<abstract xmlns="http://www.w3.org/1999/xhtml">

Effective nonlinear control of manipulators with dynamically coupled arms, like those with direct drives, is the subject of the paper. Model-based predictive control (MPC) algorithms with nonlinear state-space models and most recent disturbance attenuation technique are proposed. This technique makes controller design and online calculations simpler, avoiding necessity of dynamic modeling of disturbances or resorting to additional techniques like SMC. The core of the paper are computationally effective MPC-NPL (Nonlinear Prediction and Linearization) algorithms, where computations at every sample are divided into two parts: prediction of initial trajectories using nonlinear model, then optimization using simplified linearized model. For a comparison, a known CTC-PID algorithm, which is also model-based, is considered. It is applied in standard form and also proposed in more advanced CTC-PID2dof version. For all algorithms a comprehensive comparative simulation study is performed, for a direct drive manipulator under disturbances. Additional contribution of the paper is investigation of influence of sampling period and of computational delay time on performance of the algorithms, which is practically important when using model-based algorithms with fast sampling.
</abstract>

Effective nonlinear Predictive and CTC-PID Control of Rigid Manipulators

<abstract xmlns="http://www.w3.org/1999/xhtml">

Contemporary industry and science expectations towards technological solutions set the bar high. Current approaches to increasing the computing power of standard systems are reaching the limits of physics known to humankind. Fast, programmable systems with relatively low power consumption are a different concept for performing complex calculations. Highly parallel processing opens up a number of possibilities in the context of accelerating calculations. Application of SoC (System On Chip) with FPGA (Field-Programmable Gate Array) enables the delegating of a part of computations to the gates matrix, thereby expediting processing by using parallelization of hardware operations. This paper presents the general concept of using SoC FPGA systems to support the CPU (Central Processing Unit) in many modern tasks. While some tasks might be really hard to implement on an FPGA in a reasonable time, the SoC FPGA platform allows for easy low-level interconnections, and with such virtualized access to the hardware computing resources, it is seen as making FPGAs, or hardware in general, more accessible to engineers accustomed to high-level solutions. The concept presented in the article takes into account the limited resources of cheaper educational platforms, which, however, still provide an interesting and alternative hybrid solution to the problem of parallelization and acceleration of data processing. This allows encountered limitations to be overcome and the flexibility known from high-level solutions and high performance achieved with low-level programming to be maintained without the need for a high financial background.
</abstract>

European Organization for Nuclear Research CERN

SOC-FPGA Based Concept of Hardware Aided Quantum Simulation

<abstract xmlns="http://www.w3.org/1999/xhtml">

This article presents research results on building a model to reproduce ship vibrations based on a parallel robot with 6 degrees of freedom on the Gough-Stewart platform. Vibration data at the ship’s center of gravity, calculated by simulation software, will be inputted into the model. The regenerative control system uses a simple PID controller to control input trajectory tracking. Simulation results on Matlab/Simulink software have demonstrated the reproduction of ship vibrations within the allowable error.
</abstract>

University of Fire Prevention and Fighting

Academy of Military Science and Technology

Research to Simulate the Ship’s Vibration Regeneration System using A 6-Degree Freedom Gough-Stewart Parallel Robot

<abstract xmlns="http://www.w3.org/1999/xhtml">

In this paper, we proposed a comparative research project on the classification of various objects in satellite images using some pre-trained models of CNN (VGG-19, ResNet-50, Inception-V3, EfficientNet-B7) and R-CNN. In this research work, we have used the DOTA dataset, which combines data from 14 classes. We have implemented above-mentioned pre-trained models of CNN and R-CNN to achieve optimal results for accuracy as well as productivity in detection of various objects such as ships, tennis courts, swimming pools, vehicles, and harbors from remotely accessed images. In this study, a convolutional neural network (CNN) is used as the base model. For complex computations and for speeding up results, transfer learning is used. With the help of experimental analysis, we have discovered that R-CNN and Inception-V3 performed best out of the five pre-trained models.
</abstract>

College of Computing Sciences & Information Technology

Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO)

