Signal subspace identification in hyperspectral imagery

Terrestrial remote sensing imagery involves the acquisition of information from the Earth's surface without physical contact with the area under study. Among the remote sensing modalities, hyperspectral imaging has recently emerged as a powerful passive technology. This technology has been widely used in the fields of urban and regional planning, water resource management, environmental monitoring, food safety, counterfeit drugs detection, oil spill and other types of chemical contamination detection, biological hazards prevention, and target detection for military and security purposes [2-9]. Hyperspectral sensors sample the reflected solar radiation from the Earth surface in the portion of the spectrum extending from the visible region through the near-infrared and mid-infrared (wavelengths between 0.3 and 2.5 µm) in hundreds of narrow (of the order of 10 nm) contiguous bands [10]. This high spectral resolution can be used for object detection and for discriminating between different objects based on their spectral xharacteristics [6]. However, this huge spectral resolution yields large amounts of data to be processed. For example, the Airbone Visible/Infrared Imaging Spectrometer (AVIRIS) [11] collects a 512 (along track) X 614 (across track) X 224 (bands) X 12 (bits) data cube in 5 s, corresponding to about 140 MBs. Similar data collection ratios are achieved by other spectrometers [12]. Such huge data volumes put stringent requirements on communications, storage, and processing. The problem of signal sbspace identification of hyperspectral data represents a crucial first step in many hypersctral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction (DR) yelding gains in data storage and retrieval and in computational time and complexity. Additionally, DR may also improve algorithms performance since it reduce data dimensionality without losses in the useful signal components. The computation of statistical estimates is a relevant example of the advantages of DR, since the number of samples required to obtain accurate estimates increases drastically with the dimmensionality of the data (Hughes phnomenon) [13].

Environmental mycology in public health: fungi and mycotoxins risk assessment and management

Fungi are essential to the survival of our global ecology, but they might pose a significant threat to the health of occupants when they grow in our buildings. The exposure to fungi in homes is a significant risk factor for a number of respiratory symptoms. Well-known illnesses caused by fungi include allergy and hypersensitivity pneumonitis. Environmental monitoring for fungi and their disease agents are important aspects of exposure assessment, but few guidelines exist for interpreting their health impacts. This book answers the questions: How does one detect and measure the presence of indoor fungi? What is an acceptable level of indoor fungi? How do we relate this information to human health problems?

Environmental sustainability in superior education: case study of Lisbon College of Health Technology

Over the centuries there has been a growing trend of societies and it is possible to verify their economic growth. This growth has provided an increased pressure on natural resources, often over-reaching the boundaries of each country, which has called into question the level of environmental sustainability in different countries. Sustainability is understood as a complex concept involving ecological, social, economic dimensions and temporal urban processes. Therefore, Firmino (2009) suggests that the ecological footprint (EF) allows people to establish dependency relations between human activities and the natural resources required for such activities and for the absorption of waste generated. According to Bergh & Verbruggen (1999) the EF is an objective, impartial and one-dimensional indicator that enables people to assess the sustainability. The Superior Schools have a crucial role in building the vision of a sustainable future as a reality, because in transmitting values and environmental principles to his students, are providing that they, in exercising his professional activity, make decisions weighing the environmental values. This ensures improved quality of life. The present study aims to determine the level of environmental sustainability of the Academic Community of Lisbon College of Health Technology (ESTeSL), by calculating the EF, and describe whether a relation between Footprint and various socio-demographic characteristics of the subjects.