According to well-described literature concerning the work history of multipath mitigation in the global navigation satellite systems (GNSS), multipath is still the most dominant factor in a challenging environment. There are unperturbed harsh circumstances where GNSS signals cannot reach and smartphone navigation is not possible. The main objective of this research is to find an accurate solution for pedestrian smartphone navigation in a multipath environment. Experiments are done with micro-electro-mechanical system (MEMS) sensors mounted on a smartphone, and no extra hardware is needed. The latest Android smartphone is used to log the data files of GNSS and MEMS sensors. This scheme has been classified in the synopsis, and a rectangular route with three perpendicular turns has been selected for a pedestrian walk. The data is preprocessed using a low pass filter to remove high-frequency noise and smooth the signal. The description of accumulative error produced by the heading and step size estimation has been reduced by implementing the indices of mean cumulative heading error and cumulative step length error, respectively. In the end, the suboptimal extended Kalman filter algorithm is used to fuse the data of GNSS and pedestrian dead reckoning (PDR) for final results. In this paper, we try to give a technique to provide accurate pedestrian smartphone navigation. The fusion results show that the prospective method explores the possibility to use smartphone navigation in any case when GNSS or PDR information is not available. Substantial simulations are implemented and corroborate that the schemed method is sturdier to use in a harsh environments. The aim is to achieve high-level accuracy with an ultra-low-cost solution.

The presence of different malicious regions on a single breast reveal some necessary information for breast cancer early detection. In current computer-aided diagnosis models, different lesions contained in a single mammogram are not detected and segmented individually. Therefore, the multidetection and segmentation of the breast lesions can help the radiologists for an accurate diagnosis. This study aims to develop a model based on regional learning technique and RoI-based Convolutional neural network (CNN), which is known as Masked Regional Convolutional Neural Network embedded with Feature Pyramid Network. By using Mask RCNN-FPN, we can handle multi-detection, instance segmentation, and classification simultaneously. FPN extracts semantic features at different resolution scales and it can exhibit lesions at multiple scales. The training and testing of the model are performed on the DDSM and Inbreast respectively. In comparison, this model achieved mean average precision 0.84 for multi-detection and segmentation and 91% overall accuracy performance over SegNet and U-Net CNN encoder and decoder segmentation architecture.

Developing radio access technologies that enable reliable and low-latency vehicular communications have become of the utmost importance with the rise of interest in autonomous vehicles. The Third Generation Partnership Project (3GPP) has developed Vehicle to Everything (V2X) specifications based on the 5G New Radio Air Interface (NR-V2X) to support connected and automated driving use cases, with strict requirements to fulfill the constantly evolving vehicular applications, communication, and service demands of connected vehicles, such as ultra-low latency and ultra-high reliability. This paper presents an analytical model for evaluating the performance of NR-V2X communications, with particular reference to the sensing-based semi-persistent scheduling operation defined in the NR-V2X Mode 2, in comparison with legacy sidelink V2X over LTE, specified as LTE-V2X Mode 4. We consider a vehicle platooning scenario and evaluate the impact of multiple access interference on the packet success probability, by varying the available resources, the number of interfering vehicles, and their relative positions. The average packet success probability is determined analytically for LTE-V2X and NR-V2X, taking into account the different physical layer specifications, and the Moment Matching Approximation (MMA) is used to approximate the statistics of the signal-to-interference-plus-noise ratio (SINR) under the assumption of a Nakagami-lognormal composite channel model. The analytical approximation is validated against extensive Matlab simulations that a show good accuracy. The results confirm a boost in performance with NR-V2X against LTE-V2X, particularly for high inter-vehicle distance and a large number of vehicles, providing a concise yet accurate modeling rationale for planning and adaptation of the configuration and parameter setup of vehicle platoons, without having to resort to extensive computer simulation or experimental measurements.

Cellular vehicle-to-everything (C-V2X) communications will play a pivotal role in the realization of connected and cooperative autonomous driving. Applications such as ve-hicle platooning benefit from sidelink communications to fulfill stringent latency requirements at the cost of uncertainty in the reliability due to the multiple access interference. In this paper, we propose an analytical framework to model the performance of C-V2X sidelink communications in a vehicle platooning scenario, depending on the available resources, the number of interfering vehicles and their relative positions. A Nakagami-Lognormal composite channel model is assumed, and the moment matching approximation (MMA) is used to approximate the statistics of the signal-to-interference-plus-noise ratio (SINR). Compared to existing simulation studies in the literature, the proposed model offers a compact, yet accurate, approximation of the achievable performance in terms of packet success rate, and allows for planning and adapting the configuration and parameters setup of the vehicle platoon.

An extensive uncertainty is a big concern of deforestation, degradation and forest decentralization. Recorded cases of deforestation in northern regions of Pakistan have drawn serious involvement in the last two decades. These areas include agriculture land and source of fresh water for more than 20 million residents. Downgrading in the forest is also big damage in the ecosystem which increases the flood risk in any community. The fast development in remote sensing (RS) satellites and RS techniques in the last four decades, provides a stable, successive and efficient way for analysis of land cover and land mapping. This work is carried out the analysis of deforestation from 2000-2016. The satellite images of Landsat 4, 5 and 8 are used and processed by Arc GIS software. Maximum Likelihood (ML) supervised classification based on Bayes theorem is used and this technique classified the vegetation, water, and mountains individually. Normalized Difference Vegetation Index (NDVI) is used to calculate the downfall in forests from the vegetation area. This analysis reflected a compelling downturn in forest cover in a period of study.