Raspberry Pi Single-board Computer Research Articles

Development and Validation of Low-Cost Indoor Air Quality Monitoring System for Swine Buildings

The optimal indoor environment is associated with comfortable temperatures along with favorable indoor air quality. One of the air pollutants, particulate matter (PM), is potentially harmful to animals and humans. Most farms have monitoring systems to identify other hazardous gases rather than PM due to the sensor cost. In recent decades, the application of environmental monitoring systems based on Internet of Things (IoT) devices that incorporate low-cost sensors has elevated extensively. The current study develops a low-cost air quality monitoring system for swine buildings based on Raspberry Pi single-board computers along with a sensor array. The system collects data using 11 types of environmental variables along with temperature, humidity, CO2, light, pressure, and different types of gases, namely PM1, PM2.5, and PM10. The system is designed with a central web server that provides real-time data visualization and data availability through the Internet. It was tested in actual pig barns to ensure stability and functionality. In addition, there was a collocation test conducted by placing the system in two different pig barns to validate the sensor data. The Wilcoxon rank sum test demonstrates that there are no significant differences between the two sensor datasets, as all variables have a p-value greater than 0.05. However, except for carbon monoxide (CO), none of the variables exhibit correlation exceeding 0.5 with PM concentrations. Overall, a scalable, portable, non-complex, low-cost air quality monitoring system was successfully developed within a cost of USD 94.

Cost-effective 3D documentation device in forensic medicine

3D documentation in forensics and forensic medicine is being introduced more frequently in various institutes around the world. However, several institutes lack capacity in finances as well as staff to perform 3D documentations regularly. This technical paper aims to present a 3D documentation device that is low cost and easy to use and is a viable entry level solution for forensic medical departments. For this the small single-board computer Raspberry Pi 4 was used in conjunction with its high quality (HQ) camera module to create the 3DLamp - a flexible, low cost and easy to use documentation device. Besides a detailed description of the device this paper also presents four case examples where a 3D documentation was performed and analyses the acquired data and the created 3D models. It was found that the device returns feasible 3D models that appear usable for forensic 3D reconstructions.

State-of-the-art AI-enabled mobile device for real-time water stress detection of field crops

An AI-enabled mobile device was developed for real time identification of water stress in field crops with the aim to assist crop breeding and precision irrigation management programs. The device assembles a Raspberry Pi single board computer and a digital RGB camera. A validated, state-of-the-art, transfer-learning based deep learning model GoogLeNet was deployed on-board to process the captured images and classify those into stressed or non-stressed categories in real time. The captured images and results are displayed on a custom graphic user interface. The device is also capable of predicting stress in-house from historically collected images fed as inputs to the device. The results are produced in 200 ms post-capture/feeding of the images. The device was field-tested in wheat and maize crops and pertinent stress identification accuracies were 92.9% and 97.9%, respectively. The developed device offers a user-friendly mode to identify water stress in real time for the crop breeders, researchers, and ultimately growers for timely decision making on plant health and crop yield improvement. The device framework capabilities can be extended in the future for identifying multiple problems of various biotic and abiotic stresses in multiple crops, and their cultivars grown in various agroclimatic conditions.

An Indoor Localization System for Automotive Driving Competitions

Localization, in both indoor and outdoor settings, represents a problem that has received increased attention lately. This paper presents the development, testing, and validation of an indoor localization system for 1/10 scale vehicles based on the Robot Operating System (ROS) and ArUco marker detection. It has a distributed architecture, consisting of tens of Raspberry Pi (RPi) singleboard computers running ROS nodes and fitted with cameras, that monitor a certain area of the 14x14 m plane. The developed system has been successfully used in three editions of Bosch Future Mobility Challenge, an international student competition, where the participants are required to implement autonomous driving functionalities in an environment resembling a real-life city on small-scale automated vehicles.

TelePi: an affordable telepathology microscope camera system anyone can build and use.

