Noise Rate Research Articles

Amidst the clamor: Effects of emergency department noise on Physicians' health and attention

ObjectiveThis study aims to assess effects of noise on physicians' stress levels and attention capacities within an emergency department. MethodsIn this prospective cross-sectional study, 15 physicians from a state hospital emergency department with 300,000 annual visits provided demographics, work factors, and physiologic data. Attention was evaluated through smartphone-based Stroop tests, while noise and heart rates were monitored via smartwatches. ResultsMedian physician age was 26, with 16 months' emergency department experience. Average sound intensity was 68.80 dB. Despite noise, physicians in green/yellow areas showed increased Stroop scores (p = 0.002). Heart rate responses correlated with noise changes. End-of-shift surveys highlighted physicians' adaptability and resilience to high noise levels. ConclusionNoisy emergency departments pose health risks to physicians, but noise-related stress did not significantly affect attention, beneficial for patient care.

Simulation of RPC responses in mini-Iron Calorimeter for cosmic ray muon studies

A Prototype of the Iron Calorimeter for the India-based Neutrino Observatory is currently running in Madurai, India. This consists of twenty large area single gap Resistive Plate Chambers (RPCs) of size, ∼ 185 cm × 175 cm sandwiched between 11 layers of iron plates of thickness 5.6 cm. The total size of the mini-Iron Calorimeter(mini-ICAL) is 4 m × 4 m × 1.2 m and having a weight of 85 tons is a (1/600) scaled-down version of the ICAL detector of INO [1]. The detector is magnetized using iron plates and two sets of copper coils, each coil has 18 turns. The central region of the mini-ICAL, where the RPCs are placed, has a nearly uniform magnetic field of 1.4 T, similar to the magnetic field in the final ICAL detector. This prototype serves as the basis for the design of the demonstrators, which closely mimics the characteristics of a future ICAL. This detector is built to test the final electronics in the fringe field of the magnet and also to develop the experience to construct the ICAL detector. The mini-ICAL has been operational since 2018 and collects cosmic muon data with different configurations of RPC, and electronics, which are continuously changing for various R&D efforts. Dedicated efforts are made in parallel to measure the momentum and azimuthal angle of μ+ and μ- independently, which could be an important input to the cosmic neutrino event generators. To improve the precision of those measurements a dedicated simulation effort was made, particularly in the digitisation of the RPC signal for a real detector simulation, where efficiency, noise rate, strip multiplicity, etc. were matched with the real data collected during runs at mini-ICAL.

Origin of Discrete and Continuous Dark Noise in Rod Photoreceptors.

The detection of a single photon by a rod photoreceptor is limited by two sources of physiological noise, called discrete and continuous noise. Discrete noise occurs as intermittent current deflections with a waveform very similar to that of the single-photon response to real light and is thought to be produced by spontaneous activation of rhodopsin. Continuous noise occurs as random and continuous fluctuations in outer-segment current and is usually attributed to some intermediate in the phototransduction cascade. To confirm the origin of these noise sources, we have recorded from retinas of mouse lines with rods having reduced levels of rhodopsin, transducin, or phosphodiesterase. We show that the rate of discrete noise is diminished in proportion to the decrease in rhodopsin concentration, and that continuous noise is independent of transducin concentration but clearly elevated when the level of phosphodiesterase is reduced. Our experiments provide new molecular evidence that discrete noise is indeed produced by rhodopsin itself, and that continuous noise is generated by spontaneous activation of phosphodiesterase resulting in random fluctuations in outer-segment current.

Learning From Noisy Labels With Deep Neural Networks: A Survey.

Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data. However, the quality of data labels is a concern because of the lack of high-quality labels in many real-world scenarios. As noisy labels severely degrade the generalization performance of deep neural networks, learning from noisy labels (robust training) is becoming an important task in modern deep learning applications. In this survey, we first describe the problem of learning with label noise from a supervised learning perspective. Next, we provide a comprehensive review of 62 state-of-the-art robust training methods, all of which are categorized into five groups according to their methodological difference, followed by a systematic comparison of six properties used to evaluate their superiority. Subsequently, we perform an in-depth analysis of noise rate estimation and summarize the typically used evaluation methodology, including public noisy datasets and evaluation metrics. Finally, we present several promising research directions that can serve as a guideline for future studies.

