Sleep (system call)

About: Sleep (system call) is a research topic. Over the lifetime, 2633 publications have been published within this topic receiving 27806 citations. The topic is also known as: Sleep() & sleep().

Cognitive Performance in Short Sleep Young Adults with Different Physical Activity Levels: A Cross-Sectional fNIRS Study

Abstract: Short sleep is a common issue nowadays. The purpose of this study was to investigate prefrontal cortical hemodynamics by evaluating changes in concentrations of oxygenated hemoglobin (HbO) in cognitive tests among short-sleep young adults and to explore the relationship between sleep duration, physical activity level, and cognitive function in this specific population. A total of 46 participants (25 males and 21 females) were included in our study, and among them, the average sleep duration was 358 min/day. Stroop performance in the short sleep population was linked to higher levels cortical activation in distinct parts of the left middle frontal gyrus. This study found that moderate-to-vigorous physical activity (MVPA) was significantly associated with lower accuracy of incongruent Stroop test. The dose-response relationship between sleep duration and Stroop performance under different levels of light-intensity physical activity (LPA) and MVPA was further explored, and increasing sleep time for different PA level was associated with better Stroop performance. In summary, this present study provided neurobehavioral evidence between cortical hemodynamics and cognitive function in the short sleep population. Furthermore, our findings indicated that, in younger adults with short sleep, more MVPA was associated with worse cognitive performance. Short sleep young adults should increase sleep time, rather than more MVPA, to achieve better cognitive function.

Automated Sleep Stage Scoring Using Time-Frequency Spectra Convolution Neural Network

Abstract: Sleep stage scoring is fundamental for the examination and analysis of sleep problems. Sleep experts score sleep by analyzing brain activity, muscle activity, and eye activity. Manual sleep stage scoring is an expert-dependent, tedious, and time-consuming process. Automatic sleep stage classification (ASSC) has gained particular attention due to sleep awareness over the last few years. In this research, ASSC is proposed using deep learning methods using a single-channel electroencephalogram (EEG) signal. EEG signals contain lots of information about brain functions during sleep. The EEG features were extracted using the convolution neural network (CNN) method. Different deep learning architectures are investigated using the raw EEG epochs and their time-frequency spectra using short-time Fourier transform (STFT) and stationary wavelet transform (SWT). The end-to-end classification pipeline classifies 30-s EEG epochs into five sleep stages by extracting features from raw EEG epoch and their time–frequency representations. Deep learning models give good classification accuracy compared to the current state-of-the-art methods. It gives an overall accuracy of (Fpz-Cz: 83.7%, Pz-Oz: 83.5%), (Fpz-Cz: 85.6%, Pz-Oz: 83.6%), and (Fpz-Cz: 85.7%, Pz-Oz: 83.2%) on 20-fold subjectwise cross-validation (CV) of the sleep-EDF-v1 dataset using 1-D CNN, SWT-CNN, and STFT-CNN, respectively. The subjectwise CV performed shows more consistent performance across different subjects. The model size and performance are investigated to develop a less complex and smaller deep learning model.

Nonvolatile memory device and device sleep state control method thereof

Abstract: A nonvolatile memory device includes a device interface configured to communicate with a host. The nonvolatile memory device includes a sleep controller configured to select a sleep state from among a plurality of sleep states. The sleep controller is configured to control the device interface to enter the selected sleep state. Each of the plurality of sleep states have different resume times. The resume times are a period of time taken for the nonvolatile memory device to exit an associated sleep state. In each of the plurality of sleep states, the sleep controller is configured to remove a supply of power from physical blocks of the device interface except for a physical block used for sideband signaling.

Program wakeup time setting method and device and terminal

Abstract: The invention provides a program wakeup time setting method and device and a terminal. The program wakeup time setting method includes an acquiring step and a setting step. The acquiring step includes utilizing one of systems as an executing system to acquire current wakeup time of each program to be awaked in the systems. The setting step includes setting final wakeup time of each program to be awaked according to the current wakeup time of each program to be awaked and the preset target wakeup time interval. According to the technical scheme, wakeup times is effectively reduced, power consumption is reduced, the problem about high frequency in switching between sleep and wakeup of application programs is solved, and the purpose for prolonging standby time of the terminal is achieved.

Hypnotism-based intelligent sleep aiding method and system

Abstract: The invention relates to a hypnotism-based intelligent sleep aiding method and a hypnotism-based intelligent sleep aiding system. The method comprises the following steps: acquiring a bioelectric signal of a user in sleep; identifying the current sleep state of the user according to the bioelectric signal; when the user is in a waking state at current, playing preset hypnosis guide words to hypnotize the user, and detecting the sleep depth of the user; when the current sleep depth of the user reaches a preset sleep level, playing a sleep instruction to guide the user to fall asleep. According to the technical scheme provided by the invention, the influence on the sleep of the user caused by false intervention is avoided by identifying the sleep state of the user and performing corresponding intervention; the user is guided to fall asleep when hypnosis depth is set, so that the phenomenon that a sleep aiding effect cannot be achieved because of too low hypnosis depth is avoided, the situation of a risk caused by over-high hypnosis depth is also avoided, and the sleep aiding effect is effectively improved.