關於 block by block application ，我們在網路上蒐集到這些相關的討論、資訊與評價

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【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
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※報名及繳費方式：
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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）
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※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).

Aria Audio Limited 雅詠音響有限公司

SOULNOTE加藤の「もう少し言っておきたい！」シリーズ その241

【S-3の対向ダブルトランス構造について】

前回、開発中のS-3の内部写真を公開しましたが、まず目につくのはデカイ電源トランスが2つ、しかもサイドに横向きに取り付けられていることだと思います。トータル500VAの重いトランスをサイドに取り付けるのは非常に組み立てにくいですし、いままで1筐体1トランスを貫いてきたSOULNOTEとしては異例のダブルトランスです。今回は、何故この構造にしたかを説明したいと思います。

まず、S-3の最大の使命は、メカニズム1体型で完結するデジタル再生機器の可能性を追求することにあります。つまりクロックが同期できて、最短で配線できるメリットを追求することです。これは以前述べたとおり。

当然ながら、メカニズム1体型のデメリットもあります。メカニズムの振動と電源へのモーターノイズ混入等の問題です。これに対する最適解が対向ダブルトランス構造なのです。

メカニズムの振動については、アルミ削り出しメカベースにスパイク足をダイレクトに取り付けることで、1点で振動を排出しつつ、メカニズムの位置を確定します。メカベースはフロントパネルのみに固定され、基板が取り付くボトムシャーシに直接触れさせ無いのがポイントです。

さて、3本スパイクのうち、1本をメカニズムに使いました。で、残り2本でトランスを支えるわけです。SOULNOTEとしては異例のダブルトランスですが、そのために万全の構造にしました。

その解説の前に、何故私が1筐体1トランスに拘っているのかをおさらいします。

ダブルトランスの最大のデメリットは、振動源がダブルになることで有害な振動やリーケージフラックスがさらに混変調されて、音が滲むことです。(私の個人的な感想です。)

そこで、ダブルトランスによる振動の混変調を避けるために、トランスベースを左右に分離して、それぞれのトランスベースに直接固定されたスパイク足で振動を排出します。またトランスベースによる音質への影響は非常に大きいので、ここは細心のチューニングを施しました。ポイントはダンプをしないで共振を防ぐことにあります。そのためにトライ&エラーを繰り返し、厚みの違う2枚のアルミブロックをチタン製スペーサーで3点でフローティング固定することで最高の結果を得ました。外観のサイドパネルがそれです。単なる意匠ではありません。

またトランスを横向き対向配置する理由は、トロイダルトランスから出るリーケージフラックスの進行方向を基板やメカニズム、あるいはメカニズムから伸びるフレキシブルケーブルと「並行にする」ことにあります。これはE-2のMC基板の配置で抜群の効果を発揮しましたが、その応用です。

また、電源トランスをアナログとデジタルに分け、さらに2次巻線を細分化(トータル11巻線)とすることで、メカニズムによる有害な電源ノイズの特にアナログ電源への混入を排除できました。フロントから見て右側の250VAトランスはType-R 回路専用です。無帰還回路は電源も無帰還ですから、この独立トランスが威力を発揮しています。

パワーアンプを別物に化けさせる圧倒的な駆動力は、このType-R 専用トランスの賜物なのです。
Soulnote Kato's " I want to say a little more!" series part 241

[about the oncoming double trance structure of s-3]

Last time I published an internal photo of s-3 in development, I think that the first one is a big power transmission, and it is installed sideways on the side. It is very hard to assemble the heavy trance of total 500 VA to the side, and it is an unusual double trance as a soulnote that has been pierced by 1 cabinets. This time, I would like to explain why I made this structure.

First of all, the s-3 biggest mission is to pursue the possibility of a digital regeneration device that will be completed in a mechanism 1 body. In other words, the clock is synchronized, and it is to pursue the benefits that can be wiring at the shortest time. This is what I used to say.

Of course, there is also a disadvantages of the mechanism of 1 bodies. A problem with the vibration of the mechanism and motor noise mixing to the power supply. The Optimal Solution for this is an oncoming double trance structure.

For the vibration of the mechanism, you can attach the spike feet directly to the aluminum sharpener mecha base, and it will determine the position of the mechanism while venting the vibration in 1 points. The mecha base is fixed on the front panel, and it is the point that the board does not touch the bottom chassis directly.

Well, I used one of the 3 Spikes for the mechanism. So we can support the trance with 2 remaining. It's an unusual double trance as a soulnote, but it's a complete structure for that.

Before the commentary, I'll take a look at why I'm holding a 1 CABINET 1 Trance.

The biggest disadvantages of double trance are that the vibration source becomes double, the harmful vibration and the lee cage flux are even more mixed up, and the sound is shènmu. (my personal impressions. )))))))

So, to avoid the mixed modulation of vibration by double trance, separate the trance base to the left and right, and drain the vibration with the spike feet directly fixed to each trance base. Also, the impact of trance-based sound quality is very big, so here is a close tuning. Point is to prevent resonance without dump. That's why we repeated the try & error, and we got the best result by floating a 2-Piece Aluminum Block with a titanium spacer with a titanium spacer. The side panel of the look is that. It's not just a design.

