Journal articles: 'Sleep mode'

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Jayakumar, Hrishikesh, Arnab Raha, and Vijay Raghunathan. "Sleep-Mode Voltage Scaling." ACM Transactions on Embedded Computing Systems 16, no. 1 (November 3, 2016): 1–25. http://dx.doi.org/10.1145/2950054.

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Indraganti, Madhavi, Farsana Kutty, Reem Ali, Lulwa Al Noaimi, Saaeda Al-Bader, and Maryam Adel Al Mulla. "OCCUPANT PERCEPTION OF THERMAL COMFORT IN SLEEP ENVIRONMENTS IN QATAR." Journal of Engineering Research [TJER] 18, no. 2 (February 13, 2022): 137–45. http://dx.doi.org/10.53540/tjer.vol18iss2pp137-145.

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A thermal comfort field survey in sleep environments in winter and spring seasons in Qatar collected 833 sets of objective and subjective thermal and sleep quality responses before going to bed and after getting up. The subjects felt cooler sensations most of the time, preferred warmer sensations and the sleep environments are considered comfortable overall. Griffiths comfort temperature (Tc) was 24.3 ˚C and 20.2 ˚C in FR and AC modes respectively. Subjects used air-conditioner (AC)s adaptively in heating mode in winter. In 82.7% cases in air-conditioned (AC) mode, the comfort temperature was below the lower limit of the international standard. Subjects enjoyed quality sleep in Qatar. Overall self-declared sleep quality improved with thermal acceptability. Depth of sleep was higher in AC mode (mean = 3.86). On the other hand, mean global Pittsburgh Sleep Quality Index (PSQI) score was high in general (mean = 10.7), indicating good quality sleep, and significantly so in free-running mode (mean = 11) than in AC mode (mean = 10.4). It increased as subjects liked their AC systems. This study suggests that overcooling in spring can be avoided by increasing the air movement without compromising sleep quality.

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Xiao, Zhu, Shuangchun Li, Xiaochun Chen, Dong Wang, and Wenjie Chen. "A Load-Balancing Energy Consumption Minimization Scheme in 5G Heterogeneous Small Cell Wireless Networks Under Coverage Probability Analysis." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 07 (April 10, 2017): 1759013. http://dx.doi.org/10.1142/s0218001417590133.

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Heterogeneous small cell networks (HSCN), as a promising paradigm to increase end-user data rates and improve the overall capacity, is expected to be a key cellular architecture in 5G wireless networks. However, energy consumed in HSCN is considerable due to the massive use of small cells. In this paper, we investigate the energy consumption issue which stems from the enormous number of running small cell base stations (SBSs) deploying in the HSCN. We first propose power consumption models so as to characterize the active state and the idle state of SBSs, respectively. Then two sleep modes for SBSs tier, i.e. random sleep mode and load-awareness dynamic sleep mode, are proposed. The random sleep is designed based on a binomial distribution of the SBS operation probability. Through the analysis on activeness of SBSs, we define the operation probability for the SBS applying the proposed dynamic sleep mode is associated to its traffic load level. The closed-form expressions of success probability for coverage, which is used to decide whether an active user can connect to a SBS successfully, are derived for the proposed sleep modes. Energy consumption minimizations are presented for the two proposed sleep modes under the success probability constraint. Simulation results prove the effectiveness of the proposed two sleep modes. Different energy saving gains can be achieved via using of the energy saving strategy. The superior of the dynamic sleep mode by comparing the random sleep is also verified in terms of energy consumption, success probability and power efficiency.

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Veerappan Kousik, Nalliyanna Goundar, Yuvaraj Natarajan, Kallam Suresh, Rizwan Patan, and Amir H. Gandomi. "Improving Power and Resource Management in Heterogeneous Downlink OFDMA Networks." Information 11, no. 4 (April 10, 2020): 203. http://dx.doi.org/10.3390/info11040203.

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In the past decade, low power consumption schemes have undergone degraded communication performance, where they fail to maintain the trade-off between the resource and power consumption. In this paper, management of resource and power consumption on small cell orthogonal frequency-division multiple access (OFDMA) networks is enacted using the sleep mode selection method. The sleep mode selection method uses both power and resource management, where the former is responsible for a heterogeneous network, and the latter is managed using a deactivation algorithm. Further, to improve the communication performance during sleep mode selection, a semi-Markov sleep mode selection decision-making process is developed. Spectrum reuse maximization is achieved using a small cell deactivation strategy that potentially identifies and eliminates the sleep mode cells. The performance of this hybrid technique is evaluated and compared against benchmark techniques. The results demonstrate that the proposed hybrid performance model shows effective power and resource management with reduced computational cost compared with benchmark techniques.

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Vacca, Irene. "Vibrio enters 'sleep mode' to survive." Nature Reviews Microbiology 15, no. 9 (September 2017): 515. http://dx.doi.org/10.1038/nrmicro.2017.102.

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Indraganti, Madhavi. "Investigation of thermal comfort in bedrooms in Qatar." E3S Web of Conferences 396 (2023): 01070. http://dx.doi.org/10.1051/e3sconf/202339601070.

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Good quality sleep is essential for overall health and productivity of human beings. In a field survey in bedrooms in Qatar, 833 sets of occupant responses on thermal comfort and sleep quality before going to bed and after getting up were made together with the corresponding environmental measurements and occupant’s clothing and bedding information. Subject’s thermal sensation was on the cooler side with a preference for warmer environments mostly. People generally felt comfortable, with Griffiths comfort temperature (Tc) being 24.3 °C. and 20.2 °C in free-running (FR) and air-conditioned (AC) modes respectively. Adaptive use of air-conditioners was noted. In 82.7% cases in (AC) mode, the comfort temperature was below the lower limit of the international standard. The quality of sleep was good and overall self-declared sleep quality increased with thermal acceptability. Higher depth of sleep was noted when ACs were on. Qatar bedrooms recorded high mean global Pittsburgh Sleep Quality Index (PSQI) score in general (mean = 10.7), indicating good quality sleep. It was higher in free-running mode (mean = 11) than in AC mode (mean 10.4). It increased as subjects liked their HVAC systems, indicating the occupants perception of performance of AC systems affecting the sleep quality. This study highlights the need reduce overcooling in spring and to increase air-movement to enable free-running mode, without reducing the sleep quality.

