Significant Noise Levels Research Articles

Assessment of Four Scatter Correction Methods in In-111 SPECT Imaging: A Simulation Study.

Introduction:Detection of compton scattered photons is one of the most important factors affecting the quality of single-photon emission computed tomography (SPECT) images. In most cases, the multiple-energy window acquisition methods are used for estimation of the scatter contribution into the main energy window(s) used in imaging.Aims and Objectives:The purpose of this study is to evaluate and compare the performance of four different scatter correction methods in In-111 SPECT imaging. Due to the lack of sufficient studies in this field, it can be useful to perform a more detailed and comparative study.Materials and Methods:Four approximations for scatter correction of In-111 SPECT images are evaluated by using the Monte Carlo simulation. These methods are firstly applied on each of photopeak windows, separately. Then, the effect of the correction methods is investigated by considering both the photopeak windows. The images obtained from a simulated multiple-spheres phantom are used for the evaluation of the correction methods by using three assessment criteria, including the image contrast, relative noise, and the recovery coefficient.Results:The results of this study show that the correction methods, when using the single photopeak windows, result in increase in image contrast with a significant level of noise. In return, when both the photopeak energy windows are used for imaging, it is possible to achieve the better image characteristics.Conclusion:The use of the proposed correction methods, by considering both the photopeak windows, leads to improve the image contrast with a reasonable level of image noise.

Експериментальне дослідження частотного дискримінатора з компенсації зміни рівня вхідного сигналу

Subject and Purpose. Answering the need for powerful high-frequency signal generation and desired radar range provision, pulsed magnetrons hold the current lead as part of radar transmitters. Yet there are some disadvantages, such as significant noise level and technological difficulties concerning the frequency tuning implementation. In this connection, a direct control of the pulsed magnetron frequency with the use of the Automatic Frequency Control (AFC) causes problems. And in the receiver, the AFC of the heterodyne rather than of the magnetron is more frequently used. The heterodyne frequency is maintained at such a level that the receiver is always tuned to the frequency of signal to receive. For various signal generation arrangements, the existing methods of frequency tuning with a frequency discriminator will be analyzed towards the following development of a principal electrical schematic diagram with gaining the operating characteristics of the frequency discriminator prototype on that diagram basis. Methods and methodology. The electrical schematic diagram is developed in terms of intuitive approach and analyzed by the mathematical modeling method. Results. The functional scheme of a frequency discriminator combining advantages of both single-cycle circuit (design simplicity and ease of tuning) and two-cycle circuit (null output voltage at a given frequency of the input signal and compensation possibilities as to the input voltage amplitude varying and interference) has been developed. The principal electrical schematic diagram of this new frequency discriminator has been reported together with the measured amplitude and frequency characteristics, dynamical figures, etc. Conclusions. The novelty of the proposed concept of a heterodyne AFC system built around a frequency discriminator has been confirmed. Its advantages over the known solutions have been shown.

Benchmarking a Commercial (Sub-)THz Focal Plane Array Against a Custom-Built Millimeter-Wave Single-Pixel Camera

For the first time, the characteristics of an evolving commercial camera technology that can operate at millimeter-wave frequencies has been independently investigated. In this work, we benchmark the TeraSense camera against a custom-built single-pixel camera at W-band, for image quality and aperture reflectance. It is found that the Tera-1024 TeraSense camera exhibits limited image resolution and fidelity, with significant levels of systematic spatial noise. In a poor signal-to-noise ratio scenario, the addition of random noise exacerbates these problems. Possible causes of both beam and image distortion have been identified in quasi-optical applications, which gives important insight into the best use of (sub-)THz cameras and interpretation of their images. Inherent standing waves caused by the significant power reflectance of the camera aperture is investigated in detail. A simple W-band one-port quasi-optical scalar network analyzer is developed, to determine the levels of reflectance for both cameras, with its bespoke calibration routine derived from first principles - providing a low-cost solution for many non-destructive testing applications. It is found that the TeraSense camera (with additional RAM) and single-pixel camera (having default RAM) have measured reflectance values of 27% and 3%, respectively, over a corresponding aperture area ratio of approximately 714:1. While our single-pixel camera provides excellent image resolution and fidelity, it inherently suffers from very slow raster-scanning speeds and operational bandwidth limitations. For this reason, the TeraSense camera technology is excellent for performing qualitative measurements in real time, with the caveats outlined in this paper.