Also, the reason why the trance is sideways, is about the progression of the lee cage flux from the troi trance, and the flexible cable that stretches from the mechanism, and the flexible cable that stretches from the mechanism. This has been an excellent effect on the placement of the e-2 MC Board, but it is the application.

In addition, the power trance is divided into analog and digital, and the 2nd volume line is subdivided (total 11 volumes), and it is possible to eliminate the laced of the analog power of the harmful power noise by the mechanism. I'm here. From the front, the 250 VA trance on the right side is exclusive to type-R circuit. This independent trance is in power because the electric circuits are no longer power.

The overwhelming powered power that makes power amps different, is the gift of this type-R exclusive trance.Translated

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Pilotfly posted on their twitter ( https://twitter.com/pilotflyteam/status/1053292436624044032 ) they are going to sue several companies that infringed their direct screen view patent. However I found 2 point that could make their patent to be invalid. However if Pilotfly do win, it means that many of the brands out there would need to stop their gimbal productions and redesign their current models to not have this direct screen view design.

UPDATE:
Found their full patent in - https://patents.google.com/patent/CN206929539U/en?oq=201720603123

So the first point why I feel this innovations might not be an innovation, is that you can already achieve this with holding your gimbal in an angle where the back motor does not block the screen at all.

2nd reason is that in 2016 may 10th Alex Mos have already made one and posted it on his blog and Youtube. https://youtu.be/GVWGpBXOLOw

Because Alex Mos and not other company has patented it within 1 year of this publication this innovation would automatically go into the public domain. And Pilotfly patent application date is on May 26 2017 which is well over a year since the publication, making it a patent of a public domain which is not valid.

However I am now a lawyer and the Chinese Law might be different than the American patent laws and these are just my opinion why I feel that the Pilotfly Patent would be invalid.

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What is Pak Sao in Bruce Lee's JKD? Translated, pak sao means "slapping hand". However, pak doesn't really slap, at all. In reality, the technique of pak sao involves a great deal of information learned by close attention to details. Be sure, pak and slap do not share the same energies, nor even the same results. You may execute a slap, but not Bruce.

His was pak! Nevertheless, pak sao has still another meaning in wing chun. It's in the nature of the meaning whereby Bruce spoke when he defined Jeet Kune Do as, "pak sao and hip". Pak sao's nature is to intercept.

Thus, the name Jeet Kune Do, The Way of the Intercepting Fist. Actually, there are only three ways to intercept. Ahead of the opponent's beat, at the same time as his beat, or behind his beat. We call this a half-beat ahead, same beat, or half- beat behind.

Subscribe to Dan's Channel to learn more Wing Chun or Jeet Kune Do: http://bit.ly/DanLokSubscribe

With Pak Sao, the hand comes directly out of the center of the body to slap away an attacker's strike to one's head. Effective application of Pak Sao involves creating an angle of deflection through which the opponent's blow can be slapped away with minimal effort.

Trapping is not exclusive to JKD, but it certainly isn’t very common in most martial arts.

In case you aren’t familiar with what I’m talking about, trapping is the art of using your arms to nullify one of your opponent’s limbs so that he can’t use it to hit or to block. This allows you to then strike into that open line (that’s a general definition). It’s also called Hand Immobilization Attack (although you can also immobilize other parts of the body, too, using the same concept).

In short, trapping basically means “grabbing to hit”.

One of Dan Lok’s passions in life is martial arts. Like many young kids, after watching a Bruce Lee movie, it changed his life forever. At 17 years old, Dan started training in martial arts seriously because he was being bullied in school. It wasn’t long for Dan to learn the techniques he needed, and gain the confidence necessary to defend himself.

Dan has studied with legendary martial artist such as Bruce Lee’s original student Ted Wong (http://tedwongjkd.net) and Joe Lewis “The Worlds Greatest Fighter” (http://joelewisassociation.com), making him a second generation student of Bruce Lee - in Bruce Lee's authentic art of Jeet Kune Do (JKD). He's also a third generation student of Ip Man (Wing Chun Kung Fu).

Dan has also trained with other great instructors like Sifu Adam Chan (https://www.pragmaticmartialarts.com), Canadian lightweight boxing champion Tony "Fire Kid" Pep (https://www.facebook.com/pepboxing), and Octavio Quintero (https://www.theartofjkd.com)

For Dan, martial arts training permeates every area of life. It’s not a hobby, it’s a way of life, and it influences how he does business.

Martial arts gave him the confidence, focus, and patience to push through these obstacles and to keep fighting when he felt like giving up.

IMPORTANT NOTE: Dan is NOT a full-time martial artist and he doesn't even claim to be that good of a fighter.

He's simply a successful businessman who enjoys the art and philosophy of Bruce Lee, just like you.

He doesn't have any online martial art videos, seminars or expensive "private training" to sell you. Quite frankly, he doesn't need the money.

He simply wants to share his passion for the art of JKD (his own version of Jeet Kune Do) through his YouTube channel.

Check out the other Jeet Kune Do (JKD) Fighting Tactics and Training Videos: https://www.youtube.com/playlist?list=PLEmTTOfet46Ocn3bqnUIaAB-cTUzsAXOG

This Video: Bruce Lee's Jeet Kune Do Trapping Techniques - Pak Sao Drill
https://youtu.be/oot0ZpuF9RQ
https://youtu.be/oot0ZpuF9RQ