13091217
A Parallel Particle Swarm Optimization Algorithm with Communication Strategies
Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy is designed for solution parameters that are independent or are only loosely correlated, such as the Rosenbrock and Rastrigrin functions. The second communication strategy can be applied to parameters that are more strongly correlated such as the Griewank function. In cases where the properties of the parameters are unknown, a third hybrid communication strategy can be used. Experimental results demonstrate the usefulness of the proposed PPSO algorithm.
172381
Robust VQ-based digital watermarking for memoryless binary symmetric channel
Vector quantization (VQ) has been distinguished for its high compression rate in lossy data compression applications. And VQ-based watermarking plays a newly developed branch in digital watermarking research fields. In this paper, we propose optimized schemes for VQ-based image watermarking. We overcome the VQ index assignment problem with genetic algorithm, which is suitable for transmitting the watermarked image over noisy channels. We obtain better robustness of the watermarking algorithm against the effects caused by channel noise in our simulations after inspecting the results from several test images. In addition, to compare with existing schemes in literature, the watermarked image quality in our scheme has approximately the same quality, with better performance in robustness, to the schemes proposed by other researchers. This also proves the effectiveness of our proposed schemes in VQ-based image watermarking for copyright protection.
1155416
Co-Localization from Labeled and Unlabeled Data Using Graph Laplacian
This paper addresses the problem of recovering the locations of both mobile devices and access points from radio signals, a problem which we call colocalization, by exploiting both labeled and unlabeled data from mobile devices and access points. We first propose a solution using Latent Semantic Indexing to construct the relative locations of the mobile devices and access points when their absolute locations are unknown. We then propose a semi-supervised learning algorithm based on manifold to obtain the absolute locations of the devices. Both solutions are finally combined together in terms of graph Laplacian. Extensive experiments are conducted in wireless local-area networks, wireless sensor networks and radio frequency identification networks. The experimental results showthat we can achieve high accuracy with much less calibration effort as compared to several previous systems.
2109911
Serialized optimal relay schedules in two-tiered wireless sensor networks
In two-tiered wireless sensor networks (WSNs), sensor nodes (SNs) are scattered in clusters, and are responsible for collecting relevant information from designated areas and transmitting to an application node (AN) in the cluster. The AN then constructs a local-view for the cluster by exploring correlations among information received from nearby SNs, and sends the local-view toward a base-station that creates a global-view for the entire WSN. ANs can also relay local-views for other ANs, if the resultant network lifetime is longer. In this paper, we want to arrange inter-AN relaying optimally, which is an important process in topology control for maximizing the topological lifetime of a WSN with regard to a certain amount of initial energy provisioning. We first propose some criteria on relay candidates preselection, which can considerably reduce the overhead of obtaining an optimal relaying. We then design an algorithm to serialize the parallel relay allocation, so that each AN only needs to have one relaying AN at any time. Finally, we demonstrate the equivalency in network lifetime of the serialized inter-AN relay schedules.
13400473
Isometric path numbers of graphs
An isometric path between two vertices in a graph G is a shortest path joining them. The isometric path number of G, denoted by ip(G), is the minimum number of isometric paths needed to cover all vertices of G. In this paper, we determine exact values of isometric path numbers of complete r-partite graphs and Cartesian products of 2 or 3 complete graphs.
17371811
An Efficient Object Tracking Algorithm with Adaptive Prediction of Initial Searching Point
In object tracking, complex background frequently forms local maxima that tend to distract tracking algorithms from the real target. In order to reduce such risks, we utilize an adaptive Kalman filter to predict the initial searching point in the space of coordinate transform parameters so that both tracking reliability and computational simplicity is significantly improved. Our method tracks the changing rate of the transform parameters and makes prediction on future values of the transform parameters to determine the initial searching point. More importantly, noises in the Kalman filter are effectively estimated in our approach without any artificial assumption, which makes our method able to adapt to various target motions and searching step sizes without any manual intervention. Simulation results demonstrate the effectiveness of our algorithm.
2592435
Automatic image captioning
We examine the problem of automatic image captioning. Given a training set of captioned images, we want to discover correlations between image features and keywords, so that we can automatically find good keywords for a new image. We experiment thoroughly with multiple design alternatives on large datasets of various content styles, and our proposed methods achieve up to a 45% relative improvement on captioning accuracy over the state of the art.