No abstract

Sign language forms a communication channel among the deaf; however, automated gesture recognition could further expand their communication with the hearers. In this work, data from five-channel surface electromyogram and 3-D accelerometer from the signer's dominant hand were analyzed using intrinsic-mode entropy (IMEn) for the automated recognition of Greek sign language (GSL) isolated signs. Discriminant analysis was used to identify the effective scales of the intrinsic-mode functions and the window length for the calculation of the IMEn that contributes to the efficient classification of the GSL signs. Experimental results from the IMEn analysis applied to GSL signs corresponding to 60-word lexicon repeated ten times by three native signers have shown more than 93% mean classification accuracy using IMEn as the only source of the classification feature set. This provides a promising bed-set toward the automated GSL gesture recognition.

Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by cognitive deficits, problems in activities of daily living, and behavioral disturbances. Electroencephalogram (EEG) has been demonstrated as a reliable tool in dementia research and diagnosis. The application of EEG in AD has a wide range of interest. EEG contributes to the differential diagnosis and the prognosis of the disease progression. Additionally such recordings can add important information related to the drug effectiveness. This review is prepared to form a knowledge platform for the project entitled “Cognitive Signal Processing Lab,” which is in progress in Information Technology Institute in Thessaloniki. The team tried to focus on the main research fields of AD via EEG and recent published studies.

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-fundedBIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO SOS) project has developedthe Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and mon-itoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization LandCover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Cate-gories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM systemuses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation(EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat typemaps are derived. An additional module quantifies changes in the LCCS classes and their components,indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e.,GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protectedsites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India