Similar Noise Levels Research Articles

Influencing parameters and indicators assessment for aircraft external noise tonality perception

Reducing the annoyance related to the noise generated by aircraft flyovers near airports is a major concern of the aeronautical industry. Noise source reduction is one of the pillars of the balanced approach, but further research is still needed to understand and reduce unpleasantness and annoyance due to aircraft noise. From a psychoacoustics point of view, it is particularly important to understand and assess the effects related to perceived phenomena such as tonality which increases unpleasantness at similar noise levels. This article presents the results of a parametric study looking at the influence on tonality of frequency, emergence level and number of harmonics and the assessment of tonality indicators. The particularity of this study is the non stationarity of the tonal noise due to the Doppler effect and the low frequency investigation.

Noise and frequency propagation in natural stone processing plants

In this study, 510 noise measurements recorded in eight natural stone processing plants operating in Sivas City, Turkey were evaluated and the noise levels and dominant frequency bands of 11 different processing machines used in these plants were determined. The daily equivalent noise level of all natural stone processing plants was around or above the exposure limit value (87 dBA) specified in relevant regulations. Considering the machines and plant environment, the frequency ranges for the highest noise levels were mostly between 630 Hz - 5000 Hz, centering around 3150 Hz. Variations in the noise level of the processing machines in separate plants where similar processes were carried out was primarily related to the plant size and machine layout, the number of machines operating simultaneously and the type of natural stone processed. While the difference between the noise levels of gangsaws, bridge cutting, ST cutting, head/side cutting, trimming, cement filling, slab polishing, narrow polishing, chamfering and splitting machines were statistically significant, aging machines generated similar noise levels.

Low Frequency Electronic Noise Characterization of Au-W/β-Ga2O3 Schottky Diodes

Ultra-wide bandgap (UWBG) semiconductors have attracted tremendous amount of interest for applications in high-power electronics and solar-blind UV photodetectors. In particular, β-Ga2O3 has attracted significant research attention in recent years due to its promising electrical and optical characteristics including a large bandgap, high breakdown electrical field, and large Baliga’s figure of merit compared to well-established wide-bandgap technologies such as GaN and SiC [1].Low frequency electronic noise, which is present in all electronic devices, can be used as a diagnostic tool for semiconductor material and device characterization. By studying the low frequency noise in semiconductor materials and devices, the physics associated with the noise properties of bulk and interface defects and traps can be investigated [2]. Measurement of low frequency noise is also crucial for evaluating material quality and device reliability.In this work, we investigate the forward-bias diode parameters and low-frequency electronic noise in Au-W/β-Ga2O3 Schottky barrier diodes. The Schottky contact is formed by depositing 20 nm of Tungsten (W) using dc sputtering followed by 340 nm of Gold (Au) using e-beam evaporation, and subsequent annealing [3].We first measure the I-V characteristics and extract important diode parameters, such as the Schottky barrier height, ideality factor, and series resistance from room temperature forward-bias current-voltage characteristics. The extracted Schottky barrier height for these devices is in the range 0.7-0.8 eV and the ideality factor is approximately 1.2.We next measure the low-frequency electronic noise in Au-W/β-Ga2O3 Schottky diodes at different forward bias voltages at room temperature. We find that the current noise spectral density exhibits 1/f-type behavior for all forward bias voltages at low frequency with an exponent ranging from 0.75 to 1.15, indicating that the low frequency noise of these devices is dominated by excess (flicker) noise. No Lorentzian-shaped generation-recombination (G-R) noise is observed. At low frequencies, the shot noise contribution is found to be negligible for the values of the current in our devices, and the total noise is 1/f-limited.We also investigate the dependence of the current noise spectral density on forward bias current, revealing different relationships in different current regimes. At low currents, the noise spectral density is found to scale approximately as the square of current, whereas at high currents, the noise spectral density begins to saturate. Such noise behavior can be attributed to different noise sources dominating in different current regimes. Furthermore, using the noise figure of merit adapted for ultra-wide bandgap semiconductor devices [4], we find that the Au-W/β-Ga2O3 devices have lower noise levels compared to other emerging UWBG materials and similar noise levels as well-established technologies such as GaN.These results provide important insights into the electronic noise properties of Au-W/β-Ga2O3 Schottky diodes and show that low frequency noise characterization is an important diagnostic tool for assessing the quality and reliability of gallium oxide based wide-bandgap materials and device technologies.

