Monitoring tomato leaf disease through convolutional neural networks

Agriculture plays an essential role in Mexico’s economy. The agricultural sector has a 2.5% share of Mexico’s gross domestic product. Specifically, tomatoes have become the country’s most exported agricultural product. That is why there is an increasing need to improve crop yields. One of the elements that can considerably affect crop productivity is diseases caused by agents such as bacteria, fungi, and viruses. However, the process of disease identification can be costly and, in many cases, time-consuming. Deep learning techniques have begun to be applied in the process of plant disease identification with promising results. In this paper, we propose a model based on convolutional neural networks to identify and classify tomato leaf diseases using a public dataset and complementing it with other photographs taken in the fields of the country. To avoid overfitting, generative adversarial networks were used to generate samples with the same characteristics as the training data. The results show that the proposed model achieves a high performance in the process of detection and classification of diseases in tomato leaves: the accuracy achieved is greater than 99% in both the training dataset and the test dataset.This work was partially funded by the State Research Agency of Spain under grant number PID2020-116377RB-C21.Peer ReviewedPostprint (published version

Remotely piloted aircraft systems and a wireless sensors network for radiological accidents

In critical radiological situations, the real time information that we could get from the disaster area becomes of great importance. However, communication systems could be affected after a radiological accident. The proposed network in this research consists of distributed sensors in charge of collecting radiological data and ground vehicles that are sent to the nuclear plant at the moment of the accident to sense environmental and radiological information. Afterwards, data would be analyzed in the control center. Collected data by sensors and ground vehicles would be delivered to a control center using Remotely Piloted Aircraft Systems (RPAS) as a message carrier. We analyze the pairwise contacts, as well as visiting times, data collection, capacity of the links, size of the transmission window of the sensors, and so forth. All this calculus was made analytically and compared via network simulations.Peer ReviewedPostprint (published version

Communication technologies to design vehicle-to-vehicle and vehile-to-infrastructures applications

Intelligent Transport Systems use communication technologies to offer real-time traffic information services to road users and government managers. Vehicular Ad Hoc Networks is an important component of ITS where vehicles communicate with other vehicles and road-side infrastructures, analyze and process received information, and make decisions according to that. However, features like high vehicle speeds, constant mobility, varying topology, traffic density, etc. induce challenges that make conventional wireless technologies unsuitable for vehicular networks. This paper focuses on the process of designing efficient vehicle-to-vehicle and vehicle-to road-side infrastructure applications.Peer ReviewedPostprint (published version

Feature selection model based on EEG signals for assessing the cognitive workload in drivers

In recent years, research has focused on generating mechanisms to assess the levels of subjects’ cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools for analyzing the cognitive workload, and electroencephalographic (EEG) signals have been most frequently used due to their high precision. However, one of the main challenges in implementing the EEG signals is finding appropriate information for identifying cognitive states. Here, we present a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workloads and structures a new dataset capable of optimizing the model’s predictive process. We found that GALoRIS identifies data related to high and low cognitive workloads of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50% and maximizing the model’s predictive capacity, achieving a precision rate greater than 90%.This work has been funded by the Ministry of Science, Innovation and Universities of Spain under grant number TRA2016-77012-RPeer ReviewedPostprint (published version

SAgric-IoT: an IoT-based platform and deep learning for greenhouse monitoring

The Internet of Things (IoT) and convolutional neural networks (CNN) integration is a growing topic of interest for researchers as a technology that will contribute to transforming agriculture. IoT will enable farmers to decide and act based on data collected from sensor nodes regarding field conditions and not purely based on experience, thus minimizing the wastage of supplies (seeds, water, pesticide, and fumigants). On the other hand, CNN complements monitoring systems with tasks such as the early detection of crop diseases or predicting the number of consumable resources and supplies (water, fertilizers) needed to increase productivity. This paper proposes SAgric-IoT, a technology platform based on IoT and CNN for precision agriculture, to monitor environmental and physical variables and provide early disease detection while automatically controlling the irrigation and fertilization in greenhouses. The results show SAgric-IoT is a reliable IoT platform with a low packet loss level that considerably reduces energy consumption and has a disease identification detection accuracy and classification process of over 90%

A search for an unexpected asymmetry in the production of e(+)mu(-) and e(-)mu(+) pairs in proton-proton collisions recorded by the ATLAS detector at root s=13 TeV

