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We introduce an ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time. The proposed algorithm, named Learn^++.NSE, learns from consecutive batches of data without making any assumptions on the nature or rate of drift; it can learn from such environments that experience constant or variable rate of drift, addition or deletion of concept classes, as well as cyclical drift. The algorithm learns incrementally, as other members of the Learn^++ family of algorithms, that is, without requiring access to previously seen data. Learn^++.NSE trains one new classifier for each batch of data it receives, and combines these classifiers using a dynamically weighted majority voting. The novelty of the approach is in determining the voting weights, based on each classifier's time-adjusted accuracy on current and past environments. This approach allows the algorithm to recognize, and act accordingly, to the changes in underlying data distributions, as well as to a possible reoccurrence of an earlier distribution. We evaluate the algorithm on several synthetic datasets designed to simulate a variety of nonstationary environments, as well as a real-world weather prediction dataset. Comparisons with several other approaches are also included. Results indicate that Learn^++.NSE can track the changing environments very closely, regardless of the type of concept drift. To allow future use, comparison and benchmarking by interested researchers, we also release our data used in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a new dc offset removal algorithm for real-time simulations is proposed. Physically transferred signals in a real-time simulation interface tend to contain a dc offset component for various reasons. This component decreases the accuracy and stability of real-time simulations. The dc offset tends to change according to circumstances which include changes in the system configuration and effects of the external environment which makes the static dc offset removal scheme inefficient. The proposed algorithm can estimate and remove a dc offset component dynamically from the input signal of the hardware under test. In the proposed algorithm, the optimal constant for the condition equation is calculated by using an iterative method and is used to estimate the frequency, time, magnitude, and dc offset component of the input signal. Four sampling points are used to calculate the optimal constant in each step. The proposed algorithm has relatively fast speed and good accuracy in comparison to conventional dc offset removal algorithms. In order to verify the performances of the proposed algorithm, offline and real-time dc offset estimation simulation tests were performed. The results of the simulation tests showed that the proposed algorithm can estimate the dc offset component promptly and exactly and can be applied to real-time simulations. [ABSTRACT FROM AUTHOR]

We discuss the application of indirect learning methods in zero-sum and identical payoff learning automata games. We propose a novel decentralized version of the well-known pursuit learning algorithm. Such a decentralized algorithm has significant computational advantages over its centralized counterpart. The theoretical study of such a decentralized algorithm requires the analysis to be carried out in a nonstationary environment. We use a novel bootstrapping argument to prove the convergence of the algorithm. To our knowledge, this is the first time that such analysis has been carried out for zero-sum and identical payoff games. Extensive simulation studies are reported, which demonstrate the proposed algorithm's fast and accurate convergence in a variety of game scenarios. We also introduce the framework of partial communication in the context of identical payoff games of learning automata. In such games, the automata may not communicate with each other or may communicate selectively. This comprehensive framework has the capability to model both centralized and decentralized games discussed in this paper. [ABSTRACT FROM AUTHOR]

This paper presents computationally efficient implementations for several recent algorithms based on the iterative adaptive approach (IAA) for uniformly sampled one- and two-dimensional data sets, considering both the complete data case, and the cases when the data sets are missing samples, either lacking arbitrary locations, or having gaps or periodically reoccurring gaps. By exploiting the method's inherent low displacement rank, together with the development of suitable Gohberg–Semencul representations, and the use of data dependent trigonometric polynomials, the proposed implementations are shown to offer a reduction of the necessary computational complexity by at least one order of magnitude. Numerical simulations together with theoretical complexity measures illustrate the achieved performance gain. [ABSTRACT FROM AUTHOR]

Several types of low-power passive equalizer are investigated and optimized in this paper. The equalizer topologies include T-junction, parallel R-C, and series R-L structures. These structures can be inserted either at the driver or the receiver side at both the chip and package level to improve the channel bandwidth of central processing unit (CPU)-memory links. Using the eye area as the objective function to be maximized, we optimize these equalizers for the CPU–memory interconnection of an IBM POWER6 system with and without practical constraints on the RLC parameter values. An efficient optimization flow combined with an algorithm predicting the worst case eye diagram is proposed and employed to optimize 42 equalizer schemes. Simulation results show that, without employing any equalizer, the data eye is closed for the bit rate of 6.4 Gb/s, while the equalized schemes can work at the bit rate of 8 Gb/s. Very promising improvements in eye height and jitter are observed with little power overhead. Simulation results also show the sensitivity of the equalization schemes to the RLC values and the effect of coupling noise. [ABSTRACT FROM PUBLISHER]

In this paper, a new technology which can be used to diagnose 3-D temperature distribution of argon arc plasma is proposed. Compared with current technologies, two aspects have been improved. First, a new hybrid reconstruction technique [max-entropy, least square, and flatness reconstruction technique (MLFRT)] is presented as an algorithm system, where the numerical simulation of the MLFRT has been realized. The results show that the MLFRT has good convergence, stability, and efficiency. Second, two orthographic double-wavelength light paths have been designed in the experimental setup, which can collect intensity data in real time. Based on these data, we reconstruct the emission coefficient using the MLFRT. Then, a spectral line relative intensity method was chosen to calculate the 3-D temperature distribution, where the spectral analysis of argon arc plasma was introduced. This new technology can acquire good result by just two orthographic intensity data sets. Therefore, the experimental system is simple, and the experimental cost is low. The result is beneficial to understand the working principle of argon arc plasma, improving the level of craftsmanship. It also has good application advantages over other 3-D flow field testing, such as density field, pressure field, velocity field, etc. It shows great academic and economic value. [ABSTRACT FROM PUBLISHER]