Integrated Amphibious Distributed Acoustic Sensing for Seismic Monitoring in the Xinfengjiang Reservoir

Abstract The rapidly advancing technology of distributed acoustic sensing (DAS) has profoundly impacted the field of underwater geophysics. Our study investigates the effectiveness of DAS in underwater geological stability monitoring, with a particular focus on microseismic monitoring in the Xinfengjiang reservoir. The 6.2 km long acquisition setup, covering both land and reservoir bottom, was verified using temporary shore-based short-period seismometers to ensure reliable data acquisition in various environments. Higher background noise was observed on the land section compared with the lakebed section during the day, whereas both sections exhibited similar noise levels at night. We confirmed that the DAS system was capable of detecting distant microseismic events, some of which were previously unreported. These detections exhibited temporal and phase consistency with neighboring seismometers. Comparison of signal-to-noise ratios indicates that the lakebed section demonstrates higher sensitivity. This system delivers cost-effective performance through natural settling, negating the requirement for costly embedding methods. Moreover, the DAS system identified “comet-like” small-scale signals on the lakebed that had eluded shore-based seismometers. This exemplifies the exceptional high-density and high-resolution capabilities of DAS technology in both aquatic and terrestrial environments. This study underscores the pivotal role of the DAS technology in conducting underwater microseismic monitoring, real-time seismic monitoring, seismic mechanism research, and earthquake hazard assessment.

Power saw noise levels during steel stud cutting tasks on commercial construction sites: a tool characterization from a worker exposure standpoint.

Construction framers who cut and install steel studs as part of their daily tasks are exposed to hazardous noise levels during their work shift in large part due to the power saws they use to cut steel studs. This investigation characterized the sound pressure levels of power saws used to cut steel studs on active construction sites. Further, the length of time it took to cut various studs on a construction site was investigated to understand worker exposure times to saw noise. In general, power saws used on the study sites to cut steel studs had a mean A-weighted equivalent continuous sound pressure level (LAeq) of 107.2 dB and a C-weighted peak sound pressure level (LCpeak) of 120.1 dB. Three of the saws-the chopsaw, the cut-off saw, and the grinder-had similar noise levels, whereas the cordless circular saw had higher noise levels. It took an average of 13.2 s to cut each stud, and workers in the study used power saws to cut steel studs for an average of 371.5 s per day. This average exposure time at the average recorded sound pressure levels (SPLs) suggests these saws can increase the risk of occupational noise-induced hearing loss, according to National Institute for Occupational Safety and Health (NIOSH) recommendations.

Noise level estimation using locality preserving natural image statistics

Natural images are known to have certain regular statistical properties. These properties get modified under any artificial change or distortion in natural images. Most common form of image degradation occurs in the form of noise. The amount of degradation in noisy images is measured by estimating the noise level. Many image processing applications such as denoising, restoration, segmentation, compression etc. use noise level information as a prior; inaccurate estimate of which may impact their performance. In this article, we explore natural image statistics in locality preserving transform domain. This property groups structurally similar images/image patches when projected in the transform domain. Image patches corrupted with similar noise level get projected close by in the locality preserving domain and show consistent coefficient behaviour. In particular, we use Two Dimensional Orthogonal Locality Preserving Projection (2DOLPP) as the domain transformation technique. 2DOLPP basis, representing natural images, are learnt in advance from a set of clean images, thereby reducing the computational time significantly. Features based on natural image statistics are extracted from 2DOLPP domain representation of input image patches. Mapping from feature space to noise level is carried out using support vector regression. The proposed noise estimation approach is at par with or surpasses the state-of-the-art techniques with much less computational time. Performance of this approach is stable across a wide range of noise levels and independent of the image structure.

Improving Generalizability of PET DL Algorithms: List-Mode Reconstructions Improve DOTATATE PET Hepatic Lesion Detection Performance.