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Farrahi, Amir H., Gustavo E. Téllez, and Majid Sarrafzadeh. "Exploiting Sleep Mode for Memory Partitioning and Other Applications." VLSI Design 7, no. 3 (January 1, 1998): 271–87. http://dx.doi.org/10.1155/1998/50491.

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Sleep mode operation and exploiting it to minimize the average power consumption are of great importance in modern VLSI circuits. In general, sleep mode refers to the mode in which part(s) of the system are idle. In this paper, we study the problem of partitioning a circuit according to the activity patterns of its elements such that circuit elements with similar activity patterns are packed into the same partition. Then a partition can be placed in sleep mode during the time intervals all elements contained in that partition are idle. We formulate the partitioning problem to exploit sleep mode operation and show that the problem is NP-complete. We present polynomial time algorithms for practical classes of the problem. Applications of the problem to memory and module partitioning and clock gating are discussed. The experimental data confirm that a careful partitioning allows upto 40% more sleep time which could be exploited to minimize the average power consumption.

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Rajagopal, Anita, and Cynthia Bodkin. "1095 Treatment with Trilogy AVAPS AE Mode in Pompe Disease with Chronic Respiratory Failure Unresponsive to AVAPS ST Mode." Sleep 41, suppl_1 (April 2018): A407. http://dx.doi.org/10.1093/sleep/zsy063.1094.

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Kim, Taehwa, Seungjin Lee, Hyungwoo Choi, Hong-Shik Park, and Junkyun Choi. "An Energy-Efficient Multi-Level Sleep Strategy for Periodic Uplink Transmission in Industrial Private 5G Networks." Sensors 23, no. 22 (November 9, 2023): 9070. http://dx.doi.org/10.3390/s23229070.

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This paper proposes an energy-efficient multi-level sleep mode control for periodic transmission (MSC-PUT) in private fifth-generation (5G) networks. In general, private 5G networks meet IIoT requirements but face rising energy consumption due to dense base station (BS) deployment, particularly impacting operating expenses (OPEX). An approach of BS sleep mode has been studied to reduce energy consumption, but there has been insufficient consideration for the periodic uplink transmission of industrial Internet of Things (IIoT) devices. Additionally, 5G New Reno’s synchronization signal interval limits the effectiveness of the deepest sleep mode in reducing BS energy consumption. By addressing this issue, the aim of this paper is to propose an energy-efficient multi-level sleep mode control for periodic uplink transmission to improve the energy efficiency of BSs. In advance, we develop an energy-efficient model that considers the trade-off between throughput impairment caused by increased latency and energy saving by sleep mode operation for IIoT’s periodic uplink transmission. Then, we propose an approach based on proximal policy optimization (PPO) to determine the deep sleep mode of BSs, considering throughput impairment and energy efficiency. Our simulation results verify the proposed MSC-PUT algorithm’s effectiveness in terms of throughput, energy saving, and energy efficiency. Specifically, we verify that our proposed MSC-PUT enhances energy efficiency by nearly 27.5% when compared to conventional multi-level sleep operation and consumes less energy at 75.21% of the energy consumed by the conventional method while incurring a throughput impairment of nearly 4.2%. Numerical results show that the proposed algorithm can significantly reduce the energy consumption of BSs accounting for periodic uplink transmission of IIoT devices.

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Negelspach, David, Anna Alkozei, Alisa Huskey, and William Killgore. "0008 Wake Onset Variability Effect on Functional Connectivity in the Default Mode Network." SLEEP 47, Supplement_1 (April 20, 2024): A4. http://dx.doi.org/10.1093/sleep/zsae067.0008.

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Abstract Introduction Decoupling patterns of sleep and wake from underlying circadian oscillations is associated with poor sleep health. While this has been studied with respect to shift work, it is unclear if minor variations in normative sleep/wake patterns affect the restoration of cognitive function during sleep intervals while controlling for total sleep time. It is commonplace to adhere to a fixed sleep/wake schedule, however, this practice does not account for day-to-day variations in physiological need for sleep. We examined the relationship between sleep-interval variability and fMRI resting state functional connectivity while controlling for sleep duration. Methods Participants (n=21; 13 female; Age=23.3, SD=4.7) wore an actigraph for 7 days followed by a daytime functional magnetic resonance imaging (fMRI) session. Sleep/wake onset variability was measured from double-rated actigraphy data and cross referenced with sleep journals. Functional connectivity analysis and preprocessing of fMRI images were conducted in the CONN toolbox (SPM12). Seed-based functional connectivity maps were estimated using BOLD activity of the posterior parietal cortex node of the default mode network (DMN) as the seed region. Functional connectivity strength was represented by Fisher-transformed bivariate correlation coefficients from a weighted GLM, modeling the association between the seed and other brain regions. Results Wake onset variability (WOV) predicted functional connectivity of the DMN (controlling for age, gender, and sleep duration). WOV was significantly associated (Pthreshold <.005 ; Pfwe <.05) with a higher degree of anticorrelation between the posterior parietal node of the DMN and several regions of the frontoparietal network (FPN): Right superior frontal gyrus, Right mid-frontal gyrus, and Right frontal pole, as well as the occipital cortex and cerebellum. In contrast, sleep onset variability showed no significant changes in functional connectivity within these areas. Conclusion Greater WOV was associated with stronger inverse connectivity between the DMN and FPN. This suggests that increased WOV may improve attentional control through greater top-down suppression of the DMN. We speculate that WOV is representative of sleep satiety, resulting in voluntary arousal from sleep, rather than enforced arousal. These results suggest that sleep timing affects attentional control irrespective of duration. Support (if any) USAMRAA: W81XWH1910074

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Zheng, Xiangwei, Xiaochun Yin, Xuexiao Shao, Yalin Li, and Xiaomei Yu. "Collaborative Sleep Electroencephalogram Data Analysis Based on Improved Empirical Mode Decomposition and Clustering Algorithm." Complexity 2020 (June 13, 2020): 1–14. http://dx.doi.org/10.1155/2020/1496973.

