Contents: 

Introduction to Functional Programming (Haskell): 

• Syntax (Backus-Naur Form) 
• primitive data types, lists, tuples, strings 
• expressions, function definitions, recursion and iteration 
  • evaluation strategies of functional programs
  • higher-order functions
  • polymorphism 
  • type system and verification 
• Algebraic and abstract data types 
• input, output and Monads
• searching and sorting algorithms 

Introduction of computability: 

• Lambda calculus 
• Primitive Recursion 
• μ-Recursion 

Proofs of program properties: 

• Term Rewriting 
• Structural induction 
• Termination 

Implementation and programming technique: 

• Modular program design

• Simon Thompson: Haskell: The Craft of Functional Programming, 2nd Edition, Addison-Wesley, 1999
• Graham Hutton: Programming in Haskell, Cambridge University Press, 2007
• Bird, R./Wadler, Ph.: Einführung in Funktionale Programmierung, Hanser Verlag, 1982
• Hans Hermes: Aufzählbarkeit, Entscheidbarkeit, Berechenbarkeit, Springer-Verlag 1978

For the tutorial please refer to 19300004

Tutorien finden erst ab der 2. Vorlesungswoche statt

• Analysis of sorting algorithms: Mergesort, Quicksort, etc.
• ADTs priority queue and dictionary and corresponding data structures: heaps, hashing, binary search trees, B-trees, etc. 
• Graph algorithms: breadth first search and depth first search, topolocigal sorting, minimum spanning trees, shortest paths.
• Analysis on Strings.
• Memory-management.
• Algorithm design: divide-and-conquer, greedy algorithms, dynamic programming.
• Mathematical analysis of algorithms: running time, space.

• P. Morin: Open Data Structures, an open content textboox.
• T. H. Cormen, C. Leiserson, R. Rivest, C. Stein: Introduction to Algorithms, MIT Press, 2009.
• R. Sedgewick: Algorithms in Java (Part 1–5), Addison-Wesley, 2003.
• G. Saake, S. Sattler: Algorithmen und Datenstrukturen, dpunkt.verlag, 2013.
• M. Dietzfelbinger, K. Mehlhorn, P. Sanders. Algorithmen und Datenstrukturen: Die Grundwerkzeuge, Springer, 2014.
• M.T. Goodrich, R. Tamassia: Data Structures and Algorithms in Java, Wiley, 2014.

The module Fundamentals of Computer Engineering forms the basis for understanding the functioning of real computer systems.

Starting from logic, this module primarily covers the subject areas of switching networks and switching mechanisms, logic minimization, gates, flip-flops, memory, automatic machines and simple hardware design.

Furthermore, basic knowledge of DC and AC networks, semiconductors, transistors, CMOS, operational amplifiers, A/D and D/A converters is taught, as far as it is necessary for computer science.

In the corresponding practical course, the knowledge gained in the lecture and in the module Computer Architecture, Operating and Communication Systems are deepened with numerous practical experiments.

Based on a simple hardware platform with processor and various interfaces, the participants will learn how to program elementary drivers, extend operating system routines and control the interfaces.

Subsequently, the systems should be networked and be able to interact with their environment.

Siehe Unterlagen auf der Webseite - nur aus dem FB-Netz oder über VPN Webseite
 

See documentation on the website - only from the FB network or via VPN Webseite

The module Computer Architecture covers basic concepts of computer systems. Topics are von-Neuman/Harvard architecture, microarchitectures, RISC/CISC, micro programming, pipelining, caches, memory hierarchy, bus systems, assembler programming, multi processor systems, branch prediction, representation of numbers and other data types, computer arithmetic.

• Andrew S. Tannenbaum: Computerarchitektur, 5.Auflage, Pearson Studium, 2006
• English: Andrew S. Tanenbaum (with contributions from James R. Goodman):
• Structured Computer Organization, 4th Ed., Prentice Hall International, 2005.

Operating systems tie together the execution of applications, user experience and usability with the management of computer hardware. Starting with the tasks an operating system has to perform and the requirements it has to meet, the most important aspects of design and development of modern operating systems will be introduced:

• Structure and design of an operating system including historical summary, structures and philosophies of OS design and resources and resource management
• Threads and processes including thread management
• Scheduling including real-time scheduling
• Process interaction and inter-process communication
• Resource management including device operation, driver development, management and operation of input- and output devices
• Memory management including address spaces and virtual memory
• File system including management and operation of discs and memory hierarchy
• Distributed operating systems including distributed architectures for resource management
• Performance evaluation and modeling including overload detection and handling

Modern operating systems provide examples for different aspects and current research will be introduced. The tutorials serve to reflect the topics dealt with in the lecture and to acquire experience by developing a small operating system.

• A.S. Tanenbaum: Modern Operating Systems, 2nd Ed. Prentice-Hall, 2001
• A. Silberschatz et al.: Operating Systems Concepts with Java, 6th Ed. Wiley, 2004

The overwhelming majority of future computer systems will be characterized by communicating, embedded systems. These are found in machine controls, household appliances, motor vehicles, airplanes, intelligent buildings, etc. and will in future be increasingly integrated into networks such as the Internet.

The internship will address the architecture of embedded systems and demonstrate the differences to traditional PC architectures (e.g., real-time capability, interaction with the environment) with practical examples. The internship is based on 16- and 32-bit microcontroller systems.

The main focus of the internship is the following:

    register structures
    memory organization
    Hardware assembler and high-language programming
    I / O system and timer programming
    Interrupt system
    Watchdog logic
    Analog interface
    Bus system connection of components
    Communication (serial, CAN bus, Ethernet, radio and USB)
    Control of models and use of different sensors

• Brian W. Kernighan, Dennis M. Ritchie: The C Programming Language, Second Edition, Prentice Hall, 1988.