One of the most popular techniques to take into account the finite ground conductivity in the evaluation of the radial component of the electric field generated by a lightning return stroke is the Cooray–Rubinstein (CR) formula. As this formula is derived in the frequency domain, its application in time-domain simulation codes for the analysis of lightning-induced overvoltages on overhead lines might be challenging. In this paper, two methods allowing a simple time-domain implementation of the formula are discussed. The first one is an enhanced version of an algorithm recently proposed by two of the authors, while the second consists of a new technique, based on a suitable rational approximation of the impedance term appearing in the CR formula. This second approach is characterized by an intrinsic easiness in time-domain implementation and a considerable computational efficiency compared with conventional methods. [ABSTRACT FROM PUBLISHER]

Simultaneous localization and tracking (SLAT) in sensor networks aims to determine the positions of sensor nodes and a moving target in a network, given incomplete and inaccurate range measurements between the target and each of the sensors. One of the established methods for achieving this is to iteratively maximize a likelihood function (ML) of positions given the observed ranges, which requires initialization with an approximate solution to avoid convergence towards local extrema. This paper develops methods for handling both Gaussian and Laplacian noise, the latter modeling the presence of outliers in some practical ranging systems that adversely affect the performance of localization algorithms designed for Gaussian noise. A modified Euclidean distance matrix (EDM) completion problem is solved for a block of target range measurements to approximately set up initial sensor/target positions, and the likelihood function is then iteratively refined through majorization-minimization (MM). To avoid the computational burden of repeatedly solving increasingly large EDM problems in time-recursive operation, an incremental scheme is exploited whereby a new target/node position is estimated from previously available node/target locations to set up the iterative ML initial point for the full spatial configuration. The above methods are first derived under Gaussian noise assumptions, and modifications for Laplacian noise are then considered. Analytically, the main challenges to overcome in the Laplacian case stem from the non-differentiability of \ell_1 norms that arise in the various cost functions. Simulation results show that the proposed algorithms significantly outperform existing localization methods in the presence of outliers, while providing comparable performance for Gaussian noise. [ABSTRACT FROM PUBLISHER]

In this paper, an enhanced linear minimum mean square error (LMMSE) turbo equalization scheme is proposed for multiple-input multiple-output (MIMO) communication systems with bit-interleaved coded modulation (BICM) in the time domain and multiplexing in the space domain. The proposed turbo equalization scheme outperforms the conventional LMMSE turbo equalization by adopting two new signal processing techniques. First, it performs hybrid soft interference cancellation (HSOIC) by subtracting the soft decisions of the interfering symbols, and the soft decisions are calculated by using a hybrid of the a priori information at the equalizer input and the a posteriori information at the equalizer output. Second, it employs a novel block-wise reliability-based ordering scheme such that more “reliable” symbols are detected before the less “reliable” ones to reduce error propagation in HSOIC. The symbol reliability information is based on the symbol a priori probability, as a unique byproduct of turbo detection, thus can be obtained with very small overhead. A low-complexity approximation of the enhanced MIMO LMMSE turbo equalization is also proposed to balance the tradeoff between complexity and performance. The performance of the enhanced MIMO LMMSE turbo equalization has been verified through both numerical simulations and the undersea experimental data collected in the SPACE08 experiment launched near Martha's Vineyard, Edgartown, MA, in 2008. [ABSTRACT FROM PUBLISHER]

This paper presents a pulsewidth modulation (PWM) algorithm for a three-phase ac–dc rectifier, where the third and fifth harmonics are indicated in the input phase voltages. A mathematical analysis, which includes this voltage harmonic's components, shows that the unity input displacement factor (IDF) can be reached by appropriate evaluation of duty-cycle functions. A PWM algorithm is proposed based on this developed theoretical achievement. This approach enables current sensorless unity IDF rectification. The PWM algorithm is investigated theoretically and verified by simulations and experiment. [ABSTRACT FROM PUBLISHER]

In this paper, we present an algorithm for the exploration of an unknown graph by multiple robots, which is never worse than depth-first search with a single robot. On trees, we prove that the algorithm is optimal for two robots. For k robots, the algorithm has an optimal dependence on the size of the tree but not on its radius. We believe that the algorithm performs well on any tree, and this is substantiated by simulations. For trees with e edges and radius r, the exploration time is less than 2e/k+(1+(k/r))^k-1(2/k!)r^k-1 =(2e/k) + O((k+r)^k-1) (for r>k, \le (2e/k) + 2r^k-1), thereby improving a recent method with time O((e/\log k) + r) refid="ref2"/, and almost reaching the lower bound max ((2e/k), 2r). The model underlying undirected-graph exploration is a set of rooms connected by opaque passages; thus, the algorithm is appropriate for scenarios like indoor navigation or cave exploration. In this framework, communication can be realized by bookkeeping devices being dropped by the robots at explored vertices, the states of which are read and changed by further visiting robots. Simulations have been performed in both tree and graph explorations to corroborate the mathematical results. [ABSTRACT FROM AUTHOR]

