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We present in this paper an integrated solution to rapidly recognizing dynamic objects in surveillance videos by exploring various contextual information. This solution consists of three components. The first one is a multi-view object representation. It contains a set of deformable object templates, each of which comprises an ensemble of active features for an object category in a specific view/pose. The template can be efficiently learned via a small set of roughly aligned positive samples without negative samples. The second component is a unified spatio-temporal context model, which integrates two types of contextual information in a Bayesian way. One is the spatial context, including main surface property (constraints on object type and density) and camera geometric parameters (constraints on object size at a specific location). The other is the temporal context, containing the pixel-level and instance-level consistency models, used to generate the foreground probability map and local object trajectory prediction. We also combine the above spatial and temporal contextual information to estimate the object pose in scene and use it as a strong prior for inference. The third component is a robust sampling-based inference procedure. Taking the spatio-temporal contextual knowledge as the prior model and deformable template matching as the likelihood model, we formulate the problem of object category recognition as a maximum-a-posteriori problem. The probabilistic inference can be achieved by a simple Markov chain Mento Carlo sampler, owing to the informative spatio-temporal context model which is able to greatly reduce the computation complexity and the category ambiguities. The system performance and benefit gain from the spatio-temporal contextual information are quantitatively evaluated on several challenging datasets and the comparison results clearly demonstrate that our proposed algorithm outperforms other state-of-the-art algorithms. [ABSTRACT FROM PUBLISHER]

Though the entanglement-assisted formalism provides a universal connection between a classical linear code and an entanglement-assisted quantum error-correcting code (EAQECC), the issue of maintaining large amount of pure maximally entangled states in constructing EAQECCs is a practical obstacle to its use. It is also conjectured that the power of entanglement-assisted formalism to convert those good classical codes comes from massive consumption of maximally entangled states. We show that the above conjecture is wrong by providing families of EAQECCs with an entanglement consumption rate that diminishes linearly as a function of the code length. Notably, two families of EAQECCs constructed in the paper require only one copy of maximally entangled state no matter how large the code length is. These families of EAQECCs that are constructed from classical finite geometric LDPC codes perform very well according to our numerical simulations. Our work indicates that EAQECCs are not only theoretically interesting, but also physically implementable. Finally, these high performance entanglement-assisted LDPC codes with low entanglement consumption rates allow one to construct high-performance standard QECCs with very similar parameters. [ABSTRACT FROM PUBLISHER]

Phase-locked loop (PLL) algorithms are commonly used to track sinusoidal components in currents and voltage signals in three-phase power systems. Despite the simplicity of those algorithms, problems arise when signals have variable frequency or amplitude, or are polluted with harmonic content and measurement noise, as can be found in aircraft ac power systems where the fundamental frequency can vary in the range 360–900 Hz. To improve the quality of phase and frequency estimates in such power systems, a novel PLL scheme based on a real-time implementation of the discrete Fourier transform (DFT) is presented in this paper. The DFT algorithm calculates the amplitudes of three consecutive components in the frequency domain. These components are used to determine an error signal which is minimized by a proportional–integral loop filter in order to estimate the fundamental frequency. The integral of the estimated frequency is the estimated phase of the fundamental component, and this is fed back to the DFT algorithm. The proposed algorithm can therefore be considered to be a PLL in which phase detection is performed via a DFT-based algorithm. A comparison has been made of the performances of a standard PLL and the proposed DFT-PLL using computer simulations and through experiments. [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]

This paper presents a fast analog circuit analysis algorithm, fundamental circuit-based circuit analysis, for circuits being repeatedly modified and verified in product development. The algorithm reuses previous circuit simulation result on successive changed circuit analysis to achieve simulation operation reduction. The algorithm is implemented with SPICE simulator on linear and nonlinear circuit applications with the proposed device delta models. The experiments show that the algorithm increases the speed of the circuit simulation five to ten times over directly simulations under the same simulation accuracy. [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 describes a block-latency insertion method (LIM) for the fast transient simulation of the large networks with many coupling elements. First, the basic formulation of LIM is reviewed. Next, the block-LIM formulation for the network with many coupling elements, such as the mutual inductance, the mutual capacitance, and controlled sources is described. Then, the block-LIM algorithm is applied to the tightly coupled transmission lines, which are connected to each other by a number of mutual inductors and capacitors. Finally, some numerical results are shown, and it is confirmed that the proposed technique is useful and efficient for the simulation of the tightly coupled transmission lines. [ABSTRACT FROM AUTHOR]

This paper analyzes critical control issues associated with the deep field weakening operation of interior permanent magnet (IPM) synchronous machines. The singlecurrent-regulator control algorithm is reviewed, which controls the d-axis current actively and gives a fixed q-axis voltage command. To achieve better operational efficiency and performance, improvements are made to choose an optimal q-axis voltage for variable-speed and variable-load conditions. The control algorithm is implemented on a 50-kW IPM machine. Test results, including 7:1 constant power speed ratio operation, are presented to verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

