Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device

There is a significant high fall risk population, where individuals are susceptible to frequent falls and obtaining significant injury, where quick medical response and fall information are critical to providing efficient aid. This article presents an evaluation of compressive sensing techniques in an accelerometer-based intelligent fall detection system modelled on a wearable Shimmer biomedical embedded computing device with Matlab. The presented fall detection system utilises a database of fall and activities of daily living signals evaluated with discrete wavelet transforms and principal component analysis to obtain binary tree classifiers for fall evaluation. 14 test subjects undertook various fall and activities of daily living experiments with a Shimmer device to generate data for principal component analysis-based fall classifiers and evaluate the proposed fall analysis system. The presented system obtains highly accurate fall detection results, demonstrating significant advantages in comparison with the thresholding method presented. Additionally, the presented approach offers advantageous fall diagnostic information. Furthermore, transmitted data accounts for over 80% battery current usage of the Shimmer device, hence it is critical the acceleration data is reduced to increase transmission efficiency and in-turn improve battery usage performance. Various Matching pursuit-based compressive sensing techniques have been utilised to significantly reduce acceleration information required for transmission.Scopu

Novel Viruses of the Family Partitiviridae Discovered in Saccharomyces cerevisiae

It has been 49 years since the last discovery of a new virus family in the model yeast Saccharomyces cerevisiae. A large-scale screen to determine the diversity of double-stranded RNA (dsRNA) viruses in S. cerevisiae has identified multiple novel viruses from the family Partitiviridae that have been previously shown to infect plants, fungi, protozoans, and insects. Most S. cerevisiae partitiviruses (ScPVs) are associated with strains of yeasts isolated from coffee and cacao beans. The presence of partitiviruses was confirmed by sequencing the viral dsRNAs and purifying and visualizing isometric, non-enveloped viral particles. ScPVs have a typical bipartite genome encoding an RNA-dependent RNA polymerase (RdRP) and a coat protein (CP). Phylogenetic analysis of ScPVs identified three species of ScPV, which are most closely related to viruses of the genus Cryspovirus from the mammalian pathogenic protozoan Cryptosporidium parvum. Molecular modeling of the ScPV RdRP revealed a conserved tertiary structure and catalytic site organization when compared to the RdRPs of the Picornaviridae. The ScPV CP is the smallest so far identified in the Partitiviridae and has structural homology with the CP of other partitiviruses but likely lacks a protrusion domain that is a conspicuous feature of other partitivirus particles. ScPVs were stably maintained during laboratory growth and were successfully transferred to haploid progeny after sporulation, which provides future opportunities to study partitivirus-host interactions using the powerful genetic tools available for the model organism S. cerevisiae

CRUISE, a Tool for the Detection of Iterons in Circular Rep-Encoding Single-Stranded DNA Viruses

Iterons are short, repeated DNA sequences that are important for the replication of circular single-stranded DNA viruses. No tools that can reliably predict iterons are currently available. The CRUcivirus Iteron SEarch (CRUISE) tool is a computational tool that identifies iteron candidates near stem-loop structures in viral genomes

Biosensor comprising metal nanoparticles

[ES] La presente invención se refiere a un biosensor donde la detección del analito se realiza de forma visual por el cambio de color en las zonas del soporte en que el analito esté presente producido por las nanopartículas al ser irradiadas con una fuente de luz externa[EN] The present invention discloses a biosensor for visual detection of an analyte, based on the light to heat conversion properties of metal nanoparticles: the analyte is visually detected by the colour change in the support areas (where the analyte is present), produced as a result of the heat generated by the metal nanoparticles where they are irradiated with an external light source. Use of said biosensor in a method for the detection of analytes is also claimed.Peer reviewedUniversidad de Zaragoza, Fundación Agencia Aragonesa para la Investigación y el Desarrollo, Consejo Superior de Investigaciones Científicas (España)B1 Patente sin examen previ

Identifying Topics in Social Media Posts using DBpedia

This paper describes a method for identifying topics in text published in social media, by applying topic recognition techniques that exploit DBpedia. We evaluate such method for social media in Spanish and we provide the results of the evaluation performed

Smartphone-based object recognition with embedded machine learning intelligence for unmanned aerial vehicles

Existing artificial intelligence solutions typically operate in powerful platforms with high computational resources availability. However, a growing number of emerging use cases such as those based on unmanned aerial systems (UAS) require new solutions with embedded artificial intelligence on a highly mobile platform. This paper proposes an innovative UAS that explores machine learning (ML) capabilities in a smartphone‐based mobile platform for object detection and recognition applications. A new system framework tailored to this challenging use case is designed with a customized workflow specified. Furthermore, the design of the embedded ML leverages TensorFlow, a cutting‐edge open‐source ML framework. The prototype of the system integrates all the architectural components in a fully functional system, and it is suitable for real‐world operational environments such as seek and rescue use cases. Experimental results validate the design and prototyping of the system and demonstrate an overall improved performance compared with the state of the art in terms of a wide range of metrics

SUM’20: State-based user modelling

Capturing and effectively utilising user states and goals is becoming a timely challenge for successfully leveraging intelligent and usercentric systems in differentweb search and data mining applications. Examples of such systems are conversational agents, intelligent assistants, educational and contextual information retrieval systems, recommender/match-making systems and advertising systems, all of which rely on identifying the user state in order to provide the most relevant information and assist users in achieving their goals. There has been, however, limited work towards building such state-aware intelligent learning mechanisms. Hence, devising information systems that can keep track of the user's state has been listed as one of the grand challenges to be tackled in the next few years [1]. It is thus timely to organize a workshop that re-visits the problem of designing and evaluating state-aware and user-centric systems, ensuring that the community (spanning academic and industrial backgrounds) works together to tackle these challenges

Maximum Causal Entropy Specification Inference from Demonstrations

In many settings (e.g., robotics) demonstrations provide a natural way to specify tasks; however, most methods for learning from demonstrations either do not provide guarantees that the artifacts learned for the tasks, such as rewards or policies, can be safely composed and/or do not explicitly capture history dependencies. Motivated by this deficit, recent works have proposed learning Boolean task specifications, a class of Boolean non-Markovian rewards which admit well-defined composition and explicitly handle historical dependencies. This work continues this line of research by adapting maximum causal entropy inverse reinforcement learning to estimate the posteriori probability of a specification given a multi-set of demonstrations. The key algorithmic insight is to leverage the extensive literature and tooling on reduced ordered binary decision diagrams to efficiently encode a time unrolled Markov Decision Process. This enables transforming a naive exponential time algorithm into a polynomial time algorithm.Comment: Computer Aided Verification, 202

Systematic infrared image quality improvement using deep learning based techniques

This is the final version. Available from SPIE via the DOI in this recordInfrared thermography (IRT, or thermal video) uses thermographic cameras to detect and record radiation in the longwavelength infrared range of the electromagnetic spectrum. It allows sensing environments beyond the visual perception limitations, and thus has been widely used in many civilian and military applications. Even though current thermal cameras are able to provide high resolution and bit-depth images, there are significant challenges to be addressed in specific applications such as poor contrast, low target signature resolution, etc. This paper addresses quality improvement in IRT images for object recognition. A systematic approach based on image bias correction and deep learning is proposed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. Our main objective is to maximise the useful information on the object to be detected even when the number of pixels on target is adversely small. The experimental results show that our approach can significantly improve target resolution and thus helps making object recognition more efficient in automatic target detection/recognition systems (ATD/R).Centre for Excellence for Sensor and Imaging System (CENSIS)Scottish Funding CouncilDigital Health and Care Institute (DHI)Royal Society of EdinburghNational Science Foundation of Chin