Deep learning (DL) algorithms used for DOTATATE PET lesion detection typically require large, well-annotated training datasets. These are difficult to obtain due to low incidence of gastroenteropancreatic neuroendocrine tumors (GEP-NETs) and the high cost of manual annotation. Furthermore, networks trained and tested with data acquired from site specific PET/CT instrumentation, acquisition and processing protocols have reduced performance when tested with offsite data. This lack of generalizability requires even larger, more diverse training datasets. The objective of this study is to investigate the feasibility of improving DL algorithm performance by better matching the background noise in training datasets to higher noise, out-of-domain testing datasets. 68Ga-DOTATATE PET/CT datasets were obtained from two scanners: Scanner1, a state-of-the-art digital PET/CT (GE DMI PET/CT; n = 83 subjects), and Scanner2, an older-generation analog PET/CT (GE STE; n = 123 subjects). Set1, the data set from Scanner1, was reconstructed with standard clinical parameters (5 min; Q.Clear) and list-mode reconstructions (VPFXS 2, 3, 4, and 5-min). Set2, data from Scanner2 representing out-of-domain clinical scans, used standard iterative reconstruction (5 min; OSEM). A deep neural network was trained with each dataset: Network1 for Scanner1 and Network2 for Scanner2. DL performance (Network1) was tested with out-of-domain test data (Set2). To evaluate the effect of training sample size, we tested DL model performance using a fraction (25%, 50% and 75%) of Set1 for training. Scanner1, list-mode 2-min reconstructed data demonstrated the most similar noise level compared that of Set2, resulting in the best performance (F1 = 0.713). This was not significantly different compared to the highest performance, upper-bound limit using in-domain training for Network2 (F1 = 0.755; p-value = 0.103). Regarding sample size, the F1 score significantly increased from 25% training data (F1 = 0.478) to 100% training data (F1 = 0.713; p < 0.001). List-mode data from modern PET scanners can be reconstructed to better match the noise properties of older scanners. Using existing data and their associated annotations dramatically reduces the cost and effort in generating these datasets and significantly improves the performance of existing DL algorithms. List-mode reconstructions can provide an efficient, low-cost method to improve DL algorithm generalizability.

Thin slice photon-counting CT coronary angiography compared to conventional CT: Objective image quality and clinical radiation dose assessment.

Photon-counting CT (PCCT) is the next-generation CT scanner that enables improved spatial resolution and spectral imaging. For full spectral processing, higher tube voltages compared to conventional CT are necessary to achieve the required spectral separation. This generated interest in the potential influence of thin slice high tube voltage PCCT on overall image quality and consequently on radiation dose. This study first evaluated tube voltages and radiation doses applied in patients who underwent coronary CT angiography with PCCT and energy-integrating detector CT (EID-CT). Next, image quality of PCCT and EID-CT was objectively evaluated in a phantom study simulating different patient sizes at these tube voltages and radiation doses. We conducted a retrospective analysis of clinical doses of patients scanned on a conventional and PCCT system. Average patient water equivalent diameters for different tube voltages were extracted from the dose reports for both EID-CT and PCCT. A conical phantom made of polyethylene with multiple diameters (26/31/36cm) representing different patient sizes and containing an iodine insert was scanned with a EID-CT scanner using tube voltages and phantom diameters that match the patient scans and characteristics. Next, phantom scans were made with PCCT at a fixed tube voltage of 120kV and with CTDIVOL values and phantom diameters identical to the EID-CT scans. Clinical image reconstructions at 0.6mm slice thickness for conventional CT were compared to PCCT images with 0.4mm slice thickness. Image quality was quantified using the detectability index (d'), which estimated the visibility of a 3mm diameter contrast-enhanced coronary artery by considering noise, contrast, resolution, and human visual perception. Alongside d', noise, contrast and resolution were also individually assessed. In addition, the influence of various kernels (Bv40/Bv44/Bv48/Bv56), quantum iterative reconstruction strengths (QIR, 3/4) and monoenergetic levels (40/45/50/55keV) for PCCT on d' was investigated. In this study, 143 patients were included: 47 were scanned on PCCT (120kV) and the remaining on EID-CT (74 small-sized at 70kV, 18 medium-sized at 80kV and four large-sized at 90kV). EID-CT showed 7%-17% higher d' than PCCT with Bv40 kernel and strength four for small/medium patients. Lower monoenergetic images (40keV) helped mitigate the difference to 1%-6%. For large patients, PCCT's detectability was up to 31% higher than EID-CT. PCCT has thinner slices but similar noise levels for similar reconstruction parameters. The noise increased with lower keV levels in PCCT (≈30% increase), but higher QIR strengths reduced noise. PCCT's iodine contrast was stable across patient sizes, while EID-CT had 33% less contrast in large patients than in small-sized patients. At 120kV, thin slice PCCT enables CCTA in phantom scans representing large patients without raising radiation dose or affecting vessel detectability. However, higher doses are needed for small and medium-sized patients to obtain a similar image quality as in EID-CT. The alternative of using lower mono-energetic levels requires further evaluation in clinical practice.

