International Journal of Information Acquisition

As the first link in the information chain, Information Acquisition has become one of the most important branches in information science and technology. A basic information acquisition process is a multi-layer one that consists of the material layer for signal transduction, the data layer for sensor data fusion and the perception layer for information abstraction. While the material layer can be described as a continuous channel in information transmission, the data layer is a multiple discrete channel in the communication system. To analyze the perception layer, the μ-space theory in high dimensional geometric topology and the Γ-space theory based on statistical mechanics are introduced. A general model of information acquisition, which explains the working principle of intelligent sensors with self-adaptive function, is established according to the acquisition process and bionic principle.

This article describes the joint measures method as a new powerful method for the development of a high performance multi-sensor data/image fusion scheme at the decision level. The images are received from distributed multiple sensors, which sense the targets in different spectral bands including visible, infrared, thermal and microwave. At first, we study the decision fusion methods, including voting schemes, rank based algorithm, Bayesian inference, and the Dempster-Shafer method. Then, we extract the mathematical properties of multi-sensor local classification results and use them for modeling of the classifier performances by the two new measures, i.e. the plausibility and correctness. Then we establish the plausibility and correctness distribution vectors and matrices for introducing the two improvements of the Dempster-Shafer method, i.e. the DS (CM) and DS (PM) methods. After that we introduce the joint measures decision fusion method based on using these two measures jointly. The Joint Measures Method (JMM) can deal with any decision fusion problem in the case of uncertain local classifiers results as well as clear local classifiers results. Finally, we deploy the new and previous methods for the fusion of the two different sets of multispectral image classification local results and we also compare their reliabilities, the commission errors and the omission errors. The results obviously show that the DS (PM), DS (CM) and JMM methods which use the special properties of the local classifiers and classes, have much better accuracies and reliabilities than other methods. In addition, we show that the reliability of the JMM is at least 3% higher than all other methods.

This paper presents the design and experimental results of a Micro Power Generator (MPG) which harvests mechanical energy from its environment and converts this energy into useful electrical power. The energy transduction component is mainly a magnet and a resonating spring made using SU-8 molding and MEMS electroplating technologies. We have shown that when the MPG is packaged into an AA battery size container along with a power-management circuit that consists of rectifiers and a capacitor, it is capable of producing ~1.6 V DC when charged for less than 1 min. Our goal is to realize a MPG to function with low input mechanical frequencies while producing enough power for low-power wireless applications.

This article describes a measurement system designed to evaluate the dynamic characteristics of micromechanical structures. The system is based on Active Homodyne demodulation M-Z optic-fiber interferometer and can measure out-of-plane movement with a sensitivity of 0.1 nm. To validate the system, vibration measurements have been carried on a typical MEMS structure — the sensitive elements of a kind of uncooled IR imaging system, an array of micromachined bimaterial cantilevers (micron dimensions) — and the results are compared with the ones from FEM model analysis and another measurement method which can be known as spectrum plane optic filtering. The three results agree with each other. The first natural frequencies of the structures are determined from data obtained by vibrating the structures using a PZT excitation system. The mode shapes and modal damping will be carried out soon after a scanning system is completed.

The common approach to radial distortion is by the means of polynomial approximation, which introduces distortion-specific parameters into the camera model and requires estimation of these distortion parameters. The task of estimating radial distortion is to find a radial distortion model that allows easy undistortion as well as satisfactory accuracy. This paper presents a new class of rational radial distortion models with easy analytical undistortion formulae. Experimental results are presented to show that with this class of rational radial distortion models, satisfactory and comparable accuracy can be achieved.

This paper proposes a unified knowledge model establishing a core structure of knowledge representation in natural and artificial intelligence systems. The Concept Cell Model proposes the use of acyclic lattices to model a concept formed from a knowledge network of simpler concepts. Declarative and procedural knowledge are explicitly defined as the time-invariant and time-variant relationship of concepts. Examples of a Restaurant Servicing Concept Cell and an extended Shopping Complex Concept Cell are used to demonstrate the functionality of this model. Major existing theoretic and engineering Ontology knowledge schools are compared under this framework.

Surveillance of public places has become a worldwide concern in recent years. The ability to identify abnormal human behaviors in real-time is fundamental to the success of intelligent surveillance systems. The recognition of abnormal and suspicious human walking patterns is an important step towards the achievement of this goal. In this research, we develop an intelligent visual surveillance system that can classify normal and abnormal human walking trajectories in outdoor environments by learning from demonstration. The system takes into account both the local and global characteristics of the observed trajectories and is able to identify their normality in real-time. By utilizing support vector learning and a similarity measure based on hidden Markov models, the developed system has produced satisfactory results on real-life data during testing. Moreover, we utilize the approach of longest common subsequence (LCSS) in determining the similarity between different types of walking trajectories. In order to establish the position and speed boundaries required for the similarity measure, we compare the performance of a number of approaches, including fixed boundary values, variable boundary values, learning boundary by support vector regression, and learning boundary by cascade neural networks.

This article discusses the basic ideas and the practical applications of information acquisition in relation to psychophysics theory as implemented via the Soft-Haptics concept for Internet-based robotic teleoperation systems. In specific, a study on using acoustic display for navigation applications in Internet-based robot teleoperation is presented via psychophysics tests and experimental studies of the Internet-based teleoperated robotic systems to illustrate the ideas and concepts of this paper.