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Pulmonary pathologies are a significant global health concern, often leading to fatal outcomes if not diagnosed and treated promptly. Chest radiography serves as a primary diagnostic tool, but the availability of experienced radiologists remains limited. Advances in Artificial Intelligence (AI) and machine learning, particularly in computer vision, offer promising solutions to address this challenge. This research evaluates a deep learning model designed to detect lung cancer, specifically pulmonary nodules, along with eight other lung pathologies, using chest radiographs. The study leverages diverse datasets comprising over 135,120 frontal chest radiographs to train a Convolutional Neural Network (CNN). A two-stage classification system, utilizing ensemble methods and transfer learning, is employed to first triage images into Normal or Abnormal categories and then identify specific pathologies, including lung nodules. The deep learning model achieves notable results in nodule classification, with a top-performing accuracy of 77%, a sensitivity of 0.713, a specificity of 0.776 during external validation, and an AUC score of 0.888. Despite these successes, some misclassifications were observed, primarily false negatives. In conclusion, the model demonstrates robust potential for generalization across diverse patient populations, attributed to the geographic diversity of the training dataset. Future work could focus on integrating ETL data distribution strategies and expanding the dataset with additional nodule-type samples to further enhance diagnostic accuracy., Comment: IEEE International Workshop on Mechatronic Systems Supervision 2023

New GeV-scale dark forces coupling predominantly to quarks offer novel signatures that can be produced directly and searched for at high-luminosity colliders. We compute the photon-proton and electron-proton cross sections for producing a GeV-scale gauge boson arising from a $U(1)_B$ gauge symmetry. Our calculation relies on vector meson dominance and a phenomenological model for diffractive scattering used for vector-meson photoproduction. The parameters of our phenomenological model are fixed by performing a Markov Chain Monte Carlo fit to existing exclusive photoproduction data for $\omega$ and $\phi$ mesons. Our approach can be generalized to other GeV-scale dark gauge forces., Comment: 28 pages, 12 figures; minor revisions, matches published version

A resource-constrained unmanned aerial vehicle (UAV) can be used as a flying LoRa gateway (GW) to move inside the target area for efficient data collection and LoRa resource management. In this work, we propose deep reinforcement learning (DRL) to optimize the energy efficiency (EE) in wireless LoRa networks composed of LoRa end devices (EDs) and a flying GW to extend the network lifetime. The trained DRL agent can efficiently allocate the spreading factors (SFs) and transmission powers (TPs) to EDs while considering the air-to-ground wireless link and the availability of SFs. In addition, we allow the flying GW to adjust its optimal policy onboard and perform online resource allocation. This is accomplished through retraining the DRL agent using reduced action space. Simulation results demonstrate that our proposed DRL-based online resource allocation scheme can achieve higher EE in LoRa networks over three benchmark schemes.

The last decade has witnessed an ever-growing user demand for a better QoS (Quality Of Service) and the fast growth of connected devices still put high pressure on the legacy network infrastructures. To improve network performances, better manage the resources and have a greater control over traffic transmission, intelligent routing procedures are increasingly demanded. Modern applications in the dynamic context of new emerging networks have their own routing requirements, in terms of set of metrics to consider, their importance and thresholds to respect. The objective of this work is to design an approach based on MCDM (Multi-Criteria Decision Making) to decide complex routing problems when assuming threshold constraints on metrics. We give the mathematical framework to capture such requirements and to decide the routing. A case study is presented to advocate the benefit of using our approach.

When tackling real-life datasets, it is common to face the existence of scrambled missing values within data. Considered as 'dirty data', usually it is removed during a pre-processing step. Starting from the fact that 'making up this missing data is better than throwing out it away', we present a new approach trying to complete missing data. The main singularity of the introduced approach is that it sheds light on a fruitful synergy between generic basis of association rules and the topic of missing values handling. In fact, beyond interesting compactness rate, such generic association rules make it possible to get a considerable reduction of conflicts during the completion step. A new metric called 'Robustness' is also introduced, and aims to select the robust association rule for the completion of a missing value whenever a conflict appears. Carried out experiments on benchmark datasets confirm the soundness of our approach. Thus, it reduces conflict during the completion step while offering a high percentage of correct completion accuracy., Comment: in French

Problem Statement: Field Programmable Gate Array (FPGA) circuits play a significant role in major recent embedded process control designs. However, exploiting these platforms requires deep hardware conception skills and remains an important time consuming stage in a design flow. High Level Synthesis technique avoids this bottleneck and increases design productivity as witnessed by industry specialists. Approach: This study proposes to apply this technique for the conception and implementation of a Real Time Direct Current Machine (RTDCM) emulator for an embedded control application. Results: Several FPGA-based configuration scenarios are studied. A series of tests including design and timing-precision analysis were conducted to discuss and validate the obtained hardware architectures. Conclusion/Recommendations: The proposed methodology has accelerated the design time besides it has provided an extra time to refine the hardware core of the DCM emulator. The high level synthesis technique can be applied to the control field especially to test with low cost and short delays newest algorithms and motor models.