Optimal filters for ERP research II: Recommended settings for seven common ERP components.

In research with event-related potentials (ERPs), aggressive filters can substantially improve the signal-to-noise ratio and maximize statistical power, but they can also produce significant waveform distortion. Although this tradeoff has been well documented, the field lacks recommendations for filter cutoffs that quantitatively address both of these competing considerations. To fill this gap, we quantified the effects of a broad range of low-pass filter and high-pass filter cutoffs for seven common ERP components (P3b, N400, N170, N2pc, mismatch negativity, error-related negativity, and lateralized readiness potential) recorded from a set of neurotypical young adults. We also examined four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency). For each combination of component and scoring methods, we quantified the effects of filtering on data quality (noise level and signal-to-noise ratio) and waveform distortion. This led to recommendations for optimal low-pass and high-pass filter cutoffs. We repeated the analyses after adding artificial noise to provide recommendations for data sets with moderately greater noise levels. For researchers who are analyzing data with similar ERP components, noise levels, and participant populations, using the recommended filter settings should lead to improved data quality and statistical power without creating problematic waveform distortion.

Optimized tools and process for a better prediction of future aircraft noise perception

Reducing the annoyance related to the noise generated by aircraft flyovers near airports is a major concern of the aeronautical industry. Noise source reduction is one of the pillars of the balanced approach of the International Civil Aviation Organization of, but further research is still needed to understand and reduce unpleasantness and annoyance due to aircraft noise. From a psychoacoustics point of view, it is particularly important to understand and assess the effects related to perceived phenomena such as tonality, roughness, and beats created by produced tones whose frequencies can be more or less close to each other. Indeed, these phenomena increase unpleasantness at similar noise levels. This article describes optimized tools and overall process used for studies applied to current and future aircraft noise perception such as: noise source modeling, simulation of the propagation to the ground, 3D sound rendering on a multi-speaker system, perceptual evaluation, statistical analysis, and regression analyses between measured unpleasantness and Effective Perceived Noise Level (EPNL) and other indicators.

Seismic background noise levels in the Italian strong-motion network

Abstract. The Italian strong-motion network monitors the seismic activity in the region, with more than 585 stations with continuous data acquisition. In this study, we determine the background seismic noise characteristics of the network by using the data collected in 2022. We analyse the spatial and temporal characteristics of the background noise. It is found that most of the stations suffer from anthropogenic noises, since the strong-motion network is designed to capture the peak ground motions in populated areas. Hence, human activities enrich the low periods of noise. Therefore, land usage of the area where the stations are located affects the background noise levels. Stations can be noisier during the day, up to 12 dB, and during the weekday, up to 5 dB, in short periods. In long periods (≥ 5 s), accelerometric stations converge to similar noise levels and there are no significant daily or weekly changes. It is found that more than half of the stations exceed the background noise model designed for strong-motion stations in Switzerland by Cauzzi and Clinton (2013) in at least one of the calculated periods. We also develop an accelerometric seismic background noise model for periods between 0.0124 and 100 s for Italy by using the power spectral densities of the network. The model is in agreement with the background noise model developed by D’Alessandro et al. (2021) using broadband data for Italy in short periods, but in long periods there is no correlation among studies.

Dynamic residual Kaczmarz method for noise reducing reconstruction in magnetic particle imaging

Objective. Here, we propose a dynamic residual Kaczmarz (DRK) method as an improved reconstruction method for magnetic particle imaging (MPI) to achieve a better reconstruction quality from high-noise signals. Approach. Based on the Kaczmarz (KZ) method, we introduced a residual vector to select parts of the low-noise equations for reconstruction. In each iteration, a low-noise subset was formulated based on the residual vector. Thus, the reconstruction converged to an accurate result with less noise. Main Results. To evaluate the performance of the proposed method, it was compared with classical Kaczmarz-type methods and state-of-the-art regularization models. The numerical simulation results demonstrate that the DRK method can achieve better reconstruction quality than all other comparison methods at similar noise levels. It can acquire a signal-to-background ratio (SBR) that is five times higher than that of classical Kaczmarz-type methods at a 5 dB noise level. Furthermore, the DRK method can acquire up to 0.7 structural similarity (SSIM) indicators at a 5 dB noise level when combined with the non-negative fused Least absolute shrinkage and selection operator (LASSO) regularization model. In addition, a real experiment based on the OpenMPI data set validated that the proposed DRK method can be applied to real data and perform well. Significance. The experimental results demonstrate that the proposed DRK method can significantly improve the reconstruction quality of MPI when the signals contain high noise. It has the potential to be applied to MPI instruments that contain high signal noise, such as human-sized MPI instruments. It is beneficial for expanding the biomedical applications of MPI technology.

