The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
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...
Vegetation plays an important role in the terrestrial ecosystems. Monitoring vegetation is of great importance in understanding the climate change. Passive microwave remote sensing behaves as an attractive technique due to its penetrability and comprehensive macro scale. It is of great significance to develop a good forward model with simple form and high accuracy for the inversion. The commonly used...
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...
Metallic cables are still frequently used in access telecommunications networks, especially in the last-mile network segments, together with digital subscriber line (DSL) technologies. Recently, the G.fast system for reaching gigabit transmission speed over short metallic lines has been introduced. In order to achieve such performance, the frequency band of G.fast was extended up to 106 MHz or 212...
Due to the increasing interest of the emerging millimeter wave (mmWave) frequency band for application to cellular networks, new flexible and scalable approaches for their modeling, analysis and optimization are needed. Recently, a new approach has been proposed: it is based on the theory of point processes and it leverages tools from stochastic geometry for tractable system-level modeling, performance...
In this paper we present an adaptive variant of a fast augmented Lagrangian method for solving linearly constrained convex optimization problems arising e.g. in model predictive control for embedded linear systems. Mainly, our method relies on the combination of the excessive-gap-like smoothing technique and the inexact oracle framework, which have been presented in details in [13]. We briefly present...
Approximate computing is a paradigm for trading off program accuracy to save energy in memory or computational resources. However, determining feasible program approximations is difficult to achieve. Popular solutions involve programmer in annotating instructions or data that can be approximated. Recently, program testing based techniques have also been explored. But these are computationally expensive...
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.
Many modern applications (such as multimedia processing, machine learning, and big-data analytics) exhibit an inherent tradeoff between performance and the accuracy of the produced results. These applications allow us to investigate new, more aggressive program optimizations. We present a novel approximate optimization framework based on accuracy-aware program transformations. These transformations...
Approximate circuit design is an innovative paradigm for error-resilient image and signal processing applications. Multiplication is often a fundamental function for many of these applications. In this paper, three approximate compressors are proposed with an accuracy constraint for the partial product reduction (PPR) in a multiplier. Both approximation and truncation are considered in the approximate...
The object under study is a metric associated to each graph, called diameter constrained reliability. The exact evaluation of the diameter constrained reliability belongs to the class of NP-Hard problems, and becomes prohibitive in large graphs. In the literature, several estimation methods have been developed, inspired in statistics, combinatorics, algebra and other branches of knowledge. We are...
By relying on a stochastic geometry abstraction modeling for the locations of the base stations and by considering an accurate channel model based on measurements, the author of [1] has recently proposed a tractable mathematical framework for evaluating coverage and rate of millimeter wave cellular networks. The approach proposed in [1] however, relies on a noise-limited approximation for millimeter...
This paper contains a description of methods and algorithms for solving the generalization problem in intelligent decision support systems. For this purpose the argumentation approach for inductive concept formation is used. The methods for finding the conflicts and the generalization algorithm based on the rough set theory are proposed. It is suggested to use the argumentation, based on defeasible...
We derive a new algorithm which avoids normalization of the probability density for particle flow. The algorithm was inspired by renormalization group flow in quantum field theory. In contrast with other particle flow algorithms, this one works in k-space rather than state space. We have roughly 30 or 40 algorithms to compute particle flow, and the three best algorithms avoid computing the normalization...
This paper presents a general computational method to evaluate slowly converging infinite integrals efficiently and accurately. The method applies a subspace algorithm to the set of partial integrals and approximates them interms of complex exponentials. The residue of the exponential term with zero pole directly corresponds to the result of the integration. The method is applied to Sommerfeld integral...
Approximate query processing with relatively small random samples is an effective way to deal with many queries on large databases. However, small random samples might miss relevant records for highly selective queries due to insufficient coverage. A multidimensional index tree called the k-MDI was proposed as an effective sampling scheme for highly selective decision support queries. It has been...
In high performance computing, Monte Carlo methods are widely used to solve problems in various areas of computational physics, finance, mathematics, electrical engineering and many other fields. We present Monte Carlo methods for the Intel Xeon Phi coprocessor, to compute Feynman loop integrals in high energy physics, and integrals arising in stochastic geometry as two types of sample problems. The...
Data clustering is usually time-consuming since it by default needs to iteratively aggregate and process large volume of data. Approximate aggregation based on sample provides fast and quality ensured results. In this paper, we propose to leverage approximation techniques to data clustering to obtain the trade-off between clustering efficiency and result quality, along with online accuracy estimation...
A simple and effective method to extend the characteristic basis function pattern (CBFP) method to multiple frequencies is presented. Transformation matrices are generated from simulated basis functions at each frequency of interest. By computing model coefficients, by means of a singular value decomposition (SVD) to enhance accuracy over a wide bandwidth, using a few directional measurements of any...
The adaptive cross approximation algorithm is invoked for the fast construction of the method-of-moment matrix involving source basis functions for the currents and a set of auxiliary test functions that samples the radiated electromagnetic (EM) field. Once the adaptive cross approximation coupling matrix is constructed, the far and near fields from a source current are obtained directly through a...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.