See documents on the website - only from the FB network or via VPN website

Group A, Wednesday, 12-16 h Takustraße 9, Room K63
Group B, Thursday, 12-16 h Takustraße 9, Room K63

This course addresses communication asp. The lecture addresses topics such as:

• Basic background: protocls, services, models, communication standards;
• Principles of communication engineering: signals, coding, modulation, media;
• Data link layer: media access etc.;
• Local networks: IEEE-Standards, Ethernet, bridges;
• Network layer: routing and forwarding, Internet protocols (IPv4, IPv6);
• Transport layer: quality of service, flow control, congestion control, TCP;
• Internet: TCP/IP protocol suite;
• Applications: WWW, security, network management;
• New network concepts.

In the supplementary exercise course the students will practically apply their knowledge.

• Larry Peterson, Bruce S. Davie: Computernetze - Ein modernes Lehrbuch, dpunkt Verlag, Heidelberg, 2000
• Krüger, G., Reschke, D.: Lehr- und Übungsbuch Telematik, Fachbuchverlag Leipzig, 2000
• Kurose, J. F., Ross, K. W.: Computer Networking: A Top-Down Approach Featuring the Internet, Addi-son-Wesley Publishing Company, Wokingham, England, 2001
• Siegmund, G.: Technik der Netze, 4. Auflage, Hüthig Verlag, Heidelberg, 1999
• Halsall, F.: Data Communi-cations, Computer Networks and Open Systems 4. Auflage, Addison-Wesley Publishing Company, Wokingham, England, 1996
• Tanenbaum, A. S.: Computer Networks, 3. Auflage, Prentice Hall, Inc., New Jersey, 1996

Requirements: Basic understanding of computer networks, e.g., TI-III

We will study the history, technology and applications of cryptocurrencies and blockchain.

Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction, by Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder

We will study the history, technology and applications of cryptocurrencies and blockchain.

Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction, by Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder

Contents

The proseminar delves more deeply into topics covered in the basic classes taught by the theory group. During the winter semester, we consider advanced topics from the theory of computability and of formal languates (in continuation of "Theory of Computation"); during the summer semester, we talk about algorithms (in continuation of "Algorithms, Data Structures, and Data Abstraction").

Prerequisites

two semesters of computer science, successful completion of "Theory of Computation"

wird mit der Ankündigung bekannt gegeben

Contents

We choose a typical application area of algorithms, usually for geometric problems, and develop software solutions for it, e.g., computer graphics (representation of objects in a computer, projections, hidden edge and surface removal, lighting, raytracing), computer vision (image processing, filtering, projections, camera calibration, stereo-vision) or pattern recognition (classification, searching).

Prerequsitions

Basic knowledge in design and anaylsis of algorithms.

je nach Anwendungsgebiet

Subject of the project: either development of software together with a company (in this case: 4­ weeks fulltime August/September) or we build a so called NoSQL system. Decision in March. Further information are published in the KVV.

Wird bekannt gegeben. / To be announced.

Target group

Students in the Master's or Bachelor's programme

Prerequisites

Good programming skills, introduction to database systems.

Programming for Internet Communication Along the Stack

In this course, students will develop solutions that relate to Internet communication. We will consider all aspects of the Internet ecosystem, including lower layers up to the application layer for the current Internet as well as the Internet of Things. Participants will work in smaller groups (3-4 students), where each group will focus on a specific builing block. This will allow to adapt to interests and expertises (e.g., web vs. embedded system programming).

Note that we intend to use the results of this software project to revamp the old super computers in the entrance area of the CS building (i.e., the PDP 11 and the Russian clone). We will extend both computers by network-capabale devices, sensors, screens, and your software (a) to demonstrate the Internet of Things and (b) to give insights into the current state of the Internet backbone.

Below you find some initial topics, which we will clarify during the first meeting.

Embedded / IoT Programming

You will develop applications for embedded devices. The software base is the open source operating system RIOT.

Internet Backbone Measurements

You will develop applications that process live BGP data and check if an attack is happening. The software base is the RTRlib and BGPMON/BGPStream.

Web Interface

You will develop a web interfaces that shows the state of the super computers to remote participants. Your web frontend should be suitable for mobile devices.

If you have questions in advance, feel free to contact Matthias Wählisch.

• A. S. Tanenbaum, Modern Operating Systems, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall Press, 2007.
• Shelby, Zach, and Carsten Bormann. 6LoWPAN: The wireless embedded Internet. Vol. 43. Wiley. com, 2011.
• A. Dunkels, B. Gronvall, and T. Voigt, "Contiki - a lightweight and flexible operating system for tiny networked sensors." in LCN. IEEE Computer Society, 2004, pp. 455-462.
• P. Levis, S. Madden, J. Polastre, R. Szewczyk, K. Whitehouse, A. Woo, D. Gay, J. Hill, M. Welsh, E. Brewer, and D. Culler, "TinyOS: An Operating System for Sensor Networks," in Ambient Intelligence, W. Weber, J. M. Rabaey, and E. Aarts, Eds. Berlin/Heidelberg: Springer-Verlag, 2005, ch. 7, pp. 115-148.
• Oliver Hahm, Emmanuel Baccelli, Mesut Günes, Matthias Wählisch, Thomas C. Schmidt, "RIOT OS: Towards an OS for the Internet of Things," in Proceedings of the 32nd IEEE International Conference on Computer Communications (INFOCOM), Poster Session, April 2013.
• M.R. Palattella, N. Accettura, X. Vilajosana, T. Watteyne, L.A. Grieco, G. Boggia and M. Dohler, "Standardized Protocol Stack For The Internet Of (Important) Things", IEEE Communications Surveys and Tutorials, December 2012.
• J. Wiegelmann, Softwareentwicklung in C für Mikroprozessoren und Mikrocontroller, Hüthig, 2009

In this course you will be expected to write code. The outcome of your software project should be a concrete contribution to the RIOT code base, and take the shape of one or more pull request(s) to the RIOT github (https://github.com/RIOT-OS/RIOT). Before you start coding, refer to the starting guide

https://github.com/RIOT-OS/RIOT/wiki#wiki-start-the-riot

Mixed groups of master and bachelor students will either implement an independent project or are part of a larger project in the area of semantic technologies. They will gain in-depth programming knowledge about applications of semantic technologies and artificial intelligence techniques in the Corporate Semantic Web. They will practice teamwork and best practices in software development of large distributed systems and Semantic Web applications. The software project can be done in collaboration with an external partner from industry or standardization. It is possible to continue the project as bachelor or master thesis.

