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Thanks for the analysis! A bit off-topic, but I wanted to ask: I have a SafePal wallet with USDT, and I have the seed phrase. (alarm fetch churn bridge exercise tape speak race clerk couch crater letter). How can I transfer them to Binance?

완강까지 얼마 안남았네요 ㅎㅎ 컴공과라 정말 어려웠습니다

42:00

너무 귀한 강의 감사합니다... notation때문에 더 어렵게 느껴지네요

27:43

13:00

43:00

41:51

점점 조회수가 줄어가는게 보이는군요... 이 악물고 버티는 중입니다...

summary 20:20 ❤

Thank you so much Prof. Please, could you tell me how I can make this video editing? I mean I appear in small video, present at the same time the document that show the written explanation? Please, what is the software that you used for video recording in this manner, also the product that allow you for digital writing in the document! I'll so thankful and appreciative to you Prof :)

cisl.postech.ac.kr/class/eece233/schedule.htm

안녕하세요 교수님, 업로드해주신 강의 감사히 보고 있습니다. 아무리 생각해도 이해되지 않아 고통스러운 부분이 하나 있어서 질문을 드립니다. 1:11:40 에서, j>=2 부터의 조건부확률에 있는 conditioning bar 뒤에서, W=m1을 제거해도 된다는 것은 충분히 이해가 갑니다. 그러나 [ X_1^(n) = x_1^(n) ] 은 어떻게 제거할 수 있는 것인지 모르겠습니다. (올려주신 강의노트 handout 11a p3에서 이러한 제거를 수행하고 있는데요) 제가 이해하기로 지금 Y^(n)은, channel이 X_1^(n)을 input으로 받았을 때 내놓는 output입니다. (만약 그렇지 않다면, handout 11a p3의 마지막 등호에서 dependent/independent를 구분하는 것이 불가능할 것입니다) 이러한 의존성에 의해, [ X_1^(n) = x_1^(n) ] 에서 우변의 x_1^(n) 값이 변하면, (channel이라는 조건부분포에 의해 실현되는) Y^(n)의 실현이 변하고, 따라서 conditioning bar의 앞부분에 있는 ( X_j^(n), Y^(n) ) 의 실현 또한 달라지므로, [ X_1^(n) = x_1^(n) ] 를 함부로 제거해서는 안 되지 않는가? 라고 생각하게 되었는데요, 혹시 제가 무엇을 놓치고 있는지 궁금합니다.

안녕하세요 교수님 강의 자료는 현재 다운로드가 어렵나요?

차세대 통신 관련 강의가 많지 않은데 동영상으로 올려주셔서 감사합니다. 쉽게 설명해주시니, 업무(통신 관련 선행 기술 조사)에도 도움이 많이 됩니다.

좋은 말씀 감사합니다 교수님

안녕하세요 교수님. 멋진 강의 감사드립니다..

감사합니다 교수님. 수학과 회로를 잘 모르는 회사원인데, 덕분에 귀한 강의를 듣습니다.

우리나라는 4G LTE에서 FDD, 5G NR에서 TDD를 채택했다고 24, 25 강의에서 설명해주셨는데, 이번 강의에서는 4G에서도 TDD를 채택했다고 설명해주셨습니다. 어떤 것이 맞는건가요?

Thank you for good lecture.

감사합니다.

다음 수업을 대비하기 위해 빠르게 듣고 있습니다. 감사합니다!

24.02.22

ㅋㅋㅋㅋㅋ 유익하고 재미있네요!

안녕하세요 교수님. 저도 혹시 강의자료 password 알 수 있을까요…?

Dsp 강의도 아주 듣고싶습니다..

확률에 대한 공부를 하려고 듣는데 명강의라는 생각이 듭니다. 감사합니다.

00:00 Clarification 01:06 - Def. of ZIZO; Lemma. If Linear then ZIZO. 02:05 - Def. of Causality; 03:04 - Def. of Initial Rest. Lemma. If Initial Rest then ZIZO. 05:50 - Thm. Linear and Causal iff Linear and Initial Rest. 06:58 - Examples. Initial Rest and ZIZO but 1) nonlinear causal time-invariant memoryless 2) nonlinear causal time-varying memoryless systems. 10:34 - Lemma. When LCCDE, if ZIZO then LTI. 12:23 - Lemma. When LCCDE, if Initial Rest then causal and LTI. 15:12 Announcements 18:22 차세대 통신 및 네트워크 융합부전공 소개 19:53 Review 37:28 Convolution Property 49:52 Multiplication Property 53:02 Duality in Fourier Analysis 1:03:06 Parseval's Relation and Effect of Phase

