An Approach to Air-to-Surface Mission Planner on 3D Environments for an Unmanned Combat Aerial Vehicle

Recently, interest in mission autonomy related to Unmanned Combat Aerial Vehicles(UCAVs) for performing highly dangerous Air-to-Surface Missions(ASMs) has been increasing. Regarding autonomous mission planners, studies currently being conducted in this field have been mainly focused on creating a path from a macroscopic 2D environment to a dense target area or proposing a route for intercepting a target. For further improvement, this paper treats a mission planning algorithm on an ASM which can plan the path to the target dense area in consideration of threats spread in a 3D terrain environment while planning the shortest path to intercept multiple targets. To do so, ASMs are considered three sequential mission elements: ingress, intercept, and egress. The ingress and egress elements require a terrain flight path to penetrate deep into the enemy territory. Thus, the proposed terrain flight path planner generates a nap-of-the-earth path to avoid detection by enemy radar while avoiding enemy air defense threats. In the intercept element, the shortest intercept path planner based on the Dubins path concept combined with nonlinear programming is developed to minimize exposure time for survivability. Finally, the integrated ASM planner is applied to several mission scenarios and validated by simulations using a rotorcraft model.

The Review of Vehicular Safety Enhancement Through Wireless Protocol

Nowadays, Transportation is one of the primary and major needs of almost every human being that cannot be avoided. The road congested or road traffic are due to The increases in vehicles, which in turn results in road safety and increase accidents. In current society, there are many modern cars with tremendous features in it like, Mercedes, BMW, Audi, and Tesla and so on. Those cars are highly technical and even higher in price. Many people are not economical on buying those vehicles because the middle range vehicles are not capable of much attributes like, visualizing performance, driver safety and so on.., Anyway , the action of using something that is of highly mobile and energy limitation UVs for wireless communications also introduces many new provocation. Hence, we use Vehicle to Vehicle communication and vehicle to infrastructure communication are to reduce crashes. In addition to this, for an Electric unmanned vehicle we use Wireless charging to charge the vehicle with the help of the Tesla coil. In this system we use a (Dedicated short range communication) DSRC and Zigbee. Both DSRC and Zigbee are used for the communication between the vehicles within a certain range. Dedicated short range communication (DSRC) protocol is used for the network access since it reduces the delay in transmission time. Resentencing, there are research shows that using DSRC has a performance issue in a dense area or increased network load hence Zigbee is used along with DSRC since Zigbee perform well in dense area.

Transmission dynamics, clinical characteristics and sero-surveillance in the COVID-19 outbreak in a population dense area of Colombo, Sri Lanka April- May 2020

Background The transmission dynamics of SARS-CoV-2 varies depending on social distancing measures, circulating SARS-CoV-2 variants, host factors and other environmental factors. We sought to investigate the clinical and epidemiological characteristics of a SARS-CoV-2 outbreak that occurred in a highly dense population area in Colombo, Sri Lanka from April to May 2020. Methodology/principal findings We carried out RT-qPCR for SARS-CoV2, assessed the SARS-CoV-2 specific total and neutralizing antibodies (Nabs) in a densely packed, underserved settlement (n = 2722) after identification of the index case on 15th April 2020. 89/2722 individuals were detected as infected by RT-qPCR with a secondary attack rate among close contacts being 0.077 (95% CI 0.063–0.095). Another 30 asymptomatic individuals were found to have had COVID-19 based on the presence of SARS-CoV-2 specific antibodies. However, only 61.5% of those who were initially seropositive for SARS-CoV-2 had detectable total antibodies at 120 to 160 days, while only 40.6% had detectable Nabs. 74/89 (83.1%) of RT-qPCR positive individuals were completely asymptomatic and all 15 (16.9%) who experienced symptoms were classified as having a mild illness. 18 (20.2%) were between the ages of 61 to 80. 11/89 (12.4%) had diabetes, 8/89 (9%) had cardiovascular disease and 4 (4.5%) had asthma. Of the two viruses that were sequenced and were of the B.1 and B.4 lineages with one carrying the D614G mutation. Discussion/conclusion Almost all infected individuals developed mild or asymptomatic illness despite the presence of comorbid illnesses. Since the majority of those who were in this underserved settlement were not infected despite circulation of the D614G variant, it would be important to further study environmental and host factors that lead to disease severity and transmission.

