Light Noise Research Articles

An approach to ensure joint illumination & communication performance of a forward error corrected indoor visible light communication (VLC) system in presence of ambient light interference

Abstract In recent time of looming radio frequency (RF) spectrum crisis, visible light communication using lighting infrastructure emerged as a potential alternative at an indoor environment. This paper addresses the setback associated with ambient light interference in an indoor Visible Light Communication (VLC) system to ensure joint communication and illumination performance inside an office room. A novel VLC architecture with suitable white light emitting diode (WLED) luminaire arrangement is presented to minimize the dispersion of signal to interference plus noise ratio (SINR) across the room. Luminaires are categorized in two groups viz. data transmitting illuminants and illuminants for lighting purpose. The first group is dedicated to transmit data as well as serves the purpose of illumination. The other set creates only ambient illumination to achieve quality lighting attributes. The proposed forward error corrected receiver configuration discards the ambient light noise originated by the illuminants that serve the ambient illumination. Tail biting convolutional encoder and viterbi decoder are used at the encoding section of the transmitter and decoding section of the receiver respectively to improve bit error rate. Results obtained through MATLAB simulation shows better average bit error rate (BER) in the order of 10−8 measured at uniformly distributed 25 grid points over the working plane. At the same time achieved average horizontal illuminance with good uniformity comply with ISO recommendation.

DiffusionFR: Species Recognition of Fish in Blurry Scenarios via Diffusion and Attention

Blurry scenarios, such as light reflections and water ripples, often affect the clarity and signal-to-noise ratio of fish images, posing significant challenges for traditional deep learning models in accurately recognizing fish species. Firstly, deep learning models rely on a large amount of labeled data. However, it is often difficult to label data in blurry scenarios. Secondly, existing deep learning models need to be more effective for the processing of bad, blurry, and otherwise inadequate images, which is an essential reason for their low recognition rate. A method based on the diffusion model and attention mechanism for fish image recognition in blurry scenarios, DiffusionFR, is proposed to solve these problems and improve the performance of species recognition of fish images in blurry scenarios. This paper presents the selection and application of this correcting technique. In the method, DiffusionFR, a two-stage diffusion network model, TSD, is designed to deblur bad, blurry, and otherwise inadequate fish scene pictures to restore clarity, and a learnable attention module, LAM, is intended to improve the accuracy of fish recognition. In addition, a new dataset of fish images in blurry scenarios, BlurryFish, was constructed and used to validate the effectiveness of DiffusionFR, combining bad, blurry, and otherwise inadequate images from the publicly available dataset Fish4Knowledge. The experimental results demonstrate that DiffusionFR achieves outstanding performance on various datasets. On the original dataset, DiffusionFR achieved the highest training accuracy of 97.55%, as well as a Top-1 accuracy test score of 92.02% and a Top-5 accuracy test score of 95.17%. Furthermore, on nine datasets with light reflection noise, the mean values of training accuracy reached a peak at 96.50%, while the mean values of the Top-1 accuracy test and Top-5 accuracy test were at their highest at 90.96% and 94.12%, respectively. Similarly, on three datasets with water ripple noise, the mean values of training accuracy reached a peak at 95.00%, while the mean values of the Top-1 accuracy test and Top-5 accuracy test were at their highest at 89.54% and 92.73%, respectively. These results demonstrate that the method showcases superior accuracy and enhanced robustness in handling original datasets and datasets with light reflection and water ripple noise.

Improving the reliability of digital cameras identification by optimizing the noise portraits comparing

The issues of optimization of existing methods for digital camera sensor identification are considered. Ways of improvement of reliability of digital cameras identification is discussed. Homogeneous images were optically recorded to form a noise portrait and test sets of amateur images for 3 cameras of various types. An optimal digital filter was selected to evaluate smoothed images for obtaining noise portraits of identified cameras. Camera identification algorithm was optimized basing on a comparison of light spatial noise portraits. Application of the optimal filter and identity criterion provides an average increase of identification reliability of more than 60 times. The results can be useful in the areas of image registration and processing, security, forensics, big data analysis, etc.

Research on Algorithm of Light Strip Center Extraction Based on Deep Learning

In view of the phenomena such as under-exposure of light strips and noise interference caused by complex surfaces of objects, it is difficult for traditional light strip center extraction algorithm to achieve light strip center extraction. Therefore, this paper studies the extraction of light strip center line based on semantic segmentation network algorithm based on deep learning, uses deep learning algorithm to presegment light strips, and then uses gray prime-core method to extract light strips subpixel. Improve the stability and accuracy of center line extraction.

