A novel three-level zero-voltage zero-current switching (ZVZCS) DC/DC converter is proposed in this paper. A tapped-inductor is used to replace the normal output filter inductor, so that the circulating current in the zero-state can be reset to zero. The reset voltage and the reset time can be set conveniently just by simply changing the winding ratio of the tapped inductor. The converter achieves a zero-current tuning off for inner switching, and a zero-voltage tuning on for outer switching. No circulating current exists in the zero state, so that the loss in the on-state is reduced, and the efficiency can be improved. The experimental results verify that the ZVZCS has low voltage stress, zero-voltage and zero-current switching.

By using Mie’s theory, the boundary conditions, and some advanced mathematical knowledge, the scattering problem of a plane-wave by bi-sphere groups and of cores-traversed coherent Gauss-beams by one sphere was addressed. In each, the coefficients of the scattering-field expressions were deduced. Finally, the result was predigested and transfigured so that the available form for programming was achieved. On deducing, the former adopted the undetermined coefficient method and the latter used the plane geometry method. Moreover, the complexity of the calculation was decreased here.

In order to periodically reassess the status of the alternate path route (APR) set and to improve the efficiency of alternate path construction existing in most current alternate path routing protocols, we present a cross-layer design and ant-colony optimization based load-balancing routing protocol for ad-hoc networks (CALRA) in this paper. In CALRA, the APR set maintained in nodes is aged and reassessed by the inherent mechanism of pheromone evaporation of ant-colony optimization algorithm, and load balance of network is achieved by ant-colony optimization combining with cross-layer synthetic optimization. The efficiency of APR set construction is improved by bidirectional and hop-by-hop routing update during routing discovery and routing maintenance process. Moreover, ants in CALRA deposit simulated pheromones as a function of multiple parameters corresponding to the information collected by each layer of each node visited, such as the distance from their source node, the congestion degree of the visited nodes, the current pheromones the nodes possess, the velocity of the nodes, and so on, and provide the information to the visiting nodes to update their pheromone tables by endowing the different parameters corresponding to different information and different weight values, which provides a new method to improve the congestion problem, the shortcut problem, the convergence rate and the heavy overheads commonly existed in existing ant-based routing protocols for ad-hoc networks. The performance of the algorithm is measured by the packet delivery rate, good-put ratio (routing overhead), and end-to-end delay. Simulation results show that CALRA performs well in decreasing the route overheads, balancing traffic load, as well as increasing the packet delivery rate, etc.

This paper considers the processing of realworld imagery in the so-called Form-And-Color-And-DEpth (FACADE) framework, which features some superior mechanisms of the human vision system (HVS). FACADE framework was originally proposed by Grossberg et al. as an integrative model of the HVS to illustrate the possible procedures for visual perception of shape (the boundary contour), surface (luminance and color), and binocular depth. As a simplified, reasonable and mathematically full-fledged approach to the HVS, we saw FACADE as a promising infrastructure through which to construct a powerful image processing engine. However, in our attempts to use the approach in its original modality, to deal with real-world imagery, we found it to be inefficient and non-robust.

After re-introducing the model hierarchy and illustrating the involved cell dynamics of the FACADE framework, this paper reveals the crucial issues that lead to the deficiency and accordingly present our substitutive solutions by incorporating the mechanisms of anisotropic spatial- and diffusive orientational-competition to make the HVS-featured model efficient and robust. A computer system based on the improved FACADE engine has been implemented and tested not only with illustrative images to highlight the model characteristics, but also with some real-world imagery in both monocular and binocular situations, thereby demonstrating the ability of the FACADE-based image processing approach featuring the HVS.

Emotion plays an essential role in the adaptation and social communication of organisms. Similarly, an appropriately timed and clearly expressed emotion is a central requirement for believable interactive virtual humans. Presently, incorporating emotion into virtual humans has gained increasing attention in the academia and industry. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, e-learning, entertainment, etc. This paper introduces an emotion model of artificial psychology, in which the transition of emotion can be viewed as a Markov process and the relation of emotion, external incentive and personality can be described by a Markov decision process (MDP). In order to demonstrate the approach, this paper integrates the emotion model into a system composed of voice recognition and a realistic facial model. Thus, the model could be used for generating a variety of emotional expressions of autonomous, interactive virtual human beings.

