Rateless codes-based secure communication employing transmit antenna selection and harvest-to-jam under joint effect of interference and hardware impairments

In this paper, we propose a rateless codes-based communication protocol to provide security for wireless systems. In the proposed protocol, a source uses the transmit antenna selection (TAS) technique to transmit Fountain-encoded packets to a destination in presence of an eavesdropper. Moreover, a cooperative jammer node harvests energy from radio frequency (RF) signals of the source and the interference sources to generate jamming noises on the eavesdropper. The data transmission terminates as soon as the destination can receive a sufficient number of the encoded packets for decoding the original data of the source. To obtain secure communication, the destination must receive sufficient encoded packets before the eavesdropper. The combination of the TAS and harvest-to-jam techniques obtains the security and efficient energy via reducing the number of the data transmission, increasing the quality of the data channel, decreasing the quality of the eavesdropping channel, and supporting the energy for the jammer. The main contribution of this paper is to derive exact closed-form expressions of outage probability (OP), probability of successful and secure communication (SS), intercept probability (IP) and average number of time slots used by the source over Rayleigh fading channel under the joint impact of co-channel interference and hardware impairments. Then, Monte Carlo simulations are presented to verify the theoretical results.Web of Science217art. no. 70

Healthcare resource use and costs of severe, uncontrolled eosinophilic asthma in the UK general population

Acknowledgments The authors thank Derek Skinner (Cambridge Research Support Ltd, Oakington, Cambridge, UK) for assistance with data extraction.Peer reviewedPublisher PD

Energy harvesting-based spectrum access with incremental cooperation, relay selection and hardware noises

In this paper, we propose an energy harvesting (EH)-based spectrum access model in cognitive radio (CR) network. In the proposed scheme, one of available secondary transmitters (STs) helps a primary transmitter (PT) forward primary signals to a primary receiver (PR). Via the cooperation, the selected ST finds opportunities to access licensed bands to transmit secondary signals to its intended secondary receiver (SR). Secondary users are assumed to be mobile, hence, optimization of energy consumption for these users is interested. The EH STs have to harvest energy from the PT's radio-frequency (RF) signals to serve the PTPR communication as well as to transmit their signals. The proposed scheme employs incremental relaying technique in which the PR only requires the assistance from the STs when the transmission between PT and PR is not successful. Moreover, we also investigate impact of hardware impairments on performance of the primary and secondary networks. For performance evaluation, we derive exact and lower-bound expressions of outage probability (OP) over Rayleigh fading channel. Monte-Carlo simulations are performed to verify the theoretical results. The results present that the outage performance of both networks can be enhanced by increasing the number of the ST-SR pairs. In addition, the outage performance of both primary and secondary networks is severely degraded with the increasing of hardware impairment level. It is also shown that fraction of time used for EH and positions of the secondary users significantly impact on the system performance.Web of Science26125024

Secrecy performance enhancement for underlay cognitive radio networks employing cooperative multi-hop transmission with and without presence of hardware impairments

In this paper, we consider a cooperative multi-hop secured transmission protocol to underlay cognitive radio networks. In the proposed protocol, a secondary source attempts to transmit its data to a secondary destination with the assistance of multiple secondary relays. In addition, there exists a secondary eavesdropper who tries to overhear the source data. Under a maximum interference level required by a primary user, the secondary source and relay nodes must adjust their transmit power. We first formulate effective signal-to-interference-plus-noise ratio (SINR) as well as secrecy capacity under the constraints of the maximum transmit power, the interference threshold and the hardware impairment level. Furthermore, when the hardware impairment level is relaxed, we derive exact and asymptotic expressions of end-to-end secrecy outage probability over Rayleigh fading channels by using the recursive method. The derived expressions were verified by simulations, in which the proposed scheme outperformed the conventional multi-hop direct transmission protocol.Web of Science212art. no. 21

The Current State of Climate Change Perceptions and Policies in Vietnam: 2014 Report

