Constant Noise Research Articles

A geometric nonlinear stochastic filter for simultaneous localization and mapping

Simultaneous Localization and Mapping (SLAM) is one of the key robotics tasks as it tackles simultaneous mapping of the unknown environment defined by multiple landmark positions and localization of the unknown pose (i.e., attitude and position) of the robot in three-dimensional (3D) space. The true SLAM problem is modeled on the Lie group of SLAMn(3), and its true dynamics rely on angular and translational velocities. This paper proposes a novel geometric nonlinear stochastic estimator algorithm for SLAM on SLAMn(3) that precisely mimics the nonlinear motion dynamics of the true SLAM problem. Unlike existing solutions, the proposed stochastic filter takes into account unknown constant bias and noise attached to the velocity measurements. The proposed nonlinear stochastic estimator on manifold is guaranteed to produce good results provided with the measurements of angular velocities, translational velocities, landmarks, and inertial measurement unit (IMU). Simulation and experimental results reflect the ability of the proposed filter to successfully estimate the six-degrees-of-freedom (6 DoF) robot's pose and landmark positions.

Toward Bayesian Data Compression

AbstractIn order to handle large datasets omnipresent in modern science, efficient compression algorithms are necessary. Here, a Bayesian data compression (BDC) algorithm that adapts to the specific measurement situation is derived in the context of signal reconstruction. BDC compresses a dataset under conservation of its posterior structure with minimal information loss given the prior knowledge on the signal, the quantity of interest. Its basic form is valid for Gaussian priors and likelihoods. For constant noise standard deviation, basic BDC becomes equivalent to a Bayesian analog of principal component analysis. Using metric Gaussian variational inference, BDC generalizes to non‐linear settings. In its current form, BDC requires the storage of effective instrument response functions for the compressed data and corresponding noise encoding the posterior covariance structure. Their memory demand counteract the compression gain. In order to improve this, sparsity of the compressed responses can be obtained by separating the data into patches and compressing them separately. The applicability of BDC is demonstrated by applying it to synthetic data and radio astronomical data. Still the algorithm needs further improvement as the computation time of the compression and subsequent inference exceeds the time of the inference with the original data.

РОЗРАХУНОК ВИРОБНИЧОГО РИЗИКУ ВІД ШУМОВОГО ЗАБРУДНЕННЯ ДЛЯ БЕЗПЕКИ ПРАЦЮЮЧИХ НА ВІДКРИТОМУ ПОВІТРІ

Purpose. Studying the impact of noise pollution levels at the busiest intersections of Kyiv and determining the exceedances of normative values on the basis of national and international standards. Calculation of the probability of hearing loss under conditions of prolonged exposure to noise on the body of 40-year-old and 50-year-old workers during 30 years of service. Methods. In this paper, based on the obtained field measurement data, noise pollution on the main highways of Kyiv is analyzed. Topicality. The research is aimed at ensuring the safety of operating transport services and construction companies from the constant noise load, which creates traffic flows in the city and leads to occupational diseases. Studies have shown that at an 8-hour exposure in a worker under conditions of constant chronic noise load, there may be a shift in the audibility threshold depending on age and length of service. The method recommended by the international standard ISO1999 / 2013 can be used to determine the corrected level of noise pollution and safety of workers in the open air during the repair of highway overpasses. The scientific novelty lies in the determination of natural data on the noise load on the example of some of the busiest road junctions in Kyiv, where transport mainly moves at low speed and stands in traffic jams. The obtained average data during the working day indicate a noise load that is 18-20% higher than the normative value of workers working outdoors (80 dBA). Based on basic calculations of the probability of hearing loss because of noise pollution at an average level of 90 dBA for workers with 30 years of experience proved the need for enhanced software monitoring in areas of increased noise pollution for production sites and protection of workers for their safety. Practical significance. The research results are aimed at providing safety for workers outdoors during the repair of highways in large cities and on construction sites near major intersections. The importance of the work lies in the need to strengthen the existing dynamic control over working conditions and health of workers in the open air in the conditions of constant noise load of the city and the need to revise the requirements to the employer governing the protection of workers from noise. The research results showed that the maximum value of noise pollution in Kyiv near the main roads during the day is within the range of 87-94dBa, which is on average 11% higher than the normative value for open production sites. The probability of hearing loss of workers due to noise is 11.5% for 50-year-olds and 7% for 40-year-old workers with 30 years of service.

