Tunisia Polytechnic School

• Identifying the type and freshness level of beef where beef is primarily sold in packaged form. • Impedance spectroscopy enables non-invasive meat assessment, existing methods often rely on raw data or complex feature sets, failing to explain the rationale behind their selection or justify their complexity. • A systematic, physics-based feature engineering methodology that can classify muscle type and freshness in bovine meat simultaneously using a single neural network. • Feature selection is primarily based on significant parameter variation measured over a 14-day period, enabling a classification accuracy of up to 91,5% using both neural networks and SVM models. • The evolution of these parameters aligns with biological fundamentals, confirming the relevance of the selected features and underlying physical principles In this paper, we propose a systematic, physics-based feature engineering methodology that can classify muscle type and freshness in bovine meat simultaneously using a single neural network. We identify a minimal, yet highly informative set of features extracted from bioimpedance spectra based on observed spectral behavior and underlying physical principles. Feature selection in this study is primarily based on significant parameter variation measured over a 14-day period, particularly between days 2 or 3 and day 14 for a binary classification of meat in the two classes safe-to-sell and not-safe-to-sell. This variation serves as a strong indicator of sensitivity and contributes to achieving a high classification rate. The key features selected include parameters from the modified Fricke model and characteristic points in the α and β frequency bands (Rex, Rin, C, Im1, Im2 and Im3). We validate the stability of these features and assess the model’s interpretability. Our analysis highlights the consistent relevance of these parameters, enabling a classification accuracy of up to 91,5% using both neural networks and SVM models. The evolution of these parameters aligns with biological fundamentals, confirming the relevance of the selected features.

This paper describes a real-time system for recognizing voice commands for resource-constrained embedded devices, specifically a PIC microcontroller. While most existing speech ordering support solutions rely on high-performance processing platforms or cloud computation, the system described here performs fully embedded low-power processing locally on the device. Sound is captured through a low-cost MEMS microphone, segmented into short audio frames, and time domain features are extracted (i.e., Zero-Crossing Rate (ZCR) and Short-Time Energy (STE)). These features were chosen for low power and computational efficiency and the ability to be processed in real time on a microcontroller. For the purposes of this experimental system, a small vocabulary of four command words (i.e., “ON”, “OFF”, “LEFT”, and “RIGHT”) were used to simulate real sound-ordering interfaces. The main contribution is demonstrated in the clever combination of low-complex, lightweight signal-processing techniques with embedded neural network inference, completing a classification cycle in real time (under 50 ms). It was demonstrated that the classification accuracy was over 90% using confusion matrices and timing analysis of the classifier’s performance across vocabularies with varying levels of complexity. This method is very applicable to IoT and portable embedded applications, offering a low-latency classification alternative to more complex and resource intensive classification architectures.

The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based simulated environments demonstrated that when benchmarked against the classical MARL baseline, QI-MARL achieved an 8% improvement in path efficiency, a 12% increase in task success rate, and a 15% reduction in energy consumption. When compared with the rule-based approach, improvements reached 15%, 20%, and 26%, respectively. Ablation studies further confirmed the substantial contributions of the quantum-inspired exploration and energy-sharing mechanisms, while sensitivity and scalability analyses validated the system’s robustness across varying swarm sizes and environmental complexities. The proposed framework effectively integrates quantum-inspired AI, intelligent microgrid management, and autonomous robotics, offering a novel approach to energy coordination in cyber-physical systems. Potential applications include smart buildings, industrial campuses, and distributed renewable energy networks, where the system enables flexible, resilient, and energy-efficient robotic operations within modern electrical engineering contexts.

This study investigates the relationships between fossil fuel availability, access to renewable energy, economic growth, and CO2 emissions in BRICS countries over the period 1990–2019. Employing FMOLS, DOLS, and Method of Moments Quantile Regression, the analysis reveals that fossil fuel availability and renewable energy access both exhibit a significant negative impact on economic growth in FMOLS and DOLS estimations, suggesting a limited role in driving growth in these countries. However, fossil fuels significantly increase CO2 emissions, while renewable energy access significantly reduces emissions, highlighting their contrasting environmental effects. The quantile regression results further show that fossil fuel availability consistently hinders economic growth across all quantiles but raises CO2 emissions, whereas renewable energy positively influences economic growth and lowers emissions throughout all quantile levels. These findings underscore the complex interplay between energy sources, economic development, and environmental sustainability, offering important policy implications for promoting balanced growth and emission reductions in the BRICS economies.

As robotic systems advance in autonomy and sophistication while being used in uncertain environments, the challenge of building reliable and robust electric motors that are embedded into robotic systems has never been a more important engineering problem. Thermal distress caused by extended operation or excessive loading can negatively affect a motor’s performance and efficiency and lead to catastrophic hardware failure. This paper proposes a novel intelligent control framework that includes real-time thermal feedback for hybrid electric motors that are embedded into robotic systems. The framework relies on adaptive control techniques and lightweight machine learning techniques to estimate internal motor temperatures and dynamically change operational parameters. Unlike traditional reactive methods, this framework provides a spacious active/predictive method of heat management, while preserving efficiency and allowing for responsive control. Simulations, experimental validations, and preliminary trials that deployed real robotic systems demonstrated that our framework allows for reductions in peak temperatures by up to 18% and extends motor lifetime by 22%, while retaining control stability and a range of variations in PWM adjustments of ±12% across disparate workloads. These results demonstrate the efficacy of intelligent and thermally aware motor control architectures and processes to improve the reliability of autonomous robotic systems and open the door for next-generation embedded controllers that will allow robotic platforms to self-manage thermal effects in resilient, adaptable robots.