Reduction of Vision-Based Models for Fall Detection.

Due to the limitations that falls have on humans, early detection of these becomes essential to avoid further damage. In many applications, various technologies are used to acquire accurate information from individuals such as wearable sensors, environmental sensors or cameras, but all of these require high computational resources in many cases, delaying the response of the entire system. The complexity of the models used to process the input data and detect these activities makes them almost impossible to complete on devices with limited resources, which are the ones that could offer an immediate response avoiding unnecessary communications between sensors and centralized computing centers.

SLRProp: A Back-Propagation Variant of Sparse Low Rank Method for DNNs Reduction.

Application of deep neural networks (DNN) in edge computing has emerged as a consequence of the need of real time and distributed response of different devices in a large number of scenarios. To this end, shredding these original structures is urgent due to the high number of parameters needed to represent them. As a consequence, the most representative components of different layers are kept in order to maintain the network's accuracy as close as possible to the entire network's ones.

A Generalization Performance Study Using Deep Learning Networks in Embedded Systems.

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ.

Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People.

This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context.

Entangling non planar molecules via inversion doublet transition with negligible spontaneous emission.

We analyze theoretically the entanglement between two non-planar and light identical molecules (e.g., pyramidal NH3) that present inversion doubling due to the internal spatial inversion of their nuclear conformations by tunneling.

Smart Meeting Room Usage Information and Prediction by Modelling Occupancy Profiles.

The monitoring of small houses and rooms has become possible due to the advances in IoT sensors, actuators and low power communication protocols in the last few years. As buildings are one of the biggest energy consuming entities, monitoring them has great interest for trying to avoid non-necessary energy waste. Moreover, human behaviour has been reported as being the main discrepancy source between energy usage simulations and real usage, so the ability to monitor and predict actions as opening windows, using rooms, etc.

Plasmonic control of nonlinear two-photon absorption in graphene nanocomposites.

Nonlinear two-photon absorption in a quantum dot-graphene nanoflake nanocomposite system has been investigated. An external laser field is applied to the nanocomposite to simultaneously observe two-photon processes in the quantum dot and excite localized surface plasmons in the graphene nanodisk. This resonance condition can be achieved by tuning the plasmon resonance frequency in the graphene nanoflake via electrostatic gating.

Improvements to previous algorithms to predict gene structure and isoform concentrations using Affymetrix Exon arrays.

Background: Exon arrays provide a way to measure the expression of different isoforms of genes in an organism. Most of the procedures to deal with these arrays are focused on gene expression or on exon expression. Although the only biological analytes that can be properly assigned a concentration are transcripts, there are very few algorithms that focus on them.

Development of a novel splice array platform and its application in the identification of alternative splice variants in lung cancer.

Background: Microarrays strategies, which allow for the characterization of thousands of alternative splice forms in a single test, can be applied to identify differential alternative splicing events. In this study, a novel splice array approach was developed, including the design of a high-density oligonucleotide array, a labeling procedure, and an algorithm to identify splice events.

Results: The array consisted of exon probes and thermodynamically balanced junction probes.

SPACE: an algorithm to predict and quantify alternatively spliced isoforms using microarrays.

Exon and exon+junction microarrays are promising tools for studying alternative splicing. Current analytical tools applied to these arrays lack two relevant features: the ability to predict unknown spliced forms and the ability to quantify the concentration of known and unknown isoforms. SPACE is an algorithm that has been developed to (1) estimate the number of different transcripts expressed under several conditions, (2) predict the precursor mRNA splicing structure and (3) quantify the transcript concentrations including unknown forms.