Hankel matrix-based Mahalanobis distance for fault detection robust towards changes in process noise covariance

Abstract Statistical subspace-based change detection residuals have been developed to infer a change in the eigenstructure of linear systems. Their statistical properties have been properly evaluated in the case of a known reference and constant noise properties. Previous residuals have favored the family of null space-based approaches, whereas the possibility of using other metrics such as the Mahalanobis distance has been omitted. This paper investigates the development and study of such a norm under the premise of a varying noise covariance. Its statistical properties have been studied and tested on a numerical example of a mechanical system.

Face Image Compression and Reconstruction Based on Improved PCA

Face recognition technology has many usages in the real-world applications, and it has generated extensive interest in recent years. However, the amount of data in a digital image is growing explosively, taking up a lot of storage and transmission resources. There is a lot of redundancy in an image data representation. Thus, image compression has become a hot topic. The principal component analysis (PCA) can effectively remove the correlation of an image and condense the image information into a characteristic image with several main components. At the same time, it can restore different data images according to their principal components and meet the needs of image compression and reconstruction at diverse levels. This paper introduces an improved PCA algorithms. The covariance matrix, calculated according to a batch of training samples, is an approximation of the real covariance matrix. The matrix is relatively to the dimension of the covariance matrix, and the number of training samples is often too small. Therefore, it difficult to accurately obtain the covariance matrix. This improved PCA algorithm called 2DPCA can solve this problem effectively. By comparing it with several discrete PCA improvement algorithms, we show that the 2DPCA has a better dimensionality reduction effect. Compared with the PCA algorithm, the 2DPCA has a lower root-mean-square error under the constant noise condition.

Annie Ernaux et Édouard Louis : Écrire le réel entre bruit(s) et silence

A. Ernaux and É. Louis – writers of the real, between silence and noise In A. Ernaux’s steps, É. Louis writes to account for truth, to bear witness for the real. They experience sounds, noises and silences as particularly meaningful signs of the social classes that they live in. Their original, popular environment is made of constant noise, whether coming from inside the body or the surrounding working-place. Later as they rise socially, they realise that more privileged classes live in a filtered, muffled world. But being loud may also be a move toward freedom, an expression of the lived experience. The theatre stage is a particularly apt media for the telling of É. Louis’s stories as it enables the varied experiences to be heard and felt through their vibrations, and all noises on stage make sense.

Selected Morbidities among Workers of an Adhesive Plaster Industry in Goa

Background: The toxic nature of chemicals & pollutants associated with an adhesive plaster industry is well established. A disastrous health sequel would occur among workers of these industries due to exposure to pollution, created & stored wastes, and constant noise & bright light. Objective:To describe selected morbidities among workers in an ad-hesive plaster industry in Goa. Methodology:The study was conducted over a period of two months during which, the data of 135 workers was obtained from an Occupa-tional Health and Safety (OHS) Centre catering to an adhesive plaster industry in North Goa which conducted periodic medical check-ups of these workers. The data included socio-demographic characteristics and details regarding selected morbidities among the workers of the adhesive plaster industry. Result: Among 135 workers, the mean age was 40.96 ± 11.1 years. Majority of the participants were males. It was observed that 5 (3.7 %) had a known history of diabetes and 48 (35.6 %) were hypertensive. Near-vision abnormalities were seen among 66 (48.9 %), whereas 60 (44.4 %) had uncorrected far-vision abnormalities. Obesity was detected among 6 (4.4 %) whereas 40 (29.6 %) were overweight. Obstructive & restrictive respiratory diseases were seen in 3 (2.2 %) & 40 (29.6 %) persons respectively. Audiometry reports revealed 4 (3%) workers had mild to moderate hearing loss. Conclusion: Workers in the adhesive plaster industry suffered from several lifestyle diseases like diabetes, hypertension, obesity, as well as occupational diseases like audio-visual impairment and lung diseases. This study emphasizes the importance of periodic medical examinations for timely detection & appropriate treatment of the health conditions among such workers.