INITIAL INVESTIGATION OF A LOW-COST AUTOMOTIVE LIDAR SYSTEM

Abstract. This investigation focuses on the performance assessment of a low-cost automotive LIDAR, the Livox Mid-40 series. The work aims to examine the qualities of the sensor in terms of ranging, repeatability and accuracy. Towards these aims a series of experiments were carried out based on previous research of low-cost sensor accuracy, LIDAR accuracy investigation and TLS calibration experiments. The Livox Mid-40 series offers the advantage of a long-range detection beyond 200 m at a remarkably low cost. The preliminary results of the tests for this sensor indicate that it can be used for reality capture purposes such as to obtain coarse as-built plans and volume calculations to mention a few. Close-range experiments were conducted in an indoor laboratory setting. Long-range experiments were performed outdoors towards a building façade. Reference values in both setups were provided with a Leica RTC 360 terrestrial LIDAR system. In the close-range experiments a cross section of the point cloud shows a significant level of noise in the acquired data. At a stand-off distance of 5 m the length measurement tests reveal deviations of up to 11 mm to the reference values. Range measurement was tested up to 130 meters and shows ranging deviations of up to 25 millimetres. The authors recommend further investigation of the issues in radiometric behaviour and material reflectivity. Also, more knowledge about the internal components is needed to understand the causes of the concentric ripple effect observed at close ranges. Another aspect that should be considered is the use of targets and their design as the non-standard scan pattern prevents automated detection with standard commercial software.

Time Integrated Flux Analysis: Exploiting the Concentration Measurements Directly for Cost-Effective Metabolic Network Flux Analysis

Background: Flux analyses, such as Metabolic Flux Analysis (MFA), Flux Balance Analysis (FBA), Flux Variability Analysis (FVA) or similar methods, can provide insights into the cellular metabolism, especially in combination with experimental data. The most common integration of extracellular concentration data requires the estimation of the specific fluxes (/rates) from the measured concentrations. This is a time-consuming, mathematically ill-conditioned inverse problem, raising high requirements for the quality and quantity of data. Method: In this contribution, a time integrated flux analysis approach is proposed which avoids the error-prone estimation of specific flux values. The approach is adopted for a Metabolic time integrated Flux Analysis and (sparse) time integrated Flux Balance/Variability Analysis. The proposed approach is applied to three case studies: (1) a simulated bioprocess case studying the impact of the number of samples (experimental points) and measurements’ noise on the performance; (2) a simulation case to understand the impact of network redundancies and reaction irreversibility; and (3) an experimental bioprocess case study, showing its relevance for practical applications. Results: It is observed that this method can successfully estimate the time integrated flux values, even with relatively low numbers of samples and significant noise levels. In addition, the method allows the integration of additional constraints (e.g., bounds on the estimated concentrations) and since it eliminates the need for estimating fluxes from measured concentrations, it significantly reduces the workload while providing about the same level of insight into the metabolism as classic flux analysis methods.

Simulation of noise generated by a rotary-screw mover as a result of friction

The article describes the theoretical study of the noise generation parameters under friction of a base cylinder of a rotary-screw mover. The analysis of the influence of parameters of the base cylinder (diameter, angle of winding of the helical blade, length) and the vehicle motion parameters (speed, mass) on the noise level arising from mover interaction with a single obstacle is presented in the paper. The conditions for the occurrence of sound vibrations are shown. Analysis of the research results allows us to conclude that a significant noise level generated by the rotary-screw mover can take place only when the vehicle is moving on ice. The created model and the obtained results can be applied by developers of all-terrain vehicles.