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Sleep-related diseases seriously affect the life quality of patients. Sleep stage classification (or sleep staging), which studies the human sleep process and classifies the sleep stages, is an important reference to the diagnosis and study of sleep disorders. Many scholars have conducted a series of sleep staging studies, but the correlation between different sleep stages and the accuracy of classification still needs to be improved. Therefore, this paper proposes an automatic sleep stage classification based on EEG. By constructing an improved empirical mode decomposition and K-means experimental model, the concept of “frequency-domain correlation coefficient” is defined. In the process of feature extraction, the feature vector with the best correlation in the time-frequency domain is selected. Extraction and classification of EEG features are realized based on the K-means clustering algorithm. Experimental results demonstrate that the classification accuracy is significantly improved, and our proposed algorithm has a positive impact on sleep staging compared with other algorithms.

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Ashraf, Imran, Federico Boccardi, and Lester Ho. "SLEEP mode techniques for small cell deployments." IEEE Communications Magazine 49, no. 8 (August 2011): 72–79. http://dx.doi.org/10.1109/mcom.2011.5978418.

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Wang, R., H. Haas, J. S. Thompson, and P. M. Grant. "Sleep mode design for green base stations." IET Communications 5, no. 18 (December 16, 2011): 2606–16. http://dx.doi.org/10.1049/iet-com.2011.0104.

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Mizutani, Noboru, Katsuhisa Waseda, Kenji Asai, and Isao Katou. "Effect of pacing mode on sleep disturbance." Journal of Artificial Organs 6, no. 2 (June 2003): 106–11. http://dx.doi.org/10.1007/s10047-003-0212-1.

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Paul, Ayan, Khin Wee Lai, Juliana Usman, Mohd Yazed Ahmad, Mohafez Hamidreza, Hadizadeh Maryam, and Zhi Chao Ong. "Investigating the Effects of Ogawa Master Drive AI Automated Massage on Blood Circulation and Sleep Quality." Journal of Medical Imaging and Health Informatics 11, no. 5 (May 1, 2021): 1357–63. http://dx.doi.org/10.1166/jmihi.2021.3810.

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Sleep and stress-related disorders are increasingly becoming more prevalent among adult population. Massage therapy (MT) may assist in improving the peripheral circulation through mechanical manipulation of soft tissues and passively act upon reducing stress stimuli as well as induce relaxation and sleep. For the purpose of evaluating the efficacy of massage chair therapy (MCT), this study is divided into two parts. First, 15 participants (mean age = (21.00 ±2.00) years; body mass index (BMI) = (19.22 ±2.23)) were enrolled in a randomized controlled, cross-over, and single-blinded trial on pre- and post-treatment based sessions where skin blood flow (SBF) was measured. Except for the control mode (p > 0.05), all other mode-variants exhibit significantly increased SBF between pre-post sessions (p < 0.05). Furthermore, compared to the blood circulation and sweet dreams massage modes, deep tissue massage mode exhibited a significantly increase in SBF values (p < 0.05). In the second part of the study, 5 participants (age 24.00±3.00 years; weight 73.10±5.63 kg; height 178.28 ±10.08 cm; BMI 22.49 ±1.89) underwent MCT with only sweet dreams mode. Richards-Campbell Sleep Questionnaire and wrist actigraph were used to assess pre- and post-treatment sleep quality. After undergoing massage all subjects showed improvement in overall sleep quality. These results are suggestive that the automated MCT may potentially improve blood circulation and promote relaxation and sleep.

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Elleithy, Abdelrahman, Gonhsin Liu, and Ali Elrashidi. "A New Model of the Lifetime of Wireless Sensor Networks in Pure Water Communications." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (August 1, 2012): 24–33. http://dx.doi.org/10.24297/ijct.v3i1a.2723.

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Abstractâ€”In this paper we present an a new model for thelifetime of wireless sensor networks used for underwatercommunications in pure water. The new model for powercommunications takes into consideration parameters such aspower consumption for the active mode, power consumption forthe sleep mode, power consumption for the transient mode,transmission period, transient mode duration, sleep modeduration, and active mode duration. The power communicationsmodel is incorporated in the life time model of wireless sensornetworks. The life time model takes into consideration severalparameters such as the total number of sensors, network size,percentage of sink nodes, location of sensors, the mobility ofsensors, power consumption when nodes move and the powerconsumption of communications. The new model for powerconsumption in communications shows more accurate resultsabout the lifetime of the sensor network in comparison withpreviously published results.

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Gujar, Ninad, Seung-Schik Yoo, Peter Hu, and Matthew P. Walker. "The Unrested Resting Brain: Sleep Deprivation Alters Activity within the Default-mode Network." Journal of Cognitive Neuroscience 22, no. 8 (August 2010): 1637–48. http://dx.doi.org/10.1162/jocn.2009.21331.

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The sleep-deprived brain has principally been characterized by examining dysfunction during cognitive task performance. However, far less attention has been afforded the possibility that sleep deprivation may be as, if not more, accurately characterized on the basis of abnormal resting-state brain activity. Here we report that one night of sleep deprivation significantly disrupts the canonical signature of task-related deactivation, resulting in a double dissociation within anterior as well as posterior midline regions of the default network. Indeed, deactivation within these regions alone discriminated sleep-deprived from sleep-control subjects with a 93% degree of sensitivity and 92% specificity. In addition, the relative balance of deactivation within these default nodes significantly correlated with the amount of prior sleep in the control group (and not extended time awake in the deprivation group). Therefore, the stability and the balance of task-related deactivation in key default-mode regions may be dependent on prior sleep, such that a lack thereof disrupts this signature pattern of brain activity, findings that may offer explanatory insights into conditions associated with sleep loss at both a clinical as well as societal level.

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Huang, Muzhi, Yangpan Ou, Huabing Li, Feng Liu, Ping Li, Jingping Zhao, Bing Lang, and Wenbin Guo. "Association between abnormal default mode network homogeneity and sleep disturbances in major depressive disorder." General Psychiatry 37, no. 2 (March 2024): e101371. http://dx.doi.org/10.1136/gpsych-2023-101371.