Inflation and Risky Investments

The paper uses a Walrasian two-period financial market model with informed and uninformed constant absolute risk averse (CARA) rational investors and noise traders. The investors allocate their initial wealth between risky assets and risk-free fiat money. The analysis concentrates on the effects of decreasing value of money, or inflation, on the rational investors’ behavior and the asset market. The main findings are the following: Inflation does not affect the informed investors’ prediction coefficient but makes that of the uninformed investors diminish. Inflation does not affect rational investors’ risk but makes the asset price more sensitive to fundament-based and sentiment-based shocks. Inflation changes the market price of the risky asset rise; while it has no effects on the informed investors’ demand of the risky asset, it does affect the uninformed investors’ demand. Finally, inflation makes the asset market more volatile.

Chronic exposure to urban noise during the vocal learning period does not lead to increased song frequencies in zebra finches

It has often been observed that birds sing at a higher pitch in cities and other areas that are polluted with intense low-frequency noise. How this pattern arises remains unclear though. One prevailing idea is that songbirds adjust song frequencies to environmental noise profiles through developmental plasticity via vocal learning. However, the conclusions of previous studies testing this hypothesis are inconsistent. Here we report the findings from two song learning experiments with zebra finches (Taenopygia guttata), in which we exposed young birds to anthropogenic noise during their sensitive vocal learning period. Unlike previous studies that addressed this issue, we did not use constant synthetic noise but natural urban noise with its typical amplitude fluctuations that was broadcast at realistic sound levels. We found that noise-exposed males in neither experiment developed higher pitched songs compared to control males. This suggests that the natural fluctuations between higher and lower noise levels in cities may allow young birds to exploit relatively quiet moments to hear their tutors and themselves, permitting them to make accurate copies of even low-frequency song elements.Significance statementIf animals are to persist in urban habitats, they often must adjust their behavior to the altered conditions. Birds in cities are often observed to sing at a higher pitch, but we are largely ignorant of how this phenomenon arises. We investigated whether low-frequency traffic noise interferes with the song learning of birds so that they develop higher pitched songs. Accordingly, we played back natural traffic noise from urban bird habitats to young birds during their learning period and then analyzed their adult songs. We found that birds that learned their songs in noise did not sing at higher frequencies compared to control males that learned their song with no noise exposure. Our results show that typical traffic noise in cities may not be sufficient to interfere with vocal learning in a way that birds develop higher-pitched songs.

Background noise reduction for airborne bathymetric full waveforms by creating trend models using Optech CZMIL in the Yellow Sea of China.

Raw full waveforms of green lasers used in airborne LiDAR bathymetry (ALB) are contaminated by background and random noise related to the environment and ALB devices. Traditional thresholding methods have been widely used to reduce background noise in raw full waveforms on the basis of the assumption of constant background noise. However, background noise that is mainly related to background solar radiation and detector dark current changes over time. Thresholding methods perform poorly on the full waveforms with a wide variation range of background noise. A background noise reduction method considering its wide variation is proposed to decrease the background noise by creating trend models. First, each green full waveform is divided into two parts: pulse- and non-pulse-return waveforms. Second, a linear interpolation is conducted on the non-pulse-return waveform to impute the missing noise. Third, a low-pass filter is used to filter the random noise with high frequency in the imputed non-pulse-return waveform and obtain the trend model of background noise of the full waveform. Finally, the derived background noise model is used to decrease the background noise in the pulse-return waveform. The proposed method is applied to decrease the background noise in raw green full waveforms collected by the Optech coastal zone mapping and imaging LiDAR (CZMIL). The mean and standard deviation of residual noise in the CZMIL waveform reduced by the trend model of background noise are -0.03 and 3.5 digitizer counts, respectively. The proposed background noise reduction method is easy to apply and can reduce the background noise to a significantly low level. This method is recommended for preprocessing the raw full waveforms of green lasers collected by Optech CZMIL for ALB.