The simulation of remote-sensing hyperspectral images is a useful tool for a variety of tasks such as the design of systems, the understanding of the image formation process, and the development and validation of data processing algorithms. The lack of ground truth and the incomplete knowledge of the Martian environment make simulation studies of Mars hyperspectral images a useful tool for automated analysis of Mars data. Hyperspectral near-infrared scenes of mineral mixtures have been simulated to analyze the contributions of surface minerals, atmosphere, and sensor noise on images of Mars. Modeling the remote-sensing process creates a means for the independent analysis of the influence of the environment and instruments on the detection accuracy of the surface composition (e.g., the scene endmembers). The end-to-end model builds surface reflectance scenes based on laboratory sample spectra, creates atmospheric effects using radiative transfer routines, simulates the instrument response function using CRISM data files, and adds instrument noise from thermal and other sources. The purpose of this paper is to understand the hyperspectral remote-sensing process to eventually enable the elevated detection accuracy of minerals on the surface of Mars. The viability of a linear approximation of the complete model is also investigated. The approximation is compared to the complete model in an image classification task. [ABSTRACT FROM AUTHOR]

This paper describes a flexible and efficient new algorithm for model order reduction of parameterized systems. The method is based on the reformulation of the parameterized system as a perturbation-like parallel interconnection of the nominal transfer function and the nonparameterized transfer function sensitivities with respect to the parameter variations. Such a formulation reveals an explicit dependence on each parameter which is exploited by reducing each component system independently via a standard nonparameterized structure preserving algorithm. Therefore, the resulting smaller size interconnected system retains the structure of the original system with respect to parameter dependence. This allows for better accuracy control, enabling independent adaptive order determination with respect to each parameter and adding flexibility in simulation environments. It is shown that the method is efficiently scalable and preserves relevant system properties such as passivity. The new technique can handle fairly large parameter variations on systems whose outputs exhibit smooth dependence on the parameters, also allowing design space exploration to some degree. Several examples show that besides the added flexibility and control, when compared with competing algorithms, the proposed technique can, in some cases, produce smaller reduced models with potential accuracy gains. [ABSTRACT FROM AUTHOR]

Shape similarity and shape retrieval are very important topics in computer vision. The recent progress in this domain has been mostly driven by designing smart shape descriptors for providing better similarity measure between pairs of shapes. In this paper, we provide a new perspective to this problem by considering the existing shapes as a group, and study their similarity measures to the query shape in a graph structure. Our method is general and can be built on top of any existing shape similarity measure. For a given similarity measure, a new similarity is learned through graph transduction. The new similarity is learned iteratively so that the neighbors of a given shape influence its final similarity to the query. The basic idea here is related to PageRank ranking, which forms a foundation of Google Web search. The presented experimental results demonstrate that the proposed approach yields significant improvements over the state-of-art shape matching algorithms. We obtained a retrieval rate of 91.61 percent on the MPEG-7 data set, which is the highest ever reported in the literature. Moreover, the learned similarity by the proposed method also achieves promising improvements on both shape classification and shape clustering. [ABSTRACT FROM AUTHOR]

This paper introduces a new concept of Boolean derivatives as a fusion of partial derivatives of Boolean functions (PDBFs). Three efficient algorithms for the calculation of PDBFs are presented. It is shown that Boolean function derivatives are useful for the application of identifying the location of edge pixels in binary images. The same concept is extended to the development of a new edge detection algorithm for grayscale images, which yields competitive results, compared with those of traditional methods. Furthermore, a new measure is introduced to automatically determine the parameter values used in the thresholding portion of the binarization procedure. Through computer simulations, demonstrations of Boolean derivatives and the effectiveness of the presented edge detection algorithm, compared with traditional edge detection algorithms, are shown using several synthetic and natural test images. In order to make quantitative comparisons, two quantitative measures are used: one based on the recovery of the original image from the output edge map and the Pratt's figure of merit. [ABSTRACT FROM AUTHOR]

In this paper, we present results from a circuit simulation of a proposed electro-acoustic communications channel. The communications channel was modeled in PSpice using Redwood's version of Mason's equivalent circuit. The simulations used binary phase shift keyed communications signals with a carrier frequency of 1.12 MHz. Results obtained from the model are comparable with the results obtained experimentally. The frequency response of the model matched the measured frequency response, predicting lower frequency resonances obtained in the experimental data. The transient response of the model compares almost identically with the transient response observed experimentally. This is a significant characteristic as the acoustic communications are transient limited, which suggests that the model can be used with good confidence in the optimization of the transducers and algorithms used for acoustic communications. [ABSTRACT FROM AUTHOR]