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on Dota

<abstract xmlns="http://www.w3.org/1999/xhtml">

The new wave of performant technology devices generates massive amounts of data. These devices are used in cities, homes, buildings, companies, and more. One of the reasons for digitalizing their tasks is that over the past few years, there has been an interest in reducing carbon emissions and increasing energy efficiency to create a friendly ecosystem and protect nature. One of which granted the explosion of data. After deploying these new devices, a significant increase in the use of the other face of energy to implement the components of the new devices was noticed. Above all, the interconnection of these intelligent devices is the central concept of the Internet of Things (IoT). This domain has widened the possibilities for the interconnection of building management systems (also named Smart Grids) and devices for better energy management. Furthermore, its potential is realized only after organizing and analyzing a large amount of data. Real-time management and maintenance of big data are critical to improving energy management in buildings. The benefits of big data analytics go beyond savings on electricity bills. It can provide comfort for building users and extend the life of building equipment, enhancing the value of commercial buildings. Intelligent interconnection of a building’s technical installations (lighting, heating, hot water, photovoltaic installations, etc.) not only allows for connected management of this equipment but also meets high energy efficiency criteria that indicate an increase in comfort and energy savings. With building automation, the technical installations of a building interact optimally. In this article, we will simulate an intelligent building based on the Cisco packet tracer software. To better manage the energy consumption of our project, we will focus on the processing of data in real-time, especially since we will have a massive amount of data generated by the sensors, which makes the use of big data mandatory.
</abstract>

Researcher Professor at Faculty of Letters and Human Society

Sidi Mohammed ben Abdellah University, High Normal School

A Numerical Analysis Based Internet of Things (IoT) and Big Data Analytics to Minimize Energy Consumption in Smart Buildings

<abstract xmlns="http://www.w3.org/1999/xhtml">

With the rapid advancements in technology, the educational landscape is witnessing significant transformations in pedagogy and classroom dynamics. Two prominent technologies, Artificial Intelligence (AI) and Augmented Reality (AR), are gaining prominence in the field of education, promising to revolutionize the way teaching and learning take place. This article explores the potential benefits, challenges, and practical applications of integrating AI and AR into the teaching process to enhance student engagement and learning outcomes.
The integration of AI in education brings forth personalized learning experiences. AI-powered algorithms analyze vast amounts of student data, including learning patterns, strengths, and weaknesses, to create tailored learning paths. This individualized approach helps educators identify students’ unique needs and provide targeted support, ensuring that no student is left behind. Moreover, AI-based chatbots and virtual teaching assistants are increasingly being used to address student queries promptly, providing real-time support and fostering a more interactive learning environment.
AR, on the other hand, enables the overlay of virtual objects and information in the real-world environment.. Students can explore complex concepts through visualizations, simulations, and interactive demonstrations, facilitating a deeper understanding of abstract topics. AR also fosters collaboration and teamwork among students, promoting active learning and peer-to-peer knowledge sharing.
Combining AI and AR technologies offers a powerful synergy in the educational realm. AI can analyze ARgenerated data and adapt instructional strategies in real time, responding to individual students’ progress. This synergy not only enhances learning outcomes but also empowers teachers with data-driven insights, enabling them to make informed decisions about their teaching methodologies.
However, successfully implementing AI and AR in education comes with its challenges. Issues related to data privacy, ethical considerations, and the need for effective teacher training in utilizing these technologies require careful attention. Additionally, the digital divide can exacerbate educational inequalities, as not all students have equal access to technology outside the classroom.
Collaboration between educators, researchers, and technology developers is crucial to overcome these challenges. The development of user-friendly, accessible, and ethically sound AI and AR tools can ensure inclusivity and maximize the potential benefits of these technologies in education. The aim is to investigate whether the use of AR technology can enhance students’ understanding and mastery of physics concepts through visualizations and simulations. Spatial intelligence plays a crucial role in various subjects, including physics, as it enables students to create mental models and representations of objects and expressions. While spatial intelligence is not an innate skill, it can be developed through interactions with real and virtual objects. ARas a cutting-edge technology, has the potential to illustrate physical applications and significantly aid students in visualizing and comprehending complex physics concepts.
</abstract>

Improving Teaching Using Artificial Intelligence and Augmented Reality

<abstract xmlns="http://www.w3.org/1999/xhtml">

Simultaneous Localization and Mapping (SLAM) is applied to robots for accurate navigation. The stereo cameras are suitable for visual SLAM as they can give the depth of the visual landmarks and more precise estimations of the robot’s pose. In this paper, we present a survey of SLAM methods, either Bayesian or bioinspired. Then we present a new method of SLAM, which we call stereo Extended Kalman Filter, improving the matching by computing the innovation matrices from the left and the right images. The landmarks are computed from Oriented FAST and Rotated BRIEF (ORB) features for detecting salient points and their descriptors. The covariance matrices of the state and the robot’s map are reduced during the robot’s motion. Experiments are done on the raw images of the Kitti dataset.
</abstract>