Development of a mathematical model of scrambler-type speech-like interference generator for system of prevent speech information from leaking via acoustic and vibration channels

The protection of speech information is one of the main tasks of information protection and is a sign of a responsible attitude of an organization (company) both to its information resources and respect for partners. The object of research is the process of protecting speech information from leakage by acoustic and vibrational technical channels at the objects of information activity. An exceptional feature of such facilities is the circulation, processing and discussion of issues containing information of limited access, including state secrets. A peculiarity of Ukraine is the requirement to use exclusively technical means that have passed the relevant certification at such facilities. The basis of the active noise jamming system is a noise generator. At the same time, one of the most problematic issues is that in Ukraine only noise interference generators of the “white” noise type and its clones are allowed to be used. The systems have a number of significant drawbacks – the low protection level of intercepted speech signals from noise filtering (interference), a significant noise level in the premises to be protected, and others. A block diagram of an interference generator is proposed. And its mathematical model is also developed and researched in Matlab. In the course of the research, a comparative analysis of the signals input and synthesized by the generator was carried out, their temporal and spectral characteristics were investigated. The obtained results indicate the high efficiency of the proposed method of protecting speech information. This is due to the fact that the method of forming a speech-like interference has a number of features that provide a significant destructive effect on speech information, namely the use of a combined scrambler model with time and frequency transforms. The method takes into account the use of dynamic keys for coding systems, and the connection of third-party sources of speech signals, as well as ringing (mixing of the input and output signals) at the input of the scrambling unit. This decision excludes reengineering. The results are confirmed by the research of an experimental sample. The destructive effect of typical noise interference ( « white » noise and its clones) and the noise interference created by the proposed method are compared by the criterion of residual speech intelligibility of the speaker’s speech. Studies have shown that, provided that no more than 10 % of the level of residual intelligibility is provided, the volume level of the output signal of the noise interference generator can be reduced by almost 6 dBA.

A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence

We propose a probabilistic approach to the problem of schema mapping. Our approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of schema mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings as well as inconsistencies and incompleteness in the input. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using state-of-the-art probabilistic reasoning techniques. Our evaluation on a wide range of integration scenarios, including several real-world domains, demonstrates that CMD effectively combines data and metadata information to infer highly accurate mappings even with significant levels of noise.

Greenhouse environment modeling and simulation for microclimate control

Greenhouse plant science assays have been impacted by microclimates which causes significant level of noise to plant growth measurement data. Researchers and scientist have been randomizing pots locations, which helps to re-distribute the noise, but does not remove the noise. The impacts from microclimates can be eliminated by shuffling plants, but there has been no study on the optimization of shuffling pattern, such as the frequency and moving distance for the pots. It is important to quantitatively study the microclimates in the greenhouse, so we can optimize the shuffling pattern accordingly. The aim of this study was to propose a computer modeling approach for simulating microclimate in the greenhouse, and then use the simulation result to optimize pot movement distance and frequency. A computational greenhouse model was developed using inputs from real design, materials and location of a Purdue Lily greenhouse in West Lafayette, Indiana. Microclimate variables, including ambient temperature and lighting radiation over 24 h and 7 days were predicted with the simulation model. Thermometers and lighting sensors were also distributed in the greenhouse for the ground-truth measurements over a seven-day period. Comparison of measured microclimate variables with predicted variables obtained from the computational model demonstrated that the simulation model could precisely predict temperatures and light radiation at any time, and at different positions in the greenhouse. Optimized pot movement frequency and distance were then determined with the simulation result. The new shuffling pattern can remove over 90% of the microclimate variance but could save more than 95% shuffling efforts compared with non-stop movement.

Top-Down Auditory Plasticity: Acceptable Noise Level Predicts and Reflects the Effect of Perceptual Learning in Experience-Induced Plasticity.

In the auditory system, tinnitus and superior speech perception in noise are examples of negative and positive plasticity that can result from sensory neural hearing loss and life experiences dealing with more complex stimuli and learning, respectively. The main objective of this study was to determine the relationship between acceptable noise level (ANL) values and perceptual learning in individuals exposed to unavoidable occupational noise. Here we document a form of plasticity in top-down auditory pathways through the measurement of the acceptable noise level in 60 adults, 27 females and 33 males, with normal hearing (Amiraalam state Hospital, Tehran, Iran 2016). Individuals were assigned to one of two groups: those with and without the occupational experience of speech perception in noise. The test group had statistically significant lower acceptable noise level and significantly higher background noise level scores compared with the control group. Using acceptable noise level, we attributed differences in individuals' abilities to tolerate varying amounts of background noise and speech perception in noise function to the auditory efferent system. Working in crowded locations due to job nature can influence differences in speech perception in noise function.

