The evaluation results show the efficiency of the EM-FIREFLY method in maximum relative load and network lifetime criteria compared to other methods discussed in this article.

Development of Noise and AI-based Pavement Condition Rating Evaluation System

Development of Noise and AI-based Pavement Condition Rating Evaluation System

The majority vote process and other consensus processes on trees

The majority vote process was one of the first interacting particle systems to be investigated. It can be described briefly as follows. There are two possible opinions at each site of a graph G. At rate 1−e, the opinion at a site aligns with the majority opinion at its neighboring sites and, at rate e, the opinion at a site is randomized due to noise, where e∈[0,1] is a parameter. Despite the simple dynamics of the majority vote process, its equilibrium behavior is difficult to analyze when the noise rate is small but positive. In particular, when the underlying graph is G=Zn with n≥2, it is not known whether the process possesses more than one equilibrium. This is surprising, especially in light of the close analogy between this model and the stochastic Ising model, where much more is known. Here, we study the majority vote process on the infinite tree Td with vertex degree d. For d≥5 and small noise, we show that there are uncountably many mutually singular equilibria, with convergence to such an equilibrium occurring exponentially quickly from nearby initial states. Our methods are quite flexible and extend to a broader class of models, consensus processes. This class includes the stochastic Ising model and other processes in which the dynamics at a site depend on the number of neighbors holding a given opinion. All of our proofs are carried out in this broader context.

Temporal Encoding is Required for Categorization, But Not Discrimination.

Core auditory cortex (AC) neurons encode slow fluctuations of acoustic stimuli with temporally patterned activity. However, whether temporal encoding is necessary to explain auditory perceptual skills remains uncertain. Here, we recorded from gerbil AC neurons while they discriminated between a 4-Hz amplitude modulation (AM) broadband noise and AM rates >4Hz. We found a proportion of neurons possessed neural thresholds based on spike pattern or spike count that were better than the recorded session's behavioral threshold, suggesting that spike count could provide sufficient information for this perceptual task. A population decoder that relied on temporal information outperformed a decoder that relied on spike count alone, but the spike count decoder still remained sufficient to explain average behavioral performance. This leaves open the possibility that more demanding perceptual judgments require temporal information. Thus, we asked whether accurate classification of different AM rates between 4 and 12Hz required the information contained in AC temporal discharge patterns. Indeed, accurate classification of these AM stimuli depended on the inclusion of temporal information rather than spike count alone. Overall, our results compare two different representations of time-varying acoustic features that can be accessed by downstream circuits required for perceptual judgments.

Pediatric voice-related quality of life and acoustic analysis of voice: a study in schoolchildren

ABSTRACT Purpose: to assess the self-reported voice-related quality of life of schoolchildren without voice complaints and correlate it to acoustic parameters of voice. Methods: the research population comprised 31 children, mean age 6.5 (±0.17) years. The children’s perception of their voice-related quality of life was verified with the Pediatric Voice-Related Quality of Life Survey, which has 10 closed-ended questions and three domains. The acoustic parameters assessed were fundamental frequency, jitter, shimmer, glottal-to-noise excitation ratio, and noise rate, besides the phonatory deviation diagram, based on the analysis of the emission of the sustained vowel /ɛ/ for 5 seconds. Results: all the children obtained scores close to 100% in the three domains of the Pediatric Voice-Related Quality of Life Survey. As for the acoustic parameters, most of them presented abnormal values in the phonatory deviation diagram and in shimmer. There was a difference between girls and boys only in fundamental frequency. Conclusion: the pediatric self-reported voice-related quality of life of the children studied had a positive impact, despite the acoustic changes found in the voices. There was no correlation between the pediatric voice-related quality of life and the acoustic parameters in voice in the group studied.

NNR-GL: A Measure to Detect Co-Nonlinearity Based on Neural Network Regression Regularized by Group Lasso

For finding keys to understand and elucidate a phenomenon, it is essential to detect dependences among variables, and so measures for that have been proposed. Correlation coefficient and its variants are most common, but they only detect a linear dependence (co-linearity) between two variables. Some recent measures can detect a nonlinear dependence (co-nonlinearity) by means of kernelization or segmentation. They are supposed to handle two variables only and open to discussion with regard to performance in detection and difficulty in setup. There is room for a novel measure based on Neural Networks (NNs), since usual NNs aim at prediction but not at variable dependence detection. For the high-performance detection of co-nonlinearities among multi variables, we propose a measure called NNR-GL based on Neural Network Regression (NNR) regularized by Group Lasso (GL). NNR-GL embodies the detection through multi-input single-output regression by NNR and regularization on the input layer by GL. NNR-GL then calculates how strong the detected co-nonlinearities are by unifying the regression performance and the weights on input variables. We conducted experiments using artificial data to examine the behaviors and fundamental effectiveness of NNR-GL. The performance was estimated by a comprehensive detection performance criterion (CDP-AUC in short), which is the mean of area under curves representing true positive and true negative detections. NNR-GL achieved the values of CDP-AUC from 0.7472 to 0.9681, where 0 means complete failure and 1 means complete success in detection. These values were consistently higher than those from 0.5972 to 0.9259 of the conventional measures for all the different conditions of dependence, data size, and noise rate. Consequently, the effectiveness and robustness of NNR-GL were clearly confirmed.