Corporate Semantic Web

Corporate Semantic Web

Further information can be found on the course website

In this course, we co-educate computer scientists and product designers. Beyond experiencing interdisciplinary work, we want students to envision interactive systems that are intelligent: by this, we mean an intelligence through code that is carefully using material, form, and context, while profoundly respecting both human capabilities and vulnerabilities.

We understand this course as experimental space, where different perspectives meet, exchange, and evolve. Each semester, based on small project teams of up to five members, students are challenged to examine a specific application context. Within this context, the teams envision a new application or product concepts.

We guide this process through various carefully tuned methods that are used to spark their ideas. Students iterate through several rounds of ideation and refine their concept in different prototype versions. The most compelling or promising interaction concept, the one that allows grasping the quality and essence of the product concept is implemented in a working prototype.

Students are accompanied by a team of experienced designers and computer scientists but also by guest experts that provide feedback to the various design iterations. If needed, special workshops are organized to cover specific topics ranging from prototyping to project management. The whole course is evaluated continuously to enhance our methodological toolbox.

This course offering is a cooperation of the research group Human-Centered Computing at the institute of computer science at the Freie Universität Berlin and the Product Design Department at the Weißensee Kunsthochschule Berlin (KHB).

Besides the regular weekly meetings, the KHB provides complimentary workshops each Monday from 10 AM to 1 PM where participation for computer science students is optionally.

Each term, we have a different topic. Please check out the HCC website for further information.

Literatur, Materialien und Geräte werden während der Veranstaltung gestellt.

Sophienstrasse 22a, 2.HH, 2.Stock, 10178 Berlin

www.interdisciplinary-laboratory.hu-berlin.de

In this software project we will carry out the entire process of designing, implementing, and evaluating mobile devices (mainly Android) with a focus on usable security. We will work in small groups.

Tasks:

1. Further development of existing authentication methods and adaptation to smartphones.

2. Development of new authentication methods (some ideas already exist but new ones can also be incorporated).

3. E- mail encryption on mobile devices.

Requirements:

1. Knowledge of programming on Android devices (Alternatively, for iOS). There will be a brief preliminary test.

2. Interest in Security and Usability.

3. Participation in the planning meeting (end of summer term 2016).

Secure software engineering joins two important fields: Software engineering and information security. software engineering is the systematic use of principles, methods and tools to develop and deploy software. information security covers topics like confidentiality, integrity and availability of informations and data.

- Claudia Eckert: IT-Sicherheit, 9. Auflage, De Gruyter Oldenbourg, 2014; - Ross Anderson: Security Engineering, 2. Auflage, Wiley, 2008. Weitere Literaturhinweise werden zu den einzelnen Themenblöcken bereitgestellt.

The goal of this lecture is to teach principles, methods and tools for the development of secure software applications. To this end, basic concepts are first introduced. This is followed by process models for developing secure software and evaluating the maturity of development processes. Along the phases or process groups of software development, central principles, methods and tools are then introduced and explained. Special attention is given to threat and risk analysis, security requirements, principles and patterns for designing secure software applications, secure and insecure software implementations, security tests and evaluation of the security properties of software applications.

Data compression is a technology, which only enables a variety of applications in our information age. Even though the underlying technology is often hidden from the end user, we use data compression every day when we hear music, watch images and videos, or use applications on our smartphone.

In this course, the fundamental and most often used approaches for data compression are introduced.  We discuss theoretical foundations as well as methods used in practice.

The first part of the course deals with lossless compression, in which the original data can be reconstructed exactly. This part includes the following topics:

• Unique decodability and prefix codes
• Entropy and entropy rate as theoretical limits of lossless compression
• Optimal codes, Huffman codes
• Arithmetic coding
• Lempel-Ziv coding
• Linear prediction
• Examples from text, image and audio compression

In the second part of the course, we consider lossy compression, by which only an approximation of the original data can be reconstructed. This type of compression enables much higher compression rates and is the dominant form of compression for audio, image and video data. The second part of the course includes the following topics:

• Scalar quantization, optimal scalar quantization
• Theoretical limits of lossy compression: Rate distortion functions
• Vector quantization
• Predictive quantization
• Transform coding
• Examples from audio, image, and video compression

• Sayood, K. (2018), “Introduction to Data Compression,” Morgan Kaufmann, Cambridge, MA.
• Cover, T. M. and Thomas, J. A. (2006), “Elements of Information Theory,” John Wiley & Sons, New York.
• Gersho, A. and Gray, R. M. (1992), “Vector Quantization and Signal Compression,” Kluwer Academic Publishers, Boston, Dordrecht, London.
• Jayant, N. S. and Noll, P. (1994), “Digital Coding of Waveforms,” Prentice-Hall, Englewood Cliffs, NJ, USA.
• Wiegand, T. and Schwarz, H. (2010), “Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, vol. 4, no. 1-2.

siehe Vorlesungsbeschreibung / see course description

As the use of data in research, business and politics become increasingly important, well-designed data visualizations are needed to improve understanding, reduce human memory and support decision-making.

This course aims to familiarize students with the principles, techniques, and algorithms of data visualization.
This course teaches students the basics of the current state of data visualization. At the end of the course, students will have an understanding of the following:

1. essential visualization techniques and theory, including data models, graphical perception and methods for visual coding and interaction.
2. basic techniques and algorithms for visualizing data, including multivariate data, networks, and maps.
3. practical experience in the creation and evaluation of visualizations.