This paper proposes the waveform reconstruction method (WRM), which is utilized in the single-phase series active filter's (SAF’s) control algorithm, in order to extract the load harmonic voltage component of voltage harmonic type single-phase diode rectifier loads. Employing WRM and the line current sampling delay reduction method, a single-phase SAF compensated system provides higher harmonic isolation performance and higher stability margins compared to the system using conventional synchronous-reference-frame-based methods. The analytical, simulation, and experimental studies of a 2.5 kW single-phase SAF compensated system prove the theory. [ABSTRACT FROM AUTHOR]

In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic causal model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study. [ABSTRACT FROM AUTHOR]

A high-performance pulsewidth modulation (PWM) algorithm with reduced common-mode voltage (CMV) and satisfactory overall performance is proposed for three-phase PWM inverter drives. The algorithm combines the near-state PWM (NSPWM) method that has superior overall performance characteristics at high modulation index, and MAZSPWM, a modified form of the active zero-state PWM method (AZSPWM1), which is suitable for low modulation index range of operation. Since AZSPWM1 has line-to-line voltage pulse reversals with small zero-voltage time intervals, in its naive form, it causes overvoltages, in particular in long-cable motor drive applications. Obtained by reorganizing the duty cycles of the utilized voltage vectors of AZSPWM1, MAZSPWM has sufficiently long zero-voltage time intervals between pulse reversals such that during pulse reversals, overvoltages are avoided. The combined algorithm performs satisfactorily throughout the inverter operating range and the transition from NSPWM to MAZSPWM and vice versa is seamless. The performance of the proposed algorithm is proven by theory, computer simulations, and detailed laboratory experiments. The paper also shows that the proposed reduced CMV PWM algorithm is effective in reducing the motor leakage current, and it is most beneficial when a small common-mode inductor is included in the drive. [ABSTRACT FROM AUTHOR]

The recent development, achieved on SC devices for grid applications, requires the support of design tools in order to establish the impact of these new devices on the operation of the electrical system. Classically, standard simulation programs are used in grid design and maintenance by the utilities; but these new devices require user written models, compatible with the grid simulators. In this paper, a user written model for the integration of superconducting cables in the grid, with the capacity to simulate dynamic behavior, is presented. Results of the simulations are presented which refer to the overall grid performance. [ABSTRACT FROM AUTHOR]

In this paper, we employ selective bit mapping to improve the performance of the LDPC coded QAM scheme for terrestrial DTV broadcasting system. The threshold of message-passing decoding can be considerably lowered by selectively mapping the binary components of LDPC codeword to the positions in the m-tuples to be mapped into 2^mQAM symbols. In our approach, the mapping pattern is described by bit-mapping polynomials, based on which density evolution can be applied. The optimization algorithm is developed with two implementation concerns, using the Chinese DTMB standard as an example. Numerical results illustrate that our proposed approach can improve the decoding threshold by 0.05 dB to 0.499 dB depending on the code rate and the order of QAM modulation. Simulation results show that the actual BER improvement varies from 0.09 dB to 0.6 dB with different code-modulation combinations in both single-carrier and OFDM modes. [ABSTRACT FROM AUTHOR]

Source localization especially direction-of-arrival (DOA) estimation using sensor arrays is of considerable interest in both classical array signal processing and radar applications. Most radar systems are designed under the line-of-sight (LOS) assumption with multipath echos treated as undesired clutter noise. Strong multipath, therefore, has a negative impact on the resolution of the radar systems and their ability in accurately localizing the target. Rather than treating multipath as a detrimental effect, the paper introduces time reversal (TR) to exploit spatial/multipath diversity in improving the capability of the existing localization algorithms. In particular, we design TR based range and DOA estimators that adaptively adjust the probing radar waveforms to the multipath characteristics of the environment. The benefits of the spatial/multipath diversity in the proposed DOA and range estimators are quantified by deriving the respective Cramér–Rao bounds (CRB) and comparing them with the analytical expressions for their conventional counterparts. Numerical simulations also confirm the benefit of applying TR to source localization algorithms especially at low signal-to-noise ratios below -5 dB. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple and robust algorithm for compressive sensing (CS) signal reconstruction based on the weighted median (WM) operator. The proposed approach addresses the reconstruction problem by solving a l0-regularized least absolute deviation (l0-LAD) regression problem with a tunable regularization parameter, being suitable for applications where the underlying contamination follows a statistical model with heavier-than-Gaussian tails. The solution to this regularized LAD regression problem is efficiently computed, under a coordinate descent framework, by an iterative algorithm that comprises two stages. In the first stage, an estimation of the sparse signal is found by recasting the reconstruction problem as a parameter location estimation for each entry in the sparse vector leading to the minimization of a sum of weighted absolute deviations. The solution to this one-dimensional minimization problem turns out to be the WM operator acting on a shifted-and-scaled version of the measurement samples with weights taken from the entries in the measurement matrix. The resultant estimated value is then passed to a second stage that identifies whether the corresponding entry is relevant or not. This stage is achieved by a hard threshold operator with adaptable thresholding parameter that is suitably tuned as the algorithm progresses. This two-stage operation, WM operator followed by a hard threshold operator, adds the desired robustness to the estimation of the sparse signal and, at the same time, ensures the sparsity of the solution. Extensive simulations demonstrate the reconstruction capability of the proposed approach under different noise models. We compare the performance of the proposed approach to those yielded by state-of-the-art CS reconstruction algorithms showing that our approach achieves a better performance for different noise distributions. In particular, as the distribution tails become heavier the performance gain achieved by the proposed approach increases significantly. [ABSTRACT FROM AUTHOR]