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BackgroundSleep disturbance is a common comorbidity of major depressive disorder (MDD). However, network homogeneity (NH) changes of the default mode network (DMN) in MDD with sleep disturbances are unclear.AimsThe purpose of this study was to probe the abnormal NH in the DMN in MDD with sleep disturbances and to reveal the differences between MDD with or without sleep disturbances.MethodsTwenty-four patients with MDD and sleep disturbances (Pa_s), 33 patients with MDD without sleep disturbances (Pa_ns) and 32 healthy controls (HCs) were recruited in this study. Resting-state functional imaging data were analysed using NH.ResultsCompared with Pa_ns and HCs, Pa_s showed decreased NH in the left superior medial prefrontal cortex and increased NH in the right precuneus. There was a negative correlation between NH in the left superior medial prefrontal cortex and sleep disturbances (r=−0.42, p=0.001) as well as a positive correlation between NH in the right precuneus and sleep disturbances (r=0.41, p=0.002) in patients with MDD.ConclusionsMDD with sleep disturbances is associated with abnormal NH in the DMN, which could differentiate pa_s from pa_ns. The DMN may play a crucial role in the neurobiological mechanisms of MDD with sleep disturbances.

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Xue, Jian Bin, Song Bai Li, Ting Zhang, and Wen Hua Wang. "Type II Sleep Mode Operation for IEEE 802.16e Based WiMAX." Applied Mechanics and Materials 128-129 (October 2011): 343–49. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.343.

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To overcome the flaw of energy efficiency drop in broad band wireless communication with the short time sleep, the energy-saving mechanism of the sleep mode operation was researched in IEEE 802.16e. In this paper we propose a dynamic algorithm to tune the ratio of the sleep windows and receive windows according to the traffic load. Then, a Markov chain model was set up to analyze the energy efficiency and mean access delay. NS2 simulation results show that the proposed algorithm can achieve marked gain in energy efficiency compared to the traditional energy saving mechanism.

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Bang, Hakjeon, Jongdeog Kim, Sang-Soo Lee, and Chang-Soo Park. "Determination of Sleep Period for Cyclic Sleep Mode in XG-PON Power Management." IEEE Communications Letters 16, no. 1 (January 2012): 98–100. http://dx.doi.org/10.1109/lcomm.2011.111011.111322.

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Hakim, Galang Persada Nurani, Muhammad Hafizd Ibnu Hajar, Ahmad Firdausi, and Eko Ramadhan. "Benchmarking In Microcontroller Development Board Power Consumption For Low Power Iot Wsn Application." Jurnal Teknologi Elektro 13, no. 1 (February 8, 2022): 25. http://dx.doi.org/10.22441/jte.2022.v13i1.005.

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One of the advantages of Wireless Sensor Network would be its ability to reduce cost of communication system using node to node communication. However Wireless Sensor Network also had a disadvantage which is has limited energy which is include this as low power application. This small energy capacity has limit WSN node capability to operate for a long time. In this paper, we compare power consumption for 3 popular microcontroller development platforms that use for fast development and prototyping Wireless Sensor Network node. The power consumption was including active mode (using most energy) and deep sleep mode (using least energy) operation. From benchmarking we can see that lolin ESP32 as a microcontroller development platform has the most efficient in power consumption which is only 40 mA in active and 0.05 in deep sleep mode, compare with arduino pro mini 8 mA in active and 0.3 mA in deep sleep mode, and wemos D1 mini 74 mA in active and 0.13 mA in deep sleep mode. This low power consumption in deep sleep mode has resulting in longer operational time which is almost 48 Month for lolin ESP32

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Sun-Jong Kwon, Yun Won Chung, and Dan Keun Sung. "Queueing model of sleep-mode operation in cellular digital packet data." IEEE Transactions on Vehicular Technology 52, no. 4 (July 2003): 1158–62. http://dx.doi.org/10.1109/tvt.2003.814218.

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Werth, Esther, Peter Achermann, and Alexander A. Borbély. "II. Muscle atonia in non-REM sleep." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 283, no. 2 (August 1, 2002): R527—R532. http://dx.doi.org/10.1152/ajpregu.00466.2001.

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One of the hallmarks of rapid eye movement (REM) sleep is muscle atonia. Here we report extended epochs of muscle atonia in non-REM sleep (MAN). Their extent and time course was studied in a protocol that included a baseline night, a daytime sleep episode with or without selective REM sleep deprivation, and a recovery night. The distribution of the latency to the first occurrence of MAN was bimodal with a first mode shortly after sleep onset and a second mode 40 min later. Within a non-REM sleep episode, MAN showed a U-shaped distribution with the highest values before and after REM sleep. Whereas MAN was at a constant level over consecutive 2-h intervals of nighttime sleep, MAN showed high initial values when sleep began in the morning. Selective daytime REM sleep deprivation caused an initial enhancement of MAN during recovery sleep. It is concluded that episodes of MAN may represent an REM sleep equivalent and that it may be a marker of homeostatic and circadian REM sleep regulating processes. MAN episodes may contribute to the compensation of an REM sleep deficit.

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QU, Hong-wei. "Improved sleep mode mechanism based on IEEE 802.16e." Journal of Computer Applications 28, no. 8 (August 20, 2008): 1959–64. http://dx.doi.org/10.3724/sp.j.1087.2008.01959.

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Kamitsos, Ioannis, Paschalis Tsiaflakis, Ken J. Kerpez, Sangtae Ha, and Mung Chiang. "Stable Sleep Mode Optimization for Energy Efficient DSL." IEEE Transactions on Communications 63, no. 12 (December 2015): 5116–27. http://dx.doi.org/10.1109/tcomm.2015.2481885.

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Yuan, Zhaohui, Yuping Zhang, and Chun Jason Xue. "Sleep-aware mode assignment in wireless embedded systems." Journal of Parallel and Distributed Computing 71, no. 7 (July 2011): 1002–10. http://dx.doi.org/10.1016/j.jpdc.2010.11.006.

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Jurdak, Raja, Antonio G. Ruzzelli, and Gregory M. P. O'Hare. "Radio Sleep Mode Optimization in Wireless Sensor Networks." IEEE Transactions on Mobile Computing 9, no. 7 (July 2010): 955–68. http://dx.doi.org/10.1109/tmc.2010.35.

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Tashjian, Sarah M., Diane Goldenberg, Martin M. Monti, and Adriana Galván. "Sleep quality and adolescent default mode network connectivity." Social Cognitive and Affective Neuroscience 13, no. 3 (February 7, 2018): 290–99. http://dx.doi.org/10.1093/scan/nsy009.

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Bhaskaran, Arun M. "Adaptive Sleep Mode Mechanism for 3G WiMax Networks." IOSR Journal of Electronics and Communication Engineering 8, no. 6 (2013): 56–61. http://dx.doi.org/10.9790/2834-0865661.

