Accurate point matching based on multi-objective Genetic Algorithm for multi-sensor satellite imagery

This paper investigates a novel approach for point matching of multi-sensor satellite imagery. The feature (corner) points extracted using an improved version of the Harris Corner Detector (HCD) is matched using multi-objective optimization based on a Genetic Algorithm (GA). An objective switching approach to optimization that incorporates an angle criterion, distance condition and point matching condition in the multi-objective fitness function is applied to match corresponding corner-points between the reference image and the sensed image. The matched points obtained in this way are used to align the sensed image with a reference image by applying an affine transformation. From the results obtained, the performance of the image registration is evaluated and compared with existing methods, namely Nearest Neighbor-Random SAmple Consensus (NN-Ran-SAC) and multi-objective Discrete Particle Swarm Optimization (DPSO). From the performed experiments it can be concluded that the proposed approach is an accurate method for registration of multi-sensor satellite imagery. (C) 2014 Elsevier Inc. All rights reserved.

Performance study of a cesium iodide photocathode-based UV photon detector in Ar/CH4 mixture

The detection efficiency of a gaseous photomultiplier depends on the photocathode quantum efficiency and the extraction efficiency of photoelectrons into the gas. In this paper we have studied the performance of an UV photon detector with P10 gas in which the extraction efficiency can reach values near to those in vacuum operated devices. Simulations have been done to compare the percentage of photoelectrons backscattered in P10 gas as well as in the widely used neon-based gas mixture. The performance study has been carried out using a single stage thick gas electron multiplier (THGEM). The electron pulses and electron spectrum are recorded under various operating conditions. Secondary effects prevailing in UV photon detectors like photon feedback are discussed and its effect on the electron spectrum under different operating conditions is analyzed. (C) 2014 Chinese Laser Press

An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.

State Transition Based Embedding in Cepstrum Domain for Audio Copyright Protection

In this paper, we propose a new state transition based embedding (STBE) technique for audio watermarking with high fidelity. Furthermore, we propose a new correlation based encoding (CBE) scheme for binary logo image in order to enhance the payload capacity. The result of CBE is also compared with standard run-length encoding (RLE) compression and Huffman schemes. Most of the watermarking algorithms are based on modulating selected transform domain feature of an audio segment in order to embed given watermark bit. In the proposed STBE method instead of modulating feature of each and every segment to embed data, our aim is to retain the default value of this feature for most of the segments. Thus, a high quality of watermarked audio is maintained. Here, the difference between the mean values (Mdiff) of insignificant complex cepstrum transform (CCT) coefficients of down-sampled subsets is selected as a robust feature for embedding. Mdiff values of the frames are changed only when certain conditions are met. Hence, almost 50% of the times, segments are not changed and still STBE can convey watermark information at receiver side. STBE also exhibits a partial restoration feature by which the watermarked audio can be restored partially after extraction of the watermark at detector side. The psychoacoustic model analysis showed that the noise-masking ratio (NMR) of our system is less than -10dB. As amplitude scaling in time domain does not affect selected insignificant CCT coefficients, strong invariance towards amplitude scaling attacks is also proved theoretically. Experimental results reveal that the proposed watermarking scheme maintains high audio quality and are simultaneously robust to general attacks like MP3 compression, amplitude scaling, additive noise, re-quantization, etc.

A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.

Effects of the Spanwise Characteristic Length on the Transition Feature in the Wake of a Cylinder

The transition features of the wake behind a uniform circular cylinder at Re = 200, which is just beyond the critical Reynolds number of 3-D transition, are investigated in detail by direct numerical simulations of 3-D incompressible Navier-Stokes equations. The spanwise characteris-tic length determines the transition features and global properties of the wake.

Carbon onions: Carriers of the 217.5 nm interstellar absorption feature

The UV-VIS characteristics of carbon ions fabricated by an arch discharge in water or liquid nitrogen show characteristics that are similar to the interstellar absorption feature. Thus, high purity and well separated carbon onion samples prepared by annealing the as-fabricated powder in air at 600°C for 60 showed a constant absorption peak at 4.6 μm-1 with a variable width ranging from 1.2-1.6 μm-1. In addition to the UV-VIS data, the origin of the absorption feature at 4.6 μm-1 in carbon onions can be attributed to the collective excitations of π plasmons.