This search, a type not previously performed at ATLAS, uses a comparison of the production cross sections for e(+)mu(-) and e(-)mu(+) pairs to constrain physics processes beyond the Standard Model. It uses 139 fb(-1) of proton-proton collision data recorded at root s = 13 TeV at the LHC. Targeting sources of new physics which prefer final states containing e(+)mu(-) and e(-)mu(+), the search contains two broad signal regions which are used to provide model-independent constraints on the ratio of cross sections at the 2% level. The search also has two special selections targeting supersymmetric models and leptoquark signatures. Observations using one of these selections are able to exclude, at 95% confidence level, singly produced smuons with masses up to 640 GeV in a model in which the only other light sparticle is a neutralino when the R-parity-violating coupling lambda(23)(1)' is close to unity. Observations using the other selection exclude scalar leptoquarks with masses below 1880 GeV when g(1R)(eu) = g(1R)(mu c) = 1, at 95% confidence level. The limit on the coupling reduces to g(1R)(eu) = g(1R)(mu c) = 0.46 for a mass of 1420 GeV

Single nucleotide variations in ZBTB46 are associated with post-thrombolytic parenchymal haematoma

Haemorrhagic transformation is a complication of recombinant tissue-plasminogen activator treatment. The most severe form, parenchymal haematoma, can result in neurological deterioration, disability, and death. Our objective was to identify single nucleotide variations associated with a risk of parenchymal haematoma following thrombolytic therapy in patients with acute ischaemic stroke. A fixed-effect genome-wide meta-analysis was performed combining two-stage genome-wide association studies (n = 1904). The discovery stage (three cohorts) comprised 1324 ischaemic stroke individuals, 5.4% of whom had a parenchymal haematoma. Genetic variants yielding a P-value < 0.05 1 x 10(-5) were analysed in the validation stage (six cohorts), formed by 580 ischaemic stroke patients with 12.1% haemorrhagic events. All participants received recombinant tissue-plasminogen activator; cases were parenchymal haematoma type 1 or 2 as defined by the European Cooperative Acute Stroke Study (ECASS) criteria. Genome-wide significant findings (P < 5 x 10(-8)) were characterized by in silica functional annotation, gene expression, and DNA regulatory elements. We analysed 7 989 272 single nucleotide polymorphisms and identified a genome-wide association locus on chromosome 20 in the discovery cohort; functional annotation indicated that the ZBTB46 gene was driving the association for chromosome 20. The top single nucleotide polymorphism was rs76484331 in the ZBTB46 gene [P = 2.49 x 10(-8); odds ratio (OR): 11.21; 95% confidence interval (CI): 4.82-26.55]. In the replication cohort (n = 580), the rs76484331 polymorphism was associated with parenchymal haematoma (P = 0.01), and the overall association after meta-analysis increased (P = 1.61 x 10(-8), OR: 5.84; 95% CI: 3.16-10.76). ZBTB46 codes the zinc finger and BTB domain-containing protein 46 that acts as a transcription factor. In silica studies indicated that ZBTB46 is expressed in brain tissue by neurons and endothelial cells. Moreover, rs76484331 interacts with the promoter sites located at 20q13. In conclusion, we identified single nucleotide variants in the ZBTB46 gene associated with a higher risk of parenchymal haematoma following recombinant tissue-plasminogen activator treatment.Peer reviewe

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure &lt; 100 mmHg (n = 1127), estimated glomerular filtration rate &lt; 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Wearable sensors for evaluating driver drowsiness and high stress

