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|AC5060||Course: Mobile and wireless networks||The objective of this course is to give an introduction to the fundamentals of the wireless communications systems, the wireless network architectures, protocols, and applications. Topics of study include an overview of wireless communications and mobile computing systems, signal propagation characteristics of wireless channels, wireless channel modeling, frequency reuse/cellular/microcellular concepts, spread-spectrum modulation for wireless systems, multiple access techniques, and wireless networking standards (e.g., 2.5G, 3G, IEEE 802.11, IEEE 802.15, IEEE 802.16/WiMAX).|
|AC5070||Course: Localization and navigation techniques||Through this course, the students will be provided knowledge on different localization techniques and their applications in pervasive environments. In the first part, a categorized overview of indoor and outdoor techniques will be introduced. In the second part, the students will investigate more deeply into emerging techniques. The final part is on the use of location information in pervasive computing services.|
|AC5090||Course: System analysis and design||This course aims at providing fundamental knowledge about system analysis and design. Students understand basic analysis and design process, object oriented model, and application of object oriented model in systems analysis and design.|
|AC5100||Course: Information safety and security in pervasive environments||Firstly, this course provides fundamental knowledge about information safety and security. Then, it presents some specific characteristic of information safety and security while working with ambient environments.|
|AC5140||Course: 3D modeling and representation||Provide theories and applications in the domains of 3-D visualization, presentation and manupulating in 3-D objects as well as presenting surfaces, volumes of objects, enviroments|
|AC5170||Course: Knowledge representation and reasoning||Knowledge representation and reasoning is an important topic of artificial intelligence with the goal to represent information about the user and its environment context in a form that a computer system can utilize to solve complex tasks. It is usually considered two ways to represent this information: the first one is logical and symbolic knowledge (ontology, etc.) and, the second one the probabilistic knowledge (Bayesian networtk, etc.). Associated concepts: logical and symbolic languages, stockastic and probabilistic systems, and decision making with these algorithms.|
|AC5200||Course: Design embedded systems||An introduction to the architecture of Design embedded systems. Topics include hardware, software of embedded system, analysis, design and implementation of embedded system.|
|AC5210||Course: Multimedia databases||This course gives firstly an overview of multimedia data, multimedia database, main issues in multimedia database, then focuses on several important problems in multimedia database: text retrieval, audio retrieval, content based image retrieval and video retrieval.|
|AC6010||Course: Pervasive environments and context modeling||This course aims at providing fundamental knowledge about ambient environments. Topics of study include basic but essential aspects of ambient environments: wireless and mobile networks, sensors, user context and context awareness, context reasoning, interaction, and their applications. At the end of this course, students would have a general vision about the move from distributed computing to pervasive computing.|
|AC6020||Course: Interaction through computer vision||This course contains two main parts: basics of computer vision and vision-based human-machine interaction. The first part covers the main issues in image processing and computer vision: image acquisition, image sampling and quantization, color, point operations, segmentation, morphological image processing, image transforms, feature extraction and feature matching. The second part presents vision-based human-machine interaction: principle of vision-based human-machine interaction and several vision-based human machine interaction system such as hand gesture recognition, face detection and recognition, facial expression recognition.|
|AC6030||Course: Interaction through natural language||Upon completion of the course, the student should have gathered enough knowledge and understanding of principles behind natural language processing and their applications.|
|AC6040||Course: Radio-frequency transactions and identification||This course provides an introduction of Radio frequency and Antenna Fundamentals as well as wireless communication systems with essential information about analog and digital modulations, coding and decoding, RF and microwave specifications and wireless communication standards. It describes architectures of a radio frequency transaction and identification systems. Several applications and major research topic in this field will be mentioned along with potential solutions|
|AC6050||Course: Sensors and sensor networks||This course aims at providing to students differences elementary sensor/transducer in perceptive environment, method connect and standard for sensors. The contents include: Overview of Data Acquisition System. Types of Elementary Sensor / Transducer and signal conditioning; Architecture and main functionalities of smart sensors; Sensor network with his structure and those functions and basic standard; The examples.|
|AC6080||Course: Human-machine interaction||The course provides basic knowledge about human-system interaction. It also introduces some approaches such as WIMP, web, mobile, three-dimensional and multi-modal interaction systems, especially interaction using gestures and voice. Accomplishing the course will gives students the ability of analyzing and designing interfaces for advanced human-system interaction methods.|
|AC6150||Course: Multimedia applications||Provide advanced knowledges for students in the domain of multimedia processing including audio, image, video, text. Improve skills of students in using these technologies for development of multimedia applications.|
|AC6160||Course: Computational photography||Provide advanced knowledges for students in the domain of computer graphics, computer vision and photography.
Overcome the limitations of the traditional camera by using computational techniques to produce a richer, more vivid, perhaps more perceptually meaningful representation of visual world.
Improve skills of students in development of computer graphic or vision applications.
|AC6180||Course: Machine translation||Machine translation (MT) means automatic translation of text by computer from one natural language into another natural language. The course will provide students the overall knowledge about machine translation and the available methods in machine translation. The course then will focus on the most recent method on statistical machine translation.|
|AC6220||Course: Energy management for pervasive environments||Industrial solutions to manage energy in buildings.
Technologies available in housing.
Be able to imagine and to implement new strategies for global and optimal management in buildings.
Use of standard solvers for energy optimization problem in buildings.
|AC6230||Course: Digital signal processing||This subject will offer a approach to modeling designing, and analyzing a general systems and certain specific structures of digital systems, namely, those in the A/D-digital filter – D/A structure. In Chapter 2, definitions are presented for special types of discrete-time signals and systems with special emphasis on frequency response characteristics and aliasing property, the discrete Fourrier transform and its inverse are presented along with algorithms for fast calculation. Chapter 3 provide design procedures for digital filters, and gives some insight into the hardware realization of such digital filters. Special attention is given to interpreting the results obtained by using the discrete Fourier transform on sampled continous-time signals. Structures of special Digital Signal Processor (DSP) are presented in Chapter 4.|
|AC6310||Course: Machine learning||This course is structured in 4 chapters. Chapter 1 gives an overview of machine learning and evaluation methodology will be given. Chapter 2, we present supervised learning methods (generative/discriminative learning, parametric/non-parametric learning, bayes, neural network and support vector machine). Chapter 3 introduces some unsupervised learning methods (clustering, data reduction, kernel-based methods). Finally, some applications of machine learning are given in Chapter 4.|