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In cognitive radio (CR) networks, the multichannel access problem is an important problem, which may directly affect user applications. However, most of previous work on this problem focuses on maximizing physical layer throughput, rather than the end-to-end transmission control protocol (TCP) performance. In this paper, we propose an optimal TCP throughput based channel access scheme in CR networks,...
Secure communications in the context of civil space missions gained a major attention in the last few years, several research groups and international organizations are currently developing the required security mechanisms, based on widely used protocols of the Consultative Committee for Space Data Systems (CCSDS) Packet TM and TC family. However, given the hostile space communication environment,...
To maximize the channel utilization, an opportunistic spectrum access (OSA) strategy for a slotted secondary user (SU) overlaying an unslotted primary network under interference constraint (IC) and energy consumption constraint (ECC) is proposed. The IC is modeled by the average temporal overlap between SU and PU. On the other hand, since SU is periodic, thus the sensing period will affect the energy...
When children learn to grasp a new object, they often know several possible grasping points from observing a parent's demonstration and subsequently learn better grasps by trial and error. From a machine learning point of view, this process is an active learning approach. In this paper, we present a new robot learning framework for reproducing this ability in robot grasping. For doing so, we chose...
Image segmentation is a key technique of image processing and computer vision field. However, facing with large amount of image segmentation methods, the qualitative and quantitative evaluation of algorithms is very significant. This paper states the thoughts of high resolution RS image segmentation methods evaluation and tests it by evaluating four typical image segmentation algorithms based on features...
Due to their highly dynamic nature, vehicular ad hoc networks (VANETs) are occasionally found difficult for their parameter optimization. Some attempts have been made to challenge the difficulty by modeling VANETs mathematically to predict parameter values successfully such as average cluster size and the lifetime of a path. Nevertheless, there have been no systematic approaches to analyze both node...
Autonomous estimation of the altitude of an Unmanned Aerial Vehicle (UAV) is extremely important when dealing with flight maneuvers like landing, steady flight, etc. Vision based techniques for solving this problem have been underutilized. In this paper, we propose a new algorithm to estimate the altitude of a UAV from top-down aerial images taken from a single on-board camera. We use a semi-supervised...
Recently, opportunistic routing has been proposed to take good advantage of broadcast nature and spatial diversity to achieve high throughput, despite highly unpredictable and lossy wireless links in multi-hop wireless networks. Most previous works provide heuristic solutions to select as many candidates, and don't take inter-candidate delivery probability into account, which might suffer acknowledgement...
In the context of image segmentation, Markov random fields (MRF) are extensively used. However solution of MRF-based models is heavily dependent on how successfully the MRF energy minimization is performed. In this framework, two methodologies, complementary to each other, are proposed for random field optimization. We address the special class of models comprising a random field imposed on the probabilities...
How to improve the spectrum utility of the unlicensed uses (secondary users) without interfering the licensed spectrum holders' usage becomes a key issue in the cognitive radio system. In this paper, we consider an underlay cognitive radio system in which the secondary users can make use of the allowance of the primary users' SINR constraints and operate on the same spectrum band as the primary signals...
Many complex systems today, such as robotic networks, automobiles and automated factories, consist of hardware components whose functionality is extended or controlled by embedded software and which exhibit continuous dynamics. We address the problem of monitoring and control in such systems with a twofold contribution. First, we extend probabilistic hierarchical constraint automata (PHCA), introduced...
In this paper, we are interested in the problem of motion-blurred image blind restoration. A new method for this ill-posed problem is proposed. We present an adaptive Huber Markov Random Field (HMRF) image prior model as the regularization term, which can be suitable for motion-blurred situation, then turn the ill-posed problem to well-posed. It can preserve fine image details and edges. However,...
Graph cut as a powerful optimization technique for minimizing MRF (Markov Random Field) energy functions has been successfully applied to image segmentation. In this paper, we adopt an MRF model for object/background segmentation. The theoretical framework is based on maximum a posterior estimation via the graph-cut energy optimization method. Parameters are estimated with a novel parameter estimation...
This paper is focused on the Co-segmentation problem [1] - where the objective is to segment a similar object from a pair of images. The background in the two images may be arbitrary; therefore, simultaneous segmentation of both images must be performed with a requirement that the appearance of the two sets of foreground pixels in the respective images are consistent. Existing approaches [1, 2] cast...
Human pose estimation is the task of determining the states (location, orientation and scale) of each body part. It is important for many vision understanding applications, e.g. visual interactive gaming, immersive virtual reality, content-based image retrieval, etc. However, it remains a challenging task because of unknown image background, presence of clutter, partial occlusion and especially the...
A Markov random field (MRF) model is proposed for unsupervised image segmentation in this paper. The theoretical framework is based on Bayesian estimation via the graph-cut energy optimization method. A Gaussian is used to model the density associated with each image segment (or class), and parameters are estimated with an expectation maximization (EM) algorithm. Here we use the perceptually uniform...
Evolution algorithm (EA) has been widely used in solving optimization problem. But the theory foundation of EA is still not completely clear. This paper first puts forward optimization measurement principle, and then analyzes (crossover+mutation) EA based on it. According to optimization measurement principle we proposed, we deduce a condition that relates all parameters of EA, under which EA can...
In order to establish a brain-machine interface (BMI) system that rehabilitates damaged cerebellum function of discrete motor learning, the detection of conditional and unconditional stimuli (CS and US) onset times based on electro-physiology recordings analysis is necessary. These signals are relayed through brainstem areas called Pontine Nucleus (PN) and the Inferior Olive (IO) respectively. In...
This paper presents a factorial hidden Markov model (FHMM)-based diagnostic reasoner to handle multiple intermittent faults. The dynamic multiple fault diagnosis (DMFD) problem is to determine the most likely evolution of fault states, the one that best explains the observed test outcomes over time. In our previous research work, we have shown that the problem of diagnosing dynamic multiple faults...
Spectrum access scheme is a fundamental component in building efficient wireless networks. Conventional methods such as proactive channel assignment is costly due to large amount of protocol overhead. Also, those algorithms suffer from its inability in dealing with channel dynamics. The opportunistic methods however, spend more time on probing, and suffer from the myopic decisions as well. We present...
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