In this paper, we propose a convolutional neural network (CNN) aided automatic modulation recognition (AMR) method for a multiple antenna system. We also present two specific combination strategies, such as the relative majority voting method and arithmetic mean method to improve the classification performance in comparison with the state of the art. Our results are given to verify that the proposed method dominant exploits features and classify the modulation types with higher accuracy in comparison with the AMR employing high order cumulants (HOC) and artificial neural networks (ANN).

High-sensitive cardiac Troponin T (hsTnT) is the most frequently used biomarker for the detection of cardiomyocyte injury. Severe aortic stenosis (AS) leads to an increased left ventricular load, with the potential of myocardial injury reflected by increased TnT levels. However, there is a lack of studies showing the prevalence and prognostic role of elevated hsTnT in patients with asymptomatic AS. To examine the association between the hsTnT levels and AS severity in asymptomatic AS patients. We hypothesized that patients with more severe AS will have elevated hsTnT levels and that hsTnT levels are associated with a higher risk for aortic valve events (AVE) and all-cause mortality (ACM). We performed a post-hoc analysis in 1739 asymptomatic patients with mild to moderate-severe AS, enrolled in the randomized, double-blinded SEAS-study (Simvastatin and Ezetimibe in Aortic Stenosis). All patients had available hsTnT blood samples measured at baseline (Year 0) and Year 1. We defined moderate to severe (mod-severe) AS as a transaortic maximal outflow velocity (Vmax)>3.5 m/s combined with aortic valve area (AVA)<1.0 cm2, otherwise non-severe AS. An hsTnT>14 ng/L was high according to assay (Roche, Elecsys Troponin T hs on cobas e 601). Linear multivariable regression model examined the association of hsTnT levels to clinical and echocardiographic variables. Cox multivariable regression model evaluated competing risks and hazard ratios (HR) of outcomes while adjusting for relevant variables, including a Framingham 10-years risk score of cardiovascular diseases. The competing risks were either ACM or AVE, i.e. the first of AVR, cardiovascular death and heart failure due to AS progression. At baseline, hsTnT was high in 26% (453/1739) patients; 25% (380/1529) in non-severe and 35% (73/210) in mod-severe AS. Relative TnT change over one year was 17% (mean 1.17, SD 1.01); 15% in non-severe vs. 32% in mod-severe AS, and neither associated to AS severity, hsTnT at baseline or lipid-lowering treatment. In multivariable linear regression analysis, there were significant correlations between hsTnT at baseline and age, male gender, creatinine, left ventricular mass index and BMI (all p<0.001, R-square=0.42), but not with AS severity. In multivariable Cox regression analyses, a high hsTnT at baseline was associated with AVE 1.61 [95% CI 1.29–1.99]. In contrast, hsTnT at baseline was not associated to all-cause mortality (see figure). In asymptomatic AS patients without severe AS, high-sensitive Troponin T is not associated with AS severity in cross-sectional analyses, and its levels do not change substantially during one year of follow-up. However, patients with hsTnT >14 ng/l had a sixty percent higher independent risk of subsequent aortic valve events. Multivariable Cox regression Type of funding source: Private company. Main funding source(s): Acknowledgements: Main sponsor (SEAS): MSD Singapore Company, LLC, partnership between Merck & Co. Inc. and Schering-Plough Corporation. Blood analysis sponsor: Roche

Abstract Development of critical systems nowadays is hardly achievable without reuse of previous knowledge. Design patterns have an important role in the design of such systems as they define and document common solutions to recurring design problems. However, critical systems such as those that are safety or security related, often require specific assurances that the system is adequate to operate in a given environment. Just as with any other reused knowledge in such systems, the reuse via application of design patterns needs to be assured every time. In this paper, we present a methodology for assuring the application of design patterns in critical domains. In particular, we enrich the design patterns template to support their further assurance. We define the aspects that should be tackled during the assurance of a design pattern application. We use the information specified in the design pattern template to guide the automated instantiation of the argumentation for each design pattern application in the system. We provide tool-support for our methodology in the context of the AMASS tool-platform and evaluate it in an automotive case study.

