High performance level restoration circuits

Three high performance level restoration circuits are proposed, which outperform the existing level restoration circuits with cross-coupled PMOS, in terms of power dissipation and delay. The first configuration employs a back-bias scheme in order to eliminate the stand-by leakage caused by the low-swing input. The second one adopts a bootstrapping technique, in order to restore the low-swing signal, without dc power consumption. Finally, a level restoration circuit is proposed, based on the generation of a narrow zero-pulse, for properly controlling the output PMOS device. The presented level restoration circuits can be implemented in standard CMOS technologies. By simulating the proposed circuits on a low-swing interconnect scheme, a 60% power savings have been observed over the conventional full-swing case.     

European residential buildings and empirical assessment of the Hellenic building stock,energy consumption,emissions and potential energy savings

The existing building stock in European countries accounts for over 40% of final energy consumption in the European Union (EU) member states, of which residential use represents 63% of total energy consumption in the buildings sector. Consequently, an increase of building energy performance can constitute an important instrument in the efforts to alleviate the EU energy import dependency (currently at about 48%) and comply with the Kyoto Protocol to reduce carbon dioxide emissions. This is also in accordance to the European Directive (EPBD 2002/91/EC) on the energy performance of buildings, which is currently under consideration in all EU member states. This paper presents an overview of the EU residential building stock and focuses on the Hellenic buildings. It elaborates the methodology used to determine the priorities for energy conservation measures (ECMs) in Hellenic residential buildings to reduce the environmental impact from CO₂ emissions, through the implementation of a realistic and effective national action plan. A major obstacle that had to overcome was the need to make suitable assumptions for missing detailed primary data. Accordingly, a qualitative and quantitative assessment of scattered national data resulted to a realistic assessment of the existing residential building stock and energy consumption. This is the first time that this kind of aggregate data is presented on a national level. Different energy conservation scenarios and their impact on the reduction of CO₂ emissions were evaluated. Accordingly, the most effective ECMs are the insulation of external walls (33–60% energy savings), weather proofing of openings (16–21%), the installation of double-glazed windows (14–20%), the regular maintenance of central heating boilers (10–12%), and the installation of solar collectors for sanitary hot water production (50–80%).     

Alkali Activation of AOD Stainless Steel Slag Under Steam Curing Conditions

Crystalline argon oxygen decarburization slag, in powdery form, was investigated for its hydration potential by alkali activation and curing at 80°C. Na‐silicate and K‐silicate of the same modulus were used as activators. Isothermal calorimetry at 80°C indicated exothermic reactions in the slag pastes. When the slag mortars were cured under steam at 80°C appreciable gain in compressive strength was measured. This was attributed to C–S–H which was detected in TG, FTIR, and ²⁹Si NMR analyses. Upon hydration at 90 d, the amount of crystalline phases decreased, whereas the XRD amorphous content in the slag increased. Electron microscopy showed the formation of different morphologies of reaction products depending on the alkaline activator employed. Presence of reaction rims around the crystalline phases with a major presence of Ca, Si, and O in the reacted matrix was observed in elemental maps.     

Collaborative event annotation in tagged photo collections

Events constitute a significant means of multimedia content organization and sharing. Despite the recent interest in detecting events and annotating media content in an event-centric way, there is currently insufficient support for managing events in large-scale content collections and limited understanding of the event annotation process. To this end, this paper presents CrEve, a collaborative event annotation framework which uses content found in social media sites with the prime objective to facilitate the annotation of large media corpora with event information. The proposed annotation framework could significantly benefit social media research due to the proliferation of event-related user-contributed content. We demonstrate that, compared to a standard “browse-and-annotate” interface, CrEve leads to a 19% increase in the coverage of the generated ground truth in a large-scale annotation experiment. Furthermore, the paper discusses the results of a user study that quantifies the performance of CrEve and the contribution of different event dimensions in the event annotation process. The study confirms the prevalence of spatio-temporal queries as the prime option of discovering event-related content in a large collection. In addition, textual queries and social cues (content contributor) were also found to be significant as event search dimensions. Finally, it demonstrates the potential of employing automatic photo clustering methods with the goal of facilitating event annotation.     

Image Transformations and Blurring

Since cameras blur the incoming light during measurement, different images of the same surface do not contain the same information about that surface. Thus, in general, corresponding points in multiple views of a scene have different image intensities. While multiple-view geometry constrains the locations of corresponding points, it does not give relationships between the signals at corresponding locations. This paper offers an elementary treatment of these relationships. We first develop the notion of "ideal” and "real” images, corresponding to, respectively, the raw incoming light and the measured signal. This framework separates the filtering and geometric aspects of imaging. We then consider how to synthesize one view of a surface from another; if the transformation between the two views is affine, it emerges that this is possible if and only if the singular values of the affine matrix are positive. Next, we consider how to combine the information in several views of a surface into a single output image. By developing a new tool called "frequency segmentation,” we show how this can be done despite not knowing the blurring kernel.     

Evaluation of thermally-dried Kluyveromyces marxianus as baker’s yeast

The aim of the present study was to evaluate the use of thermally-dried Kluyveromyces marxianus as baker’s yeast. Bread samples produced by thermally-dried K. marxianus were compared with samples produced by wet K. marxianus culture and by commercial baker’s yeast. The type of the culture had no effect on loaf weight, loaf volume, specific loaf volume, density or moisture loss, in contrast to pH, total titrable acidity (TTA), and moisture content. The use of thermally-dried K. marxianus resulted in lower pH values and higher TTA, while the bread samples showed higher resistance to spoilage as counts of moulds and yeasts were significantly lower during preservation. The SPME GC/MS analysis of volatiles and the preliminary sensory evaluation showed no significant differences in the profile of aroma-related compounds or overall quality of the tested samples.     

A spatially-constrained normalized Gamma process prior

In this work, we propose a novel nonparametric Bayesian method for clustering of data with spatial interdependencies. Specifically, we devise a novel normalized Gamma process, regulated by a simplified (pointwise) Markov random field (Gibbsian) distribution with a countably infinite number of states. As a result of its construction, the proposed model allows for introducing spatial dependencies in the clustering mechanics of the normalized Gamma process, thus yielding a novel nonparametric Bayesian method for spatial data clustering. We derive an efficient truncated variational Bayesian algorithm for model inference. We examine the efficacy of our approach by considering an image segmentation application using a real-world dataset. We show that our approach outperforms related methods from the field of Bayesian nonparametrics, including the infinite hidden Markov random field model, and the Dirichlet process prior.