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The Internet of Things empowers citizens to interconnect their devices, such as smart phones, into large-scale participatory decentralized networks, which they can use to make real-time collective measurements as public good, for instance, crowd-sourcing the monitoring of traffic in a city. This approach is an alternative to big data analytics systems that are often expensive to access, privacy-intrusive...
With the aim of increasing the numerical methods' precision regarding Maxwell's equations solving, a third order staggered FDTD method is proposed in this paper. The proposed method offers a trade-off between the accuracy and the stability, through the application of a third order staggered backward differentiation for the approximation of the temporal partial derivatives, and a fourth-order central...
The feasibility of large-scale decentralized networks for local computations, as an alternative to big data systems that are often privacy-intrusive, expensive and serve exclusively corporate interests, is usually questioned by network dynamics such as node leaves, failures and rejoins in the network. This is especially the case when decentralized computations performed in a network, such as the estimation...
Using the modern elements in a radio-electronic equipment requires close attention to processes as in branch of design and production of electronic components to use the last development in the field simulation of components and in the field computer-aided design of an equipment on the basis of radio-electronic components for the purpose of support adequacy, accuracy and convergence. Possibilities...
Most of the intrusion detection systems analyze all network traffic features to identify intrusions with different classification techniques. Any intrusion detection model developed has to provide maximum accuracy with minimal false alarms. Identifying the optimal feature subset for classification is an important task for improved classification. In this paper, consistency based feature selection...
Approximate computing is gaining a lot of traction due to its potential for improving performance and consequently, energy efficiency. This project explores the potential for approximating locks. We start out with the observation that many applications can tolerate occasional skipping of computations done inside a critical section protected by a lock. This means that for certain critical sections,...
Single-ISA heterogeneous multicore processors have gained increasing popularity with the introduction of recent technologies such as ARM big.LITTLE. These processors offer increased energy efficiency through combining low power in-order cores with high performance out-of-order cores. Efficiently exploiting this attractive feature requires careful management so as to meet the demands of targeted applications...
3D point Geometric alignment is a challenging task encountered in many scientific applications related to different fields such as robotics and computer vision. For this reason, the well-known 3D registration problem has been extensively studied, and a lot of efficient 3D registration algorithms (RA) exist. Even though many surveys in the literature addressed RA's, none to our knowledge is especially...
Task Scheduling is one of the major challenges in any system, whether, parallel system, distributed system, cloud computing, where we execute more than one task at a time. In a distributed system one of issue is task scheduling, based on trusting the accuracy of the information about the resource availability. In a commercial multi-cloud environment, usually any individual providers are focused towards...
Computers and Smartphone's becomes vital part of everyday life and hence use of internet becomes more and more. Due to internet, computers are becomes vulnerable of different kinds of security threats. Therefore it is required that we need to have efficient security method in order to avoid leakage of important data or misuse of data. This security method is called as Intrusion Detection System (IDS)...
This paper excavated the review theme of clothing products by method of association rules, and built a maximum entropy model for the reviews classification. Then this paper did experimental verification to large-scale clothing product reviews classification, which verified the practical effect that maximum entropy model had in the comment text classification problems. In the process of classification,...
Surrogate modeling combined with adaptive sampling allows to greatly lessen the number of samples required to reach the desired model accuracy. However, this comes at cost of substantial computational load. The cost can be limited by less frequent model extraction. This paper describes a surrogate modeling with varying intervals between the model extractions. The intervals are aligned to the sampling...
Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth's surface and their interactions with vegetation and atmosphere. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical...
Kernel methods constitute a family of powerful machine learning algorithms, which have found wide use in remote sensing and geosciences. However, kernel methods are still not widely adopted because of the high computational cost when dealing with large scale problems, such as the inversion of radiative transfer models. This paper introduces the method of random kitchen sinks (RKS) for fast statistical...
In hyperspectral image analysis, the classification task has generally been discussed with dimensionality reduction due to high correlation and noise between the spectral features, which might cause significantly low classification performance. In supervised classification, limited training samples in proportion to the number of spectral features have also negative impacts on the classification accuracy,...
Recurrent drift, as a specific type of concept drift, is characterised by the appearance of previously seen concepts. Therefore, in those cases the learning process could be saved or at least minimized by applying an already trained classification model. In this paper we propose Fuzzy-Rec, a framework that is able to deal with recurrent concept drifts by means of a repository of classification models...
The Weighted Center Localization (WCL), which is a classical localization scheme for the Wireless Sensor Network (WSN), can't decrease the average localization errors and the maximum errors simultaneously due to the fixed weight argument. Improving the localization accuracy of WCL is to decrease the distance measurement errors or to set a proper dynamic weight argument. We have already improved the...
When conventional techniques run of steam, it is time for extreme creativity. Approximate computing provides one possible path forward by relaxing the tradition abstraction of full accuracy across the computing stack.
Process nonlinearity and time-varying behavior of industrial systems are the main factors for poor performance of online soft sensors. To ensure high predictive accuracy, adaptive soft sensor is a common practice. In this paper, an adaptive soft sensor based on moving window Gaussian process regression (GPR) is presented. To make the moving window strategy more efficient, a just-in-time learning (JITL)...
In this paper, D-Wave quantum computing Ising model is employed and evaluated for the forecasting of positive extremes of daily mean air temperature. Forecast models are designed with two to five qubits, which represent 2-, 3-, 4-, and 5-day historical data respectively. Ising model's real-valued weights and dimensionless coefficients are calculated using daily mean air temperatures from 119 places...
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