Channel Estimation and ICI Cancelation in Vehicular Channels of OFDM Wireless Communication Systems

Orthogonal frequency division multiplexing (OFDM) scheme increases bandwidth efficiency (BE) of data transmission and eliminates inter symbol interference (ISI). As a result, it has been widely used for wideband communication systems that have been developed during the past two decades and it can be a good candidate for the emerging communication systems such as fifth generation (5G) cellular networks with high carrier frequency and communication systems of high speed vehicles such as high speed trains (HSTs) and supersonic unmanned aircraft vehicles (UAVs). However, the employment of OFDM for those upcoming systems is challenging because of high Doppler shifts. High Doppler shift makes the wideband communication channel to be both frequency selective and time selective, doubly selective (DS), causes inter carrier interference (ICI) and destroys the orthogonality between the subcarriers of OFDM signal. In order to demodulate the signal in OFDM systems and mitigate ICIs, channel state information (CSI) is required. In this work, we deal with channel estimation (CE) and ICI cancellation in DS vehicular channels. The digitized model of the DS channels can be short and dense, or long and sparse. CE methods that perform well for short and dense channels are highly inefficient for long and sparse channels. As a result, for the latter type of channels, we proposed the employment of compressed sensing (CS) based schemes for estimating the channel. In addition, we extended our CE methods for multiple input multiple output (MIMO) scenarios. We evaluated the CE accuracy and data demodulation fidelity, along with the BE and computational complexity of our methods and compared the results with the previous CE procedures in different environments. The simulation results indicate that our proposed CE methods perform considerably better than the conventional CE schemes

Two Novel Methods for Accurate NLOS Detection Based on Channel Statistics

ABSTRACT Time-of-arrival (TOA) estimation is the first step of the most positioning algorithms. However in various environments especially when ultra wideband (UWB) pulses are used, TOA extraction from the received signal is challenging. UWB radio propagation bears multipath phenomenon, therefore correct identification of the first path TOA highly depends on the statistical characteristics of the environment and apprehension that the signal has been passed through the line-of-sight (LOS) channel or the non-line-of-sight (NLOS) one. In this paper, two novel NLOS identification techniques based on the multipath channel statistics are proposed. Simulations show that the first technique using the fitness equations of mean and variance of the received signal is suitable for residential and outdoor environments. The other one that compares the relative energy of two different periods of the received signal is more accurate in office and industrial environments. IEEE 802.15.4a channel models are used and two hypothesis tests are applied to distinguish between LOS and NLOS. The high accuracy identification of channel type is achieved for all mentioned environments

Association of NFKB1 gene polymorphism (rs28362491) with cardiometabolic risk factor in patients undergoing coronary angiography

Introduction: Genetic and environmental factors are involved in the pathogenesis of cardiovascular diseases (CVDs). The aim of the study was to investigate between the genotype of the NFKB1 gene and the cardiometabolic risk factor in patients undergoing coronary angiography. Methods: This cross-sectional study was conducted on 462 adults (male and women) aged between 35 and 75 years who referred to Afshar Hospital for coronary angiography in 2021- 2022. The polymerase chain reaction restriction fragment length polymorphism method was used to detect the genotype of rs28362491. Biochemical parameters were measured using commercial kits. Gensini and Syntax scores were calculated using the angiography result to assess the extent of coronary artery stenosis. We used multivariate logistic regression analysis to examine the relationship between genotype variants and cardiometabolic risk factors. Results: There was no association between variant genotypes and abnormally levels of serum alanine aminotransferase (ALT) (P value=0.51), aspartate aminotransferase (AST) (P value=0.99), triglyceride (TG) (P value=0.48), total cholesterol (P value=0.79), low density lipoprotein-cholestero (LDL-C) (P value=0.31), high-density lipoprotein-cholesterol (HDL-C) (P value=0.53), fast blood sugar (FBS) (P value=0.39), systolic blood pressure (P value=0.14), diastolic blood pressure (P value=0.64), Gensini score (P value=0.48) and syntax score (P value=0.74) in the crude model even after adjustment for confounding factors. Conclusion: We found no association between the ATTG polymorphism and cardiometabolic risk factors in patients who had coronary angiography. Further investigations are needed to assess the association between variants of 28362491 and cardiometabolic markers

