46526063
Optimal Difference Systems of Sets with Multipliers
Difference Systems of Sets (DSS) are combinatorial structures that are used in code synchronization. A DSS is optimal if the associated code has minimum redundancy for the given block length n, alphabet size q, and error-correcting capacity ρ. An algorithm is described for finding optimal DSS that admit prescribed symmetries defined by automorphisms of the cyclic group of order n, together with optimal DSS found by this algorithm.
11024065
An algorithm for optimal comma free codes with isomorphism rejection
A general algorithm for finding optimal comma-free codes and deriving upper bounds on the minimum redundancy of comma-free codes that implements the idea of isomorphism rejection is presented, together with tables with bounds on the minimum redundancy computed by using this algorithm.
4594972
New Fair Payment Protocols
Fair payment protocol is designed to guarantee fairness in electronic purchasing, that is, no party can falsely deny involvement in the transaction or having sent/received the specific items/payment. In this paper we first present an efficient fair payment protocol providing invisibility of TTP, timeliness, and standard RSA signatures as the final non-repudiation evidences. Then we present a second payment protocol which is the first one to provide abuse-freeness. Our protocols can be easily integrated into the existing electronic payment systems.
16346838
Ant-MST: An Ant-Based Minimum Spanning Tree for Gene Expression Data Clustering
We have proposed an ant-based clustering algorithm for document clustering based on the travelling salesperson scenario. In this paper, we presented an approach called Ant-MST for gene expression data clustering based on both ant-based clustering and minimum spanning trees (MST). The ant-based clustering algorithm is firstly used to construct a fully connected network of nodes. Each node represents one gene, and every edge is associated with a certain level of pheromone intensity describing the co-expression level between two genes. Then MST is used to break the linkages in order to generate clusters. Comparing to other MST-based clustering approaches, our proposed method uses pheromone intensity to measure the similarity between two genes instead of using Euclidean distance or correlation distance. Pheromone intensities associated with every edge in a fully-connected network records the collective memory of the ants. Self-organizing behavior could be easily discovered through pheromone intensities. Experimental results on three gene expression datasets show that our approach in general outperforms the classical clustering methods such as K-means and agglomerate hierarchical clustering.
15452882
On Cellular Capacity with Base Station Cooperation
This paper is concerned with cellular systems with rate constraints in fading environments. We consider a simple but asymptotically optimal matched filtering/successive interference cancellation (MF-SIC) scheme. We derive several concise and closed-form bounds for the power and spectral efficiencies of various base station cooperation strategies, which provide some useful insights into cellular systems.
27939024
Junction Tree Factored Particle Inference Algorithm for Multi-Agent Dynamic Influence Diagrams
As MMDPs are difficult to represent structural relations among Agents and MAIDs can not model dynamic environment, we present Multi-Agent dynamic influences (MADIDs). MADIDs have stronger knowledge representation ability and MADIDs may efficiently model dynamic environment and structural relations among Agents. Based on the hierarchical decomposition of MADIDs, a junction tree factored particle filter (JFP) algorithm is presented by combing the advantages of the junction trees and particle filter. JFP algorithm converts the distribution of MADIDs into the local factorial form, and the inference is performed by factor particle of propagation on the junction tree. Finally, and the results of algorithm comparison show that the error of JFP algorithm is obviously less than BK algorithm and PF algorithm without the loss of time performance.
44314
A Novel Fault Diagnosis of Analog Circuit Algorithm Based on Incomplete Wavelet Packet Transform and Improved Balanced Binary-Tree SVMs
Aiming at to the characteristics of fault diagnosis of analog circuit with tolerances, noise, circuit nonlinearities and small sample set, this paper presents a novel algorithm for the existent problems based on incomplete wavelet packet transform for feature extraction and improved balanced binary-tree support vector machines (BBSVMs) for multi-classification. Firstly, analyzing characters of the optimal wavelet packet transform and the incomplete wavelet packet transform, the conclusion that the latter is perfect for fault diagnosis of analog circuit is obtained. Secondly, in order to perform multi-class classification, binary-tree-based SVMs has been studied. By introducing the separability measure that based on the space distribution of pattern classes, an improved balanced binary-tree structure is constructed. The proposed algorithm for analog circuits with tolerances and noise is implemented, and simulation results show us that compared with several existent fault diagnosis methods, the current algorithm has highest classification speed and higher classification accuracy, which make fault diagnosis of analogy circuit on-line promising.