COVID-19 Impact on Residential Preferences in the Early-Stage Outbreak in South Korea

In the early stage of the COVID-19 pandemic in South Korea, public fear or social scaring of urban living was observed, which caused people to change their daily routines. This study examines how the COVID-19 pandemic affected residential choice and perceptions of urban living. We analyzed self-reported survey data collected from 2000 participants in Seoul, Daegu, and Kyeongbuk in South Korea between 3–6 August 2020, targeting the relatively controlled period after the first COVID-19 outbreak. Logistic regression models were used to examine concerns of urban living and residence relocation consideration. Those who were aged 30 or older, regularly commuting, not feeling healthy, with a household size of two, and living in a low-rise condominium were more likely to be concerned with urban living. Those who were aged 40 or older and living in a townhouse or a single-detached house were more likely to consider moving to a less dense area. People perceived that their daily routine changed substantially after the pandemic. Certain participant groups showed concerns of urban living and relocation consideration, suggesting housing policy implications.

A unique approach of person reidentification using auto track regression framework

Automatic Person Re-identification by video surveillance is commonly used in different applications. Perhaps the human uniqueness criteria for tracking the presence of the same person across multiple camera views and a person’s growth identification is extremely challenging. To solve the above problem, we propose an efficient Auto Track Regression System (ATRF) based on a deep learning technique that uses an eminent representation strategy along with recognition. In this work, the Auto Wiley Detective (AWD) approach is proposed for the representation of features that can collect valuable information by monitoring individuals. After obtaining important information on the characteristics, it is possible to define the personal growth identity of the generation. The OPVC (Original Pick Virtual Classifier) is used for accurate classification of the queried person from a dense area by utilizing features of a person’s growth identity extracted from feature extraction by the Auto Wiley Detection Method. The proposed Originated Pick Virtual Classifier (OPVC) uses Platt scaling (originated pick) on probit regression (virtual) to train the featured data set for accurate person re-identification, which is boosted by the Karush–Kuhn–Tucker (KKT) conditions to reduce false re-identification. Since the gallery information is trained using the Backpropagation method and smoothened analysis through approximated output, the Auto Wiley Detection Method proficiently detects the required information automatically. This also helps to detect the person query image from the database, which contains a vast collection of video images based on the similarity features identified in the query image and the detailed features extracted from the query image. The classification is completed automatically, and then the Person Re-Identification from the databases is performed accurately and efficiently. Henceforth, the proposed work effectively extracts reliable height and age estimates with improved flexibility and individual re-identifying capabilities.

Radiomic features of breast parenchyma: assessing differences between FOR PROCESSING and FOR PRESENTATION digital mammography

Objective To assess the similarity and differences of radiomics features on full field digital mammography (FFDM) in FOR PROCESSING and FOR PRESENTATION data. Methods 165 consecutive women who underwent FFDM were included. Breasts have been segmented into “dense” and “non-dense” area using the software LIBRA. Segmentation of both FOR PROCESSING and FOR PRESENTATION images have been evaluated by Bland–Altman, Dice index and Cohen’s kappa analysis. 74 textural features were computed: 18 features of First Order (FO), 24 features of Gray Level Co-occurrence Matrix (GLCM), 16 features of Gray Level Run Length Matrix (GLRLM) and 16 features of Gray Level Size Zone Matrix (GLSZM). Paired Wilcoxon test, Spearman’s rank correlation, intraclass correlation and canonical correlation have been used. Bilateral symmetry and percent density (PD) were also evaluated. Results Segmentation from FOR PROCESSING and FOR PRESENTATION gave very different results. Bilateral symmetry was higher when evaluated on features computed using FOR PROCESSING images. All features showed a positive Spearman’s correlation coefficient and many FOR-PROCESSING features were moderately or strongly correlated to their corresponding FOR-PRESENTATION counterpart. As regards the correlation analysis between PD and textural features from FOR-PRESENTATION a moderate correlation was obtained only for Gray Level Non Uniformity from GLRLM both on “dense” and “non dense” area; as regards correlation between PD and features from FOR-PROCESSING a moderate correlation was observed only for Maximal Correlation Coefficient from GLCM both on “dense” and “non dense” area. Conclusions Texture features from FOR PROCESSING mammograms seem to be most suitable for assessing breast density.

