PPT-Filtering EECS 442 – Prof. David

Fouhey Winter 2019 University of Michigan httpwebeecsumichedufouheyteachingEECS442W19 Note Ill ask the front row on the right to participate in a demo All you have to do is say a number that Ill give to you If you dont want to its fine but dont sit in the front . Until recently designers needing to perform vi deo or image analysis in real time a typical requirement of medical industrial and military systems had to resort to expensive specialized processors With the advent of fixedpoint highperformance embedd PCA facilitates dimensionality reduction for of64258ine clus tering of users and rapid computation of recommendations For a database of users standard nearestneighbor tech niques require processing time to compute recom mendations whereas Eigentaste S Smith EECS 105 Prof J S Smith Context Today we will cont inue the discussion of single transistor amplifiers by looking at common source amplifiers with source degeneration also common Emitter amplifiers with emitter degeneration We will then star Branic ky is the complemen of language esNo uring mac hine aka total TM decider complete algorithm es uring mac hine aka accepting TM recognizer semialgorithm Three Classes of Languages 1 The cursive or de cidable languages for whic there is esNo TM 1. x. kcd.com. EECS 370 Discussion. Topics Today:. Function Calls. Caller / . Callee. Saved . Registers. Call Stack. Memory Layout. Stack, Heap, Static, Text. Object Files. Symbol and Relocation Tables. 1. xkcd. EECS 370 Discussion. Exam 2. High: 97 Low: 10 Average 60.4. 2. EECS 370 Discussion. Roadmap to end of semester. Project 4 – Friday . 12/6 (Due tonight at 11:59 w/ 3 slip days). Homework 7 – Tuesday 12/7 (Tomorrow). Deep Packet Inspection. Artyom. . Churilin. Tallinn University of Technology 2011. Web filtering & DPI. Web filtering (content control) . is a way control . what content is permitted to a . user. . GraphG prof 1 prof 2 prof 3 prof 4 phd 5 stud 6 stud 7 adv adv adv adv adv sup sup GraphI 1 prof 2,3 prof 4 prof 5 phd 6,7 stud adv adv adv sup 1Fortheformaldevelopmentinthispaper,itwillbeconve-nientt GraphG prof 1 prof 2 prof 3 prof 4 phd 5 stud 6 stud 7 adv adv adv adv adv sup sup GraphI 1 prof 2,3 prof 4 prof 5 phd 6,7 stud adv adv adv sup 1Fortheformaldevelopmentinthispaper,itwillbeconve-nientt Mosharaf Chowdhury. EECS 582 – W16. 1. Stats on the 18 Reviewers. EECS 582 – W16. 2. Stats on the . 21 Papers . We’ve Reviewed. EECS 582 – W16. 3. Stats on the 21 Papers We’ve Reviewed. EECS 582 – W16. Processing The PARIS File. Deuces Wild. FILTERING OPTIONS FOR YOUR PARIS FILE. Stephen Bach, New York State Office of Temporary and Disability Services, Bureau of Program Integrity. Mark Zaleha, Ohio Department of Job and Family Services, Bureau of Program Integrity. Fouhey. .. Let’s Take An Image. Let’s Fix Things. Slide Credit: D. Lowe. We have noise in our image. Let’s replace each pixel with a . weighted. average of its neighborhood. Weights are . filter kernel. Yuci Chenh300 S Westnedge Avenue KalamazooMI 49007Telephone 213610-3570 EmailchenupjohnorgWebsite sitesgooglecom/view/yuci-chen/homeEducation Department ofEconomicsUniversity of Illinoisat Urbana- Henning Lange, Mario . Bergés. , Zico Kolter. Variational Filtering. Statistical Inference. (Expectation Maximization, Variational Inference). Deep Learning. Dynamical Systems. Variational Filtering.