The course calls for students who are interested in using data visualization in their work as well as students who develop visualization tools and systems. Basic knowledge and willingness to learn of graphics/visualization tools (e.g., D3) and data analysis tools (e.g., R) are helpful.

In addition to participating in discussions in class, students must complete several short programming and data analysis tasks as well as a final project. Students are expected to submit the results of the project in the form of a conference paper.

Please note that the course does not include exploratory approaches to the discovery of data. Instead, the course focuses on how data is visually encoded and presented to an audience after the structure of the data and its content is known.

Interactive Data Visualization for the Web, 2nd Edition. Scott Murray, O'Reilly Press. 2017. 

Yau, Nathan: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley Publishing, Inc. 2011.

Spence, Robert: Information Visualization: Design for Interaction. Pearson. 2007.

Goal:

After this course the students

• have a deep understanding of design principles of security protocols and security infrastructures.
• have knowledge of the basic security goals in cryptography and its relevance to practical use cases.
• understand, in which way well-known security protocols (TLS, PACE, EAC) achieve the security goals.
• understand the key topics of the wide-spread security infrastructure standards and apply them to practical tasks.
• are able to choose suitable protocols for a given use case.
• can evaluate the security properties of security protocols and infrastructures.

Content:   

• Security goals (CIA)
• Network security protocols (TLS)
• Security protocols for electronic ID cards
• Abstract Syntax Notation 1 (ASN.1)
• Certificates and related standards X.509/RFC5280
• Public Key Cryptography Standard Series
• Certificate-based security infrastructures (PKI)
• Practical and theoretical solutions to exercises

We will study the history, technology and applications of cryptocurrencies and blockchain.

Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction, by Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder

Secure software engineering joins two important fields: Software engineering and information security. software engineering is the systematic use of principles, methods and tools to develop and deploy software. information security covers topics like confidentiality, integrity and availability of informations and data.

- Claudia Eckert: IT-Sicherheit, 9. Auflage, De Gruyter Oldenbourg, 2014; - Ross Anderson: Security Engineering, 2. Auflage, Wiley, 2008. Weitere Literaturhinweise werden zu den einzelnen Themenblöcken bereitgestellt.

The goal of this lecture is to teach principles, methods and tools for the development of secure software applications. To this end, basic concepts are first introduced. This is followed by process models for developing secure software and evaluating the maturity of development processes. Along the phases or process groups of software development, central principles, methods and tools are then introduced and explained. Special attention is given to threat and risk analysis, security requirements, principles and patterns for designing secure software applications, secure and insecure software implementations, security tests and evaluation of the security properties of software applications.

Data compression is a technology, which only enables a variety of applications in our information age. Even though the underlying technology is often hidden from the end user, we use data compression every day when we hear music, watch images and videos, or use applications on our smartphone.

In this course, the fundamental and most often used approaches for data compression are introduced.  We discuss theoretical foundations as well as methods used in practice.

The first part of the course deals with lossless compression, in which the original data can be reconstructed exactly. This part includes the following topics:

• Unique decodability and prefix codes
• Entropy and entropy rate as theoretical limits of lossless compression
• Optimal codes, Huffman codes
• Arithmetic coding
• Lempel-Ziv coding
• Linear prediction
• Examples from text, image and audio compression

In the second part of the course, we consider lossy compression, by which only an approximation of the original data can be reconstructed. This type of compression enables much higher compression rates and is the dominant form of compression for audio, image and video data. The second part of the course includes the following topics:

• Scalar quantization, optimal scalar quantization
• Theoretical limits of lossy compression: Rate distortion functions
• Vector quantization
• Predictive quantization
• Transform coding
• Examples from audio, image, and video compression

• Sayood, K. (2018), “Introduction to Data Compression,” Morgan Kaufmann, Cambridge, MA.
• Cover, T. M. and Thomas, J. A. (2006), “Elements of Information Theory,” John Wiley & Sons, New York.
• Gersho, A. and Gray, R. M. (1992), “Vector Quantization and Signal Compression,” Kluwer Academic Publishers, Boston, Dordrecht, London.
• Jayant, N. S. and Noll, P. (1994), “Digital Coding of Waveforms,” Prentice-Hall, Englewood Cliffs, NJ, USA.
• Wiegand, T. and Schwarz, H. (2010), “Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, vol. 4, no. 1-2.

siehe Vorlesungsbeschreibung / see course description

As the use of data in research, business and politics become increasingly important, well-designed data visualizations are needed to improve understanding, reduce human memory and support decision-making.

This course aims to familiarize students with the principles, techniques, and algorithms of data visualization.
This course teaches students the basics of the current state of data visualization. At the end of the course, students will have an understanding of the following:

1. essential visualization techniques and theory, including data models, graphical perception and methods for visual coding and interaction.
2. basic techniques and algorithms for visualizing data, including multivariate data, networks, and maps.
3. practical experience in the creation and evaluation of visualizations.

The course calls for students who are interested in using data visualization in their work as well as students who develop visualization tools and systems. Basic knowledge and willingness to learn of graphics/visualization tools (e.g., D3) and data analysis tools (e.g., R) are helpful.

In addition to participating in discussions in class, students must complete several short programming and data analysis tasks as well as a final project. Students are expected to submit the results of the project in the form of a conference paper.

Please note that the course does not include exploratory approaches to the discovery of data. Instead, the course focuses on how data is visually encoded and presented to an audience after the structure of the data and its content is known.

Interactive Data Visualization for the Web, 2nd Edition. Scott Murray, O'Reilly Press. 2017. 

Yau, Nathan: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley Publishing, Inc. 2011.

Spence, Robert: Information Visualization: Design for Interaction. Pearson. 2007.

