Ground target chasing with a unmanned aerial vehicle

Image processing - mobilitatGround target chasing systems using Unmanned Aerial Vehicles are increasingly used in aerial photography and film recording. In most of the cases GPS-based system is not useful and the use of computer vision is required. In this work an alternative to common GPS-based systems is presented using the open-source ArduPilot technology. In this work, the video is transmitted by radio signal and is processed in an external computer that also controls the movements of the vehicle. The system is compound by a tracker based on the mean-shift algorithm that combines information of the color, information from visual features (ORB) and motion information of the video. The output of the tracker is used by a set of PID controllers that control the movements of the vehicle. This work presents good results and it can be a good start point for a commercial product.Los sistemas de seguimiento de blancos terrestres mediante vehículos aéreos no tripulados son cada vez más utilizados en fotografía aérea o en rodaje de películas. En muchos casos los sistemas basados en GPS no son útiles y se utiliza la visión por computador. En este proyecto, una alternativa a los sistemas basados en GPS es presentada. La tecnología usada, de código libre, es ArduPilot. La señal del video es transmitida por radio y procesada en un ordenador externo que también se ocupa de controlar los movimientos del vehículo. El sistema está formado por un tracker basado en el algoritmo mean-shift que combina la información del color, de visual features (ORB) y del movimiento de las imágenes. La salida del tracker es utilizada por un conjunto de controladores PID que se ocupan de mover el vehículo. Este trabajo presenta buenos resultados y es un buen punto de partida para un producto comercial.Els sistemes de seguiment de blancs terrestres mitjançant vehicles aeris no tripulats són cada cop més utilitzats en fotografia aèria i en rodatge de pel·lícules. En molts casos l’ús de sistemes basats en GPS no son útils i s’utilitza la visió per computador. En aquest projecte, una alternativa als sistemes basats en GPS es presentada. La tecnologia usada, de codi lliure, és ArduPilot. La senyal de vídeo es transmesa per radio i es processada en un ordinador extern que també s’ocupa de controlar els moviments del vehicle. El sistema esta format per un tracker basat en l’algoritme mean-shift que combina la informació del color, de visual features (ORB) i informació del moviment. La sortida del tracker es utilitzada per un conjunt de controladors PID que s’ocupen de moure el vehicle. Aquest treball presenta bons resultats i es un bon punt de partida per a un producte comercial

LAI-Net: Local-Ancestry Inference with Neural Networks

Local-ancestry inference (LAI), also referred to as ancestry deconvolution, provides high-resolution ancestry estimation along the human genome. In both research and industry, LAI is emerging as a critical step in DNA sequence analysis with applications extending from polygenic risk scores (used to predict traits in embryos and disease risk in adults) to genome-wide association studies, and from pharmacogenomics to inference of human population history. While many LAI methods have been developed, advances in computing hardware (GPUs) combined with machine learning techniques, such as neural networks, are enabling the development of new methods that are fast, robust and easily shared and stored. In this paper we develop the first neural network based LAI method, named LAI-Net, providing competitive accuracy with state-of-the-art methods and robustness to missing or noisy data, while having a small number of layers

Manipulation Detection in Satellite Images Using Deep Belief Networks

Satellite images are more accessible with the increase of commercial satellites being orbited. These images are used in a wide range of applications including agricultural management, meteorological prediction, damage assessment from natural disasters, and cartography. Image manipulation tools including both manual editing tools and automated techniques can be easily used to tamper and modify satellite imagery. One type of manipulation that we examine in this paper is the splice attack where a region from one image (or the same image) is inserted (spliced) into an image. In this paper, we present a one-class detection method based on deep belief networks (DBN) for splicing detection and localization without using any prior knowledge of the manipulations. We evaluate the performance of our approach and show that it provides good detection and localization accuracies in small forgeries compared to other approaches