Ex vivo coronary calcium volume quantification using a high-spatial-resolution clinical photon-counting-detector computed tomography.

Coronary artery calcification (CAC) is an important indicator of coronary disease. Accurate volume quantification of CAC is challenging using computed tomography (CT) due to calcium blooming, which is a consequence of limited spatial resolution. Ex vivo coronary specimens were scanned on an ultra-high-resolution (UHR) clinical photon-counting detector (PCD) CT scanner, and the accuracy of CAC volume estimation was compared with a state-of-the-art conventional energy-integrating detector (EID) CT, a previous-generation investigational PCD-CT, and micro-CT. CAC specimens () were scanned on EID-CT and PCD-CT using matched parameters (120kV, 9.3mGy ). EID-CT images were reconstructed using our institutional routine clinical protocol for CAC quantification. UHR PCD-CT data were reconstructed using a sharper kernel. An image-based denoising algorithm was applied to the PCD-CT images to achieve similar noise levels as EID-CT. Micro-CT images served as the volume reference standard. Calcification images were segmented, and their volume estimates were compared. The CT data were further compared with previous work using an investigational PCD-CT. Compared with micro-CT, CT volume estimates had a mean absolute percent error of for clinical PCD-CT, for EID-CT, and for previous-generation PCD-CT. Clinical PCD-CT absolute percent error was significantly () lower than both EID-CT and previous generation PCD-CT. The mean calcification CT number and contrast-to-noise ratio were both significantly () higher in clinical PCD-CT relative to EID-CT. UHR clinical PCD-CT showed reduced calcium blooming artifacts and further enabled improved accuracy of CAC quantification beyond that of conventional EID-CT and previous generation PCD-CT systems.

Deep Learning-reconstructed Parallel Accelerated Imaging for Knee MRI

Deep learning (DL) can improve image quality by removing noise from accelerated MRI. To compare the quality of various accelerated imaging applications in knee MRI with and without DL. We analyzed 44 knee MRI scans from 38 adult patients using the DL-reconstructed parallel acquisition technique (PAT) between May 2021 and April 2022. The participants underwent sagittal fat-saturated T2-weighted turbo-spin-echo accelerated imaging without DL (PAT-2 [2-fold parallel accelerated imaging], PAT-3, and PAT-4) and with DL (DL with PAT-3 [PAT-3DL] and PAT-4 [PAT-4DL]). Two readers independently evaluated subjective image quality (diagnostic confidence of knee joint abnormalities, subjective noise and sharpness, and overall image quality) using a 4-point grading system (1-4, 4=best). Objective image quality was assessed based on noise (noise power) and sharpness (edge rise distance). The mean acquisition times for PAT-2, PAT-3, PAT-4, PAT-3DL, and PAT-4DL sequences were 2:55, 2:04, 1:33, 2:04, and 1:33 min, respectively. Regarding subjective image quality, PAT-3DL and PAT-4DL scored higher than PAT-2. Objectively, DL-reconstructed imaging had significantly lower noise than PAT-3 and PAT-4 (P <0.001), but the results were not significantly different from those for PAT-2 (P >0.988). Objective image sharpness did not differ significantly among the imaging combinations (P =0.470). The inter-reader reliability ranged from good to excellent (κ = 0.761–0.832). PAT-4DL imaging in knee MRI exhibits similar subjective image quality, objective noise, and sharpness levels compared with conventional PAT-2 imaging, with an acquisition time reduction of 47%.