P068 Factors affecting sleep quality in hospitalised respiratory medicine patients

Abstract Background & Aim Hospitalised patients are at increased risk of poor sleep which can negatively impact on recovery and quality of life. This study aimed to assess sleep quality in hospitalised patients and explore the factors associated with poorer sleep. Methods A hospital-wide questionnaire completed at discharge as asked patients to rate their sleep quality during their admission. Results were compared between respiratory and non-respiratory inpatients. A more detailed questionnaire rating sleep quality and factors disrupting sleep was completed by respiratory ward inpatients at discharge and combined with medical record review of co-morbidities that may impact on sleep. Results The hospital-wide questionnaire was completed by 619 patients.28% of respiratory ward inpatients reported poor or very poor quality sleep compared to 20% of patients in a general medicine or other subspecialty ward. 83 respiratory inpatients have completed the more detailed questionnaire to date with recruitment ongoing. Sixteen patients (19.3%) reported poor or very poor sleep. These patients were compared to those who reported fair, good, or very good sleep. Age, gender distribution and BMI were similar between both groups. Noise rating was a significantly different between the groups and may predict sleep quality. Conclusion A high proportion of hospitalised patients reported poor sleep quality at our tertiary centre particularly on the respiratory inpatient ward where noise was a significant factor. Further research is needed to determine whether strategies to reduce noise will improved sleep quality and clinical outcomes.

Background noise model of spaceborne photon-counting lidars over oceans and aerosol optical depth retrieval from ICESat-2 noise data

The ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) photon-counting lidar is a revolutionary active remote sensing device and is extremely sensitive to both laser signal and solar background induced noise photons. The weak laser signals, such as aerosol and water subsurface backscattered laser photons, may be buried in noise in the daytime. A noise model for spaceborne photon-counting lidars is of vital importance in understanding and evaluating the performance of a lidar system, as well as in data processing and applications. From the reciprocity perspective, the main noise source for photon-counting lidars is solar background radiation, which in turn is the signal source of ocean color sensors. In the absence of clouds, it is possible to estimate the aerosol information using background noise data of ICESat-2 if the background noise is accurately modeled. In this study, a background noise model of photon-counting lidars over oceans is proposed that carefully considers the contributions of gas molecules, aerosol, water surface, subsurface, and detector dark noise. As the background rate from clouds are much larger than the sum of other factors, in this study, we only consider the cloudless atmosphere by using a cloud screening process. By inputting the system and environmental parameters, the theoretical predictions of noise level in six study areas have an average MAPE of ∼5% compared with ICESat-2 measured noise data. The results also indicate that without clouds, the variation of aerosols dominates the variation of ICESat-2 total background noise in open ocean areas. Then, based on this noise model, a method to estimate aerosol optical depths (AODs) over oceans is proposed solely on the ICESat-2 product including background noise rate and environmental data. Validation against MODIS (Moderate Resolution Imaging Spectroradiometer) daily AODs indicates that the average MAPE is <20% and the average correlation coefficient is 0.95 in six study areas. This method may provide a new AOD retrieval way over oceans in the daytime, when it is difficult to directly calculate AODs using lidar backscattered signal under strong background noise. In addition, the background noise of the ICESat-2 photon-counting lidar in its six channels (or pixels) has the potential to be regarded as the signal of an “ocean color camera” with only a single narrow green band. Based on this consideration, future works may be conducted to combine ocean color remote sensing with ICESat-2 photon-counting lidar.

