Construction of an indoor radio environment map using gradient boosting decision tree

Wireless Networks - Radio environment maps represent a signal strength map or a coverage area of radio networks. Constructing such maps involves gathering signal coverage information in sparse...     

Design and Implementation of a Fixed-Mobile Convergent Music Search Engine (FMC-MUSE)

Query by Singing/Humming (QBSH) is a most natural way for music search. A music search system can help music finders search songs by matching a part of melody by singing or humming. Many music information retrieval techniques have been developed to carry out music search for years. On the other hand, thanks to the rapid growth of mobile wireless Internet technologies this decade, music search applications can be implemented on hand-carried devices, such as cellular phones, to conduct music search anytime and anywhere via any available networks, such as Wi-Fi, UMTS, WiMAX to the emerging 3GPP-LTE networks. In the past, little studies had ever been revealed about how to design and implement a lightweight music search engine over a fixed or mobile Internet. In this article, we aim to elaborate a practical skeleton of developing a simple music search engine over fixed or mobile networks—a Fixed-Mobile Convergent Music Search Engine (FMC-MUSE). FMC-MUSE can process music queries by QBSH from fixed or mobile clients and return a dataset containing the search results and meta-info back to music finders via ubiquitous networks.     

诠释基桩承载力之波动方程分析应用

基桩承载力之试验方法包括常见之静载重试验和近期发展出的高应变动力试桩。其中静载重试验所耗费人力及物力颇高,施做过程亦具危险性,经常无法得到完整的试桩曲线,造成诠释之不便与困扰。鉴于此,本研究采波动方程分析仿真桩载重试验,配合现地土壤之标准贯入值,藉迭代方式修正材料性质,以使分析结果合乎试验资料,并建立外插程序方便各式诠释法使用,其结果经验证符合实际状况。     

Ambient mesoscale weather forecasting system featuring mobile augmented reality

Multimedia Tools and Applications - With rapid development of mobile technologies, people can easily obtain surrounding information through their mobile devices. Meanwhile, weather forecasting...     

Ensembled neural networks applied to modeling survival rate for the patients with out-of-hospital cardiac arrest

The purpose of this study is to use ensembled neural networks (ENN) to model survival rate for the patients with out-of-hospital cardiac arrest (OHCA). We also use seven different sensitivity analyses to find out the important variables to establish a comprehensive and objective assessment method for the OHCA patients. After pre-filtering, we obtained 4,095 data for building this ENN model. The data have been divided into 60?% data for training, 20?% data for validation, and 20?% data for testing. The 11 inputs, including response time, on-scene time, patient transfer time, time to cardiopulmonary resuscitation (CPR), CPR on the scene, using drugs, age, gender, using airway, using automated external defibrillator (AED), and trauma type, and one output variable have been selected as ENN model structure. The results have been shown that ENN can model the OHCA patients and CPR on the scene, using drugs, on-scene time, and using airway in the top 4 of these 11 important variables after 7 different sensitivity analyses. Moreover, these four variables have also been shown significant differences when we use traditional one variable statistics analysis for these variables.