Since the launching of the human genome sequencing project in the 1990s, genomic research has already achieved definite results. At the beginning of the present century, the complete genomes of several model organisms have already been sequenced, including a number of prokaryote microorganisms and the eukaryotes yeast (Saccharomyces cerevisiae), nematode (C. elegans), fruit fly (Drosophila melanogaster) and thale cress (Arabidopsis thaliana) as well as the major part of the human genome. These achievements signified that a new era of data mining and analysis on the human genome had commenced. The language of human genetics would gradually be read and understood, and the genetic information underlying metabolism, development, differentiation and evolution would progressively become known to mankind. Large amounts of data are already accumulating, but at present many of the rules that should guide the understanding of this information are yet unknown. Bioinformatics research is thus not only becoming more important, but is also faced with severe challenges as well as great opportunities.

This paper proposes a novel airport detection method, which integrates the texture features and shape features of the airport. Eight texture features, such as the mean of the region, the deviation of the region, the smoothness of the region, the skewness of a histogram, the uniformity of the region, the randomness of the region, the mean of the gradient image and the deviation of the gradient image, are used to represent the features of the region. In this method, first the long lines are detected and the regions where the lines locate are segmented. Second, support vector machine (SVM) based on Gaussian kernel is used as a classifier which discriminates the runway from other candidate regions. Experimental results show that the error rate of the proposed method is lower than those of conventional methods which detect airport only by the shape feature of runway. The detection accuracy of the proposed method is nearly ten times higher than that of Liu s methods, and the method has favorable speed for a real-time system.

The modern optics industry demands rigorous surface quality with minimum defects, which presents challenges to optics machining technologies. There are always certain defects on the final surfaces of the components formed in convent

This article studies a third-order trajectory planning method for point-to-point motion. All available instances for third-order trajectory planning are first analyzed. To distinguish those, three criteria are presented relying on trajectory characteristics. Following that, a fast preprocessing approach considering the trajectory as a whole is given based on the criteria constructed and system constraints. Also, the time-optimality of the trajectory is obtained. The relevant formulas are derived with the combination of geometrical symmetry of trajectory and area method. As a result, an accurate algorithm and its implementation procedure are proposed. The experimental results show the effectiveness and precision of the proposed method. The presented algorithm has been applied in semiconductor manufacturing equipment successfully.

The progress in dual-band dual-polarization (DBDP) shared-aperture antennas for the synthetic aperture radar (SAR) application in the last decade is reviewed. Several designs of DBDP SAR antenna arrays are introduced with their main performances, then their comparison is summarized. In addition, some techniques enhancing DBDP antenna performances are presented.

In multi-user wireless communication systems, dynamic power allocation is an important means to deal with the time-varying nature of the physical and network layers. However, the current layer optimization approach to power allocation cannot achieve the global optimum of the overall system performance. To solve this problem, a cross-layer optimization framework is presented for downlink power allocation, which takes both the channel and buffer states into account. A cross-layer optimization problem is formulated to optimize the total throughput with queue length and power constraints. An analytical solution and a low complexity dynamic programming algorithm, which are referred as water-filling in cellar (WFIC) policy, are presented to optimize the downlink power allocation. Finally, simulation results are presented to demonstrate the potential of the proposed method.

The content of water vapor in atmosphere is very little and the ratio of volume of moisture to air is about 0.1%–3%, but water vapor is the most active molecule in atmosphere. There are many absorption bands in infrared (IR) wavelength for water vapor, and water vapor is also an important factor in cloud formation and precipitation, therefore it takes a significant position in the global radiation budget and climatic changes. Because of the advantages of the high resolution, wide range, and highly automatic operation, the Raman lidar has become a new-style and useful tool to measure water vapor. In this paper, first, the new mobile Raman lidar’s structure and specifications were introduced. Second, the process method of lidar data was described. Finally, the practical and comparative experiments were made over Hefei City in China. The results of measurement show that this lidar has the ability to gain profiles of ratio of water vapor mixing ratio from surface to a height of about 8 km at night. Meanwhile, the measurement of water vapor in daytime has been taken, and the profiles of water vapor mixing ratio at ground level have been detected.

