2,812 research outputs found
Two-Hop Routing with Traffic-Differentiation for QoS Guarantee in Wireless Sensor Networks
This paper proposes a Traffic-Differentiated Two-Hop Routing protocol for
Quality of Service (QoS) in Wireless Sensor Networks (WSNs). It targets WSN
applications having different types of data traffic with several priorities.
The protocol achieves to increase Packet Reception Ratio (PRR) and reduce
end-to-end delay while considering multi-queue priority policy, two-hop
neighborhood information, link reliability and power efficiency. The protocol
is modular and utilizes effective methods for estimating the link metrics.
Numerical results show that the proposed protocol is a feasible solution to
addresses QoS service differenti- ation for traffic with different priorities.Comment: 13 page
Far-infrared observations of Circinus and NGC 4945 galaxies
Circinus and NGC 4945 are two galaxies luminous in the infrared and are characterized by compact non thermal radio nuclei, deep silicate absorption features and unusually strong water vapor maser luminosities. Moorwood and Glass (1984) have observed these galaxies extensively in the 1 to 20 micron range. In the far-infrared, observations up to 100 microns are available from the Infrared Astronomy Satellite (IRAS). In order to study the cool dust component of these galaxies, researchers observed them at 150 microns using the Tata Institute of Fundamental Research (TIFR) 100 cm balloon-borne telescope. Here, they report observations along with deconvolved maps at 50 and 100 microns obtained from the Chopped Photometric Channel (CPC) on board IRAS
Forecasting Stock Time-Series using Data Approximation and Pattern Sequence Similarity
Time series analysis is the process of building a model using statistical
techniques to represent characteristics of time series data. Processing and
forecasting huge time series data is a challenging task. This paper presents
Approximation and Prediction of Stock Time-series data (APST), which is a two
step approach to predict the direction of change of stock price indices. First,
performs data approximation by using the technique called Multilevel Segment
Mean (MSM). In second phase, prediction is performed for the approximated data
using Euclidian distance and Nearest-Neighbour technique. The computational
cost of data approximation is O(n ni) and computational cost of prediction task
is O(m |NN|). Thus, the accuracy and the time required for prediction in the
proposed method is comparatively efficient than the existing Label Based
Forecasting (LBF) method [1].Comment: 11 page
Generalization for Deep Reinforcement Learning for Inverse Kinematics of Concentric Tube Robots
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