This report was constructed to assess the current perceptions and policies regarding climate change in Vietnam. The report comprises a country report, outlining current policies relating to climate change, stakeholder mapping regarding climate change locally and nationally, and results from two stakeholder perception surveys conducted locally and nationally in Vietnam. A total of 50 stakeholders were interviewed, 25 locally and 25 nationally. The stakeholders in the survey represented government offices, universities, research institutions, NGOs, and farmers’ groups. Concerns about climate change impacts included drought, flooding, rainfall variation, and salinity intrusion. These concerns, as well as the methods in which stakeholders would like to receive climate information, varied between local and national stakeholders as well as by the type of institution that the stakeholder represented. This emphasizes a need for location- and user-specific responses to climate change

Improving Multi-task Learning via Seeking Task-based Flat Regions

Multi-Task Learning (MTL) is a widely-used and powerful learning paradigm for training deep neural networks that allows learning more than one objective by a single backbone. Compared to training tasks separately, MTL significantly reduces computational costs, improves data efficiency, and potentially enhances model performance by leveraging knowledge across tasks. Hence, it has been adopted in a variety of applications, ranging from computer vision to natural language processing and speech recognition. Among them, there is an emerging line of work in MTL that focuses on manipulating the task gradient to derive an ultimate gradient descent direction to benefit all tasks. Despite achieving impressive results on many benchmarks, directly applying these approaches without using appropriate regularization techniques might lead to suboptimal solutions on real-world problems. In particular, standard training that minimizes the empirical loss on the training data can easily suffer from overfitting to low-resource tasks or be spoiled by noisy-labeled ones, which can cause negative transfer between tasks and overall performance drop. To alleviate such problems, we propose to leverage a recently introduced training method, named Sharpness-aware Minimization, which can enhance model generalization ability on single-task learning. Accordingly, we present a novel MTL training methodology, encouraging the model to find task-based flat minima for coherently improving its generalization capability on all tasks. Finally, we conduct comprehensive experiments on a variety of applications to demonstrate the merit of our proposed approach to existing gradient-based MTL methods, as suggested by our developed theory.Comment: 29 pages, 11 figures, 6 table

Persistence of eosinophilic asthma endotype and clinical outcomes : A real-world observational study

Acknowledgments Writing and editing support, including preparation of the draft manuscript under the direction and guidance of the authors, incorporating author feedback, and manuscript submission, was provided by Crystal Murcia, PhD (CiTRUS Healthcare Communications Group). This support was funded by AstraZeneca (Gaithersburg, Maryland). Funding This work was supported by AstraZeneca. A named author is an employee of AstraZeneca; therefore, AstraZeneca was involved in the study design; collection, analysis, and interpretation of data; and the development and review of the manuscript. The decision to submit the manuscript for publication was made by the authors.Peer reviewedPublisher PD

Gender Differences in Climate Change Perception and Adaptation Strategies: The Case of Three Provinces in Vietnam’s Mekong River Delta

The Policy Information and Response Platform on Climate Change and Rice in ASEAN and its Member Countries Project (PIRCCA) is one of the trial projects funded under Flagship 4. PIRCCA has an overarching goal to enable policymakers in ASEAN countries to make informed decisions on (1) food security policies focusing on the supply and availability of rice, (2) climate change adaptation policies, and (3) gender action plans

Mortality Analyses on Systemic Corticosteroid Use : a Long-term Observational Study

Peer reviewe

Efficient Palm-Line Segmentation with U-Net Context Fusion Module

Many cultures around the world believe that palm reading can be used to predict the future life of a person. Palmistry uses features of the hand such as palm lines, hand shape, or fingertip position. However, the research on palm-line detection is still scarce, many of them applied traditional image processing techniques. In most real-world scenarios, images usually are not in well-conditioned, causing these methods to severely under-perform. In this paper, we propose an algorithm to extract principle palm lines from an image of a person's hand. Our method applies deep learning networks (DNNs) to improve performance. Another challenge of this problem is the lack of training data. To deal with this issue, we handcrafted a dataset from scratch. From this dataset, we compare the performance of readily available methods with ours. Furthermore, based on the UNet segmentation neural network architecture and the knowledge of attention mechanism, we propose a highly efficient architecture to detect palm-lines. We proposed the Context Fusion Module to capture the most important context feature, which aims to improve segmentation accuracy. The experimental results show that it outperforms the other methods with the highest F1 Score about 99.42% and mIoU is 0.584 for the same dataset.Comment: Published in 2020 International Conference on Advanced Computing and Applications (ACOMP