Features of noise radiation from gas turbines

Gas turbine unit (GTU) is the most intensive source of constant noise during normal operation. The noise of the gas turbine unit is emitted from the compressor intake, from the installation shell and from the chimney mouth of the gas turbine unit. Mathematical models for GTU of various capacities are compiled. It is shown that the determining role plays the noise from combustion. That noise is radiated from the chimney outside. Considered various factors affecting noise emission. Obtained empirical formulas for calculating the noise level at various distances depending on the power of the gas turbine. For the first time indicated the importance of taking into account regional climate factors to determine the required noise reduction from GTU. This is especially important to consider when determining the required reduction at high frequencies. The set of factors considered in this paper allows us to determine the required reduction from the gas turbine. Indicated possible measures to reduce noise from gas turbine unit and bring it to sanitary standards.

Study of the acoustic efficiency of a plate silencer of the original design

Energy facilities are sources of noise impact. One of the most powerful sources of constant noise at such facilities is power boilers and draft machines, which emit noise into the surrounding area from air intakes and chimney mouths. To reduce noise from them, dissipative plate-type silencers are mainly used. In many cases, the installation in gas and air ducts of typical dissipative plate noise silencers consisting of plates with flat sidewalls does not provide the required acoustic efficiency with a limited length of the plates and the requirement for low aerodynamic resistance. In this regard, an urgent direction of scientific research is the development of new approaches to improve the acoustic characteristics of dissipative noise mufflers. The article presents the results of field tests of the acoustic efficiency of a dissipative plate noise muffler, consisting of patented noise suppression elements. The comparison of the obtained efficiency of the muffler from the proposed elements of noise suppression with the efficiency of the known designs of plate mufflers is carried out.

A New Dichoptic Training Strategy Leads to Better Cooperation Between the Two Eyes in Amblyopia.

Recent clinical trials failed to endorse dichoptic training for amblyopia treatment. Here, we proposed an alternative training strategy that focused on reducing signal threshold contrast in the amblyopic eye under a constant and high noise contrast in the fellow eye (HNC), and compared it to a typical dichoptic strategy that aimed at increasing the tolerable noise contrast in the fellow eye (i.e., TNC strategy). We recruited 16 patients with amblyopia and divided them into two groups. Eight patients in Group 1 received the HNC training, while the other eight patients in Group 2 performed the TNC training first (Phase 1) and then crossed over to the HNC training (Phase 2). We measured contrast sensitivity functions (CSFs) separately in the amblyopic and fellow eyes when the untested eye viewed mean luminance (monocularly unmasked) or noise stimuli (dichoptically masked) before and after training at a particular frequency. The area under the log contrast sensitivity function (AULCSF) of masked and unmasked conditions, and dichoptic gain (the ratio of AULCSF of masked to unmasked condition) were calculated for each eye. We found that both dichoptic training paradigms substantially improved masked CSF, dichoptic gain, and visual acuity in the amblyopic eye. As opposed to the TNC paradigm, the HNC training produced stronger effects on masked CSFs, stereoacuity, dichoptic gain, and visual acuity in the amblyopic eye. Interestingly, the second-phase HNC training in Group 2 also induced further improvement in the masked contrast sensitivity and AULCSF in the amblyopic eye. We concluded that the HNC training strategy was more effective than the TNC training paradigm. Future design for dichoptic training should not only focus on increasing the tolerable noise contrast in the fellow eye but should also “nurture” the amblyopic eye under normal binocular viewing conditions and sustained interocular suppression.

Testing one-loop galaxy bias: Power spectrum

We test the regime of validity of the one-loop galaxy bias for a wide variety of biased tracers. Our most stringent test asks the bias model to simultaneously match the galaxy-galaxy and galaxy-mass spectrum, using the measured nonlinear matter spectrum from the simulations to test the one-loop effects from the bias expansion alone. In addition, we investigate the relevance of short-range nonlocality and halo exclusion through higher-derivative and scale-dependent noise terms, as well as the impact of using coevolution relations to reduce the number of free fitting parameters. From comparing the validity and merit of these assumptions, we find that a four-parameter model (linear, quadratic, cubic nonlocal bias, and constant shot noise) with a fixed quadratic tidal bias provides a robust modeling choice for the auto power spectrum of the less massive halos in our set of samples and their galaxy populations [up to ${k}_{\mathrm{max}}=0.35\text{ }\text{ }h/\mathrm{Mpc}$ for a sample volume of $6\text{ }\text{ }(\mathrm{Gpc}/h{)}^{3}$]. For the more biased tracers, it is most beneficial to include scale-dependent noise. This is also the preferred option when considering combinations of the auto and cross power spectrum, which might be relevant in joint studies of galaxy clustering and weak lensing. We also test the use of perturbation theory to account for matter loops through gRPT, EFT, and the hybrid approach RESPRESSO. While all these have similar performance, we find the latter to be the best in terms of validity and recovered mean posterior values, in accordance with it being based partially on simulations.