Contaminant source localization via Bayesian global optimization

Abstract. Contaminant source localization problems require efficient and robust methods that can account for geological heterogeneities and accommodate relatively small data sets of noisy observations. As realism commands hi-fidelity simulations, computation costs call for global optimization algorithms under parsimonious evaluation budgets. Bayesian optimization approaches are well adapted to such settings as they allow the exploration of parameter spaces in a principled way so as to iteratively locate the point(s) of global optimum while maintaining an approximation of the objective function with an instrumental quantification of prediction uncertainty. Here, we adapt a Bayesian optimization approach to localize a contaminant source in a discretized spatial domain. We thus demonstrate the potential of such a method for hydrogeological applications and also provide test cases for the optimization community. The localization problem is illustrated for cases where the geology is assumed to be perfectly known. Two 2-D synthetic cases that display sharp hydraulic conductivity contrasts and specific connectivity patterns are investigated. These cases generate highly nonlinear objective functions that present multiple local minima. A derivative-free global optimization algorithm relying on a Gaussian process model and on the expected improvement criterion is used to efficiently localize the point of minimum of the objective functions, which corresponds to the contaminant source location. Even though concentration measurements contain a significant level of proportional noise, the algorithm efficiently localizes the contaminant source location. The variations of the objective function are essentially driven by the geology, followed by the design of the monitoring well network. The data and scripts used to generate objective functions are shared to favor reproducible research. This contribution is important because the functions present multiple local minima and are inspired from a practical field application. Sharing these complex objective functions provides a source of test cases for global optimization benchmarks and should help with designing new and efficient methods to solve this type of problem.

Discovery of the data-driven differential equation-based models of continuous metocean process

Data-driven models are widely used in cases when the model structure is not known a priori. However, most contemporary data-driven modeling methods like neural networks and most types of simple regression cannot be interpreted. Decision trees are on contrary, interpretable, however, the model structure remains simple and thus interpretable complex models are barely obtained with decision trees. The possible tradeoff between the model complexity and interpretability is the data-driven differential equations discovery methods. At the current time, most of the methods are not able to handle the data with a significant noise level. In the paper, a new approach to the problem is proposed. The approach involves evolutionary algorithm and sparse regression and allows one to obtain various forms of equations, defined only by the number of meta-parameters instead of the pre-defined library of terms. The application of PDE discovery tool to obtain continuous metocean process equation is described.

Geodesic Distance on Gaussian Manifolds to Reduce the Statistical Errors in the Investigation of Complex Systems

In the last years the reputation of medical, economic, and scientific expertise has been strongly damaged by a series of false predictions and contradictory studies. The lax application of statistical principles has certainly contributed to the uncertainty and loss of confidence in the sciences. Various assumptions, generally held as valid in statistical treatments, have proved their limits. In particular, since some time it has emerged quite clearly that even slightly departures from normality and homoscedasticity can affect significantly classic significance tests. Robust statistical methods have been developed, which can provide much more reliable estimates. On the other hand, they do not address an additional problem typical of the natural sciences, whose data are often the output of delicate measurements. The data can therefore not only be sampled from a nonnormal pdf but also be affected by significant levels of Gaussian additive noise of various amplitude. To tackle this additional source of uncertainty, in this paper it is shown how already developed robust statistical tools can be usefully complemented with the Geodesic Distance on Gaussian Manifolds. This metric is conceptually more appropriate and practically more effective, in handling noise of Gaussian distribution, than the traditional Euclidean distance. The results of a series of systematic numerical tests show the advantages of the proposed approach in all the main aspects of statistical inference, from measures of location and scale to size effects and hypothesis testing. Particularly relevant is the reduction even of 35% in Type II errors, proving the important improvement in power obtained by applying the methods proposed in the paper. It is worth emphasizing that the proposed approach provides a general framework, in which also noise of different statistical distributions can be dealt with.