A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

In recent years, there has been a dramatic increase in the use of unmanned aerial vehicles (UAVs), particularly for small UAVs, due to their affordable prices, ease of availability, and ease of operability. Existing and future applications of UAVs include remote surveillance and monitoring, relief operations, package delivery, and communication backhaul infrastructure. Additionally, UAVs are envisioned as an important component of 5G wireless technology and beyond. The unique application scenarios for UAVs necessitate accurate air-to-ground (AG) propagation channel models for designing and evaluating UAV communication links for control/non-payload as well as payload data transmissions. These AG propagation models have not been investigated in detail when compared to terrestrial propagation models. In this paper, a comprehensive survey is provided on available AG channel measurement campaigns, large and small scale fading channel models, their limitations, and future research directions for UAV communication scenarios

Elevation and Azimuth-aided Channel Estimation Scheme for Airborne Hyperspectral Data Transmission

A channel-estimation (CE) scheme is proposed to estimate the complex amplitude, Doppler shift, angle-of-departure, and angle-of-arrival of the channel taps for sparse and doubly selective channels for hyperspectral image transmission from unmanned aircraft vehicles (UAVs) to ground stations. The proposed method is dubbed as compressed-sensing joint parameter estimation (CS-JPE) and finds the channel parameters matrix by employing a compressed-sensing (CS)-based method. Afterward, a modified version of the joint parameter estimation (JPE) is proposed as CS-JPE and is dubbed as M-CS-JPE, which employs the elevation-azimuth angles of the line-of-sight channel tap to estimate the channel parameters with higher accuracy and lower computational complexity compared to the CS-JPE scheme. For higher accuracy of the M-CS-JPE, an elevation-azimuth angle estimation is proposed and is dubbed as fractal-structure-array since it uses a fractal structure for the placement of the UAV antennas. The performance of the CE methods is appraised by simulating transmission of AVIRIS hyperspectral data via the communication channel and evaluating their accuracy for the classification after demodulation. Compared to the least-square method, the simulation results indicate up to 30-dB figure of merit in the bit-error-rate and 10 times improvement in the hyperspectral image classification fidelity

Hyperhomographies on Krasner Hyperfields

In this paper, we introduce generalized homographic transformations as hyperhomographies over Krasner hyperfields.These particular algebraic hyperstructues are quotient structures of classical fields modulo normal groups. Besides, we define some hyperoperations and investigate the properties of the derived hypergroups and H v -groups associated with the considered hyperhomographies. They are equipped hyperhomographies obtained as quotient sets of nondegenerate hyperhomographies modulo a special equivalence. Thus the symmetrical property of the equivalence relations plays a fundamental role in this constructions

Machine learning-based approach for predicting low birth weight

Abstract Background Low birth weight (LBW) has been linked to infant mortality. Predicting LBW is a valuable preventative tool and predictor of newborn health risks. The current study employed a machine learning model to predict LBW. Methods This study implemented predictive LBW models based on the data obtained from the “Iranian Maternal and Neonatal Network (IMaN Net)” from January 2020 to January 2022. Women with singleton pregnancies above the gestational age of 24 weeks were included. Exclusion criteria included multiple pregnancies and fetal anomalies. A predictive model was built using eight statistical learning models (logistic regression, decision tree classification, random forest classification, deep learning feedforward, extreme gradient boost model, light gradient boost model, support vector machine, and permutation feature classification with k-nearest neighbors). Expert opinion and prior observational cohorts were used to select candidate LBW predictors for all models. The area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1 score were measured to evaluate their diagnostic performance. Results We found 1280 women with a recorded LBW out of 8853 deliveries, for a frequency of 14.5%. Deep learning (AUROC: 0.86), random forest classification (AUROC: 0.79), and extreme gradient boost classification (AUROC: 0.79) all have higher AUROC and perform better than others. When the other performance parameters of the models mentioned above with higher AUROC were compared, the extreme gradient boost model was the best model to predict LBW with an accuracy of 0.79, precision of 0.87, recall of 0.69, and F1 score of 0.77. According to the feature importance rank, gestational age and prior history of LBW were the top critical predictors. Conclusions Although this study found that the extreme gradient boost model performed well in predicting LBW, more research is needed to make a better conclusion on the performance of ML models in predicting LBW

