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Environmental monitoring plays a crucial role in various domains, including agriculture, healthcare, and manufacturing, where optimal environmental conditions are essential for productivity and safety. In this project, we present a smart environmental monitoring system that leverages IoT (Internet of Things) technology and data analytics to monitor temperature and humidity levels in real-time. The system consists of a network of sensor nodes deployed in the target environment, comprising ESP32 microcontrollers and DHT11 sensors for data collection. The sensor nodes transmit data using the MQTT (Message Queuing Telemetry Transport) protocol to a cloud-based MQTT Broker hosted on HiveMQ Cloud. Data processing and visualization are handled by Node-RED, which subscribes to MQTT topics, processes incoming data streams, and stores them in a time-series database, InfluxDB Cloud. The collected data is then visualized in real-time using Grafana dashboards, which are embedded within a Flask web application, providing stakeholders with seamless access to actionable insights into environmental conditions. The smart environmental monitoring system offers numerous benefits, including improved decisionmaking, proactive maintenance, and enhanced productivity. Future enhancements could include the integration of additional sensors and the application of machine learning algorithms for predictive analytics. Overall, the project demonstrates the potential of IoT and data analytics in addressing real-world challenges related to environmental monitoring.

This project aims to develop an innovative technique for detecting water stress in tomato plants using deep learning and image processing techniques, and to integrate it into a mobile application for real-time monitoring. The methodology adopted includes the acquisition and preprocessing of image data, the construction and training of a deep learning model, and the development of a user-friendly mobile application. The results show a promising performance of the model in the precise detection of water stress, confirming the usefulness and usability of the developed mobile application.

This paper presents the output feedback pole assignment methods that attempt to achieve a robust solution where the arbitrary choose of the poles to be assigned are as insensitive as possible to perturbations in the closed loop system. The robust and arbitrary pole placement problem is formulated as a nonlinear and non-convex programming problem with a nonlinear constraint on matrix functions as well as a linear constraint. The numerical treatment is the subject of a comparative study of the proposed methods.

This article explores several classical techniques of facial recognition, assessing their suitability for real-time embedded systems such as digital cameras. It also includes a thorough discussion on the training requirements for various facial recognition methods. Addressing mounting concerns in security and surveillance, this project aims to develop an advanced facial recognition system using a 2D detection approach. Leveraging the Intel RealSense 415 camera connected to a Raspberry Pi 3. An introduction to Haar Cascading models is provided, highlighting their advantages, particularly their ability to ensure acceptable levels of accuracy for facial recognition in unseen image collections. Lastly, the article offers a detailed description and implementation of a functional platform, accompanied by preliminary results.

COVID-19 is a disease that particularly attacks the respiratory system. As the situation worsens, the lungs function less effectively, requiring artificial respiration support to maintain adequate oxygen levels. Moreover, the remote supervision of the patient's conditions and remote control of the parameters of a portable breathing apparatus should help decongest hospitals. Such a concept will help healthcare professionals save considerable time by avoiding round trips among patients who do not represent acute respiratory failure. In this work, we describe the basic concepts of telemonitoring of remote patients during ventilation. Our goal is to guarantee the appropriate functioning of a portable respirator including management functionalities. Thus, medical staff can remotely supervise and configure the various components of the portable respiratory system according to the patient's condition.

This work presents the design and the modelling of an improved lead acid Battery charger for solar photovoltaic applications. In this context, the control unit of the battery charger is composed of two intelligent controllers. In the first state, an MPPT controller based on an Adaptive neuro-fuzzy inference system (ANFIS) is used to extract the full maximum power provided by the PV array, in the second stage, the control unit switches to the regulator mode on the basis of a fuzzy logic control block that offers the three charging stages according to DIN 41773 standard for lead-acid battery. In order to demonstrate the performance of the ANFIS controller, this paper presents also a comparison of several MPPT techniques for solar PV applications.

Nuclear and radiological materials, essential in various sectors, pose significant risks if misused for terrorism. The detonation of a radiological dispersal device (RDD) can have catastrophic consequences. This study examines how meteorological parameters and topography affect the dispersion of radioactive clouds from an RDD attack using the HotSpot model. Simulations were based on a hypothetical scenario involving a radioactive source at a football stadium renovation site. The study assesses wind speed, atmospheric stability, and precipitation's impact on plume dispersion and ground contamination. Results highlight the crucial role of these factors in determining the extent and severity of radiological contamination, providing essential insights for risk management and emergency planning. [ABSTRACT FROM AUTHOR]

In this paper, we present a Constrained Discete Model Predictive Control (CDMPC) strategy application for relative humidity control. In this sense, and for our system inside humidity dynamics description, a green-house prototype is engaged and a state space form which fits properly a set of collected data of the greenhouse humidity dynamics is presented as mathematical model. This latest is used for the CDMPC starategy application, which purpose is to select the best control moves based on an optimization procedure regarding the constraints on the control. By the means of Matlab/ Simulink and Yalmip toolbox algorithms, numerical simulations were held to proove the effectiveness of the controller, garanteeing both the constraints feasibility and system stability.

This paper presents the modeling of an intelligent combined MPPT and Lead-Acid battery charger controller for standalone solar photovoltaic systems. It involves the control of a DC/DC buck converter through a control unit, which contains two cascaded fuzzy logic controllers (FLC), that adjusts the required duty cycle of the converter according to the state of charge and the three stage lead acid battery charging system. The first fuzzy logic controller (FLC1) consists of an MPPT controller to extract the maximum power produced by the PV array, while the second fuzzy controller (FLC2) is aimed to control the voltage across the battery to ensure the three stage charging approach. This solution of employing two distinct cascaded fuzzy controllers surmounts the drawbacks of the classical chargers in which the voltage provided to the lead acid battery is not constant owing to the effects of the MPPT control which can automatically damage the battery. Thus, the suggested control strategy has the benefit of extracting the full power against the PV array, avoiding battery damage incurred by variable MPPT voltage and increasing the battery’s lifespan.

The present paper, introduces Adaptive Neuro Fuzzy Inference System (ANFIS) as one of the most mature and intelligent methods to predicte internal temperature and relative humidity of a greenhouse system. To conduct the application of the proposed strategy, an experimenntal greenhouse equipied with several sensors and actuators is engaged. In this sense a data base was collected during a period of day time where the temperature and relative humidity dynamics were observed inpresence of others climatic parameters and the actuators’ actions. The results demonstrate that by using ANFIS method, the predictions match the target points with a good accuracy. Therefore, the effectiveness of the strategy in term of both inside climate parameters’ prediction is guaranteed.