Raspberry Pi Computer Research Articles

Camera-Based Human Gait Speed Monitoring and Tracking for Performance Assessment of Elderly Patients with Cancer.

This paper presents a camera-based device for monitoring walking gait speed. The walking gait speed data will be used for performance assessment of elderly patients with cancer and calibrating wearable walking gait speed monitoring devices. This standalone device has a Raspberry Pi computer, three cameras (two cameras for finding the trajectory and gait speed of the subject and one camera for tracking the subject), and two stepper motors. The stepper motors turn the camera platform left and right and tilt it up and down by using video footage from the center camera. The left and right cameras are used to record videos of the person walking. The algorithm for operating the proposed system is developed in Python. The measured data and calculated outputs of the system consist of times for frames, distances from the center camera, horizontal angles, distances moved, instantaneous gait speed (frame-by-frame), total distance walked, and average speed. This system covers a large Lab area of 134.3 m2 and has achieved errors of less than 5% for gait speed calculation.Clinical Relevance- This project will help specialists to adjust the chemo dosage for elderly patients with cancer. The results will be used to analyze the human walking movements for estimating frailty and rehabilitation applications, too.

A cheap electronic sensor automated trap for monitoring the flight activity period of moths

Automated pheromone dispensers disrupt the mating behaviour of pest moths by releasing pheromone during their daily activity period, which is not the same for all target species. These periods usually occur in or close to night time and last just a few hours, so automated sampling devices are needed to characterize them. However, the commercially available automated models do not provide enough temporal resolution for characterizing the short diel periods of sexual activity of moths. Thus, we built and tested a relatively cheap and simple high-temporal-resolution image-sensor insect trap. It consisted of a Raspberry Pi computer with an infrared camera operated by open-source software and housed in a plastic box. The Raspberry Pi was powered by a solar panel and rechargeable battery that were mounted on a solid and weather-proof structure made of cheap materials. Pictures were downloaded by WiFi from the Raspberry's SD card to a computer. Six traps baited either with synthetic sex pheromone or with females of Grapholita molesta (Busk) were tested in the field. The traps were sturdy, reliable and easy to use, taking pictures at 10 min intervals, 24 h a day for over two months. These pictures confirmed previous results regarding the period of sexual activity of the oriental fruit moth, which will aid in determining the optimal time for operating automated pheromone dispensers.

Design of an Infusion Monitoring System Based On Image Processing

At present, infusion monitoring heavily relies on nursing rounds or supervision. In many cases, nursing staff fail to remove patients’ cannula in a timely manner after intravenous infusion due to negligence, which leads to serious swelling and blood backflow at the chosen venipuncture area over time, causing pain and even endangering patient lives. This study designed a smart real-time liquid level detection system based on image processing to solve this difficulty. By running the canny edge detection algorithm and Hough Transform (HT) algorithm on a Raspberry Pi computer with an industrial camera, the system extracted and calculated the image’s pixels for judgment. As the number of pixels in the detected area reaches the alarm value, the system shall issue an alarm. In this experiment, a 62mm drip chamber was selected, and the system could achieve a high success rate of 98% with a set detection width of 5mm. The experimental results showed that whether the liquid level reached the alarm level could be accurately and effectively-identified utilizing the system. The information could be transmitted to the receiving end promptly with a high success rate, which verified the system’s effectiveness. Given its real-time performance and high accuracy, the system has excellent application prospects.