Prediction of postpartum hemorrhage using traditional statistical analysis and a machine learning approachAJOG Global Reports at a Glance

BACKGROUND: Early detection of postpartum hemorrhage risk factors by healthcare providers during pregnancy and the postpartum period may allow healthcare providers to act to prevent it. Developing a prediction model that incorporates several risk factors and accurately calculates the overall risk for postpartum hemorrhage is necessary. OBJECTIVE: This study used a traditional analytical approach and a machine learning model to predict postpartum hemorrhage. STUDY DESIGN: Women who gave birth at the Khaleej-e-Fars Hospital in Bandar Abbas, Iran, were evaluated retrospectively between January 1, 2020, and January 1, 2022. These pregnant women were divided into 2 groups, namely those who had postpartum hemorrhage and those who did not. We used 2 approaches for the analysis. At the first level, we used the traditional analysis methods. Demographic factors, maternal comorbidities, and obstetrical factors were compared between the 2 groups. A bivariate logistic regression analysis of the risk factors for postpartum hemorrhage was done to estimate the crude odds ratios and their 95% confidence intervals. In the second level, we used machine learning approaches to predict postpartum hemorrhage. RESULTS: Of the 8888 deliveries, we identified 163 women with recorded postpartum hemorrhage, giving a frequency of 1.8%. According to a traditional analysis, factors associated with an increased risk for postpartum hemorrhage in a bivariate logistic regression analysis were living in a rural area (odds ratio, 1.41; 95% confidence interval, 1.08–1.98); primiparity (odds ratio, 3.16; 95% confidence interval, 1.90–4.75); mild to moderate anemia (odds ratio, 5.94; 95% confidence interval 2.81–8.34); severe anemia (odds ratio, 6.01; 95% confidence interval 3.89–11.09); abnormal placentation (odds ratio, 7.66; 95% confidence interval, 2.81–17.34); fetal macrosomia (odds ratio, 8.14; 95% confidence interval, 1.02–14.47); shoulder dystocia (odds ratio, 7.88; 95% confidence interval, 1.07–13.99); vacuum delivery (odds ratio, 2.01; 95% confidence interval, 1.15–5.98); cesarean delivery (odds ratio, 1.86; 95% confidence interval, 1.12–3.79); and general anesthesia during cesarean delivery (odds ratio, 7.66; 95 % confidence interval, 3.11–9.36). According to machine learning analysis, the top 5 algorithms were XGBoost regression (area under the receiver operating characteristic curve of 99%), XGBoost classification (area under the receiver operating characteristic curve of 98%), LightGBM (area under the receiver operating characteristic curve of 94%), random forest regression (area under the receiver operating characteristic curve of 86%), and linear regression (area under the receiver operating characteristic curve of 78%). However, after considering all performance parameters, the XGBoost classification was found to be the best model to predict postpartum hemorrhage. The importance of the variables in the linear regression model, similar to traditional analysis methods, revealed that macrosomia, general anesthesia, anemia, shoulder dystocia, and abnormal placentation were considered to be weighted factors, whereas XGBoost classification considered living residency, parity, cesarean delivery, education, and induced labor to be weighted factors. CONCLUSION: Risk factors for postpartum hemorrhage can be identified using traditional statistical analysis and a machine learning model. Machine learning models were a credible approach for improving postpartum hemorrhage prediction with high accuracy. More research should be conducted to analyze appropriate variables and prepare big data to determine the best model