Design and experiment of light field shaping system for three-dimensional extended light source used in photoacoustic spectrometer

Compared with infrared laser sources, the three-dimensional incoherent extended light source has the advantages of high power, wide spectral range, and low cost. It has extremely wide applications in high-precision and multi-component photoacoustic spectrometers. However, it encounters some problems about poor directivity, low energy density, irregular shape, light field shaping needed in the design of optical system. The photoacoustic spectrometer is required to collect and optimize the radiation of the centimeter-level three-dimensional extended light source to the whole space in a small volume. Through using a series of wavelength and frequency modulation elements, the final cylindrical light field distribution with millimeter-level radius and centimeter-level length is realized. According to the concept of optical expansion and the principle of edge light, this paper breaks through the traditional design mode based on point light source in the process of optical system design and optimization. The concept of extended light source is used throughout the design process. The luminous characteristics of the three-dimensional extended light source are directly acquired by the self-designed measurement method and device which is accurately reflected in the three-dimensional extended light source model in the form of micro-element. The design of the light field shaping system of the three-dimensional extended light source for the photoacoustic spectrometer is realized by the aspheric surface, and the relevant experimental verification is carried out. Taking the Hawkeye IR-Si272 light source for example, the experimental value of the light power at the entrance of the photoacoustic cell and the sidewall noise rate of the photoacoustic spectrometer have a small deviation from their corresponding simulation values. Compared with the original condenser system, the self-designed photoacoustic spectrometer light source system increases the value of the light power at the entrance of the photoacoustic cell from 0.86W to 1.32W, and reduces the value of the sidewall noise rate from 50.3% to 19.7%. The lower limit of detection of the concentration of trace gas in the order of ppm (parts per million) is also achieved.

Link quality estimation method based on gradient boosting decision tree

In the application of wireless sensor networks, link quality estimation is the primary problem to guarantee the reliable transmission of data and the performance of the upper layer network protocol. In order to accurately evaluate link quality, a link quality estimator based on gradient boosting decision tree (GBDT) was proposed. The physical layer parameter average received signal strength indication, mean link quality indicator and mean signal noise rate is selected as the input of the GBDT estimator and the nonlinear correlation between physical layer parameters and packet received rate is analysed by using the maximum information coefficient method. Considering the influence between outliers and different dimensionality of parameters, we used the boxplot method to carry out smoothing and normalisation processing to reduce the complexity of the estimator. At last, the improved particle swarm optimisation algorithm is used to select the optimal parameter combination in the GBDT estimator. The experimental results show that compared with the support vector machine (SVM) estimator, the estimator of this paper has higher accuracy and stability.

Training Robust Deep Neural Networks on Noisy Labels Using Adaptive Sample Selection With Disagreement

Learning with noisy labels is one of the most practical but challenging tasks in deep learning. One promising way to treat noisy labels is to use the small-loss trick based on the memorization effect, that is, clean and noisy samples are identified by observing the network’s loss during training. Co-teaching+ is a state-of-the-art method that simultaneously trains two networks with small-loss selection using the “update by disagreement” strategy; however, it suffers from the problem that the selected samples tend to become noisy as the number of iterations increases. This phenomenon means that clean small-loss samples will be biased toward agreement data, which is the set of samples for which the two networks have the same prediction. This paper proposes an adaptive sample selection method to train deep neural networks robustly and prevent noise contamination in the disagreement strategy. Specifically, the proposed method calculates the threshold of the small-loss criterion by considering the loss distribution of the whole batch at each iteration. Then, the network is backpropagated by extracting samples below this threshold from the disagreement data. Combining the disagreement and agreement data of the two networks can suppress the degradation of the true-label rate of training data in a mini batch. Experiments were conducted using five commonly used benchmarks, MNIST, CIFAR-10, CIFAR-100, NEWS, and T-ImageNet to verify the robustness of the proposed method to noisy labels. The results show the proposed method improves generalization performance in an image classification task with simulated noise rates of up to 50%.

