Low Noise Sensitivity Research Articles

A Closed-Form Method With Low Noise Sensitivity for Locating a Moving Source on Earth at a Known Altitude

A Closed-Form Method With Low Noise Sensitivity for Locating a Moving Source on Earth at a Known Altitude

Noise-sensitivity during sleep in non-sedated mechanically ventilated patients is associated with weaning duration: a polysomnographic study.

Poor sleep is a major concern in intensive care units (ICUs), particularly in mechanically ventilated patients, because it is associated with longer duration of the weaning phase and higher mortality. High noise levels in ICUs are frequently reported by patients as one of the most disturbing sleep-disrupting factors but would be responsible for less than 20% of arousals. This suggests major inter-individual variability in noise sensitivity. Our objectives were to define and assess noise sensitivity in mechanically ventilated patients and to explore its association with sleep duration, sleep quality and weaning duration. We retrospectively re-analyzed polysomnographies (PSGs) recorded in 29 non-sedated patients, mechanically ventilated for at least 24h and difficult to wean (i.e. ≥ 1 spontaneous breathing trial failure). All the arousals were identified on all the PSGs. We calculated mean noise level and identified all noise peaks (an abrupt increase of noise intensity of more than 10 decibels (dBA)) preceding each arousal. Each 21-second period preceding each arousal was divided into seven 3-second bins. We built a pre-event time histogram for each PSG by counting the total number of noise peaks in each bin. If the total number of noise peaks in one bin exceeded the average of the seven bins plus 2 SD, we considered that there was a significant relationship between the number of noise peaks in this bin and the arousal. The patient was then considered as noise-sensitive. Presence of atypical sleep, proportion of sleep stages, and weaning duration were assessed. Nineteen out of 29 patients (66%) were noise-sensitive. Duration of weaning from ventilator was significantly longer in noise-insensitive patients (median [interquartile range] 2 [1-2] versus 5 [2-8] days; p < 0.01). Proportion of N1, N2, N3 sleep stages and rapid eye movement sleep were similar in noise-sensitive and noise-insensitive patients. In contrast, the proportion of patients displaying atypical sleep was higher in noise-insensitive patients. Our results report for the first time that most ICU patients were noise-sensitive. Lower noise sensitivity was associated with atypical sleep and could reflect lower brain reactivity to environment.

Magnetic Railway Sleeper Detector

In an ever expanding railway network all around the world, the need for track maintenance grows steadily. Traditionally, one major part of track maintenance is ramming large vibrating steel picks into the gravel between and under railway sleepers to compress the gravel and generate a safe substructure. Even today, maintenance personnel still have to manually locate the sleepers if they cannot be detected by computer vision systems or visually by the operator. Here we developed a first of its kind magnetic sleeper detector, even able to find sleepers, buried in gravel, undetectable by vision based systems. Our approach of magnetic detection is based on a DC magnetic field excitation and a detector moving with respect to the rail system, including the sleepers and fasteners for mounting the rails. Due to railway application constraints a large air gap between the sensor and the sleeper structure is required, which significantly complicates the magnetic sensing task for robust sleeper detection. The design and optimization of the magnetic circuit was based on extensive 3D simulation studies to ensure highest possible variation in magnetic flux density at the sensor locations for absence and presence of a sleeper. Furthermore, a low noise and high sensitivity electronic circuit has been realized to cope with sensor signal offsets from unknown or changing sensor orientations with respect to the earth’s magnetic field, or magnetic interferences from other trains potentially passing by during active measurements. Since we only want to detect sleepers in close vicinity of the moving sensor system, digital signal processing of the acquired signals can easily compensate for disturbing slowly changing or static field components within real world application scenarios. We demonstrate that magnetic detection of even buried sleepers on railway tracks is possible for distances of up to 172 mm between the sensor and the sleeper. This enables an even higher level of railway maintenance automation previously impossible in certain scenarios.

A Label-Free Photoelectrochemical Biosensor Based on ZnO/Cs3MnBr5 Heterogeneous Films for Alpha-Fetoprotein Detection.