Goal:

After this course the students

• have a deep understanding of design principles of security protocols and security infrastructures.
• have knowledge of the basic security goals in cryptography and its relevance to practical use cases.
• understand, in which way well-known security protocols (TLS, PACE, EAC) achieve the security goals.
• understand the key topics of the wide-spread security infrastructure standards and apply them to practical tasks.
• are able to choose suitable protocols for a given use case.
• can evaluate the security properties of security protocols and infrastructures.

Content:   

• Security goals (CIA)
• Network security protocols (TLS)
• Security protocols for electronic ID cards
• Abstract Syntax Notation 1 (ASN.1)
• Certificates and related standards X.509/RFC5280
• Public Key Cryptography Standard Series
• Certificate-based security infrastructures (PKI)
• Practical and theoretical solutions to exercises

We will study the history, technology and applications of cryptocurrencies and blockchain.

Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction, by Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder

Secure software engineering joins two important fields: Software engineering and information security. software engineering is the systematic use of principles, methods and tools to develop and deploy software. information security covers topics like confidentiality, integrity and availability of informations and data.

- Claudia Eckert: IT-Sicherheit, 9. Auflage, De Gruyter Oldenbourg, 2014; - Ross Anderson: Security Engineering, 2. Auflage, Wiley, 2008. Weitere Literaturhinweise werden zu den einzelnen Themenblöcken bereitgestellt.

The goal of this lecture is to teach principles, methods and tools for the development of secure software applications. To this end, basic concepts are first introduced. This is followed by process models for developing secure software and evaluating the maturity of development processes. Along the phases or process groups of software development, central principles, methods and tools are then introduced and explained. Special attention is given to threat and risk analysis, security requirements, principles and patterns for designing secure software applications, secure and insecure software implementations, security tests and evaluation of the security properties of software applications.

The class focuses on topics such as

• general principles of algorithm design,
• network flows,
• number-theoretic algorithms (including the RSA crypto system),
• string matching,
• NP-completeness,
• approximation algorithms for hard problems,
• arithmetic algorithms and circuits, fast fourier transform.

• Cormen, Leiserson, Rivest, Stein: Introduction to Algorithms, 2nd Ed. McGraw-Hill 2001
• Kleinberg, Tardos: Algorithm Design Addison-Wesley 2005.

Target audience

All Master and Bachelor students who are interested in algorithms.

Prerequisites

Basic familiarity with the design and analysis of algorithms.

The lecture aims at a deeper understanding of cryptographic algorithms, especially which design criteria have to be considered for the development of secure encryption algorithms. For that purpose we will get to know and evaluate different cryptanalytic methods for symmetrical and asymmetrical encryption techniques – e.g. linear and differential cryptanalysis on block ciphers, correlation attacks on stream ciphers and algorithms to solve the factorization problem and the discrete logarithm problem. Weaknesses in the implementation, e.g. to exploit side-channel attacks, will be discussed only peripherally.

We talk about approximation algorithms.

Wird noch bekannt gegeben.

Target Audience

Masters students in Computer Science or Mathematics, advanced Bachelor students.

Prerequisites

"Advanced Algorithms" or a similar class

Course objectives 

Security is a prerequisite of modern wireless communication systems and user services. In this course we will introduce a new security paradigm for communication systems called physical layer security (PLS). PLS is an advanced method envisioned to meet the advanced requirements of future communication systems such as low latency ('fast security') and 'plug & secure' functionality. PLS is based on a well-defined mathematical theory exploiting the inherent properties of the wireless channel such as noise and fading. The goal of the course is to present the information-theoretic foundations of PLS including the definition of secrecy capacity definitions for the point-to-point wireless channel with an eavesdropper ('wiretap channel') as well as secret key agreement from stochastic channel measurements.

The course will also take on the attackers view and address potential attacks. Finally, several potential deployment scenarios such as the Internet of Things will be discussed.

Course Content

• Overview of security schemes in communication systems: layer model, asymmetric/symmetric encryption, PLS classifications
• Wiretap channel: basic definitions and assumptions, secrecy capacity, strong/weak secrecy, coding methods, MIMO channel, multiuser aspects
• Secret key agreement: source models, key generation, common randomness, reciprocal wireless channel, correlation properties
• Attacker models: passive/active, 'man in the middle', jamming
• Deployment scenarios: lightweight security, wormhole detection

Matthieu Bloch and Joao Barros: Physical Layer Security: From Information Theory to Security Engineering. Cambridge University Press, 2011

We talk about approximation algorithms.

Wird noch bekannt gegeben.

Target Audience

Masters students in Computer Science or Mathematics, advanced Bachelor students.

Prerequisites

"Advanced Algorithms" or a similar class

Course objectives 

Security is a prerequisite of modern wireless communication systems and user services. In this course we will introduce a new security paradigm for communication systems called physical layer security (PLS). PLS is an advanced method envisioned to meet the advanced requirements of future communication systems such as low latency ('fast security') and 'plug & secure' functionality. PLS is based on a well-defined mathematical theory exploiting the inherent properties of the wireless channel such as noise and fading. The goal of the course is to present the information-theoretic foundations of PLS including the definition of secrecy capacity definitions for the point-to-point wireless channel with an eavesdropper ('wiretap channel') as well as secret key agreement from stochastic channel measurements.

The course will also take on the attackers view and address potential attacks. Finally, several potential deployment scenarios such as the Internet of Things will be discussed.

Course Content

• Overview of security schemes in communication systems: layer model, asymmetric/symmetric encryption, PLS classifications
• Wiretap channel: basic definitions and assumptions, secrecy capacity, strong/weak secrecy, coding methods, MIMO channel, multiuser aspects
• Secret key agreement: source models, key generation, common randomness, reciprocal wireless channel, correlation properties
• Attacker models: passive/active, 'man in the middle', jamming
• Deployment scenarios: lightweight security, wormhole detection

Matthieu Bloch and Joao Barros: Physical Layer Security: From Information Theory to Security Engineering. Cambridge University Press, 2011

The lecture aims at a deeper understanding of cryptographic algorithms, especially which design criteria have to be considered for the development of secure encryption algorithms. For that purpose we will get to know and evaluate different cryptanalytic methods for symmetrical and asymmetrical encryption techniques – e.g. linear and differential cryptanalysis on block ciphers, correlation attacks on stream ciphers and algorithms to solve the factorization problem and the discrete logarithm problem. Weaknesses in the implementation, e.g. to exploit side-channel attacks, will be discussed only peripherally.

