31791732
Cluster-Based Self-organizing Neuro-fuzzy System with Hybrid Learning Approach for Function Approximation
A novel hybrid cluster-based self-organizing neuro-fuzzy system (HC-SONFS) is proposed for dynamic function approximation and prediction. With the mechanism of self-organization, fuzzy rules are generated in the form of clusters using the proposed self-organization method to achieve compact and sufficient system structure if the current structure of knowledge base is insufficient to satisfy the required performance. A hybrid learning algorithm combining the well-known random optimization (RO) and the least square estimation (LSE) is use for fast learning. An example of chaos time series for system identification and prediction is illustrated. Compared to other approaches, excellent performance of the proposed HC-SONFS is observed.
40002746
Soft computing approach to feature extraction
Based on both wavelet theory and fuzzy theory, a soft computing system (SCS) is proposed for feature extraction of signals. The proposed SCS approach possesses the advantages of soft decision-making on wavelet coefficients for feature extraction, adaptive selectivity of mapping factors to coarse-to-fine resolution, and compact form of feature representation with the SCS feature-extractor. Fuzzy sets are used to provide a robust representation for signal information, and wavelet transform is used to decompose a signal into detail and approximation signals. At a given resolution, the detail and approximation signals are inputted to the proposed SCS to extract signal features at that resolution level. The sensitivity in feature extraction of the proposed approach can be adapted by tuning the fuzzy sets for the detail and approximation signals. At different resolutions, the signal can be examined and suitable features can be extracted. Examples of both one-dimensional signals and two-dimensional fingerprint images are used to illustrate the proposed soft computing approach for feature extraction and pattern recognition. The results show that the extracted features are sensitive enough to distinguish the similar signal from the different ones and are robust enough to tolerate noise corruption.
14325657
An approach to reducing the labeling cost of Markov random fields within an infinite label space
This work proposes a novel idea, called SOIL, for reducing the computational complexity of the maximum a posteriori optimization problem using Markov random fields (MRFs). The local characteristics of MRFs are employed so that the searching in a virtually infinite label space is confined in a small finite space. Globally, the number of labels allowed is as many as the number of image sites while locally the optimal label is sampled from a space consisting of the labels assigned to the four-neighbor plus a random one. Neither the prior knowledge about the number of classes nor the estimation phase of the class number is required in this work. The proposed method is applied to the problem of texture segmentation and the result is compared with those obtained from conventional methods.
14389597
Symmetrical path-cycle covers of a graph and polygonal graphs
A near-polygonal graph is a graph @C which has a set C of m-cycles for some positive integer m such that each 2-path of @C is contained in exactly one cycle in C. If m is the girth of @C then the graph is called polygonal. We introduce a method for constructing near-polygonal graphs with 2-arc transitive automorphism groups. As special cases, we obtain several new infinite families of polygonal graphs.
1502705
Attack Probability Based Deterministic Key Predistribution Mechanism for Non-Uniform Sensor Deployment
Secure communication is a challenging problem in wireless sensor networks. In this paper, we investigate an attack probability-based deterministic key predistribution method for non-uniform sensor deployment. First, we design a reasonable model for group-based deployment to estimate the attack probability of each deployment group. Second, our proposed model further considers the location of data sink to meet the practical situation. Third, based on this attack model, a deterministic key predistribution mechanism is presented for group-based deployment. The proposed method mainly aims at preserving the integrity of network so that the functionality of each sensor node can be used. To evaluate the proposed method, the issues of memory overhead, communication overhead, and the fraction of total communications compromised after adversaries launch node capture attacks are studied analytically and numerically.
10480954
Isomorphisms of Finite Cayley Digraphs of Bounded Valency
LetGbe a finite group,Sa subset ofG\{1}, and let Cay(G,S) denote the Cayley digraph ofGwith respect toS. If, for all subsetsS,TofG\{1} of size at mostm, Cay(G,S)?Cay(G,T) implies thatS?=Tfor some??Aut(G), thenGis called anm-DCI-group. In this paper, we prove that, form?2, allm-DCI-groups are of the formU×V, where (|U|, |V|)=1,Uis abelian andVbelongs to an explicitly determined list of groups. Moreover Sylow subgroups of such groups satisfy some very restrictive conditions.
16331610
Calcification Descriptor and Relevance Feedback Learning Algorithms for Content-Based Mammogram Retrieval
In recent years a large number of digital mammograms have been generated in hospitals and breast screening centers. To assist diagnosis through indexing those mammogram databases, we proposed a content-based image retrieval framework along with a novel feature extraction technique for describing the degree of calcification phenomenon revealed in the mammograms and six relevance feedback learning algorithms, which fall in the category of query point movement, for improving system performance. The results show that the proposed system can reach a precision rate of 0.716 after five rounds of relevance feedback have been performed.