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In this paper, we propose a novel Octantal Nearest-neighbor Structure and core points based fingerprint matching scheme. A novel fingerprint feature named the octantal nearest-neighbor structure (ONNS) is defined. Based on the ONNS, the minutiae pairing algorithm is conducted to find the corresponding minutiae pairs, and a novel algorithm is developed to evaluate the translational and rotational parameters between the input and the template fingerprints. Core point based orientation pairing is performed thereafter. Matching score is calculated. Experimental results on the FVC2004 fingerprint databases show the good performance of the proposed algorithm. Keywords: Core point; Corresponding minutia pair; Fingerprint alignment; Fingerprint matching. [ABSTRACT FROM AUTHOR]

The approximation capability of fuzzy systems heavily depends on the shapes of the chosen fuzzy membership functions. When fuzzy systems are applied in adaptive control, computational complexity and generalization capability are another two important indexes we must consider. Inspired by the conclusion drawn by S.Mitaim and B.Kosko and wavelet analysis and SVM, the scaling kernel-based fuzzy system SKFS(Scaling Kernel-based Fuzzy System) is presented as a new simplified fuzzy system in this paper, based on Sinc x membership functions. SKFS can approximate any function in L2(R), with much less computational complexity than classical fuzzy systems. Compared with another simplified fuzzy system GKFS(Gaussian Kernel-based Fuzzy System) using Gaussian membership functions, SKFS has a better approximation and generalization capabilities, especially in the coexistence of linearity and nonlinearity. Therefore, SKFS is very suitable for fuzzy control. Finally, several experiment results are used to demonstrate the effectiveness of the new simplified fuzzy system SKFS. [ABSTRACT FROM AUTHOR]

In many applications of wireless sensor networks (WSNs), the location information of users is very important. In this paper we present a localization method without on-body sensor (LWOS). The basic idea is that when a person is standing between a pair of transceivers, the human body will attenuate the received signal. From the detected attenuation of Received Signal Strength Indication (RSSI), LWOS can detect and localize people directly utilizing the wireless communication in WSNs. No additional sensor is needed and users do not need to wear a sensor node any more. A signal-shielding device is used at the transmitter side to minify the interference of RSSI variability from multi-path effects. Experiment results show a good capability of localizing a single user in an indoor environment. [ABSTRACT FROM AUTHOR]

As long leading-time hydrological forecast is a complex non-linear procedure, traditional methods are easy to get slow convergence and low efficiency. The basic relevance vector machine (BRVM) and the developed sequential relevance vector machine (SRVM) are employed to forecast multi-step ahead hydrological time series. The relevance vector machine is a sparse approximate Bayesian kernel method, and it provides full probabilistic forecasting results, which is helpful for hydrological engineering decision. BRVM and SRVM are respectively applied to the annual coming runoff forecast of Three Gorges hydropower station as case study. When compared with auto regression moving average models, BRVM exhibits high model efficiency and provides satisfying forecasting precision. SRVM is potential for its increased freedom and adaptive model selection mechanism. Comparison is also made within direct forecast and iterative one-step ahead forecasting for multi-step ahead forecasting, and the latter shows the ability of highlighting the model performance. [ABSTRACT FROM AUTHOR]

We propose to use a low order autoregressive (AR) model to describe the temporal structure of source. Then we adopt Variational Bayesian (VB) method for source separation which models additive noise into the mixing system. The approach integrates the source probabilistic model and noise probabilistic model. Its goal is to approximate the actual probability density function of hidden variables and parameters using its approximating posterior distribution by minimizing the misfit between them. The advantage of our VB-AR algorithm is that it can exploit the temporal nature of source signals and avoid overfitting in the separating process. This algorithm can also identify the AR order. Experiments on artifact and real-world speech signals are used to verify our proposed algorithms. As a result, the lower AR source model improves the separation. The performance of the algorithm is compared with that of i.i.d. separation algorithm and the second-order separation algorithm. [ABSTRACT FROM AUTHOR]