Telepathology facilitates histological diagnoses through sharing expertise between pathologists. However, the associated costs are high and frequently prohibitive, especially in low-resource settings, where telepathology would paradoxically be of paramount importance due to a paucity of pathologists.We have constructed a telepathology system (TelePi) with a budget of < €120 using the small, single-board computer Raspberry Pi Zero and its High-Quality Camera Module in conjunction with a standard microscope and open-source software. The system requires no maintenance costs or service contracts, has a small footprint, can be moved and shared across several microscopes, and is independent from other computer operating systems. TelePi uses a responsive and high-resolution web-based live stream which allows remote consultation between two or more locations. TelePi can serve as a telepathology system for remote diagnostics of frozen sections. Additionally, it can be used as a standard microscope camera for teaching of medical students and for basic research. The quality of the TelePi system compared favorable to a commercially available telepathology system that exceed its cost by more than 125-fold. Additionally, still images are of publication quality equal to that of a whole slide scanner that costs 800 times more.In summary, TelePi is an affordable, versatile, and inexpensive camera system that potentially enables telepathology in low-resource settings without sacrificing image quality.

Research into LED RGB Light Fixture with Adjustable Radiation Spectrum for Aquarium Lighting

The article presents the results of a study of an aquarium lighting device prototype developed based on a RGB LED strip using computer technologies for measurement of its spectral characteristics on the basis of practical studies of the effect of chromaticity on flora and fauna of water bodies. The studies were carried out at Light and Engineering Metrology resource sharing centre of the Electronics and Light Engineering Institute of N.P. Ogarev Mordovia State University. For development of the prototype of an aquarium lighting device based on RGB LED strip, the results of colorimetric measurements of special fluorescent aquarium lamps made by Hagen and Havells Sylvania Europe LTD (Germany) were used. The laboratory set based on a Raspberry Pi single-board computer and software for colorimetric calculations and set control was developed for connection and adjustment of spectral characteristics of the RGB LED strip. The colorimetric characteristics of the prototype were compared to those of special fluorescent lamps for aquarium lighting. Satisfactory repeatability of spectra and CCT was demonstrated for the developed LED-based prototype aquarium lighting device. These studies will allow us to identify further areas for updating of functional and software solutions to make it possible to adjust spectral characteristics and to make them close to optimal levels for specific species.

An edge-computing framework for operational modal analysis of offshore wind-turbine tower

With massive construction of offshore wind turbines (OWT), structural health monitoring (SHM) techniques have been applied to estimate the working conditions and provide early warnings of structural damage. This study proposes an edge-computing-based SHM system for operational modal analysis (OMA) of an OWT. ThssCe system comprised an open-source data acquisition (DAQ) device, a Raspberry Pi single-board computer, and associated drivers, protocols, and algorithms. The DAQ device can measure the acceleration response of an operating OWT subjected to environmental excitation and transmit raw signals to the Raspberry Pi according to the UDP protocol. The data are further processed to obtain the first-order frequency via an OMA algorithm developed by combining Kalman filtering, the random decrement technique (RDT), and the stochastic subspace identification (SSI) method. The proposed SHM system was implemented in a scaled OWT model to verify its feasibility for field use. The experimental results indicate that (1) the edge-computing-based SHM system can accurately measure the response of the OWT; (2) the proposed algorithm can estimate the modal information of the OWT during operation; (3) the variation in the first-order frequency induced by lateral loads can be identified by the SHM system. Unlike a centralized SHM system, the proposed edge-computing-based SHM system does not require massive data transmission, making it promising for offshore environments.

