236 research outputs found
Bidirectional propagation method for analysis of reflection on radio networks
In this paper, our aim is to evaluate and analyze two propagation methods over mobile IPv6 networks which are bidirectional tunneling and routing optimization and both deliver a packet from a correspondent node to the mobile node and vice versa via specific tunnel. To this end we propose a mobile IPv6 scenario by including real-time applications such as video- conferencing. As a result of the evaluation, routing optimization method reduces end-to-end delay and packet delay variation because the number of packets experience tunneling is reduced by this method. This results in increasing network performance
Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review
Vehicular congestion is directly impacting the efficiency of the transport
sector. A wireless sensor network for vehicular clients is used in Internet of
Vehicles based solutions for traffic management applications. It was found that
vehicular congestion detection by using Internet of Vehicles based connected
vehicles technology are practically feasible for congestion handling. It was
found that by using Fog Computing based principles in the vehicular wireless
sensor network, communication in the system can be improved to support larger
number of nodes without impacting performance. In this paper, connected
vehicles technology based vehicular congestion identification techniques are
studied. Computing paradigms that can be used for the vehicular network are
studied to develop a practically feasible vehicular congestion detection system
that performs accurately for a large coverage area and multiple scenarios. The
designed system is expected to detect congestion to meet traffic management
goals that are of primary importance in intelligent transportation systems
Personalized fall detection monitoring system based on learning from the user movements
Personalized fall detection system is shown to provide added and more
benefits compare to the current fall detection system. The personalized model
can also be applied to anything where one class of data is hard to gather. The
results show that adapting to the user needs, improve the overall accuracy of
the system. Future work includes detection of the smartphone on the user so
that the user can place the system anywhere on the body and make sure it
detects. Even though the accuracy is not 100% the proof of concept of
personalization can be used to achieve greater accuracy. The concept of
personalization used in this paper can also be extended to other research in
the medical field or where data is hard to come by for a particular class. More
research into the feature extraction and feature selection module should be
investigated. For the feature selection module, more research into selecting
features based on one class data
A Vector Matroid-Theoretic Approach in the Study of Structural Controllability Over F(z)
In this paper, the structural controllability of the systems over F(z) is
studied using a new mathematical method-matroids. Firstly, a vector matroid is
defined over F(z). Secondly, the full rank conditions of [sI-A|B] are derived
in terms of the concept related to matroid theory, such as rank, base and
union. Then the sufficient condition for the linear system and composite system
over F(z) to be structurally controllable is obtained. Finally, this paper
gives several examples to demonstrate that the married-theoretic approach is
simpler than other existing approaches
Modeling Based on Elman Wavelet Neural Network for Class-D Power Amplifiers
In Class-D Power Amplifiers (CDPAs), the power supply noise can intermodulate
with the input signal, manifesting into power-supply induced intermodulation
distortion (PS-IMD) and due to the memory effects of the system, there exist
asymmetries in the PS-IMDs. In this paper, a new behavioral modeling based on
the Elman Wavelet Neural Network (EWNN) is proposed to study the nonlinear
distortion of the CDPAs. In EWNN model, the Morlet wavelet functions are
employed as the activation function and there is a normalized operation in the
hidden layer, the modification of the scale factor and translation factor in
the wavelet functions are ignored to avoid the fluctuations of the error
curves. When there are 30 neurons in the hidden layer, to achieve the same
square sum error (SSE) , EWNN needs 31 iteration steps,
while the basic Elman neural network (BENN) model needs 86 steps. The
Volterra-Laguerre model has 605 parameters to be estimated but still can't
achieve the same magnitude accuracy of EWNN. Simulation results show that the
proposed approach of EWNN model has fewer parameters and higher accuracy than
the Volterra-Laguerre model and its convergence rate is much faster than the
BENN model
Measuring the similarity of PML documents with RFID-based sensors
The Electronic Product Code (EPC) Network is an important part of the
Internet of Things. The Physical Mark-Up Language (PML) is to represent and
de-scribe data related to objects in EPC Network. The PML documents of each
component to exchange data in EPC Network system are XML documents based on PML
Core schema. For managing theses huge amount of PML documents of tags captured
by Radio frequency identification (RFID) readers, it is inevitable to develop
the high-performance technol-ogy, such as filtering and integrating these tag
data. So in this paper, we propose an approach for meas-uring the similarity of
PML documents based on Bayesian Network of several sensors. With respect to the
features of PML, while measuring the similarity, we firstly reduce the
redundancy data except information of EPC. On the basis of this, the Bayesian
Network model derived from the structure of the PML documents being compared is
constructed.Comment: International Journal of Ad Hoc and Ubiquitous Computin
Body sensor network for mobile health monitoring, a diagnosis and anticipating system
A system capable of mobile heath monitoring was
designed and implemented from the first principles. The system
is capable of measuring vital physiological parameters, interpret
the measured signals, and provide a sense of monitoring and
biofeedback to the user. In the case, a medical emergency
is detected notifications are sent to a medical team for the
analysis. The system was designed through the investigation of
various biosignal extraction methods. An optical pulse oximeter
sensor was designed along with the required software algorithms.
The photoplethysmographic signals were extracted and used to
calculate heart rate, saturation of oxygen, and pulse transits time.
The pulse transit time was utilized in the estimation of continuous
blood pressure measurement. The pulse oximetry implementation
applies reflectance-based sensing on the user’s fingertip and palm.
Skin temperature is also measurable through the use of a digital
temperature sensor. The system is capable of providing feedback
to the user by means of a smartphone application receiving data
from the device through Bluetooth. The measurement of the
proposed physiological parameters were successfully measured
with the appropriate degree of accuracy required by medical
standards. The system is capable of health monitoring through
accurate measurements, providing the user with feedback and
successfully identifying medical stress resulting in the sending of
a notification to a medical doctor.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7361hb201
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