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Vu, T. H., V. C. Luc, N. T. Quan, T. Thanh, N. H. Thanh, and P. N. Nam. "Sleep Mode and Wakeup Method for OpenFlow Switches." Journal of Low Power Electronics 10, no. 3 (September 1, 2014): 347–53. http://dx.doi.org/10.1166/jolpe.2014.1328.

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Caughey, Aaron B. "Poor sleep in pregnancy and mode of delivery." American Journal of Obstetrics and Gynecology 193, no. 3 (September 2005): 1286. http://dx.doi.org/10.1016/j.ajog.2005.02.122.

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Chiaraviglio, Luca, Delia Ciullo, Marco Mellia, and Michela Meo. "Modeling sleep mode gains in energy-aware networks." Computer Networks 57, no. 15 (October 2013): 3051–66. http://dx.doi.org/10.1016/j.comnet.2013.07.011.

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Rho, Y., and S. Vijayan. "0124 A Prefrontal-Amygdala Network Model of the Cellular and Circuit-Level Mechanisms of Emotional Memory Consolidation During the Awake State and REM Sleep." Sleep 43, Supplement_1 (April 2020): A49. http://dx.doi.org/10.1093/sleep/zsaa056.122.

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Abstract Introduction Rapid eye movement (REM) sleep has been implicated in the consolidation of emotional memories. Our recent work found a candidate system for REM-related memory consolidation. We showed that during REM sleep, the frontal cortices are dominated by theta (4–8 Hz) oscillations and bursts of beta (15–35 Hz) activity. Studies suggest that rhythmic interactions between the frontal cortices and limbic structures, in particular the amygdala, play a critical role in the consolidation of emotional memories. However, the mechanisms responsible for memory consolidation during these rhythmic interactions during REM sleep remain unknown. Methods We used biophysically based neural models to build a large-scale network model of the prefrontal cortex (PFC) and amygdala (AMY) and incorporated synaptic plasticity mechanisms, such as spike-timing dependent plasticity (STDP), into the connections between these two regions. Norepinephrine (NE) and serotonin (SE) levels were manipulated to mimic the different physiological conditions during the awake state and REM sleep. Results We were able to reproduce the oscillatory dynamics observed in experimental studies and identify cell-type specific synaptic changes caused by STDP. During the awake state, PFC connections to all cell types of the AMY become strengthened when PFC neurons provide theta frequency inputs, with the connections strengthening to a greater extent when inputs are in burst mode rather than single spike mode. When the PFC provides beta inputs, we see the exact opposite relationship: synaptic strengths become weaker when inputs are in burst mode rather than single spike mode. During REM sleep conditions, the connections to all principal cell types of the AMY become strengthened, with synaptic connections to some subtypes of pyramidal cells becoming stronger than others. Surprisingly, however, the synaptic connections to the interneurons become weaker in response to theta frequency inputs. Conclusion Using our large-scale network model, we show how the levels of the neurotransmitters NE and SE during the awake state and REM sleep affect oscillatory dynamics and in turn influence the strengthening or weakening of connections related to emotional memories. Support United States Army Research Office, Award number ARO W91lNF-17-1-0300

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Parajuli, Madan, Amy W. Amara, and Mohamed Shaban. "Deep-learning detection of mild cognitive impairment from sleep electroencephalography for patients with Parkinson’s disease." PLOS ONE 18, no. 8 (August 3, 2023): e0286506. http://dx.doi.org/10.1371/journal.pone.0286506.

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Parkinson’s disease which is the second most prevalent neurodegenerative disorder in the United States is a serious and complex disease that may progress to mild cognitive impairment and dementia. The early detection of the mild cognitive impairment and the identification of its biomarkers is crucial to support neurologists in monitoring the progression of the disease and allow an early initiation of effective therapeutic treatments that will improve the quality of life for the patients. In this paper, we propose the first deep-learning based approaches to detect mild cognitive impairment in the sleep Electroencephalography for patients with Parkinson’s disease and further identify the discriminative features of the disease. The proposed frameworks start by segmenting the sleep Electroencephalography time series into three sleep stages (i.e., two non-rapid eye movement sleep-stages and one rapid eye movement sleep stage), further transforming the segmented signals in the time-frequency domain using the continuous wavelet transform and the variational mode decomposition and finally applying novel convolutional neural networks on the time-frequency representations. The gradient-weighted class activation mapping was also used to visualize the features based on which the proposed deep-learning approaches reached an accurate prediction of mild cognitive impairment in Parkinson’s disease. The proposed variational mode decomposition-based model offered a superior accuracy, sensitivity, specificity, area under curve, and quadratic weighted Kappa score, all above 99% as compared with the continuous wavelet transform-based model (that achieved a performance that is almost above 92%) in differentiating mild cognitive impairment from normal cognition in sleep Electroencephalography for patients with Parkinson’s disease. In addition, the features attributed to the mild cognitive impairment in Parkinson’s disease were demonstrated by changes in the middle and high frequency variational mode decomposition components across the three sleep-stages. The use of the proposed model on the time-frequency representation of the sleep Electroencephalography signals will provide a promising and precise computer-aided diagnostic tool for detecting mild cognitive impairment and hence, monitoring the progression of Parkinson’s disease.

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Zhang, Lincong, Lei Guo, Yejun Liu, Kefeng Wei, and Weigang Hou. "A Multicast-Traffic-Oriented Energy-Saving Algorithm with a Hybrid Sleep Mode for EPONs." Journal of Electrical and Computer Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6785752.

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High energy consumption in Ethernet Passive Optical Networks (EPONs) has caused intense research on energy-saving methods in recent years. The most common method for EPON energy saving is to cause optical network units (ONUs) to sleep which are idle or low loaded. With the popularity of multimedia applications, the ONUs carry not only unicast traffic but also more and more multicast traffic. However, existing studies mainly focus on ONUs that support only unicast traffic. The many differences between multicast traffic and unicast traffic make it necessary to design novel energy-saving methods. This paper proposes a multicast-traffic-oriented energy-saving algorithm for EPONs, called the energy-saving algorithm for multicast traffic (ESMT). To save as much energy as possible, the proposed algorithm uses a hybrid sleep mode (composed of a deep sleep state and an independent sleep mode) in which not only can ONU enter a deep sleep state, but also the ONU transmitter and receiver can sleep independently. Simulation results show that the proposed algorithm, which is oriented to multicast traffic in EPONs, is more energy efficient than other algorithms.