Faculty of Sciences, Mohammed V University in Rabat

Intelligent Processing and Security Systems Team

Faculty of Sciences, Mohammed V University in Rabat, 4

Simultaneous Localization and Mapping of a Mobile Robot With Stereo Camera Using ORB Features

<abstract xmlns="http://www.w3.org/1999/xhtml">

The design method and the time-variant finite impulse response (FIR) architecture for real-time estimation of fractional and integer differentials and integrals are presented in this paper. The proposed FIR architecture is divided into two parts. Small-phase filtering, integer differentiation, and fractional differential and integration on the local data are performed by the first part, which is time-invariant. The second part, which is time-variant, handles fractional and global differentiation and integration. The separation of the two parts is necessary because real-time matrix inversion or an extensive analytical solution, which can be computationally intensive for high-order FIR architectures, would be required by a single time-variant FIR architecture. However, matrix inversion is used in the design method to achieve negligible delay in the filtered, differentiated, and integrated signals. The optimum output obtained by the method of least squares results in the negligible delay. The experimental results show that fractional and integer differentiation and integration can be performed by the proposed solution, although the fractional differentiation and integration process is sensitive to the noise and limited resolution of the measurements. In systems that require closed-loop control, disturbance observation, and real-time identification of model parameters, this solution can be implemented.
</abstract>

Design of Small-Phase Time-Variant Low-Pass Digital Fractional Differentiators and Integrators

<abstract xmlns="http://www.w3.org/1999/xhtml">

The widespread adoption of Electronic Healthcare Records has resulted in an abundance of healthcare data. This data holds significant potential for improving healthcare services by providing valuable clinical insights and enhancing clinical decision-making. This paper presents a patient classification methodology that utilizes a multiclass and multilabel diagnostic approach to predict the patient’s clinical class. The proposed model effectively handles comorbidities while maintaining a high level of accuracy. The implementation leverages the MIMIC III database as a data source to create a phenotyping dataset and train the models. Various machine learning models are employed in this study. Notably, the natural language processing-based One-Vs-Rest classifier achieves the best classification results, maintaining accuracy and F1 scores even with a large number of classes. The patient diagnostic class prediction model, based on the International Classification of Diseases 9, showcased in this paper, has broad applications in diagnostic support, treatment prediction, clinical assistance, recommender systems, clinical decision support systems, and clinical knowledge discovery engines.
</abstract>

SVKM’s, Narsee Monjee Institute of Management Studies

Choithram Interventional Spine & Pain Centre

Diagnostics Based Patient Classification for Clinical Decision Support Systems

AHEAD OF PRINT

Volume 18 (2024): Issue 2 (June 2024)

Volume 18 (2024): Issue 1 (March 2024)

Volume 17 (2023): Issue 4 (December 2023)

Volume 17 (2023): Issue 3 (September 2023)

Volume 17 (2023): Issue 2 (June 2023)

Volume 17 (2023): Issue 1 (March 2023)

Volume 16 (2022): Issue 4 (December 2022)

Volume 16 (2022): Issue 3 (August 2022)

Volume 16 (2023): Issue 2 (May 2023)

Volume 16 (2022): Issue 1 (March 2022)

Journal of Automation, Mobile Robotics and Intelligent Systems

Journal of Automation, Mobile Robotics and Intelligent Systems (ISSN 1897-8649 (Print) / ISSN 2080-2145 (Online) publishes high-quality, state-of-the-art technical and position papers in the fields of theory, design and applications of broadly perceived intelligent technologies, notably based on artificial intelligence and computational intelligence, in particular for the analysis and solution of scientific and engineering problems and systems.  JAMRIS is an international peer-reviewed, open-access, scientific journal, disseminating the highest quality research in the following　 fields:    Automation, Systems and Control,  Autonomous Systems,  Multiagent Systems,  Decision-making and Decision Support,  Robotics,  Mechatronics,  Data Sciences,  New Computing Paradigms, etc.   Supporting the realization of Open Science, all scientific articles published in the Journal of Automation, Mobile Robotics and Intelligent Systems are free of charge.   The journal is an open-access journal that allows free unlimited access to all its contents by all users without any restrictions upon publication.  Journal of Automation, Mobile Robotics and Intelligent Systems has been published 4 times a year since 2007.  Journal of Automation, Mobile Robotics and Intelligent Systems (JAMRIS) is published in printed and electronic version by Sciendo in cooperation with £ukasiewicz - Industrial Research Institute for Automation and Measurements PIAP, located in Warsaw, Poland.  Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 