73 research outputs found
Parameter selection and performance comparison of particle swarm optimization in sensor networks localization
Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors\u27 memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm
Grouping Boundary Proposals for Fast Interactive Image Segmentation
Geodesic models are known as an efficient tool for solving various image
segmentation problems. Most of existing approaches only exploit local pointwise
image features to track geodesic paths for delineating the objective
boundaries. However, such a segmentation strategy cannot take into account the
connectivity of the image edge features, increasing the risk of shortcut
problem, especially in the case of complicated scenario. In this work, we
introduce a new image segmentation model based on the minimal geodesic
framework in conjunction with an adaptive cut-based circular optimal path
computation scheme and a graph-based boundary proposals grouping scheme.
Specifically, the adaptive cut can disconnect the image domain such that the
target contours are imposed to pass through this cut only once. The boundary
proposals are comprised of precomputed image edge segments, providing the
connectivity information for our segmentation model. These boundary proposals
are then incorporated into the proposed image segmentation model, such that the
target segmentation contours are made up of a set of selected boundary
proposals and the corresponding geodesic paths linking them. Experimental
results show that the proposed model indeed outperforms state-of-the-art
minimal paths-based image segmentation approaches
Geodesic Models with Convexity Shape Prior
The minimal geodesic models based on the Eikonal equations are capable of
finding suitable solutions in various image segmentation scenarios. Existing
geodesic-based segmentation approaches usually exploit image features in
conjunction with geometric regularization terms, such as Euclidean curve length
or curvature-penalized length, for computing geodesic curves. In this paper, we
take into account a more complicated problem: finding curvature-penalized
geodesic paths with a convexity shape prior. We establish new geodesic models
relying on the strategy of orientation-lifting, by which a planar curve can be
mapped to an high-dimensional orientation-dependent space. The convexity shape
prior serves as a constraint for the construction of local geodesic metrics
encoding a particular curvature constraint. Then the geodesic distances and the
corresponding closed geodesic paths in the orientation-lifted space can be
efficiently computed through state-of-the-art Hamiltonian fast marching method.
In addition, we apply the proposed geodesic models to the active contours,
leading to efficient interactive image segmentation algorithms that preserve
the advantages of convexity shape prior and curvature penalization.Comment: This paper has been accepted by TPAM
A New Stochastic Geometry Model of Coexistence of Wireless Body Sensor Networks
Stochastic geometry, in particular Poission point process theory, has been widely used in the last decade to provide models and methods to analyze wireless networks. It is a branch of mathematics which deals with the study of random point processes. There are various models for point processes, typically based on but going beyond the classic homogeneous Poisson point process. Poisson point process cannot be used to model the spatial distribution of the simultaneously active transmitters. A novel framework has been presented for modeling the intensity of simultaneous active transmitters of a random carrier sense multiple access wireless sensor network. This thinning rule uses a second-neighbors distance-dependent method, which controls too many nodes deleted of points close together
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Implantable and Biodegradable Macroporous Iron Oxide Frameworks for Efficient Regeneration and Repair of Infracted Heart
The construction, characterization and surgical application of a multilayered iron oxide-based macroporous composite framework were reported in this study. The framework consisted of a highly porous iron oxide core, a gelatin-based hydrogel intermediary layer and a matrigel outer cover, which conferred a multitude of desirable properties including excellent biocompatibility, improved mechanical strength and controlled biodegradability. The large pore sizes and high extent of pore interconnectivity of the framework stimulated robust neovascularization and resulted in substantially better cell viability and proliferation as a result of improved transport efficiency for oxygen and nutrients. In addition, rat models with myocardial infraction showed sustained heart tissue regeneration over the infract region and significant improvement of cardiac functions following the surgical implantation of the framework. These results demonstrated that the current framework might hold great potential for cardiac repair in patients with myocardial infraction
ECG Signal Denoising and Reconstruction Based on Basis Pursuit
The electrocardiogram (ECG) is widely used for the diagnosis of heart diseases. However, ECG signals are easily contaminated by different noises. This paper presents efficient denoising and compressed sensing (CS) schemes for ECG signals based on basis pursuit (BP). In the process of signal denoising and reconstruction, the low-pass filtering method and alternating direction method of multipliers (ADMM) optimization algorithm are used. This method introduces dual variables, adds a secondary penalty term, and reduces constraint conditions through alternate optimization to optimize the original variable and the dual variable at the same time. This algorithm is able to remove both baseline wander and Gaussian white noise. The effectiveness of the algorithm is validated through the records of the MIT-BIH arrhythmia database. The simulations show that the proposed ADMM-based method performs better in ECG denoising. Furthermore, this algorithm keeps the details of the ECG signal in reconstruction and achieves higher signal-to-noise ratio (SNR) and smaller mean square error (MSE)
Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization
Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm
A Multi-Threading Algorithm to Detect and Remove Cycles in Vertex- and Arc-Weighted Digraph
A graph is a very important structure to describe many applications in the real world. In many applications, such as dependency graphs and debt graphs, it is an important problem to find and remove cycles to make these graphs be cycle-free. The common algorithm often leads to an out-of-memory exception in commodity personal computer, and it cannot leverage the advantage of multicore computers. This paper introduces a new problem, cycle detection and removal with vertex priority. It proposes a multithreading iterative algorithm to solve this problem for large-scale graphs on personal computers. The algorithm includes three main steps: simplification to decrease the scale of graph, calculation of strongly connected components, and cycle detection and removal according to a pre-defined priority in parallel. This algorithm avoids the out-of-memory exception by simplification and iteration, and it leverages the advantage of multicore computers by multithreading parallelism. Five different versions of the proposed algorithm are compared by experiments, and the results show that the parallel iterative algorithm outperforms the others, and simplification can effectively improve the algorithm's performance
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