Digital ecosystems (DEs) are based on a large amount of distributed data, and these data are gathered from physical devices, particularly from wireless sensor networks (WSNs). Due to the inherent stringent bandwidth and energy constraints, energy-efficient mechanisms of performance measurement are the key to the proper operation of WSNs and thereby important for the data reliability of DEs. This paper presents a novel algorithm, i.e., Loss Inference based on Passive Measurement (LIPM), to infer WSN link loss performance. The LIPM algorithm passively monitors the application traffic between sensor nodes and the sink (base station), and then uses network tomography technology to infer the network internal performance. Furthermore, contour maps, the well-known representation of data, are first taken into account in WSN loss performance inference, which can help the LIPM algorithm identify lossy areas rapidly. Finally, the algorithm is validated through simulations and exhibits good performance and scalability. [ABSTRACT FROM AUTHOR]

Electronically controlled air suspension systems have been used in vehicles to improve ride comfort and handling safety by adjusting vehicle height. This paper proposes a new nonlinear controller to adjust the height of the vehicle sprung mass (height control) and to regulate the roll and pitch angles of the vehicle body (leveling control) by an air suspension system. A sliding mode control algorithm is designed to improve the tracking accuracy of the control and to overcome nonlinearities and uncertainties in the air suspension system. A mathematical model of the air suspension system is formulated in a nonlinear affine form to describe the dynamic behavior of the system and to derive the control algorithm. The sliding mode observer is also designed to estimate the pressures inside four air springs. The effectiveness and performance of the proposed control algorithm are verified by simulations and actual vehicle tests. [ABSTRACT FROM PUBLISHER]

Although the recently proposed single-implicit-equation-based input voltage equations (IVEs) for the independent double-gate (IDG) MOSFET promise faster computation time than the earlier proposed coupled-equations-based IVEs, it is not clear how those equations could be solved inside a circuit simulator as the conventional Newton–Raphson (NR)-based root finding method will not always converge due to the presence of discontinuity at the G-zero point (GZP) and nonremovable singularities in the trigonometric IVE. In this paper, we propose a unique algorithm to solve those IVEs, which combines the Ridders algorithm with the NR-based technique in order to provide assured convergence for any bias conditions. Studying the IDG MOSFET operation carefully, we apply an optimized initial guess to the NR component and a minimized solution space to the Ridders component in order to achieve rapid convergence, which is very important for circuit simulation. To reduce the computation budget further, we propose a new closed-form solution of the IVEs in the near vicinity of the GZP. The proposed algorithm is tested with different device parameters in the extended range of bias conditions and successfully implemented in a commercial circuit simulator through its Verilog-A interface. [ABSTRACT FROM PUBLISHER]

This paper describes new strategies to improve the transient response time of harmonic detection using adaptive filters applied to shunt active power filters. Two cases are presented and discussed, both using an adaptive notch filter, but one uses the least mean square algorithm to adjust the coefficients and the other uses the recursive least squares algorithm. The synchronization of the adaptive notch filter orthogonal input signals, which are generated by the Clarke transformation of the load currents, is achieved automatically without the need of a phase-locked loop. This procedure significantly reduces the real-time computation burden. Simulations using Matlab/Simulink are presented to clarify the algorithm, and practical implementation is performed using the DSP Texas Instruments TMS320F2812. The experimental results are presented and discussed. [ABSTRACT FROM AUTHOR]

In this paper, we present a modular and pipeline architecture for lifting-based multilevel 2-D DWT, without using line-buffer and frame-buffer. Overall area-delay product is reduced in the proposed design by appropriate partitioning and scheduling of the computation of individual decomposition-levels. The processing for different levels is performed by a cascaded pipeline structure to maximize the hardware utilization efficiency (HUE). Moreover, the proposed structure is scalable for high-throughput and area-constrained implementation. We have removed all the redundancies resulting from decimated wavelet filtering to maximize the HUE. The proposed design involves L pyramid algorithm (PA) units and one recursive pyramid algorithm (RPA) unit, where R=N/P, L=\lceil \log4P\rceil and P is the input block size, M and N, respectively, being the height and width of the image. The entire multilevel DWT is computed by the proposed structure in MR cycles. The proposed structure has O(8R\times 2^L) cycles of output latency, which is very small compared to the latency of the existing structures. Interestingly, the proposed structure does not require any line-buffer or frame-buffer, unlike the existing folded structures which otherwise require a line-buffer of size O(N) and frame-buffer of size O(M/2\times N/2) for multilevel 2-D computation. Instead of those buffers, the proposed structure involves only local registers and RAM of size O(N). The saving of line-buffer and frame-buffer achieved by the proposed design is an important advantage, since the image size could very often be as large as 512 \times 512. From the simulation results we find that, the proposed scalable structure offers better slice-delay-product (SDP) for higher throughput of implementation since the on-chip memory of this structure remains almost unchanged with input block size. It has 17% less SDP than the best of the corresponding existing structures on average, for different input-block sizes and image sizes. It involves 1.92 times more transistors, but offers 12.2 times higher throughput and consumes 52% less power per output (PPO) compared to the other, on average for different input sizes. [ABSTRACT FROM AUTHOR]

