Advanced Topics in Communications Electronics

Vortragende/r (Mitwirkende/r)
Nummer0820675069
Art
Umfang3 SWS
SemesterSommersemester 2022
UnterrichtsspracheEnglisch
Stellung in StudienplänenSiehe TUMonline

Teilnahmekriterien

Beschreibung

The Concepts and Applications of the Artificial Intelligence of Things (AIoT) The Artificial Intelligence of Things (AIoT) is the convergence of Artificial Intelligence (AI) technologies and the Internet-of-Things (IoT) infrastructure to build intelligent applications with a connected network of devices. Some major AIoT application domains include smart wearables to continuously monitor users’ health and fitness, smart homes to provide a comfortable and personalized living space, smart cities to intergrade municipal services for the convenience of its citizens, and smart industries to optimize manufacturing operations, logistics and supply chain. AIoT has already transformed (will continue to transform) how we interact with our surroundings. This course will provide an in-depth coverage on existing and emerging AIoT application domains and the related supporting technologies, such as IoT programming, machine learning and deep neural networks, GPU and FPGA for deep learning acceleration, and various computing systems that facilitate the rapid realization and growth of AIoT. Detailed topics include definition and characteristics of AIoT; IoT enabling technologies; smart domains and applications; IoT systems; IoT design methodology; machine learning and deep learning; embedded GPU and FPGA for AIoT; IoT servers and cloud; data analytics for IoT; cognitive computing; cognitive systems design; cognitive application workload; IoT security; hands-on learning experience to build AIoT systems through the Node-RED framework; and various case studies such as smart city, smart home and IoT for healthcare. This course will be a project-driven course. Throughout this course, students will build an end-to-end AIoT solution.

Inhaltliche Voraussetzungen

Medium level of programming skills in one of the primary languages such as Python and C. Basic experience in cloud computing. A passion for learning new things.

Studien-, Prüfungsleistung

In class presentation 10% Homework 30%: A total of 3 homework assignments; each worth 10% Midterm 20% Final project 40% Late Submission: • All homework assignments are due in the class on the due date at the beginning of the lecture. No late acceptance. • In the event of unexpected illness, job interviews, and family matters, students can contact the teaching staff with requests for extensions on assignments. For general conflicts, such as interviews, you must request an extension at least 48 hours before the assignment due date. For unexpected matters, such as sudden illness, shorter notice is acceptable with documentation (IE: a doctor’s note).

Empfohlene Literatur

Notes and slides from the instructors.

Links


Vollständiges Lehrangebot

Bachelorbereich: BSc-EI, MSE, BSEEIT

 

WS

SS

Diskrete Mathematik für Ingenieure (BSEI, EI00460)

Discrete Mathematics for Engineers (BSEEIT) (Schlichtmann) (Januar)

 

P/P

WS

SS

Entwurf digitaler Systeme mit VHDL u. System C (BSEI, EI0690) (Ecker)

P/m

 

SS

Entwurfsverfahren für integrierte Schaltungen (MSE, EI43811) (Schlichtmann)

P/P

WS

 

Methoden der Unternehmensführung (BSEI, EI0481) (Weigel)

-/P

WS

 

Praktikum System- und Schaltungstechnik (BSEI, EI0664) (Schlichtmann et al.)

--

 

SS

Schaltungssimulation (BSEI, EI06691) (Gräb/Schlichtmann)

P/P

 

Masterbereich: MSc-EI, MSCE, ICD

 

SS

Advanced Topics in Communication Electronics (MSCE, MSEI, EI79002)

 

WS

 

Electronic Design Automation (MSCE, MSEI, EI70610) (B. Li, Tseng)

-/?

WS

 

Design Methodology and Automation (ICD) (Schlichtmann) (Nov)

--

WS

SS

Machine Learning: Methods and Tools (MSCE, MSEI, EI71040) (Ecker)

O/O

WS

SS

SS

Mathematical Methods of Circuit Design (MSCE, MSEI, EI74042) (Gräb)

Simulation and Optimization of Analog Circuits (ICD) (Gräb) (Mai)

P/P

--

WS

 

Mixed Integer Programming and Graph Algorithms in Engineering Problems (MSCE, MSEI, EI71059) (Tseng)

-/Om

WS

SS

Numerische Methoden der Elektrotechnik (MSEI, EI70440) (Schlichtmann oder Gräb)

P/P

WS

WS

SS

Seminar VLSI-Entwurfsverfahren (MSEI, EI7750) (Schlichtmann/Müller-Gritschneder)

Seminar on Topics in Electronic Design Automation (MSCE, EI77502) (Schlichtmann/Müller-Gritschneder)

P/P

P/P

WS

SS

Synthesis of Digital Systems (MSCE, MSEI, EI70640) (Müller-Gritschneder)

P/P

WS

 

Testing Digital Circuits (MSCE, MSEI, EI50141) (Otterstedt)

-/?

WS

 

Timing of Digital Circuits (MSCE, MSEI, EI70550) (B. Li, Zhang)

-/O

WS

SS

VHDL System Design Laboratory (MSCE, MSEI, EI7403) (Schlichtmann)

O/O

 

Die Spalte ganz rechts bezeichnet die Form der Vorlesung/Prüfung im SS 2022. O=online, P=physische Präsenz, m=mündlich, Präsenz oder online. Version: 08.02.2022

The column on the very right denotes the type of course/exam in SS 2022. O=online, P=physical presence, m=oral, presence or online. Version: February 8, 2022

 

MSE: Munich School of Engineering (TUM)

BSEEIT: Bachelor in Electrical Engineering and Information Technology (TUM-Asia)

ICD: Master of Science in Integrated Circuit Design (TUM-Asia)

MSCE: Master of Science in Communications Engineering (TUM)

MSEI: Master of Science in Elektrotechnik und Informationstechnik

BSEI: Bachelor of Science in Elektrotechnik und Informationstechnik

 

Aktuelle Infos zur Lehre/Current information on teaching: https://www.tum.de/die-tum/aktuelles/coronavirus/studium/, www.ei.tum.de