Synthetic Training Data in AI-Driven Quality Inspection: The Significance of Camera, Lighting, and Noise Parameters.

Industrial-quality inspections, particularly those leveraging AI, require significant amounts of training data. In fields like injection molding, producing a multitude of defective parts for such data poses environmental and financial challenges. Synthetic training data emerge as a potential solution to address these concerns. Although the creation of realistic synthetic 2D images from 3D models of injection-molded parts involves numerous rendering parameters, the current literature on the generation and application of synthetic data in industrial-quality inspection scarcely addresses the impact of these parameters on AI efficacy. In this study, we delve into some of these key parameters, such as camera position, lighting, and computational noise, to gauge their effect on AI performance. By utilizing Blender software, we procedurally introduced the "flash" defect on a 3D model sourced from a CAD file of an injection-molded part. Subsequently, with Blender's Cycles rendering engine, we produced datasets for each parameter variation. These datasets were then used to train a pre-trained EfficientNet-V2 for the binary classification of the "flash" defect. Our results indicate that while noise is less critical, using a range of noise levels in training can benefit model adaptability and efficiency. Variability in camera positioning and lighting conditions was found to be more significant, enhancing model performance even when real-world conditions mirror the controlled synthetic environment. These findings suggest that incorporating diverse lighting and camera dynamics is beneficial for AI applications, regardless of the consistency in real-world operational settings.

Quantification and suppression of optical non-orthogonality and light intensity noise in all-optical hot atomic sensing systems

All-optical hot atomic sensing systems such as optical pumping magnetometers (OPM) and comagnetometers, nuclear magnetic resonance (NMR) magnetometers and gyroscopes, have the ability to sense magnetic field, anomalous field and inertial rotation, thus having a wide potential for applications in precision measurement, fundamental physics research and inertial navigation. System noise limits the minimum value of the signal that can be measured and there is coupling effect between the noises. Therefore, the quantification and suppression of various noise are challenging and should be investigated. As one of the major noises, the light intensity fluctuation of the probe light and the pump light affects the sensitivity of the all-optical hot atomic sensing systems. However, there has been no in-depth research on the quantification of the light intensity noise. In this paper, a method of measuring the light intensity noise is proposed, which can be applied to kinds of all-optical hot atomic sensing systems. For verification, the effects of probe and pump light intensity fluctuations on system noise are simulated and experimentally verified in a spin exchange relaxation free (SERF) comagnetometer. Besides, it is found that the optical non-orthogonality amplifies the probe light intensity noise by more than 3.4 times and a quantification method of optical non-orthogonality is proposed. Finally, a suppression method of the light intensity noise is presented by suppressing the optical non-orthogonality and the noise is suppressed by 59%. A universal light intensity noise quantification method is proposed for all-optical hot atomic sensing systems, and a light intensity noise suppression method is proposed for SERF comagnetometers, which can be applied to the suppression of noises coupled with atomic polarization.

Printer source identification of quick response codes using residual attention network and smartphones

Quick response codes are widely utilized in commodity anti-counterfeiting, and their traceability via smartphones is an effective authentication method. However, quick response codes are susceptible to illegal duplication, and the images captured by smartphones are easily affected by changes in light and environmental noise, leading to unsatisfactory verification results. To address these issues, this paper introduces a novel approach by combining the squeeze-excitation attention module with bottleneck residual block. It presents a squeeze-excitation bottleneck residual network for printer source identification of quick response codes. The squeeze-excitation attention module pays more attention to the features that represent the printer attributes, reduces the interference of useless information, and has low computational consumption; while the bottleneck residual block has the advantages of few parameters, strong feature extraction capability, and good expandability. Thus, the performance can be improved effectively only with a small increasing in parameters. The experimental results verify that the proposed method achieves an accuracy of 98.77% under smartphone capture conditions, it outperforms other convolutional neural network-based methods in terms of identification accuracy. The deep learning model proposed in this paper can be generalized and applied to the printer source identification of paper content in the civil, criminal investigation and judicial fields.

