2454790
Synchrony and Desynchrony in Neural Oscillator Networks
An novel class of locally excitatory, globally inhibitory oscillator networks is proposed. The model of each oscillator corresponds to a standard relaxation oscillator with two time scales. The network exhibits a mechanism of selective gating, whereby an oscillator jumping up to its active phase rapidly recruits the oscillators stimulated by the same pattern, while preventing others from jumping up. We show analytically that with the selective gating mechanism the network rapidly achieves both synchronization within blocks of oscillators that are stimulated by connected regions and desynchronization between different blocks. Computer simulations demonstrate the network's promising ability for segmenting multiple input patterns in real time. This model lays a physical foundation for the oscillatory correlation theory of feature binding, and may provide an effective computational framework for scene segmentation and figure/ground segregation.
748050
Oriented statistical nonlinear smoothing filter
This paper presents a nonlinear smoothing method which is based on an orientation-sensitive probability measure. By incorporating geometrical constraints through the coupling structure, we obtain a robust nonlinear smoothing algorithm. Even when noise is substantial the proposed smoothing algorithm can still preserve salient boundaries. Compared with anisotropic diffusive approaches, the proposed nonlinear algorithm not only performs better in preserving boundaries but also has a non-uniform stable state, whereby reliable results are available within a fixed number of iterations independent of images. A system using the proposed method and LEGION network has been developed and applied in noisy image segmentation and hydrographic feature extraction from digital ortho-photo quadrangles. Experimental results using synthetic and real images are provided.
873936
Singing Voice Separation from Monaural Recordings
Separating singing voice from music accompaniment has wide applications in areas such as automatic lyrics recognition and alignment, singer identification, and music information retrieval. Compared to the extensive studies of speech separation, singing voice separation has been little explored. We propose a system to separate singing voice from music accompaniment from monaural recordings. The system has three stages. The singing voice detection stage partitions and classifies an input into vocal and non-vocal portions. Then the predominant pitch detection stage detects the pitch contour of the singing voice for vocal portions. Finally the separation stage uses the detected pitch contour to group the time-frequency segments of the singing voice. Quantitative results show that the system performs well in singing voice separation.
648343
Texture classification using spectral histograms
Based on a local spatial/frequency representation,we employ a spectral histogram as a feature statistic for texture classification. The spectral histogram consists of marginal distributions of responses of a bank of filters and encodes implicitly the local structure of images through the filtering stage and the global appearance through the histogram stage. The distance between two spectral histograms is measured using chi(2)-statistic. The spectral histogram with the associated distance measure exhibits several properties that are necessary for texture classification. A filter selection algorithm is proposed to maximize classification performance of a given dataset. Our classification experiments using natural texture images reveal that the spectral histogram representation provides a robust feature statistic for textures and generalizes well. Comparisons show that our method produces a marked improvement in classification performance. Finally we point out the relationships between existing texture features and the spectral histogram, suggesting that the latter may provide a unified texture feature.
11782513
Synchronization in Relaxation Oscillator Networks with Conduction Delays
We study locally coupled networks of relaxation oscillators with excitatory connections and conduction delays and propose a mechanism for achieving zero phase-lag synchrony. Our mechanism is based on the observation that different rates of motion along different nullclines of the system can lead to synchrony in the presence of conduction delays. We analyze the system of two coupled oscillators and derive phase compression rates. This analysis indicates how to choose nullclines for individual relaxation oscillators in order to induce rapid synchrony. The numerical simulations demonstrate that our analytical results extend to locally coupled networks with conduction delays and that these networks can attain rapid synchrony with appropriately chosen nullclines and initial conditions. The robustness of the proposed mechanism is verified with respect to different nullclines, variations in parameter values, and initial conditions.
6139139
A dynamically coupled neural oscillator network for image segmentation
We propose a dynamically coupled neural oscillator network for image segmentation. Instead of pair-wise coupling, an ensemble of oscillators coupled in a local region is used for grouping. We introduce a set of neighborhoods to generate dynamical coupling structures associated with a specific oscillator. Based on the proximity and similarity principles, two grouping rules are proposed to explicitly consider the distinct cases of whether an oscillator is inside a homogeneous image region or near a boundary between different regions. The use of dynamical coupling makes our segmentation network robust to noise on an image, and unlike image processing algorithms no iterative operation is needed for noise removal. For fast computation, a segmentation algorithm is abstracted from the underlying oscillatory dynamics, and has been applied to synthetic and real images. Simulation results demonstrate the effectiveness of our oscillator network in image segmentation.
46198564
Reasoning about Surfaces Using Differential Zero and Ideal Decomposition
This paper presents methods for zero and ideal decomposition of partial differential polynomial systems and the application of these methods and their implementations to deal with problems from the local theory of surfaces. We show how to prove known geometric theorems and to derive unknown relations automatically. In particular, an algebraic relation between the first and the second fundamental coefficients in a very compact form has been derived, which is more general and has smaller degree than a relation discovered previously by Z. Li. Moreover, we provide symmetric expressions for Li's relation and clarify his statement. Some examples of theorem proving and computational difficulties encountered in our experiments are also discussed.
16321846
Semantics modeling based image retrieval system using neural networks
Semantics based image retrieval techniques show a promising direction to the development of CBIR systems. To design such systems, semantics modeling is one of the most difficult tasks. This paper aims to develop a semantics modeling approach using neural networks. In our work, a neural network is utilized to memorize the semantic patterns within the images. An intelligent image retrieval system is designed based on this model. User's relevance feedback is used for enhancing the retrieval performance. Experimental results from the prototype system demonstrate the effectiveness of the proposed approach.
8901566
SAT Based Predicate Abstraction for Hardware Verification
Predicate abstraction is an important technique for extracting compact finite state models from large or infinite state systems. Predicate abstraction uses decision procedures to compute a model which is amenable to model checking, and has been used successfully for software verification. Little work however has been done on applying predicate abstraction to large scale finite state systems, most notably, hardware, where the decision procedures are SAT solvers. We consider predicate abstraction for hardware in the framework of Counterexample-Guided Abstraction Refinement where in the course of verification, the abstract model has to be repeatedly refined. The goal of the refinement is to eliminate spurious behavior in the abstract model which is not present in the original model, and gives rise to false negatives (spurious counterexamples).
35035776
Hardware-software partitioning of real-time operating systems using Hopfield neural networks
Abstract The hardware–software automated partitioning of a real-time operating system in the system-on-a-chip (SoC-RTOS partitioning) is a NP-complete problem, and a crucial step in the hardware–software co-design of SoC. In this paper, a new model for SoC-RTOS partitioning is introduced, which can help in understanding the essence of the SoC-RTOS partitioning. A discrete Hopfield neural network approach for implementing the SoC-RTOS partitioning is proposed, where a novel energy function, operating equation and coefficients of the neural network are redefined. Simulations are carried out with comparison to other optimization techniques. Experimental results demonstrate the feasibility and effectiveness of the proposed method.
24532367
Elimination procedures for mechanical theorem proving in geometry
In this paper, methods for the algebraic decision problem of mechanical theorem proving in elementary geometry are described on the basis of some elimination procedures for polynomial systems. The methods can determine whether or not a geometric theorem is generically true and whether it is true or false for each of the components including degenerate ones, with projection and irreducible decomposition. Theorems which have been proved using an implementation of the methods in Maple include the Simson Theorem, Butterfly Theorem, Secant Theorem, Feuerbach Theorem, Steiner Theorem, Steiner-Lehmus Theorem, Morely Theorem and Thébault-Taylor Theorem, of which some are taken as illustrative examples in the paper.