Highly sensitive and specific biomarker detection is of outstanding importance for the diagnosis and treatment of cancers. Herein, we developed robust photoelectrochemical (PEC) biosensors with low background noise and high sensitivity based on a heterojunction, which can improve semiconductor photoelectric properties by limiting the recombination of photogenerated electron-hole pairs and successfully widening the range of light absorption. Alpha-fetoprotein (AFP) was used as a target model to examine the analytical performances of the designed PEC biosensors. ZnO/Cs3MnBr5 heterogeneous film with a uniform porous structure and large surface area enhanced electron transfer and biomolecule immobilization, and significantly increased the photocurrent response. Under the optimal conditions, the designed PEC biosensor exhibited a linear detection range of 0.01-500 ng/mL and a detection limit of 12 pg/mL. In addition, this PEC biosensor performed well when testing human serum samples and exhibited good repeatability, stability over time, and specificity, showing enormous potential for the detection of cancer markers in future biological and clinical research.

Partial Discharge Fault Diagnosis in Power Transformers Based on SGMD Approximate Entropy and Optimized BILSTM.

Partial discharge (PD) fault diagnosis is of great importance for ensuring the safe and stable operation of power transformers. To address the issues of low accuracy in traditional PD fault diagnostic methods, this paper proposes a novel method for the power transformer PD fault diagnosis. It incorporates the approximate entropy (ApEn) of symplectic geometry mode decomposition (SGMD) into the optimized bidirectional long short-term memory (BILSTM) neural network. This method extracts dominant PD features employing SGMD and ApEn. Meanwhile, it improves the diagnostic accuracy with the optimized BILSTM by introducing the golden jackal optimization (GJO). Simulation studies evaluate the performance of FFT, EMD, VMD, and SGMD. The results show that SGMD-ApEn outperforms other methods in extracting dominant PD features. Experimental results verify the effectiveness and superiority of the proposed method by comparing different traditional methods. The proposed method improves PD fault recognition accuracy and provides a diagnostic rate of 98.6%, with lower noise sensitivity.

Synthesis of eco-friendly multifunctional dextran microbeads for multiplexed assays

There has been an increasing demand for simultaneous detection of multiple analytes in one sample. Microbead-based platforms have been developed for multiplexed assays. However, most of the microbeads are made of non-biodegradable synthetic polymers, leading to environmental and human health concerns. In this study, we developed an environmentally friendly dextran microbeads as a new type of multi-analyte assay platform. Biodegradable dextran was utilized as the primary material. Highly uniform magnetic dextran microspheres were successfully synthesized using the Shirasu porous glass (SPG) membrane emulsification technique. To enhance the amount of surface functional groups for ligand conjugation, we coated the dextran microbeads with a layer of dendrimers via a simple electrostatic adsorption process. Subsequently, a unique and efficient click chemistry coupling technique was developed for the fluorescence encoding of the microspheres, enabling multiplexed detection. The dextran microbeads were tested for 3-plex cytokine analysis, and exhibited excellent biocompatibility, stable coding signals, low background noise and high sensitivity.

US2Mask: Image-to-mask generation learning via a conditional GAN for cardiac ultrasound image segmentation

Cardiac ultrasound (US) image segmentation is vital for evaluating clinical indices, but it often demands a large dataset and expert annotations, resulting in high costs for deep learning algorithms. To address this, our study presents a framework utilizing artificial intelligence generation technology to produce multi-class RGB masks for cardiac US image segmentation. The proposed approach directly performs semantic segmentation of the heart’s main structures in US images from various scanning modes. Additionally, we introduce a novel learning approach based on conditional generative adversarial networks (CGAN) for cardiac US image segmentation, incorporating a conditional input and paired RGB masks. Experimental results from three cardiac US image datasets with diverse scan modes demonstrate that our approach outperforms several state-of-the-art models, showcasing improvements in five commonly used segmentation metrics, with lower noise sensitivity. Source code is available at https://github.com/energy588/US2mask.

