Search results for " image processing."
showing 10 items of 2265 documents
Modeling of neuron-astrocyte interaction : application to signal and image processing
2022
The introduction of the tripartite synapse and the discovery of calcium wave propagation motivated our research to explore the potential of astrocytes as active components in brain circuits. For decades, astrocytes have been considered passive cells whose primary function is metabolic and structural support to neurons; however, recent physiological measurements suggest that astrocytes modulate neural communication, strengthen synaptic efficacy, enhance synchronization, and promote homeostasis. Inspired by these biological functions, this research aimed to implement astrocytes in artificial spiking networks for deep learning applications. First, we modeled the biological interaction between …
Mathematical models for Multi Container Loading Problems with practical constraints
2019
Abstract We address the multi container loading problem of a company that serves its customers’ orders by building pallets with the required products and loading them into trucks. The problem is solved by using integer linear models. To be useful in practice, our models consider three types of constraints: geometric constraints, so that pallets lie completely inside the trucks and do not overlap; weight constraints, defining the maximum weights supported by a truck and by each axle, as well as the position of the centre of gravity of the cargo; and dynamic stability constraints. These last constraints forbid empty spaces between pallets to avoid cargo displacement when the truck is moving, …
A Framework to Evaluate Autonomic Behaviours for Intelligent Truck Parking
2016
This paper presents a multi-agent platform to evaluate different strategies to manage the negotiated management of parking spaces in road rest areas. The system dynamically adapts itself to the preferences and needs of the drivers of goods about parking requests. The system is shown to be robust to incidents regarding the closure of road rest areas and allows a conversational interaction of new parking requests through an Android based mobile application.
IoT-MAAC: Multiple Attribute Access Control for IoT environments
2020
Access Control is an important security service that should be considered in IoT environments in order to offer reliable IoT services. Access control in IoT environments concerns not only the access of IoT users to IoT services and objects, but also the access of IoT objects to IoT gateways. In this paper, we specify an access control mechanism that considers the access of IoT objects to IoT gateways in order to enhance the reliability of IoT data provided by the IoT objects. Our proposed access control mechanism, called IoT-MAAC (Multi Attribute Access Control), allows retrieving requested data from the most reliable and secured IoT objects among the available objects in the IoT environmen…
The Average State Complexity of the Star of a Finite Set of Words Is Linear
2008
We prove that, for the uniform distribution over all sets Xof m(that is a fixed integer) non-empty words whose sum of lengths is n, $\mathcal{D}_X$, one of the usual deterministic automata recognizing X*, has on average $\mathcal{O}(n)$ states and that the average state complexity of X*is i¾?(n). We also show that the average time complexity of the computation of the automaton $\mathcal{D}_X$ is $\mathcal{O}(n\log n)$, when the alphabet is of size at least three.
On achieving near-optimal “Anti-Bayesian” Order Statistics-Based classification fora asymmetric exponential distributions
2013
Published version of a Chapter in the book: Computer Analysis of Images and Patterns. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-40261-6_44 This paper considers the use of Order Statistics (OS) in the theory of Pattern Recognition (PR). The pioneering work on using OS for classification was presented in [1] for the Uniform distribution, where it was shown that optimal PR can be achieved in a counter-intuitive manner, diametrically opposed to the Bayesian paradigm, i.e., by comparing the testing sample to a few samples distant from the mean - which is distinct from the optimal Bayesian paradigm. In [2], we showed that the results could be extended for a few sym…
“Anti-Bayesian” parametric pattern classification using order statistics criteria for some members of the exponential family
2013
This paper submits a comprehensive report of the use of order statistics (OS) for parametric pattern recognition (PR) for various distributions within the exponential family. Although the field of parametric PR has been thoroughly studied for over five decades, the use of the OS of the distributions to achieve this has not been reported. The pioneering work on using OS for classification was presented earlier for the uniform distribution and for some members of the exponential family, where it was shown that optimal PR can be achieved in a counter-intuitive manner, diametrically opposed to the Bayesian paradigm, i.e., by comparing the testing sample to a few samples distant from the mean. A…
Solutions to the 1-harmonic flow with values into a hyper-octant of the N-sphere
2013
Abstract We announce existence results for the 1-harmonic flow from a domain of R m into the first hyper-octant of the N -dimensional unit sphere, under homogeneous Neumann boundary conditions. The arguments rely on a notion of “geodesic representative” of a BV-vector field on its jump set.
THE 1-HARMONIC FLOW WITH VALUES IN A HYPEROCTANT OF THE N-SPHERE
2014
We prove the existence of solutions to the 1-harmonic flow — that is, the formal gradient flow of the total variation of a vector field with respect to the [math] -distance — from a domain of [math] into a hyperoctant of the [math] -dimensional unit sphere, [math] , under homogeneous Neumann boundary conditions. In particular, we characterize the lower-order term appearing in the Euler–Lagrange formulation in terms of the “geodesic representative” of a BV-director field on its jump set. Such characterization relies on a lower semicontinuity argument which leads to a nontrivial and nonconvex minimization problem: to find a shortest path between two points on [math] with respect to a metric w…
Bot recognition in a Web store: An approach based on unsupervised learning
2020
Abstract Web traffic on e-business sites is increasingly dominated by artificial agents (Web bots) which pose a threat to the website security, privacy, and performance. To develop efficient bot detection methods and discover reliable e-customer behavioural patterns, the accurate separation of traffic generated by legitimate users and Web bots is necessary. This paper proposes a machine learning solution to the problem of bot and human session classification, with a specific application to e-commerce. The approach studied in this work explores the use of unsupervised learning (k-means and Graded Possibilistic c-Means), followed by supervised labelling of clusters, a generative learning stra…