scholarly journals Human sensory dominance is modulated by stimulus temporal uncertainty rather than by spatial uncertainty

2019 ◽  
Vol 19 (10) ◽  
pp. 20
Author(s):  
Pi-Chun Huang ◽  
Yi-Chuan Chen
Keyword(s):  
Spatial Uncertainty ◽  
Temporal Uncertainty ◽  
Sensory Dominance

scholarly journals A MAC Protocol of Concurrent Scheduling Based on Spatial-Temporal Uncertainty for Underwater Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xin Liu ◽  
Xiujuan Du ◽  
Meiju Li ◽  
Lijuan Wang ◽  
Chong Li
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Mac Protocol ◽  
Average Energy ◽  
Propagation Delay ◽  
Spatial Uncertainty ◽  
Underwater Sensor Networks ◽  
Temporal Uncertainty ◽  
Concurrent Scheduling ◽  
Time Division

Underwater sensor networks (UWSNs) are characterized by large energy consumption, limited power supply, low bit rate, and long propagation delay, as well as spatial-temporal uncertainty, which present both challenges and opportunities for media access control (MAC) protocol design. The time-division transmissions can effectively avoid collisions since different nodes transmit packets at different period of time. Nevertheless, in UWSNs with long propagation delay, in order to avoid collisions, the period of time is subject to be long enough, which results in poor channel utilization and low throughput. In view of the long and different propagation delay between a receiving node and multiple sending nodes in UWSNs, as long as there is no collision at the receiving node, multiple sending nodes can transmit packets simultaneously. Therefore, in this paper, we propose a MAC protocol of concurrent scheduling based on spatial-temporal uncertainty called CSSTU-MAC (concurrent scheduling based on spatial-temporal uncertainty MAC) for UWSNs. The CSSTU-MAC protocol utilizes the characteristics of temporal-spatial uncertainty as well as long propagation delay in UWSNs to achieve concurrent transmission and collision avoidance. Simulation results show that the CSSTU-MAC protocol outperforms the existing MAC protocol with time-division transmissions in terms of average energy consumption and network throughput.

Neuroergonomic Dynamics Associated With Spatial Uncertainty During Vigilance Task Performance

2012 ◽  
Author(s):  
Matthew E. Funke ◽  
Joel S. Warm ◽  
Gerald Matthews ◽  
Gregory J. Funke ◽  
Peter Chiu ◽  
...  
Keyword(s):  
Task Performance ◽  
Vigilance Task ◽  
Spatial Uncertainty

Spatial uncertainty associated with stereotaxic coordinates of neuroimaging results

2009 ◽  
Vol 40 (01) ◽  
Author(s):  
SB Eickhoff ◽  
AR Laird ◽  
C Grefkes ◽  
L Wang ◽  
K Zilles ◽  
...  
Keyword(s):  
Spatial Uncertainty

scholarly journals Using Spatial Validity and Uncertainty Metrics to Determine the Relative Suitability of Alternative Suites of Oceanographic Data for Seabed Biotope Prediction. A Case Study from the Barents Sea, Norway

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 48
Author(s):  
Margaret F.J. Dolan ◽  
Rebecca E. Ross ◽  
Jon Albretsen ◽  
Jofrid Skarðhamar ◽  
Genoveva Gonzalez-Mirelis ◽  
...  
Keyword(s):  
Barents Sea ◽  
Predictor Variable ◽  
Environmental Data ◽  
Spatial Uncertainty ◽  
Benthic Habitat ◽  
Habitat Distribution ◽  
Distribution Models ◽  
Entire Study ◽  
Oceanographic Data ◽  
Uncertainty Analyses

The use of habitat distribution models (HDMs) has become common in benthic habitat mapping for combining limited seabed observations with full-coverage environmental data to produce classified maps showing predicted habitat distribution for an entire study area. However, relatively few HDMs include oceanographic predictors, or present spatial validity or uncertainty analyses to support the classified predictions. Without reference studies it can be challenging to assess which type of oceanographic model data should be used, or developed, for this purpose. In this study, we compare biotope maps built using predictor variable suites from three different oceanographic models with differing levels of detail on near-bottom conditions. These results are compared with a baseline model without oceanographic predictors. We use associated spatial validity and uncertainty analyses to assess which oceanographic data may be best suited to biotope mapping. Our results show how spatial validity and uncertainty metrics capture differences between HDM outputs which are otherwise not apparent from standard non-spatial accuracy assessments or the classified maps themselves. We conclude that biotope HDMs incorporating high-resolution, preferably bottom-optimised, oceanography data can best minimise spatial uncertainty and maximise spatial validity. Furthermore, our results suggest that incorporating coarser oceanographic data may lead to more uncertainty than omitting such data.

scholarly journals Dynamic Control of Response Criterion in Premotor Cortex during Perceptual Detection under Temporal Uncertainty

Neuron ◽  
2015 ◽  
Vol 86 (4) ◽  
pp. 1067-1077 ◽  
Author(s):  
Federico Carnevale ◽  
Victor de Lafuente ◽  
Ranulfo Romo ◽  
Omri Barak ◽  
Néstor Parga
Keyword(s):  
Premotor Cortex ◽  
Dynamic Control ◽  
Response Criterion ◽  
Temporal Uncertainty

CSMC

2021 ◽  
Vol 5 (4) ◽  
pp. 1-22
Author(s):  
Hai Wang ◽  
Baoshen Guo ◽  
Shuai Wang ◽  
Tian He ◽  
Desheng Zhang
Keyword(s):  
Large Scale ◽  
Mobile Network ◽  
Signal Propagation ◽  
Spectrum Management ◽  
External Information ◽  
Temporal Uncertainty ◽  
Large Area ◽  
Fine Grained ◽  
Map Construction ◽  
Spatio Temporal

The rise concern about mobile communication performance has driven the growing demand for the construction of mobile network signal maps which are widely utilized in network monitoring, spectrum management, and indoor/outdoor localization. Existing studies such as time-consuming and labor-intensive site surveys are difficult to maintain an update-to-date finegrained signal map within a large area. The mobile crowdsensing (MCS) paradigm is a promising approach for building signal maps because collecting large-scale MCS data is low-cost and with little extra-efforts. However, the dynamic environment and the mobility of the crowd cause spatio-temporal uncertainty and sparsity of MCS. In this work, we leverage MCS as an opportunity to conduct the city-wide mobile network signal map construction. We propose a fine-grained city-wide Cellular Signal Map Construction (CSMC) framework to address two challenges including (i) the problem of missing and unreliable MCS data; (ii) spatio-temporal uncertainty of signal propagation. In particular, CSMC captures spatio-temporal characteristics of signals from both inter- and intra- cellular base stations and conducts missing signal recovery with Bayesian tensor decomposition to build large-area fine-grained signal maps. Furthermore, CSMC develops a context-aware multi-view fusion network to make full use of external information and enhance signal map construction accuracy. To evaluate the performance of CSMC, we conduct extensive experiments and ablation studies on a large-scale dataset with over 200GB MCS signal records collected from Shanghai. Experimental results demonstrate that our model outperforms state-of-the-art baselines in the accuracy of signal estimation and user localization.

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