Square-root cubature Kalman filter (SCKF) is more effective for nonlinear state estimation than an unscented Kalman filter. In this paper, we study the design of nonlinear filters based on SCKF for the system with one step noise correlation and abrupt state change. First, we give the SCKF that deals with the one step correlation between process and measurement noises, SCKF-CN in short. Second, we introduce the idea of a strong tracking filter to construct the adaptive square-root factor of the prediction error covariance with a fading factor, which makes SCKF-CN obtain outstanding tracking performance to the system with target maneuver or abrupt state change. Accordingly, the tracking performance of SCKF is greatly improved. A universal nonlinear estimator is proposed, which can not only deal with the conventional nonlinear filter problem with high dimensionality and correlated noises, but also achieve an excellent strong tracking performance towards the abrupt change of target state. Three simulation examples with a bearings-only tracking system are illustrated to verify the efficiency of the proposed algorithms.

Most stream data classification algorithms apply the supervised learning strategy which requires massive labeled data. Such approaches are impractical since labeled data are usually hard to obtain in reality. In this paper, we build a clustering feature decision tree model, CFDT, from data streams having both unlabeled and a small number of labeled examples. CFDT applies a micro-clustering algorithm that scans the data only once to provide the statistical summaries of the data for incremental decision tree induction. Micro-clusters also serve as classifiers in tree leaves to improve classification accuracy and reinforce the any-time property. Our experiments on synthetic and real-world datasets show that CFDT is highly scalable for data streams while generating high classification accuracy with high speed.

Missing values occur in bio-signal processing for various reasons, including technical problems or biological characteristics. These missing values are then either simply excluded or substituted with estimated values for further processing. When the missing signal values are estimated for electroencephalography (EEG) signals, an example where electrical signals arrive quickly and successively, rapid processing of high-speed data is required for immediate decision making. In this study, we propose an incremental expectation maximization principal component analysis (iEMPCA) method that automatically estimates missing values from multivariable EEG time series data without requiring a whole and complete data set. The proposed method solves the problem of a biased model, which inevitably results from simply removing incomplete data rather than estimating them, and thus reduces the loss of information by incorporating missing values in real time. By using an incremental approach, the proposed method also minimizes memory usage and processing time of continuously arriving data. Experimental results show that the proposed method assigns more accurate missing values than previous methods.

We studied the architecture of embedded computing systems from the viewpoint of power consumption in memory systems and used a selective-code-compression (SCC) approach to realize our design. Based on the LZW (Lempel-Ziv-Welch) compression algorithm, we propose a novel cost effective compression and decompression method. The goal of our study was to develop a new SCC approach with an extended decision policy based on the prediction of power consumption. Our decompression method had to be easily implemented in hardware and to collaborate with the embedded processor. The hardware implementation of our decompression engine uses the TSMC 0.18 μm-2p6m model and its cell-based libraries. To calculate power consumption more accurately, we used a static analysis method to estimate the power overhead of the decompression engine. We also used variable sized branch blocks and considered several features of very long instruction word (VLIW) processors for our compression, including the instruction level parallelism (ILP) technique and the scheduling of instructions. Our code-compression methods are not limited to VLIW machines, and can be applied to other kinds of reduced instruction set computer (RISC) architecture.

An appropriate mathematical model can help researchers to simulate, evaluate, and control a proton exchange membrane fuel cell (PEMFC) stack system. Because a PEMFC is a nonlinear and strongly coupled system, many assumptions and approximations are considered during modeling. Therefore, some differences are found between model results and the real performance of PEMFCs. To increase the precision of the models so that they can describe better the actual performance, optimization of PEMFC model parameters is essential. In this paper, an artificial bee swarm optimization algorithm, called ABSO, is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications. For studying the usefulness of the proposed algorithm, ABSO-based results are compared with the results from a genetic algorithm (GA) and particle swarm optimization (PSO). The results show that the ABSO algorithm outperforms the other algorithms.

Classification can be regarded as dividing the data space into decision regions separated by decision boundaries. In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective. Thus, a decision tree can be regarded as a classifier tree, in which each classifier on a non-root node is trained in decision regions of the classifier on the parent node. Meanwhile, the NBTree algorithm, which generates a classifier tree with the C4.5 algorithm and the naive Bayes classifier as the root and leaf classifiers respectively, can also be regarded as training naive Bayes classifiers in decision regions of the C4.5 algorithm. We propose a second division (SD) algorithm and three soft second division (SD-soft) algorithms to train classifiers in decision regions of the naive Bayes classifier. These four novel algorithms all generate two-level classifier trees with the naive Bayes classifier as root classifiers. The SD and three SD-soft algorithms can make good use of both the information contained in instances near decision boundaries, and those that may be ignored by the naive Bayes classifier. Finally, we conduct experiments on 30 data sets from the UC Irvine (UCI) repository. Experiment results show that the SD algorithm can obtain better generalization abilities than the NBTree and the averaged one-dependence estimators (AODE) algorithms when using the C4.5 algorithm and support vector machine (SVM) as leaf classifiers. Further experiments indicate that our three SD-soft algorithms can achieve better generalization abilities than the SD algorithm when argument values are selected appropriately.

We investigate the issue of synchronizing a blinking coupling mobile agent network through a blinking adaptation strategy, where each agent with blinking wave emission behavior not only adjusts its blinking period according to the local property of its neighbors, but also coordinates its blinking phase with those of neighboring agents. In leading the agents to blink orderly with a blinking period commensurate with the characteristic time of the dynamical oscillator, the presented blinking adaptation strategy works effectively in guaranteeing the synchronous motion of the considered network when the power density is large. In addition, the influence of the controlling parameter and moving velocity on network evolution is studied by assessing the convergence time.

This paper presents a new approach for solving a class of infinite horizon nonlinear optimal control problems (OCPs). In this approach, a nonlinear two-point boundary value problem (TPBVP), derived from Pontryagin’s maximum principle, is transformed into a sequence of linear time-invariant TPBVPs. Solving the latter problems in a recursive manner provides the optimal control law and the optimal trajectory in the form of uniformly convergent series. Hence, to obtain the optimal solution, only the techniques for solving linear ordinary differential equations are employed. An efficient algorithm is also presented, which has low computational complexity and a fast convergence rate. Just a few iterations are required to find an accurate enough suboptimal trajectory-control pair for the nonlinear OCP. The results not only demonstrate the efficiency, simplicity, and high accuracy of the suggested approach, but also indicate its effectiveness in practical use.

A low temperature drift curvature-compensated complementary metal oxide semiconductor (CMOS) bandgap reference is proposed. A dual-differential-pair amplifier was employed to add compensation with a high-order term of TlnT (T is the thermodynamic temperature) to the traditional 1st-order compensated bandgap. To reduce the offset of the amplifier and noise of the bandgap reference, input differential metal oxide semiconductor field-effect transistors (MOSFETs) of large size were used in the amplifier and to keep a low quiescent current, these MOSFETs all work in weak inversion. The voltage reference’s temperature curvature has been further corrected by trimming a switched resistor network. The circuit delivers an output voltage of 3 V with a low dropout regulator (LDO). The chip was fabricated in Taiwan Semiconductor Manufacturing Company (TSMC)’s 0.35-μm CMOS process, and the temperature coefficient (TC) was measured to be only 2.1×10⁻⁶/°C over the temperature range of −40–125 °C after trimming. The power supply rejection (PSR) was −100 dB @ DC and the noise was 42 μV (rms) from 0.1 to 10 Hz.