The method of primary processing of thermograms obtained using small-size thermal imagers

The purpose of the article is to develop and study the method of noise removal on low spatial resolution thermograms obtained by commercial small-sized thermal imaging cameras, which due to their weight and sizes can be used on mini-UAVs for thermal monitoring of power facilities. The main parameter that carries information about the operation mode of the object and the presence of defects during thermal monitoring is the temperature distribution over the surface of the object, which can be recorded by a thermal camera. The characteristics of commercial thermal imagers with uncooled arrays were analyzed. Due to low cost and mass, their usage can significantly reduce the cost of the monitoring system by reducing both the cost of the payload and the requirements for UAV payload capabilities. An issue of thermal imaging is a relatively low resolution of thermograms and a significant level of noise on them, which requires the image processing methods that would minimize the noise level. The process of obtaining thermograms using a compact thermal imager in real-time was considered. A method of removing the main types of thermogram distortions, based on an adaptive digital median filter, was proposed and implemented. The process of occurrence of a thermal gradient that occurs during the operation of the bolometric matrix was analyzed, and a method for was removal is given. It is shown that the proposed methods allow almost completely eliminated the thermogram artifacts.

Complex Scene Classification of PoLSAR Imagery Based on a Self-Paced Learning Approach

Existing polarimetric synthetic aperture radar (PolSAR) image classification methods cannot achieve satisfactory performance on complex scenes characterized by several types of land cover with significant levels of noise or similar scattering properties across land cover types. Hence, we propose a supervised classification method aimed at constructing a classifier based on self-paced learning (SPL). SPL has been demonstrated to be effective at dealing with complex data while providing classifier. In this paper, a novel Support Vector Machine (SVM) algorithm based on SPL with neighborhood constraints (SVM_SPLNC) is proposed. The proposed method leverages the easiest samples first to obtain an initial parameter vector. Then, more complex samples are gradually incorporated to update the parameter vector iteratively. Moreover, neighborhood constraints are introduced during the training process to further improve performance. Experimental results on three real PolSAR images show that the proposed method performs well on complex scenes.

Wavelet analyses and comparative denoised signals of meteorological factors of the namibian atmosphere

With population growth and climate change increasing the exposure of communities and assets to extreme hydrological events, such as floods and droughts, it is crucial to make accurate and timely early warning, climate adaptation and water management information available that can help minimize the loss of the limited quantities of water in arid regions. Trend estimation using wavelets has continued to attract ubiquitous applications in various fields including atmospheric and water studies. While Fourier analysis employs big waves, wavelet analysis uses small waves. Since wavelets localise features in a signal data to different scales, we can therefore preserve vital signal features while removing noise in the signal. Hence the basic notion of wavelet denoising or wavelet thresholding is that the wavelet transform results in a sparse representation for many real-world signals, meaning that the wavelet transform concentrates signal features in a few large-magnitude wavelet coefficients. Since small wavelet coefficients are typically noise, we can “shrink” or remove them without affecting the signal quality.This paper employs wavelet analyses of some selected meteorological factors of the Namibian atmosphere to investigate the noise in the data collected from a number of Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) stations, for the years 2012 to 2015. The results show some significant levels of noise around the thresholds of various optimal time series peaks of the data. Hence the denoised data serve to represent fairly refined representations of data for the given period. Moreover, the results of comparing the optimal game model solutions for the original and denoised data specifically identified some weather stations that are “humidity noise stable” and “temperature noise stable”. Thus, these suggest that despite the noise in the respective meteorological data for these stations, the effects of the noise in the data appear to be manageable or optimally controllable, within the given period.

A Generalized Logistic-Gaussian-Complex Signal Model for the Restoration of Canopy SWIR Hyperspectral Reflectance