In this paper, we investigate various channel estimators that exploit channel sparsity in the time and/or Doppler domain for a multicarrier underwater acoustic system. We use a path-based channel model, where the channel is described by a limited number of paths, each characterized by a delay, Doppler scale, and attenuation factor, and derive the exact inter-carrier- interference (ICI) pattern. For channels that have limited Doppler spread we show that subspace algorithms from the array processing literature, namely Root-MUSIC and ESPRIT, can be applied for channel estimation. For channels with Doppler spread, we adopt a compressed sensing approach, in form of Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP) algorithms, and utilize overcomplete dictionaries with an increased path delay resolution. Numerical simulation and experimental data of an OFDM block-by-block receiver are used to evaluate the proposed algorithms in comparison to the conventional least-squares (LS) channel estimator. We observe that subspace methods can tolerate small to moderate Doppler effects, and outperform the LS approach when the channel is indeed sparse. On the other hand, compressed sensing algorithms uniformly outperform the LS and subspace methods. Coupled with a channel equalizer mitigating ICI, the compressed sensing algorithms can effectively handle channels with significant Doppler spread. [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 ω - 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]

The altimeter range is corrected for tropospheric humidity by means of microwave radiometer measurements (Envisat/MWR, Jason-1/JMR, Jason-2/AMR). Over an open ocean, the altimeter/radiometer combination is satisfactory. However, in coastal areas, radiometer measurements are contaminated by the surrounding land surfaces, and the humidity retrieval method is not appropriate anymore. In this paper, a variational assimilation technique is proposed to retrieve the wet tropospheric correction near coasts. The method is first developed on simulations using the data from a meteorological model. A performance assessment is performed, as well as a comparison with a standard algorithm. The method is then applied on actual measurements, thus evaluating its feasibility. [ABSTRACT FROM AUTHOR]

Scheduling problems are already difficult on traditional parallel machines, and they become extremely challenging on heterogeneous clusters. In this paper, we deal with the problem of scheduling multiple applications, made of collections of independent and identical tasks, on a heterogeneous master-worker platform. The applications are submitted online, which means that there is no a priori (static) knowledge of the workload distribution at the beginning of the execution. The objective is to minimize the maximum stretch, i.e., the maximum ratio between the actual time an application has spent in the system and the time this application would have spent if executed alone. On the theoretical side, we design an optimal algorithm for the offline version of the problem (when all release dates and application characteristics are known beforehand). We also introduce a heuristic for the general case of online applications. On the practical side, we have conducted extensive simulations and MPI experiments, showing that we are able to deal with very large problem instances in a few seconds. Also, the solution that we compute totally outperforms classical heuristics from the literature, thereby fully assessing the usefulness of our approach. [ABSTRACT FROM AUTHOR]

In the first part of this work, we analyze the effect of discretization on the accuracy of fluorescence diffuse optical tomography (FDOT). Our error analysis provides two new error estimates which present a direct relationship between the error in the reconstructed fluorophore concentration and the discretization of the forward and inverse problems. In this paper, based on these error estimates, we develop two new adaptive mesh generation algorithms for the numerical solutions of the forward and inverse problems in FDOT, with the objective of error reduction in the reconstructed optical images due to discretization while keeping the size of the discretized forward and inverse problems within the allowable limits. We present three-dimensional numerical simulations to demonstrate the improvements in accuracy, resolution and detectability of small heterogeneities in reconstructed images provided by the use of the new adaptive mesh generation algorithms. Finally, we compare our algorithms both analytically and numerically with the existing conventional adaptive mesh generation algorithms. [ABSTRACT FROM AUTHOR]

This paper studies the problem of blind source separation (BSS) from instantaneous mixtures with the assumption that the source signals are mutually correlated. We propose a novel approach to BSS by using precoders in transmitters. We show that if the precoders are properly designed, some cross-correlation coefficients of the coded signals can be forced to be zero at certain time lags. Then, the unique correlation properties of the coded signals can be exploited in receiver to achieve source separation. Based on the proposed precoders, a subspace-based algorithm is derived for the blind separation of mutually correlated sources. The effectiveness of the algorithm is illustrated by simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, we propose a high-resolution autoregressive moving average (ARMA) modeling technique for signals which are a sum of sinusoids embedded in colored noise. The approach is based on a special ARMA model. We show that an approximation to this model can be found through the central solution of Nevanlinna-Pick interpolation. In this context, it can reach a very fine resolution with a special arrangement of filterbank poles. A very efficient iterative algorithm will then be presented to achieve such desired arrangement. We also derive theoretical expressions for the variance of interpolation values for both continuous and mixed spectra for complex poles. Computer simulations show that the approach is very powerful in joint power spectrum and frequency estimation and provides superior performance with respect to traditional techniques. [ABSTRACT FROM AUTHOR]