ATHENA I—An architecture for near real-time physiological signal monitoring and pattern detection

Smart healthcare has wide application possibilities, including physiological signals analysis. With the development and improvement of microelectronic devices and sensors, wearable sensors can acquire physiological signals with quality and support remote health monitoring. Due to the growing use of these devices, there is a need for easy and flexible ways to use them in health monitoring. To contribute in this context, we developed the ATHENA I, an architecture focused on using wearables to acquire physiological signals and applying artificial intelligence to classify health patterns in real-time. Because of the constant improvement of wearable devices, we focused on developing a flexible and efficient architecture. Since many health centers outside the central regions of the metropolis have reduced connectivity, we designed an architecture capable of operating entirely outside the data network. The architecture prototype features BITalino wearable sensor and a Raspberry Pi single-board computer. For a complete prototype evaluation, we performed stress-inducing experiments conducted by psychologists to acquire a new dataset of physiological signs. We used this dataset in pattern classification in offline operations and near real-time situations. Our purpose with these experiments is to predict stress, being able to differentiate the normal state from the stress state. The classification results for the stress patterns showed promising results, with accuracy above 98.72% for binary classification (stress, no-stress); and 92.72% for classification with three classes (pre-stress, stress, and post-stress). In the real-time classification, we obtained an accuracy of 69.00%. The architecture presented excellent communication and operation stability. During the experiments, the architecture performed short and long acquisitions efficiently.

Hydrogel-enzyme micropatch array format for chemical mapping: A proof of concept

Conventional sensing methods report on concentrations of analytes in a single point of sampled medium or provide an average value. However, distributions of substances on surfaces of sampled objects often exhibit intricate inhomogeneities. In order to obtain snapshots of the chemical distributions on surfaces, we have developed enzyme-loaded hydrogel arrays (5 × 5 and 10 × 10). The acrylic 10 × 10 array base contains 100 holes, which are filled with agarose hydrogel containing assay enzymes and substrates. Such arrays can be exposed to the analyzed surfaces to collect minute amounts of analytes. Following a brief incubation, they are subsequently visualized in a custom-built array reader device. The reader incorporates a light-emitting diode-based light source, miniature camera, and Raspberry Pi single-board computer. Two Python programs capture and analyze the images of the array to extract pixel saturation values corresponding to individual hydrogel micropatches. The method has been thoroughly optimized for mapping of glucose and lactic acid. The optimized parameters were: contact time, agarose concentration, substrate concentration, enzyme concentration ratio, and enzyme concentration. The array biosensor was further tested by mapping glucose distribution in fruit/vegetable cross-sections (apple, guava, and cucumber) and lactic acid distribution in cheese. We think that this new hydrogel-based chemical mapping method can find applications in studies related to food science, plant physiology, clinical chemistry, and forensics; wherever the distributions of analytes on the tested surfaces need to be assessed.

FluoroPi Device With SmartProbes: A Frugal Point-of-Care System for Fluorescent Detection of Bacteria From a Pre-Clinical Model of Microbial Keratitis.

Rapid and accurate diagnosis of microbial keratitis (MK) could greatly improve patient outcomes. Here, we present the development of a rapid, accessible multicolour fluorescence imaging device (FluoroPi) and evaluate its performance in combination with fluorescent optical reporters (SmartProbes) to distinguish bacterial Gram status. Furthermore, we show feasibility by imaging samples obtained by corneal scrape and minimally invasive corneal impression membrane (CIM) from ex vivo porcine corneal MK models. FluoroPi was built using a Raspberry Pi single-board computer and camera, light-emitting-diodes (LEDs), and filters for white-light and fluorescent imaging, with excitation and detection of bacterial optical SmartProbes: Gram-negative, NBD-PMX (exmax 488 nm); Gram positive, Merocy-Van (exmax 590 nm). We evaluated FluoroPi with bacteria (Pseudomonas aeruginosa and Staphylococcus aureus) isolated from ex vivo porcine corneal models of MK by scrape (needle) and CIM with the SmartProbes. FluoroPi provides <1 µm resolution and was able to readily distinguish bacteria isolated from ex vivo models of MK from tissue debris when combined with SmartProbes, retrieved by both scrape and CIM. Single bacteria could be resolved within the field of view, with limits of detection demonstrated as 103 to 104 CFU/mL. Sample preparation prior to imaging was minimal (wash-free), and imaging and postprocessing with FluoroPi were straightforward, confirming ease of use. FluoroPi coupled with SmartProbes provides effective, low-cost bacterial imaging, delineating Gram-negative and Gram-positive bacteria directly sampled from a preclinical model of MK. This study provides a crucial stepping stone toward clinical translation of a rapid, minimally invasive diagnostic approach for MK.