Interface engineering by constructing vertical junction for reduced noise and improved sensitivity in 2D photodetector

Two-dimensional (2D) materials have been widely demonstrated as promising candidates for next generation photodetectors, while the noticeable channel current is still a limiting factor for photodetection sensitivity. In this work, the interface engineering has been developed by constructing a vertical pn and Schottky junction in the 2D WS2 channel, resulting in a reduced dark current and noise spectral density, significantly improving the sensitivity. Specifically, the WS2 bottom surface is coupled with p-type tellurium (Te) nanoribbon and gold (Au) stripes, thus a vertical pn and Schottky junction can be constructed at WS2/Te and WS2/Au interface, respectively. In both device architectures, the dark current and electric noise are much suppressed due to the formation of depletion region in WS2 channel. Meanwhile, the out-of-plane built-in electric field at junction can facilitate the separation of photo-excited electron–hole pairs, which subsequently yields a faster temporal response. For the WS2/Au device, the incident light can be reflected by the bottom Au and propagate through the WS2 layer again, further boosting the photo-absorption, thus the photodetection sensitivity. The engineered WS2 photodetectors exhibit the noise spectral density as low as 5.36 × 10−14 A Hz−1/2 and high specific detectivity (D*) up to 1.12 × 1011 Jones, which has one–two orders of magnitude improvement compared to the pristine device. This work provides an effective and universal interface engineering strategy to achieve low noise and high sensitivity in 2D photodetectors.

Is Recognition of Speech in Noise Related to Memory Disruption Caused by Irrelevant Sound?

Listeners with normal audiometric thresholds show substantial variability in their ability to understand speech in noise (SiN). These individual differences have been reported to be associated with a range of auditory and cognitive abilities. The present study addresses the association between SiN processing and the individual susceptibility of short-term memory to auditory distraction (i.e., the irrelevant sound effect [ISE]). In a sample of 67 young adult participants with normal audiometric thresholds, we measured speech recognition performance in a spatial listening task with two interfering talkers (speech-in-speech identification), audiometric thresholds, binaural sensitivity to the temporal fine structure (interaural phase differences [IPD]), serial memory with and without interfering talkers, and self-reported noise sensitivity. Speech-in-speech processing was not significantly associated with the ISE. The most important predictors of high speech-in-speech recognition performance were a large short-term memory span, low IPD thresholds, bilaterally symmetrical audiometric thresholds, and low individual noise sensitivity. Surprisingly, the susceptibility of short-term memory to irrelevant sound accounted for a substantially smaller amount of variance in speech-in-speech processing than the nondisrupted short-term memory capacity. The data confirm the role of binaural sensitivity to the temporal fine structure, although its association to SiN recognition was weaker than in some previous studies. The inverse association between self-reported noise sensitivity and SiN processing deserves further investigation.

Difference of Convex Functions Programming With Machine-Learning Prior for the Imaging Problem in Electrical Capacitance Tomography.

The electrical capacitance tomography technology has potential benefits for the process industry by providing visualization of material distributions. One of the main technical gaps and impediments that must be overcome is the low-quality tomogram. To address this problem, this study introduces the data-guided prior and combines it with the electrical measurement mechanism and the sparsity prior to produce a new difference of convex functions programming problem that turns the image reconstruction problem into an optimization problem. The data-guided prior is learned from a provided dataset and captures the details of imaging targets since it is a specific image. A new numerical scheme that allows a complex optimization problem to be split into a few less difficult subproblems is developed to solve the challenging difference of convex functions programming problem. A new dimensionality reduction method is developed and combined with the relevance vector machine to generate a new learning engine for the forecast of the data-guided prior. The new imaging method fuses multisource information and unifies data-guided and measurement physics modeling paradigms. Performance evaluation results have validated that the new method successfully works on a series of test tasks with higher reconstruction quality and lower noise sensitivity than the popular imaging methods.

