Classification of hyperspectral remote-sensing images using discriminative linear projections

In this study we apply a variant of a recently proposed linear subspace method, the Neighbourhood Component Analysis (NCA), to the task of hyperspectral classification. The NCA algorithm explicitly utilizes the classification performance criterion to obtain the optimal linear projection. NCA assumes nothing about the form of each class and the shape of the separating surfaces. In some cases we would like to weight the penalty function for different types of misclassifications of the algorithm. A modification of the NCA cost function is introduced for this case. Experimental studies are conducted on hyperspectral images acquired by two sensors: the Airborne Visible/Infrared Imaging Spectroradiometer (AVIRIS) and AISA-EAGLE. Experimental results confirm the superiority of the NCA classifier in the context of hyperspectral data classification over methodologies that were previously suggested.     

An update on chromosome deletion and microdeletion syndromes

Chromosome deletion and microdeletion syndromes account for an increasing number of clinically recognizable genetic conditions. New deletion syndromes continue to be characterized, and a number of previously described syndromes are being found to be due to chromosomal deletions or microdeletions. Fluorescent in situ hybridization technologies are in wide clinical use to diagnose deletion and microdeletion syndromes, and future uses of these technologies will provide prognostic information for patients and their parents, as the genes responsible for the phenotypic aspects of various deletion syndromes are identified. Future research studies will focus on delineating critical deletion intervals at a molecular level, and identifying candidate genes for the phenotypic features of deletion and microdeletion syndromes, toward the goal of understanding the pathology of the abnormal developmental and physiologic processes involved in each syndrome.     

Orthodontics for the handicapped child

The broad spectrum of difficulties that exist in providing orthodontic treatment for handicapped children is well known and few practitioners offer such a service. The present article offers an analysis of the problems involved and provides some practical answers designed to overcome them. It describes some of the newer modalities that have become available to the dental profession over the last few years and shows how advantage may be gained by their exploitation, in orthodontic treatment. Additionally, some of the older and largely-forgotten methods, appropriately modified and updated, are represented in this context, where they find particular relevance. Guidelines are given to enable the orthodontic profession to gain therapeutic access to a section of the population that urgently requires treatment.     

Correlating gate sinking and electrical performance of pseudomorphic high electron mobility transistors

Ti interdiffusion from the Ti/Pt/Au gate into the AlGaAs Schottky barrier layer (SBL) of 0.25-μm GaAs Pseudomorphic High Electron Mobility Transistors (PHEMTs) has been studied using the accelerated life testing technique. Based on measurements and modeling, analytical expressions for quantitative correlation between the positive pinch-off voltage (V_P) shift as well as the saturation drain current (I_Dsat) decrease and the physical damage occurring during gate sinking has been developed. It is suggested that the main cause for device failure is the growth of the TiAs phase leading to the decrease in the SBL thickness. Additionally, it is suggested that V_P may be used as a better indicator for device degradation than I_Dsat since it is linearly proportional to the degrading physical characteristic – the Schottky barrier layer thickness.     

Detecting unknown computer worm activity via support vector machines and active learning

To detect the presence of unknown worms, we propose a technique based on computer measurements extracted from the operating system. We designed a series of experiments to test the new technique by employing several computer configurations and background application activities. In the course of the experiments, 323 computer features were monitored. Four feature-ranking measures were used to reduce the number of features required for classification. We applied support vector machines to the resulting feature subsets. In addition, we used active learning as a selective sampling method to increase the performance of the classifier and improve its robustness in the presence of misleading instances in the data. Our results indicate a mean detection accuracy in excess of 90?%, and an accuracy above 94?% for specific unknown worms using just 20 features, while maintaining a low false-positive rate when the active learning approach is applied.     

The emergence of social science research on nanotechnology

This article examines the development of social science literature focused on the emerging area of nanotechnology. It is guided by the exploratory proposition that early social science work on emerging technologies will draw on science and engineering literature on the technology in question to frame its investigative activities, but as the technologies and societal investments in them progress, social scientists will increasingly develop and draw on their own body of literature. To address this proposition the authors create a database of nanotechnology-social science literature by merging articles from the Web of Science’s Social Science Citation Index and Arts and Humanities Citation Index with articles from Scopus. The resulting database comprises 308 records. The findings suggest that there are multiple dimensions of cited literature and that social science citations of other social scientists’ works have increased since 2005.