The integration of third-generation (3G) cellular networks and IEEE 802.16e networks is an important interworking case within the forthcoming fourth-generation heterogeneous wireless networks. In this paper, we extend the virtual partitioning with preemption technique for admission control in cellular/802.16e interworking and evaluate its performance. First, we describe the mobility scenario between 3G cellular networks and IEEE 802.16e networks in terms of the horizontal (intrasystem) and vertical (intersystem) handoffs that can occur and derive the corresponding handoff rate equations. We then propose admission-control algorithms for connection requests that consider the class of service (i.e., real time or nonreal time) and the type of user (i.e., new or handoff). For the handoff requests, a different priority is assigned to each type of handoff. A joint connection- and packet-level optimization approach is used for quality-of-service (QoS) provisioning. The accuracy of the analytical model is validated via simulations. Numerical results show significant performance improvement. The blocking probabilities for new connection requests can be reduced by 70% when the joint QoS optimization approach is used. [ABSTRACT FROM AUTHOR]

In cognitive radio (CR) networks, secondary users can cooperatively sense the spectrum to detect the presence of primary users. In this paper, we propose a fully distributed and scalable cooperative spectrum-sensing scheme based on recent advances in consensus algorithms. In the proposed scheme, the secondary users can maintain coordination based on only local information exchange without a centralized common receiver. Unlike most of the existing decision rules, such as the OR-rule or the 1-out-of-N rule, we use the consensus of secondary users to make the final decision. Simulation results show that the proposed consensus scheme can have significant lower missing detection probabilities and false alarm probabilities in CR networks. It is also demonstrated that the proposed scheme not only has proven sensitivity in detecting the primary user's presence but also has robustness in choosing a desirable decision threshold. [ABSTRACT FROM AUTHOR]

This paper presents a watermarking algorithm that is suitable for the data represented in floating- or fixed-point numbers. By keeping the modulation information, every value in the original object can be restored with the smallest error. For the sake of exact recovery, the redundancy in the watermarked object should be exploited to save the recovery information. We analyze the security of the algorithm and enhance it by adding a pseudorandom (PR) sequence to the watermarked object. The algorithm can be applied in the measurement to embed some useful information in a distortion-free way. We implement it on the Virtual Reality Modeling Language (VRML) models, and the experimental results show its efficacy. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of modeling systems from measurements of their frequency response. For multiport devices, currently available techniques are expensive. We propose a new approach which is based on a system-theoretic tool, the Loewner matrix pencil constructed in the context of tangential interpolation. Several implementations are presented. They are fast, accurate, they build low order models and are especially designed for a large number of terminals. Moreover, they identify the underlying system, rather than merely fitting the measurements. The numerical results show that our algorithms yield smaller models in less time, when compared to vector fitting. [ABSTRACT FROM AUTHOR]

Model checking for large-scale systems is extremely difficult due to the state explosion problem. Creating useful abstractions for model checking task is a challenging problem, often involving many iterations of refinement. In this paper we consider techniques for model checking in the counterexample-guided abstraction refinement. The state separation problem is one popular approach in counterexample-guided abstraction refinement, and it poses the main hurdle during the refinement process. To achieve effective minimization of the separation set, we present a novel probabilistic learning approach based on the sample learning technique, evolutionary algorithm, and effective heuristics. We integrate it with the abstraction refinement framework in the VIS [1] model checker. We include experimental results on model checking to compare our new approach to recently published techniques. The benchmark results show that our approach has overall speedup of more than 56 percent against previous techniques. Our work is the first successful integration of evolutionary algorithm and abstraction refinement for model checking. [ABSTRACT FROM AUTHOR]

This paper is concerned with the blind identification of passband and baseband Hammerstein channels using cumulants of the received sequence. The channel is excited by common communication signals such as QAM, PSK and OFDM; sparseness of the higher order cumulant lags is exploited. Exact expressions and algorithms involving the output cumulants are developed. Performance is assessed by simulations. [ABSTRACT FROM AUTHOR]

We have developed a general-purpose Monte Carlo simulation code, called penMesh, that combines the accuracy of the radiation transport physics subroutines from PENELOPE and the flexibility of a geometry based on triangle meshes. While the geometric models implemented in most general-purpose codes-such as PENELOPE's quadric geometry-impose some limitations in the shape of the objects that can be simulated, triangle meshes can be used to describe any free-form (arbitrary) object. Triangle meshes are extensively used in computer-aided design and computer graphics. We took advantage of the sophisticated tools already developed in these fields, such as an octree structure and an efficient ray-triangle intersection algorithm, to significantly accelerate the triangle mesh ray-tracing. A detailed description of the new simulation code and its ray-tracing algorithm is provided in this paper. Furthermore, we show how it can be readily used in medical imaging applications thanks to the detailed anatomical phantoms already available. In particular, we present a whole body radiography simulation using a triangulated version of the anthropomorphic NCAT phantom. An example simulation of scatter fraction measurements using a standardized abdomen and lumbar spine phantom, and a benchmark of the triangle mesh and quadric geometries in the ray-tracing of a mathematical breast model, are also presented to show some of the capabilities of penMesh. [ABSTRACT FROM AUTHOR]