The influence of layer and voxel geological modelling strategy on groundwater modelling results

Reliable groundwater model predictions are dependent on representative models of the geological environment, which can be modelled using several different techniques. In order to inform the choice of the geological modelling technique, the differences between a layer modelling approach and a voxel modelling approach were analyzed. The layer model consist of stratigraphically ordered surfaces, while the voxel model consist of a structured mesh of volumetric pixels. Groundwater models based on the two models were developed to investigate their impact on groundwater model predictions. The study was conducted in the relatively data-dense area Egebjerg, Denmark, where both a layer model and a voxel model has been developed based on the same data and geological conceptualization. The characteristics of the two methodologies for developing the geological models were shown to have a direct impact on the resulting models. The differences between the layer and the voxel models were however shown to be diverse and not related to larger conceptual elements with few exceptions. The analysis showed that the geological modelling approaches had an influence on preferred parameter values and thereby groundwater model predictions of hydraulic head, groundwater budget terms and particle tracking results. A significance test taking into account the predictive distributions showed that for many predictions the differences between the models were significant. The results suggest that the geological modelling strategy has an influence on groundwater model predictions even if based on the same geological conceptualization.

Performance Evaluation of DSRC-Zigbee Heterogeneous Network for Intelligent Transportation System

Transportation is one of the primary needs of human beings that cannot be avoided, with the increasing vehicle can lead to a congested road situation which can lead to less safe road safety. An Intelligent Transportation System (ITS) can be used to increase road safety. This system uses a Dedicated Short-Range Communication (DSRC) protocol for network access due to its low latency transmission time. Unfortunately, there is research shows that DSRC has a performance issue in a dense area or increased network load. This problem is mostly solved with a heterogeneous network DSRC-LTE but utilizes mobile phone networks that dynamically change can lead to inconsistent and unpredictable network performance. There is some research about ZigBee for ITS shows that it is decent enough for non-critical applications. Thus, the authors try to utilize Zigbee to create the DSRC-Zigbee heterogeneous network so that the network is independent for the ITS application. The proposed heterogeneous network is a fixed model due to its simple architecture. OMNeT++ and Simulation of Urban Mobility (SUMO) are used to evaluate the performance of the network in an urban area with various scenarios. The simulation result shows that the proposed heterogeneous network is capable to improve the messages dissemination rate by 15.78% and 1.22% in a certain scenario compared to the homogeneous network DSRC only.

Is Mammographic Breast Density an Endophenotype for Breast Cancer?

Mammographic breast density (MBD) is a strong and highly heritable predictor of breast cancer risk and a biomarker for the disease. This study systematically assesses MBD as an endophenotype for breast cancer—a quantitative trait that is heritable and genetically correlated with disease risk. Using data from the family-based kConFab Study and the 1994/1995 cross-sectional Busselton Health Study, participants were divided into three status groups—cases, relatives of cases and controls. Participant’s mammograms were used to measure absolute dense area (DA) and percentage dense area (PDA). To address each endophenotype criterion, linear mixed models and heritability analysis were conducted. Both measures of MBD were significantly associated with breast cancer risk in two independent samples. These measures were also highly heritable. Meta-analyses of both studies showed that MBD measures were higher in cases compared to relatives (β = 0.48, 95% CI = 0.10, 0.86 and β = 0.41, 95% CI = 0.06, 0.78 for DA and PDA, respectively) and in relatives compared to controls (β = 0.16, 95% CI = −0.24, 0.56 and β = 0.16, 95% CI = −0.21, 0.53 for DA and PDA, respectively). This study formally demonstrates, for the first time, that MBD is an endophenotype for breast cancer.