Search results for " function"
showing 10 items of 9395 documents
Improving IEEE 802.11 Performance in Chain Topologies through Distributed Polling and Network Coding
2009
Wireless multi-hop networks often rely on the use of IEEE 802.11 technology. Despite of the robustness of the IEEE 802.11 Distributed Coordination Function (DCF) for working in various network scenarios, it has been proven that critical inefficiencies can arise in the case of multi-hop packet forwarding. In this paper, we propose a MAC scheme, based on the virtualization of the Point Coordination Function, optimized for working on chain topologies with bidirectional traffic flows. Our scheme is based on a token-like access mechanism coupled with network coding. The basic idea is the use of multiple Point Coordinators (PCs) along the node chain, which are elected by passing special token fra…
Psychophysical response to electrocutaneous stimulation.
1984
A method is presented to determine a reliable stimulus-sensation relationship particularly suitable for electrocutaneous stimulation. An experimental intensity-discrimination curve was obtained through simple psychophysical comparison tasks, and sensory response was inferred from integration of a JND's density function. The psychophysical response resembles a power law, although departures cannot be described in terms of a unique exponent. An estimate of binary information capacity per electrode is also given as a feature of a stimulation procedure that preserves a low value of the size-intensity product.
Modeling user preferences in content-based image retrieval: A novel attempt to bridge the semantic gap
2015
This paper is concerned with content-based image retrieval from a stochastic point of view. The semantic gap problem is addressed in two ways. First, a dimensional reduction is applied using the (pre-calculated) distances among images. The dimension of the reduced vector is the number of preferences that we allow the user to choose from, in this case, three levels. Second, the conditional probability distribution of the random user preference, given this reduced feature vector, is modeled using a proportional odds model. A new model is fitted at each iteration. The score used to rank the image database is based on the estimated probability function of the random preference. Additionally, so…
A principled approach to network-based classification and data representation
2013
Measures of similarity are fundamental in pattern recognition and data mining. Typically the Euclidean metric is used in this context, weighting all variables equally and therefore assuming equal relevance, which is very rare in real applications. In contrast, given an estimate of a conditional density function, the Fisher information calculated in primary data space implicitly measures the relevance of variables in a principled way by reference to auxiliary data such as class labels. This paper proposes a framework that uses a distance metric based on Fisher information to construct similarity networks that achieve a more informative and principled representation of data. The framework ena…
Spectral clustering with the probabilistic cluster kernel
2015
Abstract This letter introduces a probabilistic cluster kernel for data clustering. The proposed kernel is computed with the composition of dot products between the posterior probabilities obtained via GMM clustering. The kernel is directly learned from the data, is parameter-free, and captures the data manifold structure at different scales. The projections in the kernel space induced by this kernel are useful for general feature extraction purposes and are here exploited in spectral clustering with the canonical k-means. The kernel structure, informative content and optimality are studied. Analysis and performance are illustrated in several real datasets.
Fuzzy sigmoid kernel for support vector classifiers
2004
This Letter proposes the use of the fuzzy sigmoid function presented in (IEEE Trans. Neural Networks 14(6) (2003) 1576) as non-positive semi-definite kernel in the support vector machines framework. The fuzzy sigmoid kernel allows lower computational cost, and higher rate of positive eigenvalues of the kernel matrix, which alleviates current limitations of the sigmoid kernel.
Multi-Authored Manuscripts and Speedup in Academic Publishing
2014
It is unfair to count a n-authored paper as one paper for each coauthor, i.e., as n papers: this is “feeding the multitude”. Sharing the credit among coauthors by percentages or by simply dividing by n is fairer but somewhat harsh. So, we propose to take into account the productivity gains of parallelization by introducing a team bonus function that multiplies the allocation thereby increasing the credit allocated to each coauthor.The degree of parallelization cannot be determined exogenously discipline by discipline. So, one may propose that each team of coauthors indicates how the labor was organized to produce the paper. Unfortunately, the coauthors may systematically bias their answers …
Capacity studies of spatially correlated MIMO Rice channels
2010
In this paper, we have studied the statistical properties of the capacity of spatially correlated multiple-input multiple-output (MIMO) Rice channels. We have derived an exact closed-form expression for the probability density function (PDF) and an exact expression for the cumulative distribution function (CDF) of the channel capacity for single-input multiple-output (SIMO) and multiple-input single-output (MISO) systems. Furthermore, an accurate closed-form expression has been derived for the level-crossing rate (LCR) and an accurate expression has been obtained for the average duration of fades (ADF) of the SIMO and MISO channel capacities. For the MIMO case, we have investigated the PDF,…
Feature extraction from remote sensing data using Kernel Orthonormalized PLS
2007
This paper presents the study of a sparse kernel-based method for non-linear feature extraction in the context of remote sensing classification and regression problems. The so-called kernel orthonormalized PLS algorithm with reduced complexity (rKOPLS) has two core parts: (i) a kernel version of OPLS (called KOPLS), and (ii) a sparse (reduced) approximation for large scale data sets, which ultimately leads to rKOPLS. The method demonstrates good capabilities in terms of expressive power of the extracted features and scalability.
A Comparative Analysis of Multiple Biasing Techniques for $Q_{biased}$ Softmax Regression Algorithm
2021
Over the past many years the popularity of robotic workers has seen a tremendous surge. Several tasks which were previously considered insurmountable are able to be performed by robots efficiently, with much ease. This is mainly due to the advances made in the field of control systems and artificial intelligence in recent years. Lately, we have seen Reinforcement Learning (RL) capture the spotlight, in the field of robotics. Instead of explicitly specifying the solution of a particular task, RL enables the robot (agent) to explore its environment and through trial and error choose the appropriate response. In this paper, a comparative analysis of biasing techniques for the Q-biased softmax …