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ภาพเปรียบเทียบระหว่างกล้อง Ricoh GR III and the new Ricoh GR IIIx
และ GR III model สามารถ อัพเดทฟีเจอร์ที่เปลี่ยนไปผ่าน firmware update
- เลนส์ 26.1mm (เทียบเท่า 40mm) f/2.8 lens มี built-in 2-stop ND filter
- Face และ eye detection AF modes ใหม่
- ปรับปรุงและเพิ่มเติม in-camera image editing options
- มีฟังกชั่น Auto resize เมื่อ transfer ผ่าน wireless
- มี “Deep” DOF priority
- Ricoh GR IIIx มี accessories ต่างๆใหม่
//ม่วนใจ๋
父母與孩子相處方式不能是「朋友式」相處，也不可能永遠上而下單向式。我要讓他們知道每個角色都有其本份，孩子要讀書要孝順父母，父母要把孩子照顧好，成為孩子的role model，令他們信服，讓他們知道父母是值得尊重，值得他們愛錫，最重要的就是父母永遠在他身邊，支持他們，聆聽他們。輕鬆的時候，我亦能成為他最好玩的朋友。對於三個孩子相處，以下幾點是我一直堅守：
👏🏻鼓勵式學習
興趣班老師我是特別挑選過，老師不能一味靠惡，用鼓勵式方法教學，另外也不能完全遷就小朋友，要有責任感，落堂要他們自己收拾，要多謝老師授課，尊師重道。
🧐讓孩子作決定
我很討厭後悔和怪責的感覺，所以每個決定要想得遠，考慮得一清二楚。除了關乎教育，生活上的事情我先會跟他們分析，讓孩子自己做決定。記得有次兒子睡前要求食糖，我說如果現在吃的話3日都不能吃，他一口答應。第一天還口硬說不吃糖也沒什麼大不了🤷🏻♀️晚上就忍唔住要求吃糖，我跟他說「我都好想俾你食，但你許下承諾，你都唔想break promise嗎？我也不會對你break promise，媽媽會同你一齊堅守！」最後我們一起堅持到3日，他從此沒提出過睡前吃糖。
❌拒絕用負面字眼
我會係賞同無賞，不會懲罰，絕不會用負面字眼。
🚫父母和孩子之間都有界線
我不會讓孩子看我的電話，跟他們說 “THIS IS MY PHONE”! 當然，破解我電話密碼自然成為他們的生活日常，以為我有set face detection login就將電話在我面前fing來fing去😂另外，我不允許孩子與我們同床睡覺，我會告訴他，媽媽和爸爸經常陪伴你，但我們也有自己相處的時間啊！
💪🏻🙌🏻媽媽永遠在你身邊
男仔性格較大情大性，好少分享心底話，但我會經常跟他們說：「媽媽好喜歡聽你哋講嘢，有咩事都可以同媽媽講。」讓他們知道媽媽永遠在你身邊。
🤷🏻♀️以退為進
和小孩相處就是要鬥智鬥力腦筋急轉彎。同一件事上你不可能永遠只是yes or no，他們會習慣的，有時要給他「估你唔到」的回應出奇制勝。有次兒子鬧脾氣說不要出門，甚至鬥氣說「我唔需要朋友」，我回應一句「哈哈哈，講笑咩你我知你唔係真係咁諗嘅！」強硬下去只會氣氛更差，他也想不到平時有威嚴的我會這樣回應化解危機。
送二仔最後的一天的幼稚園，心情悲喜交集。Mama & papa wish you happy always. ❤️
#EstherSham
#hkig
#hkmom
#hkmami
#hkmama
#hkmommyblogger
#motherhood
【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).
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specifications-
NETWORK Technology
GSM / HSPA / LTE
LAUNCH Announced 2016, April
Status Available. Released 2016, June
BODY Dimensions 149.4 x 73.9 x 7.3 mm (5.88 x 2.91 x 0.29 in)
Weight 156 g (5.50 oz)
SIM Single SIM (Nano-SIM) or Dual SIM (Nano-SIM, dual stand-by)
DISPLAY Type IPS LCD capacitive touchscreen, 16M colors
Size 5.3 inches (~70.1% screen-to-body ratio)
Resolution 1440 x 2560 pixels (~554 ppi pixel density)
Multitouch Yes
Protection Corning Gorilla Glass 4
- Always-on display
- LG Optimus UX 5.0 UI
PLATFORM OS Android 6.0.1 (Marshmallow), upgradable to 7.0 (Nougat)
Chipset Qualcomm MSM8976 Snapdragon 652
CPU Octa-core (4x1.8 GHz Cortex-A72 & 4x1.2 GHz Cortex-A53)
GPU Adreno 510
MEMORY Card slot microSD, up to 256 GB (dedicated slot) - single-SIM model
microSD, up to 256 GB (uses SIM 2 slot) - dual-SIM model
Internal 32 GB, 3 GB RAM
CAMERA Primary Dual 16 MP (f/1.8) + 8 MP (f/2.4), laser autofocus, OIS (3-axis), LED flash
Features Geo-tagging, touch focus, face/smile detection, panorama, HDR
Video 2160p@30fps, 1080p@30fps, HDR, stereo sound rec.
Secondary 8 MP, f/2.0, 1080p@30fps
SOUND Alert types Vibration; MP3, WAV ringtones
Loudspeaker Yes
3.5mm jack Yes
- Active noise cancellation with dedicated mic
COMMS WLAN Wi-Fi 802.11 a/b/g/n/ac, dual-band, Wi-Fi Direct, DLNA, hotspot
Bluetooth 4.2, A2DP, LE, aptX
GPS Yes, with A-GPS, GLONASS
NFC Yes
Infrared port Yes
Radio FM radio
USB 2.0, Type-C 1.0 reversible connector, USB On-The-Go
FEATURES Sensors Fingerprint (rear-mounted), accelerometer, gyro, proximity, compass, barometer, color spectrum
Messaging SMS(threaded view), MMS, Email, Push Mail, IM
Browser HTML5
Java No
- Fast battery charging (Quick Charge 3.0)
- MP4/DviX/XviD/H.264/WMV player
- MP3/WAV/FLAC/eAAC+/WMA player
- Photo/video editor
- Document editor
BATTERY Removable Li-Ion 2800 mAh battery
Stand-by Up to 380 h (3G)
Talk time Up to 20 h (3G)
MISC Colors Silver, Titan, Gold
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https://www.youtube.com/watch?v=Zkdt27uJ0GY
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