Comparative Analysis of the Prox Penalty and Bregman Algorithms for Image Denoising

Image restoration is an interesting ill-posed problem. It plays a critical role in the concept of image processing. We are looking for an image that is as near to the original as possible among images that have been skewed by Gaussian and additive noise. Image deconstruction is a technique for restoring a noisy image after it has been captured. The numerical results achieved by the prox-penalty method and the split Bregman algorithm for anisotropic and isotropic TV denoising problems in terms of image quality, convergence, and signal noise rate (SNR) are compared in this paper. It should be mentioned that isotropic TV denoising is faster than anisotropic. Experimental results indicate that the prox algorithm produces the best high-quality output (clean, not smooth, and textures are preserved). In particular, we obtained (21.4, 21) the SNR of the denoising image by the prox for sigma 0.08 and 0.501, such as we obtained (10.0884, 10.1155) the SNR of the denoising image by the anisotropic TV and the isotropic TV for sigma 0.08 and (-1.4635, -1.4733) for sigma 0.501.

Batch testing and noise rate optimization of MRPC3b for CBM-TOF/STAR-eTOF

The Compressed Baryonic Matter (CBM) experiment is one of the major scientific spectrometers of the future Facility for Antiproton and Ion Research (FAIR) in Darmstadt. As one of the core sub-systems in CBM experiment for charged hadron identification, the Time-of-Flight (TOF) system is required to have a time resolution better than 80 ps. According to the final state particle flux distribution, the CBM-TOF will be constructed with several types of Multi-gap Resistive Plate Chambers (MRPC). In the outer region of the TOF wall where the particle fluxes are around 1 kHz/cm2, MRPCs with ultra-thin float glass electrodes are considered as a cost effective solution. MRPC3b prototypes have been developed and tested with excellent performance which could meet all the requirements. Before the construction of CBM-TOF, approximately 80 MRPC3bs are assembled for the STAR endcap TOF (STAR-eTOF) upgrade at RHIC as part of the FAIR Phase-0 programs for CBM-TOF which provides a valuable opportunity for detector stability test under high flux environments. This paper will introduce the batch test of the MRPC3bs for STAR-eTOF upgrade. Time resolution of better than 70 ps and efficiency of around 95% are achieved. Notably, during the batch test, it has been observed that the noise rates of the two edge strips in each counter are significantly higher than those of the middle strips. Simulations with Computer Simulation Technology (CST) Studio Suite are carried out and several kinds of MRPC prototypes are designed and tested accordingly. Based on the simulation and test results, the design of the MRPC3b has been further optimized by modify the shape and position of High-Voltage injection pad and increasing the distance between the glass edge and both end strips, resulting in a significant suppression of noise rates in the edge strips.

MinJoT: Multimodal infusion Joint Training for noise learning in text and multimodal classification problems

Amidst the critical role that high-quality labeled data plays in advancing machine learning, the persistence of noise within widely-used datasets remains a challenge. While noise learning has gained traction within machine learning, particularly in computer vision, its exploration in text and multimodal classification domains has lagged. Furthermore, a comprehensive comparison of noise learning techniques in text and multimodal classification has been lacking, partly due to variations in experimental noise settings across prior studies. Addressing these gaps, this research introduces a pioneering Multimodal Infusion Joint Training (MinJoT) framework featuring a novel co-regularized loss function that seamlessly integrates multimodal information during joint training. This framework notably excels in maintaining model robustness amidst noisy data environments. Adapting established noise learning methods from computer vision to text classification, the study conducts extensive experiments across five English and Chinese textual and multimodal datasets, involving four methods, five noise modes, and seven noise rates. Critically, this work challenges the implicit assumption that widely-used datasets are devoid of noise, revealing that these datasets indeed encompass noise levels ranging from 0.61% to 15.77% which is defined as intrinsic noise in this study. For the first time, the study investigates the impact of intrinsic noise on model performance, categorizing it into distinct levels of ambiguity. To facilitate accurate method comparison, a new dataset, Golden-Chnsenticorp (G-Chnsenticorp), is introduced, carefully crafted to be free of intrinsic noise. This research establishes the inaugural noise learning benchmark for text classification, while simultaneously pioneering the first noise learning framework tailored for multimodal sentiment classification. Through these contributions, the study advances the understanding of noise learning in text and multimodal contexts, providing a novel framework, benchmarks, and insights to propel the field forward.