The problem considered in this paper is detection and estimation of multiple radiation sources using a time series of radiation counts from a collection of sensors. A Bayesian framework is adopted. Source detection is approached as a model selection problem in which competing models are compared using partial Bayes factors. Given the number of sources, the posterior mean is the minimum mean square error estimator of the source parameters. Exact calculation of the partial Bayes factors and the posterior mean is not possible due to the presence of intractable integrals. Importance sampling using progressive correction is proposed as a computationally efficient method for approximating these integrals. Previously proposed algorithms have been restricted to one or two sources. A simulation analysis shows that the proposed methods can detect and accurately estimate the parameters of four sources with reasonable computational expense. [ABSTRACT FROM AUTHOR]

Future ancillary services provided by photovoltaic (PV) systems could facilitate their penetration in power systems. In addition, low-power PV systems can be designed to improve the power quality. This paper presents a single-phase PV system that provides grid voltage support and compensation of harmonic distortion at the point of common coupling thanks to a repetitive controller. The power provided by the PV panels is controlled by a Maximum Power Point Tracking algorithm based on the incremental conductance method specifically modified to control the phase of the PV inverter voltage. Simulation and experimental results validate the presented solution. [ABSTRACT FROM AUTHOR]

A novel scheme for three-phase grid-connected photovoltaic (PV) generation systems is presented in this paper. The scheme is based on two insulated strings of PV panels, each one feeding the dc bus of a standard two-level three-phase voltage-source inverter (VSI). The inverters are connected to the grid by a three-phase transformer having open-end windings on the inverter side. The resulting conversion structure performs as a multilevel power active filter (equivalent to a three-level inverter), doubling the power capability of a single VSI with given voltage and current ratings. The multilevel voltage waveforms are generated by an improved space-vector-modulation algorithm, suitable for the implementation in industrial digital signal processors. An original control method has been introduced to regulate the dc-link voltages of each VSI, according to the voltage reference given by a single maximum power point tracking controller. The proposed regulation system has been verified by numerical simulations and experimental tests with reference to different operating conditions. [ABSTRACT FROM AUTHOR]

Through-the-wall imaging techniques with ultrawideband(UWB) radars are promising candidates for non-destructive testing and reliable human detection, especially in disaster areas, where victims are buried under collapsed walls. These applications require high-resolution target imaging to identify the object shape, such as a human body. We have already proposed a highquality 3-dimensional (3-D) imaging algorithm in the form of Envelope, that is aimed at near field sensing for non-contact measurement or target identification for robots. Envelope achieves realtime accurate 3-D imaging with group mapping from multiple observed ranges to target points, and offers a reliable image even in noisy situations. However, this method does not maintain its quality for through-the-wall imaging because an observed range shift due to wall penetration causes a serious distortion in the image. This paper presents a high-resolution 3-D imaging algorithm by modifying the original Envelope, and which gives a more accurate object shape behind a wall. Furthermore, to enhance the resolution of the estimated images, this method is combined with a direct waveform compensation method, known as spectrum offset correction. Numerical simulations and an experiment verify that our proposed method achieves high-resolution 3-D imaging for through-the-wall radar applications. [ABSTRACT FROM AUTHOR]

Indirect and direct methods have been developed for reconstructing parametric images from dynamic positron emission tomography (PET) data. Indirect methods are simple and easy to implement because reconstruction and kinetic modeling are performed in two separate steps. Direct methods estimate parametric images directly from dynamic PET sinograms and, in theory, can be statistically more efficient, but the algorithms are often difficult to implement and are very specific to the kinetic model being used. This paper presents a class of generalized algorithms for direct reconstruction of parametric images that are relatively easy to implement and can be adapted to different kinetic models. The proposed algorithms use optimization transfer principle to convert the maximization of a penalized likelihood into a pixel-wise weighted least squares (WLS) kinetic fitting problem at each iteration. Thus, it can employ existing WLS algorithms developed for kinetic models. The proposed algorithms resemble the empirical iterative implementation of the indirect approach, but converge to a solution of the direct formulation. Computer simulations showed that the proposed direct reconstruction algorithms are flexible and achieve a better bias-variance tradeoff than indirect reconstruction methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a low-complexity decision-directed channel-estimation scheme, which offers improved performance for space-frequency-block-coded orthogonal frequency-division multiplexing (SFBC-OFDM) systems under doubly selective (i.e., frequency selective and fast fading) channels. First, we propose a square-root-free inverse-QR-decomposition-based groupwise recursive (SIQR-GR) channel-estimation algorithm employing sequences of scaled Givens rotations. The proposed SIQR-GR scheme recursively processes data decisions corresponding to the SFBC groups within each OFDM block to improve system performance under doubly selective channels. Furthermore, the avoidance of square roots and the utilization of scaled Givens rotations ensure that the proposed SIQR-GR scheme is computationally efficient. Second, we derive semi-analytical expressions for the normalized mean square error (NMSE) performance of SIQR-GR and verify the accuracy of the NMSE analysis via numerical simulations. Finally, we provide performance and complexity comparisons between SIQR-GR and previously proposed schemes. These comparisons show an excellent performance-complexity tradeoff achieved by SIQR-GR over the previous solutions under various channel conditions. [ABSTRACT FROM AUTHOR]

