Watermarking capacity control for dynamic payload embedding

Despite significant improvements in capacity-distortion performance, a computationally efficient capacity control is still lacking in the recent watermarking schemes. In this paper, we propose an efficient capacity control framework to substantiate the notion of watermarking capacity control to be the process of maintaining “acceptable” distortion and running time, while attaining the required capacity. The necessary analysis and experimental results on the capacity control are reported to address practical aspects of the watermarking capacity problem, in dynamic (size) payload embedding.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Evolutionary optimization and game strategies for advanced multi-disciplinary design : applications to aeronautics and UAV design

Many complex aeronautical design problems can be formulated with efficient multi-objective evolutionary optimization methods and game strategies. This book describes the role of advanced innovative evolution tools in the solution, or the set of solutions of single or multi disciplinary optimization. These tools use the concept of multi-population, asynchronous parallelization and hierarchical topology which allows different models including precise, intermediate and approximate models with each node belonging to the different hierarchical layer handled by a different Evolutionary Algorithm. The efficiency of evolutionary algorithms for both single and multi-objective optimization problems are significantly improved by the coupling of EAs with games and in particular by a new dynamic methodology named “Hybridized Nash-Pareto games”. Multi objective Optimization techniques and robust design problems taking into account uncertainties are introduced and explained in detail. Several applications dealing with civil aircraft and UAV, UCAV systems are implemented numerically and discussed. Applications of increasing optimization complexity are presented as well as two hands-on test cases problems. These examples focus on aeronautical applications and will be useful to the practitioner in the laboratory or in industrial design environments. The evolutionary methods coupled with games presented in this volume can be applied to other areas including surface and marine transport, structures, biomedical engineering, renewable energy and environmental problems.

Fusion of Multisensor Data:Review and Comparative Analysis

Image fusion is a formal framework which is expressed as means and tools for the alliance of multisensor, multitemporal, and multiresolution data. Multisource data vary in spectral, spatial and temporal resolutions necessitating advanced analytical or numerical techniques for enhanced interpretation capabilities. This paper reviews seven pixel based image fusion techniques - intensity-hue-saturation, brovey, high pass filter (HPF), high pass modulation (HPM), principal component analysis, fourier transform and correspondence analysis.Validation of these techniques on IKONOS data (Panchromatic band at I m spatial resolution and Multispectral 4 bands at 4 in spatial resolution) reveal that HPF and HPM methods synthesises the images closest to those the corresponding multisensors would observe at the high resolution level.

A vector measure for the intelligence of a question-answering (Q-A) system

The problem of quantification of intelligence of humans, and of intelligent systems, has been a challenging and controversial topic. IQ tests have been traditionally used to quantify human intelligence based on results of test designed by psychologists. It is in general very difficult to quantify intelligence. In this paper the authors consider a simple question-answering (Q-A) system and use this to quantify intelligence. The authors quantify intelligence as a vector with three components. The components consist of a measure of knowledge in asking questions, effectiveness of questions asked, and correctness of deduction. The authors formalize these parameters and have conducted experiments on humans to measure these parameters

Measurement of Stress-Strain Response of A Rammed Earth Prism In Compression Using Fiber Bragg Grating Sensors

A comparative study of strain response and mechanical properties of rammed earth prisms, has been made using Fiber Bragg Grating (FBG) sensors (optical) and clip-on extensometer (electro-mechanical). The aim of this study is to address the merits and demerits of traditional extensometer vis-à-vis FBG sensor; a uni-axial compression test has been performed on a rammed earth prism to validate its structural properties from the stress - strain curves obtained by two different methods of measurement. An array of FBG sensors on a single fiber with varying Bragg wavelengths (..B), has been used to spatially resolve the strains along the height of the specimen. It is interesting to note from the obtained stress-strain curves that the initial tangent modulus obtained using the FBG sensor is lower compared to that obtained using clip-on extensometer. The results also indicate that the strains measured by both FBG and extensometer sensor follow the same trend and both the sensors register the maximum strain value at the same time.

mRegistry: a registry representation for fault diagnosis

Microsoft Windows uses the notion of registry to store all configuration information. The registry entries have associations and dependencies. For example, the paths to executables may be relative to some home directories. The registry being designed with faster access as one of the objectives does not explicitly capture these relations. In this paper, we explore a representation that captures the dependencies more explicitly using shared and unifying variables. This representation, called mRegistry exploits the tree-structured hierarchical nature of the registry, is concept-based and obtained in multiple stages. mRegistry captures intra-block, inter-block and ancestor-children dependencies (all leaf entries of a parent key in a registry put together as an entity constitute a block thereby making the block as the only child of the parent). In addition, it learns the generalized concepts of dependencies in the form of rules. We show that mRegistry has several applications: fault diagnosis, prediction, comparison, compression etc.