Near field radar employing ultrawideband (UWB) signals with its high range resolution has great promise for various sensing applications. It enables non-contact measurement of precision devices with specular surfaces like an aircraft fuselage and wing, or a robotic sensor that can identify a human body in invisible situations. As one of the most promising radar algorithms, the range points migration (RPM) was proposed. This achieves fast and accurate surface extraction, even for complex-shaped objects, by eliminating the difficulty of connecting range points. However, in the case of a more complex shape whose variation scale is less than a pulsewidth, it still suffers from image distortion caused by multiple interference signals with different waveforms. As a substantial solution, this paper proposes a novel range extraction algorithm by extending the Capon method, known as frequency domain interferometry (FDI). This algorithm combines reference signal optimization with the original Capon method to enhance the accuracy and resolution for an observed range into which a deformed waveform model is introduced. The results obtained from numerical simulations and an experiment with bi-static extension of the RPM prove that super-resolution UWB radar imaging is accomplished by the combination between the RPM and the extended Capon methods, even for an extremely complex-surface target including edges. [ABSTRACT FROM AUTHOR]

In this paper, a model-based fault prognosis method is developed for hybrid systems with multiple incipient faults. The concept of augmented global analytical redundancy relations is proposed for the identification of degradation of components, such as sensors and actuators, which cannot be described by physical parameters. In addition, multiple incipient faults are considered in a complex hybrid system, and these faults can develop during a mode when the faults are not detectable. The unknown degradation characteristic of each incipient fault is identified with the closest matching one of some prescribed dynamic models. The resultant degradation model will serve as a base for prognosis. In the process of fault detection and isolation, the degradation models and faults are identified using a multiple-adaptive-hybrid-particle-swarm-optimization algorithm. The proposed methodology and algorithm are verified with simulation as well as experiments. [ABSTRACT FROM PUBLISHER]

This correspondence paper presents an algorithm in which the independent components of link lengths are used as a medium to analyze the forward kinematics of a six-degree-of-freedom Stewart platform. The link lengths are firstly transformed into independent components through independent component analysis. Then, the value of positional variables is computed by using the Nelder–Mead algorithm by taking advantage of the relationships between the independent components and the positional variables. Simulations have been conducted to test the proposed algorithm. The experimental results show that the proposed algorithm can achieve a better performance than the other published algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we present a novel approach to segment dense, homogeneous objects in a tomographic reconstruction (or tomogram). A popular method to extract such objects from a tomogram is global thresholding, in which the threshold value is determined from the image histogram. However, accurate threshold selection is not straightforward, since, due to noise or artefacts in the reconstruction, the histogram does not always contain a clear, separate peak for the dense object. We propose a new threshold estimation approach, segmentation inconsistency minimization, that exploits the available projection data to determine the optimal global threshold. The proposed algorithm was tested on simulation data and on experimental \muCT data. The results show that this method results in more accurate segmentations, compared to alternative threshold selection methods. [ABSTRACT FROM AUTHOR]

As the output power of photovoltaic (PV) array changes with the change of external environment and load, the maximum power point tracking (MPPT) technology is needed to bring the efficiency of PV device into full play. In this paper, the chaos search theory is first applied on the MPPT technology of PV system; the chaos search algorithm based on dual carrier increases the adequacy of chaos search and overcomes the blindness of traditional chaos search, thus improves the search efficiency and solves the problem of multiple MPP under partially shadowing conditions. Comparative tests show that the proposed method can track the step response quickly and accurately; at the same time get better optimization result. Simulation and experimental results showed the effectiveness and good performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a new two-terminal impedance-based fault-location algorithm, which takes into account the distributed parameter line model. The algorithm utilizes unsynchronized measurements of voltages and currents from two ends of a line and is formulated in terms of the fundamental frequency phasors of symmetrical components of the measured signals. First, an analytical synchronization of the unsynchronized measurements is performed with use of the determined synchronization operator. Then, the distance to fault is calculated as for the synchronized measurements. Simultaneous usage of two kinds of symmetrical components for determining the synchronization operator makes that the calculations are simple, noniterative, and at the same time highly accurate. The developed fault-location algorithm has been thoroughly tested using signals of Alternate Transients Program-Electromagnetic Transients Program versatile simulations of faults on a transmission line. The presented evaluation shows the validity of the developed fault-location algorithm and its high accuracy. [ABSTRACT FROM AUTHOR]

Target tracking algorithms usually treat the probability of detection as independent of the target state. In most cases, this assumption is not true, with subsequent degradation in the target tracking performance from both expected and optimal levels. One typical example is the Doppler frequency based clutter rejection, the other is obfuscation (shadowing) of ground based targets, and the third is antijamming notch filtering. This dependence of the probability of target detection on the target trajectory state modulates the measurement likelihood, which, in turn, introduces measurement nonlinearity. In this paper, we first present a general algorithm for target tracking in clutter when the probability of detection is target state dependent, and then proceed to an algorithm where both target state estimate and the probability of detection are modeled by Gaussian mixtures. The probability of target existence is recursively updated as the track quality measure used for false track discrimination. A two-sensor-based ground maneuvering target tracking in clutter simulation validates this approach. [ABSTRACT FROM AUTHOR]

