378,326 research outputs found
A Wildfire Prediction Based on Fuzzy Inference System for Wireless Sensor Networks
The study of forest fires has been traditionally considered as an important
application due to the inherent danger that this entails. This phenomenon
takes place in hostile regions of difficult access and large areas. Introduction of
new technologies such as Wireless Sensor Networks (WSNs) has allowed us to
monitor such areas. In this paper, an intelligent system for fire prediction based
on wireless sensor networks is presented. This system obtains the probability of
fire and fire behavior in a particular area. This information allows firefighters to
obtain escape paths and determine strategies to fight the fire. A firefighter can
access this information with a portable device on every node of the network. The
system has been evaluated by simulation analysis and its implementation is being
done in a real environment.Junta de Andalucía P07-TIC-02476Junta de Andalucía TIC-570
From Vehicular Networks to IoT for Smart Roads: How a Communication Engineer Can Help Solve Transportation Problems (Invited Talk)
Intelligent transportation system (ITS) is an important development that applies advanced sensing, communication, big data analysis and control technologies to ground transportation in order to improve safety, mobility and efficiency. This talk will begin with a brief introduction to our work in vehicular networks, which started more than ten years ago. As we delve deeper into vehicular networks and interact more frequently with transportation stakeholders, we realize that ITS is a truly cross-disciplinary area, in order for vehicular networks to achieve its desired impact, we need to think beyond the traditional communication domain, and start to ponder the deeper-level questions of what fundamental changes can be brought by advanced sensing and communication techniques to transportation and how the applications of advanced sensing and communication techniques can help solve crucial transportation problems. To this end, we will introduce our more recent work of developing advanced IoT technology to transform our roads into smart roads, which in the shorter term, make our roads safer and more efficient while providing the fine-grained real-time traffic information for traffic management; in the longer term, provide the much-needed road infrastructure support for the future booming CAV revolution
The development of hybrid intelligent systems for technical analysis based equivolume charting
This dissertation proposes the development of a hybrid intelligent system applied to technical analysis based equivolume charting for stock trading. A Neuro-Fuzzy based Genetic Algorithms (NF-GA) system of the Volume Adjusted Moving Average (VAMA) membership functions is introduced to evaluate the effectiveness of using a hybrid intelligent system that integrates neural networks, fuzzy logic, and genetic algorithms techniques for increasing the efficiency of technical analysis based equivolume charting for trading stocks --Introduction, page 1
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Cognitive Radio Networks: Highlights of Information Theoretic Limits, Models and Design
In recent years, the development of intelligent, adaptive wireless devices called cognitive radios, together with the introduction of secondary spectrum licensing, has led to a new paradigm in communications: cognitive networks. Cognitive networks are wireless networks that consist of several types of users: often a primary user (the primary license-holder of a spectrum band) and secondary users (cognitive radios). These cognitive users employ their cognitive abilities to communicate without harming the primary users. The study of cognitive networks is relatively new and many questions are yet to be answered. In this article we highlight some of the recent information theoretic limits, models, and design of these promising networks.Engineering and Applied Science
Lifelong Generative Modeling
Lifelong learning is the problem of learning multiple consecutive tasks in a
sequential manner, where knowledge gained from previous tasks is retained and
used to aid future learning over the lifetime of the learner. It is essential
towards the development of intelligent machines that can adapt to their
surroundings. In this work we focus on a lifelong learning approach to
unsupervised generative modeling, where we continuously incorporate newly
observed distributions into a learned model. We do so through a student-teacher
Variational Autoencoder architecture which allows us to learn and preserve all
the distributions seen so far, without the need to retain the past data nor the
past models. Through the introduction of a novel cross-model regularizer,
inspired by a Bayesian update rule, the student model leverages the information
learned by the teacher, which acts as a probabilistic knowledge store. The
regularizer reduces the effect of catastrophic interference that appears when
we learn over sequences of distributions. We validate our model's performance
on sequential variants of MNIST, FashionMNIST, PermutedMNIST, SVHN and Celeb-A
and demonstrate that our model mitigates the effects of catastrophic
interference faced by neural networks in sequential learning scenarios.Comment: 32 page
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