Kyandoghere Kyamakya

University of Klagenfurt

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Jean Chamberlain Chedjou

University of Buea

Fadi Al Machot

Norwegian University of Life Sciences

Witesyavwirwa Vianney Kambale

Tshwane University of Technology

Ahmad Haj Mosa

University of Klagenfurt

Alireza Fasih

University of Klagenfurt

Vahid Tavakkoli

University of Klagenfurt

Kabeh Mohsenzadegan

University of Klagenfurt

Mouhannad Ali

Universiti Malaysia Perlis

K. Jobmann

Leibniz University Hannover

Claude Takenga

Covenant University

Cooperative Institutions

University of Klagenfurt

University of Kinshasa

Tashkent University of Information Technology

Institute for Interdisciplinary Studies of Austrian Universities

Leibniz University Hannover

Université de Dschang

Hanoi University of Science and Technology

Université de Yaoundé I

University of Udine

Austrian Institute of Technology

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A Hybrid AI Framework Integrating Ontology Learning, Knowledge Graphs, and Large Language Models for Improved Data Model Translation in Smart Manufacturing and Transportation

Kabeh Mohsenzadegan Vahid Tavakkoli Witesyavwirwa Vianney Kambale Kyandoghere Kyamakya

Interoperability among diverse data standards is crucial for advancing digital technologies in smart manufacturing and transportation. This paper studies and presents a hybrid AI framework that integrates Ontology Learning (OL), Knowledge Graphs (KGs), and Large Language Models (LLMs) to enhance data translation across different standards. Focusing on IEEE 1451, ISO 15926, and IEC 61499, which exemplify the challenges of translating between distinct data models, we evaluate the performance of OL, KGs, and LLMs in terms of accuracy, scalability, efficiency, robustness, and flexibility. The findings indicate that the hybrid framework effectively leverages OL for semantic structuring, KGs for relational modeling, and LLMs for linguistic and contextual processing. This integration significantly improves the accuracy and adaptability of data translations, offering a comprehensive solution tailored to the complex environments of smart manufacturing and transportation, thereby advancing cross-standard data interoperability.

Adaptability

Robustness

10.1109/sdf63218.2024.10773919

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Practical considerations of optimal three-dimensional indoor localization

J. Schroeder Stefan Galler Kyandoghere Kyamakya K. Jobmann

The problem of locating objects and people in indoor environments is lately gaining high interest in research and industry. In this context, the geometrical constellation of source and sensors plays an important role and heavily influences the accuracy for the position estimate. This paper uses existing optimal solutions of the problem of three-dimensional TDOA localization, and relates them to practical application. The extension is necessary, since the optimal solution is only valid for one single position and not for a complete area, where a localization capability is desired. Therefore, it is shown by simulations that the optimum is very useful for localization purposes in typical indoor scenarios. In addition, real-world measurements using an ultra-wideband (UWB) localization system have been carried out in two geometrical scenarios. It can be shown that the simulations, which are based on theoretical derivations, are a good approximation of the reality. Finally, it can be demonstrated that the optimal solution can be adapted to typical indoor environments and still yield accurate three-dimensional positioning

Position (finance)

10.1109/mfi.2006.265598

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Citations (13)

Robust contrast enhancement by a coupled oscillatory paradigm

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering (2011)

Muhammad Ahsan Latif Jean Chamberlain Chedjou Kyandoghere Kyamakya

Purpose An image contrast enhancement is one of the most important low‐level image pre‐processing tasks required by the vision‐based advanced driver assistance systems (ADAS). This paper seeks to address this important issue keeping the real time constraints in focus, which is especially vital for the ADAS. Design/methodology/approach The approach is based on a paradigm of nonlinear‐coupled oscillators in image processing. Each layer of the colored images is treated as an independent grayscale image and is processed separately by the paradigm. The pixels with the lowest and the highest gray levels are chosen and their difference is enhanced to span all the gray levels in an image over the entire gray level range, i.e. [0 1]. This operation enhances the contrast in each layer and the enhanced layers are finally combined to produce a color image of a much improved quality. Findings The approach performs robust contrast enhancement as compared to other approaches available in the relevant literature. Generally, other approaches do need a new setting of parameters for every new image to perform its task, i.e. contrast enhancement. These approaches are not useful for real‐time applications such as ADAS. Whereas, the proposed approach presented in this paper performs contrast enhancement for different images under the same setting of parameters, hence giving rise to the robustness in the system. The unique setting of parameters is derived through a bifurcation analysis explained in the paper. Originality/value The proposed approach is novel in different aspects. First, the proposed paradigm comprises of coupled differential equations, and therefore, offers a continuous model as opposed to other approaches in the relevant literature. This continuity in the model is an inherent feature of the proposed approach, which could be useful in realizing real‐time image processing with an analog implemented circuit of the approach. Furthermore, a novel framework combining coupled oscillatory paradigm and cellular neural network is also possible to achieve ultra‐fast solution in image contrast enhancement.