The Global Meteor Network – Methodology and first results

ABSTRACT The Global Meteor Network (GMN) utilizes highly sensitive low-cost CMOS video cameras which run open-source meteor detection software on Raspberry Pi computers. Currently, over 450 GMN cameras in 30 countries are deployed. The main goal of the network is to provide long-term characterization of the radiants, flux, and size distribution of annual meteor showers and outbursts in the optical meteor mass range. The rapid 24-h publication cycle the orbital data will enhance the public situational awareness of the near-Earth meteoroid environment. The GMN also aims to increase the number of instrumentally observed meteorite falls and the transparency of data reduction methods. A novel astrometry calibration method is presented which allows decoupling of the camera pointing from the distortion, and is used for frequent pointing calibrations through the night. Using wide-field cameras (88° × 48°) with a limiting stellar magnitude of +6.0 ± 0.5 at 25 frames per second, over 220 000 precise meteoroid orbits were collected since 2018 December until 2021 June. The median radiant precision of all computed trajectories is 0.47°, 0.32° for $\sim 20{{\ \rm per\ cent}}$ of meteors which were observed from 4 + stations, a precision sufficient to measure physical dispersions of meteor showers. All non-daytime annual established meteor showers were observed during that time, including five outbursts. An analysis of a meteorite-dropping fireball is presented which showed visible wake, fragmentation details, and several discernible fragments. It had spatial trajectory fit errors of only ∼40 m, which translated into the estimated radiant and velocity errors of 3 arcmin and tens of meters per second.

Residential Water Meters as Edge Computing Nodes: Disaggregating End Uses and Creating Actionable Information at the Edge.

We present a new, open source, computationally capable datalogger for collecting and analyzing high temporal resolution residential water use data. Using this device, execution of water end use disaggregation algorithms or other data analytics can be performed directly on existing, analog residential water meters without disrupting their operation, effectively transforming existing water meters into smart, edge computing devices. Computation of water use summaries and classified water end use events directly on the meter minimizes data transmission requirements, reduces requirements for centralized data storage and processing, and reduces latency between data collection and generation of decision-relevant information. The datalogger couples an Arduino microcontroller board for data acquisition with a Raspberry Pi computer that serves as a computational resource. The computational node was developed and calibrated at the Utah Water Research Laboratory (UWRL) and was deployed for testing on the water meter for a single-family residential home in Providence City, UT, USA. Results from field deployments are presented to demonstrate the data collection accuracy, computational functionality, power requirements, communication capabilities, and applicability of the system. The computational node’s hardware design and software are open source, available for potential reuse, and can be adapted to specific research needs.

Image analysis for the automatic phenotyping of Orobanche cumana tubercles on sunflower roots

BackgroundThe parasitic plant Orobanche cumana is one of the most important threats to sunflower crops in Europe. Resistant sunflower varieties have been developed, but new O. cumana races have evolved and have overcome introgressed resistance genes, leading to the recurrent need for new resistance methods. Screening for resistance requires the phenotyping of thousands of sunflower plants to various O. cumana races. Most phenotyping experiments have been performed in fields at the later stage of the interaction, requiring time and space. A rapid phenotyping screening method under controlled conditions would need less space and would allow screening for resistance of many sunflower genotypes. Our study proposes a phenotyping tool for the sunflower/O. cumana interaction under controlled conditions through image analysis for broomrape tubercle analysis at early stages of the interaction.ResultsWe optimized the phenotyping of sunflower/O. cumana interactions by using rhizotrons (transparent Plexiglas boxes) in a growth chamber to control culture conditions and Orobanche inoculum. We used a Raspberry Pi computer with a picamera for acquiring images of inoculated sunflower roots 3 weeks post inoculation. We set up a macro using ImageJ free software for the automatic counting of the number of tubercles. This phenotyping tool was named RhizOSun. We evaluated five sunflower genotypes inoculated with two O. cumana races and showed that automatic counting of the number of tubercles using RhizOSun was highly correlated with manual time-consuming counting and could be efficiently used for screening sunflower genotypes at the tubercle stage.ConclusionThis method is rapid, accurate and low-cost. It allows rapid imaging of numerous rhizotrons over time, and it enables image tracking of all the data with time kinetics. This paves the way toward automatization of phenotyping in rhizotrons that could be used for other root phenotyping, such as symbiotic nodules on legumes.

