669,229 research outputs found
Adjacency Matrix Based Energy Efficient Scheduling using S-MAC Protocol in Wireless Sensor Networks
Communication is the main motive in any Networks whether it is Wireless
Sensor Network, Ad-Hoc networks, Mobile Networks, Wired Networks, Local Area
Network, Metropolitan Area Network, Wireless Area Network etc, hence it must be
energy efficient. The main parameters for energy efficient communication are
maximizing network lifetime, saving energy at the different nodes, sending the
packets in minimum time delay, higher throughput etc. This paper focuses mainly
on the energy efficient communication with the help of Adjacency Matrix in the
Wireless Sensor Networks. The energy efficient scheduling can be done by
putting the idle node in to sleep node so energy at the idle node can be saved.
The proposed model in this paper first forms the adjacency matrix and
broadcasts the information about the total number of existing nodes with depths
to the other nodes in the same cluster from controller node. When every node
receives the node information about the other nodes for same cluster they
communicate based on the shortest depths and schedules the idle node in to
sleep mode for a specific time threshold so energy at the idle nodes can be
saved.Comment: 20 pages, 2 figures, 14 tables, 5 equations, International Journal of
Computer Networks & Communications (IJCNC),March 2012, Volume 4, No. 2, March
201
User data dissemination concepts for earth resources
Domestic data dissemination networks for earth-resources data in the 1985-1995 time frame were evaluated. The following topics were addressed: (1) earth-resources data sources and expected data volumes, (2) future user demand in terms of data volume and timeliness, (3) space-to-space and earth point-to-point transmission link requirements and implementation, (4) preprocessing requirements and implementation, (5) network costs, and (6) technological development to support this implementation. This study was parametric in that the data input (supply) was varied by a factor of about fifteen while the user request (demand) was varied by a factor of about nineteen. Correspondingly, the time from observation to delivery to the user was varied. This parametric evaluation was performed by a computer simulation that was based on network alternatives and resulted in preliminary transmission and preprocessing requirements. The earth-resource data sources considered were: shuttle sorties, synchronous satellites (e.g., SEOS), aircraft, and satellites in polar orbits
How far generated data can impact Neural Networks performance?
The success of deep learning models depends on the size and quality of the
dataset to solve certain tasks. Here, we explore how far generated data can aid
real data in improving the performance of Neural Networks. In this work, we
consider facial expression recognition since it requires challenging local data
generation at the level of local regions such as mouth, eyebrows, etc, rather
than simple augmentation. Generative Adversarial Networks (GANs) provide an
alternative method for generating such local deformations but they need further
validation. To answer our question, we consider noncomplex Convolutional Neural
Networks (CNNs) based classifiers for recognizing Ekman emotions. For the data
generation process, we consider generating facial expressions (FEs) by relying
on two GANs. The first generates a random identity while the second imposes
facial deformations on top of it. We consider training the CNN classifier using
FEs from: real-faces, GANs-generated, and finally using a combination of real
and GAN-generated faces. We determine an upper bound regarding the data
generation quantity to be mixed with the real one which contributes the most to
enhancing FER accuracy. In our experiments, we find out that 5-times more
synthetic data to the real FEs dataset increases accuracy by 16%.Comment: Conference Publication in Proceedings of the 18th International Joint
Conference on Computer Vision, Imaging and Computer Graphics Theory and
Applications - Volume 5: VISAPP, 10 page
On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-based Bio-Signal Decoding in BCI Speller Applications
Brain-computer interfaces (BCI) harnessing Steady State Visual Evoked Potentials (SSVEP) manipulate the frequency and phase of visual stimuli to generate predictable oscillations in neural activity. For BCI spellers, oscillations are matched with alphanumeric characters allowing users to select target numbers and letters. Advances in BCI spellers can, in part, be accredited to subject-speci?c optimization, including; 1) custom electrode arrangements, 2) ?lter sub-band assessments and 3) stimulus parameter tuning. Here we apply deep convolutional neural networks (DCNN) demonstrating cross-subject functionality for the classi?cation of frequency and phase encoded SSVEP. Electroencephalogram (EEG) data are collected and classi?ed using the same parameters across subjects. Subjects ?xate forty randomly cued ?ickering characters (5 ×8 keyboard array) during concurrent wet-EEG acquisition. These data are provided by an open source SSVEP dataset. Our proposed DCNN, PodNet, achieves 86% and 77% of?ine Accuracy of Classi?cation across-subjects for two data capture periods, respectively, 6-seconds (information transfer rate= 40bpm) and 2-seconds (information transfer rate= 101bpm). Subjects demonstrating sub-optimal (< 70%) performance are classi?ed to similar levels after a short subject-speci?c training period. PodNet outperforms ?lter-bank canonical correlation analysis (FBCCA) for a low volume (3channel) clinically feasible occipital electrode con?guration. The networks de?ned in this study achieve functional performance for the largest number of SSVEP classes decoded via DCNN to date. Our results demonstrate PodNet achieves cross-subject, calibrationless classi?cation and adaptability to sub-optimal subject data and low-volume EEG electrode arrangements
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
Performance evaluation of WMN-GA for different mutation and crossover rates considering number of covered users parameter
Node placement problems have been long investigated in the optimization field due to numerous applications in location science and classification. Facility location problems are showing their usefulness to communication networks, and more especially from Wireless Mesh Networks (WMNs) field. Recently, such problems are showing their usefulness to communication networks, where facilities could be servers or routers offering connectivity services to clients. In this paper, we deal with the effect of mutation and crossover operators in GA for node placement problem. We evaluate the performance of the proposed system using different selection operators and different distributions of router nodes considering number of covered users parameter. The simulation results show that for Linear and Exponential ranking methods, the system has a good performance for all rates of crossover and mutation.Peer ReviewedPostprint (published version
A Survey on Continuous Time Computations
We provide an overview of theories of continuous time computation. These
theories allow us to understand both the hardness of questions related to
continuous time dynamical systems and the computational power of continuous
time analog models. We survey the existing models, summarizing results, and
point to relevant references in the literature
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