00:00 예습, 복습의 중요성 01:35 목적있는 훈련 04:16 지난 강의 수정, 보완 - Dirac delta as a limit of a Gaussian pulse 06:26 - <-> 의 의미 07:48 - impulse train in TD and FD 09:34 다음 학기 관련 과목 소개 11:21 Review of CM18 - CTFT and L^1 function - Fourier-Plancherel TF and L^2 function 17:00 - examples of CTFT pairs and time-frequency product 19:08 - complex sinusoidal w/ radian freq omega_0<-> 2pi delta (omega-omega_0) 20:42 - Properties of CTFT 25:02 Preview of CM19 26:10 CM19 - Convolution Property 34:20 - differentiator, integrator and their frequency responses 42:48 - ideal LPF and its impulse response, sinc function 49:57 - cascading filters 51:50 - int of product of sinc functions, Work in the FD!

00:00 Clarification 00:08 - 주기 N 인 서로 다른 complex sinusoidal signals의 수 06:00 - 서로 다른 complex sinusoidal signals의 수 07:30 - Periodicity of DT frequency response 09:30 - frequency response of an LTI system with real-valued impulse response 17:30 - LPF + HPF = BPF? 20:07 HW09 22:40 Exam01 23:19 clickable time stamp etc. 25:40 Review 30:15 Preview 31:25 continuous-time Fourier Transform

00:00 Message from Instructor 00:05 - Exceed expectations. 01:50 - 배우고 익히기 05:20 - 의도적 연습의 3F 06:11 - 단순화, 시각화 08:23 중간시험 결과 12:00 Course map and plan 13:13 Review of CM 15 13:39 - CTFS of impulse train 17:13 sampling using impulse train 19:48 - Product of CTFS coefficients of two periodic signals and Periodic convolution 22:55 - Average power of a periodic signal and CTFS coefficients: a Parseval’s relation 30:48 - Concept of Frequency Domain 36:23 - Properties of DTFS/CTFS 42:43 LN 07 49:57 complex gain of an LTI system as a function of frequency = frequency response 53:34 Examples of filter design using LTI systems; notch filter 1:02:55 magnitude and phase responses of an LTI system 1:04:27 non-ideal LPF, HPF,BPF 1:08:07 Ideal LPF, HPF, BPF and cut-off frequencies 1:10:50 DT Averager/Smoother 1:15:28 DT edge detector, DT differentiator

Conditional Expected Value 42:05 Moments 49:15 Markov's Inequality 1:16:40

Review 00:00 Chebyshev Inequality 24:30 Chernoff Inequality 26:45

Conditional CDF 40:20 Conditional PMF 42:10

Total Probability Theorem 1:12:50 Bayes' Theorem 1:18:43

안녕하세요 교수님, 해당 과목에 관심이 생겨 유튜브 강좌를 통해 수강 해보고자 하는데, 강의 자료에 비밀번호가 있어 댓글 남기게 되었습니다. 혹시 비밀번호를 알 수 있을까요? 감사합니다.

FONC 1:08 Def. of a feasible direction at x=x_0 7:00 (CORRECTION!!) x_0 + alpha d \in Omega, for all 0<alpha\leq alpha_0 7:28 little o and an example 11:07 Def. of a directional derivative 17:00 Lemma. For a continuously differentiable function, the directional derivative of f at x_0 in the direction of d is ... 21:06 FONC for an interior point to be a local minimizer 25:25 Theorem If x^* is a local minimizer of an unconstrained optimization problem then ... 26:35 Corollary If x^* is a local minimizer of a constrained optimization problem and an interior point then ... 27:30 Def. of an interior point of a constrained optimization problem 29:54 Proof of the theorem 36:12 Proof of the lemma 38:34 Zeroth Order Necessary Condition (ZONC) to be a local minimizer SONC 42:03 g(alpha):= f(x_0+alpha d) => g'(0) = ... g''(0) = ... 47:20 Theorem 48:45 (CORRECTION!!) d^T grad f(x^*) = 0 must be one of the conditions!!! 51:51 (CORRECTION!!) d^T grad f(x^*) = 0 is not the consequence of other assumptions but one of the assumptions. 53:55 (CORRECTION!!) d^T grad f(x^*) = 0 is not always true for a local minimizer at boundary. 54:38 Corollary 56:50 Summary: ZONC, FONC, SONC

강의 감사합니다

0:44 '함수'와 '함수의 그래프'의 차이 4:30 min f(x) vs. min. f(x) vs. argmin f(x) 8:00 a global vs. a local minimizer 13:14 the strict global minimizer, a strict local minimizer 15:24 the full characterization of a solution set vs. a local minimizer, uniqueness, 25:45 A Theorem of Weierstrass: existence of a minimizer and a maximizer 28:50 an open set 39:38 conditions for local minimizers 44:38 proof of FONC 51:20 directional derivative