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Zhou, Wenzhe, and Zilu Liu. "Design and Optimization of Hotel Management Information System Based on Artificial Intelligence." Scientific Programming 2022 (June 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/2445343.

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With the improvement of people’s living standards, the traditional hotel management model has been unable to meet the needs of customers. The traditional hotel management model also has the defects of low efficiency. The hotel management model is also gradually developing towards the direction of intelligence. The combination of artificial intelligence technology and hotel management can not only improve the operation efficiency of the hotel but also solve the operation cost of the hotel. For customers, artificial intelligence technology can bring smarter and more comfortable accommodation conditions to customers. This study uses the convolutional neural network (CNN) and long short-term memory (LSTM) technology in artificial intelligence technology to conduct related research on the in-store mode, entertainment mode, sleep mode, and out-of-store mode in hotel management. CNN is used to extract the spatial features of hotel management, and LSTM is used to extract the temporal features of hotel management. The research results show that CNN and LSTM technology can help hotel management achieve intelligent management and optimization. CNN and LSTM techniques can better predict related factors in-store entry mode, entertainment mode, and sleep mode. For the correlated predictions of these four modes, the maximum prediction error is only 2.81%. The linear correlation coefficient also reached above 0.96. The relevant parameters of artificial intelligence technology are also suitable for the optimization and design of hotel information systems.

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Shen, Xue Li, and Ying Le Fan. "Sleep Stage Classification Based on EEG Signals by Using Improved Hilbert-Huang Transform." Applied Mechanics and Materials 138-139 (November 2011): 1096–101. http://dx.doi.org/10.4028/www.scientific.net/amm.138-139.1096.

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Research on automatic sleep staging based on EEG signals has a significant meaning for objective evaluation of sleep quality. An improved Hilbert-Huang transform method was applied to time-frequency analysis of non-stable EEG signals for the sleep staging in this paper. In order to settle the frequency overlapping problem of intrinsic mode function obtained from traditional HHT, wavelet package transform was introduced to bandwidth refinement of EEG before the empirical mode decomposition was conducted. This method improved the time-frequency resolution effectively. Then the intrinsic mode functions and their marginal spectrums would be calculated. Six common spectrum energies (or spectral energy ratios) were selected as characteristic parameters. Finally, a probabilistic nearest neighbor method for statistical pattern recognition was applied to optimal decision. The experiment data was from the Sleep-EDF database of MIT-BIH. The classification results showed that the automatic sleep staging decisions based on this method conformed roughly with the manual staging results and were better than those obtained from traditional HHT obviously. Therefore, the method in this paper could be applied to extract features of sleep stages and provided necessary dependence for automatic sleep staging.

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Raymann, Roy, Nyayabrata Nayak, Nathaniel Watson, Luke Gahan, and Elie Gottlieb. "0351 Towards Interpreting Consumer Sleep Data: Distributions of Sleep Scores." Sleep 45, Supplement_1 (May 25, 2022): A158. http://dx.doi.org/10.1093/sleep/zsac079.348.

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Abstract Introduction With the rise of sleep measurement technology becoming widely available to the public, it has become apparent that traditional sleep metrics might not be best suited for a lay audience. Most consumer industry has started including a metric that would capture sleep quality, although the exact calculations of these scores remain proprietary. These novel outcome metrics require a set of reference values in order to become interpretable. Here, we provide reference values for the parameters SleepScore, BodyScore and MindScore as included in the SleepScore Labs non-contact radiofrequency sleep measurement devices. Methods SleepScore is a sleep quality metric that includes objectively measured total sleep time (TST), sleep onset latency (SOL) and sleep stage durations, normalized for aged and sex, using reference values of Ohayon et al (2004), ranging from 0-100. BodyScore reflects the normalized amount of deep sleep, whereas MindScore reflects the normalized amount of REM, ranging from 0-100. Data from 40,862 S+ and Max users between 18 and 98 years old were used to calculate distribution statistics. Results The average age of users was 53±15 years old. Individual scores of SleepScore, BodyScore and MindScore ranged from 0-100 and their distribution was left-skewed. SleepScore averaged 81±11, with the first quartile (Q1) at 73, median at 81 and third quartile (Q3) at 88, and a mode of 89. BodyScore averaged 81±10 with Q1 at 73, median at 80 and Q3 at 86, and a mode of 84. MindScore averaged 78±10 with Q1 at 72, median at 79 and Q3 at 84, and a mode of 83. Despite being algorithmically normalized for age, average SleepScore increased from 70 to 88 across the age range, BodyScore increased from 71 to 89, and MindScore increased from 75 to 81. Conclusion SleepScores, BodyScores and MindScores presented to the average consumer will mostly show them a number in the low 70 to high 80 range. This distribution was intentionally created as being left-skewed to prevent triggering anxiety that may contribute to orthosomnia. Despite the intent to create a normalized score that would not be impacted by age, the data show an increase of scores by age. Support (If Any)

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Zeng, Xiaobo, Min Zhu, Lu Wang, and Xiaohan Sun. "Optimization of sleep period in watchful sleep mode for power-efficient passive optical networks." Photonic Network Communications 35, no. 3 (December 12, 2017): 300–308. http://dx.doi.org/10.1007/s11107-017-0747-3.

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RAMCHARAN, EION J., JAMES W. GNADT, and S. MURRAY SHERMAN. "Burst and tonic firing in thalamic cells of unanesthetized, behaving monkeys." Visual Neuroscience 17, no. 1 (January 2000): 55–62. http://dx.doi.org/10.1017/s0952523800171056.

