Search results for "Algorithm"
showing 10 items of 4887 documents
An overview and issues of the sky radiometer technology and SKYNET
2020
Abstract. This paper overviews the progress in the sky radiometer technology and development of the network called SKYNET. It is found that the technology has produced useful on-site calibration methods, retrieval algorithms, and data analyses from the sky radiometer observation of aerosol, cloud, water vapor and ozone. Increasing collaborations of users in the SKYNET community are becoming a useful platform for research and operation. The paper also presents issues of the technology for future development.
A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning
2017
The aim of this study is to combine Biological Target Volume (BTV) segmentation and Gross Target Volume (GTV) segmentation in stereotactic neurosurgery.Our goal is to enhance Clinical Target Volume (CTV) definition, including metabolic and morphologic information, for treatment planning and patient follow-up.We propose a fully automatic approach for multimodal PET and MR image segmentation. This method is based on the Random Walker (RW) and Fuzzy C-Means clustering (FCM) algorithms. A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is presented, considering volume…
Generalization of Canny–Deriche filter for detection of noisy exponential edge
2002
This paper presents a generalization of the Canny-Deriche filter for ramp edge detection with optimization criteria used by Canny (signal-to-noise ratio, localization, and suppression of false responses). Using techniques similar to those developed by Deriche, we derive a filter which maximizes the product of the first two criteria under the constraint of the last one. The result is an infinite length impulse response filter which leads to a stable third-order recursive implementation. Its performance shows an increase of the signal-to-noise ratio in the case of blurred and noisy images, compared to the results obtained from Deriche's filter.
A Multiresolution Approach Based on MRF and Bak–Sneppen Models for Image Segmentation
2006
The two major Markov Random Fields (MRF) based algorithms for image segmentation are the Simulated Annealing (SA) and Iterated Conditional Modes (ICM). In practice, compared to the SA, the ICM provides reasonable segmentation and shows robust behavior in most of the cases. However, the ICM strongly depends on the initialization phase. In this paper, we combine Bak-Sneppen model and Markov Random Fields to define a new image segmentation approach. We introduce a multiresolution technique in order to speed up the segmentation process and to improve the restoration process. Image pixels are viewed as lattice species of Bak-Sneppen model. The a-posteriori probability corresponds to a local fitn…
Spatial graphs and Convolutive Models
2020
In the last two decades, many complex systems have benefited from the use of graph theory, and these approaches have shown robust applicability in the field of finance, computer circuits and in biological systems. Large scale models of brain systems make also a great use of random graph models. Graph theory can be instrumental in modeling the connectivity and spatial distribution of neurons, through a characterization of the relative topological properties. However, all approaches in studying brain function have been so far limited to use experimental constraints obtained at a macroscopic level (e.g. fMRI, EEG, MEG, DTI, DSI). In this contribution, we present a microscopic use (i.e. at the …
Maintaining Dynamic Minimum Spanning Trees: An Experimental Study
2010
AbstractWe report our findings on an extensive empirical study on the performance of several algorithms for maintaining minimum spanning trees in dynamic graphs. In particular, we have implemented and tested several variants of the polylogarithmic algorithm by Holm et al., sparsification on top of Frederickson’s algorithm, and other (less sophisticated) dynamic algorithms. In our experiments, we considered as test sets several random, semi-random and worst-case inputs previously considered in the literature together with inputs arising from real-world applications (e.g., a graph of the Internet Autonomous Systems).
Parallel Random Search and Tabu Search for the Minimal Consistent Subset Selection Problem
1998
The Minimal Consistent Subset Selection (MCSS) problem is a discrete optimization problem whose resolution for large scale instances requires a prohibitive processing time. Prior algorithms addressing this problem are presented. Randomization and approximation techniques are suitable to face the problem, then random search and meta-heuristics are proposed and consequently Tabu Search strategies are applied and evaluated. Parallel computing helps to reduce processing time and/or produce better results; different approaches for designing parallel tabu search are analyzed.
Towards adaptive localization in wireless sensor networks
2012
It is often useful or even indispensable to know the locations of the nodes in a wireless sensor network (WSN), from both network functionality and applications point of view. A potential yet one of the most challenging method is to utilize received signal strength indicator (RSSI) for distance estimation. Albeit easily adopted, using of RSSI is notorious for imprecise distance estimates which can lead to inaccuracies in location estimates. In this paper, we study RSSI-based localization in WSNs through simulation experiments. We evaluate and compare a hybrid algorithm and its modifications with well-known range-based and range-free localization algorithms. Simulation results indicate that …
Nonlinear Optical Characterization of InP@ZnS Core-Shell Colloidal Quantum Dots Using 532 nm, 10 ns Pulses
2021
InP@ZnS core-shell colloidal quantum dots (CQDs) were synthesized and characterized using the z-scan technique. The nonlinear refraction and nonlinear absorption coefficients (γ = −2 × 10−12 cm2 W−1, β = 4 × 10−8 cm W−1) of these CQDs were determined using 10 ns, 532 nm pulses. The saturable absorption (β = −1.4 × 10−9 cm W−1, Isat = 3.7 × 108 W cm−2) in the 3.5 nm CQDs dominated at small intensities of the probe pulses (I ≤ 7 × 107 W cm−2) followed by reverse saturable absorption at higher laser intensities. We report the optical limiting studies using these CQDs showing the suppression of propagated nanosecond radiation in the intensity range of 8 × 107–2 × 109 W cm−2. The role of nonline…
Low-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition with Spatial Sparsity Constraint
2022
Tucker decomposition can provide an intuitive summary to understand brain function by decomposing multi-subject fMRI data into a core tensor and multiple factor matrices, and was mostly used to extract functional connectivity patterns across time/subjects using orthogonality constraints. However, these algorithms are unsuitable for extracting common spatial and temporal patterns across subjects due to distinct characteristics such as high-level noise. Motivated by a successful application of Tucker decomposition to image denoising and the intrinsic sparsity of spatial activations in fMRI, we propose a low-rank Tucker-2 model with spatial sparsity constraint to analyze multi-subject fMRI dat…