Classification of audio radar signals using radial basis function neural networks

Radial basis function (RBF) neural networks are used to classify real-life audio radar signals that are collected by a ground surveillance radar mounted on a tank. Currently, a human operator is required to operate the radar system to discern among signals bouncing off tanks, vehicles, planes, and so on. The objective of this project is to investigate the possibility of using a neural network to perform this target recognition task, with the aim of reducing the number of personnel required in a tank. Different signal classification methods in the neural net literature are considered. The first method employs a linear autoregressive (AR) model to extract linear features of the audio data, and then perform classification on these features, i.e, the AR coefficients. AR coefficient estimations based on least squares and higher order statistics are considered in this study. The second approach uses nonlinear predictors to model the audio data and then classifies the signals according to the prediction errors. The real-life audio radar data set used here was collected by an AN/PPS-15 ground surveillance radar and consists of 13 different target classes, which include men marching, a man walking, airplanes, a man crawling, and boats, etc. It is found that each classification method has some classes which are difficult to classify. Overall, the AR feature extraction approach is most effective and has a correct classification rate of 88% for the training data and 67% for data not used for training.     

Reducing Algorithm Complexity for Computing an Aggregate Uncertainty Measure

In the theory of evidence, two kinds of uncertainty coexist, nonspecificity and discord. An aggregate uncertainty (AU) measure has been defined to include these two kinds of uncertainty, in an aggregate fashion. Meyerowitz et al. proposed an algorithm for calculating AU and validated its practical usage. Although this algorithm was proven to be absolutely correct by Klir and Wierman, in some cases, it remains too complex. In fact, when the cardinality of the frame of discernment is very large, it can be impossible to calculate AU. Therefore, based on Klir's and Harmanec's seminal work, we give some justifications for restricting the computation of AU(Bel) to the core of the corresponding belief function, and we also propose an algorithm to calculate AU(Bel), the F-algorithm, which reduces the computational complexity of the original algorithm of Meyerowitz et al. We prove that this algorithm gives the same results as Meyerowitz's algorithm, and we outline conditions under which it reduces the computational complexity significantly. Moreover, we illustrate the use of the F-algorithm in computing AU in a practical scenario of target identification.     

Pharmacokinetics of liposomal amphotericin B (Ambisome) in critically ill patients

The liposomal formulation of amphotericin B (AmBisome) greatly reduces the acute and chronic side effects of the parent drug. The present study describes the pharmacokinetic characteristics of AmBisome applied to 10 patients at a dose of 2.8 to 3.0 mg/kg of body weight and compares them to the pharmacokinetics observed in 6 patients treated with amphotericin B deoxycholate at the standard dose of 1.0 mg/kg. Interpatient variabilities of amphotericin B peak concentrations (Cmax) and areas under concentration-time curves (AUC) were 8- to 10-fold greater for patients treated with AmBisome than for patients treated with amphotericin B deoxycholate. At the threefold greater dose of AmBisome, median Cmaxs were 8.4-fold higher (14.4 versus 1.7 microg/ml) and median AUCs exceeded those observed with amphotericin B deoxycholate by 9-fold. This was in part explained by a 5.7-fold lower volume of distribution (0.42 liters/kg) in AmBisome-treated patients. The elimination of amphotericin B from serum was biphasic for both formulations. However, the apparent half-life of elimination was twofold shorter for AmBisome (P = 0.03). Neither hemodialysis nor hemofiltration had a significant impact on AmBisome pharmacokinetics as analyzed in one patient. In conclusion, the liposomal formulation of amphotericin B significantly (P = 0.001) reduces the volume of drug distribution, thereby allowing for greater drug concentrations in serum. The low toxicity of AmBisome therefore cannot readily be explained by its serum pharmacokinetics.     

Summary Maps for Lifelong Visual Localization

Robots that use vision for localization need to handle environments that are subject to seasonal and structural change, and operate under changing lighting and weather conditions. We present a framework for lifelong localization and mapping designed to provide robust and metrically accurate online localization in these kinds of changing environments. Our system iterates between offline map building, map summary, and online localization. The offline mapping fuses data from multiple visually varied datasets, thus dealing with changing environments by incorporating new information. Before passing these data to the online localization system, the map is summarized, selecting only the landmarks that are deemed useful for localization. This Summary Map enables online localization that is accurate and robust to the variation of visual information in natural environments while still being computationally efficient. We present a number of summary policies for selecting useful features for localization from the multisession map, and we explore the tradeoff between localization performance and computational complexity. The system is evaluated on 77 recordings, with a total length of 30 kilometers, collected outdoors over 16 months. These datasets cover all seasons, various times of day, and changing weather such as sunshine, rain, fog, and snow. We show that it is possible to build consistent maps that span data collected over an entire year, and cover day‐to‐night transitions. Simple statistics computed on landmark observations are enough to produce a Summary Map that enables robust and accurate localization over a wide range of seasonal, lighting, and weather conditions.     

