5G Radar and Wi-Fi Based Machine Learning on Drone Detection and Localization

Drone usages have been proliferating for various government initiatives, commercial benefits and civilian leisure purposes. Drone mismanagement especially civilian usage drones can easily expose threat and vulnerability of the Government Public Key Infrastructures (PKI) that hold crucial operations, affecting the survival and economic of the country. As such, detection and location identification of these drones are crucial immediately prior to their payload action. Existing drone detection solutions are bulky, expensive and hard to setup in real time. With the advent of 5G and Internet of Things (IoT), this paper proposes a cost effective bistatic radar solution that leverages on 5G cellular spectrum to detect the presence and localize the drone. Coupled with K-Nearest Neighbours (KNN) Machine Learning (ML) algorithm, the features of Non- Line of Sight (NLOS) transmissions by 5G radar and Received Signal Strength Indicator (RSSI) emitted by drone are used to predict the location of the drone. The proposed 5G radar solution can detect the presence of a drone in both outdoor and indoor environment with accuracy of 100%. Furthermore, it can localize the drone with an accuracy of up to 75%. These results have shown that a cost effective radar machine learning system, operating on the 5G cellular network spectrum can be developed to successfully identify and locate a drone in real-time

First somatic mutation of E2F1 in a critical DNA binding residue discovered in well-differentiated papillary mesothelioma of the peritoneum

10.1186/gb-2011-12-9-r96Genome biology129R9

Germline breast cancer susceptibility genes, tumor characteristics, and survival.

BACKGROUND: Mutations in certain genes are known to increase breast cancer risk. We study the relevance of rare protein-truncating variants (PTVs) that may result in loss-of-function in breast cancer susceptibility genes on tumor characteristics and survival in 8852 breast cancer patients of Asian descent. METHODS: Gene panel sequencing was performed for 34 known or suspected breast cancer predisposition genes, of which nine genes (ATM, BRCA1, BRCA2, CHEK2, PALB2, BARD1, RAD51C, RAD51D, and TP53) were associated with breast cancer risk. Associations between PTV carriership in one or more genes and tumor characteristics were examined using multinomial logistic regression. Ten-year overall survival was estimated using Cox regression models in 6477 breast cancer patients after excluding older patients (≥75years) and stage 0 and IV disease. RESULTS: PTV9genes carriership (n = 690) was significantly associated (p < 0.001) with more aggressive tumor characteristics including high grade (poorly vs well-differentiated, odds ratio [95% confidence interval] 3.48 [2.35-5.17], moderately vs well-differentiated 2.33 [1.56-3.49]), as well as luminal B [HER-] and triple-negative subtypes (vs luminal A 2.15 [1.58-2.92] and 2.85 [2.17-3.73], respectively), adjusted for age at diagnosis, study, and ethnicity. Associations with grade and luminal B [HER2-] subtype remained significant after excluding BRCA1/2 carriers. PTV25genes carriership (n = 289, excluding carriers of the nine genes associated with breast cancer) was not associated with tumor characteristics. However, PTV25genes carriership, but not PTV9genes carriership, was suggested to be associated with worse 10-year overall survival (hazard ratio [CI] 1.63 [1.16-2.28]). CONCLUSIONS: PTV9genes carriership is associated with more aggressive tumors. Variants in other genes might be associated with the survival of breast cancer patients. The finding that PTV carriership is not just associated with higher breast cancer risk, but also more severe and fatal forms of the disease, suggests that genetic testing has the potential to provide additional health information and help healthy individuals make screening decisions

Polygenic risk scores for prediction of breast cancer risk in Asian populations.

PURPOSE: Non-European populations are under-represented in genetics studies, hindering clinical implementation of breast cancer polygenic risk scores (PRSs). We aimed to develop PRSs using the largest available studies of Asian ancestry and to assess the transferability of PRS across ethnic subgroups. METHODS: The development data set comprised 138,309 women from 17 case-control studies. PRSs were generated using a clumping and thresholding method, lasso penalized regression, an Empirical Bayes approach, a Bayesian polygenic prediction approach, or linear combinations of multiple PRSs. These PRSs were evaluated in 89,898 women from 3 prospective studies (1592 incident cases). RESULTS: The best performing PRS (genome-wide set of single-nucleotide variations [formerly single-nucleotide polymorphism]) had a hazard ratio per unit SD of 1.62 (95% CI = 1.46-1.80) and an area under the receiver operating curve of 0.635 (95% CI = 0.622-0.649). Combined Asian and European PRSs (333 single-nucleotide variations) had a hazard ratio per SD of 1.53 (95% CI = 1.37-1.71) and an area under the receiver operating curve of 0.621 (95% CI = 0.608-0.635). The distribution of the latter PRS was different across ethnic subgroups, confirming the importance of population-specific calibration for valid estimation of breast cancer risk. CONCLUSION: PRSs developed in this study, from association data from multiple ancestries, can enhance risk stratification for women of Asian ancestry

European polygenic risk score for prediction of breast cancer shows similar performance in Asian women

Abstract: Polygenic risk scores (PRS) have been shown to predict breast cancer risk in European women, but their utility in Asian women is unclear. Here we evaluate the best performing PRSs for European-ancestry women using data from 17,262 breast cancer cases and 17,695 controls of Asian ancestry from 13 case-control studies, and 10,255 Chinese women from a prospective cohort (413 incident breast cancers). Compared to women in the middle quintile of the risk distribution, women in the highest 1% of PRS distribution have a ~2.7-fold risk and women in the lowest 1% of PRS distribution has ~0.4-fold risk of developing breast cancer. There is no evidence of heterogeneity in PRS performance in Chinese, Malay and Indian women. A PRS developed for European-ancestry women is also predictive of breast cancer risk in Asian women and can help in developing risk-stratified screening programmes in Asia