Predicting discrete-time bifurcations with deep learning

Many natural and man-made systems are prone to critical transitions—abrupt and potentially devastating changes in dynamics. Deep learning classifiers can provide an early warning signal for critical transitions by learning generic features of bifurcations from large simulated training data sets. So far, classifiers have only been trained to predict continuous-time bifurcations, ignoring rich dynamics unique to discrete-time bifurcations. Here, we train a deep learning classifier to provide an early warning signal for the five local discrete-time bifurcations of codimension-one. We test the classifier on simulation data from discrete-time models used in physiology, economics and ecology, as well as experimental data of spontaneously beating chick-heart aggregates that undergo a period-doubling bifurcation. The classifier shows higher sensitivity and specificity than commonly used early warning signals under a wide range of noise intensities and rates of approach to the bifurcation. It also predicts the correct bifurcation in most cases, with particularly high accuracy for the period-doubling, Neimark-Sacker and fold bifurcations. Deep learning as a tool for bifurcation prediction is still in its nascence and has the potential to transform the way we monitor systems for critical transitions.

Experimental analysis of biomimetic silencer to reduce exhaust noise in pneumatic devices

Pneumatic devices such as pneumatic artificial muscles (PAMs) generate excessive noise during operation, thus inducing safety hazards and becoming a nuisance to nearby communities. In this study, biomimetic silencers consisting of polyurethane foam and slit structures that mimic shark gill slits to reduce the exhaust noise caused by pneumatic devices were developed and experimentally analyzed. Two separate experimental approaches were employed to evaluate and analyze the performance of the developed biomimetic silencer. During these experiments involving the compressor and PAM exhaust noise measurements, the sound pressure signals, noise, and flow rate were obtained for the cases of with and without silencer. The results demonstrated that the slit structure of the developed silencer decreased the relative velocity with the surrounding air, thus resulting in approximately 6 dB improvement in noise reduction performance of the developed silencer compared with that of a general pneumatic silencer. Although the use of porous materials in the silencer causes an exhaust time delay, the delay is insignificant compared with that of a general pneumatic silencer in terms of the exhaust area. Thus, the developed silencer is an effective solution for reducing the exhaust noise in pneumatic devices, particularly in situations where noise reduction is critical.

Low frequency noise study of entertainment venues

Noise impacts from entertainment venues have long been a challenge to communities, local government and venue operators. A study into the decay rate of low frequency music noise from venues was undertaken in the Entertainment Precinct of Northbridge, Western Australia, for the Department of Water and Environmental Regulation (DWER). A drone mounted measurement method was utilised to gain an understanding of venue noise emissions via roof elements, combined with on-ground measurements and software noise modelling. The aerial measurements proved effective at low frequency, allowing for a more comprehensive analysis of the ten venues assessed. While the study revealed several challenges and discoveries, the concept of aerial noise measurement was proven to be useful in this application. The findings will further contribute to the development of guidelines and recommendations for noise management in Northbridge, ensuring a better acoustic environment for residents and visitors while maintaining the viability of the venues in the area.

EVALUATING THE ROBUSTNESS OF YOLO OBJECT DETECTION ALGORITHM IN TERMS OF DETECTING OBJECTS IN NOISY ENVIRONMENT

Our daily lives are impacted by object detection in many ways, such as automobile driving, traffic control, medical fields, etc. Over the past few years, deep learning techniques have been widely used for object detection. Several powerful models have been developed over the past decade for this purpose. The YOLO architecture is one of the most important cutting-edge approaches to object detection. Researchers have used YOLO in their object detection tasks and obtained promising results. Since the YOLO algorithm can be used as an object detector in critical domains, it should provide a quite high accuracy both in noisy and noise-free environments. Consequently, in this study, we aim to carry out an experimental study to test the robustness of the YOLO v5 object detection algorithm when applied to noisy environments. To this end, four case studies have been conducted to evaluate this algorithm's ability to detect objects in noisy images. Specifically, four datasets have been created by injecting an original quality image dataset with different ratios of Gaussian noise. The YOLO v5 algorithm has been trained and tested using the original high-quality dataset. Then, the trained YOLO algorithm has been tested using the created noisy image datasets to monitor the changes in its performance in proportion to the injected Gaussian noise ratio. To our knowledge, this type of performance evaluation study did not conduct before in the literature. Furthermore, there are no such noisy image datasets have been shared before for conducting these types of studies. The obtained results showed that the YOLO algorithm failed to handle the noisy images efficiently besides degrading its performance in proportion to noise rates.

