VALENCE: affective visualisation using EEG

VALENCE is an interactive visualisation controlled by live brainwave monitoring. We used a wireless EEG headset to monitor the player's alpha waves (an indicator of relaxation) and valence (an indicator of emotion or arousal). The game world is an emergent system of attractive and repulsive forces responding to EEG input.     

FAO’s AVHRR-based Agricultural Stress Index System (ASIS) for global drought monitoring

Agricultural production is highly dependent on climate variability in many parts of the world. In particular, drought may severely reduce crop yields, potentially affecting food availability at local, regional, and global scales. The Food and Agriculture Organization of the United Nations (FAO) operates the Global Early Warning System (GIEWS), which monitors global food supply and demand. One of the key challenges is to obtain synoptic information on a recurrent and timely basis about drought-affected agricultural zones. This is needed to quickly identify areas requiring immediate attention. The Agricultural Stress Index System (ASIS), based on imagery from the Advanced Very High Resolution Radiometer (AVHRR) sensors on board the National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational Satellite (METOP) satellites, was specifically developed to meet this need. The system is based on a methodology developed by Rojas, Vrieling, and Rembold over the African continent. This approach has been modified and adapted to the global scale to produce an agricultural stress index (ASI) representing, per administrative unit, the percentage of cropland (or pasture) areas affected by drought over the growing season. The vegetation health index (VHI), based on normalized difference vegetation index (NDVI) and temperature anomalies, is used as a drought indicator. A fused time series of AVHRR data from METOP and NOAA was used to produce a consistent time series of VHI at 1 km resolution. Global phenology maps, indicating the number of growing seasons and their start and end dates, were derived from a multi-annual image set of SPOT-Vegetation (1999–2011). The VHI time series and phenology maps were then combined to produce the ASI for the years 1984 to the present. This allowed evaluation of the suitability of the ASIS to identify drought using historical reports and ancillary data. As a result of this analysis, ASIS was positively evaluated to support the FAO early warning system.     

Early design stage exploration of fixed-length block structured architectures

An important challenge concerning the design of future microprocessors is that current design methodologies are becoming impractical due to long simulation runs and due to the fact that chip layout considerations are not incorporated in early design stages. In this paper, we show that statistical modeling can be used to speed up the architectural simulations and is thus viable for early design stage explorations of new microarchitectures. In addition, we argue that processor layouts should be considered in early design stages in order to tackle the growing importance of interconnects in future technologies. In order to show the applicability of our methodology which combines statistical modeling and processor layout considerations in an early design stage, we have applied our method on a novel architectural paradigm, namely a fixed-length block structured architecture. A fixed-length block structured architecture is an answer to the scalability problem of current architectures. Two important factors prevent contemporary out-of-order architectures from being scalable to higher levels of parallelism in future deep-submicron technologies: the increased complexity and the growing domination of interconnect delays. In this paper, we show by using statistical modeling and processor layout considerations, that a fixed-length block structured architecture is a viable architectural paradigm for future microprocessors in future technologies thanks to the introduction of decentralization and a reduced register file pressure.     

Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients

Sub-pixel mapping and sub-pixel sharpening are techniques for increasing the spatial resolution of sub-pixel image classifications. The proposed method makes use of wavelets and artificial neural networks. Wavelet multiresolution analysis facilitates the link between different resolution levels. In this work a higher resolution image is constructed after estimation of the detail wavelet coefficients with neural networks. Detail wavelet coefficients are used to synthesize the high-resolution approximation. The applied technique allows for both sub-pixel sharpening and sub-pixel mapping. An algorithm was developed on artificial imagery and tested on artificial as well as real synthetic imagery. The proposed method resulted in images with higher spatial resolution showing more spatial detail than the source imagery. Evaluation of the algorithm was performed both visually and quantitatively using established classification accuracy indices.     

Porous,Sintered Glass‐Ceramics from Inorganic Polymers Based on Fayalite Slag

The present paper deals with the synthesis of porous, sintered glass‐ceramics obtained at temperatures below 1150°C, originating from inorganic polymers based on fayalite slag. Firing led to the evaporation of water, dehydroxylation, and oxidation of Fe²⁺ above 345°C. For heating >700°C, the Si–O stretching band shifted from the 1160 and 750 cm^?1 to the 1255 and 830 cm^?1 region, due to a structural reorganization of the amorphous phase, whereas Fe–O bands appeared at 550 cm^?1. The final microstructure consisted predominantly of an amorphous phase, hematite, and franklinite. The open porosity and compressive strength decreased and increased, respectively, as the firing temperature increased. The final values suggest properties comparable to that of structural lightweight concrete, still, the materials synthesized herein, are lighter, and made primarily from secondary resources.     

A sub‐pixel mapping algorithm based on sub‐pixel/pixel spatial attraction models

Soft classification techniques avoid the loss of information characteristic to hard classification techniques when handling mixed pixels. Sub‐pixel mapping is a method incorporating benefits of both hard and soft classification techniques. In this paper an algorithm is developed based on sub‐pixel/pixel attractions. The design of the algorithm is accomplished using artificial imagery but testing is done on artificial as well as real synthetic imagery. The algorithm is evaluated both visually and quantitatively using established classification accuracy indices. The resulting images show increased accuracy when compared to hardened soft classifications.     

What's in a manner of speaking? Children's sensitivity to partner-specific referential precedents.

Do young children form “referential pacts”? If a person has referred to an object with a certain term (e.g., the horse), will children expect this person to use this term in the future but allow others to use a different expression (e.g., the pony)? One hundred twenty-eight children between 3 and 5 years old co-operated with an experimenter (E1) to move toys to new locations on a shelf. E1 established referential terms for all toys in a warm-up game. Then, either the original partner, E1, or a new partner, E2, played a second game with the same toys. In this game, the experimenters referred to toys using either their original terms from the warm-up game or new terms. Children were slower to react to new terms than old, and this difference in reaction times was greater in the original partner condition (but only on the first trial). Children sometimes protested at the use of new terms, doing so regardless of their interlocutor's identity. We contrast these findings with those for adults and discuss their implications for the debate regarding the nature of referential pacts. (PsycINFO Database Record (c) 2010 APA, all rights reserved)