4,868 research outputs found
Cooperative Wideband Spectrum Sensing Based on Joint Sparsity
COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON JOINT SPARSITY
By Ghazaleh Jowkar, Master of Science
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University
Virginia Commonwealth University 2017
Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering
In this thesis, the problem of wideband spectrum sensing in cognitive radio (CR) networks using sub-Nyquist sampling and sparse signal processing techniques is investigated. To mitigate multi-path fading, it is assumed that a group of spatially dispersed SUs collaborate for wideband spectrum sensing, to determine whether or not a channel is occupied by a primary user (PU). Due to the underutilization of the spectrum by the PUs, the spectrum matrix has only a small number of non-zero rows. In existing state-of-the-art approaches, the spectrum sensing problem was solved using the low-rank matrix completion technique involving matrix nuclear-norm minimization. Motivated by the fact that the spectrum matrix is not only low-rank, but also sparse, a spectrum sensing approach is proposed based on minimizing a mixed-norm of the spectrum matrix instead of low-rank matrix completion to promote the joint sparsity among the column vectors of the spectrum matrix. Simulation results are obtained, which demonstrate that the proposed mixed-norm minimization approach outperforms the low-rank matrix completion based approach, in terms of the PU detection performance. Further we used mixed-norm minimization model in multi time frame detection. Simulation results shows that increasing the number of time frames will increase the detection performance, however, by increasing the number of time frames after a number of times the performance decrease dramatically
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
SimpleTrack:Adaptive Trajectory Compression with Deterministic Projection Matrix for Mobile Sensor Networks
Some mobile sensor network applications require the sensor nodes to transfer
their trajectories to a data sink. This paper proposes an adaptive trajectory
(lossy) compression algorithm based on compressive sensing. The algorithm has
two innovative elements. First, we propose a method to compute a deterministic
projection matrix from a learnt dictionary. Second, we propose a method for the
mobile nodes to adaptively predict the number of projections needed based on
the speed of the mobile nodes. Extensive evaluation of the proposed algorithm
using 6 datasets shows that our proposed algorithm can achieve sub-metre
accuracy. In addition, our method of computing projection matrices outperforms
two existing methods. Finally, comparison of our algorithm against a
state-of-the-art trajectory compression algorithm show that our algorithm can
reduce the error by 10-60 cm for the same compression ratio
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Algorithms design for improving homecare using Electrocardiogram (ECG) signals and Internet of Things (IoT)
Due to the fast growing of population, a lot of hospitals get crowded from the huge amount of
patients visits. Moreover, during COVID-19 a lot of patients prefer staying at home to minimize
the spread of the virus. The need for providing care to patients at home is essential. Internet
of Things (IoT) is widely known and used by different fields. IoT based homecare will help
in reducing the burden upon hospitals. IoT with homecare bring up several benefits such as
minimizing human exertions, economical savings and improved efficiency and effectiveness. One
of the important requirement on homecare system is the accuracy because those systems are
dealing with human health which is sensitive and need high amount of accuracy. Moreover,
those systems deal with huge amount of data due to the continues sensing that need to be
processed well to provide fast response regarding the diagnosis with minimum cost requirements.
Heart is one of the most important organ in the human body that requires high level of caring.
Monitoring heart status can diagnose disease from the early stage and find the best medication
plan by health experts. Continues monitoring and diagnosis of heart could exhaust caregivers
efforts. Having an IoT heart monitoring model at home is the solution to this problem. Electrocardiogram
(ECG) signals are used to track heart condition using waves and peaks. Accurate
and efficient IoT ECG monitoring at home can detect heart diseases and save human lives.
As a consequence, an IoT ECG homecare monitoring model is designed in this thesis for detecting
Cardiac Arrhythmia and diagnosing heart diseases. Two databases of ECG signals are used;
one online which is old and limited, and another huge, unique and special from real patients
in hospital. The raw ECG signal for each patient is passed through the implemented Low
Pass filter and Savitzky Golay filter signal processing techniques to remove the noise and any
external interference. The clear signal in this model is passed through feature extraction stage
to extract number of features based on some metrics and medical information along with feature extraction algorithm to find peaks and waves. Those features are saved in the local database to
apply classification on them. For the diagnosis purpose a classification stage is made using three
classification ways; threshold values, machine learning and deep learning to increase the accuracy.
Threshold values classification technique worked based on medical values and boarder lines. In
case any feature goes above or beyond these ranges, a warning message appeared with expected
heart disease. The second type of classification is by using machine learning to minimize the
human efforts. A Support Vector Machine (SVM) algorithm is proposed by running the algorithm
on the features extracted from both databases. The classification accuracy for online and hospital
databases was 91.67% and 94% respectively. Due to the non-linearity of the decision boundary, a
third way of classification using deep learning is presented. A full Multilayer Perceptron (MLP)
Neural Network is implemented to improve the accuracy and reduce the errors. The number of
errors reduced to 0.019 and 0.006 using online and hospital databases.
While using hospital database which is huge, there is a need for a technique to reduce the amount
of data. Furthermore, a novel adaptive amplitude threshold compression algorithm is proposed.
This algorithm is able to make diagnosis of heart disease from the reduced size using compressed
ECG signals with high level of accuracy and low cost. The extracted features from compressed
and original are similar with only slight differences of 1%, 2% and 3% with no effects on machine
learning and deep learning classification accuracy without the need for any reconstructions. The
throughput is improved by 43% with reduced storage space of 57% when using data compression.
Moreover, to achieve fast response, the amount of data should be reduced further to provide
fast data transmission. A compressive sensing based cardiac homecare system is presented.
It gives the channel between sender and receiver the ability to carry small amount of data.
Experiment results reveal that the proposed models are more accurate in the classification of
Cardiac Arrhythmia and in the diagnosis of heart diseases. The proposed models ensure fast
diagnosis and minimum cost requirements. Based on the experiments on classification accuracy,
number of errors and false alarms, the dictionary of the compressive sensing selected to be 900.
As a result, this thesis provided three different scenarios that achieved IoT homecare Cardiac
monitoring to assist in further research for designing homecare Cardiac monitoring systems. The experiment results reveal that those scenarios produced better results with high level of accuracy
in addition to minimizing data and cost requirements
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