Mass testing of SiPMs for the CMVD at IICHEP

A Cosmic Muon Veto Detector (CMVD) is being built around the mini-Iron Calorimeter (mini-ICAL) detector at the transit campus of the India based Neutrino Observatory, Madurai. The CMV detector will be made using extruded plastic scintillators with embedded wavelength shifting (WLS) fibres which propagate re-emitted photons of longer wavelengths to silicon photo-multipliers (SiPMs). The SiPMs detect these scintillation photons, producing electronic signals. The design goal for the cosmic muon veto efficiency of the CMV is &gt;99.99% and fake veto rate less than 10^{-5}−5. A testing system was developed, using an LED driver, to measure the noise rate and gain of each SiPM, and thus determine its overvoltage (V_{ov}ov). This paper describes the test results and the analysed characteristics of about 3.5k SiPMs.

Dry Friction and Wear Behavior of Laser-Sintered Graphite/Carbon Fiber/Polyamide 12 Composite.

Carbon fiber-reinforced polymers (CFRPs) are being used extensively in modern industries that require a high strength-to-weight ratio, such as aerospace, automotive, motorsport, and sports equipment. However, although reinforcement with carbon fibers improves the mechanical properties of polymers, this comes at the expense of abrasive wear resistance. Therefore, to efficiently utilize CFRPs in dry sliding contacts, solid lubricant is used as a filler. Further, to facilitate the fabrication of objects with complex geometries, selective laser sintering (SLS) can be employed. Accordingly, in the present work, graphite-filled carbon fiber-reinforced polyamide 12 (CFR-PA12) specimens were prepared using the SLS process to explore the dry sliding friction and wear characteristics of the composite. The test specimens were aligned along four different orientations in the build chamber of the SLS machine to determine the orientation-dependent tribological properties. The experiments were conducted using a pin-on-disc tribometer to measure the coefficient of friction (COF), interface temperature, friction-induced noise, and specific wear rate. In addition, scanning electron microscopy (SEM) of tribo-surfaces was conducted to specify the dominant wear pattern. The results indicated that the steady-state COF, contact temperature, and wear pattern of graphite-filled CFR-PA12 are orientation-independent and that the contact temperature is likely to approach an asymptote far below the glass transition temperature of amorphous PA12 zones, thus eliminating the possibility of matrix softening. Additionally, the results showed that the Z-oriented specimen exhibits the lowest level of friction-induced noise along with the highest wear resistance. Moreover, SEM of tribo-surfaces determined that abrasive wear is the dominant wear pattern.

Two‐Stage, Low Noise Quantum Frequency Conversion of Single Photons from Silicon‐Vacancy Centers in Diamond to the Telecom C‐Band

AbstractThe silicon‐vacancy center in diamond holds great promise as a qubit for quantum communication networks. However, since the optical transitions are located within the visible red spectral region, quantum frequency conversion to low‐loss telecommunication wavelengths becomes a necessity for its use in long‐range, fiber‐linked networks. This work presents a highly efficient, low‐noise quantum frequency conversion device for photons emitted by a silicon‐vacancy (SiV) center in diamond to the telecom C‐band. By using a two‐stage difference‐frequency mixing scheme, spontaneous parametric down‐conversion (SPDC) noise is circumvented and Raman noise is minimized, resulting in a very low noise rate of 10.4 ± 0.7 photons per second as well as an overall device efficiency of 35.6%. By converting single photons from SiV centers, it demonstrates the preservation of photon statistics upon conversion.

