1,434 research outputs found
Context-Aware Resource Allocation in Cellular Networks
We define and propose a resource allocation architecture for cellular
networks. The architecture combines content-aware, time-aware and
location-aware resource allocation for next generation broadband wireless
systems. The architecture ensures content-aware resource allocation by
prioritizing real-time applications users over delay-tolerant applications
users when allocating resources. It enables time-aware resource allocation via
traffic-dependent pricing that varies during different hours of day (e.g. peak
and off-peak traffic hours). Additionally, location-aware resource allocation
is integrable in this architecture by including carrier aggregation of various
frequency bands. The context-aware resource allocation is an optimal and
flexible architecture that can be easily implemented in practical cellular
networks. We highlight the advantages of the proposed network architecture with
a discussion on the future research directions for context-aware resource
allocation architecture. We also provide experimental results to illustrate a
general proof of concept for this new architecture.Comment: (c) 2015 IEEE. Personal use of this material is permitted. Permission
from IEEE must be obtained for all other uses, in any current or future
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this work in other work
NeuTM: A Neural Network-based Framework for Traffic Matrix Prediction in SDN
This paper presents NeuTM, a framework for network Traffic Matrix (TM)
prediction based on Long Short-Term Memory Recurrent Neural Networks (LSTM
RNNs). TM prediction is defined as the problem of estimating future network
traffic matrix from the previous and achieved network traffic data. It is
widely used in network planning, resource management and network security. Long
Short-Term Memory (LSTM) is a specific recurrent neural network (RNN)
architecture that is well-suited to learn from data and classify or predict
time series with time lags of unknown size. LSTMs have been shown to model
long-range dependencies more accurately than conventional RNNs. NeuTM is a LSTM
RNN-based framework for predicting TM in large networks. By validating our
framework on real-world data from GEEANT network, we show that our model
converges quickly and gives state of the art TM prediction performance.Comment: Submitted to NOMS18. arXiv admin note: substantial text overlap with
arXiv:1705.0569
A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction
Network Traffic Matrix (TM) prediction is defined as the problem of
estimating future network traffic from the previous and achieved network
traffic data. It is widely used in network planning, resource management and
network security. Long Short-Term Memory (LSTM) is a specific recurrent neural
network (RNN) architecture that is well-suited to learn from experience to
classify, process and predict time series with time lags of unknown size. LSTMs
have been shown to model temporal sequences and their long-range dependencies
more accurately than conventional RNNs. In this paper, we propose a LSTM RNN
framework for predicting short and long term Traffic Matrix (TM) in large
networks. By validating our framework on real-world data from GEANT network, we
show that our LSTM models converge quickly and give state of the art TM
prediction performance for relatively small sized models.Comment: Submitted for peer review. arXiv admin note: text overlap with
arXiv:1402.1128 by other author
INTERACTIVE EMIRATE SIGN LANGUAGE E-DICTIONARY BASED ON DEEP LEARNING RECOGNITION MODELS
According to the ministry of community development database in the United Arab Emirates (UAE) about 3065 people with disabilities are hearing disabled (Emirates News Agency - Ministry of Community Development). Hearing-impaired people find it difficult to communicate with the rest of society. They usually need Sign Language (SL) interpreters but as the number of hearing-impaired individuals grows the number of Sign Language interpreters can almost be non-existent. In addition, specialized schools lack a unified Sign Language (SL) dictionary, which can be linked to the Arabic language being of a diglossia nature, hence many dialects of the language co-exist. Moreover, there are not sufficient research work in Arabic SL in general, which can be linked to the lack of unification in the Arabic Sign Language. Hence, presenting an Emirate Sign Language (ESL) electronic Dictionary (e-Dictionary), consisting of four features, namely Dictation, Alpha Webcam, Vocabulary, and Spell, and two datasets (letters and vocabulary/sentences) to help the community in exploring and unifying the ESL. The vocabulary/sentences dataset was recorded by Azure Kinect and includes 127 signs and 50 sentences, making a total of 708 clips, performed by 4 Emirate signers with hearing loss. All the signs were reviewed by the head of the Community Development Authority in UAE for compliance. ESL e-Dictionary integrates state-of-the-art methods i.e., Automatic Speech recognition API by Google, YOLOv8 model trained on our dataset, and an algorithm inspired by bag of words model. Experimental results proved the usability of the e-Dictionary in real-time on laptops. The vocabulary/sentences dataset will be publicly offered in the near future for research purposes
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