A reliable and precise model of the environment is of the highest importance for autonomous vehicles. Occupancy grids are a well-known approach for environment modeling and are a crucial part of multiple autonomous driving functionalities. The standard method is to use a single 2D occupancy grid to model the environment using nonground points. In this paper, we propose a decentralized occupancy grid filtering chain (pipeline) where a high-density 64-layer LiDAR provided the input to our pipeline. Our approach enables us to obtain detailed 2D and 3D models of the environment simultaneously. The pipeline was validated on different scenarios in both simulation and real world. The performance of the designed occupancy grid pipeline was evaluated by the proposed key performance indicators (KPIs) based on accuracy. The results have shown that the approach was able to extract free space information with a high degree of accuracy, while reducing the size of the unobserved area in the grid compared to the standard methods and achieving real-time performance.

Introduction Successful rehabilitation of the growing number of older citizens receiving healthcare services can lead to preservation of functional independence and improvement in quality of life. Adequate intake of dietary protein and physical training are key factors in counteracting the age-related decline in strength performance and physical function. However, during rehabilitation, many older people/persons have insufficient protein intake, and difficulties in performing exercise training with sufficient intensity and volume. The primary aim of this trial is to investigate if individualised physical exercise training programmes combined with increased protein intake (IPET+P) can improve measures on all International Classification of Functioning, Disability and Health levels, such as strength, gait speed and health-related quality of life, when compared with care as usual in municipality-based rehabilitation alone (usual care, UC) or care as usual in combination with increased protein intake (UC+P). Further, the trial investigates whether UC+P will potentiate more significant improvements in outcome measures than UC. Methods and analysis The trial is a three-armed multicentre, block-randomised controlled trial consisting of a 12-week intervention period with a 1-year follow-up. Citizens above 65 years referred to rehabilitation in the municipality without restricting comorbidities are eligible. Participants are randomised to either a UC group, a UC group with protein supplementation receiving 27.5 g protein/day (UC+P), or an IPET+P supplementation of 27.5 g protein/day. The Short Musculoskeletal Function Assessment questionnaire is the primary outcome. Ethics and dissemination Approvals from The Ethics Committee in Region Zealand, Denmark (SJ-758), and the General Data Protection Regulation at the University of Southern Denmark, Odense (10.330) have been obtained. Trial registration number NCT04091308

Low power wide area networks support the success of long range Internet of things applications such as agriculture, security, smart cities and homes. This enormous popularity, however, breeds new challenging problems as the wireless spectrum gets saturated which increases the probability of collisions and performance degradation. To this end, smart spectrum decisions are needed and will be supported by wireless technology recognition to allow the networks to dynamically adapt to the ever changing environment where fair co-existence with other wireless technologies becomes essential. In contrast to existing research that assesses technology recognition using machine learning on powerful graphics processing units, this work aims to propose a deep learning solution using convolutional neural networks, cheap software defined radios and efficient embedded platforms such as NVIDIA’s Jetson Nano. More specifically, this paper presents low complexity near-real time multi-band sub-GHz technology recognition and supports a wide variety of technologies using multiple settings. Results show accuracies around 99%, which are comparable with state of the art solutions, while the classification time on a NVIDIA Jetson Nano remains small and offers real-time execution. These results will enable smart spectrum management without the need of expensive and high power consuming hardware.