Cumulative spectrum distribution entropy for rotating machinery fault diagnosis

Entropy-based methods have shown promise in detecting dynamic changes in non-linear signals and have been widely applied in fault diagnosis for rotating machinery. However, these methods have limitations when it comes to capturing frequency-domain information of fault features, as they are primarily based on time-domain signals. To address this issue, this paper proposes a new entropy measure called cumulative spectrum distribution entropy (CSDEn), which is based on the cumulative distribution of the spectrum and considers both frequency probability and frequency values in the spectrum domain. The proposed method is evaluated using synthetic signals and experimental data from different bearing and gear working states. The results show that CSDEn outperforms other widely used entropy measures in detecting dynamic changes and measuring signal complexity with low noise sensitivity and high computing efficiency. Nonparametric Mann–Whitney U tests reveal significant differences between different working states for proposed CSDEn method, and compared with other entropy methods, CSDEn achieves the highest recognition rates in diagnosing different bearing and gear working states. Moreover, proposed CSDEn method demonstrates its effectiveness in addressing the challenges of small sample datasets and strong noise interference, making it highly competitive in real industrial applications.

Recent Advances in Photoelectrochemical Sensors for Analysis of Toxins and Abused Drugs in the Environment

Toxic pollutants in the environment, such as toxins and abused drugs, have posed a major threat to human health and ecosystem security. It is extremely desirable to develop simple, low-cost, sensitive, and reliable techniques for the detection of these pollutants in the environment. As a booming analytical method, photoelectrochemical (PEC) sensors possess low background noise and high sensitivity. The performances of PEC sensors are fundamentally related to the photoelectric conversion efficiency, which mainly depends on the properties of photoactive materials. This review aims to summarize the engineered photoactive materials, i.e., semiconductors and semiconductor-based heterojunctions, as well as their actual applications, with emphasis on sensing mechanisms in PEC sensors for the analysis of toxins and abused drugs in the environment. Finally, the future research perspectives in this field are also discussed.

EEG alpha wave responses to sounds from neighbours in high-rise wood residential buildings

This study explored electroencephalogram (EEG) alpha waves (α-EEG) in response to neighbours’ sounds in wood residential buildings. Experiments were carried out in a laboratory to collect α-EEG data in distinct acoustics scenarios. A series of impact and airborne sounds were generated using loudspeakers and subwoofers, while the participants sat comfortably in a simulated living room wearing EEG headsets. Impact sounds were those of footsteps of adults walking on floors equipped with different timber floor configurations, whereas airborne sounds were of speech and music digitally filtered to resemble the good and poor sound insulation performances of lightweight vertical partitions. The sound sources were presented both individually and in combination (e.g. footsteps combined with music or speech). Noise sensitivity and attitudes towards neighbours were introduced as non-acoustic factors. The study highlighted significantly higher α-EEG in response to footsteps heard through floors characterised by low impact sound pressure levels (SPL) and to music heard through partition walls with low sound reduction indices. The effective duration of the autocorrelation function, τe, was computed to investigate subjective preference, and significant differences between sounds heard at various SPLs were identified for speech and music. Footsteps sounds in combination with an airborne source elicited higher α-EEG when compared to single footsteps sounds. Participants with self-reported low noise-sensitivity and positive attitude towards neighbours showed significantly larger α-EEG responses when exposed to sounds from neighbours than those who had high noise-sensitivity and negative attitude towards neighbours.

Au@BN-enhanced laser desorption/ionization mass spectrometry and imaging for determination of fipronil and its metabolites in food and biological samples