In this paper, we propose two simple orthogonal frequency-division multiplexing (OFDM) mean-delay estimation schemes, i.e., a channel impulse response (CIR)-based scheme and a differential scheme, which are applicable for adaptive algorithms including improved channel estimation and fast handover. Instead of time-domain processing which requires additional inverse fast Fourier transform (IFFT), the mean delay can be obtained in the proposed CIR-based scheme using the estimated frequency-domain CIR. When cell-specific preamble sequences are employed, the mean delay can also be estimated without CIR estimation in the proposed differential scheme using cross correlation of differentially correlated received signals, enabling further complexity reduction and performance improvement when the CIR estimation is not sufficiently accurate. The performance of the proposed schemes is verified by computer simulation. [ABSTRACT FROM AUTHOR]

In this paper, we propose an attraction-repulsion expectation-maximization (AREM) algorithm for image reconstruction and sensor field estimation. We rely on a new method for density estimation to address the problems of image reconstruction from limited samples and sensor field estimation from randomly scattered sensors. Density estimation methods often suffer from undesirable phenomena such as over-fitting and over-smoothing. Specifically, various density estimation techniques based on a Gaussian mixture model (GMM) tend to cluster the Gaussian functions together, thus resulting in over-fitting: On the other hand, other approaches repel the Gaussian functions and yield over-smooth density estimates. We propose a method that seeks an equilibrium between over-fitting and over-smoothing in density estimation by incorporating attraction and repulsion forces among the Gaussian functions and determining the optimal balance between the competing forces experimentally. We model the attractive and repulsive forces by introducing the Gibbs and inverse Gibbs distributions, respectively. The maximization of the likelihood function augmented by the Gibbs density mixture is solved under the expectation-maximization (EM) method. Computer simulation results are provided to demonstrate the effectiveness of the proposed AREM algorithm in image reconstruction and sensor field estimation. [ABSTRACT FROM AUTHOR]

This paper explores the use of kurtosis in synchronizing with a QPSK burst transmission. The start and end of the burst are a priori unknown and are detected without the use of pilot signals. After frame level synchronization, signal level synchronization is performed. The symbol timing, carrier frequency and carrier phase are recovered without the assistance of known symbols or synchronization words in the data stream, i.e. totally blind synchronization. The performance of frame, symbol and carrier synchronization algorithms is evaluated through computer simulations. [ABSTRACT FROM AUTHOR]

An iterative receiver for a multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system is considered to jointly decode the transmitted bits and estimate the channel state. The receiver consists of a list detector, a turbo decoder, and a channel estimator that is based on the space-alternating generalized expectation-maximization (SAGE) algorithm. This paper proposes a way to improve the convergence of the iterative detection and decoding by using a priori information to also recalculate the candidate list, aside from the log-likelihood ratios (LLRs) of the coded bits. A new list parallel interference cancellation (PlC) detector is derived to approximate an a posteriori probability (APP) algorithm with reduced complexity and minimal losses of performance. Further- more, the organization of spectrally efficient decision-directed (DD) SAGE channel estimation under a constrained number of detector-decoder iterations is optimized by computer simulations, and the SAGE algorithm itself is modified for nonconstant envelope constellations. The list recalculation is shown to improve convergence. It is also shown that the list PlC detector with good initialization outperforms the K-best list sphere detector (LSD) in the case of small list sizes, whereas the complexities of the algorithms are of the same order. Despite the low preamble density and fast-fading channel, the proposed iterative receiver shows robust performance. [ABSTRACT FROM AUTHOR]

The aim of this study is to determine a robust prediction algorithm that can be used to correct the measured specific absorption rate (SAR) in a homogeneous phantom when its complex permittivity deviates from standardized reference values. Results are analyzed over a frequency range of 30-6000 MHz. Both measurements and numerical simulations are presented. Several antenna sizes and distances to the phantom are investigated so as to study a large range of SAR distributions. It is demonstrated that the prediction algorithm, while developed using dipole antennas, also works well for mobile telephone models. Employing the prediction algorithm reduces the SAR measurement uncertainty, thereby improving the reproducibility of SAR compliance assessment between laboratories. Another benefit of the algorithm is that it enables the use of broadband tissue-equivalent liquids, whose dielectric parameters are not currently within the tight tolerances of existing standards. The use of broadband liquids reduces the cost of SAR measurement. The method presented in this paper is of benefit to the IEEE 1528 and IEC 62209 measurement standards. [ABSTRACT FROM AUTHOR]

