Spatial Distribution of Religious Sites in China: A web-based data-rich application using Esri

The Online Spiritual Atlas of China (OSAC), created by the Center on Religion and Chinese Society at Purdue, was constructed as a complement to the print volume, Atlas of Religion in China: Social and Geographical Contexts, by Fenggang Yang (Brill, 2018), as a way to visually demonstrate the extent and distribution of religious sites in China. OSAC is power by ArcGIS online, and some features were developed with ArcGIS JavaScript SDK. The site allows users to visualize the spatial distribution of individual religious sites in China, as well as see how provinces, prefectures, and counties compare with each other in terms of the number of religious sites. Currently, the data comes from China’s 2004 Economic Census, which listed 72,887 religious sites from all of China’s 31 provinces or provincial-level regions and municipalities, but there are plans to update this with data gleaned from other sources in the future.. It also allows for user-generated edits or additions to the religious site information. In addition, OSAC uses the ArcGIS Storymap platform to display visual essays for a select number of specific religious sites

Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images.

Although extreme learning machines (ELM) have been successfully applied for the classification of hyperspectral images (HSIs), they still suffer from three main drawbacks. These include: 1) ineffective feature extraction (FE) in HSIs due to a single hidden layer neuron network used; 2) ill-posed problems caused by the random input weights and biases; and 3) lack of spatial information for HSIs classification. To tackle the first problem, we construct a multilayer ELM for effective FE from HSIs. The sparse representation is adopted with the multilayer ELM to tackle the ill-posed problem of ELM, which can be solved by the alternative direction method of multipliers. This has resulted in the proposed multilayer sparse ELM (MSELM) model. Considering that the neighboring pixels are more likely from the same class, a local block extension is introduced for MSELM to extract the local spatial information, leading to the local block MSELM (LBMSELM). The loopy belief propagation is also applied to the proposed MSELM and LBMSELM approaches to further utilize the rich spectral and spatial information for improving the classification. Experimental results show that the proposed methods have outperformed the ELM and other state-of-the-art approaches

Short Block-length Codes for Ultra-Reliable Low-Latency Communications

This paper reviews the state of the art channel coding techniques for ultra-reliable low latency communication (URLLC). The stringent requirements of URLLC services, such as ultra-high reliability and low latency, have made it the most challenging feature of the fifth generation (5G) mobile systems. The problem is even more challenging for the services beyond the 5G promise, such as tele-surgery and factory automation, which require latencies less than 1ms and failure rate as low as $10^{-9}$. The very low latency requirements of URLLC do not allow traditional approaches such as re-transmission to be used to increase the reliability. On the other hand, to guarantee the delay requirements, the block length needs to be small, so conventional channel codes, originally designed and optimised for moderate-to-long block-lengths, show notable deficiencies for short blocks. This paper provides an overview on channel coding techniques for short block lengths and compares them in terms of performance and complexity. Several important research directions are identified and discussed in more detail with several possible solutions.Comment: Accepted for publication in IEEE Communications Magazin