An image enhancement method for cable tunnel inspection robot

Influenced by multiple factors such as low light intensity, dispersed light, excessive dust, and excessive ambient noise inside cable tunnels, the images captured by cable tunnel inspection robots have shortcomings such as low contrast, low pixel values, and high noise. To improve the image quality, this paper proposes an image enhancement method suitable for cable tunnel inspection robot. First, in this paper, a bivariate hybrid optimization module using the alternating direction multiplier method and the adaptive learning rate acceleration SVRG algorithm is constructed to achieve image pre-processing. Second, a feature extraction module combining a U-Net network and a coordinate attention mechanism is constructed to extract features from the original image and the pre-processed image. Third, the progressive feature fusion module and the image recovery module are constructed to fuse the above features and are combined with the original image to obtain the enhanced image. Finally, the pixel compensation module is constructed to compensate for the features of the enhanced image and recover the lost texture details.

DUTC Net: A novel deep ulcer tissue classification network with stage prediction and treatment plan recommendation

Background and objectivesThe Chronic injuries caused by the venous valve called as Varicose ulcers. The pressure in the veins is increased as the valves of the veins allows blood to flow in backward direction. Detecting and segmenting varicose ulcer wound region is a separate step prior to the classification operation. In this work the novel customized Deep Ulcer Tissue Classification Network is proposed and it is named as “DUTC Net”. The goal of the proposed DUTC Net is i) to classify the tissue type by generating tissue map using segmented ulcer wound image ii) to identify the stages of ulcer and iii) Percentage of affected tissues in the wound. MethodsThe dataset contains various stages of varicose ulcer wound images. The first step is to preprocess the ulcer wound image to remove light artifacts and noise. After preprocessing segmentation is done to isolate the region of the varicose ulcer using active contour algorithm. The segmented area can be fed into proposed DUTC Net to get the respective tissue map of the ulcer wound images. The tissue map images are assigned as input to DUTC Net at the phase II. In this phase II, DUTC Net learns the features from the tissue map of the phase I and classifies the stages and percentage of affected tissues in the varicose ulcer wound image. ResultsWhile experimental analysis, the proposed method DUTC Net results sensitivity 97.9%, specificity 98% and accuracy 97.2% for the average of tissue classification and sensitivity 97.1%, specificity 98% and accuracy 97.5% for the average of stage classification. ConclusionThe proposed work is mainly used to classify the varicose ulcer stages, tissues types and affected area using DUTC Net to investigate the patients status and treatment methods.

The effect of light pollution and noise pollution on birds

It is common knowledge that human society is developing faster and faster nowadays. As human society is developing, the cities are expanding. As the cities are expanding, more and more people are now in the city. To help these people live a better life, more and more resources are produced. After producing most of the products nowadays, there comes pollution. In this way, pollution is getting more and more serious. All of the pollution that is already in a city do not only affect human but also affect birds. One of the most common non pollution is air pollution, almost every city in the world has air pollution. But there are also two other kinds of pollution that is also harming the society and the animals in a serious way. They are light pollution and sound pollution. This passage focuses on these two types of pollution. Light pollution will cause birds to sleep less and may affect their migration way. The birds may be killed halfway on migration way because of the light may lead them to a loss of direction. Sound pollution also reduces the bird sleeping quality. This kind of pollution also cause birds to get food more difficult. By analyzing the effects of these pollution, some ways to help birds may be figured out.

Ophthalmic Manifestations of Migraine in a Nigerian Tertiary Health Facility.

Migraine is a group of headache syndromes, with a prevalence of 5%-25%. Migraine is a complex recurrent headache disorder, often unilateral, throbbing or pulsating in nature aggravated by physical activity, bright light, and loud noises. Symptoms associated with migraine without aura are nausea, anorexia, and varying degrees of ophthalmic problems ranging from visual aura, ocular symptoms, and ophthalmoplegia. The study determined the pattern and ophthalmic manifestations of migraine in Aminu Kano Teaching Hospital, Kano, Nigeria. This was an observational cross-sectional study. Ethical clearance was obtained from the research ethical committee of Aminu Kano Teaching Hospital (AKTH). A total of 254 patients diagnosed with migraine using international headache society criteria attending neurology clinic in AKTH were interviewed and examined using a structured questionnaire which captured the biodata, pattern of migraine and ocular symptoms associated with migraine. The data obtained were collated and analysed using Statistical Package for the Social Sciences, version 23. Of the 254 patients examined, there were 95 (37.4%) males and 159 (62.6%) females, (M:F = 1:2) with mean age of 28.9 ± 9.7 years. Migraine without aura was the commonest type seen in 55.8% of patients. Most patients had unilateral headaches. About 52.8% and 57.5% had headaches that were throbbing in nature. The headache lasted for 72 h in 48.2% and 48 h in 30.3%. It was triggered by physical stress in 49.6% and lack of sleep in 46.5%. The commonest associated symptom was nausea in 44.9%, tinnitus in 39.0%, and vomiting in 29.1%. While the commonest ocular/visual symptom was photophobia in 76% of patients. Most of the patients had family history of migraine (62.8%). Migraine without aura (MWOA) was found to be significantly associated with female gender (P = 0.001; OR = 2.48; 95% CI: 1.45-4.25). Scotoma, fortifying spectra were significantly associated with migraine with aura (MWA; P = 0.0004; OR = 28.46; 95% CI: 11.53-70.35). Migraine is one of the most common types of primary headache. There is a female preponderance. Migraine has significant association with visual disturbances and ocular symptoms.