This paper presents an intra-fame rate control algorithm for ultralow delay H.264/AVC coding. The main goal of the proposed scheme is to allocate a proper bit budget to each macroblock (MB), based on its complexity (or activity). However, since a coder needs to start encoding a frame before buffering the whole frame for very low delay video streaming applications, the relative complexity of each MB can not be known in advance. So, the proposed algorithm predicts a relative complexity of a current MB from complexities of its spatially/temporally neighboring MBs, and then allocates a proper bit budget to the MB using the predicted complexity. Finally, quantization parameter of each MB is obtained by comparing the generated bits and allocated bit budget, and is refined for improving coding performance, based on information from the previous frame. Even though the required buffer size is only around one-third of average bits per a frame in order to achieve very low coding delay, the proposed algorithm provides reliable coding performance compared to the conventional schemes. Simulation results show that the proposed algorithm can prevent the buffer overflow or underflow and it provides better coding performance. [ABSTRACT FROM AUTHOR]

This paper describes a simultaneous localization and mapping (SLAM) algorithm for use in unstructured environments that is effective regardless of the geometric complexity of the environment. Features are described using B-splines as modeling tool, and the set of control points defining their shape is used to form a complete and compact description of the environment, thus making it feasible to use an extended Kalman-filter (EKF) based SLAM algorithm. This method is the first known EKF-SLAM implementation capable of describing general free-form features in a parametric manner. Efficient strategies for computing the relevant Jacobians, perform data association, initialization, and map enlargement are presented. The algorithms are evaluated for accuracy and consistency using computer simulations, and for effectiveness using experimental data gathered from different real environments. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new robust congestion- control strategy for a differentiated-services (DiffServ) network. A sliding-mode-based variable structure control technique is utilized to design a congestion controller on the basis of a fluid flow model. The performance of our proposed scheme is then demonstrated on a DiffServ network. Our proposed algorithm is developed for an interconnected network as a hop-by-hop distributed-control approach. A formal analysis of the controlled network is conducted, where it is shown that the error dynamics of the overall time-delay-dependent system is ℒ∞ stable. Numerical simulations are provided to demonstrate the capabilities of our proposed methodology. [ABSTRACT FROM AUTHOR]

This paper generalizes the information-theoretic optimality of minimum mean square error successive interference cancellation in layered space-time coding with rate and power control. Based on this derivation, a new concept relying on partial feedback is introduced, whose core idea is to exploit an additional degree of freedom relative to the partitioning of transmit antennas. Taking into account this additional degree of freedom, together with power control and decoding order, allows the reduction of the quantization noise induced by the user of finite discrete-rate sets at the transmitter. However, the simultaneous optimization of all those degrees of freedom proves to be computationally intensive and would result in a tremendous feedback load. Practical algorithms are thus proposed to achieve this optimization with a reasonable complexity and a limited amount feedback. Monte-Carlo simulations show that those algorithms perform close to the theoretical limits. [ABSTRACT FROM AUTHOR]

In this work, a simulation environment for the development of flow-related ultrasound algorithms is presented. Ultrasound simulations of realistic Doppler signals require accurate modeling of blood flow. Instead of using analytically described flow behavior, complex blood movement can be derived from velocity fields obtained with computational fluid dynamics (CFD). By further modeling blood as a collection of point scatterers, resulting RF-signals can be efficiently retrieved using an existing ultrasound simulation model. The main aim of this paper is to elaborate on creating CFD-based phantoms for ultrasound simulations. The coupling of a computed flow field with an ultrasound model offers flexible control of flow and ultrasound imaging parameters, beneficial for improving and developing imaging algorithms. The proposed method was validated in a straight tube with a stationary parabolic velocity profile and further demonstrated by an eccentrically stenosis carotid bifurcation. The estimated flow velocities are in good agreement with the CFD reference, both for color flow imaging and pulsed-wave doppler simulations. The presented method can also be extended to include wall mechanics simulations in future work. [ABSTRACT FROM AUTHOR]

Providing evidence for the refutation of a property is an essential, if not the most important, feature of model checking. This paper considers algorithms for counterexample generation for probabilistic CTL formulas in discrete-time Markov chains. Finding the strongest evidence (i.e., the most probable path) violating a (bounded) until-formula is shown to be reducible to a single-source (hop-constrained) shortest path problem. Counterexamples of smallest size that deviate most from the required probability bound can be obtained by applying (small amendments to) k-shortest (hop-constrained) paths algorithms. These results can be extended to Markov chains with rewards, to LTL model checking, and are useful for Markov decision processes. Experimental results show that, typically, the size of a counterexample is excessive. To obtain much more compact representations, we present a simple algorithm to generate (minimal) regular expressions that can act as counterexamples. The feasibility of our approach is illustrated by means of two communication protocols: leader election in an anonymous ring network and the Crowds protocol. [ABSTRACT FROM AUTHOR]

