關於 uav課程 ，我們在網路上蒐集到這些相關的討論、資訊與評價

新北創力坊 InnoSquare 要跟 新北市-亞馬遜AWS聯合創新中心 一起舉行 Demo Day The Innovators 5 了！本屆活動因應疫情首度改為線上形式，並與 攜手，推薦19間新創團隊展示遠距醫療、智能工廠、零售無人機、線上旅遊、未來城市與新生活等兼具科技創新與商業化解決方案，共同演繹台灣數位新未來。
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📍雅渼醫療器材｜家用洗腎患者使用的遠距管理系統
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📍衣圖｜二手換衣平台
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手刀報名請點我👇
https://www.accupass.com/go/ntpcawsjic_demoday5

（來自高師地理，粉絲系友的投稿）
高中地理選修上冊：空間資訊科技
災害事故調查如何運用地理資訊

提供高中教師備課資料，以及社會大眾知悉
https://bit.ly/3my4xd4　（檔案連結）
https://www.facebook.com/ULTRAdc/posts/4250559768289960

#空拍機（#UAV）拍攝以作為後續 #3D建模 使用，

無人機救援包括如邊防監控、消防監控、環境保護、刑偵反恐、治安巡邏等具體應用。其在突發救援任務中能有效規避地面障礙，快速準確的到達指定現場，利用熱成像儀等高新技術產品把實時訊息回傳指揮中心，為指揮人員決策提供依據。

無人機在、科研、教育、精準農業、智慧城市、勘察、場景巡檢等具體應用中，#測繪 是關鍵的一環。

也會下載列車的列車自動保護系統（Automatic Train Protection， 簡稱ATP）和「列車監視控制系統」（Train Control Monitor System，簡稱TCMS）資訊，以及搜集監視器或民眾拍攝之影像供後續調查。

#點雲（point cloud）是指透過3D掃描器所取得之資料型式。掃描資料以點的型式記錄，每一個點包含有三維座標，有些可能含有色彩資訊（R,G,B）或物體反射面強度。

#正射影像
相當於是正攝投影的航攝相片，但是實際通過航拍得到的航攝相片是中心投影，而且還存在因為相片傾斜和地面起伏產生的像點位移。

#光達，LiDAR，是英文「light detection and ranging」的縮寫，是一種光學遙感技術，它通過向目標照射一束光，通常是一束脈衝雷射來測量目標的距離等參數。雷射雷達在測繪學、考古學、地理學、地形、地質、林業、遙感以及大氣物理等領域都有應用。

#資訊彙整平台
2021-04-02
太魯閣號列車脫軌事故災情整合平台

關於20210402太魯閣號第 408 車次意外，
匆促地找了些圖片、影片。

可以簡單談談 #地理資訊系統的應用
#運輸與交通
問題探究：#都市機能與城鄉關係
或者趁學生對於事件印象深刻
先談談選修課程裡的「#空間資訊科技」
#空間資料的種類與獲取
#空間資訊的呈現
問題探究：應用 #地理媒體
向他人分享自己對某個社會空間議題的看法

身為地理人，總是要做些甚麼吧

統整作者：郭展榮（高師地理系友）
協力作者：巫師地理

【演講】2019/11/19 (二) @工四816 (智易空間)，邀請到Prof. Geoffrey Li（Georgia Tech, USA）與Prof. Li-Chun Wang（NCTU, Taiwan） 演講「Deep Learning based Wireless Resource Allocation／Deep Learning in Physical Layer Communications／Machine Learning Interference Management」

IBM中心特別邀請到Prof. Geoffrey Li（Georgia Tech, USA）與Prof. Li-Chun Wang（NCTU, Taiwan）前來為我們演講，歡迎有興趣的老師與同學報名參加！

演講標題：Deep Learning based Wireless Resource Allocation／Deep Learning in Physical Layer Communications／Machine Learning Interference Management
演 講 者：Prof. Geoffrey Li與Prof. Li-Chun Wang
時 間：2019/11/19(二) 9:00 ~ 12:00
地 點：交大工程四館816 (智易空間)
活動報名網址：https://forms.gle/vUr3kYBDB2vvKtca6

報名方式：
費用：（費用含講義、午餐及茶水）
1.費用：(1) 校內學生免費，校外學生300元/人 (2) 業界人士與老師1500/人
2.人數：60人，依完成報名順序錄取（完成繳費者始完成報名程序）