The effect of coupling responses in face sheet in the bending of composite sandwich beam

As composite structures are increasingly being used in the aviation industry, it is critical to understand the structural behaviours of these modern materials. It is common to have the carbon fibre face sheets in composites to have certain orientations to achieve the desired properties. Hence, this report aims to investigate the effect of coupling responses of the face sheets acted on by bending loads which are commonly encountered in aerodynamics. To investigate the coupling responses, a four point bending experiment was done using both carbon fibre beams and composite sandwich beams. The effect of the coupling responses could easily be seen on the carbon fibre beams but not on the composite sandwich beams. A finite element method software, Abaqus/CAE, was then employed to simulate the experiments. The carbon fibre beam experiments were used to validate the simulated results from Abaqus/CAE. This gives the assurance of the accuracy and reliability of the software and that further studies could be continued on the software. The results were compiled and correlations were presented in graphical formats. The significant findings from these results were then analysed. Concluding the report are the limitations in the experiment and recommendations for future work and development which would allow for better measurement techniques to be employed.Bachelor of Engineering (Aerospace Engineering

UAV-based weed detection in Chinese cabbage using deep learning

Weeds are unwanted plants on agricultural soil. They always competing for sunlight, nutrient, space and water with economic crops. Uncontrolled weed growth can cause both significant economic and ecological loss. Hence, weeds should be efficiently differentiated from the crops for the smart spraying solution. In this study, the Convolutional Neural Network (CNN) was used to perform weed detection amongst the commercial crop of Chinese cabbage, using the acquired images by Unmanned Aerial Vehicles. The acquired images were preprocessed and subsequently segmented into the crop, soil, and weed classes using the Simple Linear Iterative Clustering Superpixel algorithm. The segmented images were then used to construct the CNN-based classifier. The Random Forest (RF) was applied to compare with the performance of CNN. The results showed that the CNNachieved a higher overall accuracy of 92.41% than the 86.18% attained by R

Nadir correction of AIRS radiances

A statistical method to correct for the limb effect in off-nadir Atmospheric Infrared Sounder (AIRS) channel radiances is described, using the channel radiance itself and principal components (PCs) of the other channel radiances to account for the multicollinearity. A method of selecting an optimal set of predictors is proposed and demonstrated for one- and two-PC predictors. Validation results with a subset of AIRS channels in the spectral region 649–2664 cm−1 show that the mean nadir-corrected brightness temperature (BT) is largely independent of scan angle. More than 66% of the channels have a root-mean-square (rms) bias less than 0.10 K after nadir correction. Limb effect on the standard deviation (SD) of BT is discernible at larger scan angles, mainly for the atmospheric windows and the water vapor channels around 6.7 μm. After nadir correction, nearly all atmospheric window channels unaffected by solar glint and more than 76% of water vapor channels examined have BT SDs brought closer to nadir values. For the window channels affected by solar glint (wavenumber > 2490 cm−1), BT SDs at the scan angles with the strongest impact from solar reflection were improved on average by more than 0.6 K after nadir correction.Published versio

Toward a mesoscale observation network in Southeast Asia

The current weather observation network in Southeast Asia is unable to support the accurate monitoring and prediction of the region's predominantly convective weather. Establishing a multisensor mesoscale observation network comprising automated in situ instruments and atmospheric remote sensors (including weather radar) over land and exploiting weather satellite data especially over the sea would significantly improve the quantity and quality of data and benefit numerical weather prediction and tropical atmospheric science research. Several technical and organizational challenges need to be overcome in order to attain this goal. It is hoped that this article would motivate closer regional coordination in plans for developing infrastructure for atmospheric observations for weather research and forecasts in Southeast Asia.Published versio

Stochastic modelling of rainfall from satellite data

Satellite-based rainfall monitoring is widely used for climatological studies because of its full global coverage but it is also of great importance for operational purposes especially in areas such as Africa where there is a lack of ground-based rainfall data. Satellite rainfall estimates have enormous potential benefits as input to hydrological and agricultural models because of their real time availability, low cost and full spatial coverage. One issue that needs to be addressed is the uncertainty on these estimates. This is particularly important in assessing the likely errors on the output from non-linear models (rainfall-runoff or crop yield) which make use of the rainfall estimates, aggregated over an area, as input. Correct assessment of the uncertainty on the rainfall is non-trivial as it must take account of • the difference in spatial support of the satellite information and independent data used for calibration • uncertainties on the independent calibration data • the non-Gaussian distribution of rainfall amount • the spatial intermittency of rainfall • the spatial correlation of the rainfall field This paper describes a method for estimating the uncertainty on satellite-based rainfall values taking account of these factors. The method involves firstly a stochastic calibration which completely describes the probability of rainfall occurrence and the pdf of rainfall amount for a given satellite value, and secondly the generation of ensemble of rainfall fields based on the stochastic calibration but with the correct spatial correlation structure within each ensemble member. This is achieved by the use of geostatistical sequential simulation. The ensemble generated in this way may be used to estimate uncertainty at larger spatial scales. A case study of daily rainfall monitoring in the Gambia, west Africa for the purpose of crop yield forecasting is presented to illustrate the method