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Thalamic relay cells fire in two distinct modes, burst or tonic, and the operative mode is dictated by the inactivation state of low-threshold, voltage-gated, transient (or T-type) Ca2+ channels. Tonic firing is seen when the T channels are inactivated via membrane depolarization, and burst firing is seen when the T channels are activated from a hyperpolarized state. These response modes have very different effects on the relay of information to the cortex. It had been thought that only tonic firing is seen in the awake, alert animal, but recent evidence from several species suggests that bursting may also occur. We have begun to explore this issue in macaque monkeys by recording from thalamic relay cells of unanesthetized, behaving animals. In the lateral geniculate nucleus, the thalamic relay for retinal information, we found that tonic mode dominated responses both during alert behavior as well as during sleep. We nonetheless found burst firing present during the vigilant, waking state. There was, however, considerably more burst mode firing during sleep than wakefulness. Surprisingly, we did not find the bursting during sleep to be rhythmic. We also recorded from relay cells of the somatosensory thalamus. Interestingly, not only did these somatosensory neurons exhibit much more burst mode activity than did geniculate cells, but bursting during sleep was highly rhythmic. It thus appears that the level and nature of relay cell bursting may not be constant across all thalamic nuclei.

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Olcese, Umberto, Steve K. Esser, and Giulio Tononi. "Sleep and Synaptic Renormalization: A Computational Study." Journal of Neurophysiology 104, no. 6 (December 2010): 3476–93. http://dx.doi.org/10.1152/jn.00593.2010.

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Recent evidence indicates that net synaptic strength in cortical and other networks increases during wakefulness and returns to a baseline level during sleep. These homeostatic changes in synaptic strength are accompanied by corresponding changes in sleep slow wave activity (SWA) and in neuronal firing rates and synchrony. Other evidence indicates that sleep is associated with an initial reactivation of learned firing patterns that decreases over time. Finally, sleep can enhance performance of learned tasks, aid memory consolidation, and desaturate the ability to learn. Using a large-scale model of the corticothalamic system equipped with a spike-timing dependent learning rule, in agreement with experimental results, we demonstrate a net increase in synaptic strength in the waking mode associated with an increase in neuronal firing rates and synchrony. In the sleep mode, net synaptic strength decreases accompanied by a decline in SWA. We show that the interplay of activity and plasticity changes implements a control loop yielding an exponential, self-limiting renormalization of synaptic strength. Moreover, when the model “learns” a sequence of activation during waking, the learned sequence is preferentially reactivated during sleep, and reactivation declines over time. Finally, sleep-dependent synaptic renormalization leads to increased signal-to-noise ratios, increased resistance to interference, and desaturation of learning capabilities. Although the specific mechanisms implemented in the model cannot capture the variety and complexity of biological substrates, and will need modifications in line with future evidence, the present simulations provide a unified, parsimonious account for diverse experimental findings coming from molecular, electrophysiological, and behavioral approaches.

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Li, Tiefeng, Caiwen Ma, and WenHua Li. "The System Power Control Unit Based on the On-Chip Wireless Communication System." Scientific World Journal 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/939254.

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Currently, the on-chip wireless communication system (OWCS) includes 2nd-generation (2G), 3rd-generation (3G), and long-term evolution (LTE) communication subsystems. To improve the power consumption of OWCS, a typical architecture design of system power control unit (SPCU) is given in this paper, which can not only make a 2G, a 3G, and an LTE subsystems enter sleep mode, but it can also wake them up from sleep mode via the interrupt. During the sleep mode period, either the real-time sleep timer or the global system for mobile (GSM) communication sleep timer can be used individually to arouse the corresponding subsystem. Compared to previous sole voltage supplies on the OWCS, a 2G, a 3G, or an LTE subsystem can be independently configured with three different voltages and frequencies in normal work mode. In the meantime, the voltage supply monitor, which is an important part in the SPCU, can significantly guard the voltage of OWCS in real time. Finally, the SPCU may implement dynamic voltage and frequency scaling (DVFS) for a 2G, a 3G, or an LTE subsystem, which is automatically accomplished by the hardware.

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Killgore, William, Samantha Jankowski, Kymberly Henderson-Arredondo, Lindsey Hildebrand, Christopher Trapani, Daniel Lucas, Emmett Suckow, et al. "0332 Improving Sleep with Continuous Theta Burst Transcranial Magnetic Stimulation (TMS) of the Default Mode Network." SLEEP 46, Supplement_1 (May 1, 2023): A147—A148. http://dx.doi.org/10.1093/sleep/zsad077.0332.

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Abstract Introduction Chronic insomnia is a disorder associated with increased cognitive arousal. Insomnia is also associated with activation/connectivity within the default mode network (DMN) of the brain, consistent with the hyperarousal theory. We hypothesized that suppression of the DMN with a type of repetitive transcranial magnetic stimulation (rTMS) known as continuous theta burst stimulation (cTBS) would lead to improved overnight sleep. Methods Twenty participants (12 female; age=26.9, SD=6.6 years) meeting criteria for insomnia/sleep disorder completed a counterbalanced sham-controlled crossover study in which they served as their own controls on two separate nights of in-laboratory polysomnography (PSG) monitored sleep. In-lab visits occurred on separate weeks at least 5 days apart. Sessions included two resting state functional magnetic resonance imaging (fMRI) sessions separated by a brief 40 second cTBS rTMS session applied over an easily accessible cortical surface node of the DMN located at the left inferior parietal lobe. After scanning/stimulation, participants were allowed an 8-hour sleep opportunity from 2300 to 0700 monitored with PSG. Results One session of active cTBS significantly altered functional connectivity (p<.05, FDR corrected) within the DMN, while the sham condition produced no changes in functional connectivity from pre- to post-treatment. After controlling for age and IQ, active treatment was associated with significant (p<.05) improvements in PSG measured Total Sleep Time (TST; ηp2=.28), latency to Slow Wave Sleep (SWS; ηp2=.23), Sleep Efficiency (SE; ηp2=.28), and a lower Arousal Index (ηp2=.22). Overall, individuals obtained 16 minutes more sleep after active cTBS compared to sham. Moreover, changes in brain connectivity following cTBS significantly (p<.05) predicted sleep outcomes, including TST, sleep latency, SE, minutes of wake, SWS, number of awakenings, arousals, and arousal index, and a trend toward increased REM (p=.06). Conclusion A brief targeted 40-second stimulation with cTBS altered DMN brain functioning, and improved PSG measured sleep outcomes during the night following stimulation. The effect sizes often exceeded those reported for other established treatments such as cognitive behavioral therapy for insomnia or hypnotic sleep medications. Further work involving multiple stimulations over several days or weeks will be necessary to demonstrate the potential utility of this approach as a treatment for insomnia. Support (if any) W81XWH2010173

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Hill, Sean, and Giulio Tononi. "Modeling Sleep and Wakefulness in the Thalamocortical System." Journal of Neurophysiology 93, no. 3 (March 2005): 1671–98. http://dx.doi.org/10.1152/jn.00915.2004.