Affordable and Easy Data Exploration of NIR Spectra Using Chemometric Techniques

The pharmaceutical industry relies heavily on analytical techniques for identifying and characterizing drug compounds. In this experiment, undergraduate pharmacy students explored the applications of near-infrared spectroscopy for pharmaceutical analysis and its potential for identifying patterns and relationships among pharmaceutical samples. In addition, they were introduced to the chemometrics field using classical pretreatment and data exploration methods. To reduce the need for expensive instrumental equipment, we have developed a small and affordable device based on the DLP NIRscan Nano EVM by Texas Instruments using free software solutions and a 3D-printed case for Raspberry Pi single-board computers. The goal of the experiment was to guide students through all the necessary steps for measuring and analyzing samples using NIR spectroscopy and to encourage their learning through the application of mathematical methods in a defined laboratory setting.

Development of FPGA-based standalone, portable TDCR system

A standalone, lightweight, portable, FPGA-based triple to double coincidence ratio (TDCR) system is developed. The optical chamber of the system is fabricated in a dual, coaxial cylindrical geometry, with the inner cylinder made up of Teflon and the outer one of aluminium. Three square PMTs, each having an active cathode area of 2.3 cm × 2.3 cm, are housed in the optical chamber. The variation in detection efficiency to identify the optimum kB (ionization quenching parameter) value is achieved by changing the vial position vertically with respect to the centre line of the PMTs. The coincidence analyzer implemented in a field programmable gate array (FPGA) transfers the pulse counts and associated parameters in real time to a Raspberry Pi single board computer (SBC). The TDCR algorithm to compute the efficiency is implemented in the SBC. In addition, the SBC is interfaced with a local 7” touchscreen to provide an intuitive graphical user interface (GUI) for operating the standalone instrument. The system performance is validated with 3H and 14C radionuclide standards.

Students Attendance Monitoring &amp; Accessing Control Based Raspberry - PI

Abstract: This project is "IOT based facial recognition attendance monitoring system". In order to maintain student attendance records, this project focuses on facial detection and radio frequency identification cards for an attendance recorder system. In class databases, student faces are already preserved. If the image from the database and the one taken by the raspberry pi camera match, the student's attendance will be tracked over time. Additionally, RF ID cards are utilized to regulate entry in certain areas. The ARM1176JZF-S core is used by the credit card-sized Raspberry Pi single-board computer. System on a Chip is a technique for packing all the circuitry required to run a computer onto a single chip. An operating system is required for Raspberry Pi to launch. In order to cut costs, the Raspberry Pi does not have any on-board non-volatile memory, which is typically used to hold file systems, Linux kernels, and boot loaders in more conventional embedded systems. To accomplish this, an SD/MMC card slot is included. the application-specific boot load after

Tele-ultrasound imaging using smartphones and single-board PCs

BACKGROUND: Mobile devices are widely available and their computational performance increases. Nonetheless, medicine should not be an exception: single-board computers and mobile phones are crucial aides in telehealth.&#x0D; AIM: To explore tele-ultrasound scope using smartphones and single-board computers&#x0D; MATERIALS AND METHODS: This study focused on capturing ultrasound videos using external video recording devices connected via USB. Raspberry Pi single-board computers and Android smartphones have been used as platforms to host a tele-ultrasound server. Used software: VLC, Motion, and USB camera. A remote expert assessment was performed with mobile devices using the following software: VLC acted as a VLC server, Google Chrome for OS Windows 7 and OS Android was used in the remaining scenarios, and Chromium browser was installed on the Raspberry Pi computer.&#x0D; OUTCOMES: The UTV007 chip-based video capture device produces better images than the AMT630A-based device. The optimum video resolution was 720576 and 25 frames per second. VLC and OBS studios are considered the most suitable for a raspberry-based ultrasound system owing to low equipment and bandwidth requirements (0.640.17 Mbps for VLC; 0.5 Mbps for OBS studio). For Android phone OS, the ultrasound system was set with the USB camera software, although it required a faster network connection speed (5.20.3 Mbps).&#x0D; CONCLUSION: The use of devices based on single-board computers and smartphones implements a low-cost tele-ultrasound system, which potentially improves the quality of studies performed through distance learning and consulting doctors. These solutions can be used in remote regions for field medicine tasks and other possible areas of m-health.

