A novel application of a microaccelerometer for target classification

This paper presents a novel method of target classification by means of a microaccelerometer. Its principle is that the seismic signals from moving vehicle targets are detected by a microaccelerometer, and targets are automatically recognized by the advanced signal processing method. The detection system based on the microaccelerometer is small in size, light in weight, has low power consumption and low cost, and can work under severe circumstances for many different applications, such as battlefield surveillance, traffic monitoring, etc. In order to extract features of seismic signals stimulated by different vehicle targets and to recognize targets, seismic properties of typical vehicle targets are researched in this paper. A technique of artificial neural networks (ANNs) is applied to the recognition of seismic signals for vehicle targets. An improved back propagation (BP) algorithm and ANN architecture have been presented to improve learning speed and avoid local minimum points in error curve. The improved BP algorithm has been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. Through experiments, it can be proven that target seismic properties acquired are correct, ANN is effective to solve the problem of classification and recognition of moving vehicle targets, and the microaccelerometer can be used in vehicle target recognition.

Trophic classification of non-perennial reservoirs utilized for the development of culture-based fisheries, Sri Lanka

The aim of this study was to check the suitability of some trophic models developed for temperate regions to classify the non-perennial reservoirs of Sri Lanka in order to manage culture-based fisheries of those reservoirs. A limnological study was carried out in 45 non-perennial reservoirs, which have been randomly selected for stocking of fish fingerlings for the development of culture-based fisheries. High total phosphorous (TP) content in relation to algal biomass indicates high non-algal turbidity in all reservoirs. Carlson's trophic state indices (TSI) measured on the basis of Secchi disc depth [TSI (SDD)], TP [TSI (TP)] and chlorophyll a [TSI (Chl-a)] show that the 45 reservoirs studied are characterized by TSI (TP) = TSI (SDD) > TSI (Chl-a), indicating that non-algal particulate matter or colour dominates underwater light attenuation. As TSI (Chl-a) is positively correlated to culture-based fisheries yield, it is useful for planning culture-based fisheries development strategies in non-perennial reservoirs of Sri Lanka, and has the potential to be used elsewhere in the tropics for comparable developments.

Spectral kernels for classification

Spectral methods, as an unsupervised technique, have been used with success in data mining such as LSI in information retrieval, HITS and PageRank in Web search engines, and spectral clustering in machine learning. The essence of success in these applications is the spectral information that captures the semantics inherent in the large amount of data required during unsupervised learning. In this paper, we ask if spectral methods can also be used in supervised learning, e.g., classification. In an attempt to answer this question, our research reveals a novel kernel in which spectral clustering information can be easily exploited and extended to new incoming data during classification tasks. From our experimental results, the proposed Spectral Kernel has proved to speedup classification tasks without compromising accuracy.

A public sector scale for codes of ethics classification

This article examines the results of a study conducted of the top 100 public sector units in Sweden. These units comprise 40 entities of government, 40 municipalities, and 20 county councils. The aim of the study was to examine and describe the codes of ethics in these Swedish public sector units. This research reports on the responses of 27 public sector units that possessed a code of ethics. The principal contribution of this work is a customized PUB SEC-scale to measure and evaluate the content of codes of ethics artefacts in public sector units. The PUB SEC-scale differs to a large extent from the current private sector-scales (PRlSEC-scales) in literature, due to the specific characteristics of the public sector.