1693393
An Evaluation of Confidence Bound Estimation Methods for Neural Networks
When artificial neural networks (ANN) are used in prediction problems, it is usually desirable that some form of confidence bound is placed on the predicted value. Methods to estimate the confidence bound are available. However, these methods are valid under certain assumptions, which are rarely satisfied in practice. The behavior of the estimated confidence bound is not well understood when the assumptions are violated. We have designed some test functions to examine the behavior. The experimental results give us some guidelines on how the confidence estimation methods should be used in our application, which is to predict rock porosity values from seismic data for oil reservoir characterisation.
1256891
A Supervised Approach to the Evaluation of Image Segmentation Methods
Evaluation is an important step in developing a segmentation algorithm for an image analysis system. We first give a review of segmentation evaluation methods, and then demonstrate how a supervised evaluation method based on shape features is used in the development of a segmentation algorithm for fluorescence images of white blood cells.
21010264
Fast Computation of Three-Dimensional Geometric Moments Using a Discrete Divergence Theorem and a Generalization to Higher Dimensions
Abstract The three-dimensional Cartesian geometric moments (for short 3-D moments) are important features for 3-D object recognition and shape description. To calculate the moments of objects in a 3-D image by a straightforward method requires a large number of computing operations. Some authors have proposed fast algorithms to compute the 3-D moments. However, the problem of computation has not been well solved since all known methods require computations of order N 3 , assuming that the object is represented by an N × N × N voxel image. In this paper, we present a discrete divergence theorem which can be used to compute the sum of a function over an n -dimensional discrete region by a summation over the discrete surface enclosing the region. As its corollaries, we give a discrete Gauss's theorem for 3-D discrete objects and a discrete Green's theorem for 2-D discrete objects. Using a fast surface tracking algorithm and the discrete Gauss's theorem, we design a new method to compute the geometric moments of 3-D binary objects as observed in 3-D discrete images. This method reduces the computational complexity significantly, requiring computation of O ( N 2 ). This reduction is demonstrated experimentally on two 3-D objects. We also generalize the method to deal with high-dimensional images. Some 3-D moment invariants and shape features are also discussed.
20306091
Fast and exact computation of Cartesian geometric moments using discrete Green's theorem
Abstract Green's theorem evaluates a double integral over a planar object by a simple integration along the boundary of the object. It can be used in fast computation of Cartesian geometric moments of two-dimensional binary objects, since the object shape is totally determined by the boundary. Li and Shen [Pattern Recognition 24, 807–813 (1991)] used an approximation of Green's theorem to deal with discrete objects. This method is fast, but not accurate. In this paper, we present a discrete version of Green's theorem, which evaluates a double sum over a two-dimensional discrete object by a simple summation along the discrete boundary of the object. By using the discrete Green's theorem, we propose a new algorithm for fast computation of the geometric moments. The new algorithm is faster than the previous methods, and gives exactly the same results as if a double sum were used. The importance of the exact computation is discussed by examining the accuracy of Hu's moment invariants and some other moment based shape features, which are frequently used in various shape description and pattern recognition tasks. A fast method for computing moments of regions in gray level images, using the discrete Green's theorem, is also presented. We show that the moments of this type are useful in structural texture analysis.
12558325
fast Computation of 3-D Geometric Moments Using a Discrete Gauss' Theorem
A discrete Gauss' theorem is presented. Using a fast surface tracking algorithm and the discrete Gauss' theorem, we design a new method to compute the Cartesian geometric moments of 3-D objects. Compared to previous methods to compute such moments, the new method reduces the computational complexity significantly.
45184
CRAMES: compressed RAM for embedded systems
Memory is a scarce resource in many embedded systems. Increasing memory often increases packaging and cooling costs, size, and energy consumption. This paper presents CRAMES, an efficient software-based RAM compression technique for embedded systems. The goal of CRAMES is to dramatically increase effective memory capacity without hardware design changes, while maintaining high performance and low energy consumption. To achieve this goal, CRAMES takes advantage of an operating system's virtual memory infrastructure by storing swapped-out pages in compressed format. It dynamically adjusts the size of the compressed RAM area, protecting applications capable of running without it from performance or energy consumption penalties. In addition to compressing working data sets, CRAMES also enables efficient in-RAM filesystem compression, thereby further increasing RAM capacity. CRAMES was implemented as a loadable module for the Linux kernel and evaluated on a battery-powered embedded system. Experimental results indicate that CRAMES is capable of doubling the amount of RAM available to applications. Execution time and energy consumption for a broad range of examples increase only slightly, by averages of 0.35% and 4.79%. In addition, this work identifies the software-based compression algorithms that are most appropriate for low-power embedded systems.
15933152
Homeostatic and tendency-based CPU load predictions
The dynamic nature of a resource-sharing environment means that applications must be able to adapt their behavior in response to changes in system status. Predictions of future system performance can be used to guide such adaptations. In this paper, we present and evaluate several new one-step-ahead and low-overhead time series prediction strategies that track recent trends by giving more weight to recent data. We present results that show that a dynamic tendency prediction model with different ascending and descending behavior performs best among all strategies studied. A comparative study conducted on a set of 38 machine load traces shows that this new predictor achieves average prediction errors that are between 2% and 55% less (36% less on average) than those incurred by the predictors used within the popular Network Weather Service system.
236427
Efficient XML query processing in mediators
With the advent of XML as the standard for data representation and exchange over the Internet, there are ever increasing applications; E-commerce application in particular, using XML as a mediator model. It raises the issue of how to evaluate XML queries efficiently in mediator systems. The authors address the problem of multiple query optimization in an XML based mediator. Because the XML query language differs from SQL/OQL in its pattern matching and regular path expression constructs, the method of identifying common subexpressions of multiple XML queries is different accordingly. We present the steps to identify common subexpressions. To obtain the global query plan, we propose some heuristics to reduce the whole search space. By using a statistical method to estimate the usage frequency of the common subexpressions in XML queries, a selective caching and materializing of remote data can be accurately made, and this will greatly improve query response time.
3016307
Weighted Information Entropy: A Method for Estimating the Complex Degree of Infrared Images' Backgrounds
The validity of using the weighted information entropy to estimate the complex degree of the infrared images' backgrounds is discussed in this paper. A Butterworth high pass filter whose cut-off frequency can be adaptively regulated to meet the different backgrounds is proposed to restrain the different clutters. Since the backgrounds usually have some random change in the video sequences, an adaptive binarisation criterion for small target detection is also presented. Experimental results show the robustness of our method.
974543
A framework for sensor management in wireless and heterogeneous sensor network
This paper proposes a framework for sensor configuration and management in wireless multi sensor network system, which is responsible for taking decisions in order to coordinate the assignment and scheduling of sensors best suited for the application. A sensor manager operates over a heterogeneous sensor network, which provides sensory data from multiple types of sensors. The application of this work is tracking the movement of objects in a moderately occupied confined space. Simulations were run to study the operations of the proposed sensor manage in such application. The simulation results are compared to other published results to show the effectiveness of this work.