This paper presents a new distributed Bayesian optimization algorithm (BOA) to overcome the efficiency problem when solving NP scheduling problems. The proposed approach integrates BOA into the co-evolutionary schema, which builds up a concurrent computing environment. A new search strategy is also introduced for local optimization process. It integrates the reinforcement learning (RL) mechanism into the BOA search processes, and then uses the mixed probability information from BOA (post-probability) and RL (pre-probability) to enhance the cooperation between different local controllers, which improves the optimization ability of the algorithm. The experiment shows that the new algorithm does better in both optimization (2.2 %) and convergence (11.7 %), compared with classic BOA.

The preliminary design results of a 1-MW, Ku-band gyrotron traveling wave amplifier (gyro-TWA) are presented. Operating at the second cyclotron harmonic of the TE₁₁ mode, the amplifier characterizes good stability even in the case of no distributed losses loaded, which could potentially allow it to be operated at high average power. Large signal simulation shows that the amplifier can generate a saturated peak power of about 1 MW with efficiency of 26.6%, gain of 31 dB, and 3-dB bandwidth of about 1 GHz when driven by a 100 kV, 40 A electron beam with 5% axial velocity spread.

Closed-form bounds for the end-to-end perform-ance of multihop communications with non-regenerative relays over Nakagami-m fading channels are investigated. Upper and lower bounds of the end-to-end signal-to-noise ratio (SNR) are first developed by using the monotonicity. Then, the probability density functions (PDFs), the cumulative? distribution? functions, ?and? the? moment-generating functions (MGFs) of the bounds are derived. Using these results, the bounds for the outage and average bit error probability (ABEP) are obtained. Numerical and simulation results are executed to validate the tightness of the proposed bounds.

Radar high-resolution range profiles (HRRPs) are typical high-dimensional and interdimension dependently distributed data, the statistical modeling of which is a challenging task for HRRP-based target recognition. Supposing that HRRP samples are independent and jointly Gaussian distributed, a recent work [Du L, Liu H W, Bao Z. IEEE Transactions on Signal Processing, 2008, 56(5): 1931-1944] applied factor analysis (FA) to model HRRP data with a two-phase approach for model selection, which achieved satisfactory recognition performance. The theoretical analysis and experimental results reveal that there exists high temporal correlation among adjacent HRRPs. This paper is thus motivated to model the spatial and temporal structure of HRRP data simultaneously by employing temporal factor analysis (TFA) model. For a limited size of high-dimensional HRRP data, the two-phase approach for parameter learning and model selection suffers from intensive computation burden and deteriorated evaluation. To tackle these problems, this work adopts the Bayesian Ying-Yang (BYY) harmony learning that has automatic model selection ability during parameter learning. Experimental results show stepwise improved recognition and rejection performances from the two-phase learning based FA, to the two-phase learning based TFA and to the BYY harmony learning based TFA with automatic model selection. In addition, adding many extra free parameters to the classic FA model and thus becoming even worse in identifiability, the model of a general linear dynamical system is even inferior to the classic FA model.

Buildings worldwide account for nearly 40% of global energy consumption. The biggest energy consumer in buildings is the heating, ventilation and air conditioning (HVAC) systems. In HVAC systems, chillers account for a major portion of the energy consumption. Maintaining chillers in good conditions through early fault detection and diagnosis is thus a critical issue.

In this paper, the fault detection and diagnosis for an air-cooled chiller with air coming from outside in variable flow rates is studied. The problem is difficult since the air-cooled chiller is operating under major uncertainties including the cooling load, and the air temperature and flow rate. A potential method to overcome the difficulty caused by the uncertainties is to perform fault detection and diagnosis based on a gray-box model with parameters regarded as constants. The method is developed and verified by us in another paper for a water-cooled chiller with the uncertainty of cooling load. The verification used a Kalman filter to predict parameters of a gray-box model and statistical process control (SPC) for measuring and analyzing their variations for fault detection and diagnosis. The gray-box model in the method, however, requires that the air temperature and flow rate be nearly constant. By introducing two new parameters and deleting data points with low air flow rate, the requirement can be satisfied and the method can then be applicable for an air-cooled chiller. The simulation results show that the method with the revised model and some data points dropped improved the fault detection and diagnosis (FDD) performance greatly. It can detect both sudden and gradual air-cooled chiller capacity degradation and sensor faults as well as their recoveries.