Because of their deployment in dispersed locations on the lowest voltage portions of the grid, photovoltaic (PV) systems pose unique challenges to power system engineers. Computer models that accurately simulate the relevant behavior of PV systems would thus be of high value. However, most of today's models either do not accurately model the dynamics of the maximum power point trackers (MPPTs) or anti-islanding algorithms, or they involve excessive computational overhead for this application. To address this need, a MATLAB/Simulink model of a single-phase grid-connected PV inverter has been developed and experimentally tested. The development of the PV array model, the integration of the MPPT with an averaged model of the power electronics, and the Simulink implementation are described. It is experimentally demonstrated that the model works well in predicting the general behaviors of single-phase grid-connected PV systems. This paper concludes with a discussion of the need for a full gradient-based MPPT model, as opposed to a commonly used simplified MPPT model. [ABSTRACT FROM AUTHOR]

Finding the positions of nodes in an ad hoc wireless sensor network (WSN) with the use of the incomplete and noisy distance measurements between nodes as well as anchor position information is currently an important and challenging research topic. However, most WSN localization studies have considered that the anchor positions and the signal propagation speed are perfectly known which is not a valid assumption in the underwater and underground scenarios. In this paper, semi-definite programming (SDP) algorithms are devised for node localization in the presence of these uncertainties. The corresponding Cramér-Rao lower bound (CRLB) is also produced. Computer simulations are included to contrast the performance of the proposed algorithms with the conventional SDP method and CRLB. [ABSTRACT FROM AUTHOR]

Abstract-This paper presents a new collaborative peer-to-peer (P2P) streaming framework for heterogeneous bandwidth capacity of clients. The proposed architecture aims for two primary goals: 1) providing higher streaming quality by making best use of the extra available bandwidth that might exist among heterogeneous clients and 2) providing robustness and resilience to high churn rate of peers by introducing redundancy, both in network paths (multisender overlay) and data (multilayered video content). For achieving these goals, we employ a gossip-based data-driven scheme for partnership formation and layered video coding for bandwidth adaptivity. To solve multilayer bitstream allocation problem, we proposed two algorithms, namely optimized transmission policy and graceful degradation scheme algorithms. The proposed system has a complete self-regulation and in a decentralized fashion. Extensive simulations show that the proposed system achieves higher quality of service through peer-assisted streaming and layered video coding. The results also show that the system outperforms some previous schemes in system overhead and reliability for dynamic node behavior. [ABSTRACT FROM AUTHOR]

The problem of segmentation of tracking sequences is of central importance in a multitude of applications. In the current paper, a different approach to the problem is discussed. Specifically, the proposed segmentation algorithm is implemented in conjunction with estimation of the dynamic parameters of moving objects represented by the tracking sequence. While the information on objects' motion allows one to transfer some valuable segmentation priors along the tracking sequence, the segmentation allows substantially reducing the complexity of motion estimation, thereby facilitating the computation. Thus, in the proposed methodology, the processes of segmentation and motion estimation work simultaneously, in a sort of "collaborative" manner. The Bayesian estimation framework is used here to perform the segmentation, while Kalman filtering is used to estimate the motion and to convey useful segmentation information along the image sequence. The proposed method is demonstrated on a number of both computed-simulated and real-life examples, and the obtained results indicate its advantages over some alternative approaches. [ABSTRACT FROM AUTHOR]

Symbol timing offset (STO) can result in intersymbol interference (ISI) and a rotated phase which value is proportional to the subcarrier index at the FFT output in an OFDM receiver. In order to avoid ISI, the FFT window start position has to be put in advance of the estimated point obtained by coarse STO estimation algorithms. But a large number of forward-shift samples will deteriorate the estimation of data subcarrier channels requiring interpolation from pilot subcarrier channels due to the phase rotation caused by a residual STO. In this paper we analyze the influence of STO on channel interpolation and propose a new compensation method for channel correction with STO. From the performance analysis of simulation results in the DVB-T application, the new algorithm not only has a better performance but also is attractive in using a simple residual STO estimation method such that few pilots are required for fine STO estimation compared to conventional approaches. [ABSTRACT FROM AUTHOR]

The most popular algorithms for blind equalization are the constant-modulus algorithm (CMA) and the Shalvi-Wein- stein algorithm (SWA). It is well-known that SWA presents a higher convergence rate than CMA, at the expense of higher computational complexity. If the forgetting factor is not sufficiently close to one, if the initialization is distant from the optimal solution, or if the signal-to-noise ratio is low, SWA can converge to undesirable local minima or even diverge. In this paper, we show that divergence can be caused by an inconsistency in the nonlinear estimate of the transmitted signal, or (when the algorithm is implemented in finite precision) by the loss of positiveness of the estimate of the autocorrelation matrix, or by a combination of both. In order to avoid the first cause of divergence, we propose a dual-mode SWA. In the first mode of operation, the new algorithm works as SWA; in the second mode, it rejects inconsistent estimates of the transmitted signal. Assuming the persistence of excitation condition, we present a deterministic stability analysis of the new algorithm. To avoid the second cause of divergence, we propose a dual-mode lattice SWA, which is stable even in finite-precision arithmetic, and has a computational complexity that increases linearly with the number of adjustable equalizer coefficients. The good performance of the proposed algorithms is confirmed through numerical simulations. [ABSTRACT FROM AUTHOR]