Data compression is a technology, which only enables a variety of applications in our information age. Even though the underlying technology is often hidden from the end user, we use data compression every day when we hear music, watch images and videos, or use applications on our smartphone.

In this course, the fundamental and most often used approaches for data compression are introduced.  We discuss theoretical foundations as well as methods used in practice.

The first part of the course deals with lossless compression, in which the original data can be reconstructed exactly. This part includes the following topics:

• Unique decodability and prefix codes
• Entropy and entropy rate as theoretical limits of lossless compression
• Optimal codes, Huffman codes
• Arithmetic coding
• Lempel-Ziv coding
• Linear prediction
• Examples from text, image and audio compression

In the second part of the course, we consider lossy compression, by which only an approximation of the original data can be reconstructed. This type of compression enables much higher compression rates and is the dominant form of compression for audio, image and video data. The second part of the course includes the following topics:

• Scalar quantization, optimal scalar quantization
• Theoretical limits of lossy compression: Rate distortion functions
• Vector quantization
• Predictive quantization
• Transform coding
• Examples from audio, image, and video compression

• Sayood, K. (2018), “Introduction to Data Compression,” Morgan Kaufmann, Cambridge, MA.
• Cover, T. M. and Thomas, J. A. (2006), “Elements of Information Theory,” John Wiley & Sons, New York.
• Gersho, A. and Gray, R. M. (1992), “Vector Quantization and Signal Compression,” Kluwer Academic Publishers, Boston, Dordrecht, London.
• Jayant, N. S. and Noll, P. (1994), “Digital Coding of Waveforms,” Prentice-Hall, Englewood Cliffs, NJ, USA.
• Wiegand, T. and Schwarz, H. (2010), “Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, vol. 4, no. 1-2.

siehe Vorlesungsbeschreibung / see course description

This course gives an introduction to cryptography and cryptographic key management, as well as an introduction to cryptographic protocols and their application in the field of security in distributed systems. Relevant mathematical tools will be developed accordingly. In addition, the lecture addresses the importance of implementation details in the context of IT system security.

• Jonathan Katz and Yehuda Lindell, Introduction to Modern Cryptography, 2008
• Lindsay N. Childs, A Concrete Introduction to Higher Algebra. Springer Verlag, 1995.
• Johannes Buchmann, Einfuehrung in die Kryptographie. Springer Verlag, 1999.

Weitere noch zu bestimmende Literatur und Primärquellen.

Requirements: Participants must have a good mathematical understanding and good knowledge of computer security and networking.

• propositional logic and mathematical proof techniques
• Boolean formulae und Boolean functions, disjunctive and conjunctive normal form, satisfiability, resolution calculus
• set theory:  sets, relations, equivalence and order relations, functions
• natural numbers, the induction principle, cardinal numbers
• first-order logic und mathematical structures
• combinatorics: counting techniques, binomial coefficents and Stirling numbers, recurrences, pigeon hole principle
• graph theory: graphs and their representations, paths and cycles in graphs, trees

• Christoph Meinel, Martin Mundhenk: Mathematische Grundlagen der Informatik, Teubner; 2. Auflage 2002
• Uwe Schöning: Logik für Informatiker, B.I.-Wissenschaftsverlag; 5. Auflage 2000
• Kenneth H. Rosen: Discrete Mathematics and its Applications, Mc-Graw Hill; 1999
• M. Aigner: Diskrete Mathematk, Vieweg, 5. Auflage 2004

1. Grundlagen, Elementare Logik, Geordnete Paare, Relationen, Funktionen, Definitionsbereich und Wertebereich einer Funktion. Umkehrfunktion (Injektivität, Surjektivität).
2. Zahlen. Vollständige Induktion. Rechnen in R, C.
3. Anordnung von R. Maximum und Minimum, Supremum und Infimum reeller Mengen. Supremums/Infimums-Vollständigkeit von R. Betrag einer reellen Zahl. Q ist dicht in R.
4. Folgen und Reihen. Grenzwerte, Cauchyfolgen. Konvergenzkriterien. Reihen und grundlegende Konvergenzprinzipien.
5. Topologische Aspekte von R. Offene, abgeschlossene und kompakte reelle Mengen.
6. Funktionenfolgen, Funktionenreihen, Potenzreihen.
7. Eigenschaften von Funktionen. Beschränktheit, Monotonie. Konvexität.
8. Stetigkeit. Grenzwerte und Stetigkeit von Funktionen. Gleichmäßige Stetigkeit. Zwischenwertsätze. Stetigkeit und Kompaktheit.
9. Differenzierbarkeit. Begriff der Ableitung. Differentiationsregeln. Mittelwertsätze. Lokale und globale Extrema. Krümmung. Monotonie. Konvexität.
10. Elementare Funktionen. Rationale Funktionen. Wurzelfunktionen. Exponentialfunktionen. Winkelfunktionen, Hyperbolische Funktionen. Reeller Logarithmus. Reelle Arcus-Funktionen. Kurvendiskussionen.
11. Anfänge der Integralrechnung

• Spivak: Calculus
• Abott: Understanding Analysis
• Forster: Analysis I
• Hildebrandt: Analysis I

Content:

• Basic terms/concepts: sets, maps, equivalence relations, groups, rings,
• fields
• Linear equation systems: solvability criteria, Gauss algorithm
• Vector spaces: linear independence, generating systems and bases, dimension,
• subspaces, quotient spaces, cross products in R3
• Linear maps: image and rank, relationship to matrices, behaviour under
• change of basis
• Dual vector spaces: multilinear forms, alternating and symmetric bilinear
• forms, relationship to matices, change of basis
• Determinants: Cramer's rule, Eigenvalues and Eigenvectors

Prerequisites:

Participation in the preparatory course (Brückenkurs) is highly recommended.