Cerebellar model articulation controller (CMAC) is a popular associative memory neural network that imitates human’s cerebellum, which allows it to learn fast and carry out local generalization efficiently. This research aims to integrate evolutionary computation into fuzzy CMAC Bayesian Ying-Yang (FCMAC-BYY) learning, which is referred to as FCMAC-EBYY, to achieve a synergetic development in the search for optimal fuzzy sets and connection weights. Traditional evolutionary approaches are limited to small populations of short binary string length and as such are not suitable for neural network training, which involves a large searching space due to complex connections as well as real values. The methodology employed by FCMAC-EBYY is coevolution, in which a complex solution is decomposed into some pieces to be optimized in different populations/species and then assembled. The developed FCMAC-EBYY is compared with various neuro-fuzzy systems using a real application of traffic flow prediction.

We discuss the nature of complex number and its effect on complex-valued neural networks (CVNNs). After we review some examples of CVNN applications, we look back at the mathematical history to elucidate the features of complex number, in particular to confirm the importance of the phaseand-amplitude viewpoint for designing and constructing CVNNs to enhance the features. This viewpoint is essential in general to deal with waves such as electromagnetic wave and lightwave. Then, we point out that, although we represent a complex number as an ordered pair of real numbers for example, we can reduce ineffective degree of freedom in learning or self-organization in CVNNs to achieve better generalization characteristics. This merit is significantly useful not only for waverelated signal processing but also for general processing with frequency-domain treatment through Fourier transform.

To improve the classical lossless compression of low efficiency, a method of image lossless compression with high efficiency is presented. Its theory and the algorithm implementation are introduced. The basic approach of medical image lossless compression is then briefly described. After analyzing and implementing differential plus code modulation (DPCM) in lossless compression, a new method of combining an integer wavelet transform with DPCM to compress medical images is discussed. The analysis and simulation results show that this new method is simpler and useful. Moreover, it has high compression ratio in medical image lossless compression.

Conventional control systems used for regulated power supplies, including the proportional integral and derivation (PID) controller, have some serious disadvantages. The PID controller has a delayed feedback associated with the control action and requires a lot of mathematical derivations. This paper presents a novel controlling system based on the artificial neural network (ANN), which can be used to regulate the output voltage of the DC power supply. Using MATLAB^TM, the designed control system was tested and analyzed with two types of back-propagation algorithms. This paper presents the results of the simulation that includes sum-squared error (SSE) and mean-squared error (MSE), and gives a detailed comparison of these values for the two algorithms. Hardware verification of the new system, using RS232 interface and Microsoft Visual Basic 6.0, was implement- ed, showing very good consistency with the simulation results. The proposed control system, compared to PID and other conventional controllers, requires less mathematical derivation in design and it is easier to implement.

This paper analyzed the operating principles and power and torque characteristics of the wind turbine and the direct current motor (DC motor), and investigated the operating characteristics of the wind turbine compared to that of the DC motor. The torque imitation scheme, which has good performance and high feasibility, together with the whole wind turbine imitation system, was provided. The wind turbine imitation system includes not only a hardware platform composed of PC, data-collection board and thyristor-based velocity-regulator, but also monitor software of wind turbine imitation. The experimental results of different occasions verify the correctness and feasibility of the wind turbine imitation scheme proposed in this paper, which provided a valid idea for wind turbine imitation and investigation of wind power generation techniques in the laboratory.

Nowadays, two trends appear in the application of sensor networks in which both multi-service and quality of service (QoS) are supported. In terms of the goal of low energy consumption and high connectivity, the control on topology is crucial. The algorithm of topology control based on quantum genetic algorithm in sensor networks is proposed. An advantage of the quantum genetic algorithm over the conventional genetic algorithm is demonstrated in simulation experiments. The goals of high connectivity and low consumption of energy are reached.