Strongly robust computational ghost imaging based on nearest neighbor filtering

Computational ghost imaging is a new optical imaging technique that obtains a reconstructed image of a target from a set of illumination patterns, but the quality of the reconstructed image is affected by ambient light noise. In this paper, two nearest neighbor filtered illumination patterns generated based on nonlinear noise filtering algorithm are proposed, which can effectively reduce the effect of ambient light in ghost imaging. When additional noise is added, compared with the reconstructed images of random illumination pattern, Hadamard illumination pattern and linear filter illumination pattern, the SNR of the reconstructed images of two different neighbor filtering illumination patterns is increased by 470 %, 1863 %, 75 % and 517 %, 2022 %, 90 %, respectively. In simulated noise environments, neighbor filtering illumination patterns reduce most of the effects of noise and results in high quality reconstructed images for a variety of target objects. In the experiments, these new illumination patterns has a strong robustness, and the reconstructed images obtained are sharper and contain more detailed information than random illumination pattern, Hadamard illumination pattern, and linearly filtered illumination pattern. Nearest neighbor filtering illumination patterns apply the nonlinear filtering method to computational ghost imaging and obtains excellent imaging results, which provides a new denoising idea for computational ghost imaging.

A Study of Two Periodogram Algorithms for Improving the Detection of Small Transiting Planets

The sensitivities of two periodograms are compared for weak signal planet detection in transit surveys: the widely used Box Least Squares (BLS) algorithm following light curve detrending and the Transit Comb Filter (TCF) algorithm following autoregressive ARIMA modeling. Small depth transits are injected into light curves with different simulated noise characteristics. Two measures of spectral peak significance are examined: the periodogram signal-to-noise ratio (S/N) and a false alarm probability (FAP) based on the generalized extreme value distribution. The relative performance of the BLS and TCF algorithms for small planet detection is examined for a range of light curve characteristics, including orbital period, transit duration, depth, number of transits, and type of noise. We find that the TCF periodogram applied to ARIMA fit residuals with the S/N detection metric is preferred when short-memory autocorrelation is present in the detrended light curve and even when the light curve noise had white Gaussian noise. BLS is more sensitive to small planets only under limited circumstances with the FAP metric. BLS periodogram characteristics are inferior when autocorrelated noise is present due to heteroscedastic noise and false period detection. Application of these methods to TESS light curves with known small exoplanets confirms our simulation results. The study ends with a decision tree that advises transit survey scientists on procedures to detect small planets most efficiently. The use of ARIMA detrending and TCF periodograms can significantly improve the sensitivity of any transit survey with regularly spaced cadence.

The Built Environment’s Role in Supporting Dementia Inclusive Communities: A Case Study of Canterbury‐Bankstown City

AbstractBackgroundInclusive and enabling cities are fundamental to the wellbeing of people impacted by dementia. How we implement these ideals in multicultural communities through the built environment remains understudied. The experience of place must be considered in the context of dementia‐friendly community (DFC) models to fully enable people experiencing dementia to remain autonomous, integrated, and connected. This study aimed to explore how diverse individuals with dementia perceive the built environment and its impacts on everyday activities using a mixed methods approach.MethodThis study was set in Canterbury‐Bankstown, New South Wales, Australia where a high proportion of people impacted by dementia speak English, Arabic, and/or Vietnamese. Seventeen people experiencing dementia, their care partners, and former carers (English: n = 7; Arabic: n = 5; Vietnamese: n = 5) were recruited for a semi‐structured interview, and two of these dyads (English: n = 1; Vietnamese n = 1) audited a local shopping centre and hospital using the DFC‐environmental assessment tool (DFC‐EAT). Interviews and audits were conducted in‐language with bilingual researchers, who then transcribed the data into English for analysis. Similarities and differences were identified across the interview transcripts and audit data to identify issues with the existing built environment and improvement opportunities.ResultWe found that participants would limit or avoid going outdoors for everyday activities because of safety concerns (especially when using paths and carparks). Furthermore, overwhelming indoor experiences were characterised by unfamiliar designs and fittings (particularly with bathroom facilities), unsupported movement and way‐finding due to excessive or insufficient signage, poor lighting, and high noise. Participants recommended greater access to door‐to‐door transport that caters to people experiencing dementia, establishing quiet resting areas, and improving navigation through signage that uses standardised symbols (e.g., for toilet facilities).ConclusionPeople experiencing dementia and carers reported an array of issues concerning built environment design and accessibility features that excluded them from everyday activities. These findings were translated into an implementation plan that informed the City of Canterbury‐Bankstown Council’s community planning efforts and the shopping centre’s facility operations. Critical areas for improvement in the built environment would benefit from co‐design practices that are inclusive and representative of the community.