Frequently Updated Maps and their Public Display

The display of maps on computer monitors in a public setting can be used to emphasize their value in conveying spatial patterns. For thematic maps, by removing the possibility for interaction, more attention can be focused on the mapped distributions. Maps that lend themselves best for public display are those that are frequently updated, such as weather maps. Other types of frequently updated maps (FUMs) include those of earthquakes, air pollution, and health conditions, such as the spread of a virus. These types of maps are increasingly provided through the internet in an interactive format, making the resultant maps less suited for public display. Described here are available maps that could be displayed in a public setting, and a method to make maps for quick display based on available data. A series of these maps can then be assembled and shown in a continuous loop. The display of maps for the public can be implemented using the low-cost, Raspberry Pi computer. Maps that are suitable for public display, instructions for implementation and the required code are available at: maps.unomaha.community/FUMPD/About.html.

Online System for Monitoring and Analysis of the Operation of a Small Photovoltaic Plant

This article proposes an integrated system for monitoring and analysis of the operation of a small photovoltaic plant with the possibility of remote access via the Internet. The system has been built on the territory of Varna Technical University and is based on a mini computer Raspberry Pi 3B + using a Linux operating system. The monitoring is performed by keeping track of the environmental parameters and the input-output parameters of the photovoltaic inverter. Data are presented for a period of three months: October 1, 2020 to December 31, 2020. The results are visualized by appropriate graphs, demonstrating the change in the observed indicators, both for the entire specified period and for a randomly selected day.

Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.

PrintrLab incubator: A portable and low-cost CO2 incubator based on an open-source 3D printer architecture.

Growth in open-source hardware designs combined with the decreasing cost of high-quality 3D printers have supported a resurgence of in-house custom lab equipment development. Herein, we describe a low-cost (< $400), open-source CO2 incubator. The system is comprised of a Raspberry Pi computer connected to a 3D printer controller board that has controls for a CO2 sensor, solenoid valve, heater, and thermistors. CO2 is supplied through the sublimation of dry ice stored inside a thermos to create a sustained 5% CO2 supply. The unit is controlled via G-Code commands sent by the Raspberry Pi to the controller board. In addition, we built a custom software application for remote control and used the open-source Grafana dashboard for remote monitoring. Our data show that we can maintain consistent CO2 and temperature levels for over three days without manual interruption. The results from our culture plates and real-time PCR indicate that our incubator performed equally well when compared to a much more expensive commercial CO2 incubator. We have also demonstrated that the antibiotic susceptibility assay can be performed in this low-cost CO2 incubator. Our work also indicates that the system can be connected to incubator chambers of various chamber volumes.

Camera Based Smart Surveillance System

This paper is the survey of Smart camera based surveillance monitoring system using Raspberry pi. Camera based surveillance is important in all sectors, they can be colleges and hospitals, shopping malls and other challenging indoor and outdoor environments require high end cameras. This paper focus on low-cost project on single board computer Raspberry Pi. This is new technology and far less expensive and, it is being used as a main platform for video detection and acquisition. It can be used with involvement of mobile network (internet) to provide essential security and surveillance to our properties and for other control applications. The security system records information and transmits it via network to a Smart Phone using web application Raspberry pi.

Colour Detection Based Robotic Cart Using Raspberry Pi

One of the most critical subjects of embedded vision is color tracking in real time. Many computer vision applications begin by detecting and tracking moving objects in video scenes. Customers arriving at hypermarkets may benefit from this concept. A color detection algorithm locates pixels in an image that fit a predetermined color scheme. To differentiate detected pixels from the rest of the image, the color of the detected pixels can be modified. The robot is programmed to track objects by turning left and right to keep the target in view and driving forward and backward to keep the distance between the robot and the object steady. By maintaining a surrounding distance, detection of other objects of the same color pattern is ignored. By keeping a safe distance between the user and the robot, other objects of the same color pattern are not detected. The camera on an ARM11 Raspberry Pi computer attached to the robot is used to capture images. Using inbuilt python files, the acquired image is processed to locate the color using RGB varying pattern methodology. To make the product work smarter, this system also includes automatic billing via RFID reader and tag. The new concept of image processing domain is based on this device theory.