High availability communication networks with very low failure rates are often designed by using physical diversity, i.e., the traffic between a given pair of nodes is routed by using several physically disjoint paths. The selection of the pair of routes that maximizes the connectivity of a node is not an easy problem, because such connectivity cannot be expressed as an additive function of the availability of links and nodes in the path pairs. Previous algorithms for searching the optimal route use additive costs, which can be a loose assumption either when high failure rates can be locally present, or when fully disjoints paths do not exist. In this paper, we construct a Bayesian network to encode the probabilistic relation between the connectivity of a node and the availability of nodes and links. The problem of selecting an optimal pair of routes becomes equivalent to optimizing the structure of the Bayesian network. By introducing appropriate approximations on the double route availability equations, we propose a new algorithm, which outperforms other classical methods when there are sporadic high error rate elements in the network. Simulations and an application example in an electric transport telecontrol network show the performance of the method when compared to standard search. [ABSTRACT FROM AUTHOR]

Speed control during startup is one of the most critical tasks of gas turbine power plant operation. This paper introduces the PI fuzzy gain-scheduling (PI-FGS) controller to solve the foremost speed-control problems, including tracking of the acceleration pattern and rejection of disturbances caused by operation events throughout startup. Fundamentally, the PI-FGS synthesizes a GS controller from multiple locally tuned generalized proportional integral (PI) algorithms by means of a fuzzy system. The PI-FGS structure permits independent tuning of the tracking and rejection responses at any operating point. In addition, tuning can be carried out on-site based on the operator experience and inspection of the current plant response. Simulation experiments show that the PI-FGS may improve speed-control performance well over that provided by commercially available speed controllers, which are based on a single conventional PI algorithm. [ABSTRACT FROM PUBLISHER]

To achieve superior performance, rate-distortion optimized motion estimation (ME) for variable block size (RDO-VBSME) is often used in state-of-the-art video coding systems such as the H.264 JM software. However, the complexity of RDO-VBSME is very high both for software and hardware implementations. In this paper, we propose a hardware-friendly ME algorithm called RDOMFS with a novel hardware-friendly rate-distortion (RD)-like cost function, and a hardware-friendly modified motion vector predictor. Simulation results suggest that the proposed RDOMFS can achieve essentially the same RD performance as RDO-VBSME in JM. We also propose a matching hardware architecture with a novel Smart Snake Scanning order which can achieve very high data re-use ratio and data throughout. It is also reconfigurable because it can achieve variable data re-use ratio and can process variable frame size. The design is implemented with TSMC 0.18 \mum CMOS technology and costs 103 k gates. At a clock frequency of 63 MHz, the architecture achieves real-time 1920\,\times\,1080 RDO-VBSME at 30 frames/s. At a maximum clock frequency of 250 MHz, it can process 4096\,\times\,2160 at 30 frames/s. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of path planning in strong current fields. In such situations, existing approaches are subject to incorrectness and incompleteness issues. That is, they may return physically infeasible paths or no path at all, even if a feasible path exists. That is why we propose here a new approach called the sliding wavefront expansion. This algorithm, which combine an appropriate cost function and continuous optimization techniques, guarantees the existence of a path with an arbitrary precision. The validity and the global optimality of the path are theoretically proven. Simulation results on realistic environments, which is based on actual wind charts, are also provided. [ABSTRACT FROM AUTHOR]

This paper discusses a self-consistent set of modern computational concepts providing an effective approach to the circuit-level harmonic-balance (HB) simulation of nonlinear microwave systems of complex topology. The system is automatically split into the interconnection of a near-optimal number of nonlinear blocks at run time. The resulting structure is then exploited by the domain-partitioning concept. A block-wise constant spectrum is used rather than a common spectrum by considering for each block only the set of spectral lines that are relevant to its electrical function, which leads to a very significant reduction in the number of problem unknowns. System simulation under digitally modulated RF drive is reduced to a sequence of modified multitone HB analyses that are backward coupled through the envelope dynamics. Besides providing high numerical efficiency, this set of techniques opens the way to an effective co-simulation of RF and baseband transceiver sections. [ABSTRACT FROM AUTHOR]

This paper presents a three-dimensional finite-difference time-domain (FDTD) scheme in cylindrical coordinates with an improved algorithm for accommodating the numerical singularity associated with the polar axis. The regularization of this singularity problem is realized based on Faraday's law (for updating the Hr-component) and Ampere's law (for updating the E\varphi-, Ez-components). The proposed algorithm has been detailed and verified against a problem with a known solution obtained from a commercial electromagnetic simulation package. The numerical scheme is also illustrated by modeling high-frequency RF field-human body interactions in magnetic resonance imaging (MRI). The results demonstrate the accuracy and capability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Driving simulators are increasingly being used for driver evaluation and/or education. In this paper, we describe the design and the modeling aspects of a 2-degree-of-freedom (2-DOF) low-cost motion platform allowing the rendering of the longitudinal and yaw movements. This prototype will be used to study various configurations of motion rendering and the impact of these variants on controllability and simulator sickness. The whole motion platform is considered as two coupled systems that are linked mechanically. The first system consists of a motorized rail for the longitudinal movement, which is mounted on top of the second system, which is a motorized turret allowing rotation of the platform. We present the platform mechanics and a number of experimental studies that have been carried out to obtain a characterization of the platform capabilities and frequency responses, as well as to assess platform performance in a classical drive operation. First conclusions and directions of future work are presented. [ABSTRACT FROM PUBLISHER]

