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Traffic accidents have become a major issue for researchers, academia, government and vehicle manufacturers over the last few years. Many accidents and emergency situations frequently occur on the road. Unfortunately, accidents lead to health injuries, destruction of some infrastructure, bad traffic flow, and more importantly these events cause deaths of hundreds of thousands of people due to not getting treatment in time. Thus, we need to develop an efficient and smart emergency system to ensure the timely arrival of an ambulance service to the place of the accident in order to provide timely medical help to those injured. In addition, we also need to communicate promptly with other entities such as hospitals so that they can make appropriate arrangements and provide timely medical information to emergency personnel on the scene including alerting those related to the injured person(s). In this paper, we have developed an intelligent protocol that uses connected and autonomous vehicles’ scenarios in Intelligent Transportation System (ITS) so that prompt emergency services can be provided to reduce the death rate caused. The proposed protocol smartly connects with all the relevant entitles during the emergency while maintaining a smooth traffic flow for the arrival of the ambulance service. Moreover, our protocol also mitigates the broadcasting of messages circulating over the network for delay sensitive tasks. The evaluation results, based on the performance metrics such as channel collision, average packet delay, packet loss, and routing-overhead demonstrate that our proposed protocol outperforms previously proposed protocols such as Emergency Message Dissemination for Vehicular (EMDV), Contention Based Broadcasting (CBB), and Particle Swarm Optimization Contention-based Broadcast (PCBB) protocols. Finally, we discuss several issues and challenges that need to be addressed in the network in order to achieve more a reliable, efficient, connected, and autonomous vehicular network.

Satellite images are important to get information related to the Earth's resources and environment. Landsat, a series of Earth-observing satellite missions, tracks use of land and records changes in it due to natural and human activities. Automated temporal analysis and prediction of landscape has various benefits in its planning and development. In this work, we use Conditional Adversarial Networks for the Landsat satellite image prediction. We not only learned a mapping from input images to output images, i.e., mapping from satellite images from 1999–2000 to satellite images from 2009–2010, but we also learned a loss function to train this mapping. The results are satisfactory in the sense that they capture the general trends over time.

The elderly population experiences great variability in health, disability, age-related changes, polymorbidity, and associated polypharmacy. Polypharmacy refers to the simultaneous use of multiple drugs to treat a single condition. Polypharmacy often leads to high healthcare costs, unpropitious events, confusion, and errors in the management of the individual’s health. The remote monitoring approach to medicine, especially for elderly populations in rural regions, with limited access to care, has significantly increased. Continuous monitoring of appropriate medication prescription, consistent review of medication lists, and re-evaluation of patient needs are crucial for ensuring that polypharmacy is minimized, and therefore the patient wellbeing is maximized. This study aims to present a comprehensive catalog of information in uniform terminology, define the general definition and features of polypharmacy in elderly people from rural areas, and enable the reader to use that information optimally for their specific application. Our study reveals the current understanding of polypharmacy in elderly individuals from rural regions. We proposed a novel system design for the remote monitoring of elderly patients to minimize polypharmacy. We aim to develop a unique mobile-based application for patient uses and a web-based application for doctors. Our applications will communicate with the medical IoT devices connected with the patients to obtain data. By using our application, providers can monitor their elderly patients’ health data and will be able to make better informed decisions for prescribing medications. Our approach will help elderly people in rural areas and their providers minimize adverse effects due to polypharmacy.

The renewable sources particularly wind resources are highly intermittent in nature. The WRF is the state-of-the-art mesoscale software to predict wind resources. In this study, the prediction skills of WRF forecasting software is tested for the wind speed of the wind farm in Pakistan. The WRF excellently interpolated the weather data to the terrain description of the wind farm's area, which lies in the complex terrain of the Jhimpir wind corridor. Wind speed predicted by the WRF is then compared with the observed speed of the met mast in the wind farm. The WRF predicts wind speed with the quite precision, for June the mean bias error of −0.85 ms−1, mean absolute error of 1.30 ms−1, and root mean square value of 1.63 ms−1, for July mean bias error of −0.85 ms−1 and root mean square error is 1.66 ms−1, for the January errors were high, the mean absolute error of 1.87 ms−1, mean bias error is −0.15 ms−1 and root mean square error of 2.42 ms−1 is observed. Wind direction is also predicted with great accuracy, with the mean error in the wind direction of 33°. Overall, the WRF overestimate the wind speeds when mean speeds are high.

