Low-effort place recognition with WiFi fingerprints using deep learning

Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions

Endmember extraction algorithms from hyperspectral images

During the last years, several high-resolution sensors have been developed for hyperspectral remote sensing applications. Some of these sensors are already available on space-borne devices. Space-borne sensors are currently acquiring a continual stream of hyperspectral data, and new efficient unsupervised algorithms are required to analyze the great amount of data produced by these instruments. The identification of image endmembers is a crucial task in hyperspectral data exploitation. Once the individual endmembers have been identified, several methods can be used to map their spatial distribution, associations and abundances. This paper reviews the Pixel Purity Index (PPI), N-FINDR and Automatic Morphological Endmember Extraction (AMEE) algorithms developed to accomplish the task of finding appropriate image endmembers by applying them to real hyperspectral data. In order to compare the performance of these methods a metric based on the Root Mean Square Error (RMSE) between the estimated and reference abundance maps is used

Annotating Synapses in Large EM Datasets

Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics. To date, electron microscopy (EM) is the most proven technique for identifying and quantifying synaptic connections. As advances in EM make acquiring larger datasets possible, subsequent manual synapse identification ({\em i.e.}, proofreading) for deciphering a connectome becomes a major time bottleneck. Here we introduce a large-scale, high-throughput, and semi-automated methodology to efficiently identify synapses. We successfully applied our methodology to the Drosophila medulla optic lobe, annotating many more synapses than previous connectome efforts. Our approaches are extensible and will make the often complicated process of synapse identification accessible to a wider-community of potential proofreaders

Endmember extraction algorithms from hyperspectral images

During the last years, several high-resolution sensors have been developed for hyperspectral remote sensing applications. Some of these sensors are already available on space-borne devices. Space-borne sensors are currently acquiring a continual stream of hyperspectral data, and new efficient unsupervised algorithms are required to analyze the great amount of data produced by these instruments. The identification of image endmembers is a crucial task in hyperspectral data exploitation. Once the individual endmembers have been identified, several methods can be used to map their spatial distribution, associations and abundances. This paper reviews the Pixel Purity Index (PPI), N-FINDR and Automatic Morphological Endmember Extraction (AMEE) algorithms developed to accomplish the task of finding appropriate image endmembers by applying them to real hyperspectral data. In order to compare the performance of these methods a metric based on the Root Mean Square Error (RMSE) between the estimated and reference abundance maps is used

Plastic deformation at high temperatures of pure and Mn-doped GaSb

In this work the plastic behavior of GaSb and Mn-doped GaSb at high temperature has been analyzed. Several experiments at different constant load and temperatures around 500 °C were carried out. The parameters used in the Haasen model have been obtained experimentally and compared with the ones obtained from simulations

The sustainable transformation of business events: Sociodemographic variables as determinants of attitudes toward sustainable academic conferences

Purpose – This study aimed to assess whether sociodemographic variables explain significant differences in attitudes towards transforming academic conferences into more sustainable events. Design/methodology/approach – An analytical model of participants' attitudes towards sustainable conferences based on literature review as well as the theories of reasoned action and planned behavior was developed and applied to a sample of 532 surveyed individuals from 68 countries who regularly attended academic conferences in the last five years prior to 2020. The results were refined using statistical and computational techniques to achieve more empirically robust conclusions. Findings – Results reveal that sociodemographic variables such as attendees' gender and age explain differences in attitudes. Women and older adults have stronger pro-environmental attitudes regarding event sustainability. On the other hand, attitudes towards more sustainable academic conferences are quite strong and positive overall. More sustainable events' venues, catering, conference materials, and accommodations strongly influence attendees' attitudes towards more sustainable conferences. The strength of attitudes was weaker towards transportation. Originality/value – To our best knowledge, this research is the first to assess whether sociodemographic variables explain significant differences in attitudes towards the sustainable transformation of academic conferences.info:eu-repo/semantics/publishedVersio

Distribución de elementos menores y trazas en casiteritas de distintos tipos de yacimientos españoles

[Resumen] En este trabajo se presenta, por primera vez, la composición química de muestras de casi teri tas pertenecientes a distintos tipos de yacimientos españoles, local izados a lo largo del Macizo Hespérico. Se establecen correlaciones entre los caracteres geoquímicos y genéticos, así como, entre el hábito y el color con la tipología del yacimiento: Bipirámides de tonalidades oscuras, junto con una escasa ó nula maclación, son típicas de los depósitos de diseminación y pegmatíticos. Prismas apuntados en pirámides, con una extensa gama de color y abundante maclación I son característicos de yacimientos filonianos.[Abstract] This study presents the chemical composi tion of cassi teri tes samples from different kinds of Spanish deposi ts, for first time. The correlations between geochemical and genetic characteristics are presented, and also, between habi t and colour wi th the type of deposit: Bipyramids of dark tonali ties wi th a li ttle or null twining are characteristic of dissemination and pegmati tic deposits. Pointed prims in pyramid wi th a wide range of colours and abundant twining are characteristic of lode deposit

Room-temperature ferromagnetism in the mixtures of the TiO₂ and Co₃O₄ powders

We report here the observation of ferromagnetism (FM) at 300 K in mixtures of TiO₂ and Co₃O₄ powders despite the antiferromagnetic and diamagnetic characters of both oxides, respectively. The ferromagnetic behavior is found in the early stages of reaction and only for TiO₂ in anatase structure; no FM is found for identical samples prepared with rutile-TiO². Optical spectroscopy and x-ray absorption spectra confirm a surface reduction of octahedral Co^(+3) -> Co^(+2) in the mixtures which is in the origin of the observed magnetism