It is well known that the motion of a target induces range migration, especially for high-resolution synthetic aperture radar (SAR) systems. Ground moving target imaging necessitates the correction of the unknown range migration. To finely refocus a moving target, one must accurately obtain the motion parameters for compensating the target trajectory. However, in practice, these parameters usually cannot be precisely estimated. This paper proposes a new imaging approach for ground moving targets without a priori knowledge of their motion parameters. In the devised method, the azimuth compression function is constructed in range frequency domain, which can eliminate the coupling effect between range and azimuth. Theoretical analysis confirms that the methodology can precisely focus targets without interpolation procedure. The effectiveness of the proposed imaging technique is demonstrated by both simulated and real airborne SAR data. [ABSTRACT FROM AUTHOR]

Multibaseline phase unwrapping is a critical processing procedure of multibaseline synthetic aperture radar interferometry (InSAR). It has an advantage over single-baseline phase unwrapping in discontinuous-terrain-height estimation. In this paper, the pixels' combination information of multiple InSAR interferograms with different baseline lengths is deeply investigated, and the term ambiguity vector is proposed to represent a pixel's ambiguity number of multiple interferograms. It is also revealed that pixels with the same ambiguity vector have an exclusive pattern among them. A fast cluster-analysis (CA)-based method is proposed for multibaseline phase unwrapping. In this method, all pixels are first clustered into different groups according to their patterns, and then, information of the cluster center is used to unwrap the phases of pixels group by group. Simulation results are shown to verify the effectiveness, efficiency, and noise robustness of CA multibaseline phase-unwrapping method. [ABSTRACT FROM AUTHOR]

The emergence of multicore and many-core processors has introduced new opportunities and challenges to electronic design automation research and development. While the availability of increasing parallel computing power holds new promise to address many challenges in computer-aided design (CAD), the leverage of hardware parallelism can only be possible with a new generation of parallel CAD applications. In this paper, we propose a novel hierarchical multialgorithm (MA) parallel circuit simulation approach and its multicore implementation to expedite one of the most fundamental CAD applications: transistor-level transient circuit simulation. In our parallel circuit simulation approach, we create two levels of parallelism. At the higher level of parallelism, we start multiple simulation algorithms in parallel for a given simulation task. Interalgorithm communication is established to enable simulation algorithms to exchange useful information so that they could advance faster than without doing so. At the lower level of parallelism, each algorithm within the MA framework utilizes fine-grained parallel techniques such as parallel device evaluation and parallel matrix solve to fully harness the available hardware resources. By combining the two levels of parallelism, the computing power of the multicore or many-core processor platforms can be fully utilized to achieve superlinear speedup in circuit simulation. [ABSTRACT FROM AUTHOR]

In this paper, we consider model combination methods for adaptive filtering that perform unbiased estimation. In this widely studied framework, two adaptive filters are run in parallel, each producing unbiased estimates of an underlying linear model. The outputs of these two filters are combined using another adaptive algorithm to yield the final output of the system. Overall, we require that the final algorithm produce an unbiased estimate of the underlying model. We later specialize this framework where we combine one filter using the least-mean squares (LMS) update and the other filter using the least-mean fourth (LMF) update to decrease cross correlation in between the outputs and improve the overall performance. We study the steady-state performance of previously introduced methods as well as novel combination algorithms for stationary and nonstationary data. These algorithms use stochastic gradient updates instead of the variable transformations used in previous approaches. We explicitly provide steady-state analysis for both stationary and nonstationary environments. We also demonstrate close agreement with the introduced results and the simulations, and show for this specific combination, more than 2 dB gains in terms of excess mean square error with respect to the best constituent filter in the simulations. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to obtain a discrete-time state-space representation of an electrical network using the nodal [G] matrix of the Electromagnetic Transients Program (EMTP) solution. Compared with conventional state-space solutions, the nodal EMTP solution is computationally very efficient. Compared with the phasor solutions used in transient stability analysis, the proposed approach may capture a wider range of eigenvalues and system operating states. An important advantage of extracting the system eigenvalues from the EMTP solution is the ability of the EMTP to follow the characteristics of nonlinearities. In addition, the algorithm can be used as a tool to identify network partitioning subsystems suitable for real-time hybrid power system simulator environments, including the implementation of multitime-scale solutions. The proposed technique can be implemented as an extension of an EMTP-based simulator. Within our research group at UBC, it is aimed at extending the capabilities of our real-time PC-cluster Object Virtual Network Integrator (OVNI) simulator. [ABSTRACT FROM AUTHOR]

This paper proposes a variable-dimension optimization approach to address the high dimensionality issues in solving the unit commitment problem. This method introduces the concept of adaptive search space dimension. The proposed approach is implemented in particle swarm optimization algorithm. The optimization process starts with an arbitrary problem dimension, adapts with respect to the swarm progress and finally selects the optimal dimensional space. The efficiency of this method is tested on a ten-unit test system. The results are compared with binary programming and fixed duty cycle approaches. The simulation results show that the proposed method results in considerable reduction of problem dimension, faster convergence and improved quality of the final solution. [ABSTRACT FROM AUTHOR]

