Scalable Solutions for Automated Single Pulse Identification and Classification in Radio Astronomy

Data collection for scientific applications is increasing exponentially and is forecasted to soon reach peta- and exabyte scales. Applications which process and analyze scientific data must be scalable and focus on execution performance to keep pace. In the field of radio astronomy, in addition to increasingly large datasets, tasks such as the identification of transient radio signals from extrasolar sources are computationally expensive. We present a scalable approach to radio pulsar detection written in Scala that parallelizes candidate identification to take advantage of in-memory task processing using Apache Spark on a YARN distributed system. Furthermore, we introduce a novel automated multiclass supervised machine learning technique that we combine with feature selection to reduce the time required for candidate classification. Experimental testing on a Beowulf cluster with 15 data nodes shows that the parallel implementation of the identification algorithm offers a speedup of up to 5X that of a similar multithreaded implementation. Further, we show that the combination of automated multiclass classification and feature selection speeds up the execution performance of the RandomForest machine learning algorithm by an average of 54% with less than a 2% average reduction in the algorithm's ability to correctly classify pulsars. The generalizability of these results is demonstrated by using two real-world radio astronomy data sets.Comment: In Proceedings of the 47th International Conference on Parallel Processing (ICPP 2018). ACM, New York, NY, USA, Article 11, 11 page

Radio Resource Management for Unmanned Aerial Vehicle Assisted Wireless Communications and Networking

In recent years, employing unmanned aerial vehicles (UAVs) as aerial communication platforms or users is envisioned as a promising solution to enhance the performance of the existing wireless communication systems. However, applying UAVs for information technology applications also introduces many new challenges. This thesis focuses on the UAV-assisted wireless communication and networking, and aims to address the challenges through exploiting and designing efficient radio resource management methods. Specifically, four research topics are studied in this thesis. Firstly, to address the constraint of network heterogeneity and leverage the benefits of diversity of UAVs, a hierarchical air-ground heterogeneous network architecture enabled by software defined networking is proposed, which integrates both high and low altitude platforms into conventional terrestrial networks to provide additional capacity enhancement and expand the coverage of current network systems. Secondly, to address the constraint of link disconnection and guarantee the reliable communications among UAVs as aerial user equipment to perform sensing tasks, a robust resource allocation scheme is designed while taking into account the dynamic features and different requirements for different UAV transmission connections. Thirdly, to address the constraint of privacy and security threat and motivate the spectrum sharing between cellular and UAV operators, a blockchain-based secure spectrum trading framework is constructed where mobile network operators and UAV operators can share spectrum in a distributed and trusted environment based on blockchain technology to protect users' privacy and data security. Fourthly, to address the constraint of low endurance of UAV and prolong its flight time as an aerial base station for delivering communication coverage in a disaster area, an energy efficiency maximization problem jointly optimizing user association, UAV's transmission power and trajectory is studied in which laser charging is exploited to supply sustainable energy to enable the UAV to operate in the sky for a long time

Hierarchical Resource Allocation Framework for Hyper-Dense Small Cell Networks

This paper considers joint power control and subchannel allocation for co-tier interference mitigation in extremely dense small cell networks, which is formulated as a combinatorial optimization problem. Since it is intractable to obtain the globally optimum assignment policy for existing techniques due to the huge computation and communication overheads in ultra-dense scenario, in this paper, we propose a hierarchical resource allocation framework to achieve a desirable solution. Speci cally, the solution is obtained by dividing the original optimization problem into four stages in partially distributed manner. First, we propose a divide-and-conquer strategy by invoking clustering technique to decompose the dense network into smaller disjoint clusters. Then, within each cluster, one of the small cell access points is elected as a cluster head to carry out intra-cluster subchannel allocation with a low-complexity algorithm. To tackle the issue of inter-cluster interference, we further develop a distributed learning-base coordination mechanism. Moreover, a local power adjustment scheme is also presented to improve the system performance. Numerical results verify the ef ciency of the proposed hierarchical scheme, and demonstrate that our solution outperforms the state-of-the-art methods, especially for hyper-dense networks

Optimising seed portfolios to cope ex ante with risks from bad weather: evidence from a recent maize farmer survey in China

Using 4-year panel data collected from surveying 640 farmers from leading maize producing provinces in China, this study analyses how maize farmers cope with anticipated risks from bad weather by strategically adjusting variety portfolios, with particular interest in farmers’ strategies in choosing and combining new and old varieties. While diversification was commonly demonstrated to be an effective means to reduce risk in most previous studies, our empirical results indicate that, in facing anticipated risks from bad weather, Chinese maize farmers tend to use fewer new varieties and allocate more land to old varieties. The lack of knowledge about weather tolerance of new varieties might be the major reason for this practice. As new varieties often have higher yield potential relative to old varieties due to technological progress, this finding suggests that Chinese maize farmers might be trading yield potential against risk reduction from bad weather. Furthermore, this study shows that maize farmers’ variety adoption is significantly related to farmers’ land conditions as well as their access to credit markets and technique extensions, suggesting that a well designed policy intervention could offset or partially offset the anticipation of adverse weather impacts on farmers’ variety choices and therefore on maize production

Optimising seed portfolios to cope ex ante with risks from bad weather: evidence from a recent maize farmer survey in China

Using 4-year panel data collected from surveying 640 farmers from leading maize producing provinces in China, this study analyses how maize farmers cope with anticipated risks from bad weather by strategically adjusting variety portfolios, with particular interest in farmers’ strategies in choosing and combining new and old varieties. While diversification was commonly demonstrated to be an effective means to reduce risk in most previous studies, our empirical results indicate that, in facing anticipated risks from bad weather, Chinese maize farmers tend to use fewer new varieties and allocate more land to old varieties. The lack of knowledge about weather tolerance of new varieties might be the major reason for this practice. As new varieties often have higher yield potential relative to old varieties due to technological progress, this finding suggests that Chinese maize farmers might be trading yield potential against risk reduction from bad weather. Furthermore, this study shows that maize farmers’ variety adoption is significantly related to farmers’ land conditions as well as their access to credit markets and technique extensions, suggesting that a well designed policy intervention could offset or partially offset the anticipation of adverse weather impacts on farmers’ variety choices and therefore on maize production

Domestic Solid Waste Discharge and Its Determinants in Rural China

The purpose of this paper is to make a quantitative analysis of the rural domestic solid wastes discharge volume, structure and Determinants. The data comes from one large field survey in 6 provinces in China, The results show that, in 2010, the discharge volume of rural domestic solid wastes was 236 million tons. Policy factors and socio-economic conditions significantly influence the discharge volume of rural domestic solid waste. The implementation of certain policies can significantly reduce the discharge volume. The relationship between discharge volume and income per capita shows an obvious inverted U-shaped curve