Gold nanoparticles (AuNPs) represent an attractive inorganic matrix for laser desorption/ionization mass spectrometry (LDI-MS) detection of low-molecular-weight analytes; however, their direct use is hindered by severe aggregation. To limit AuNPs aggregation, hexagonal boron nitride nanosheets (h-BNNs) were employed as supports to improve their desorption/ionization efficiency. Thus, Au@BN was synthesized and systematically characterized. It showed low background noise and high sensitivity for LDI-MS of fipronil and its metabolites. Au@BN-assisted LDI-MS was validated using complex samples including blueberry juice, green tea beverage, and fish muscle, achieving low detection limits (0.05–0.20 µg·L−1 for liquid media, 0.82–1.25 ng·g−1 for fish muscle), wide linear ranges (0.2–100 µg·L−1 for liquid media, 3.00–1000 ng·g−1 for fish muscle), high reproducibility (7.55%–13.7%), and satisfactory recoveries (82.62%–109.1%). Furthermore, spatial distributions of analytes in strawberries and zebrafish were successfully imaged. This strategy allows for the quantitative analysis of other small molecules in complex substrates.

Be Quiet! Effects of Competing Speakers and Individual Characteristics on Listening Comprehension for Primary School Students.

Students learn in noisy classrooms, where the main sources of noise are their own voices. In this sound environment, students are not equally at risk from background noise interference during lessons, due to the moderation effect of the individual characteristics on the listening conditions. This study investigates the effect of the number of competing speakers on listening comprehension and whether this is modulated by selective attention skills, working memory, and noise sensitivity. Seventy-one primary school students aged 10 to 13 years completed a sentence comprehension task in three listening conditions: quiet, two competing speakers, and four competing speakers. Outcome measures were accuracy, listening effort (response times and self-reported), motivation, and confidence in completing the task. Individual characteristics were assessed in quiet. Results showed that the number of competing speakers has no direct effects on the task, whilst the individual characteristics were found to moderate the effect of the listening conditions. Selective attention moderated the effects on accuracy and response times, working memory on motivation, and noise sensitivity on both perceived effort and confidence. Students with low cognitive abilities and high noise sensitivity were found to be particularly at risk in the condition with two competing speakers.

A Design Method of Active Photoelectric Detection Sensor Based on 1-D Multiunit p-i-n Detector and Its Detection Model

This article develops a new sensor that can detect projectiles in all-weather with high sensitivity and a large field of view. Two high-power line lasers are used to design an active photoelectric detection sensor with a fan-shaped detection plane. The size of the designed fan-shaped photoelectric detection receiving plane is the same as the luminous fan-shaped plane light source to make the two fan-shaped planes coincide with each other, forming an active detection screen. Based on the principle of active photoelectric detection sensor, a linear array photoelectric detector is selected, in which multiple unit p-i-n detectors are spliced together, and an amplifier processing circuit with low noise and high sensitivity is designed. Combined with the geometric structural features of the active detection screen, the radiation characteristics calculation model of the projectile in a fan-shaped detection plane is developed using a five-parameters bidirectional reflectance distribution function (BRDF). Finally, the total radiation intensity calculation function of the projectile’s surface is derived considering the influence of environmental factors and the output signal and signal-to-noise ratio (SNR) model of the active photoelectric detection sensor is set up. Comparative experiments under different environmental illuminations verify that the detection ability has been significantly improved in active photoelectric detection sensors.

Pathways to Environmental Inequality: How Urban Traffic Noise Annoyance Varies across Socioeconomic Subgroups.

The article investigates how socioeconomic background affects noise annoyance caused by residential road traffic in urban areas. It is argued that the effects of socioeconomic variables (migration background, education, and income) on noise annoyance tend to be underestimated because these effects are mainly indirect. We specify three indirect pathways. (1) A "noise exposure path" assumes that less privileged households are exposed to a higher level of noise and therefore experience stronger annoyance. (2) A "housing attributes path" argues that less privileged households can shield themselves less effectively from noise due to unfavorable housing conditions and that this contributes to annoyance. (3) Conversely, an "environmental susceptibility path" proposes that less privileged people are less concerned about the environment and have a lower noise sensitivity, and that this reduces their noise annoyance. Our analyses rest on a study carried out in four European cities (Mainz and Hanover in Germany, Bern and Zurich in Switzerland), and the results support the empirical validity of the three indirect pathways.