Review of the differentiation of a quadratic function 1:04 a quadratic form as a function of a real vector 1:30 cf) the differentiation of a quadratic form as a function of a complex vector; the Wirtinger calculus 4:57 Def. a quadratic form 8:15 the gradient of a quadratic function 22:30 cf) the differentiation of a quadratic form as a function of a complex vector Review of little o and Big O 27:42 two cases: for sufficiently large something vs. for sufficiently small something 32:24 How to memorize: Big O is for BOunding vs. little o is for limit 33:23 주의사항 35:50 ~ vs. = 38:40 Big O and little o are actually set of functions. 41:54 Definition of Big O 46:54 Definition of little o 49:12 Examples 57:06 When do we use O and o? 59:56 Def. the set of continuously differentiable functions 1:07:15 Series representation of a function

0:00 이 과목은 Flipped Learning 과목이므로 본 강의인 영어 강의를 보고 이 우리말 help session을 보셔야 합니다. 7:42 Review of CM#01 classification of optimization problems 17:42 Smooth objective function과 미분 18:30 gradient vector of a scalar-valued function 25:05 Hessian matrix of a scalar-valued function vs. Jacobian matrix of a vector-valued function 38:45 Taylor Series Representation of a function 49:45 quadratic form

Syllabus 0:00 Syllabus Review 8:48 -교과서 소개 E. K. P. Chong and S. H. Zak, An Introduction to Optimization, 4th Ed. 10:06 -참고문헌 소개 S. Boyd and L. Vandenberghe, Convex Optimization. 15:30 -참고문헌 소개 Linear Algebra and Optimization for Machine Learning, Mathematics for Machine Learning 25:38 -Scope of this course and Course Objectives 27:30 -Course Outline 29:40 -Pre-Requisites and Course Requirements 강의 34:50 강의 시작 39:30 AI와 Optimization 40:35 최적화 문제의 분류: 고등학교 문제 예시 55:25 - Constrained or Unconstrained optimizations 55:45 - Single-criterion vs. multi-criterion optimizations 1:05:25 - Discrete vs. Continuous optimizations

감사합니다 교수님!!

00:00 Announcement 2:38 Overview 4:30 Review 17:51 Scheduler 32:45 Ch 8 Models 34:36 8.1 Graphs 47:26 Ch 10 QoS 58:00 Ch 11 Physical Layer 1:15:00 Ch 12 Additional Topic

00:00 Announcements 02:32 Correction 1 05:02 Correction 2 15:42 Review 34:16 Preview 34:41 9.1 Cellular Network 37:30 9.2 Technology Evolution 45:18 9.3 Key Aspect of LTE 52:22 LTE System Architechture 58:25 Data Link Layer 1:08:06 Scheduler 1:13:10 LTE Advanced 1:14:43 Enhanced MIMO Support/CoMP 1:15:12 9.5 5G

00:00 Announcements 04:10 Corrections + review 21:17 Fast retransmission & recovery 추가 설명 22:46 Chapter 9 LTE, NR (Cellular Wireless Communication Networks) 28:05 Introduction - 통신의 역사 34:10 Introduction - (wireless telegraphy) 40:00 Introduction - PSTN 43:27 Introduction - PSTN + non-cellular 51:30 Introduction - PSTN + cellular (7 cell patern) 1:08:25 WiFi & Cellular의 차이

00:00 Announcements 04:51 Overview 05:09 Review(ch7. Transport) - 07:26 UDP, TCP - 10:45 Error control 18:57 SACK(Selective ACKnowledgements) 20:08 Timers 29:25 Congestion Control 38:16 Congestion Control 그림 설명 45:36 Fast Retransmit 47:21 Fast Recovery 53:21 Terminology 54:33 ch7 흐름 정리 57:05 Flow Control 1:05:30 Chapter 9 Preview

00:00 Announcements 05:31 Overview 05:51 Review 11:43 Preview (1. CH. 7) - 11:53 (a. trans) - 13:02 (b. port) - 13:23 (c. Handshake) 13:45 Preview (2, 3. TCP) 16:33 Preview (4, 5. Control) 19:37 Chapter 7 19:52 Introduction 25:41 7.1 Transport Services 32:08 TCP 그림 설명 36:48 7.2 Transport Header(UDP) - 38:52 5G UR/LLC -40:25-- 재밌는 농담- 44:04 TCP segment 53:20 7.3 TCP states (fig 7.4 설명) 57:34 7.4 Error Control 1:06:52 Stop And Wait & Go back N 1:14:42 SACK