Orthogonal frequency division multiplexing (OFDM) can fully use the frequency band and transmit data at high speeds. The ADSP-TS101 is a high performance digital signal processor (DSP) with good properties that include parallel processing and a high speed. Aimed at the real-time processing requirement of the OFDM algorithm, an underwater acoustic communication system with real-time processing capability is carried out. The system is mainly composed of multiple ADSP-TS101s, a multi-channel synchronous sample module and a field programmable gate array (FPGA) chip. The multiprocessor structure is made up of a cluster/data flow associated multiprocessing parallel processing structure as the operation kernel, and a multi-channel synchronous sample module is designed to realize no phase warp among the multiple channels  data at the same time. The digital modulation/demodulation methods are applied to the OFDM algorithm. Through experiments in a lake, the results show that the system has good stability and real-time processing capability, thus satisfying the design requirements.

Spectral leakage caused by synchronous error in a nonsynchronous sampling system is an important cause that reduces the accuracy of spectral analysis and harmonic measurement. This paper presents a software sampling frequency adaptive algorithm that can obtain the actual signal frequency more accurately, and then adjusts sampling interval base on the frequency calculated by software algorithm and modifies sampling frequency adaptively. It can reduce synchronous error and impact of spectral leakage; thereby improving the accuracy of spectral analysis and harmonic measurement for power system signal where frequency changes slowly. This algorithm has high precision just like the simulations show, and it can be a practical method in power system harmonic analysis since it can be implemented easily.

To improve the computational performance of the fuzzy C-means (FCM) algorithm used in dataset clustering with large numbers, the concepts of the equivalent samples and the weighting samples based on eigenvalue distribution of the samples in the feature space were introduced and a novel fast cluster algorithm named weighted fuzzy C-means (WFCM) algorithm was put forward, which came from the traditional FCM algorithm. It was proved that the cluster results were equivalent in dataset with two different cluster algorithms: WFCM and FCM. Furthermore, the WFCM algorithm had better computational performance than the ordinary FCM algorithm. The experiment of the gray image segmentation showed that the WFCM algorithm is a fast and effective cluster algorithm.

The single-input single-output (SISO)j-stepahead predictor for generalized predictive control (GPC) controllers was traditionally derived using the polynomial approach through the Diophantine equations. An equivalent version of the predictor in a state-space form is available in the literature. In this paper, a z-domain analysis of the multiple input multiple output (MIMO) extension of the statespace predictor is carried out, and then an MIMO j-step-ahead predictor in polynomial form based on the controlled autoregressive moving average model is derived. The predictor enables us to simplify the GPC algorithm design for multivariable systems. In the SISO case the predictor is just the traditional GPC predictor, therefore this paper gives rigorous proof of the equivalence between the traditional GPC predictor and the state-space predictor.

On the basis of opto-mechanical effect and micro electromechanical system (MEMS) technology, a novel substrate-free focal plane array (FPA) with the thermal isolated structure for uncooled infrared imaging is developed, even as alternate evaporated Au on SiN cantilever is used for thermal isolation. A human thermal image is obtained successfully by using the infrared imaging system composed of the FPA and optical detecting system. The experiment results show that the realization of thermal isolation structure in substrate-free FPA increases the temperature rise of the deflecting leg effectively, whereas the noise equivalent temperature difference (NETD) is about 200 mK.

Communication signals should be estimated by a single trial in a brain-computer interface. Since the relativity of visual evoked potentials from different sites should be stronger than those of the spontaneous electroencephalogram (EEG), this paper adopted the time-lock averaged signals from multi-channels as features. 200 trials of EEG recordings evoked by target or non-target stimuli were classified by the support vector machine (SVM). Results show that a classification accuracy of higher than 97% can be obtained by merely using the 250–550 ms time section of the averaged signals with channel Cz and Pz as features. It suggests that a possible approach to boost communication speed and simplify the designation of the brain-computer interface (BCI) system is worthy of an attempt in this way.

The design of the gain-scheduled H₂/H_∞ filter for polytopic discrete-time systems is investigated. By introducing additional slack variables, a new mixed H₂/H_∞ performance criterion is proposed, which provides a decoupling between the Lyapunov matrix and system matrices. Based on the new performance criterion, a sufficient condition for the existence of the gain-scheduled H₂/H_∞ filter is derived. Furthermore, the filter design problem is converted into a convex optimization problem with linear matrix inequality (LMI) constraints. Simulation results show the effectiveness of the proposed approach.