Scattered light monitoring system at the Virgo interferometer: performance improvement and automation based on O3 data

Scattered light, also referred to as scattering, is a nonlinear non stationary noise that can affect data acquired by ground-based laser interferometers for the detection of gravitational waves. A methodology for the identification and daily monitoring of scattering sources, based on the tvf-EMD algorithm, was applied to a large dataset of 132 days of data. Time series of the differential arm motion (DARM) degree of freedom acquired by the Virgo detector during the third LIGO-Virgo scientific run, so called O3, which lasted from 1 April 2019 to 27 March 2020, were considered. The analysis focused on correlation with suspended West end optical bench (SWEB) position data, as SWEB was a known culprit of scattering witnessed in DARM during O3. Different configurations were tested, improving performances with respect to previously obtained results and at the same time making the methodology fully automated. This allows to employ it as a monitoring system both during the phases of detector’s upgrade and in scientific runs such as the fourth scientific run, O4, currently scheduled to start on 2023. The higher values of correlation obtained suggest that tvf-EMD could improve the performance of scattered light noise subtraction from DARM.

Evaluation of the sensory environment in a large tertiary ICU

BackgroundICU survival is improving. However, many patients leave ICU with ongoing cognitive, physical, and/or psychological impairments and reduced quality of life. Many of the reasons for these ongoing problems are unmodifiable; however, some are linked with the ICU environment. Suboptimal lighting and excessive noise contribute to a loss of circadian rhythms and sleep disruptions, leading to increased mortality and morbidity. Despite long-standing awareness of these problems, meaningful ICU redesign is yet to be realised, and the ‘ideal’ ICU design is likely to be unique to local context and patient cohorts. To inform the co-design of an improved ICU environment, this study completed a detailed evaluation of the ICU environment, focussing on acoustics, sound, and light.MethodsThis was an observational study of the lighting and acoustic environment using sensors and formal evaluations. Selected bedspaces, chosen to represent different types of bedspaces in the ICU, were monitored during prolonged study periods. Data were analysed descriptively using Microsoft Excel.ResultsTwo of the three monitored bedspaces showed a limited difference in lighting levels across the day, with average daytime light intensity not exceeding 300 Lux. In bedspaces with a window, the spectral power distribution (but not intensity) of the light was similar to natural light when all ceiling lights were off. However, when the ceiling lights were on, the spectral power distribution was similar between bedspaces with and without windows. Average sound levels in the study bedspaces were 63.75, 56.80, and 59.71 dBA, with the single room being noisier than the two open-plan bedspaces. There were multiple occasions of peak sound levels > 80 dBA recorded, with the maximum sound level recorded being > 105 dBA. We recorded one new monitor or ventilator alarm commencing every 69 s in each bedspace, with only 5% of alarms actioned. Acoustic testing showed poor sound absorption and blocking.ConclusionsThis study corroborates other studies confirming that the lighting and acoustic environments in the study ICU were suboptimal, potentially contributing to adverse patient outcomes. This manuscript discusses potential solutions to identified problems. Future studies are required to evaluate whether an optimised ICU environment positively impacts patient outcomes.

