基于多特征匹配的快速星图识别

Automatic fingerprint identification systems use level-1 and level-2 features for fingerprint identification. However, forensic examiners utilize inherent level-3 details along with level-2 features. Existing level-3 feature extraction algorithms are computationally expensive to be used for identification. This paper presents a novel algorithm for fast level-3 feature extraction and identification. The algorithm starts with computing local image quality score using redundant discrete wavelet transform. A fast curve evolution algorithm is then used to extract four level-3 features namely, pores, ridge contours, dots, and incipient ridges. Along with level-1 and level-2 features, these level-3 features are used in a Delaunay triangulation based indexing algorithm. Finally, quality-based likelihood ratio is used to further improve the identification performance. Experiments conducted on a high resolution fingerprint database containing rolled, slap and latent images indicate that the algorithm offers significant benefits for fast fingerprint identification.

With the continuous development of optical fiber sensing technology, the Optical Fiber Pre-Warning System (OFPS) has been widely used in various fields. The OFPS identifies the type of intrusion based on the detected vibration signal to monitor the surrounding environment. Aiming at the real-time requirements of OFPS, this paper presents a fast algorithm to accelerate the detection and recognition processing of optical fiber intrusion signals. The algorithm is implemented in an embedded system that is composed of a digital signal processor (DSP). The processing flow is divided into two parts. First, the dislocation processing method is adopted for the sum processing of original signals, which effectively improves the real-time performance. The filtered signals are divided into two parts and are parallel processed by two DSP boards to save time. Then, the data is input into the identification module for feature extraction and classification. Experiments show that the algorithm can effectively detect and identify the optical fiber intrusion signals. At the same time, it accelerates the processing speed and meets the real-time requirements of OFPS for detection and identification.

The recently developed generalized linear least squares (GLLS) algorithm has been found very useful in non-uniformly sampled biomedical signal processing and parameter estimation. In this paper, the algorithm is used for the identification of a compartmental model with a pair of repeated eigenvalues based on the nonuniformly sampled noisy data. A case study is presented, which demonstrates that the algorithm is able to select the most suitable model for the system from the non-uniformly sampled noisy signals.

Eulerian fast motion identification algorithm for deformation measurement of cable-stayed bridge

A novel fingerprint recognition algorithm suitable for poor quality fingerprint is proposed, and implementation considerations to realize fingerprint recognition access controllers for residential applications are discussed. It is shown that optimizing spatial sampling interval of fingerprint image has equivalent effect of optimizing high limit frequency of low-pass filter in the process of phase based correlation. The processing time is 83% shorter for the former than the latter. An ASIC has been designed, and it is shown that fingerprint matching based access controller for residential applications can be successfully realized.

A fast and reliable nuclide identification algorithm is important to the detection of radioactive materials, and the emergence of a sequential Bayesian algorithm provides a new direction of research on radioactive detection. This article proposes a Bayesian nuclide identification algorithm based on the energy spectrum and deploys the algorithm on a microprocessor for real-time online processing. The algorithm builds a probability model based on the natural background radiation and radioactive source radiation and a decision function based on sequence testing. The processor conducts a posteriori estimation on the collected energy spectrum at fixed time intervals and updates the decision function. A laboratory self-developed CeBr3 detector was used in testing. Four radioactive sources, namely 241Am, 133Ba, 137Cs, and 60Co, were used in experiments with fixed and moving radioactive sources. The experimental results show that the proposed algorithm can rapidly identify mixed and mobile radioactive sources.

Research on rapid extraction of internal resistance of lithium battery based on short-time transient response

Cooperation coevolution with fast interdependency identification for large scale optimization

Ellipticity pivot star method for autonomous star identification

Core-periphery (CP) structure is frequently observed in networks where the nodes form two distinct groups: a small, densely interconnected core and a sparse periphery. Borgatti and Everett (Borgatti, S. P., & Everett M. G. (2000). Models of core/periphery structures. Social Networks, 21(4), 375–395.) proposed one of the most popular methods to identify and quantify CP structure by comparing the observed network with an “ideal” CP structure. While this metric has been widely used, an improved algorithm is still needed. In this work, we detail a greedy, label-switching algorithm to identify CP structure that is both fast and accurate. By leveraging a mathematical reformulation of the CP metric, our proposed heuristic offers an order-of-magnitude improvement on the number of operations compared to a naive implementation. We prove that the algorithm monotonically ascends to a local maximum while consistently yielding solutions within 90% of the global optimum on small toy networks. On synthetic networks, our algorithm exhibits superior classification accuracies and run-times compared to a popular competing method, and on one-real- world network, it is 340 times faster.

An algorithm for fast extraction and identification of star target

Fast ℓ1-recursive total least squares algorithm for sparse system identification

A derivation of fundamental fast transversal filter equations comes to a 7M fast RLS algorithm for system identification. Yet, this algorithm is not stable due to numerical errors resulting from implementation on a hardware with finite precision arithmetic. The known stabilization methods with feedback of numerical errors require a judicious choice of the feedback gains and depend on the properties of the exciting sequence, as simulations have shown. A new method to stabilize the algorithm is introduced that does not depend on the characteristics of the excitation signal. By inserting leakage factors in the filter equations, a steady contraction of error components is reached. >

A Fast Approximation Algorithm for Set-Membership System Identification

MIMO system identification is a fundamental concern in a variety of applications. Various iterative and recursive MIMO system identification algorithms exist in the literature. The iterative algorithms suffer from high computational cost due to large matrix computations and recursive algorithms suffer from slow convergence speed. This paper proposes a fast recursive exact least squares algorithm for MIMO system identification with fast convergence speed and low computational cost. The recursions of the algorithm give rise to a lattice structure. The lattice structure is less sensitive to round-off errors, coefficients variations and can also be used for model order reduction. Although in this work we estimate an FIR (finite impulse response) model of the MIMO system, the framework can also be used for IIR (infinite impulse response) models, and for estimating linear periodically time varying (LPTV) and multivariable systems. The theory proposed is validated using simulation results.