MIMO Underwater Visible Light Communications: Comprehensive Channel Study, Performance Analysis, and Multiple-Symbol Detection

In this paper, we analytically study the bit error rate (BER) performance of underwater visible light communication (UVLC) systems with binary pulse position modulation (BPPM). We simulate the channel fading-free impulse response (FFIR) based on Monte Carlo numerical method to take into account the absorption and scattering effects. Additionally, to characterize turbulence effects, we multiply the aforementioned FFIR by a fading coefficient which for weak oceanic turbulence can be modeled as a lognormal random variable (RV). Moreover, to mitigate turbulence effects, we employ multiple transmitters and/or receivers, i.e., spatial diversity technique over UVLC links. Closed-form expressions for the system BER are provided, when equal gain combiner (EGC) is employed at the receiver side, thanks to Gauss-Hermite quadrature formula and approximation to the sum of lognormal RVs. We further apply saddle-point approximation, an accurate photon-counting-based method, to evaluate the system BER in the presence of shot noise. Both laser-based collimated and light emitting diode (LED)-based diffusive links are investigated. Since multiple-scattering effect of UVLC channels on the propagating photons causes considerable inter-symbol interference (ISI), especially for diffusive channels, we also obtain the optimum multiple-symbol detection (MSD) algorithm to significantly alleviate ISI effects and improve the system performance. Our numerical analysis indicates good matches between the analytical and photon-counting results implying the negligibility of signal-dependent shot noise, and also between analytical results and numerical simulations confirming the accuracy of our derived closed-form expressions for the system BER. Besides, our results show that spatial diversity significantly mitigates fading impairments while MSD considerably alleviates ISI deteriorations

Statistical Studies of Fading in Underwater Wireless Optical Channels in the Presence of Air Bubble, Temperature, and Salinity Random Variations (Long Version)

Optical signal propagation through underwater channels is affected by three main degrading phenomena, namely absorption, scattering, and fading. In this paper, we experimentally study the statistical distribution of intensity fluctuations in underwater wireless optical channels with random temperature and salinity variations as well as the presence of air bubbles. In particular, we define different scenarios to produce random fluctuations on the water refractive index across the propagation path, and then examine the accuracy of various statistical distributions in terms of their goodness of fit to the experimental data. We also obtain the channel coherence time to address the average period of fading temporal variations. The scenarios under consideration cover a wide range of scintillation index from weak to strong turbulence. Moreover, the effects of beam-collimator at the transmitter side and aperture averaging lens at the receiver side are experimentally investigated. We show that the use of a transmitter beam-collimator and/or a receiver aperture averaging lens suits single-lobe distributions such that the generalized Gamma and exponential Weibull distributions can excellently match the histograms of the acquired data. Our experimental results further reveal that the channel coherence time is on the order of $10^{-3}$ seconds and larger which implies to the slow fading turbulent channels

Impaired aortic distensibility measured by computed tomography is associated with the severity of coronary artery disease.

Impaired aortic distensibility index (ADI) is associated with cardiovascular risk factors. This study evaluates the relation of ADI measured by computed tomographic angiography (CTA) with the severity of coronary atherosclerosis in subjects with suspected coronary artery disease (CAD). Two hundred and twenty-nine subjects,age 63 ± 9 years, 42% female, underwent coronary artery calcium (CAC) scanning and CTA, and their ADI and Framingham risk score (FRS) were measured. End-systolic and end-diastolic (ED) cross-sectional-area(CSA) of ascending-aorta (AAo) was measured 15-mm above the left-main coronary ostium. ADI was defined as: [(Δlumen-CSA)/(lumen-CSA in ED × systemic-pulse-pressure) × 10(3)]. ADI measured by 2D-trans-thoracic echocardiography (TTE) was compared with CTA-measured ADI in 26 subjects without CAC. CAC was defined as 0, 1-100, 101-400 and 400+. CAD was defined as luminal stenosis 0, 1-49% and 50%+. There was an excellent correlation between CTA- and TTE-measured ADI (r(2)=0.94, P=0.0001). ADI decreased from CAC 0 to CAC 400+; similarly from FRS 1-9% to FRS 20% + (P<0.05). After adjustment for risk factors, the relative risk for each standard deviation decrease in ADI was 1.66 for CAC 1-100, 2.26 for CAC 101-400 and 2.32 for CAC 400+ as compared to CAC 0; similarly, 2.36 for non-obstructive CAD and 2.67 for obstructive CAD as compared to normal coronaries. The area under the ROC-curve to predict significant CAD was 0.68 for FRS, 0.75 for ADI, 0.81 for CAC and 0.86 for the combination (P<0.05). Impaired aortic distensibility strongly correlates with the severity of coronary atherosclerosis. Addition of ADI to CAC and traditional risk factors provides incremental value to predict at-risk individuals