Hyperspectral screen-image-synthesis meter with scattering-noise suppression

The screen image synthesis (SIS) meter was originally proposed as a high-speed measurement tool, which fused the measured data from multiple sample-rotational angles to produce a whole-field measurement result. However, it suffered from stray light noise and lacked the capability of spectrum measurement. In this study, we propose an SIS system embedded with a snapshot hyperspectral technology, which was based on a dispersion image of the sparse sampling screen (SSS). When a photo was captured, it was transformed and calibrated to hyperspectral data at a specific sample-rotational angle. After the hyperspectral data in all sample-rotational angles were captured, an SIS image-fusion process was then applied to get the whole field hyperspectral data. By applying SSS to the SIS meter, we not only create a screen image synthesis hyperspectral meter but also effectively address the issue of stray-light noise. In the experiment, we analyze its correctness by comparing the hyperspectral value with a one-dimensional spectrum goniometer (ODSG). We also show the 2D color temperature coefficient distribution and compare it with the ODSG. Experimental results also demonstrate the feasibility in terms of both spectrum distribution meter and color coefficient temperature distribution meter.

High Dynamic Pixel Structure Based on an Adaptive Integrating Capacitor.

Infrared image sensing technology has received widespread attention due to its advantages of not being affected by the environment, good target recognition, and high anti-interference ability. However, with the improvement of the integration of the infrared focal plane, the dynamic range of the photoelectric system is difficult to improve, that is, the restrictive trade-off between noise and full well capacity is particularly prominent. Since the capacitance of the inversion MOS capacitor changes with the gate-source voltage adaptively, the inversion MOS capacitor is used as the capacitor in the infrared pixel circuit, which can solve the contradiction between noise in low light and full well capacity in high light. To this end, a highly dynamic pixel structure based on adaptive capacitance is proposed, so that the capacitance of the infrared image sensor can automatically change from 6.5 fF to 37.5 fF as the light intensity increases. And based on 55 nm CMOS process technology, the performance parameters of an infrared image sensor with a 12,288 × 12,288 pixel array are studied. The research results show that a small-size pixel of 5.5 µm × 5.5 µm has a large full well capacity of 1.31 Me- and a variable conversion gain, with a noise of less than 0.43 e- and a dynamic range of more than 130 dB.

Compression performance and low-frequency damping characteristics of pyramidal lattice cylinder skeleton structure

The lattice structure has obvious advantages over traditional materials in terms of light weight, energy absorption, vibration and noise reduction, and is therefore widely used in many fields such as shipping and aerospace. In this work, a cylinder skeleton structure is designed based on pyramidal lattice structure by circumferential and axial array arrangement. The load-bearing capacity of the pyramidal lattice cylinder skeleton structure under quasi-static compression and its axial vibration characteristics are investigated by numerical method. The effects of key element geometric parameters such as diameter of metal wire, circumferential angle and axial angle on the compression and vibration damping performances of the pyramidal lattice cylinder skeleton structure were investigated. The results show that the developed structure has excellent compression and vibration damping capacity. It is able to provide a pronounced damping effect in the low-frequency range of 204-367 Hz, and the attenuation intensity of elastic wave in this frequency range can be up to 20dB.

Ultradian sleep cycles: Frequency, duration, and associations with individual and environmental factors—A retrospective study

ObjectiveSleep varies between individuals in response to sleep-wake history and various environmental factors, including light and noise. Here we report on the intranight variation of the ultradian nonrapid eye movement-rapid eye movement (NREM-REM) sleep cycle in 369 participants who have contributed to different laboratory studies from 1994 to 2020 at the Centre for Chronobiology, Basel, Switzerland. ResultsWe observed a large interindividual variability in sleep cycle duration, including NREM and REM sleep episodes in healthy participants who were given an 8-hour sleep opportunity at habitual bedtime in controlled laboratory settings. The median sleep cycle duration was 96 minutes out of 6064 polysomnographically-recorded cycles. The number and duration of cycles were not normally distributed, and the distribution became narrower for NREM sleep and wider for REM sleep later in the night. The first cycle was consistently shorter than subsequent cycles, and moderate presleep light or nocturnal noise exposure had no significant effects on ultradian sleep cycle duration. Age and sex significantly affected NREM and REM sleep duration, with older individuals having longer NREM and shorter REM sleep particularly in the end of the night, and females having longer NREM sleep episodes. High sleep pressure (ie, sleep deprivation) and low sleep pressure (ie, multiple naps) altered ultradian sleep cycles, with high sleep pressure leading to longer NREM sleep in the first cycle, and low sleep pressure leading to longer REM sleep episodes. Positive correlations were observed between N2 and NREM duration, and between N1 and REM duration. Weak intrasleep REM sleep homeostasis was also evident in our data set. ConclusionsWe conclude that ultradian sleep cycles are endogenous biological rhythms modulated by age, sex, and sleep homeostasis, but not directly responsive to (moderate levels of) environmental cues in healthy good sleepers.