AN INTERACTIVE COMPUTER FRAMEWORK FOR LEARNING GENETICS

To enhance the effectiveness of learning genetics, we have developed a series of individual computer programs integrating interactivity with animated processes. It was noted that, although the content of the programs varied, the programs all contained a number of common features, including the ability to display text and images, present animated content, and interact with the user. These common features led us to the development of an innovative and unified framework of integrated functions for modeling and simulations. The framework, named "GeneAct" was developed to standardize and accelerate the development of the computer based genetics learning programs and was used as the application programming interface (API). The API allows the content to be imbued with rich text (text with multi-formats), static images, and animations; and it also allows the program to be interactive.

HILBERT–HUANG TRANSFORM FOR FEATURE EXTRACTION OF TEMPERATURE MODULATED MOS SENSORS

This work investigates the potential use of temperature modulation of MOS gas sensors combined with the Hilbert–Huang transform (HHT) as a feature extraction mechanism for MOS-based electronic noses. Five samples each of ethyl acetate, ethanol and isopropanol were prepared. The response of each of four sensors in an array was decomposed using empirical mode decomposition and the marginal Hilbert spectrum was computed. A set of 72 frequency components was extracted from marginal Hilbert spectrum response of each sensor in an array of four sensor to produce a 288 element fingerprint of each sample. The fingerprints were successfully clustered using PCA and classified using a SVM neutral network.

SUPPORTING DISTRESS SIGNALS OVER LOW EARTH ORBIT MOBILE SATELLITE SYSTEMS FOR EMERGENCY INFORMATION ACQUISITION

Low earth orbit (LEO) satellite systems allow a broad range of services to be provided using small, lightweight, cellular-like portable telephones. Exploiting LEO satellites to support distress signals for aircrafts, ships and international travelers is explored in the current paper. A multi-service priority-oriented algorithm is proposed for handling voice, data and emergency signals over LEO satellites. The emergency signal is privileged with service priority so that rescue operation can be carried out as soon as possible. The priority mechanism includes channel reservation as well as joining a queue if no free channel is available as long as the request is roaming in the handover area. In addition, a simplified but efficient approach is suggested for locating the object of an imminent danger situation. As LEO satellites are non-geostationary, the visible period of each spot-beam is small. Consequently, a teletraffic model, that accommodates the mobility of spot-beams as well as the resulting handover rate, is developed in order to gauge the performance of the proposed algorithm. Numerical results for access denying and service-dropping rates are presented for nominal system parameters.

LANE-BASED DIRECTION MARKING RECOGNITION USING HU MOMENTS

In intelligent vehicle system, it is significant to detect and identify road markings for vehicles to follow traffic regulation. This paper proposes a method to recognize direction markings on road surface, which is on the basis of detected lanes and uses Hu moments. First of all, the detection of lanes is based on horizontal luminance difference, which converts the RGB color image to the luminance image, calculates the horizontal luminance difference, obtains the candidate points of lanes' edge and uses least square method to fit the lanes. Secondly, with the detected lines as guide for the search of candidate marking, the paper extracts Hu moments of candidate marking, calculates its Mahalanobis distance to every marking type and classifies it to the type which has the minimal distance with the candidate marking. From the simulation results, the method to detect lanes is more effective and time-efficient than canny or sobel edge detection methods; the method to recognize direction marking is effective and has a high accuracy.

ARTIFICIAL ENDOCRINE SYSTEM: A NEW PARADIGM OF KNOWLEDGE DISCOVERY

We propose an artificial endocrine system (AES) for extracting the knowledge from database so that effective and reliable decision rules can be constructed. The proposed AES mimics the functionality of biological endocrine system (BES) to some extent. A mathematical model is proposed for expressive representation of endocrine system as well as for homeostasis. Further, different aspects of our proposed "Artificial endocrine system for knowledge discovery" (AESKD) have been compared with state of art classifiers e.g., support vector machine, neural network, radial basis function (RBF) network and K-NN for some bench mark datasets.

CODING AND INTERLEAVING SCHEMES FOR INFORMATION ACQUISITION FROM WIRELESS SENSOR NETWORKS

Transmission of data over wireless sensor networks (WSNs) poses significant constraints on the energy and bandwidth of the communication system. We consider the problem of decision fusion in a distributed detection system in a classical parallel fusion structure by incorporating the fading channel layer that is omnipresent in WSNs. We use channel coding and transmit diversity schemes, equal gain combining (EGC) and maximum ratio combining (MRC). We employ a multi-carrier modulation scheme over the wireless communication channel. This paper proposes the use of Hamming, low-density parity-check (LDPC) coding, or Viterbi coding. Interleaving is also suggested in this paper using chaotic Baker map randomization for the reduction of the bit error rate (BER). Moreover, the chaotic interleaving adds a degree of security to the transmitted data. Simulation results show that the application of coding and interleaving achieves a performance improvement in the WSN.

ARTISTIC COMPLEXITY AND SALIENCY: TWO FACES OF THE SAME COIN?

Many computer vision problems consist of making a suitable content description of images usually aiming to extract the relevant information content. In case of images representing paintings or artworks, the information extracted is rather subject-dependent, thus escaping any universal quantification. However, we proposed a measure of complexity of such kinds of oeuvres which is related to brain processing. The artistic complexity measures the brain inability to categorize complex nonsense forms represented in modern art, in a dynamic process of acquisition that most involves top-down mechanisms. Here, we compare the quantitative results of our analysis on a wide set of paintings of various artists to the cues extracted from a standard bottom-up approach based on visual saliency concept. In every painting inspection, the brain searches for more informative areas at different scales, then connecting them in an attempt to capture the full impact of information content. Artistic complexity is able to quantify information which might have been individually lost in the fruition of a human observer thus identifying the artistic hand. Visual saliency highlights the most salient areas of the paintings standing out from their neighbours and grabbing our attention. Nevertheless, we will show that a comparison on the ways the two algorithms act, may manifest some interesting links, finally indicating an interplay between bottom-up and top-down modalities.

RESEARCH OF PRINTING QUALITY DETECTION BASED ON IMAGE RESTORATION

When kernel methods are applied to detect the defection, there is a need to select the training samples, because kernel methods are based on the statistical learning theory. To extract the defects, the pre-image is calculated. In this paper, a sampling algorithm based on the alignment is designed to improve the calculation efficiency, where kernel alignment can measure the similarity between different kernel functions and matrices. A local linear algorithm is proposed to calculate the pre-image. When obtain the 0–1 difference image, an algorithm is designed to determine whether there are defects. An algorithm is designed to calculate the center coordinates and the areas of defects in the 0–1 image. Using this method, the accuracy of detection can be improved, because the method can remove the effect from recovery errors. When using the algorithms on a data set of printing products, the experiment results show that the detection results are more accurately than using the difference matrix.

A NOVEL FRAMEWORK FOR TRACKING ONLINE COMMUNITY INTERACTION IN SOCIAL NETWORK

This paper focuses on a design of improved framework and analysis of existing framework which exploits certain algorithms for tracking online community in social network. Tracking of online community is an imperative task where the goal is to identify meaningful group structures in the dynamic social network and consider the problem of the evolution of groups of users in dynamic scenarios. Existing frameworks for tracking community in social network have some limitation which makes it less scalable and computationally inefficient. This novel framework facilitates scalable tracking communities over the time in social networks and offers efficient methods to deal with the problems which are offered in most of the existing frameworks.