Human Motion Planning Based on Recursive Dynamics and Optimal Control Techniques

This paper presents an efficient optimal control and recursive dynamics-based computer animation system for simulating and controlling themotion of articulated figures. A quasi-Newton nonlinear programmingtechnique (super-linear convergence) is implemented to solve minimumtorque-based human motion-planning problems. The explicit analyticalgradients needed in the dynamics are derived using a matrix exponentialformulation and Lie algebra. Cubic spline functions are used to make thesearch space for an optimal solution finite. Based on our formulations,our method is well conditioned and robust, in addition to beingcomputationally efficient. To better illustrate the efficiency of ourmethod, we present results of natural looking and physically correcthuman motions for a variety of human motion tasks involving open andclosed loop kinematic chains.     

A collision resolution algorithm for clump-free fast moving cloth

Cloth animation is an important area of computer graphics due to its numerous applications. However, so far a fast moving cloth with multiple wrinkles has been difficult to animate because of the cloth clump problem. Cloth clumps are the frozen areas where cloth pieces are clustered unnaturally — an obstacle in making a realistic cloth animation. Hence we present a novel cloth collision resolution algorithm that prevents clump formation during fast cloth motions. The goal of our resolution algorithm is to make cloth move swiftly without having any unnatural frozen cloth clumps, while preventing any cloth-cloth and rigid-cloth penetrations at any moment of a simulation. The non-penetration status of cloth is maintained without the formation of cloth clumps regardless of the speed of cloth motion. Our algorithm is based on a particular order that we found in the resolution of cloth collisions, and can be used with any structural modeling approaches such as spring-masses or finite elements. This paper includes several realistic simulation examples involving fast motions that are clump-free.     

Reverse nearest neighbors in large graphs

A reverse nearest neighbor (RNN) query returns the data objects that have a query point as their nearest neighbor (NN). Although such queries have been studied quite extensively in Euclidean spaces, there is no previous work in the context of large graphs. In this paper, we provide a fundamental lemma, which can be used to prune the search space while traversing the graph in search for RNN. Based on it, we develop two RNN methods; an eager algorithm that attempts to prune network nodes as soon as they are visited and a lazy technique that prunes the search space when a data point is discovered. We study retrieval of an arbitrary number k of reverse nearest neighbors, investigate the benefits of materialization, cover several query types, and deal with cases where the queries and the data objects reside on nodes or edges of the graph. The proposed techniques are evaluated in various practical scenarios involving spatial maps, computer networks, and the DBLP coauthorship graph.     

Aerosol lidar intercomparison in the framework of the EARLINET project. 1. Instruments

In the framework of the European Aerosol Research Lidar Network to Establish an Aerosol Climatology (EARLINET), 19 aerosol lidar systems from 11 European countries were compared. Aerosol extinction or backscatter coefficient profiles were measured by at least two systems for each comparison. Aerosol extinction coefficients were derived from Raman lidar measurements in the UV (351 or 355 nm), and aerosol backscatter profiles were calculated from pure elastic backscatter measurements at 351 or 355, 532, or 1064 nm. The results were compared for height ranges with high and low aerosol content. Some systems were additionally compared with sunphotometers and starphotometers. Predefined maximum deviations were used for quality control of the results. Lidar systems with results outside those limits could not meet the quality assurance criterion. The algorithms for deriving aerosol backscatter profiles from elastic lidar measurements were tested separately, and the results are described in Part 2 of this series of papers [Appl. Opt. 43, 977-989 (2004)]. In the end, all systems were quality assured, although some had to be modified to improve their performance. Typical deviations between aerosol backscatter profiles were 10% in the planetary boundary layer and 0.1 x 10(-6) m(-1) sr(-1) in the free troposphere.     

Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions

Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality in women. In the last decade, ultrasound along with digital mammography has come to be regarded as the gold standard for breast cancer diagnosis. Automatically detecting tumors and extracting lesion boundaries in ultrasound images is difficult due to their specular nature and the variance in shape and appearance of sonographic lesions. Past work on automated ultrasonic breast lesion segmentation has not addressed important issues such as shadowing artifacts or dealing with similar tumor like structures in the sonogram. Algorithms that claim to automatically classify ultrasonic breast lesions, rely on manual delineation of the tumor boundaries. In this paper, we present a novel technique to automatically find lesion margins in ultrasound images, by combining intensity and texture with empirical domain specific knowledge along with directional gradient and a deformable shape-based model. The images are first filtered to remove speckle noise and then contrast enhanced to emphasize the tumor regions. For the first time, a mathematical formulation of the empirical rules used by radiologists in detecting ultrasonic breast lesions, popularly known as the "Stavros Criteria" is presented in this paper. We have applied this formulation to automatically determine a seed point within the image. Probabilistic classification of image pixels based on intensity and texture is followed by region growing using the automatically determined seed point to obtain an initial segmentation of the lesion. Boundary points are found on the directional gradient of the image. Outliers are removed by a process of recursive refinement. These boundary points are then supplied as an initial estimate to a deformable model. Incorporating empirical domain specific knowledge along with low and high-level knowledge makes it possible to avoid shadowing artifacts and lowers the chance of confusing similar tumor like structures for the lesion. The system was validated on a database of breast sonograms for 42 patients. The average mean boundary error between manual and automated segmentation was 6.6 pixels and the normalized true positive area overlap was 75.1%. The algorithm was found to be robust to 1) variations in system parameters, 2) number of training samples used, and 3) the position of the seed point within the tumor. Running time for segmenting a single sonogram was 18 s on a 1.8-GHz Pentium machine.     

