Optimization of Point Selection on Digital Ink Curves

2012 ◽  
Author(s):  
Rui Hu ◽  
Stephen M. Watt
Keyword(s):  
Point Selection ◽  
Digital Ink

scholarly journals Joint optimization of network selection and task offloading for vehicular edge computing

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Lujie Tang ◽  
Bing Tang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He
Keyword(s):  
Large Scale ◽  
Vehicular Networks ◽  
Access Point ◽  
Edge Computing ◽  
Network Selection ◽  
Network Access ◽  
Task Execution ◽  
Point Selection ◽  
Vehicle Movement ◽  
Task Offloading

AbstractTaking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

Candidate point selection using a self-attention mechanism for generating a smooth volatility surface under the SABR model

2021 ◽  
Vol 173 ◽  
pp. 114640
Author(s):  
Hyeonuk Kim ◽  
Kyunghyun Park ◽  
Junkee Jeon ◽  
Changhoon Song ◽  
Jungwoo Bae ◽  
...  
Keyword(s):  
Attention Mechanism ◽  
Point Selection ◽  
Sabr Model ◽  
Volatility Surface ◽  
Candidate Point

scholarly journals Beehive Air Sampling and Sensing Device Operation in Apicultural Applications—Methodological and Technical Aspects

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4019
Author(s):  
Andrzej Szczurek ◽  
Monika Maciejewska
Keyword(s):  
Gas Sensing ◽  
Operation Mode ◽  
Air Sampling ◽  
Sampling Point ◽  
Sampling System ◽  
Point Selection ◽  
Bee Colony ◽  
Temporal Aspects ◽  
High Quality Information ◽  
Device Operation

The basis of effective beekeeping is the information about the state of the bee colony. A rich source of respective information is beehive air. This source may be explored by applying gas sensing. It allows for classifying bee colony states based on beehive air measurements. In this work, we discussed the essential aspects of beehive air sampling and sensing device operation in apicultural applications. They are the sampling method (diffusive vs. dynamic, temporal aspects), sampling system (sample probe, sampling point selection, sample conditioning unit and sample delivery system) and device operation mode (‘exposure-cleaning’ operation). It was demonstrated how factors associated with the beehive, bee colony and ambient environment define prerequisites for these elements of the measuring instrument. These requirements have to be respected in order to assure high accuracy of measurement and high-quality information. The presented results are primarily based on the field measurement study performed in summer 2020, in three apiaries, in various meteorological conditions. Two exemplars of a prototype gas sensing device were used. These sensor devices were constructed according to our original concept.

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