#高雄市 因產業特殊關係,設有多個產業園區,如智慧科技創新園區、體感科技產業園區、和發產業園區、岡山本洲產業園區等。而產業園區的成立也帶動高雄的企業發展,也激發出創新創業能量得以扶持草創企業成長,部分產業園區帶有創業基地的功能,扮演資助新創企業成長的角色。高雄市政府積極鏈結國際性 #加速器 ,與全球最大新創加速器「Plug and Play Tech Center」簽訂國際合作意向書,協助高雄體感團隊鏈結 #矽谷 #創投 、#業師、#科技社群 等創業資源。高雄育成中心同樣超過半數附屬於大專院校內。
For English, check out the post by Startup Island TAIWAN!
同時也有1部Youtube影片,追蹤數超過1,440的網紅WEBの学校 ペンシルアカデミー|PencilAcademy !,也在其Youtube影片中提到,https://www.pencil-academy.org シリコンバレー最大のインキュベーション施設 PnP(Plug and Play Tech Center)にて、チャットワークのシリコンバレーインターシップの0期生のペンシルの覚田が語る「シリコンバレーで働いてみよう!」ということで、チャッ...
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plug and play tech center 在 InnoVEX Facebook 的精選貼文
【2021 InnoVEX Pitch: Judges Lineup Reveal!📣】
Shawn Dehpanah, Executive Vice President of Corporate Innovation, Plug and Play Tech Center
Shawn Dehpanah, a highly accomplished & agile Executive Vice President at Plug and Play, also a leader who quickly overcomes profit & growth challenges in many new markets!💪 Shawn started his journey in Silicon Valley, from engineer to business development departments, and now he is leading Play and Play with 20 years of overseas working experiences and strong backgrounds in both technologies and Startup Ecosystems! Plug and Play is a global innovation platform, corporate with over 30,000 startups and 500 official partners such as in-house VC and famous companies. Currently, they have 35 locations globally!🌏⚡
Beloved Partners🤝: Plug and Play, Plug and Play APAC
With a strong Judging Panel, InnoVEX creates more opportunities for you, and it's your time to take the chance! Registration ends on 7/2, sign-up now!🔥👉https://bit.ly/3tNBx2R
#EVENT #technology #Startup #win #innovation #iot #ai #business #vr #car #health #Smart #Data #New #usa
plug and play tech center 在 國立陽明交通大學電子工程學系及電子研究所 Facebook 的最佳貼文
【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).
plug and play tech center 在 WEBの学校 ペンシルアカデミー|PencilAcademy ! Youtube 的最讚貼文
https://www.pencil-academy.org
シリコンバレー最大のインキュベーション施設 PnP(Plug and Play Tech Center)にて、チャットワークのシリコンバレーインターシップの0期生のペンシルの覚田が語る「シリコンバレーで働いてみよう!」ということで、チャットワークのシリコンバレーインターシップの内容や魅力をチャットワークCEOの山本社長にインタビューを受けたい。また私が「Plug and Play Tech Center」に来たのは5回目のとなり本日ペンシルの新会社である米国独立法人の「ペンシルインク」のシリコンバレーオフィスの契約に来た。無事契約が終わりIDカードもその場で発行して貰った。
なにからなにまでチャットワークの山本社長のお陰だ、ありがとう!
https://www.pencil-academy.org
チャットワーク・インターンシップはこちらです。
http://svintern.chatwork.com