Literatur:
Siegfried Bosch, Lineare Algebra, 4. Auflage, Springer-Verlag, 2008;
Tammo tom Dieck, Lineare Algebra, Skript, Universität Göttingen Version vom 7.10.2014

Contents:

• number systems: from natural numbers to real numbers, completeness property of the reals
• polynomials: roots of polynomials, polynomial interpolation, rational functions
• special functions:  exponential function, logarithm, trigonometric functions
• complex numbers: exponential function for complex numbers, complex roots
• convergence of sequences and series, convergence of functions, continuous functions, O-notation
• differential calculus: derivative of a function,  interpretations and applications of the derivative
• intergral calculus: primitive functions, definite integrals,  fundamental theorem of calculus, applications
• power series
• basics of stochastics: probability spaces, discrete and continuous random variables, expected value and variance of random variables

• Kurt Meyberg, Peter Vachenauer: Höhere Mathematik 1, Springer-Verlag, 6. Auflage 2001
• Dirk Hachenberger: Mathematik für Informatiker, Pearson 2005
• Peter Hartmann: Mathematik für Informatiker, Vieweg, 4. Auflage 2006
• Thomas Westermann: Mathematik für Ingenieure mit Maple 1, Springer-Verlag, 4. Auflage 2005

The sign-up for the tutorial sessions will be announced in due time.

1. Grundlagen, Elementare Logik, Geordnete Paare, Relationen, Funktionen, Definitionsbereich und Wertebereich einer Funktion. Umkehrfunktion (Injektivität, Surjektivität).
2. Zahlen. Vollständige Induktion. Rechnen in R, C.
3. Anordnung von R. Maximum und Minimum, Supremum und Infimum reeller Mengen. Supremums/Infimums-Vollständigkeit von R. Betrag einer reellen Zahl. Q ist dicht in R.
4. Folgen und Reihen. Grenzwerte, Cauchyfolgen. Konvergenzkriterien. Reihen und grundlegende Konvergenzprinzipien.
5. Topologische Aspekte von R. Offene, abgeschlossene und kompakte reelle Mengen.
6. Funktionenfolgen, Funktionenreihen, Potenzreihen.
7. Eigenschaften von Funktionen. Beschränktheit, Monotonie. Konvexität.
8. Stetigkeit. Grenzwerte und Stetigkeit von Funktionen. Gleichmäßige Stetigkeit. Zwischenwertsätze. Stetigkeit und Kompaktheit.
9. Differenzierbarkeit. Begriff der Ableitung. Differentiationsregeln. Mittelwertsätze. Lokale und globale Extrema. Krümmung. Monotonie. Konvexität.
10. Elementare Funktionen. Rationale Funktionen. Wurzelfunktionen. Exponentialfunktionen. Winkelfunktionen, Hyperbolische Funktionen. Reeller Logarithmus. Reelle Arcus-Funktionen. Kurvendiskussionen.
11. Anfänge der Integralrechnung

• Spivak: Calculus
• Abott: Understanding Analysis
• Forster: Analysis I
• Hildebrandt: Analysis I

Content:

• Basic terms/concepts: sets, maps, equivalence relations, groups, rings,
• fields
• Linear equation systems: solvability criteria, Gauss algorithm
• Vector spaces: linear independence, generating systems and bases, dimension,
• subspaces, quotient spaces, cross products in R3
• Linear maps: image and rank, relationship to matrices, behaviour under
• change of basis
• Dual vector spaces: multilinear forms, alternating and symmetric bilinear
• forms, relationship to matices, change of basis
• Determinants: Cramer's rule, Eigenvalues and Eigenvectors

Prerequisites:

Participation in the preparatory course (Brückenkurs) is highly recommended.

Literatur:
Siegfried Bosch, Lineare Algebra, 4. Auflage, Springer-Verlag, 2008;
Tammo tom Dieck, Lineare Algebra, Skript, Universität Göttingen Version vom 7.10.2014

Current topics from the area of medical imaging will be worked on and presented. Amongst these are applications from radiography, computer tomography, nuclear medicine, magnetic resonance tomography, and medical ultrasonics.

Website

http://www.charite.de/medinfo/Studium/wahllehre.php

Target Group

MSc students

Preconditions

Having visited the lecture and tutorial Medical Imaging

Advanced module data visualization and data mining.

Wird bekannt gegeben.

Preliminary discussion date to be announced.

Contents

Advanced topcis in algorithm design with a changing focus. The topic is determined newly in each semester. For example, we might consider algorithms for problems on graphs, such as connectivity, shortest paths, or network flows.

Target audience

Masters students in computer science and mathematics.

Recommended prerequisites

"Advanced algorithms" or a similar class.

Spezialliteratur aus Zeitschriften

Current topics from the area of medical imaging will be worked on and presented. Amongst these are applications from radiography, computer tomography, nuclear medicine, magnetic resonance tomography, and medical ultrasonics.

Website

http://www.charite.de/medinfo/Studium/wahllehre.php

Target Group

MSc students

Preconditions

Having visited the lecture and tutorial Medical Imaging

Advanced module data visualization and data mining.

Wird bekannt gegeben.

Preliminary discussion date to be announced.

Description:

This seminar covers different topics from the research area of autonomous cars. We will focus on questions which can be the basis for Bachelor or Master theses as:

vision based person or vehicle recognition, map generation from trajectories, V2V and V2X communication, object tracking and prediction, sensory data fusion, localization using map matching, recognition of drivable areas and free spaces using stereo vision, path planning with A*, RRT, potential fields, automatic sensor calibration, map data formats.