The continuum of the SWIR (short-wave infrared) signals from 1320 to 1650 nm contains valuable information for effectively diagnosing water, chlorophyll, and nitrogen content. The SWIR spectra of in situ spectroradiometric data and airborne spectrometric images are frequently contaminated by significant noise. Based on a Logistic-Gaussian complex signal model (LGCM), the noise-free signals at 1330–1349 and 1411–1430 nm wavelengths can provide critical bases for restoring the 1350–1410 nm wavelength signals for a single point of data. This paper proposes a generalized LGCM (GLGCM) technique to expand the ability of LGCM to process large data with variant reflectance values. A 12-year-old red cypress plantation located in a central Taiwan temperate forest was selected for this study. Hundreds of reflectance spectra of tree crowns were obtained using an ASD FR Spectroradiometer. The in-laboratory blank test showed that the GLGCM technique was able to achieve sufficient performance with an RMSE (root mean square error) of 0.0015 ± 0.0005 and 0.0011 ± 0.0005 for the front-edge and end-edge signal bases respectively, and 0.0014 ± 0.0006 in between the two signal bases. A significant level of noise between −0.2 and 0.4 was successfully removed from the in situ contaminated reflectance in the 1350–1410 nm wavelengths. The estimation bias for the signals of front-edge and end-edge bases was low, averaging 0.0031 ± 0.0003 and 0.0032 ± 0.0012. The consistency between the blank test and the in situ experimental results indicates that the GLGCM technique has potential in using batch processing to fix the problem of the noisy SWIR spectra in spectroradiometeric data and also airborne spectrometric images.

Amphibian sacculus and the forced Kuramoto model with intrinsic noise and frequency dispersion.

The amphibian sacculus (AS) is an end organ that specializes in the detection of low-frequency auditory and vestibular signals. In this paper, we propose a model for the AS in the form of an array of phase oscillators with long-range coupling, subject to a steady load that suppresses spontaneous oscillations. The array is exposed to significant levels of frequency dispersion and intrinsic noise. We show that such an array can be a sensitive and robust subthreshold detector of low-frequency stimuli, though without significant frequency selectivity. The effects of intrinsic noise and frequency dispersion are contrasted. Intermediate levels of intrinsic noise greatly enhance the sensitivity through stochastic resonance. Frequency dispersion, on the other hand, only degrades detection sensitivity. However, frequency dispersion can play a useful role in terms of the suppression of spontaneous activity. As a model for the AS, the array parameters are such that the system is poised near a saddle-node bifurcation on an invariant circle. However, by a change of array parameters, the same system also can be poised near an emergent Andronov-Hopf bifurcation and thereby function as a frequency-selective detector.

Time resolved analysis algorithm for ramped Langmuir probe to study temporal evolution of plasma parameters in ROBIN

ROBIN (Rf Operated Beam source in India for Negative ion research) is an Inductively Coupled Plasma (ICP) device, which is powered by a 1 MHz Radio Frequency (RF) generator of 100 kW power. During the experiment in ROBIN source in IPR, plasma is monitored and characterized by two independent diagnostics based on optical emission spectroscopy (OES) and two separate Langmuir probes (LP) powered by ramped bias voltage. In the present work, an algorithm has been developed in LabVIEW to analyse the digitized LP signal to study the time evolution of different plasma parameters. The software is developed for both real time as well as post hoc analysis of Langmuir probe data and is normally applicable for low temperature, non magnetized, collisionless plasmas. The unprocessed LP data contains significant noise level and so noise filtration is adopted. In present case, Savitzky-Golay (SG) noise filtering method is used for data conditioning for its ability to preserve the area under the signal curve. The analysis software is implemented in ROBIN data acquisition setup to study the plasma evolution in ROBIN in-situ, to optimize the operational parameter regime.

Designing a Low-Cost, Single-Supply ECG System for Suppression of Movement Artifact from Contaminated Magnetocardiogram.

Measurement of the late potentials and His-bundle activity is crucial for many clinical studies using the noncontact and noninvasive magnetocardiography (MCG) technique; these weak signals are extracted by averaging many cardiac cycles aligned using the R-peak of the cardiac cycle identified using an electrocardiography (ECG) lead. ECG is measured simultaneously with MCG using a conventional dual-supply ECG amplifier, which requires either two separate batteries or a single battery with a switching voltage inverter circuit for its proper operation. The ECG circuitry based on two separate batteries requires a relatively large voltage supply (-18 to +18 V). The single-supply (low voltage: 0-9 V) ECG circuitry may be implemented using a switching voltage inverter; however, this mode of operation introduces switching noise in the system. The objective of the present work is to overcome these problems by carefully designing a low-voltage, single-supply ECG system, which can be used simultaneously with the MCG setup without introducing a significant level of additional noise in the MCG measurement system.