Quantum authentication method based on key-controlled maximally mixed quantum state encryption

Quantum authentication is a fundamental first step that ensures secure quantum communication. Although various quantum authentication methods have been proposed recently, their implementation efficiency is limited. This paper proposes a key-controlled maximally mixed quantum state encryption (MMQSE) method using only a single qubit, unitary operation, minimized quantum transmissions, and a single qubit measurement, which improves implementation feasibility and operation efficiency. We applied it to representative quantum authentication applications, namely, quantum identity and message authentication. The security of our authentication schemes was verified by analyzing the relationship between the integral ratio of Uhlmann’s fidelity and probability of successful eavesdropping. Moreover, we demonstrate the higher authentication efficiency of the proposed scheme in a real quantum-channel noise environment. The upper bound of the valid noise rate was quantified using the integral ratio of Uhlmann’s fidelity in a noise environment. Finally, the optimal number of authentication sequences was estimated.

DRF codes: Deep SNR-robust feedback codes

We present a new Deep Neural Network (DNN)-based error correction code for fading channels with output feedback, called the Deep SNR-Robust Feedback (DRF) code. At the encoder, parity symbols are generated by a Long Short Term Memory (LSTM) network based on the message, as well as the past forward channel outputs observed by the transmitter in a noisy fashion. The decoder uses a bidirectional LSTM architecture along with a Signal to Noise Ratio (SNR)-aware attention NN to decode the message. The proposed code overcomes two major shortcomings of DNN-based codes over channels with passive output feedback: (i) the SNR-aware attention mechanism at the decoder enables reliable application of the same trained NN over a wide range of SNR values; (ii) curriculum training with batch size scheduling is used to speed up and stabilize training while improving the SNR-robustness of the resulting code. We show that the DRF codes outperform the existing DNN-based codes in terms of both the SNR-robustness and the error rate in an Additive White Gaussian Noise (AWGN) channel with noisy output feedback. In fading channels with perfect phase compensation at the receiver, DRF codes learn to efficiently exploit knowledge of the instantaneous fading amplitude (which is available to the encoder through feedback) to reduce the overhead and complexity associated with channel estimation at the decoder. Finally, we show the effectiveness of DRF codes in multicast channels with feedback, where linear feedback codes are known to be strictly suboptimal. These results show the feasibility of automatic design of new channel codes using DNN-based language models.

Large Area Picosecond Photodetector for the Upgrade II of the LHCb RICH

The Large Area Picosecond Photodetector (LAPPD) is a new generation microchannel plate (MCP) photodetector that is currently capturing the attention of detector physicists thanks to its excellent timing resolution, high gain and low dark count rate. It consists of a photocathode that responds to a single photon by producing a photoelectron that enters a pair of microchannel plates, where it is multiplied to a million electrons in a fast-rising pulse that is easily detected. The speed of this process provides the time resolution essential for high intensity environments such as the high luminosity Large Hadron Collider (LHC) at the CERN laboratory. The LAPPD has a large sensitive area, 200 × 200 mm 2, which reduces the number of sensors that are needed. The increased particle collision rate in the future LHC-HL phase represents a great challenge for the Ring Imaging Cherenkov (RICH) photodetectors in the LHCb experiment. The LHCb collaboration is working for the Upgrade II that will take place during the Long Shutdown 4 of the LHC (>2032). The LAPPD produced and commercialised by INCOM (US) represents one of the possible alternatives for the future RICH photodetectors, thanks to its good time resolution, low dark noise rate and high gain. The LHCb Edinburgh group is involved in the Upgrade II R&D programme, testing and characterising an LAPPD in the laboratory of the University. The LAPPD technology, the current Edinburgh setup and performance from first tests of the photodetector will be presented.

Data Fusion for Displacement Estimation and Tracking of UAV Quadrotor in Dynamic Motion

The fusion of MIMU and GPS data is generally used to estimate the displacement and tracking of quadrotor UAVs. Meanwhile, displacement estimation inaccuracies during dynamic motion often occur. This error is caused by noise and limited sensor sampling rate especially occurs when the quadrotor changes its attitude rapidly to generate an instantaneous horizontal force. This paper proposes data fusion based on Kalman filter to estimate orientation and displacement. Experiments were also carried out to verify displacement accuracy, i.e. in single-axis and multi-axis sensor motions. The algorithm combines data from MIMU and GPS sensors so that acceleration data is filled in points where GPS data is not available. With this method, the predicted displacement from the MIMU sensor can be corrected every second with data from the GPS and produce accurate displacement and trajectory estimates.