Study of a fixed-lag Kalman smoother for input and state estimation in vibrating structures

ABSTRACT This paper presents a numerical study of an augmented Kalman filter extended with a fixed-lag smoother. The smoother solves the joint input and state estimation problem based on sparse vibration measurements. Two numerical examples are examined in order to study the influence of model errors and measurement noise on the estimate quality. From simulations of a simply supported beam, it is shown that estimates from the smoother are superior to those of a conventional Kalman filter, both when the level of model error and measurement noise are increased. By studying simulations of a truck component, the improvement due to smoothing over a conventional Kalman filter is shown to be even greater when the model error is present in both the eigenfrequencies and the mode shapes. In addition, a sensitivity analysis of a tuning methodology with the assumption of constant noise covariance matrices is performed. The result indicates that the proposed tuning methodology results in stable estimates with a good trade-off between estimator adaptability and noise sensitivity. The presented approach of tuning and evaluating the estimates is therefore suggested as a guideline for using the fixed-lag smoother when solving input and state estimation problems in vibrating structures.

Spoofing Attack on Ultrasonic Distance Sensors Using a Continuous Signal.

Ultrasonic distance sensors use an ultrasonic pulse’s time of flight to calculate the distance to the reflecting object. Widely used in industry, these sensors are an important component in autonomous vehicles, where they are used for such tasks as object avoidance and altitude measurement. The proper operation of such autonomous vehicles relies on sensor measurements; therefore, an adversary that has the ability to undermine the sensor’s reliability can pose a major risk to the vehicle. Previous attempts to alter the measurements of this sensor using an external signal succeeded in performing a denial-of-service (DoS) attack, in which the sensor’s reading showed a constant value, and a spoofing attack, in which the attacker could control the measurement to some extent. However, these attacks require precise knowledge of the sensor and its operation (e.g., timing of the ultrasonic pulse sent by the sensor). In this paper, we present an attack on ultrasonic distance sensors in which the measured distance can be altered (i.e., spoofing attack). The attack exploits a vulnerability discovered in the ultrasonic sensor’s receiver that results in a fake pulse that is produced by a constant noise in the input. A major advantage of the proposed attack is that, unlike previous attacks, a constant signal is used, and therefore, no prior knowledge of the sensor’s relative location or its timing behavior is required. We demonstrate the attack in both a lab setup (testbed) and a real setup involving a drone to demonstrate its feasibility. Our experiments show that the attack can interfere with the proper operation of the vehicle. In addition to the risk that the attack poses to autonomous vehicles, it can also be used as an effective defensive tool for restricting the movement of unauthorized autonomous vehicles within a protected area.

Unsupervised Deep Learning for Laboratory-Based Diffraction Contrast Tomography

An important leap forward for the 3D community is the possibility to perform non-destructive 3D microstructural imaging in the home laboratories. This possibility is profiled by a recently developed technique—laboratory X-ray diffraction contrast tomography (LabDCT). As diffraction spots in LabDCT images are the basis for 3D reconstruction of microstructures, it is critical to get their identification as precise as possible. In the present work we use a deep learning (DL) routine to optimize the identification of the spots. It is shown that by adding an artificial simple constant background noise to a series of forward simulated LabDCT diffraction images, DL can be trained and then learn to remove high frequency noise and low frequency radial gradients in brightness in the real experimental LabDCT images. The training of the DL routine is unsupervised in the sense that no human intervention is needed for labelling the data. The reduction in high frequency noise and low frequency radial gradients in brightness is demonstrated by comparing line profile scans through the experimental and the DL output images. Finally, the implications of this reduction procedure on the spot identification are analysed and possible improvements are discussed.