An Improvement of SNR for Simultaneously Wireless Power and Data Transfer System with Full-Duplex Communication Mode

The power interference is always inevitable in the simultaneous wireless power and data transfer (SWPDT) system resulting in the lower signal to noise rate (SNR) and may cause the failure of communication. Besides, the interference between bidirectional data source is also the problem during the full-duplex communication. To solve these problems, in this article, an improvement of SNR for SWPDT system with full-duplex communication mode is proposed. In the power transfer channel, the double-sided LCCL compensation topology is selected due to its good performance of blocking the power interference on data transmission. Besides, the constant current characteristic of primary coil could guarantee the stability of power transmission. In the data transfer channel, a novel data transmission based on the dual-resonant structure is proposed to suppress the ipsilateral interference, and the full-duplex communication is achieved by two different frequencies. An optimization method of power and data transfer channel to improve SNR further is proposed after the analysis on interferences of power transmission and ipsilateral data source. Finally, the feasibility of proposed topology and the correctness of the parameter design method are verified by the simulation and experimental results.

Dynamic RFID Data Filtering and Application

Radio frequency Identification “RFID” Technology uses the radio frequency waves to transfer the RFID data between RFID readers and tags which are used to identify objects/employees without line of sight. The RFID data which is captured by the tag reader may contain false readings, noise, and duplicates which implies data filtering and cleaning. Therefore, it is necessary to develop efficient processing algorithms of RFID data. This pa-per presents a dynamic technique to filter the RFID data, elimi-nate duplicates and filter noise. Data filtering during employee identification in the work place enhances the performance of employee attendance management systems. The proposed sys-tem compared to the Denoising and duplication Elimination approach under different arrival rates at a rate 0.1 tag/sec and under the noise rate at rate between 0.085-0.01 tag/sec.

An Optimization Method for Simultaneous Wireless Power and Data Transfer Systems

Simultaneous wireless power and data transfer (SWPDT) technology is usually needed in wireless power transfer (WPT) systems. In this paper, to achieve the efficient and stable SWPDT, the signal to noise rate (SNR) is first optimized through the analysis of data transmission gain and the interference of power transfer on data transfer. In order to reduce the interference of power channel to data transfer channel, the compensation topology of double-sided LCC and the band-stop networks are used. To solve the problem of SNR limitation, the coupling relationships between each circuit are taken into consideration and a matching method of resonance parameters to improve the SNR is presented. Based on the relationship between SNR and the parameters of data transfer channel, an optimization design method focused on parameters of data transfer channel for improving the SNR is presented. Finally, the feasibility and the correctness of the parameter design method proposed in this paper is verified by simulation and experiment.

Quantum magic rectangles: Characterization and application to certified randomness expansion

We study a generalization of the Mermin-Peres magic square game to arbitrary rectangular dimensions. After exhibiting some general properties, these rectangular games are fully characterized in terms of their optimal win probabilities for quantum strategies. We find that for $m \times n$ rectangular games of dimensions $m,n \geq 3$ there are quantum strategies that win with certainty, while for dimensions $1 \times n$ quantum strategies do not outperform classical strategies. The final case of dimensions $2 \times n$ is richer, and we give upper and lower bounds that both outperform the classical strategies. Finally, we apply our findings to quantum certified randomness expansion to find the noise tolerance and rates for all magic rectangle games. To do this, we use our previous results to obtain the winning probability of games with a distinguished input for which the devices give a deterministic outcome, and follow the analysis of C. A. Miller and Y. Shi [SIAM J. Comput. 46, 1304 (2017)].

Directed transport of symmetrically periodic system induced by "color" breaking of noise.

In general, no transport can emerge in a spatially symmetric periodic system subjected to an unbiased dichotomous periodic driving. Here, we used a noise, which switches synchronously with the driving in three cases [switch between Gaussian white noise and colored noise, two colored noises with different colors (e.g., autocorrelation rate), and Gaussian white noise and harmonic velocity noise], to drive such a symmetric system. Numerical results for the cases indicate that the directed transport of the symmetric system can be induced merely by the color breaking (the difference in two autocorrelation rates) of the switch noise. The amplitude of current depends on the difference, i.e., the greater the difference, the greater the current. Also, the greater autocorrelation rate between the two noises determines the direction of current. The current as a function of the noise intensity for all cases has in common that appropriate noise intensity induces optimal transport. Further investigations show that the color breaking comes from the difference of barrier heights between the left and right-tilted potentials induced by the different autocorrelation rates.