Noise power spectrum properties of deep learning–based reconstruction and iterative reconstruction algorithms: Phantom and clinical study

PurposeTo compare the noise power spectrum (NPS) properties and perform a qualitative analysis of hybrid iterative reconstruction (IR), model-based IR (MBIR), and deep learning–based reconstruction (DLR) at a similar noise level in clinical study and compare these outcomes with those in phantom study. MethodsA Catphan phantom with an external body ring was used in the phantom study. In the clinical study, computed tomography (CT) examination data of 34 patients were reviewed. NPS was calculated from DLR, hybrid IR, and MBIR images. The noise magnitude ratio (NMR) and the central frequency ratio (CFR) were calculated from DLR, hybrid IR, and MBIR images relative to filtered back-projection images using NPS. Clinical images were independently reviewed by two radiologists. ResultsIn the phantom study, DLR with a mild level had a similar noise level as hybrid IR and MBIR with strong levels. In the clinical study, DLR with a mild level had a similar noise level as hybrid IR with standard and MBIR with strong levels. The NMR and CFR were 0.40 and 0.76 for DLR, 0.42 and 0.55 for hybrid IR, and 0.48 and 0.62 for MBIR. The visual inspection of the clinical DLR image was superior to that of the hybrid IR and MBIR images. ConclusionDeep learning–based reconstruction improves overall image quality with substantial noise reduction while maintaining image noise texture compared with the CT reconstruction techniques.

MCA-Based Multi-Channel Fusion Attacks Against Cryptographic Implementations

Multi-channel fusion attacks (MCFAs) are a more effective kind of side-channel analysis approaches for evaluating the side-channel vulnerability of a cryptographic device as compared to side-channel attacks exploiting only mono-channel leakage. However, research for MCFAs is insufficient to explore their greatest potential. Considering that multi-channel leakages are generated by the same crypto device and collected individually, the paper holds the opinion that there exists a linear combination of the multi-channel leakages or leakage features or attack results to launch a fusion attack with the highest performance. Correspondingly, this paper develops three optimal strategies of MCFAs on the basis of multiple correlation analysis (MCA). In addition, a new MCA-based leakage fusion metric applied for measuring whether or not to combine a channel with other channels for fusion attacks is constructed as well. The proposed metric can select the channels with similar noise levels for combination and reduce the possibility of negative influences on leakage fusion caused by high-noise channels. Effectiveness of the proposed optimal fusion attacks and the new fusion metric are verified by practical experiments in typical scenarios, respectively.

Mission design aspects for the mass change and geoscience international constellation (MAGIC)

SUMMARYThe Mass Change and Geoscience International Constellation (MAGIC) is planned as the first realization of a double-pair low-low satellite-to-satellite (ll-sst) tracking gravity mission consisting of a polar and an inclined satellite pair. Due to the much increased spatial and temporal resolution and multidirectionality of the data to be collected by this mission, new possibilities regarding the resolvability of mass transport processes in space and time are expected. In order to maximize the scientific and societal outcome of this mission, an optimization of both the mission design as well as the methods to process the expected data is fundamental. Using numerical closed-loop simulations, we investigate the impact of several key mission design aspects on the gravity retrieval from a double-pair constellation such as the planned MAGIC mission. Specifically, we show how the choice of the second pair’s inclination poses a trade-off between a reduction of retrieval errors at latitudes covered by data from both pairs and at higher latitudes, thereby requiring a compromise between the latitude-dependent accuracy requirements of different user groups. One of the key mission goals is to provide fast-track gravity products with short latency for operational service applications. Towards the estimation of such short-term gravity fields of a few days, we investigate if coordinating the polar and inclined pairs’ orbits to achieve a stable ground-track coverage is necessary for obtaining a homogeneous accuracy of subsequent gravity solutions. Indeed, combining two freely drifting, uncontrolled orbits significantly degrades short-term gravity fields in time periods in which both pairs show coinciding ground track gaps. Finally, we analyse the relative performance of the two satellite pairs. Double-pair scenarios that are strongly dominated by the inclined pair’s data reveal degraded gravity solutions when co-estimating daily gravity fields as de-aliasing strategy. This effect can be mitigated by choosing a more balanced double-pair configuration, for example by choosing similar orbit heights and instrument noise levels for both satellite pairs. The findings presented in our study will serve to optimize the system design of the upcoming MAGIC constellation.