Pipelined recurrent fuzzy neural networks for nonlinear adaptive speech prediction.

A class of pipelined recurrent fuzzy neural networks (PRFNNs) is proposed in this paper for nonlinear adaptive speech prediction. The PRFNNs are modular structures comprising a number of modules that are interconnected in a chained form. Each module is implemented by a small-scale recurrent fuzzy neural network (RFNN) with internal dynamics. Due to module nesting, the PRFNNs offer a number of desirable attributes, including decomposition of the modeling task, enhanced temporal processing capabilities, and multistage dynamic fuzzy inference. Tuning of the PRFNN adaptable parameters is accomplished by a series of gradient descent methods with different weighting of the modules and the decoupled extended Kalman filter (DEKF) algorithm, based on weight grouping. Extensive experimentation is carried out to evaluate the performance of the PRFNNs on the speech prediction platform. Comparative analysis shows that the PRFNNs outperform the single-RFNN models in terms of the prediction gains that are obtained and computational efficiency. Furthermore, PRFNNs provide considerably better performance compared to pipelined recurrent neural networks, for models with similar model complexity.     

Amplitude Variability in Simulated Incoherent Seismic Ground Motions

This note compares in detail four commonly used schemes for the simulation of spatially variable ground motions. Emphasis is placed not only on the conformity of the simulations with the power and cross spectral density of the random field but, also, on the examination of the consistency of the simulations with the homogeneity condition, and the (Fourier) amplitude variability of the simulations. It is shown that, whereas three techniques that simulate ground motions in parallel satisfy the homogeneity requirement, produce simulations with random amplitudes, and amplitude and phase variability consistent with that of recorded data, one technique that simulates motions in sequence does not.     

Development of NiO-based thin film structures as efficient H2 gas sensors operating at room temperatures

P-type NiO thin films have been developed on high resistivity Si and SiO₂ substrates by a pulsed laser deposition technique using an ArF^? 193 nm excimer laser at deposition temperature of 300 °C and in 40 Pa partial oxygen pressure. Structures based on such NiO films as host material in the form of Au-NiO Schottky diodes have been subsequently developed under vacuum. In a different procedure, an n-SnO₂ layer has been deposited by a CVD technique on a NiO film to produce a p/n heterojunction. The sensing properties of all above structures have been tested upon exposure to a H₂ flow in air ambient gas at various operating temperature ranging from 30 to 180 °C. For the NiO films, the optimum temperature was about 150 °C exhibiting a sensitivity of 94%. After surface sensitization of NiO by Au the NiO films showed an H₂ response at operating temperature of 30 °C. The sensitivity of p-NiO/n-SnO₂ heterojunction devices was extracted from I-V measurements in air and under H₂ flow mixed in air. In this case a dramatic increase of the sensitivity was achieved at operating temperature of 30 °C for a forward bias of 0,2 V.     

Detecting unauthorized and compromised nodes in mobile ad hoc networks

Security of mobile ad hoc networks (MANET) has become a more sophisticated problem than security in other networks, due to the open nature and the lack of infrastructure of such networks. In this paper, the security challenges in intrusion detection and authentication are identified and the different types of attacks are discussed. We propose a two-phase detection procedure of nodes that are not authorized for specific services and nodes that have been compromised during their operation in MANET. The detection framework is enabled with the main operations of ad hoc networking, which are found at the link and network layers. The proposed framework is based on zero knowledge techniques, which are presented through proofs.     

The economics of integration of DVB‐S with terrestrial technologies and the role of dynamic bandwidth management

This paper presents the technoeconomic aspects of network solutions based on integration of satellite and terrestrial, based on ADSL, WLAN and DVB‐T technologies, architectures. Following a discussion of the general deployment context, the business case framework is described in terms of areas served, services offered, tariffs, demand assumptions, as well as network architecture and dimensioning rules. The key cost items are presented and economic estimations are provided in the case of telecom operators, which integrate satellite bandwidth with terrestrial technologies in different area types. The evaluated business cases have shown that the satellite rental cost (airtime cost) level is one of the most critical parameter for the economic indicator of present value. Therefore, agreements with satellite operators for spectrum usage will give operators space for business opportunities and acceptable profit margins. In addition, dynamic bandwidth allocation schemes can exploit the aggregated traffic within satellite transmission, which can benefit satellite, telecom and cable operators offering entertainment on broadband business as well as new entrants in broadband business, equipment vendors, broadcasters and TV companies. Copyright © 2007 John Wiley & Sons, Ltd.