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Original title in Czech: Výpočetní technika a informatikaFITAbbreviation: DVI4Acad. year: 2018/2019
Programme: Computer Science and Engineering
Length of Study: 4 years
Accredited from: 1.1.2007Accredited until: 31.12.2024
Profile
The goal of the doctoral study programme is to provide outstanding graduates from the MSc study programme with a specialised university education of the highest level in certain fields of information technology, including especially the areas of information systems, computer-based systems and computer networks, computer graphics and multimedia, and intelligent systems. The education obtained within this study programme also comprises a training and attestation for scientific work.
Guarantor
prof. RNDr. Milan Češka, CSc.
Issued topics of Doctoral Study Program
The topic focuses algorithms of computer graphics and generally computer image synthesis. Its main goal is to research algorithms so that their features and application possibilities are better understood, so that they are deeply analyzed, so that they are improved or newly created. The programming work is expected in C, C++, C#, assembly language, CUDA, OpenCl, VHDL, or other languages. If suitable, they can be efficiently implemented e.g. in CPU, in CPU with acceleration through SSE instructions, in embeded systems, in embedded systems with FPGA, or in other systems, such as x86/64, ARM, Xeon PHI, or other cores. Algorithms of interest include 3D model processing and acquisition. The algorithms of interest include:
After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.
Tutor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.
Tutor: Sekanina Lukáš, prof. Ing., Ph.D.
Tutor: Matoušek Petr, doc. Ing., Ph.D., M.A.
The project is concerned with advanced methods of computational photography. The aim is to research new computational photography methods, which comprises software solutions potentially supported by new optics and/or hardware. Our interest is on HDR image and video processing, color-to-grayscale conversions, spectral imaging, and others.
Tutor: Čadík Martin, doc. Ing., Ph.D.
Tutor: Zbořil František, doc. Ing., Ph.D.
The project is concerned with advanced rendering and global illumination methods. The aim is to research new photorealistic (physically accurate) as well as non-photorealistic (NPR) simulations of interaction of light with the 3D scene. Cooperation and research visits with leading research labs are possible (Adobe, USA, MPII Saarbrücken, Německo, Disney Curych, Švýcarsko, INRIA Bordeaux, Francie).
Tutor: Vašíček Zdeněk, doc. Ing., Ph.D.
Tutor: Hanáček Petr, doc. Dr. Ing.
Tutor: Chudý Peter, doc. Ing., Ph.D., MBA
The goal of the work is to research and create algorithms that will allow for running augmented reality on mobile (ultramobile) devices. It mainly concerns algorithms of pose estimation in the space by the means of computer vision and by using sensors embedded in the device. Furthermore, the work will elaborate on algorithms of rendering of virtual elements into the real-world scene and on applications of augmented reality on mobile devices.
Tutor: Herout Adam, prof. Ing., Ph.D.
Tutor: Vojnar Tomáš, prof. Ing., Ph.D.
Tutor: Jaroš Jiří, doc. Ing., Ph.D.
Tutor: Kolář Dušan, doc. Dr. Ing.
Tutor: Zbořil František, doc. Ing., CSc.
Tutor: Smrž Pavel, doc. RNDr., Ph.D.
Device profile is characteristics of a device that is created by monitoring of running processes on a device and network communication of the device. The profile includes statistics and metadata about the device behaviour in active or passive state. Knowing device profiles helps network administrators and users to know how device communicates without explicit user interaction, e.g., during user data synchronization in cloud, software updates, application data synchronization (emails, calendar), etc. Knowledge of the device profile can be used to identify different types of network attacks, malware contagion, or unauthorized access and process running. The research will include selection of device profile data, implementation of the tool for retrieving such data, device profiling and identification of deviations in network communcation using clustering or automated filtering. This topic is a part of research project Integrated Platform for Analysis of Digital Data from Security Incidents (Tarzan).
Tutor: Drahanský Martin, prof. Ing., Ph.D.
Modern operating systems (OS) must meet many requirements not only in terms of flexibility and efficiency of their execution on recent computing platforms, but also in terms of dependability of their kernels and services they provide to the application layer. The aim of the project is:
The project can be oriented into various directions, such as low-power applications / OS or application / OS designed to run in an embedded or a multi-core environment. During the project, a "conventional" OS such as Unix, Linux, Android, Windows, iOS or a specialized OS such as QNX, uC/OS-I (II, III), FreeRTOS, MQX can be utilized.
Tutor: Strnadel Josef, Ing., Ph.D.
Tutor: Růžička Richard, doc. Ing., Ph.D., MBA
Tutor: Janoušek Vladimír, doc. Ing., Ph.D.
The topic focuses embedded image, video and/or signal processing. Its main goal is to research capabilities of "smart" and "small" units that have such features that allow for their applications requiring smyll, hidden, distributed, low power, mechanically or climatically stressed systems suitable of processing of some signal input. Exploitation of such systems is perspective and wide and also client/server and/or cloud systems. The units themselves can be based on CPU/DSP/GPU, programmable hardware, or their combination. Smart cameras can be considered as well. Applications of interest include:
A possibility exists in collaboration on grant projects, especially the newly submitted TAČR, H2020, ECSEL ones (potentially employment or scholarship possible).