Students can propose interesting topics, too. The seminar will be taught as a block course.

Subject of the project: either development of software together with a company (in this case: 4­ weeks fulltime August/September) or we build a so called NoSQL system. Decision in March. Further information are published in the KVV.

Wird bekannt gegeben. / To be announced.

Target group

Students in the Master's or Bachelor's programme

Prerequisites

Good programming skills, introduction to database systems.

In this course, we co-educate computer scientists and product designers. Beyond experiencing interdisciplinary work, we want students to envision interactive systems that are intelligent: by this, we mean an intelligence through code that is carefully using material, form, and context, while profoundly respecting both human capabilities and vulnerabilities.

We understand this course as experimental space, where different perspectives meet, exchange, and evolve. Each semester, based on small project teams of up to five members, students are challenged to examine a specific application context. Within this context, the teams envision a new application or product concepts.

We guide this process through various carefully tuned methods that are used to spark their ideas. Students iterate through several rounds of ideation and refine their concept in different prototype versions. The most compelling or promising interaction concept, the one that allows grasping the quality and essence of the product concept is implemented in a working prototype.

Students are accompanied by a team of experienced designers and computer scientists but also by guest experts that provide feedback to the various design iterations. If needed, special workshops are organized to cover specific topics ranging from prototyping to project management. The whole course is evaluated continuously to enhance our methodological toolbox.

This course offering is a cooperation of the research group Human-Centered Computing at the institute of computer science at the Freie Universität Berlin and the Product Design Department at the Weißensee Kunsthochschule Berlin (KHB).

Besides the regular weekly meetings, the KHB provides complimentary workshops each Monday from 10 AM to 1 PM where participation for computer science students is optionally.

Each term, we have a different topic. Please check out the HCC website for further information.

Literatur, Materialien und Geräte werden während der Veranstaltung gestellt.

Sophienstrasse 22a, 2.HH, 2.Stock, 10178 Berlin

www.interdisciplinary-laboratory.hu-berlin.de

Subject of the project: either development of software together with a company (in this case: 4­ weeks fulltime August/September) or we build a so called NoSQL system. Decision in March. Further information are published in the KVV.

Wird bekannt gegeben. / To be announced.

Target group

Students in the Master's or Bachelor's programme

Prerequisites

Good programming skills, introduction to database systems.

In this course, we co-educate computer scientists and product designers. Beyond experiencing interdisciplinary work, we want students to envision interactive systems that are intelligent: by this, we mean an intelligence through code that is carefully using material, form, and context, while profoundly respecting both human capabilities and vulnerabilities.

We understand this course as experimental space, where different perspectives meet, exchange, and evolve. Each semester, based on small project teams of up to five members, students are challenged to examine a specific application context. Within this context, the teams envision a new application or product concepts.

We guide this process through various carefully tuned methods that are used to spark their ideas. Students iterate through several rounds of ideation and refine their concept in different prototype versions. The most compelling or promising interaction concept, the one that allows grasping the quality and essence of the product concept is implemented in a working prototype.

Students are accompanied by a team of experienced designers and computer scientists but also by guest experts that provide feedback to the various design iterations. If needed, special workshops are organized to cover specific topics ranging from prototyping to project management. The whole course is evaluated continuously to enhance our methodological toolbox.

This course offering is a cooperation of the research group Human-Centered Computing at the institute of computer science at the Freie Universität Berlin and the Product Design Department at the Weißensee Kunsthochschule Berlin (KHB).

Besides the regular weekly meetings, the KHB provides complimentary workshops each Monday from 10 AM to 1 PM where participation for computer science students is optionally.

Each term, we have a different topic. Please check out the HCC website for further information.

Literatur, Materialien und Geräte werden während der Veranstaltung gestellt.

Sophienstrasse 22a, 2.HH, 2.Stock, 10178 Berlin

www.interdisciplinary-laboratory.hu-berlin.de

Contents:

The most frequent tasks in Computer Vision are object (or event) detection and object tracking. In contrast to the field of image processing we often work on a sequence of images (a.k.a. video). In the lecture we will review a number of essential landmark publications and learn about cutting edge technologies of today.

Content

Software development in small independent teams in the context of the research area of the working group Database and Information Systems. Reading of scientific literature and implementation of the described solutions. Regular presentations of the milestones and the scientific background.

A project seminar serves as preparation of a thesis (bachelor or master) in the AG DB. In the summer semester the focus lies on software development and event-based systems for mobile applications (event data, geo data, smart phone apps).

Wird bekannt gegeben.

Requirement

ALP I-III, Foundations of Datenbase Systems, good programming knowledge.

Content

This class will give an introduction to robotics. It will be structured into the following parts:

• Generating motion and and dynamic control: This chapter will cover coordinate frames, non-holonomic constraints, Ackermann-drive (in analogy to street cars), PID.
• Planning: Planning around obstacles, path finding, Dijkstra, A*, configuration space obstacles, RRTs, lattice planners, gradient methods, potential fields, splines.
• Localization and mapping: state estimation problem, Bayesian filter, Odometry, Particle & Kalman filter, Extended and Unscented Kalman-Filter, simultaneous localization and mapping (SLAM).
• Vision and perception: SIFT, HOG-features, Deformable parts models, hough transform, lane detection, 3d-point clouds, RANSAC .

After these lectures, students will be able to design basic algorithms for motion, control and state estimation for robotics.

The lecture will be in German, accompanying materials in English.

Literatur:

Bruno Siciliano, Oussama Khatib: Handbook of Robotics (partially online at Googlebooks) Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani: Robotics: modelling, planning and control Steven LaValle: Planning Algorithms Sebastian Thrun, Wolfram Burgard, Dieter Fox: Probabilistic Robotics

Students interested in robotics with application to autonomous vehicles. Voraussetzungen: As a prerequisite, student should have basic knowledge of maths, in particular linear algebra and a bit of optimization. Students will work with a real model car in the robotics lab.