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.FLOR will redefine the paradigms under which remote pilots and Remotely Piloted Aircraft Systems have been designed and operated during the last decades; i.e. a dedicated crew manages a single aircraft, indistinctive from being the latter manned or unmanned. Most of the existing and ongoing investigations related to the insertion of RPAS into air traffic have addressed the impact of operating a single aircraft. However, it has never been considered the advantatges of operating of a fleet of RPAS managed within a dedicated ground facility involving a specialised team of remote pilots. Currently, only the military applies RPAS in a large scale, and although in that specific environment the pressure to reduce operational costs is increasingly high, nevertheless the armed services do not have to face the pressure of an open market. FLOR intends to investigate the requirements for a new generation of RPAS systems that support a revolutionary operational paradigm: each pilot operates multiple RPAS in a certain phase of flight; pilots transfer the flight responsavility as the RPAS operation progresses. FLOR improves the way in which RPAS operators employ both aircraft and remote pilots, especially in terms of economic efficiency and safety. The improvements will come from multiple sources, such as a better exploitation of the vehicle availability as it will not be limited to the capabilities or duty time of a single crew. Further improvements are a more advantageous use of personnel resources. Each individual can be allocated to a certain tasks that better fit his competences and capabilities and those tasks may be adjusted dynamically over time. Pilots may supervise multiple RPAs during the extensive and uneventful cruise portions of flights, while may focus on individual vehicles to manage the critical parts of the flight, or to respond to emergency/abnormal situations. The ground based flight control of the aircraft fleet is improved by centralizing the RPAS piloting in a small network of highly capable infrastructures which will guarantee the desired levels of certification, fault-tolerance, and service continuity. Finally, from a safety perspective, the possibility of transferring the control of the RPAS for a change of pilot or to another piloting centre in case of need, will allow an unprecedented extra safety measure. The FLOR concept will prove effective for extensive commercial point-to-point cargo operations, but also when conducting surveillance operations, e.g. disaster monitoring with a fleet of MALE/HALE RPAS or operating a fleet of solar powered RPAS slowly loitering at extremely high altitude. This issue is often addressed as some of the most feasible deployment of RPAS, as those operations might be dangerous or exhausting for pilots in manned aircraft. Within the most recent European Commissions update of the Aviation Strategy for Europe the huge potential of drones in the sense of unmanned aircraft systems (UAS) is highlighted. EC is asking for R&D in reducing risks associated to UAS. In addition EC sees the need for improving related technologies and operations to unleash their full market potential. FLOR paves the way for the efficient application of UAS operated as RPAS with respect to their fleet management and control.Peer Reviewe

Wearable sensors for evaluating driver drowsiness and high stress

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.FLOR will redefine the paradigms under which remote pilots and Remotely Piloted Aircraft Systems have been designed and operated during the last decades; i.e. a dedicated crew manages a single aircraft, indistinctive from being the latter manned or unmanned. Most of the existing and ongoing investigations related to the insertion of RPAS into air traffic have addressed the impact of operating a single aircraft. However, it has never been considered the advantatges of operating of a fleet of RPAS managed within a dedicated ground facility involving a specialised team of remote pilots. Currently, only the military applies RPAS in a large scale, and although in that specific environment the pressure to reduce operational costs is increasingly high, nevertheless the armed services do not have to face the pressure of an open market. FLOR intends to investigate the requirements for a new generation of RPAS systems that support a revolutionary operational paradigm: each pilot operates multiple RPAS in a certain phase of flight; pilots transfer the flight responsavility as the RPAS operation progresses. FLOR improves the way in which RPAS operators employ both aircraft and remote pilots, especially in terms of economic efficiency and safety. The improvements will come from multiple sources, such as a better exploitation of the vehicle availability as it will not be limited to the capabilities or duty time of a single crew. Further improvements are a more advantageous use of personnel resources. Each individual can be allocated to a certain tasks that better fit his competences and capabilities and those tasks may be adjusted dynamically over time. Pilots may supervise multiple RPAs during the extensive and uneventful cruise portions of flights, while may focus on individual vehicles to manage the critical parts of the flight, or to respond to emergency/abnormal situations. The ground based flight control of the aircraft fleet is improved by centralizing the RPAS piloting in a small network of highly capable infrastructures which will guarantee the desired levels of certification, fault-tolerance, and service continuity. Finally, from a safety perspective, the possibility of transferring the control of the RPAS for a change of pilot or to another piloting centre in case of need, will allow an unprecedented extra safety measure. The FLOR concept will prove effective for extensive commercial point-to-point cargo operations, but also when conducting surveillance operations, e.g. disaster monitoring with a fleet of MALE/HALE RPAS or operating a fleet of solar powered RPAS slowly loitering at extremely high altitude. This issue is often addressed as some of the most feasible deployment of RPAS, as those operations might be dangerous or exhausting for pilots in manned aircraft. Within the most recent European Commissions update of the Aviation Strategy for Europe the huge potential of drones in the sense of unmanned aircraft systems (UAS) is highlighted. EC is asking for R&D in reducing risks associated to UAS. In addition EC sees the need for improving related technologies and operations to unleash their full market potential. FLOR paves the way for the efficient application of UAS operated as RPAS with respect to their fleet management and control.Peer Reviewe