Embedded Internet of Things (IoT) Enviromental Monitoring System Using Raspberry PI

Background: Measuring and monitoring the temperature and humidity levels in a computer laboratory is crucial for ensuring the safety of the facility and the comfort of those using it. The latest technology for collecting this data is the Internet of Things (IoT), which uses sensors to monitor these parameters remotely. Aim: This paper presents an IoT system that assesses the temperature and humidity levels in a computer laboratory using a Raspberry Pi single-board computer and a DHT11 sensorMethod: The major contribution of this research is it utilizes the ThingSpeak Cloud database which provide the result faster within the shortest interval time which improve the temperature and humidity values more than the traditional Message Queue Telemetry Transport (MQTT). The DHT11 sensor captures the temperature and humidity data, which is then uploaded to the servers in real-time. This allows laboratory managers and decision-makers to monitor the laboratory's condition through a web portal or a mobile application installed on their Android or iOS devices from anywhere.Results:The results show that the ThingSpeak Cloud database provides the result of the temperatureas to 31.5°C and the relative humidity 74.8%.

IMPROVEMENT OF THE METHOD OF TESTING THE DYNAMIC PROPERTIES OF WHEELED MACHINES WITH THE USE OF ARTIFICIAL INTELLIGENCE

The method of testing vehicles for dynamic position stability has been improved, which makes it possible to assesstheir lateral stability when driving on irregularities. The dynamic stability coefficient is proposed for use as an assessmentcriterion. The application of the developed method allows to obtain an assessment of stability in real time during theoperation of transport means.The structural scheme has been developed to ensure the reliability of the operation of wheeled vehicle, based onreducing the influence of the driver's qualifications as an element of the "driver-vehicle-road conditions" system on itsposition stability, taking into account the results of monitoring the technical condition.To conduct experiments and evaluate the results, the mobile registration and measurement complex (MRMC) wasused to assess and improve the safety of exploitation of wheeled vehicles, taking into account modern means andapproaches. A single-board computer Raspberry Pi version 3B + serves as a platform for the mobile registration andmeasurement complex. The architecture of MRMC is modular; it supports the installation of additional inertial sensors,photo and video fixation devices, GPS, as well as devices compatible with USB. Operating system – Raspbian based onDebian distribution (GNU / Linux).To further automate the process of controlling a wheeled vehicle, the promising scheme for the operation of theregistration and measuring complex with elements of artificial intelligence was developed, which allows, without theparticipation of the driver, to carry out the necessary actions to control it and ensure, including operational safety.The results can be used to assess the suitability and performance of wheeled vehicles.

Evaluation of Sources of Artificial Light at Night With an Autonomous Payload in a Sounding Balloon Flight

The presence of artificial light at night is not only limiting astronomical observations but has been linked to negative effects on human health and behavior of wildlife. New measurement systems are therefore needed to monitor artificial light emissions and their time evolution; MINLU autonomous payload has been designed and tested at University of Padova to provide complete aerial observations of artificial light sources over extended areas, with the capability to be integrated either on stratospheric balloons or drones. The implemented architecture is based on commercial components and is controlled by a Raspberry PI single board computer with the capability of uninterrupted operation up to 5 hours. The payload was successfully launched with a stratospheric sounding balloon on July 8th 2021 from Lajatico (Tuscany) and performed continuous analysis of emission sources up to the burst altitude of 34 km. The work will describe the calibration activity of the imaging unit which includes commercial cameras with dedicated filters used as luminance measuring device and raw spectrometer and present the elaboration of georeferenced images after reconstruction of the unit's inertial pointing along flight trajectory using combined GPS and IMU data integration.

