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All‐in‐one, versatile and low‐cost experimental set‐up to implement environmental stochasticity in mesocosms (PiStoch)
Environmental stochasticity in abiotic factors is inherent to ecosystems and is exacerbated by global change. However, experimental protocols typically cancel this factor by using cyclic or constant conditions, limiting the study of environmental variations. This simplification highlights the need for better methodological tools to control fluctuating environmental variables.
We present here a guidance and solution for generating and implementing stochastic environmental conditions through our Raspberry Pi System for environmental Stochasticity (PiStoch). This low‐cost, low‐tech and scalable method for mesocosm experiments also has the potential to replicate any other form of variability, including cyclic patterns.
This system successfully reproduced stochastic time series in temperature and oxygen manipulation experiments. Testing with two biological case studies (macrophyte biomass and freshwater fish oxygen consumption), it demonstrated that thermal stochasticity had stronger effects than mean temperature, highlighting the importance of studying fluctuating conditions.
Developing accessible methods to study organismal responses to environmental stochasticity is essential for improving the realism of laboratory experiments and enhancing the accuracy of physiological and ecological predictions.
Résumé
La stochasticité environnementale est inhérente aux écosystèmes et exacerbée par le changement climatique actuel. Cependant, les protocoles expérimentaux en laboratoire ne prennent pas en compte ce paramètre n’utilisant que des conditions cycliques ou constantes, limitant ainsi la connaissance des effets des variations environnementales sur le vivant. Cette simplification expérimentale souligne la nécessité de disposer d’outils méthodologiques performants pour contrôler les fluctuations des variables environnementales en laboratoire.
Nous présentons ici un appareillage accompagné d’un guide pour générer et mettre en œuvre des conditions environnementales stochastiques grâce à un système appelé PiStoch et basé sur un Raspberry Pi. Cette méthode peu coûteuse, simple à mettre en place et évolutive permet de reproduire toute forme de variabilité au sein de mésocosmes.
Ce système a reproduit avec succès des séries temporelles stochastiques lors d’expériences sur des variations de température et d’oxygène. Testé sur deux modèles biologiques différents (macrophytes et poissons d’eau douce), il a démontré qu’à moyenne égale, la stochasticité thermique avait des effets plus importants que la température constante, soulignant l’importance d’étudier ces conditions fluctuantes.
Le développement de méthodes accessibles pour étudier les réponses des organismes à la stochasticité environnementale est essentiel pour améliorer le réalisme des expériences en laboratoire et accroître la précision des prédictions physiologiques et écologiques.
Journal Article
Raspberry Pi for dummies
In Raspberry Pi For Dummies, 3rd Edition veteran tech authors Sean McManus and Mike Cook make it easier than ever to get you up and running on your Raspberry Pi, from setting it up, downloading the operating system, and using the desktop environment to editing photos, playing music and videos, and programming with Scratch--and everything in between.
Book
Design and Implementation of a Real-Time Smart Home Management System Considering Energy Saving
One of the most challenging problems related to the operation of smart microgrids is the optimal home energy management scheme with multiple and conflicting objectives. Moreover, there is a noticeable increase in homes equipped with renewable energy sources (RESs), where the coordination of loads and generation can achieve extra savings and minimize peak loads. In this paper, a solar-powered smart home with optimal energy management is designed in an affordable and secure manner, allowing the owner to control the home from remote and local sites using their smartphones and PCs. The Raspberry Pi 4 B is used as the brain of the proposed smart home automation management system (HAMS). It is used to collect the data from the existing sensors and store them, and then take the decision. The home is monitored using a graphical interface that monitors room temperature, humidity, smoke, and lighting through a set of sensors, as well as PIR sensors to monitor the people movement. This action enables remote control of all home appliances in a safe and emission-free manner. This target is reached using Cayenne, which is an IoT platform, in addition to building some codes related to some appliances and sensors not supported in Cayenne from scratch. Convenience for people with disabilities is considered by using the Amazon Echo Dot (Alexa) to control home appliances and the charging point by voice, implementing the associated code for connecting the Raspberry pi with Alexa from scratch, and simulating the system on LabVIEW. To reach the optimal operation and reduce the operating costs, an optimization framework for the home energy management system (HEMS) is proposed. The operating costs for the day amounted to approximately 16.039 €. There is a decrease in the operating costs by about 23.13%. The consumption decreased after using the smart HAMS by 18.161 kWh. The results of the optimization also show that the least area that can be used to install solar panels to produce the desired energy with the lowest cost is about 118.1039 m2, which is about 23.62% of the total surface area of the home in which the study was conducted. The obtained results prove the effectiveness of the proposed system in terms of automation, security, safety, and low operating costs.