Robustness

10.1108/03321641111133299

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Continuous wavelet analysis of rainfall fluctuations at interannual and decennial scales on the south-eastern part in the Democratic Republic of the Congo between 1940 and 1997

Deleted Journal (2016)

Albert Mbata Muliwavyo Jean Marie Tshitenge Mbuebue Edmond Phuku Phuati Kyandoghere Kyamakya François Tondozi Keto

Wavelet analysis was applied to the standardized rainfall time series in the south-eastern in the Democratic Republic of the Congo in order to determine periods of rainfall fluctuations in this region at interannual and decennial timescales and to assess the influence of regional or remote climate phenomena on the rainfall of that sector between 1940 and 1997. The local, the global and the scale-averaged wavelet power spectrum of the rainfall time series of that sector indicate an important fluctuation between practically 1960 and 1970. Wavelet coherence spectral analysis shows that the southern oscillation El Nino phenomenon, the Western Hemisphere Warm Pool, the Atlantic Ocean climate variability and the Indian Ocean dipole have a very weak influence on rainfall on this territory. Scale-averaged wavelet coherence in 2-16 years band is lower than 0.5 whatever the climate index considered. Other factors must also be considered in the study of rainfall variability in this study area.

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MIDCO – Machine-Vision Based Context-Aware Intelligent Driver Co-Pilot

Kyandoghere Kyamakya Simone Fuchs Bernhard Lamprecht Simone Fuchs Bernhard Lamprecht

Machine Vision

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ICT and COMPRAM to assess road Traffic Congestion Management in Kinshasa

Antoine Kazadi Kayisu Meera K. Joseph Kyandoghere Kyamakya

Traffic Congestion Management (TCM) in a megacity like Kinshasa, capital of the DR Congo, is a knowledge and real life problem of complex nature. Here, the authors describe the TCM problem through 9 phases of the layer 1 of the COMPRAM methodology. TCM is a worldwide complex societal problem and specifically in Kinshasa where it presents a set of characteristics such as 'chaotic' driver behaviour, road potholes and the road network physiognomy doesn't respond to the supply-versus demand-side equation. The other complex problems include the absence of road planning with consideration to demographic parameters and car ownership increase, no suitable traffic operations infrastructure and limited funds for both maintaining existing roads and building additional ones. To solve this TCM problem, the authors propose a TTCMP (Triangular Traffic Congestion Management Process) framework as an output based layer 1 of COMPRAM by identifying types and sources of congestion, followed by a TCM problem description and a set of technical elements for 'curbing' traffic congestion with an overview on a Bluetooth based technology for traffic data collection as an adapted ICT4D solution for a low-income city like Kinshasa.

Megacity

Capital city

Car ownership

10.23919/istafrica.2017.8102338

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Development of a Smart Chair Sensors System and Classification of Sitting Postures with Deep Learning Algorithms

Sensors (2022)

Taraneh Aminosharieh Najafi Antonio Carlos Abramo Kyandoghere Kyamakya Antonio Affanni

Nowadays in modern societies, a sedentary lifestyle is almost inevitable for a majority of the population. Long hours of sitting, especially in wrong postures, may result in health complications. A smart chair with the capability to identify sitting postures can help reduce health risks induced by a modern lifestyle. This paper presents the design, realization and evaluation of a new smart chair sensors system capable of sitting postures identification. The system consists of eight pressure sensors placed on the chair's sitting cushion and the backrest. A signal acquisition board was designed from scratch to acquire data generated by the pressure sensors and transmit them via a Wi-Fi network to a purposely developed graphical user interface which monitors and stores the acquired sensors' data on a computer. The designed system was tested by means of an extensive sitting experiment involving 40 subjects, and from the acquired data, the classification of the respective sitting postures out of eight possible postures was performed. Hereby, the performance of seven deep-learning algorithms was assessed. The best accuracy of 91.68% was achieved by an echo memory network model. The designed smart chair sensors system is simple and versatile, low cost and accurate, and it can easily be deployed in several smart chair environments, both for public and private contexts.

Sitting

10.3390/s22155585

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Citations (17)

On the Introduction of Nonlinear Dynamics in Image Processing with Applications

VDI Verlag eBooks (2019)

Mashiat Zahan J. Ch. Chedjou Kyandoghere Kyamakya

Dynamics

10.51202/9783186864109-199

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COMPRAM Assessment and System Dynamics Modeling and Simulation of Car-Following Model for Degraded Roads