Smart Quarantine Environment Privacy through IoT Gadgets Using Blockchain

The coronavirus, formerly known as COVID-19, has caused massive global disasters. As a precaution, most governments imposed quarantine periods ranging from months to years and postponed significant financial obligations. Furthermore, governments around the world have used cutting-edge technologies to track citizens’ activity. Thousands of sensors were connected to IoT (Internet of Things) devices to monitor the catastrophic eruption with billions of connected devices that use these novel tools and apps, privacy and security issues regarding data transmission and memory space abound. In this study, we suggest a blockchain-based methodology for safeguarding data in the billions of devices and sensors connected over the internet. Various trial secrecy and safety qualities are based on cutting-edge cryptography. To evaluate the proposed model, we recommend using an application of the system, a Raspberry Pi single-board computer in an IoT system, a laptop, a computer, cell phones and the Ethereum smart contract platform. The models ability to ensure safety, effectiveness and a suitable budget is proved by the Gowalla dataset results.

92 Mb/s Fat-Intrabody Communication (Fat-IBC) With Low-Cost WLAN Hardware.

The human subcutaneous fat layer, skin and muscle together act as a waveguide for microwave transmissions and provide a low-loss communication medium for implantable and wearable body area networks (BAN). In this work, fat-intrabody communication (Fat-IBC) as a human body-centric wireless communication link is explored. To reach a target 64 Mb/s inbody communication, wireless LAN in the 2.4 GHz band was tested using low-cost Raspberry Pi single-board computers. The link was characterized using scattering parameters, bit error rate (BER) for different modulation schemes, and IEEE 802.11n wireless communication using inbody (implanted) and onbody (on the skin) antenna combinations. The human body was emulated by phantoms of different lengths. All measurements were done in a shielded chamber to isolate the phantoms from external interference and to suppress unwanted transmission paths. The BER measurements show that, except when using dual on-body antennas with longer phantoms, the Fat-IBC link is very linear and can handle modulations as complex as 512-QAM without any significant degradation of the BER. For all antenna combinations and phantoms lengths, link speeds of 92 Mb/s were achieved using 40 MHz bandwidth provided by the IEEE 802.11n standard in the 2.4 GHz band. This speed is most likely limited by the used radio circuits, not the Fat-IBC link. The results show that Fat-IBC, using low-cost off-the-shelf hardware and established IEEE 802.11 wireless communication, can achieve high-speed data communication within the body. The obtained data rate is among the fastest measured with intrabody communication.

Maka Niu: A low-cost, modular imaging and sensor platform to increase observation capabilities of the deep ocean

The deep sea (&amp;gt;200 m) is vast, covering 92.6% of the seafloor and largely unexplored. Imaging and sensor platforms capable of surviving the immense pressures at these depths are expensive and often engineered by individuals and institutions in affluent countries as unique, monolithic vehicles that require significant expertise and investment to build, operate, and maintain. Maka Niu was co-designed with a global community of deep-sea researchers. It is a low-cost, modular imaging and sensor platform that leverages off-the-shelf commodity hardware along with the efficiencies of mass production to decrease the price per unit and allow more communities to explore previously unseen regions of the deep ocean. Maka Niu combines a Raspberry Pi single-board computer, a Pi Camera Module V2, and a novel pressure housing and viewport combination capable of withstanding 1,500 m water depth. Other modules, including high-lumen LEDs, can be engineered to use the same battery charging and control system and form factor, allowing for an ever-increasing number of capabilities to be added to the system. After deployment, imagery and sensor data are wirelessly uploaded to Tator, an integrated media management and machine learning backend for automated analysis and classification. Maka Niu’s mobile mission programming and data management systems are designed to be user-friendly. Here, Maka Niu is described in detail along with data and imagery recorded from deployments around the world.