Communication systems using LabVIEW

LabVIEW enables engineers to simulate various communication and control systems. LabVIEW helps to create Virtual Instruments (VIs) which are the files with which the user interacts to accomplish the required task. In this paper, the AM system implementation in LabVIEW is explained in detail along with the observed waveforms. The AM system is implemented using two separate VIs i.e. Transmitter_AM.vi and Receiver_AM.vi. Each VI has two parts: Front Panel and the Block Diagram. The Front Panel is usually the interface the user interacts with and observes results. The block diagram contains the blocks used to implement the functionality required for the operation of the VI. The individual blocks in the block diagram are called the sub VIs. The user may or may not need to make changes in the block diagram of the VI during the execution of the LabVIEW program

Gestational diabetes mellitus in Cameroon: prevalence, risk factors and screening strategies

Copyright \ua9 2024 Sobngwi, Sobngwi-Tambekou, Katte, Echouffo-Tcheugui, Balti, Kengne, Fezeu, Ditah, Tchatchoua, Dehayem, Unwin, Rankin, Mbanya and Bell.Background: The burden of gestational diabetes (GDM) and the optimal screening strategies in African populations are yet to be determined. We assessed the prevalence of GDM and the performance of various screening tests in a Cameroonian population. Methods: We carried out a cross-sectional study involving the screening of 983 women at 24-28 weeks of pregnancy for GDM using serial tests, including fasting plasma (FPG), random blood glucose (RBG), a 1-hour 50g glucose challenge test (GCT), and standard 2-hour oral glucose tolerance test (OGTT). GDM was defined using the World Health Organization (WHO 1999), International Association of Diabetes and Pregnancy Special Group (IADPSG 2010), and National Institute for Health Care Excellence (NICE 2015) criteria. GDM correlates were assessed using logistic regressions, and c-statistics were used to assess the performance of screening strategies. Findings: GDM prevalence was 5\ub79%, 17\ub77%, and 11\ub70% using WHO, IADPSG, and NICE criteria, respectively. Previous stillbirth [odds ratio: 3\ub714, 95%CI: 1\ub727-7\ub776)] was the main correlate of GDM. The optimal cut-points to diagnose WHO-defined GDM were 5\ub79 mmol/L for RPG (c-statistic 0\ub762) and 7\ub71 mmol/L for 1-hour 50g GCT (c-statistic 0\ub776). The same cut-off value for RPG was applicable for IADPSG-diagnosed GDM while the threshold was 6\ub75 mmol/L (c-statistic 0\ub761) for NICE-diagnosed GDM. The optimal cut-off of 1-hour 50g GCT was similar for IADPSG and NICE-diagnosed GDM. WHO-defined GDM was always confirmed by another diagnosis strategy while IADPSG and GCT independently identified at least 66\ub79 and 41\ub70% of the cases. Interpretation: GDM is common among Cameroonian women. Effective detection of GDM in under-resourced settings may require simpler algorithms including the initial use of FPG, which could substantially increase screening yield

Impact of concomitant mitral regurgitation on transvalvular gradient and flow in severe aortic stenosis: a systematic ex vivo analysis of a subentity of low-flow low-gradient aortic stenosis

AIMS Evaluation of aortic stenosis (AS) is based on echocardiographic measurement of mean pressure gradient (MPG), flow velocity (Vmax) and aortic valve area (AVA). The objective of the present study was to analyse the impact of systemic haemodynamic variables and concomitant mitral regurgitation (MR) on aortic MPG, Vmax and AVA in severe AS. METHODS AND RESULTS A pulsatile circulatory model was designed to study function and interdependence of stenotic aortic (AVA: 1.0 cm², 0.8 cm² and 0.6 cm²) and insufficient mitral prosthetic valves (n=8; effective regurgitant orifice area [EROA] 0.4 cm²) using Doppler ultrasound. In the absence of severe MR, a stepwise increase of stroke volume (SV) and a decrease of AVA was associated with a proportional increase of aortic MPG. When MR with EROA 0.4 cm² was introduced, forward SV decreased significantly (70.9±1.1 ml vs. 60.8±1.6 ml vs. 47.4±1.1 ml; p=0.02) while MR volume increased proportionally. This was associated with a subsequent reduction of aortic MPG (57.1±9.4 mmHg vs. 48.6±13.8 mmHg vs. 33.64±9.5 mmHg; p=0.035) and Vmax (5.09±0.4 m/s vs. 4.91±0.73 m/s vs. 3.75±0.57 m/s; p=0.007). Calculated AVA remained unchanged (without MR: AVA=0.53±0.04 cm² vs. with MR: AVA=0.52±0.05 cm²; p=ns). In the setting of severe AS without MR, changes of vascular resistance (SVR) and compliance (C) did not impact on aortic MPG (low SVR and C: 66±13.8 mmHg and 61.1±20 mmHg vs. high SVR and C: 60.9±9.2 mmHg and 71.5±13.5 mmHg; p=ns) In concomitant severe MR, aortic MPG and Vmax were not significantly reduced by increased SVR (36.6±2.2 mmHg vs. 34.9±5.6 mmHg, p=0.608; 3.89±0.18 m/s vs. 3.96±0.28 m/s; p=ns). CONCLUSIONS Systemic haemodynamic variables and concomitant MR may potentially affect diagnostic accuracy of echocardiographic AS evaluation. As demonstrated in the present study, MPG and Vmax are flow-dependent and significantly reduced by a reduction of forward SV from concomitant severe MR, resulting in another entity of low-flow low-gradient aortic stenosis. In contrast, calculated AVA appears to be a robust parameter of AS evaluation if severe MR is present. Changes of SVR and C did not affect the diagnostic accuracy of AS evaluation