Experimental realization of dynamic fluence field optimization for proton computed tomography

Proton computed tomography (pCT) has high accuracy and dose efficiency in producing spatial maps of the relative stopping power (RSP) required for treatment planning in proton therapy. With fluence-modulated pCT (FMpCT), prescribed noise distributions can be achieved, which allows to decrease imaging dose by employing object-specific dynamically modulated fluence during the acquisition.For FMpCT acquisitions we divide the image into region-of-interest (ROI) and non-ROI volumes. In proton therapy, the ROI volume would encompass all treatment beams. An optimization algorithm then calculates dynamically modulated fluence that achieves low prescribed noise inside the ROI and high prescribed noise elsewhere. It also produces a planned noise distribution, which is the expected noise map for that fluence, as calculated with a Monte Carlo simulation. The optimized fluence can be achieved by acquiring pCT images with grids of intensity modulated pencil beams. In this work, we interfaced the control system of a clinical proton beam line to deliver the optimized fluence. Using three phantoms we acquired images with uniform fluence, with a constant noise prescription, and with an FMpCT task. Image noise distributions as well as fluence maps were compared to the corresponding planned distributions as well as to the prescription. Furthermore, we propose a correction method that removes image artifacts stemming from the acquisition with pencil beams having a spatially varying energy distribution that is not seen in clinical operation. RSP accuracy of FMpCT scans was compared to uniform scans and was found to be comparable to standard pCT scans.While we identified technical improvements for future experimental acquisitions, in particular related to an unexpected pencil beam size reduction and a misalignment of the fluence pattern, agreement with the planned noise was satisfactory and we conclude that FMpCT optimized for specific image noise prescriptions is experimentally feasible.

Contribution of noise reduction pre-processing and microphone directionality strategies in the speech recognition in noise in adult cochlear implant users.

Refinement currently offered in new sound processors may improve noise listening capability reducing constant background noise and enhancing listening in challenging signal-to-noise conditions. This study aimed to identify whether the new version of speech processor preprocessing strategy contributes to speech recognition in background noise compared to the previous generation processor. This was a multicentric prospective cross-sectional study. Post-lingually deaf adult patients, with at least 1year of device use and speech recognition scores above 60% on HINT sentences in quiet were invited. Speech recognition performance in quiet and in noise with sound processors with previous and recent technologies was assessed under four conditions with speech coming from the front: (a) quiet (b) fixed noise coming from the front, (c) fixed noise coming from the back, and (d) adaptive noise ratios with noise coming from the front. Forty-seven cochlear implant users were included. No significant difference was found in quiet condition. Performance with the new processor was statistically better than the previous sound processor in all three noisy conditions (p < 0.05). With fixed noise coming from the back condition, speech recognition was 62.9% with the previous technology and 73.5% on the new one (p < 0.05). The mean speech recognition in noise was also statistically higher, with 5.8dB and 7.1dB for the newer and older technologies (p < 0.05), respectively. New technology has shown to provide benefits regarding speech recognition in noise. In addition, the new background noise reduction technology, has shown to be effective and improves speech recognition in situations of more intense noise coming from behind.

Anthropogenic noise influences on marine soundscape variability across coastal areas

Acoustic data was collected across 15 sites distributed through the coastal area of Rio de Janeiro, Brazil, encompassing regions of different natural characteristics. Noise levels and the acoustic complexity index were calculated. Quantity and composition of anthropogenic sound sources varied across recording sites, with at least one type of sound source being registered in each location. A cluster analysis using third-octave levels from eight frequency bands divided recording sites into two groups, one considered as impacted by anthropogenic noise and the other as less-impacted. The noisiest recording locations were those with higher numbers of anthropogenic sound sources, specifically large ships. It was evidenced that anthropogenic noise affects not only noise levels, but also low-frequency acoustic complexity, which decreased in the presence of vessel traffic. The constant noise input of human activities tended to mask natural variability in the soundscape at lower frequencies.