FIBER QUALITY PREDICTION USING NIR SPECTRAL DATA: TREE-BASED ENSEMBLE LEARNING VS DEEP NEURAL NETWORKS

The growing applications of near infrared (NIR) spectroscopy in wood quality control and monitoring necessitates focusing on data-driven methods to develop predictive models. Despite the advancements in analyzing NIR spectral data, literature on wood science and engineering has mainly uti- lizedthe classic model development methods, such as principal component analysis (PCA) regression or partial least squares (PLS) regression, with relatively limited studies conducted on evaluating machine learning (ML) models, and specifically, artificial neural networks (ANNs). This couldpotentially limit the performance of predictive models, specifically for some wood properties, such as tracheid width that are both time-consuming tomeasure and challenging to predict using spectral data. This study aims to enhance the prediction accuracy for tracheid width using deep neural networks and tree-based ensemble learning algorithms on a dataset consisting of 2018 samples and 692 features (NIR spectra wavelengths). Accord- ingly, NIR spectra were fed into multilayer perceptron (MLP), 1 dimensional-convolutional neural net- works (1D-CNNs), random forest, TreeNet gradient-boosting, extreme gradient-boosting (XGBoost), and light gradient-boosting machine (LGBM). It was of interest to study the performance of the models with and without applying PCA to assess how effective they would perform when analyzing NIR spectra with- out employing dimensionality reduction on data. It was shown that gradient-boosting machines outper- formed the ANNs regardless of the number of features (data dimension). Allthe models performed better without PCA. It is concluded that tree-based gradient-boosting machines could be effectively used for wood characterization utilizing a medium-sized NIR spectral dataset

Copy number analysis from whole-exome sequencing data revealed a novel homozygous deletion in PARK7 leads to severe early-onset Parkinson's disease

Parkinson's disease (PD), a neurodegenerative disease characterized by both motor neuron and non-motor neuron symptoms, is the most frequent neurodegenerative disease after Alzheimer's disease. Both genetic and environmental factors take part in disease etiology. Most cases are considered complex multifactorial diseases. About 15% of PD appear in the familial form, and about 5% of all cases arise from a single gene mutation. Among Mendelian causes of PD, PARK7 is one of the autosomal recessive forms due to loss-of-function mutations in both gene alleles. Both single nucleotide variants (SNVs) and copy number variations (CNVs) are observed in PARK7. This study presents an Iranian family with familial PD where some relatives had psychiatric disorders. A homozygous 1617 bp deletion in a female with early-onset PD was detected through copy-number analysis from whole-exome sequencing (WES) data in this consanguineous family. Further investigation by surveying microhomology revealed that the actual size of the deletion is 3,625 bp. This novel CNV that was in the PARK7gene is supposed to co-relation with early-onset PD and infertility in this family

Low fingertip temperature rebound measured by digital thermal monitoring strongly correlates with the presence and extent of coronary artery disease diagnosed by 64-slice multi-detector computed tomography

Previous studies showed strong correlations between low fingertip temperature rebound measured by digital thermal monitoring (DTM) during a 5 min arm-cuff induced reactive hyperemia and both the Framingham Risk Score (FRS), and coronary artery calcification (CAC) in asymptomatic populations. This study evaluates the correlation between DTM and coronary artery disease (CAD) measured by CT angiography (CTA) in symptomatic patients. It also investigates the correlation between CTA and a new index of neurovascular reactivity measured by DTM. 129 patients, age 63 ± 9 years, 68% male, underwent DTM, CAC and CTA. Adjusted DTM indices in the occluded arm were calculated: temperature rebound: aTR and area under the temperature curve aTMP-AUC. DTM neurovascular reactivity (NVR) index was measured based on increased fingertip temperature in the non-occluded arm. Obstructive CAD was defined as ≥50% luminal stenosis, and normal as no stenosis and CAC = 0. Baseline fingertip temperature was not different across the groups. However, all DTM indices of vascular and neurovascular reactivity significantly decreased from normal to non-obstructive to obstructive CAD [(aTR 1.77 ± 1.18 to 1.24 ± 1.14 to 0.94 ± 0.92) (P = 0.009), (aTMP-AUC: 355.6 ± 242.4 to 277.4 ± 182.4 to 184.4 ± 171.2) (P = 0.001), (NVR: 161.5 ± 147.4 to 77.6 ± 88.2 to 48.8 ± 63.8) (P = 0.015)]. After adjusting for risk factors, the odds ratio for obstructive CAD compared to normal in the lowest versus two upper tertiles of FRS, aTR, aTMP-AUC, and NVR were 2.41 (1.02–5.93), P = 0.05, 8.67 (2.6–9.4), P = 0.001, 11.62 (5.1–28.7), P = 0.001, and 3.58 (1.09–11.69), P = 0.01, respectively. DTM indices and FRS combined resulted in a ROC curve area of 0.88 for the prediction of obstructive CAD. In patients suspected of CAD, low fingertip temperature rebound measured by DTM significantly predicted CTA-diagnosed obstructive disease