Super-Resolution Thermal Imaging Using Uncooled Infrared Sensors for Non-Destructive Testing of Adhesively Bonded Joints

To compensate for the inability of inexpensive uncooled infrared (IR) sensors to perform high-speed thermography detection with low noise and high sensitivity in the field of non-destructive testing (NDT), a post-processing method for uncooled IR sensors based on compressive sensing is proposed in this paper to perform super-resolution NDT thermography. Super-resolution IR phase images can be generated by first using a sparse representation of the low-resolution (LR) IR images and a sparse dictionary generated from randomly sampled high-resolution (HR) raw training image patches to generate an HR IR image sequence, and, subsequently, implementing IR thermography NDT. To verify the reliability of the proposed method, super-resolution IR images were combined with pulsed phase thermography (PPT) to verify bonded joint structures in carbon fibre-reinforced polymer (CFRP). Compared to other methods, the proposed method reduces the mean square error (MSE) by 11.13% and yields a better visual effect on the PPT phase image reconstructed using three-fold super-resolution. The proposed method not only produces sharp defect edges, but also retains the original texture, which is expected to enable wider application of uncooled IR sensors in NDT applications in the future.

Dispersion entropy-based Lempel-Ziv complexity: A new metric for signal analysis

Lempel-Ziv complexity (LZC) is one of the most important metrics for detecting dynamic changes in non-linear signals, but due to its dependence on binary conversion, LZC tends to lose some of the effective information of the time series, while the noise immunity is not guaranteed and cannot be applied to the detection of real-world signals. To address these limitations, we have developed a dispersion entropy-based LZC (DELZC) based on the normal cumulative distribution function (NCDF) and dispersion permutation patterns. In DELZC, the time series are first processed by NCDF to increase the number of classes and thus reduce the loss of information, and in addition, the dispersive entropy (DE) in terms of the ordinal number of the permutation pattern is considered to replace the binary conversion of LZC, thus improving the ability to capture the dynamic changes in the time series. In signal analysis using a set of time series, several easy-to-understand concepts are used to demonstrate the superiority of DELZC over other three LZC metrics in detecting the dynamic variability of the signals, namely LZC, dispersion LZC (DLZC) and permutation LZC (PLZC). The synthetic signal experiments demonstrate the superiority of DELZC in detecting the dynamic changes of time series and characterizing the complexity of signal, and also have lower noise sensitivity. Moreover, DELZC has the best performance in diagnosing four states of rolling bearing fault signals and classifying five types of ship radiation noise signals, with higher recognition rates than LZC, PLZC and DLZC.

Low-Noise Optical Accelerometers: Bridging the Gaps among Geophones, Accelerometers, and Broadbands in a Deep Borehole

Abstract Earthquakes and other seismic sources produce waves with frequency content spanning many orders of magnitude. Recording a broader frequency band of interest has historically required deploying multiple instruments designed to work the best within limited, overlapping frequency ranges. Here, we detail a 300 m deep borehole deployment of a sensor package, including three new optical accelerometers that can potentially replace many dedicated instruments with a single, low-noise sensor. These instruments are designed with a flat frequency response from 0.005 to 1500 Hz, spanning the flat response segments of broadband sensors and geophones, as well as a low-noise floor and high sensitivity. The sensors have been functioning normally for over four years, fully grouted at depths of &amp;gt;100 m. Year-long power spectral density (PSD) profiles show that the optical accelerometers have a lower noise floor than a colocated geophone for all frequencies, with 20 dB noise reduction at 250 Hz. PSD comparisons to a broadband sensor installed at the surface show a 5–30 dB noise reduction for the optical accelerometer above 1 Hz, although this is likely due, in part, to the broadband sensor being subjected to much higher surface noise. At periods &amp;gt;5 s, the broadband sensor shows up to 20 dB lower noise than the optical accelerometer, which in turn has up to 50 dB lower noise floor than the colocated geophone. Finally, modeling the Brune displacement spectrum for theoretical seismicity within 1 km of the borehole shows that the optical accelerometers could potentially deliver a detection threshold improvement of one magnitude unit relative to the colocated geophone.