Effects of Combining Multi-Core Processors in Creating Super-Computers Using Raspberry Pis

The vast resources of old working computers are getting bigger and bigger. Companies that consider these old computers as obsolete but still working put these computers to the storage, or worse, junk. Different entities are more and more relying on supercomputers to replace their ordinary and old computers. Supercomputers have become a basic necessity in this generation. It has a wide variety of functions that make our daily work easier. Having a well-built supercomputer with excellent specifications is a must; however, it comes with a cost. The computers can be clustered to create a supercomputer. The computers used in these clustered supercomputers have the exact specifications, such as uniform models with the same number of cores per processor and the same memory capacity. This study focuses on creating a supercomputer using different processors with a different number of cores per processor, processor speed, and different memory capacity. This study can be a valuable system for entities that have old computers in their workplace. They can reuse their old computers and combine them with other computers to create a supercomputer, thus reducing waste and recycling old resources. This study will be using different types of Raspberry Pi (RPi) computers combining old and new models to simulate the scenario of combining multi-core processors in creating supercomputers.

MATHEMATICAL MODELS PRODUCTIVITY OF CLUSTER SYSTEM BASED ON RASPBERRY PI 3B+

Context. High-performance computing systems are needed to solve many scientific problems and to work with complex applied problems. Previously, real parallel data processing was supported only by supercomputers, which are very limited and difficult to access. Currently, one way to solve this problem is to build small, cheap clusters based on single-board computers Raspberry Pi.&#x0D; Objective. The goal of the work is the creation of a complex criterion for the efficiency of the cluster system, which could properly characterize the operation of such a system and find the dependences of the performance of the cluster system based on Raspberry Pi 3B+ on the number of boards in it with different cooling systems.&#x0D; Method. It is offered to apply in the analysis of small cluster computer systems the complex criterion of efficiency of work of cluster system which will consider the general productivity of cluster computer system, productivity of one computing element in cluster computer system, electricity consumption by cluster system, electricity consumption per one computing element, the cost of calculating 1 Gflops cluster computer system, the total cost of the cluster computer system.&#x0D; Results. The developed complex criterion of cluster system efficiency was used to create an experimental cluster system based on single-board computers Raspberry Pi 3B+. Mathematical models of the dependence of the performance of a small cluster system based on single-board computers Raspberry Pi 3B+ depending on the number of boards in it with different cooling systems have also been developed.&#x0D; Conclusions. The conducted experiments confirmed the expediency of using the developed complex criterion of efficiency of the cluster system and allow to recommend it for use in practice when creating small cluster systems. Prospects for further research are to determine the weights of the constituent elements of the complex criterion of efficiency of the cluster system, as well as in the experimental study of the proposed weights.

The DAQ system of the 12,000 channel CMS high granularity calorimeter prototype

The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC) [1]. Concomitant with this increase will be an increase in the number of interactions in each bunch crossing and a significant increase in the total ionising dose and fluence. One part of this upgrade is the replacement of the current endcap calorimeters with a high granularity sampling calorimeter equipped with silicon sensors, designed to manage the high collision rates [2]. As part of the development of this calorimeter, a series of beam tests have been conducted with different sampling configurations using prototype segmented silicon detectors. In the most recent of these tests, conducted in late 2018 at the CERN SPS, the performance of a prototype calorimeter equipped with ≈12,000 channels of silicon sensors was studied with beams of high-energy electrons, pions and muons. This paper describes the custom-built scalable data acquisition system that was built with readily available FPGA mezzanines and low-cost Raspberry Pi computers.

Observation of atmospheric gravity waves using a Raspberry Pi camera module on board the International Space Station

We identified and computed the horizontal wavelengths of atmospheric gravity waves in clouds using a visible camera installed on a window of the Columbus module of the International Space Station (ISS) and controlled by a Raspberry Pi computer. The experiment was designed in the context of the Astro Pi challenge, a project run by ESA in collaboration with the Raspberry Pi Foundation, where students are allowed the opportunity to write a code to be executed at the ISS. A code was developed to maximize the probability of capturing images of clouds while the ISS is orbiting the Earth. Several constraints had to be fulfilled such as the experiment duration limit (3 h) and the maximum data size (3 GB). After receiving the data from the ISS, small-scale gravity waves were observed in different regions in the northern hemisphere with horizontal wavelengths in the range of 3.9 to 5.1km.

