Implementing PID Control on Arduino Uno for Air Temperature Optimization

This research investigates the precise regulation of liquid filling in tanks, specifically focusing on water storage systems. It employs the Proportional-Integral-Derivative (PID) control method in conjunction with an HC-SR04 ultrasonic sensor and an Arduino Uno microcontroller. Given the paramount importance of water as a resource, accurate management of its storage is of utmost significance. The PID control method, known for its rapid responsiveness, minimal overshoot, and robust stability, effectively facilitates this task. Integrating the ultrasonic sensor and microcontroller further augments the precision of water level regulation. The article expounds upon the foundational principles of the PID control method and elucidates its application in the context of liquid tank filling. It offers a comprehensive insight into the hardware configuration, encompassing pivotal components such as the Arduino Uno microcontroller, HC-SR04 ultrasonic sensor, and the L298 driver responsible for water pump control. The experimental approach is meticulous, presenting results from tests involving the Proportional Controller, Proportional Integral (PI) Controller, and Proportional Integral Derivative (PID) Controller. These tests rigorously analyze the impact of varying Proportional Gain (Kp), Integral Gain (Ki), and Derivative Gain (Kd) parameters on crucial performance metrics such as response time, overshoot, and steady-state error. The findings underscore the critical importance of an optimal parameter configuration, emphasizing the delicate equilibrium between response speed, precision, and error minimization. This research significantly advances PID control implementation in liquid tank filling, offering insights that pave the way for developing more efficient liquid management systems across various sectors. The identified optimal parameter configuration is Kp = 5.0, Ki = 0.3, and Kd = 0.2

Genetic diversity in Tunisian horse breeds

This study aimed at screening genetic diversity and differentiation in four horse breeds raised in Tunisia, the Barb, Arab-Barb, Arabian, and English Thoroughbred breeds. A total of 200 blood samples (50 for each breed) were collected from the jugular veins of animals, and genomic DNA was extracted. The analysis of the genetic structure was carried out using a panel of 16 microsatellite loci. Results showed that all studied microsatellite markers were highly polymorphic in all breeds. Overall, a total of 147 alleles were detected using the 16 microsatellite loci. The average number of alleles per locus was 7.52 (0.49), 7.35 (0.54), 6.3 (0.44), and 6 (0.38) for the Arab-Barb, Barb, Arabian, and English Thoroughbred breeds, respectively. The observed heterozygosities ranged from 0.63 (0.03) in the English Thoroughbred to 0.72 in the Arab-Barb breeds, whereas the expected heterozygosities were between 0.68 (0.02) in the English Thoroughbred and 0.73 in the Barb breeds. All FST values calculated by pairwise breed combinations were significantly different from zero (p  <  0.05) and an important genetic differentiation among breeds was revealed. Genetic distances, the factorial correspondence, and principal coordinate analyses showed that the important amount of genetic variation was within population. These results may facilitate conservation programs for the studied breeds and enhance preserve their genetic diversity

3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge

Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, developing automated algorithms for teeth analysis presents significant challenges due to variations in dental anatomy, imaging protocols, and limited availability of publicly accessible data. To address these challenges, the 3DTeethSeg'22 challenge was organized in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2022, with a call for algorithms tackling teeth localization, segmentation, and labeling from intraoral 3D scans. A dataset comprising a total of 1800 scans from 900 patients was prepared, and each tooth was individually annotated by a human-machine hybrid algorithm. A total of 6 algorithms were evaluated on this dataset. In this study, we present the evaluation results of the 3DTeethSeg'22 challenge. The 3DTeethSeg'22 challenge code can be accessed at: https://github.com/abenhamadou/3DTeethSeg22_challengeComment: 29 pages, MICCAI 2022 Singapore, Satellite Event, Challeng

Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project

Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF)method, and performed by analyzing patterns and fluorescence intensity. This paper introduces the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold Standard database is used for optimization of aCAD(Computer AidedDetection) solution and for the assessment of its added value, in order to be applied along with an Immunologist as a second Reader in detection of autoantibodies. This CAD system is able to identify on IIF images the fluorescence intensity and the fluorescence pattern. Preliminary results show that CAD, used as second Reader, appeared to perform better than Junior Immunologists and hence may significantly improve their efficacy; compared with two Junior Immunologists, the CAD system showed higher Intensity Accuracy (85,5% versus 66,0% and 66,0%), higher Patterns Accuracy (79,3% versus 48,0% and 66,2%), and higher Mean Class Accuracy (79,4% versus 56,7% and 64.2%)

Preliminary results of the project A.I.D.A. (Auto Immunity: Diagnosis Assisted by computer)

In this paper, are presented the preliminary results of the A.I.D.A. (Auto Immunity: Diagnosis Assisted by computer) project which is developed in the frame of the cross-border cooperation Italy-Tunisia. According to the main objectives of this project, a database of interpreted Indirect ImmunoFluorescence (IIF) images on HEp 2 cells is being collected thanks to the contribution of Italian and Tunisian experts involved in routine diagnosis of autoimmune diseases. Through exchanging images and double reporting; a Gold Standard database, containing around 1000 double reported IIF images with different patterns including negative tests, has been settled. This Gold Standard database has been used for optimization of a computing solution (CADComputer Aided Detection) and for assessment of its added value in order to be used along with an immunologist as a second reader in detection of auto antibodies for autoimmune disease diagnosis. From the preliminary results obtained, the CAD appeared more powerful than junior immunologists used as second readers and may significantly improve their efficacy

Alternative methods for test and calibration of MEMS : application to convective accelerometer

Le test et la calibration des MEMS sont des enjeux complexes à cause de leur nature multi-domaines. Ils nécessitent l'application de stimuli physiques, en utilisant des équipements de test coûteux, afin de tester et de calibrer leurs spécifications. L'objectif de cette thèse est de développer des méthodes alternatives et purement électriques pour tester et calibrer un accéléromètre MEMS convectif. Premièrement, un modèle comportemental du capteur est développé et validé en se basant sur des simulations FEM. Il inclut l'influence de tous les paramètres géométriques sur la sensibilité du capteur. Deuxièmement, le modèle est utilisé pour simuler des fautes dans le but d'identifier la corrélation qui peut exister entre la sensibilité du capteur à l'accélération et certains paramètres électriques. Troisièmement, cette corrélation est exploitée pour développer des méthodes de test et de calibration alternatives où la sensibilité est estimée en effectuant uniquement des mesures électriques et sans appliquer de stimuli physiques (accélérations). L'efficacité de ces méthodes est ainsi démontrée. Finalement, deux architectures permettant l'auto-test et l'auto-calibration sur puce sont proposées.MEMS test and calibration are challenging issues due to the multi-domain nature of MEMS devices. They therefore require the application of physical stimuli, using expensive test equipments, to test and to calibrate their specifications. The main objective of this thesis is to develop alternative electrical-only test and calibration procedures for MEMS convective accelerometers.First, a behavioral model that includes the influence of sensor geometrical parameters on sensitivity is developed and validated with respect to FEM simulations. Second, the model is used to perform fault simulations and to identify correlation that may exist between device sensitivity to acceleration and some electrical parameters. Third, this correlation is exploited to develop alternative test and calibration methods where the sensitivity is estimated using only electrical measurements and without applying any physical stimulus (acceleration). The efficiency of these methods is demonstrated. Finally, two architectures that allow on-chip test and calibration are proposed