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When the brain goes from wakefulness to sleep, cortical neurons begin to undergo slow oscillations in their membrane potential that are synchronized by thalamocortical circuits and reflected in EEG slow waves. To provide a self-consistent account of the transition from wakefulness to sleep and of the generation of sleep slow waves, we have constructed a large-scale computer model that encompasses portions of two visual areas and associated thalamic and reticular thalamic nuclei. Thousands of model neurons, incorporating several intrinsic currents, are interconnected with millions of thalamocortical, corticothalamic, and both intra- and interareal corticocortical connections. In the waking mode, the model exhibits irregular spontaneous firing and selective responses to visual stimuli. In the sleep mode, neuromodulatory changes lead to slow oscillations that closely resemble those observed in vivo and in vitro. A systematic exploration of the effects of intrinsic currents and network parameters on the initiation, maintenance, and termination of slow oscillations shows the following. 1) An increase in potassium leak conductances is sufficient to trigger the transition from wakefulness to sleep. 2) The activation of persistent sodium currents is sufficient to initiate the up-state of the slow oscillation. 3) A combination of intrinsic and synaptic currents is sufficient to maintain the up-state. 4) Depolarization-activated potassium currents and synaptic depression terminate the up-state. 5) Corticocortical connections synchronize the slow oscillation. The model is the first to integrate intrinsic neuronal properties with detailed thalamocortical anatomy and reproduce neural activity patterns in both wakefulness and sleep, thereby providing a powerful tool to investigate the role of sleep in information transmission and plasticity.

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Jin, Shun Fu, Rong Yu Fan, and Li Chen. "Performance Analysis of a Simple Power Saving Scheme for Energy Efficient Ethernet." Applied Mechanics and Materials 151 (January 2012): 583–86. http://dx.doi.org/10.4028/www.scientific.net/amm.151.583.

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In order to improve the energy efficiency and build a green Ethernet, we propose a simple power saving (SPS) scheme with a burst transmission for Energy Efficient Ethernet. In SPS scheme, let the link transfer to the sleep mode as soon as no packets are ready for transmission, whereas let the link return to the awake mode when a fixed number of packets arrive during the sleep mode. We build a vacation queueing model with N strategy to describe the working principle of SPS scheme. By using the method of embedded Markov chain, the formulas of performance measures are given. Finally, numerical results are provided to show the impact of traffic load on system performance.

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van Raan, Anthony F. J. "Sleeping beauties gain impact in overdrive mode." Scientometrics 126, no. 5 (March 2, 2021): 4311–32. http://dx.doi.org/10.1007/s11192-021-03910-5.

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AbstractIn this study we focus on characteristics of SBs that have not or hardly been investigated previously. We find that the choice of the awakening period in the selection of SBs has consequences for the measured citation patterns. Focusing on medical SBs we analyze patterns in the time-development of the citation impact of SBs; the influence of self-citations on the awakening process; and the occurrence of medical research fields to which the SBs and their citing papers belong. An important finding is that SBs are generally characterized by a sleep that becomes less and less deep instead of a permanent deep sleep. The sleeping period is followed by a phase-transition-like jump as a start of the awakening period and a remarkable regularity is found for the citation impact immediately before and after the jump.

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Liu, Jian, Liman Zhao, Xiaofeng Du, and Guanxing Xu. "Study on the Ideal Matching Mode of Sleep Time and High Academic Performance of High School Students in China and Its Early Warning Mechanism." Best Evidence in Chinese Education 6, no. 2 (November 23, 2020): 845–61. http://dx.doi.org/10.15354/bece.20.ar074.

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Based on a sample survey of high schools in Province S of China, this study used quantitative statistical analysis to explore the ideal matching mode of sleep time and high academic performance and established a multi-level early warning mechanism for schools that sacrifice student sleep for high academic performance. The results showed that “students achieve the best academic performance when they sleep for eight hours or more.” This is an ideal matching mode for schools to ensure the healthy development of students and build a good educational environment. Teachers, schools, education administrators, and parents should hold correct educational values and view comprehensively the relationship between students’ sleep time and academic performance. For schools that sacrifice students' sleep time and blindly pursue high grades, a multi-level early warning mechanism should be established and their rectification should be supervised.

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Deboer, T., and I. Tobler. "Natural hypothermia and sleep deprivation: common effects on recovery sleep in the Djungarian hamster." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 271, no. 5 (November 1, 1996): R1364—R1371. http://dx.doi.org/10.1152/ajpregu.1996.271.5.r1364.

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Sleep, daily torpor, and hibernation have been considered to be homologous processes. However, in the Djungarian hamster, daily torpor is followed by an increase in slow-wave activity (SWA; electroencephalogram power density 0.75-4.0 Hz) that is similar to the increase observed after sleep deprivation. A positive correlation was found between torpor episode length and the subsequent increase in SWA, which was highest when SWA was assumed to increase with a saturating exponential function. Thus the increase in SWA propensity during daily torpor followed similar kinetics as during waking, supporting the hypothesis that when the animal is in torpor it is incurring a sleep debt. An alternative hypothesis, proposing that the mode of arousal causes the subsequent SWA increase, was tested by warming the animals during emergence from daily torpor. Irrespective of mode of arousal, more non-rapid eye movement (NREM) sleep and a similar SWA increase was found after torpor. The data are compatible with a putative neuronal restorative function for sleep associated with the expression of SWA in NREM sleep. During torpor, when brain temperature is low, this function is inhibited, whereas the need for restoration accumulates. Recovery takes place only after return to euthermia.

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QU, Hong-ei. "Research of the sleep mode based on IEEE 802.16e." Journal of Computer Applications 28, no. 6 (August 20, 2008): 1494–97. http://dx.doi.org/10.3724/sp.j.1087.2008.01494.

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., Linawati, Gede Sukadarmika, and Ridho Yurham. "Sleep Mode Strategy for Energy Saving on 3G Network." International Journal of Multimedia and Ubiquitous Engineering 12, no. 2 (February 28, 2017): 129–42. http://dx.doi.org/10.14257/ijmue.2017.12.2.10.

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Journal articles on the topic 'Sleep mode'

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