Tutor: Kreslíková Jitka, doc. RNDr., CSc.
Tutor: Meduna Alexandr, prof. RNDr., CSc.
Tutor: Beran Vítězslav, doc. Ing., Ph.D.
Tutor: Fučík Otto, doc. Dr. Ing.
The project deals with image and video quality assessment metrics (IQM). The aim is to explore new ways how to incorporate human visual system properties into IQM. In particular, we will consider perception of HDR images, and utilization of additional knowledge (in form of metadata, 3D information, etc.) about the tested scenes using machine learning (e.g. neural networks).
The project is concerned with advanced methods of image processing. The aim is to research new methods using machine learning, in particular deep convolutional neural networks.
Tutor: Hruška Tomáš, prof. Ing., CSc.
The topic focuses algorithms of image, video, and/or signal processing. Its main goal is to research and in-depth analyze existing algorithms and discover new ones so that they have desirable features and so that they are possible to efficiently implement. Such efficient implementation can be but does not necessarily have to be part of the work but it is important to prepare the algorithms so that they can be efficiently implemented e.g. in CPU, in CPU with acceleration through SSE instructions, in embeded systems, in embedded systems with FPGA, in Intel Xeon PHI, in extremely low power systems, or in other environments. The programming work is expected in C, C++, C#, assembly language, CUDA, OpenCl, VHDL, or other languages. The application possibilities of the algorithms are also important and the application can be but does not have to be part of the work. The algorithms/applications of interest include:
SCADA (Supervisory Control And Data Acquisition) systems and industrial IoT networks provides control and monitoring of industrial processes and devices. In the past, SCADA communication was usually transmitted over dedicated data links with Token Ring or HDLC protocols. Today, SCADA and industrial IoT communication is transmitted over TCP/IP or connected to the Internet. This rises serious security concerns. DLMS/COSEM, IEC 104 or IEC 61850 communication is usually used for energy smart metering and control where security incidents are critical. One of the solution is to provide security monitoring of these protocols using extended flow network that can be used to detect security incidents. This dissertation will be focused on behavior of industrial IoT and SCADA communication and detection of common threats (malformed packets, DoS attacks, forged commands, unauthorized data acquisition). The goal of the dissertation is to propose and verify new approaches for IoT network intrusion detection using flow monitoring and anomaly detection. This topic is a part of international research project IoT Monitoring and Forensics (IRONSTONE).
Internet of Thing is a communication platform that interconnects different types of home devices (home IoT networks) or industrial devices (industrial IoT networks). These devices usually lack sufficient protection against network attacks. Unfortunately, these attacks can cause serious demages. Besides intentional attacks, also malfuctioning or failures can cause serious demages. Thus IoT monitoring becomes a new domain of network monitoring and management. It includes monitoring of device behaviour, monitoring data acquisition, device settings, etc. Security monitoring focuses on detection of attack and anomalies in communication. Traditional methods used in security monitoring have limited scope of usage because IoT communication differs from common communication patterns on Internet. Thus, it is necessary to extend these method or propose a new approach how to analyze IoT monitoring metadata.The goal of this disertation is to analyze different method of IoT security monitoring and define how to protect these network against common threats. This advanced monitoring system should be implemented into existing SIEM systems. This topic is a part of international research IoT Monitoring and Forensics (IRONSTONE) by TACR.
The proposed dissertation deals with a subset of techniques for extracting meaningful information from speech: voice activity detection, transcription, keyword spotting, speaker recognition, language recognition and other possible modalities. It includes investigation into relevant signal processing and machine learning, and experimentation on standard speech data-sets. The topic is related to several projects running in the BUT Speech@FIT group, see http://speech.fit.vutbr.cz/projects. The candidate should have strong background in mathematics, linear algebra and statistics, and experience in one or more of the following disciplines: signal processing, speech signal processing, machine learning, natural language processing, data-mining. He/she should be experienced with usual scientific programming and scripting languages (C, Matlab, Python). Experience with at least one of machine learning/speech toolkits (Theano, Keras, PyTorch, CNTK, Chainer, KALDI, HTK) is a plus. As the the group is international, a good working knowledge of English is required.
Tutor: Černocký Jan, prof. Dr. Ing.
Tutor: Kotásek Zdeněk, doc. Ing., CSc.
Recent systems are becoming more and more demanding not only from the viewpoints of flexibility and efficiency of their operation on modern computing platforms, but also from the viewpoint of their activities and the services they provide and how they are managed.The aim of the project is:
Modern systems must meet many requirements not only in terms of flexibility and efficiency of their execution on recent computing platforms, but also in terms of security. The aim of the project is:
Tutor: Burget Radek, doc. Ing., Ph.D.