Epitaxial CdSe/PbSe Heterojunction Growth and MWIR Photovoltaic Detector

A novel Epitaxial Cadmium Selenide (CdSe) on Lead Selenide (PbSe) type-II heterojunction photovoltaic detector has been demonstrated by Molecular Beam Epitaxy (MBE) growth of n-type CdSe on p-type PbSe single crystalline film. The use of Reflection High-Energy Electron Diffraction (RHEED) during the nucleation and growth of CdSe indicates high-quality single-phase cubic CdSe. This is a first-time demonstration of single crystalline and single phase CdSe growth on single crystalline PbSe, to the best of our knowledge. The current-voltage characteristic indicates a p-n junction diode with a rectifying factor over 50 at room temperature. The detector structure is characterized by radiometric measurement. A 30 μm × 30 μm pixel achieved a peak responsivity of 0.06 A/W and a specific detectivity (D*) of 6.5 × 108 Jones under a zero bias photovoltaic operation. With decreasing temperature, the optical signal increased by almost an order of magnitude as it approached 230 K (with thermoelectric cooling) while maintaining a similar level of noise, achieving a responsivity of 0.441 A/W and a D* of 4.4 × 109 Jones at 230 K.

Likelihood-based bilateral filters for pre-estimated basis sinograms using photon-counting CT.

Noise amplification in material decomposition is an issue for exploiting photon-counting computed tomography (PCCT). Regularization techniques and neighborhood filters have been widely used, but degraded spatial resolution and bias areconcerns. This paper proposes likelihood-based bilateral filters that can be applied to pre-estimated basis sinograms to reduce the noise while minimally affecting spatial resolution andaccuracy. The proposed method needs system models (e.g., incident spectrum, detector response) to calculate the likelihood. First, it performs maximum likelihood (ML)-based estimation in the projection domain to obtain basis sinograms. The estimated basis sinograms suffer from severe noise but are asymptotically unbiased without degrading spatial resolution. Then it calculates the neighborhood likelihoods for a given measurement at the center pixel using the neighborhood estimates and designs the weights based on the distance of likelihoods. It is also analyzed in terms of statistical inference, and then two variations of the filter are introduced: one that requires the significance level instead of the empirical hyperparameter. The other is a measurement-based filter, which can be applied when accurate estimates are given without the system models. The proposed methods were validated by analyzing the local property of noise and spatial resolution and the global trend of noise and bias using numerical thorax and abdominal phantoms for a two-material decomposition (water and bone). They were compared to the conventional neighborhood filters and the model-based iterative reconstruction with an edge-preserving penalty applied in the basisimages. The proposed method showed comparable or superior performance for the local and global properties to conventional methods in many cases. The thorax phantom: The full width at half maximum (FWHM) decreased by -2%-31% (-2 indicates that it increased by 2% compared to the best performance from conventional methods), and the global bias was reduced by 2%-19% compared to other methods for similar noise levels (local: 51% of the ML, global: 49%) in the water basis image. The FWHM decreased by 8%-31%, and the global bias was reduced by 9%-44% for similar noise levels (local: 44% of the ML, global: 36%) in the CT image at 65 keV. The abdominal phantom: The FWHM decreased by 10%-32%, and the global bias was reduced by 3%-35% compared to other methods for similar noise levels (local: 66% of the ML, global: 67%) in the water basis image. The FWHM decreased by up to -11%-47%, and the global bias was reduced by 13%-35% for similar noise levels (local: 71% of the ML, global: 70%) in the CT image at 60 keV. This paper introduced the likelihood-based bilateral filters as a post-processing method applied to the ML-based estimates of basis sinograms. The proposed filters effectively reduced the noise in the basis images and the synthesized monochromatic CT images. It showed the potential of using likelihood-based filters in the projection domain as a substitute for conventional regularization or filteringmethods.

ACOUSTIC CHARACTERISTICS OF ROAD SURFACES

The article is focused on the tyre/road noise and vibration measurements of the motorway network in the Czech Republic between 2012 and 2020. Three different surfaces were measured, namely a conventional stone mastic asphalt pavement (SMA 11S) and two cement concrete pavements with different treatments – burlap drag and exposed aggregate. A basic statistical analysis of the noise values in different years after laying was performed. Cement concrete treated by burlap drag method has lower noise values than SMA 11S. This is to be expected as burlap drag method is considered a low-noise cement concrete treatment. However, it has worse skid resistance properties and so the exposed aggregate cement concrete (EACC) began to be used. SMA 11S and EACC have very similar noise levels in all years tested, suggesting that they behave acoustically in the same way. Vibrations were investigated during field measurements with an accelerometer mounted on the CPX trailer. The noise and vibration results could thus be compared with each other. There is a positive correlation between them.