International Journal of Information Acquisition

In this paper, an efficient and low-cost cellphone-commandable mobile manipulation system is described. Aiming at house and elderly caring, this system can be easily commanded through common cellphone network to efficiently grasp objects in household environment, utilizing several low-cost off-the-shelf devices. Unlike the visual servo technology using high quality vision system with high cost, the household-service robot may not afford to such high quality vision servo system, and thus it is essential to use some of low-cost device. However, it is extremely challenging to have the said vision for precise localization, as well as motion control. To tackle this challenge, we developed a realtime vision system with which a reliable grasping algorithm combining machine vision, robotic kinematics and motor control technology is presented. After the target is captured by the arm camera, the arm camera keeps tracking the target while the arm keeps stretching until the end effector reaches the target. However, if the target is not captured by the arm camera, the arm will take a move to help the arm camera capture the target under the guidance of the head camera. This algorithm is implemented on two robot systems: the one with a fixed base and another with a mobile base. The results demonstrated the feasibility and efficiency of the algorithm and system we developed, and the study shown in this paper is of significance in developing a service robot in modern household environment.

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.

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.

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 telemedicine system will provide sustainable, comprehensive, low-cost, fast, private, and convenient access to medical consultation and diagnosis for patients from remote locations. The telemedicine system addressed in this paper consists of a sensor jacket, which is worn by the patient for medical monitoring. The signals sensed through the jacket are processed and transmitted through a public telecommunication link, to a medical professional in a hospital at distance. The medical professional interacts with the patient through audio and video links, and simultaneously examines the data transmitted by the monitoring system. Medical assessment, diagnosis, and prescription are carried out on this basis. Sensing and signal processing are paramount to providing the patient data to the medical professional in an accurate and effective manner. This paper presents some relevant issues and techniques. Specific examples of electrocardiograms and respiratory signals are provided to illustrate the applicable signal conditioning approaches. Results are presented to demonstrate the feasibility and the effectiveness of these methods.