Three‐dimensional localization for the MagneBike inspection robot

The MagneBike inspection robot is a climbing robot equipped with magnetic wheels. The robot is designed to drive on three‐dimensional (3D) complexly shaped pipe structures; therefore it is necessary to provide 3D visualization tools for the user, who remotely controls the robot out of sight. The localization system is required to provide a 3D map of the unknown environment and the 3D location of the robot in the environment's map. The localization strategy proposed in this paper consists of combining 3D odometry with 3D scan registration. The odometry model is based on wheel encoders and a three‐axis accelerometer. Odometry enables the tracking of the robot trajectory between consecutive 3D scans and is used as a prior for the scan matching algorithm. The 3D scan registration facilitates the construction of a 3D map of the environment and refines the robot position computed with odometry. This paper describes in detail the implementation of the localization concept. It presents the lightweight, small‐sized 3D range finder that has been developed for the MagneBike. It also proposes an innovative 3D odometry model that estimates the local surface curvature to compensate for the absence of angular velocity inputs. The different tools are characterized in detail based on laboratory and field experiments. They show that the localization concepts reliably track the robot moving in the specific application environment. We also describe various techniques to optimize the 3D scanning process, which is time consuming, and to compensate for the identified limitations. These techniques are useful inputs for the future automatization of the robot's control and optimization of its localization process. © 2010 Wiley Periodicals, Inc.     

Calibrated,Registered Images of an Extended Urban Area

We describe a dataset of several thousand calibrated, time-stamped, geo-referenced, high dynamic range color images, acquired under uncontrolled, variable illumination conditions in an outdoor region spanning several hundred meters. The image data is grouped into several regions which have little mutual inter-visibility. For each group, the calibration data is globally consistent on average to roughly five centimeters and 0 1°, or about four pixels of epipolar registration. All image, feature and calibration data is available for interactive inspection and downloading at http://city.lcs.mit.edu/data.Calibrated imagery is of fundamental interest in a variety of applications. We have made this data available in the belief that researchers in computer graphics, computer vision, photogrammetry and digital cartography will find it of value as a test set for their own image registration algorithms, as a calibrated image set for applications such as image-based rendering, metric 3D reconstruction, and appearance recovery, and as input for existing GIS applications.     

Modelling collective decision making in groups and crowds: Integrating social contagion and interacting emotions, beliefs and intentions

Collective decision making involves on the one hand individual mental states such as beliefs, emotions and intentions, and on the other hand interaction with others with possibly different mental states. Achieving a satisfactory common group decision on which all agree requires that such mental states are adapted to each other by social interaction. Recent developments in social neuroscience have revealed neural mechanisms by which such mutual adaptation can be realised. These mechanisms not only enable intentions to converge to an emerging common decision, but at the same time enable to achieve shared underlying individual beliefs and emotions. This paper presents a computational model for such processes. As an application of the model, an agent-based analysis was made of patterns in crowd behaviour, in particular to simulate a real-life incident that took place on May 4, 2010 in Amsterdam. From available video material and witness reports, useful empirical data were extracted. Similar patterns were achieved in simulations, whereby some of the parameters of the model were tuned to the case addressed, and most parameters were assigned default values. The results show the inclusion of contagion of belief, emotion, and intention states of agents results in better reproduction of the incident than non-inclusion.     

Neural data fusion algorithms based on a linearly constrained leastsquare method

Two novel neural data fusion algorithms based on a linearly constrained least square (LCLS) method are proposed. While the LCLS method is used to minimize the energy of the linearly fused information, two neural-network algorithms are developed to overcome the ill-conditioned and singular problems of the sample covariance matrix arisen in the LCLS method. The proposed neural fusion algorithms are samples for implementation using both software and hardware. Compared with the existing fusion methods, the proposed neural data fusion method has an unbiased statistical property and does not require any a priori knowledge about the noise covariance. It is shown that the proposed neural fusion algorithms converge globally to the optimal fusion solution when the sample covariance matrix is singular, and converge globally with exponential rate when the sample covariance matrix is nonsingular. We apply the proposed neural fusion method to image and signal fusion, and it is shown that the quality of the solution can be greatly enhanced by the proposed technique.     

Masdar生态广场,阿拉伯联合酋长国

阿联酋新的生态城市广场上,巨大的伞状物提供了可移动的阴凉,同时白天储存热量,夜晚将其释放。这一基于向日葵特性的伞状物创意,使得LAVA一举赢得了国际竞赛。Masdar是阿联酋第一个零碳排放、零污染的城市,距阿布扎比17km,预计将由7期建成。