International Journal of Information Acquisition

Airborne and spaceborne remote sensors can acquire invaluable information about earth surface, which have many important applications. The acquired information usually is represented as two-dimensional grids, i.e. images. One of techniques to processing such images is Independent Component Analysis (ICA), which is particularly useful for classifying objects with unknown spectral signatures in an unknown image scene, i.e. unsupervised classification. Since the weight matrix in ICA is a square matrix for the purpose of mathematical tractability, the number of objects that can be classified is equal to the data dimensionality, i.e. the number of spectral bands. When the number of sensors (or spectral channels) is very small (e.g. a 3-band CIR photograph and 6-band Landsat image with the thermal band being removed), it is impossible to classify all the different objects present in an image scene using the original data. In order to solve this problem, we present a data dimensionality expansion technique to generate artificial bands. Its basic idea is to use nonlinear functions to capture and highlight the similarity/dissimilarity between original spectral measurements, which can provide more data with additional information for detecting and classifying more objects. The results from such a nonlinear band generation approach are compared with a linear band generation method using cubic spline interpolation of pixel spectral signatures. The experiments demonstrate that nonlinear band generation approach can significantly improve unsupervised classification accuracy, while linear band generation method cannot since no new information can be provided. It is also demonstrated that ICA is more powerful than other frequently used unsupervised classification algorithms such as ISODATA.

A new silicon-based amperometric microelectrode biosensor produced using bulk micromachining technology is presented here. Bulk micromachining, platinization and polymerization of pyrrole enhance sensitive coefficient, thus helping to miniaturize its dimensions and reduce unit cost. To our knowledge, platinization and polymerization of pyrrole is first used consecutively for microelectrode surface modification. Successful experimental results have been achieved for glucose detection. Compared to conventional amperometric biosensors and amperometric microelectrode biosensors made with surface micromachining technology, it has several advantages, such as smaller sensing surface area (1 mm × 1 mm), lower detection limit (1×10^-4M), larger sensitive coefficient (39.640 nAmM^-1mm^-2), broader linear range (1 × 10^-4-1 × 10^-2M), better replicability (3.2% RSD for five respective detections) and stability (enzyme efficiency remains well above 95% after being stored for a month), easier to be made into arrays and to be integrated with processing circuitry, etc.

A new approach for rapid detection of hazardous gas like domestic liquefied petroleum gas (LPG) using a single SnO₂ gas sensor based on dynamic measurement was studied. The working principles of static and dynamic measurements were compared and experimental and analytical profiles of the dynamic measurement was also investigated. Experimental results showed that hazardous gas such as LPG had obvious properties by using dynamic measurement. The qualitative analysis was also studied by means of fast Fourier transform (FFT), and power spectrum showed that different properties appeared on different concentrations of LPG.

A new type of solid-state electrochemical sensor was studied in this paper. By mixing proper content of polyacrylonitrile (PAN)-Polyvinylpyrrolidone (PVP)-Dimethylsulfoxide (DMSO)-Tetraethylammonium perchlorate (TEAP), an elastic and porous polymeric solid electrolyte was developed. According to the microelectrode theory and modulated pulse potential technique, an electrochemical sensor that can realize the synchronous and fast detection of CO₂ and O₂ at room temperature was designed. Experimental data showed that this polymer electrolyte exhibited ionic conductivity of the order of 10^-4S/cm at room temperature, and also showed that the sensor had many advantages such as small volume, convenient usage and certain extent stability.

We develop a novel energy-efficient routing protocol called the THEEM (Two-Hop Energy-Efficient Mesh) protocol for wireless sensor networks. In the THEEM protocol, a comprehensive and integrated treatment is employed to achieve energy efficiency. In specific, a two-hop in-mesh transmission scheme and a centralized mesh (cluster) formulation method are employed, along with other design innovations, such as the concepts of mesh layer/column, the power-aware assignment of mesh heads and a low-energy media access protocol. Simulation results show that the THEEM protocol is able to reduce energy consumption quite significantly compared to currently existing protocols. Equal energy dissipation among all sensor nodes in a network is also achieved. In addition, the protocol maximizes network data throughput.

The relationship between modeling error of the system and widening multiple of the working frequency band was analyzed quantitatively and its spectrums were obtained. They offered a criterion to estimate the feasibility and effect in engineering practice of the compensating method to improve the sensor's dynamic characteristics, and proved that the compensating method has good reliability. Conclusions were drawn clearly: For a first-order system, the compensation effect is conspicuous, which only relates to modeling error but is independent of time constant; for a second-order system, the compensation effect is determined by modeling error and damping ratio. Within a wide range, if the damping ratio is larger, the modeling precision required to obtain the same compensation effect can be lower. Through quantitatively analysis, we found that the modeling precision in dynamic compensation with analogy or digital filter should not be so high as one is taken for granted currently.

For 3D Eucledean reconstruction, the challenging problem is that only the hypothesis of intrinsic parameters can be used to retrieve the camera parameters without additional information. In this paper, we propose a method to find out the camera's variable intrinsic parameters using the scene invariable conics (SIC). The experiment results are presented and analysed, which show good performance of this proposed method.

The vibrational acoustic signals collected on pipelines contain substantial information concerning the working status of pipelines. They can be used for pinpointing leaks in buried pipelines. However, because of the complexity of signal composition and the heavy corruption of signals by ambient noises, it is essential to set up an appropriate signal model and scheme of analysis and synthesis in order to extract leak signatures and specify leak locations. In addition, the features of vibrational acoustic signals vary with materials, sizes and buried conditions of tubular pipes. It is difficult to pre-determine the knowledge of signals, such as the spectral knowledge. Here, an adaptive detection and estimation strategy is proposed based on LMS adaptive filtering and modified Wavelet denoising. The feasibility leak detection and location estimation is first automatically analysed by the signal processing procedure without any prerequisite on signals and then the procedure adaptively finds better estimate. With the proposed schemes, even where collected signals are heavily blurred by bursting interferences and present non-stationary, the instrument may also carry on detection and achieve effective results.