Intelligent Systems and Advanced Telecommunication
Modern intelligent systems are systems which autonomously or semi-autonomously achieve their objectives through a wealth of experience or knowledge acquired from the environment while pervasively integrating the information processing into our daily life by engaging heterogeneous computational devices and systems simultaneously into objects that we manipulate. When endowed with an IP address and outfitted with sensing, actuating and identification devices, these objects become smart objects capable of interacting with humans and among themselves to provide different services to different users “anytime”, “anywhere” and using “anything” in a heterogeneous environment that involves a number of applications, communication protocols, operating systems, processors, devices, and architectures. Such pervasive computing presents many issues in terms of design, implementation and optimization.
The aim of the Intelligent Systems and Advanced Telecommunication (ISAT) research group is to advance the science and engineering of intelligent systems and their applications through the development of intelligent smart cards, robotic systems, intelligent models, smart sensor/actuator networks, intelligent information systems, etc. Our main research focus lies on the design, modelling, simulation and prototype implementation of intelligent systems and networks derived from (1) novel human computer interaction by investigating speech and vision based interfaces and (2) exact and heuristic optimization methods derived from the artificial intelligence statistics to solve telecommunication problems.
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Our Recent Publications
- Trajectory planing for cooperating unmanned aerial vehicles in the IoT
- E Tuyishimire, A Bagula, S Rekhis, N Boudriga. IoT 2022, 3(1), 147-168, MDPI
- A Novel Epidemic Model for the Interference Spread in the Internet of Things
- E Tuyishimire, J Niyigena, F Tubanambazi, J Rutikanga, P Gatabazi, A Bagula, E Niyigaba. Information. 2022, 13(4), 181. MDPI
- Environment 4.0: An IoT-Based Pollution Monitoring Model
- N Mbayo, H Maluleke, O Ajayi, A Bagula. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2022. 443, p 291-304, Springer, Cham.
- WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes
- O Ajayi, A Bagula, H Maluleke, Z Gaffoor, N Jovanovic, K Pietersen. 2022. IEEE Access 10, 48318-48337
- Assessing the Impact of Noise Pollution on Real Estate Prices using Smart Phones
- O Ajayi, H Maluleke, A Bagula. 2022 Conference on Information Communications Technology and Society (ICTAS)(pp.1-6). IEEE
- Modelling DDoS Attacks in IoT Networks using Machine Learning
- P Machaka, O Ajayi, H Maluleke, F Kahenga, A Bagula, K Kyamakya. arXiv preprint arXiv:2112.05477
- Smart city Citizens’ service provision using participatory design and participatory sensing: Lessons for developing cities
- S Kyakulumbye, S Pather, A Bagula. 2020 IST-Africa Conference (IST-Africa), 1-12.
- Cyber Physical Systems Dependability Using CPS-IOT Monitoring
- A Bagula, O Ajayi, H Maluleke. Sensors 2021, 21(8), 2761.
- A Formal and Efficient Routing Model for Persistent Traffics in the Internet of Things
- E Tuyishimire, BA Bagula. In 2020 Conference on Information Communications Technology and Society (ICTAS) (pp. 1-6). IEEE.
- Real-time data muling using a team of heterogeneous unmanned aerial vehicles
- E Tuyishimire, A Bagula, S Rekhis, N Boudriga
arXiv preprint arXiv:1912.08846
Our Recent Publications
Efficient Airborne Network Clustering for 5G Backhauling and Fronthauling
Big data analytics and its role to support groundwater management in the Southern African development community
Priority Based Traffic Pre-emption System for Medical Emergency Vehicles in Smart Cities
Predictive Models for Mitigating COVID-19 Outbreak
A novel management model for dynamic sensor networks using diffusion sets
Modelling and analysis of interference diffusion in the internet of things: an epidemic model
Archiving 4.0: Application of Image Processing and Machine Learning for the Robben Island Mayibuye Archives
Automatic Sign Language Manual Parameter Recognition (II): Comprehensive System Design
Automatic Sign Language Manual Parameter Recognition (I): Survey
Computer Vision Algorithms for Image Segmentation, Motion Detection, and Classification
Department of Computer Science, Faculty of Natural Science, University of the Western Cape.
CAMS Building, Robert Sobukwe Rd. Bellville, Cape Town 7535, South Africa