2,088 research outputs found
Enhanced Performance Cooperative Localization Wireless Sensor Networks Based on Received-Signal-Strength Method and ACLM
There has been a rise in research interest in wireless sensor networks (WSNs) due to the potential for his or her widespread use in many various areas like home automation, security, environmental monitoring, and lots more. Wireless sensor network (WSN) localization is a very important and fundamental problem that has received a great deal of attention from the WSN research community. Determining the relative coordinate of sensor nodes within the network adds way more aiming to sense data. The research community is extremely rich in proposals to deal with this challenge in WSN. This paper explores the varied techniques proposed to deal with the acquisition of location information in WSN. In the study of the research paper finding the performance in WSN and those techniques supported the energy consumption in mobile nodes in WSN, needed to implement the technique and localization accuracy (error rate) and discuss some open issues for future research. The thought behind Internet of things is that the interconnection of the Internet-enabled things or devices to every other and human to realize some common goals. WSN localization is a lively research area with tons of proposals in terms of algorithms and techniques. Centralized localization techniques estimate every sensor node's situation on a network from a central Base Station, finding absolute or relative coordinates (positioning) with or without a reference node, usually called the anchor (beacon) node. Our proposed method minimization error rate and finding the absolute position of nodes
Framingham Heart Study
This paper describes the Framingham Heart Study one of the most important epidemiological studies ever conducted, and the underlying analytics that led to our current understanding of cardiovascular disease. The logistic regression algorithm is used to analyse the Framingham data set and predict the heart risk of a patient
A Privacy-Preserving Outsourced Data Model in Cloud Environment
Nowadays, more and more machine learning applications, such as medical
diagnosis, online fraud detection, email spam filtering, etc., services are
provided by cloud computing. The cloud service provider collects the data from
the various owners to train or classify the machine learning system in the
cloud environment. However, multiple data owners may not entirely rely on the
cloud platform that a third party engages. Therefore, data security and privacy
problems are among the critical hindrances to using machine learning tools,
particularly with multiple data owners. In addition, unauthorized entities can
detect the statistical input data and infer the machine learning model
parameters. Therefore, a privacy-preserving model is proposed, which protects
the privacy of the data without compromising machine learning efficiency. In
order to protect the data of data owners, the epsilon-differential privacy is
used, and fog nodes are used to address the problem of the lower bandwidth and
latency in this proposed scheme. The noise is produced by the
epsilon-differential mechanism, which is then added to the data. Moreover, the
noise is injected at the data owner site to protect the owners data. Fog nodes
collect the noise-added data from the data owners, then shift it to the cloud
platform for storage, computation, and performing the classification tasks
purposes
Synthesizing Multiple Boolean Functions using Interpolation on a Single Proof
It is often difficult to correctly implement a Boolean controller for a
complex system, especially when concurrency is involved. Yet, it may be easy to
formally specify a controller. For instance, for a pipelined processor it
suffices to state that the visible behavior of the pipelined system should be
identical to a non-pipelined reference system (Burch-Dill paradigm). We present
a novel procedure to efficiently synthesize multiple Boolean control signals
from a specification given as a quantified first-order formula (with a specific
quantifier structure). Our approach uses uninterpreted functions to abstract
details of the design. We construct an unsatisfiable SMT formula from the given
specification. Then, from just one proof of unsatisfiability, we use a variant
of Craig interpolation to compute multiple coordinated interpolants that
implement the Boolean control signals. Our method avoids iterative learning and
back-substitution of the control functions. We applied our approach to
synthesize a controller for a simple two-stage pipelined processor, and present
first experimental results.Comment: This paper originally appeared in FMCAD 2013,
http://www.cs.utexas.edu/users/hunt/FMCAD/FMCAD13/index.shtml. This version
includes an appendix that is missing in the conference versio
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