Journal Article
Learn electronics with Raspberry Pi : physical computing with circuits, sensors, outputs, and projects
Learning electronics can be tremendous fun -- your first flashing LED circuit is a reason to celebrate! But where do you go from there, and how can you move into more challenging projects without spending a lot of money on proprietary kits? One excellent answer is Raspberry Pi. Raspberry Pi is everywhere, it's inexpensive, and it's a wonderful tool for teaching about electronics and programming. Learn Electronics with Raspberry Pi shows you how to make a variety of cool projects using the Pi with programming languages like Scratch and Python, with no experience necessary. You'll learn how the Pi works, how to work with Raspbian Linux on the Pi, and how to design and create electronic circuits. You'll then create projects like an arcade game, disco lights, and infrared transmitter, and an LCD display. You'll also learn how to control Minecraft's Steve with a joystick and how to build a Minecraft house with a Pi, and even how to control a LEGO train with a Pi. You'll even learn how to create your own robot, including how to solder and even design a printed circuit board! Learn how to design and build electronic circuits, and even how to make a PCB Learn how to make fun projects like an arcade game, a robot, and a Minecraft controller while learning about sensors and how devices talk to each other Get started programming the Pi with Scratch and Python.
Book
PYM: a new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments
Background
Plant science uses increasing amounts of phenotypic data to unravel the complex interactions between biological systems and their variable environments. Originally, phenotyping approaches were limited by manual, often destructive operations, causing large errors. Plant imaging emerged as a viable alternative allowing non-invasive and automated data acquisition. Several procedures based on image analysis were developed to monitor leaf growth as a major phenotyping target. However, in most proposals, a time-consuming parameterization of the analysis pipeline is required to handle variable conditions between images, particularly in the field due to unstable light and interferences with soil surface or weeds. To cope with these difficulties, we developed a low-cost, 2D imaging method, hereafter called PYM. The method is based on plant leaf ability to absorb blue light while reflecting infrared wavelengths. PYM consists of a Raspberry Pi computer equipped with an infrared camera and a blue filter and is associated with scripts that compute projected leaf area. This new method was tested on diverse species placed in contrasting conditions. Application to field conditions was evaluated on lettuces grown under photovoltaic panels. The objective was to look for possible acclimation of leaf expansion under photovoltaic panels to optimise the use of solar radiation per unit soil area.
Results
The new PYM device proved to be efficient and accurate for screening leaf area of various species in wide ranges of environments. In the most challenging conditions that we tested, error on plant leaf area was reduced to 5% using PYM compared to 100% when using a recently published method. A high-throughput phenotyping cart, holding 6 chained PYM devices, was designed to capture up to 2000 pictures of field-grown lettuce plants in less than 2 h. Automated analysis of image stacks of individual plants over their growth cycles revealed unexpected differences in leaf expansion rate between lettuces rows depending on their position below or between the photovoltaic panels.
Conclusions
The imaging device described here has several benefits, such as affordability, low cost, reliability and flexibility for online analysis and storage. It should be easily appropriated and customized to meet the needs of various users.
Journal Article
A Throughput Request Satisfaction Method for Concurrently Communicating Multiple Hosts in Wireless Local Area Network
Nowadays, the IEEE 802.11 wireless local area network (WLAN) has been widely used for Internet access services around the world. Then, the unfairness or insufficiency in meeting the throughput request can appear among concurrently communicating hosts with the same access point (AP), which should be solved by sacrificing advantageous hosts. Previously, we studied the fairness control method by adopting packet transmission delay at the AP. However, it suffers from slow convergence and may not satisfy different throughput requests among hosts. In this paper, we propose a throughput request satisfaction method for providing fair or different throughput requests when multiple hosts are concurrently communicating with a single AP. To meet the throughput request, the method (1) measures the single and concurrent throughput for each host, (2) calculates the channel occupying time from them, (3) derives the target throughput to achieve the given throughput request, and (4) controls the traffic by applying traffic shaping at the AP. For evaluations, we implemented the proposal in the WLAN testbed system with one Raspberry Pi AP and up to five hosts, and conducted extensive experiments in five scenarios with different throughput requests. The results confirmed the effectiveness of our proposal.
Journal Article
Learn robotics with Raspberry Pi : build and code your own moving, sensing, thinking robots
\"A beginner's guide to building and coding robots with the Raspberry Pi microcomputer. Each chapter teaches progressively complex lessons, from wireless control to line following, through detailed instructions and projects\"-- Provided by publisher.
Book
Portable Python Projects
Get to know the latest Raspberry Pi hardware, and create awesome automation solutions for home or work that don't require an electronics degree, cumbersome add-ons, or expensive third-party subscription services. Create easy-to-run Python scripts on your own that make your Pi do things that would have required a team of automation experts to build only a few years ago.
Connect to and control popular home automation lighting systems from a Raspberry Pi. Trigger autonomous actions based on movement, temperature, and timer events. Power on your own computer and appliances using your voice. Remotely control infrared-enabled consumer electronics, create chatbots to retrieve personalized items of interest, and implement a temperature-monitoring room fan. These are just some of the projects that the book will show you how to make. Most projects can be completed and operational in under an hour, and do not require any messy schematics or a spaghetti bowl of wires and breadboard-attached circuits to operate.
Control your home or office exactly the way you want instead of relying on an expensive mysterious box of third-party technology to do it for you.
eBook