Obstructive Sleep Apnea (OSA) is a very common sleep disorder which affects a significant portion of the population. The gold standard for monitoring OSA, and the treatment efficacy is through a Polysomnogram (PSG). A PSG measures a multitude of biological signals to determine the severity of OSA. However, a PSG is expensive, and causes discomfort for the patients. The proposed system allows for home-based monitoring that is affordable and much more comfortable for the patient than the conventional PSG. It makes use of 3 biological signals including a single channel frontal electroencephalogram (EEG) from the FP2-A1 positions, blood oxygen saturation (SpO 2 ) reading, and recording of snores. In the proposed system, every individual event is logged into a locally stored file, and if there are at least 2 OSA events happening at the same time, the OSA episode is deemed serious and a buzzer will sound to wake the patient. A message is also sent to the caregiver / next-of-kin to notify them.

Visual robot perception has been challenging to successful robot manipulation in noisy, cluttered and dynamic environments. While some perception systems fail to provide an adequate semantics of the scene, others fail to present appropriate learning models and training data. Another major issue encountered in some robot perception systems is their inability to promptly respond to robot control programs whose realtimeness is crucial.This paper proposes an architecture to robot vision for manipulation tasks that addresses the three issues mentioned above. The architecture encompasses a generator of training datasets and a learnable scene describer, coined as RobotVQA for Robot Visual Question Answering. The architecture leverages the power of deep learning to predict and photo-realistic virtual worlds to train. RobotVQA takes as input a robot scene’s RGB or RGBD image, detects all relevant objects in it, then describes in realtime each object in terms of category, color, material, shape, openability, 6D-pose and segmentation mask. Moreover, RobotVQA computes the qualitative spatial relations among those objects. We refer to such a scene description in this paper as scene graph or semantic graph of the scene. In RobotVQA, prediction and training take place in a unified manner. Finally, we demonstrate how RobotVQA is suitable for robot control systems that interpret perception as a question answering process.

In recent years, wireless mesh network (WMN) has attracted researchers due to its wide coverage of network area. Unlike linear topology network, WMN eliminates the dead node issue which known to influence the packet reachability. Hence, the implementation of WMN has opened a vast opportunity to numerous applications mainly in the oil and gas industry. The architecture and distribution area of pipeline in downstream sectors of oil and gas industry suits the usage of WMN with grid topology which led to the performance study of routing protocols presented in this paper. The main concern in such application is the data loss during traffic since any collected data is vital in revealing new insights to avoid machinery malfunction. The data loss can be seen through the delivery ratio mainly caused by congestion as the network size increases. During congestion, additional issues will stem out such as throughput degradation, increasing end-to-end delay, etc. Thus, this paper presents three types of routing protocols; (1) reactive, (2) proactive, and (3) hybrid routing protocol to examine the feasibility of WMN towards the application using metrics such as average delivery ratio, throughput, average end-to-end delay, and fairness of the network.

A compact rectangular slotted patch antenna has been presented in this paper. The proposed antenna has been designed for enhancing the bandwidth as well as suppressing the higher order harmonics having a partial ground structure (PGS). The dimension of the antenna is $60 \times 49 \times 1.59$ mm3 where it is engaged on a substrate whose name is Rogers Duroid 5870. A rectangular slot patch antenna which has dual U-shaped parasitic shred has been planned whereas the bandwidth is 6.5 times extensive than reference antenna. It operates both S and C-band which are used in wireless and optical communication, amateur television repeaters and ISM band application.

The nuclear reactor plays a key role in a nuclear power plant (NPP) for electricity generation. Various types of reactors are available around the world, among them VVER-1200 reactor is generation III+ and considered one of the safest reactors in the world but as it is an electrical system so there must be some possibility of faults. Prior knowledge about fault situations is needed to take the necessary steps to withstand the fault situation. In this paper, the fault situation on VVER-1200 reactor is made by PCTRAN VVER-1200 (2006) prototype software. Types of fault were considered include loss of normal feedwater flow, steam generator tube rupture, break loss of coolant accident and their possible consequences on various parameters such as a change in reactivity, steam flow, feedwater flow, pressure, etc. shown by transient behavior analysis.

In this article a high power (320 watt), high efficiency (greater than 56 %) LDMOS based power amplifier configuration is proposed. Proposed compact class-AB amplifier is designed using non-linear model of MRF8P29300, targeting S-band. Along with optimization using ADS, hybrid couplers are used to improve the performance of proposed amplifier, providing output power of 56 dBm. Other than design details, paper also present electromagnetic and thermal analysis for better understanding of amplifier characteristics. Moreover, measured results indicate that proposed amplifier achieved 14dB gain, gain flatness