The long‐wavelength geoid is sensitive to Earth's mantle density structure as well as radial variations in mantle viscosity. We present a suite of inversions for the radial viscosity profile using whole‐mantle models that jointly constrain the variations in density, shear‐ and compressional‐wavespeeds using full‐spectrum tomography. We use a Bayesian approach to identify a collection of viscosity profiles compatible with the geoid, while enabling uncertainties to be quantified. Depending on tomographic model parameterization and data weighting, it is possible to obtain models with either positive‐ or negative‐buoyancy in the large low shear velocity provinces. We demonstrate that whole‐mantle density models in which density and VS variations are correlated imply an increase in viscosity below the transition zone, often near 1,000 km. Many solutions also contain a low‐viscosity channel below 650 km. Alternatively, models in which density is less‐correlated with VS—which better fit normal mode data—require a reduced viscosity region in the lower mantle. This feature appears in solutions because it reduces the sensitivity of the geoid to buoyancy variations in the lowermost mantle. The variability among the viscosity profiles obtained using different density models is indicative of the strong nonlinearities in modeling the geoid and the limited resolving power of the geoid kernels. We demonstrate that linearized analyses of model resolution do not adequately capture the posterior uncertainty on viscosity. Joint and iterative inversions of viscosity, wavespeeds, and density using seismic and geodynamic observations are required to reduce bias from prior assumptions on viscosity variation and scalings between material properties.

A new modified ion-selective electrode with membranes of LaF3 single crystals with different internal contacts (solid steel or electrolyte) and with FexOy nanoparticles as loading was developed. The best response characteristic with linear potential change was found in the fluoride concentration range from 10−1 to 3.98 × 10−7 M. The detection limit for the electrolyte contact was determined at 7.41 × 10−8 M with a regression coefficient of 0.9932, while the regression coefficient for the solid contact was 0.9969. The potential change per concentration decade ranged from 50.3 to 62.4 mV, depending on whether the contact was solid or electrolytic. The prepared modified electrode has a long lifetime, as well as the possibility of application in different positions (solid contact), and it can also be used for the determination of iron ions. The electrode characterization was performed with scanning electron microscopy and elemental analysis with the technique of electron-dispersive X-ray spectroscopy.

Coronary chronic total occlusion (CTO) is characterized by the presence of collateral blood vessels which can provide additional blood supply to CTO-artery dependent myocardium. Successful CTO recanalization is followed by significant decrease in collateral donor artery blood flow and collateral derecruitment. Study aim was to assess time-dependent changes in coronary flow reserve (CFR) in collateral donor artery after CTO recanalization and identify factors that influence these changes. Our study enrolled 31 patients with CTO scheduled for percutaneous coronary intervention (PCI). Non-invasive CFR was measured before PCI in collateral donor artery, and 24h and 6 months post-PCI in CTO and collateral donor artery. Gated SPECT MIBI was performed before PCI, while quality of life was assessed by Seattle angina questionnaire (SAQ) pre-PCI, and 6 months after PCI. Collateral donor artery showed significant increase in CFR 24h after CTO recanalization compared to pre-PCI values (2.30±0.49 vs. 2.71±0.45, p=0.005), which remained unchanged after 6 months (2.68±0.24). Maximum baseline blood flow velocity of the collateral donor artery showed significant decrease measured 24h post-PCI compared to pre-PCI values (0.28±0.06 vs. 0.24±0.04m/s), and remained similar after 6-months. There was no significant difference in maximum hyperemic blood flow velocity pre-PCI, 24h and 6 months post-PCI. CFR change of the collateral donor artery 24h post-PCI compared to pre-PCI values showed inverse correlation with left ventricle ejection fraction (LVEF) measured on SPECT. CFR changes showed no correlation with the changes in quality of life assessed by SAQ post-PCI compared to pre-PCI. Significant increase in CFR of the collateral donor artery was observed within 24h after successful recanalization of CTO artery, which maintained constant after the 6 months follow-up. This increase was largely driven by the significant reduction in the maximum baseline blood flow velocity within 24h after CTO recanalization compared to pre-PCI values. Our results suggest that possible benefit of CTO recanalization could be the improvement in physiology of the collateral donor artery. Type of funding source: None