A High Performance Communication Architecture for a Smart Micro-Grid Testbed Using Customized Edge Intelligent Devices (EIDs) With SPI and Modbus TCP/IP Communication Protocols

This paper presents a high performance 4-layer communication architecture for a smart micro-grid testbed which consists of a 2 kVA Distributed Energy Resource (DER) inverter with PV and battery channels capable of advanced grid supportive and grid forming functions in the Process layer, a Raspberry Pi computer in the Interface layer, a customized Edge Intelligent Device (EID) in the Sub-station layer, and an end-to-end solar energy optimization platform (e-SEOP) in the Supervisory layer. The Raspberry Pi serves as a communication interface which communicates with the DER inverter using Serial Peripheral Interface (SPI) communication and talks to the EID using Modbus TCP/IP protocol. This paper provides a convenient and comprehensive solution to SPI and Modbus TCP/IP communication implementation in a smart micro-grid. The challenges of coordination between communication and system control and between different communication protocols have been addressed, which leads to a boost in the communication efficiency and makes the system highly scalable, flexible and adaptive. The proposed communication architecture as well as the micro-grid test bench have been validated through hardware experiments. The results indicate a reliable and efficient communication among different layers, which facilitates large-scale status monitoring of edge devices and coordinated control of the entire system.

The Raspberry Pi auto-aligner: Machine learning for automated alignment of laser beams

We present a novel solution to automated beam alignment optimization. This device is based on a Raspberry Pi computer, stepper motors, commercial optomechanics and electronic devices, and the open-source machine learning algorithm M-LOOP. We provide schematic drawings for the custom hardware necessary to operate the device and discuss diagnostic techniques to determine the performance. The beam auto-aligning device has been used to improve the alignment of a laser beam into a single-mode optical fiber from manually optimized fiber alignment, with an iteration time of typically 20minutes. We present example data of one such measurement to illustrate device performance.

Assessment of low-cost wireless sensors for structural health monitoring applications

Abstract Structural Health Monitoring (SHM) programs play an essential task in the field of civil engineering, especially for assessing safety conditions involving large structures such as viaducts, bridges, stadiums, and tall buildings. In fact, some of these structures are monitored 24 hours a day, 7 days a week, to supply dynamic measurements that can be used for the identification of structural problems, e.g., presence of cracks, excessive vibration, damage, among others. SHM programs may provide automated assessment of structural health by processing vibration data obtained from sensors attached to the structure. Frequently, SHM uses wired systems, which are usually expensive due to the necessity of continuous maintenance and are not always suitable for sensing remote structures. Conversely, commercial wireless systems often demand high implementation costs. Hence, this paper proposes the use of a low-cost wireless sensing system based on the single board computer Raspberry Pi, which significantly reduces implementation expenses while keeping data’s integrity. The wireless communication is performed in real-time through a local wireless network, responsible for sending and receiving vibration data. The proposed system is validated by comparing its results with a commercial wired system through a series of controlled experimental applications. The results suggest that the proposed system is suitable for civil SHM applications.

COMPUTER VISION BASED ON RASPBERRY PI SYSTEM

The paper focused on designing and developing a Raspberry Pi based system employing a camera which is able to detect and count objects within a target area. Python was the programming language of choice for this work. This is because it is a very powerful language, and it is compatible with the Pi. Besides, it lends itself to rapid application development and there are online communities that program Raspberry Pi computer using python. The results show that the implemented system was able to detect different kinds of objects in a given image. The number of objects were also generated displayed by the system. Also the results show an average efficiency of 90.206% was determined. The system is therefore seen to be highly reliable.