※報名及繳費方式：
1.報名：請至報名網址填寫資料
2.繳費：
（1）親至交大工程四館813室完成繳費(前來繳費者請先致電)
（2）匯款資訊如下：
戶名: 曾紫玲（國泰世華銀行 竹科分行013）
帳號: 075506235774 (國泰世華銀行 竹科分行013）
匯款後請提供姓名、匯款時間以及匯款帳號後五碼以便對帳
※將於上課日發放課程繳費領據

聯絡方式：曾紫玲 Tel：03-5712121分機54599 Email：tzuling@nctu.edu.tw

Abstract:
1.Deep Learning based Wireless Resource Allocation
【Abstract】
Judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless network performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. However, as wireless networks become increasingly diverse and complex, such as high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning represents a promising alternative due to its remarkable power to leverage data for problem solving. In this talk, I will present our research progress in deep learning based wireless resource allocation. Deep learning can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first present our research results in using deep learning to solve linear sum assignment problems (LSAP) and reduce the complexity of mixed integer non-linear programming (MINLP), and introduce graph embedding for wireless link scheduling. We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.

2.Deep Learning in Physical Layer Communications
【Abstract】
It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of the conventional communication systems. In this talk, we present our recent work in DL in physical layer communications. DL can improve the performance of each individual (traditional) block in the conventional communication systems or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection, and some experimental results. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN). At the end of the talk, we provide some potential research topics in the area.

3.Machine Learning Interference Management
【Abstract】
In this talk, we discuss how machine learning algorithms can address the performance issues of high-capacity ultra-dense small cells in an environment with dynamical traffic patterns and time-varying channel conditions. We introduce a bi adaptive self-organizing network (Bi-SON) to exploit the power of data-driven resource management in ultra-dense small cells (UDSC). On top of the Bi-SON framework, we further develop an affinity propagation unsupervised learning algorithm to improve energy efficiency and reduce interference of the operator deployed and the plug-and-play small cells, respectively. Finally, we discuss the opportunities and challenges of reinforcement learning and deep reinforcement learning (DRL) in more decentralized, ad-hoc, and autonomous modern networks, such as Internet of things (IoT), vehicle -to-vehicle networks, and unmanned aerial vehicle (UAV) networks.

Bio:
Dr. Geoffrey Li is a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include statistical signal processing and machine learning for wireless communications. In these areas, he has published around 500 referred journal and conference papers in addition to over 40 granted patents. His publications have cited by 37,000 times and he has been listed as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters almost every year since 2001. He has been an IEEE Fellow since 2006. He received 2010 IEEE ComSoc Stephen O. Rice Prize Paper Award, 2013 IEEE VTS James Evans Avant Garde Award, 2014 IEEE VTS Jack Neubauer Memorial Award, 2017 IEEE ComSoc Award for Advances in Communication, and 2017 IEEE SPS Donald G. Fink Overview Paper Award. He also won the 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech.

Li-Chun Wang (M'96 -- SM'06 -- F'11) received Ph. D. degree from the Georgia Institute of Technology, Atlanta, in 1996. From 1996 to 2000, he was with AT&T Laboratories, where he was a Senior Technical Staff Member in the Wireless Communications Research Department. Currently, he is the Chair Professor of the Department of Electrical and Computer Engineering and the Director of Big Data Research Center of of National Chiao Tung University in Taiwan. Dr. Wang was elected to the IEEE Fellow in 2011 for his contributions to cellular architectures and radio resource management in wireless networks. He was the co-recipients of IEEE Communications Society Asia-Pacific Board Best Award (2015), Y. Z. Hsu Scientific Paper Award (2013), and IEEE Jack Neubauer Best Paper Award (1997). He won the Distinguished Research Award of Ministry of Science and Technology in Taiwan twice (2012 and 2016). He is currently the associate editor of IEEE Transaction on Cognitive Communications and Networks. His current research interests are in the areas of software-defined mobile networks, heterogeneous networks, and data-driven intelligent wireless communications. He holds 23 US patents, and have published over 300 journal and conference papers, and co-edited a book, “Key Technologies for 5G Wireless Systems,” (Cambridge University Press 2017).