Fish inspired biomimetic ionic polymer metal composite pectoral fins using labriform propulsion

Ionic polymer metal composites (IPMC) are a new class of smart materials that have attractive characteristics such as muscle like softness, low voltage and power consumption, and good performance in aqueous environments. Thus, IPMC’s provide promising application for biomimetic fish like propulsion systems. In this paper, we design and analyze IPMC underwater propulsor inspired from swimming of Labriform fishes. Different fish species in nature are source of inspiration for different biomimetic flapping IPMC fin design. Here, three fish species with high performance flapping pectoral fin locomotion is chosen and performance analysis of each fin design is done to discover the better configurations for engineering applications. In order to describe the behavior of an active IPMC fin actuator in water, a complex hydrodynamic function is used and structural model of the IPMC fin is obtained by modifying the classical dynamic equation for a slender beam. A quasi-steady blade element model that accounts for unsteady phenomena such as added mass effects, dynamic stall, and the cumulative Wagner effect is used to estimate the hydrodynamic performance of the flapping rectangular shape fin. Dynamic characteristics of IPMC actuated flapping fins having the same size as the actual fins of three different fish species, Gomphosus varius, Scarus frenatus and Sthethojulis trilineata, are analyzed with numerical simulations. Finally, a comparative study is performed to analyze the performance of three different biomimetic IPMC flapping pectoral fins.

Algorithm for mining outliers in categorical data through ranking

The rapid growth in the field of data mining has lead to the development of various methods for outlier detection. Though detection of outliers has been well explored in the context of numerical data, dealing with categorical data is still evolving. In this paper, we propose a two-phase algorithm for detecting outliers in categorical data based on a novel definition of outliers. In the first phase, this algorithm explores a clustering of the given data, followed by the ranking phase for determining the set of most likely outliers. The proposed algorithm is expected to perform better as it can identify different types of outliers, employing two independent ranking schemes based on the attribute value frequencies and the inherent clustering structure in the given data. Unlike some existing methods, the computational complexity of this algorithm is not affected by the number of outliers to be detected. The efficacy of this algorithm is demonstrated through experiments on various public domain categorical data sets.

Unsupervised feature selection for outlier detection in categorical data using mutual information

Outlier detection in high dimensional categorical data has been a problem of much interest due to the extensive use of qualitative features for describing the data across various application areas. Though there exist various established methods for dealing with the dimensionality aspect through feature selection on numerical data, the categorical domain is actively being explored. As outlier detection is generally considered as an unsupervised learning problem due to lack of knowledge about the nature of various types of outliers, the related feature selection task also needs to be handled in a similar manner. This motivates the need to develop an unsupervised feature selection algorithm for efficient detection of outliers in categorical data. Addressing this aspect, we propose a novel feature selection algorithm based on the mutual information measure and the entropy computation. The redundancy among the features is characterized using the mutual information measure for identifying a suitable feature subset with less redundancy. The performance of the proposed algorithm in comparison with the information gain based feature selection shows its effectiveness for outlier detection. The efficacy of the proposed algorithm is demonstrated on various high-dimensional benchmark data sets employing two existing outlier detection methods.

Data Fusion of Dual Foot-Mounted INS to Reduce the Systematic Heading Drift

In this report, we investigate the problem of applying a range constraint in order to reduce the systematic heading drift in a foot-mounted inertial navigation system (INS) (motion-tracking). We make use of two foot-mounted INS, one on each foot, which are aided with zero-velocity detectors. A novel algorithm is proposed in order to reduce the systematic heading drift. The proposed algorithm is based on the idea that the separation between the two feet at any given instance must always lie within a sphere of radius equal to the maximum possible spatial separation between the two feet. A Kalman filter, getting one measurement update and two observation updates is used in this algorithm.

Classification of Vehicles Using Magnetic Field Angle Model

This paper presents an efficient approach to the modeling and classification of vehicles using the magnetic signature of the vehicle. A database was created using the magnetic signature collected over a wide range of vehicles(cars). A sensor dependent approach called as Magnetic Field Angle Model is proposed for modeling the obtained magnetic signature. Based on the data model, we present a novel method to extract the feature vector from the magnetic signature. In the classification of vehicles, a linear support vector machine configuration is used to classify the vehicles based on the obtained feature vectors.