Reliability of tree-like multicast overlays caused by nodes' abrupt failures is considered as one of the major problems for the Internet application-layer media streaming service [1]. In this paper, we address this problem by designing a distributed and light-weighted protocol named the instantaneous reliability oriented protocol (IRP). Unlike most of existing empirical solutions, we first define the overlay reliability problem formally, and propose a protocol containing a node joining algorithm (IRP-Join), a node preemption algorithm (IRP-Preempt), and a node switching algorithm (IRP-Switch) for reactively constructing and repairing the overlay, as well as proactively maintaining the overlay. With the formal problem presentation, we set up a paradigm for solving the overlay reliability problem by theoretically proving the effectiveness of our algorithms. Moreover, by comparing IRP with existing solutions via simulation-based experiments and real-world deployment, we show that IRP achieves a better reliability, while incurs fewer structural adjustments on the multicast overlay, thus, providing a superior overall performance. [ABSTRACT FROM AUTHOR]

This paper focuses on the benefits of adaptive control for permanent-magnet synchronous machines; a novel method of online parameter estimation for such machines has been developed. Two recursive least square algorithm segments, a fast and a slow one, are uniquely combined in real time with rich enough data from the machine to estimate all four machine parameters instead of a subset of these. Simulation and experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The enhanced distributed channel access (EDCA) mechanism of the IEEE 802.11e standard provides quality-of-service (QoS) support through service differentiation by using different medium-access-control (MAC) parameters for different stations. The configuration of these parameters, however, is still an open research challenge, as the standard provides only a set of fixed recommended values that do not take into account the current wireless local area network (WLAN) conditions and, therefore, lead to suboptimal performance. In this paper, we propose a novel algorithm for EDCA that, given the throughput and delay requirements of the stations that are present in the WLAN, computes the optimal configuration of the EDCA parameters. We first present a throughput and delay analysis that provides the mathematical foundation upon which our algorithm is based. This analysis is validated through simulations of different traffic sources (both data and real time) and EDCA configurations. We then propose a mechanism to derive the optimal configuration of the EDCA parameters, given a set of performance criteria for throughput and delay. We assess the effectiveness of the configuration provided by our algorithm by comparing it against 1) the recommended values by the standard, 2) the results from an exhaustive search over the parameter space, and 3) previous configuration proposals, which are both standard and nonstandard compliant. Results show that our configuration outperforms all other approaches. [ABSTRACT FROM AUTHOR]

Relay selection is crucial in improving the performance of wireless cooperative networks. Most previous works for relay selection use the current observed channel conditions to make the relay-selection decision for the subsequent frame. However, this memoryless channel assumption is often not realistic given the time-varying nature of some mobile environments. In this paper, we consider finite-state Markov channels in the relay-selection problem. Moreover, we also incorporate adaptive modulation and coding, as well as residual relay energy in the relay-selection process. The objectives of the proposed scheme are to increase spectral efficiency, mitigate error propagation, and maximize the network lifetime. The formulation of the proposed relay-selection scheme is based on recent advances in stochastic control algorithms. The obtained relay-selection policy has an indexability property that dramatically reduces the computation and implementation complexity. In addition, there is no need for a centralized control point in the network, and relays can freely join and leave from the set of potential relays. Simulation results are presented to show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Reading efficiency is one of the key factors to evaluate Radio Frequency Identification (RFID) systems. For the system using multi-branch protocols, the performance would be better if the tags are properly divided into multiple groups. This paper firstly gives the closed-form of system efficiency for binary tree algorithm. Based on the theoretical analysis, the optimal branches number is derived. An efficient multi-branch tree (EMBT) algorithm is proposed subsequently, along with a tag number estimation algorithm and performance evaluation. Both theoretical analysis and simulation results indicate that multi-branch tree algorithm has better performance than the conventional binary tree algorithm. System identification efficiency of the proposed method can achieve above 45%, while that of the binary tree algorithm is only 34.8%. [ABSTRACT FROM AUTHOR]

A synthetic aperture radar (SAR) raw data simulator is an important tool for testing the system parameters and the imaging algorithms. In this paper, a scene raw data simulator based on an inverse w-k algorithm for bistatic SAR of a translational invariant case is proposed. The differences between simulations of monostatic and bistatic SAR are also described. The algorithm proposed has high precision and can be used in long-baseline configuration and for single-pass interferometry. Implementation details are described, and plenty of simulation results are provided to validate the algorithm. [ABSTRACT FROM AUTHOR]

Recent advances in electro-optical systems make them ideal for undersampling multiband signals with very high carrier frequencies. In this paper, we propose a new scheme for sampling and reconstructing of a multiband sparse signals that occupy a small part of a given broad frequency range under the constraint of a small number of sampling channels. The locations of the signal bands are not known a priori. The scheme, which we call synchronous multirate sampling (SMRS), entails gathering samples synchronously at few different rates whose sum is significantly lower than the Nyquist sampling rate. The signals are reconstructed by finding a solution of an underdetermined system of linear equations by applying a pursuit algorithm and assuming that the solution is composed of a minimum number of bands. The empirical reconstruction success rate is higher than obtained using previously published multicoset scheme when the number of sampling channels is small and the conditions for a perfect reconstruction in the multicoset scheme are not fulfilled. The practical sampling system which is simulated in our work consists of three sampling channels. Our simulation results show that a very high empirical success rate is obtained when the total sampling rate is five times higher than the total signal support of a complex signal with four bands. By comparison, a multicoset sampling scheme obtains a very high empirical success rate with a total sampling rate which is three times higher than the total signal support. However, the multicoset scheme requires 14 channels. [ABSTRACT FROM AUTHOR]