SALAI-Net: species-agnostic local ancestry inference network

Availability and implementation: We provide an open source implementation and links to publicly available data at github.com/AI-sandbox/SALAI-Net. Data is publicly available as follows: https://www.internationalgenome.org (1000 Genomes), https://www.simonsfoundation.org/simons-genome-diversity-project (Simons Genome Diversity Project), https://www.sanger.ac.uk/resources/downloads/human/hapmap3.html (HapMap), ftp://ngs.sanger.ac.uk/production/hgdp/hgdp_wgs.20190516 (Human Genome Diversity Project) and https://www.ncbi.nlm.nih.gov/bioproject/PRJNA448733 (Canid genomes).Local ancestry inference (LAI) is the high resolution prediction of ancestry labels along a DNA sequence. LAI is important in the study of human history and migrations, and it is beginning to play a role in precision medicine applications including ancestry-adjusted genome-wide association studies (GWASs) and polygenic risk scores (PRSs). Existing LAI models do not generalize well between species, chromosomes or even ancestry groups, requiring re-training for each different setting. Furthermore, such methods can lack interpretability, which is an important element in each of these applications. We present SALAI-Net, a portable statistical LAI method that can be applied on any set of species and ancestries (species-agnostic), requiring only haplotype data and no other biological parameters. Inspired by identity by descent methods, SALAI-Net estimates population labels for each segment of DNA by performing a reference matching approach, which leads to an interpretable and fast technique. We benchmark our models on whole-genome data of humans and we test these models’ ability to generalize to dog breeds when trained on human data. SALAI-Net outperforms previous methods in terms of balanced accuracy, while generalizing between different settings, species and datasets. Moreover, it is up to two orders of magnitude faster and uses considerably less RAM memory than competing methods.This paper was published as part of a special issue financially supported by ECCB2022. Some of the computing for this project was performed on the Sherlock cluster at Stanford University. We would like to thank Stanford University and the Stanford Research Computing Center for providing computational resources and support that contributed to these research results. A.G.I. and D.M.M. received support from NIH under award R01HG010140. Conflict of Interest: AGI is a co-founder of Galatea Bio Inc.Peer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarObjectius de Desenvolupament Sostenible::3 - Salut i Benestar::3.4 - Per a 2030, reduir en un terç la mortalitat prematura per malalties no transmissibles, mitjançant la prevenció i el tractament, i promoure la salut mental i el benestarPostprint (published version

Assessing sleep health in a European population: results of the catalan health survey 2015

Objective To describe the overall sleep health of the Catalan population using data from the 2015 Catalan Health Survey and to compare the performance of two sleep health indicators: sleep duration and a 5-dimension sleep scale (SATED). Methods Multistage probability sampling representative of the non-institutionalized population aged 15 or more years, stratified by age, gender and municipality size, was used, excluding nightshift-workers. A total of 4385 surveys were included in the analyses. Associations between sleep health and the number of reported chronic diseases were assessed using non-parametric smoothed splines. Differences in the predictive ability of age-adjusted logistic regression models of self-rated health status were assessed. Multinomial logistic regression models were used to assess SATED determinants. Results Overall mean (SD) sleep duration was 7.18 (1.16) hours; and SATED score 7.91 (2.17) (range 0–10), lower (worse) scores were associated with increasing age and female sex. Alertness and efficiency were the most frequently impaired dimensions across age groups. SATED performed better than sleep duration when assessing self-rated health status (area under the curve = 0.856 vs. 0.798; p-value <0.001), and had a linear relationship with the number of reported chronic diseases, while the sleep duration relationship was u-shaped. Conclusions Sleep health in Catalonia is associated with age and gender. SATED has some advantaged compared to sleep duration assessment, as it relates linearly to health indicators, has a stronger association with self-rated health status, and provides a more comprehensive assessment of sleep health. Therefore, the inclusion of multi-dimensional sleep health assessment tools in national surveys should be considered.This work was cofunded by Ministerio de Economía y Competitividad [COFUND2014-51501]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript