Raspberry Pi Single-board Computer Research Articles

Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification.

Electrooculography (EOG) signals have been widely used in Human-Computer Interfaces (HCI). The HCI systems proposed in the literature make use of self-designed or closed environments, which restrict the number of potential users and applications. Here, we present a system for classifying four directions of eye movements employing EOG signals. The system is based on open source ecosystems, the Raspberry Pi single-board computer, the OpenBCI biosignal acquisition device, and an open-source python library. The designed system provides a cheap, compact, and easy to carry system that can be replicated or modified. We used Maximum, Minimum, and Median trial values as features to create a Support Vector Machine (SVM) classifier. A mean of 90% accuracy was obtained from 7 out of 10 subjects for online classification of Up, Down, Left, and Right movements. This classification system can be used as an input for an HCI, i.e., for assisted communication in paralyzed people.

Using the Raspberry Pi single-board computer for solving problems in MATLAB when developing an underwater vehicle

This article represents description of the developed concept of an underwater remotely controlled vehicle (URCV). Explains the steps required to establish a connection between the MATLAB computational environment and the Raspberry Pi single-board computer. The ways of using the Raspberry Pi hardware in MATLAB are considered. Using the Raspberry Pi hardware resources allows you to establish a connection between the computer environment and the outside world.

Multi-Model Biometric Authentication System for Smart Attendance

To increase the success rate in academics, attendance is an essential aspect for every student in schools and degree colleges. In olden days, this attendance is manually taken by teachers with pen and paper method, which consumes more amount of time in their busy management scheduling era. To make this attendance taking more comfortable and more accurate, a multi model biometric system for attendance monitoring system is proposed using a Raspberry Pi single-board computer. The camera and biometric device which is connected to the system gathers Information regarding the students by recognizing their faces and their fingerprint simultaneously. If both of them match with the student details stored in the database, then the system will be sending an alert about the student presence in the class. The student details which is stored into the database is collected from the students initially. By using these details like images and fingerprints the system is trained by using a Convolutional Neural Network (CNN) Machine Learning Algorithm.

Access Control Mechanism for IoT using Blockchain

In our everyday lives, IoT plays a vital role. It is crucial to sense, capture and share data from connected devices via internet. Existing system proposed centralized client/server approach where central authority keeps a record of all the activities. Failure of such centralized authority makes the whole system fail. A decentralized / distributed approach is therefore needed if a single failure point is avoided. In this paper contains information to integrating Blockchain in IoT ecosystem in order to achieve access control. We proposed smart contract based architecture which consist multiple permission contract, one decision contract and one entry contract, to achieve distributed and secure IoT device access control. To conclude system framework, we provide a case study in an IoT system with two laptops and one Raspberry Pi single-board computers, where the PCs, DC and EC are implemented based on the Ethereum smart contract platform to achieve the access control.

Driver Drowsiness Detection System using Machine Learning Algorithms

Road crashes are the most common forms of accidents and deaths worldwide, and the significant reasons for these accidents are usually drunken, drowsiness and reckless behaviour of the driver. According to the World Health Organization, road traffic injuries have risen to 1.25 billion worldwide, which makes driver drowsiness detection a major potential area to avert numerous sleep-induced road accidents. This project proposes an idea to detect drowsiness using machine learning algorithms, hence alarming the driver in real-time to prevent a collision. The model uses the Haar Cascade algorithm, along with the OpenCV library to monitor the real-time video of the driver and to detect the eyes of the driver. The system uses the Eye Aspect Ratio (EAR) concept to determine if the eyes are open or closed. We also feed a data-set file consisting of the facial features data-points to train the machine learning algorithm. The model inspects each frame of the video, which helps to recognize the state of the driver. Furthermore, a Raspberry Pi single-board computer, combined with a camera module and an alarm system, facilitates the project to emulate a compact drowsiness detection system suitable for different automobiles.

An automated testing device for continuous measurement of the hysteretic water retention curve of granular media

The water retention curve (WRC), representing an important key for the modelling of hydro-mechanical behaviour of unsaturated soils, is still not fully understood, because it originates from microscopic hydraulic and capillary phenomena. Furthermore, its experimental measurement, especially for cyclic drainage and imbibition paths, is challenging and time-consuming. In this contribution, a recently developed low-cost easy-to-use miniature testing device for the investigation of the WRC of unsaturated granular soils, such as coarse-grained sand and a packing of glass beads, is presented. With the help of the new device, that can be controlled by a Raspberry Pi single-board computer, the hysteretic WRC can be investigated in a conventional macroscopic approach by plotting the macroscopic specimen degree of saturation versus measured matric suction. The test set-up allows an automatic measurement of the WRC which is measured continuously following a programmed test procedure. In addition to the technical realisation of the new device, this contribution focuses on macroscopic results of water retention tests. Moreover, the testing device has been designed in a miniaturised size, in order to obtain microscopic insights into the phase distribution during cyclic drainage and imbibition paths with the help of computed tomography in future applications.

A versatile and compact surface plasmon resonance spectrometer based on single board computer.

The widespread diffusion of low-cost but high-performance hardware is enhancing the realization of scientific equipment with features at the research laboratory level. In this paper, we demonstrate hardware implementation of a surface plasmon resonance compact device with high accuracy and measurement times appropriate for many applications. Image acquisition is realized by a Raspberry Pi single board computer with a camera module, and a Python code is used to process data. A flexible optical setup can work in two different configurations, namely, the inspection mode and angle resolved measurement mode. The inspection mode is used to precisely locate the light-emitting diode interrogation beam on the sample, avoiding uneven or faulty regions. The measurement mode allows us to monitor in real time the position of the minimum reflectivity with subpixel resolution. Performance tests show a resolution in the bulk refractive index of 4.9 × 10-6 refractive index units for 10 s acquisition time.

Prototype Implementation and Evaluation of a Blockchain Component on IoT Devices

Abstract With the greater availability and reduced dimensions of complex electronics, an Internet of Things (IoT) will emerge in the future, in which machines use sensors to collect and evaluate information about the physical world and share it with other machines without having to rely on human interaction. Blockchain technology encompasses features such as trustworthiness and decentralization and these features can be advantageous in the IoT context, as they can help to ensure data integrity in an Internet of Things unmonitored by humans. In the course of this paper, we investigate to what extent the use of blockchain technology is feasible for autonomous devices in IoT. For this purpose, a prototypical client blockchain component is built using the single-board computer Raspberry Pi and a simulated Ethereum blockchain. This prototype will be examined for aspects like proper functionality, response times and performance.

Real-Time Model Predictive Control of Rigid Body Motion via Discretization Using the Cayley Map

This paper presents a fast nonlinear model predictive control method for rigid body dynamical systems such as spacecraft or aerial vehicles on the special Euclidean group SE(3). The focus of this research is on the real-time execution of the optimal control on a low-cost embedded computer. The position and orientation of a rigid body are expressed as a 6-dimensional vector via the Cayley map for SE(3). Based on the representation, the motion can be exactly discretized on the algebra se(3). As a result, coarse sampling intervals can be selected to reduce the number of prediction steps. Furthermore, the recursive discretization technique is applied to effectively reduce the decision variables of the nonlinear optimization problem by eliminating states from them. Simulation results on a Raspberry Pi single-board computer are given to prove that the present model predictive control method is feasible in real time. The effectiveness of the present controller is further verified by an experiment using a fully actuated hexarotor unmanned aerial vehicle.

Development of Low-Cost Point-of-Care Technologies for Cervical Cancer Prevention Based on a Single-Board Computer.

Cervical cancer disproportionally affects women in low- and middle-income countries, in part due to the difficulty of implementing existing cervical cancer screening and diagnostic technologies in low-resource settings. Single-board computers offer a low-cost alternative to provide computational support for automated point-of-care technologies. Here we demonstrate two new devices for cervical cancer prevention that use a single-board computer: 1) a low-cost imaging system for real-time detection of cervical precancer and 2) a low-cost reader for real-time interpretation of lateral flow-based molecular tests to detect cervical cancer biomarkers. Using a Raspberry Pi computer to provide real-time image collection and processing, we developed: 1) a low-cost, portable high-resolution microendoscope system (PiHRME); and 2) a low-cost automatic lateral flow test reader (PiReader). The PiHRME acquired high-resolution (n}{}4.4~mu text{m}n) images of the cervix at half the cost of existing high-resolution microendoscope systems; image analysis algorithms based on convolutional neural networks were implemented to provide real-time image interpretation. The PiReader acquired and analyzed images of a point-of-care human papillomavirus (HPV) serology test with the same contrast and accuracy as a standard flatbed high-resolution scanner coupled to a laptop computer, for less than one-fifth of the cost. Raspberry Pi single-board computers provide a low-cost means to implement point-of-care tools with automatic image analysis. This work demonstrates the promise of single-board computers to develop and translate low-cost, point-of-care technologies for use in low-resource settings.

Application of single-board computers in experimental research on unsaturated soils

In this contribution, the application of single-board computers for the investigation of the hydro-mechanical behaviour of unsaturated granular soils is presented. Single-board computers, such as the Raspberry Pi or Arduino, have recently experienced a hype of applications in school and university teaching, in the maker scene, amongst hobbyists, but also in research. In combination with easy to learn and open programming languages, such as Python, individual experimental set-ups for research in unsaturated soil mechanics, using actuators and sensors can be easily developed with the help of different programmable hardware, such as stepper motors, analog-to-digital converters and other controller boards. For the experimental application in imaging of unsaturated granular soils by computed tomography (CT), we present a miniaturized uniaxial compression device for the measurement of unsaturated shear strength and capillary cohesion in CT-experiments. The device has already been applied for CT-imaging of the development of water distribution and capillary bridges in between different shear steps. Furthermore, a new fully programmable hydraulic experimental set-up for the automated investigation of transient hydraulic paths of the water retention curve of granular media is presented. Both devices have been developed in the framework of the Raspberry Pi single-board computer and Python programming language with simple and relatively inexpensive hardware components. In addition to the technical development of the testing devices, experimental results of the hydro-mechanical behaviour of unsaturated sand and glass beads, derived from uniaxial compression tests and water retention tests, will be presented.

Информационные системы оценки технологических достоинств пшеницы

В практике пищевой промышленности востребованы высокоточные и экспрессные методики определения технологических качеств пшеницы и прогнозировании будущих потребительских свойств продуктов ее переработки. Перспективным является использование для этих целей комплексного показателя твердозерности, однако на сегодняшний день его определение сопряжено с низкой точностью, высокими трудозатратами, использованием дорогостоящего инструментария. Целесообразны разработки автоматизированных систем анализа размолотого зерна в помольных линиях на мукомольных предприятиях. Такие системы могут использоваться для определения целевого назначения производимой муки. В статье описана автоматизированная система оценки технологических свойств продуктов переработки зерна в процессе помола. В основе системы лежат методики формирования данных о форме и размерах частиц в потоках размола зерна. Для этого производимые цифровой камерой Sony IMX219 микроснимки частиц размола зерна подвергались обработке микрокомпьютером Raspberry Pi 3 разработанным программным обеспечением на основе библиотеки алгоритмов компьютерного зрения OpenCV. Для реализации работы такой системы в помольных линиях производства была сконструирована лабораторная установка. Поскольку для успешного анализа частиц компьютерным зрением необходимо устранение эффекта слипания частиц, пользовались для этой цели совместным влиянием электростатических полей и вибрации. Оптимальные технические параметры подобраны опытным путем - электрическое напряжение около 24 кВ и частота вибраций анализируемых образцов размола зерна муки около 45 Гц. Для получения высокого напряжения использовался генератор коротких импульсов на основе высокочастотных тиристоров ТЧ63. Меняя напряжение питания генератора можно плавно менять напряжение на выходе от 1 до 35 кВ. Один вывод высоковольтного источника питания заземлен, второй вывод подключен к датчику-регистратору частиц размола. Разработан алгоритм оценки твердозерности пшеницы на основе данных о форме и размерах частиц в потоках размола зерна – построено соответствующее регрессионное уравнение. Для оценки точности алгоритма сравнивали результаты оценки твердозерности образцов зерна с референтным методом – по показателю микротвердости - способности зерна сопротивляться деформации (вдавливанию). Микротвердость зерна оценивали на микротвердомере ПМТ-3 с квадратной пирамидкой. Установленные эмпирические зависимости позволяют производить оценку твердозерности зерна - с точностью не менее 3 %. Благодаря экспрессности такой оценки технологических свойств продуктов переработки зерна, имеется возможность вмешаться в процесс помола и скорректировать его для повышения эффективности.

Система управления пъезостакером для лазерного инклинометра

In the present article discusses the system of autonomous control based on single-board computer Raspberry Pi 3B for piezoelectric actuator. This system allow to control piezoelectric actuator which used for stabilization of the laser beam in the laser inclinometer.

Design and implementation of a low-cost, open source IoT-based SCADA system using ESP32 with OLED, ThingsBoard and MQTT protocol

Distributed assets, such as hybrid power system components, require reliable, timely, and secure coordinated data monitoring and control systems. Supervisory Control and Data Acquisition (SCADA) is a technology for the coordinated monitoring and control of such assets. However, SCADA system designs and implementations have largely been proprietary, mostly pricey and therefore economically unjustifiable for smaller applications. With proprietary SCADA systems, there is also the problem of interoperability with the existing components such as power electronic converters, energy storage systems, and communication systems since these components are usually from multiple vendors. Therefore, an open source SCADA system represents the most flexible and most cost-effective SCADA option for such assets. In this paper, we present the design and implementation of a low-cost, open source SCADA system based on the most recent SCADA architecture, the Internet of Things (IoT). The proposed SCADA system consists of current and voltage sensors for data collection, an ESP32 micro-controller with organic light-emitting diode (OLED) display, for receiving and processing the sensor data, and ThingsBoard IoT server for historic data storage and human machine interactions. For the sensor data transfer from the ESP32 to the ThingsBoard IoT server, Message Queuing Telemetry Transport (MQTT) protocol is implemented for data transfer over a local Wi-Fi connection with the MQTT Client configured on the ESP32, and the ThingsBoard server node serving as the MQTT Broker. The ThingsBoard IoT server is locally installed with PostgreSQL database on a Raspberry Pi single-board computer and hosted locally on MUN Network for data integrity and security. To test the performance of the developed open source SCADA system solution, it was setup to acquire and process the current, voltage and power of a standalone solar photovoltaic system for remote monitoring and supervisory control. The overall system design procedures and testing, as well as the created dashboards and alarms on the ThingsBoard IoT server platform are presented in the paper.

STEREO VISION APPLYING OPENCV AND RASPBERRY PI

Abstract. This article points out the single board computer Raspberry Pi and the related camera modules for image acquisition. Particular attention is directed to stereoscopic image recording and post processing software applying OpenCV. A design of a camera network is created and applied to a field application. The OpenCV computer vision library and its Python binding provides some script samples to encourage users developing their own custom tailored scripts. Stereoscopic recording is intended for extended base lines without a mechanical bar. Image series will be taken in order to wipe out moving objects from the frames. And finally the NoIR camera made infrared photography possible with low effort. Computer, accupack and lens board are assembled in a 3D printed housing operated by a mobile device.

Shake-it-off: a simple ultrasonic cryo-EM specimen-preparation device.

Although microscopes and image-analysis software for electron cryomicroscopy (cryo-EM) have improved dramatically in recent years, specimen-preparation methods have lagged behind. Most strategies still rely on blotting microscope grids with paper to produce a thin film of solution suitable for vitrification. This approach loses more than 99.9% of the applied sample and requires several seconds, leading to problematic air-water interface interactions for macromolecules in the resulting thin film of solution and complicating time-resolved studies. Recently developed self-wicking EM grids allow the use of small volumes of sample, with nanowires on the grid bars removing excess solution to produce a thin film within tens of milliseconds from sample application to freezing. Here, a simple cryo-EM specimen-preparation device that uses components from an ultrasonic humidifier to transfer protein solution onto a self-wicking EM grid is presented. The device is controlled by a Raspberry Pi single-board computer and all components are either widely available or can be manufactured by online services, allowing the device to be constructed in laboratories that specialize in cryo-EM rather than instrument design. The simple open-source design permits the straightforward customization of the instrument for specialized experiments.

Exploiting open source 3D printer architecture for laboratory robotics to automate high-throughput time-lapse imaging for analytical microbiology.

Growth in open-source hardware designs combined with the low-cost of high performance optoelectronic and robotics components has supported a resurgence of in-house custom lab equipment development. We describe a low cost (below $700), open-source, fully customizable high-throughput imaging system for analytical microbiology applications. The system comprises a Raspberry Pi camera mounted on an aluminium extrusion frame with 3D-printed joints controlled by an Arduino microcontroller running open-source Repetier Host Firmware. The camera position is controlled by simple G-code scripts supplied from a Raspberry Pi singleboard computer and allow customized time-lapse imaging of microdevices over a large imaging area. Open-source OctoPrint software allows remote access and control. This simple yet effective design allows high-throughput microbiology testing in multiple formats including formats for bacterial motility, colony growth, microtitre plates and microfluidic devices termed ‘lab-on-a-comb’ to screen the effects of different culture media components and antibiotics on bacterial growth. The open-source robot design allows customization of the size of the imaging area; the current design has an imaging area of ~420 × 300mm, which allows 29 ‘lab-on-a-comb’ devices to be imaged which is equivalent 3480 individual 1μl samples. The system can also be modified for fluorescence detection using LED and emission filters embedded on the PiCam for more sensitive detection of bacterial growth using fluorescent dyes.

Pembangunan Prototaip Sistem Audio Pintar Kawalan-Suara Berasaskan Platform Raspberry Pi

We report a prototype audio system that can be controlled by means of voice commands. The operations of this audio player – such as song playback and volume control – can be effectively controlled by verbal commands alone without requiring any button-press or dial-turn, enabling a user to operate it with full hands. This feature could be particularly useful in situations when the user’s view and attention are pre-occupied, as in the case of driving or cycling. A voice-command device also has the advantage of a smaller form factor and simpler product casing, due to fewer dials and buttons. While there are a wide variety of commercial audio systems, only very limited few allow voice-control operations. This voice-controlled audio system is low-cost, open-source, and capable of “smart” features that include notifying weather forecasts, automatic volume control, and automatic shutdown. Its platform is the single-board computer Raspberry Pi, which runs speech-to-text processes that record and transcribe a given voice command, such as “Play music” and “Volume up.” The Raspberry Pi also connects to cloud-based services – and upon voice requests – can check and tell real-time information such as date, time, and weather. The system’s average response time to a given voice command was measured to be about 5 seconds. A simple circuit of LED indicators was also designed to prompt users to give voice commands at the right window of time, therefore improving system responsiveness and overall user experience. This prototype can be extended to voice-control other appliances in an integrated smart home environment.

Design of Security System Based on Raspberry-PI

Abstract As a critical constituent of many associations’ protection and security precedence, video surveillance has set up its importance and benefits numerous instances with the aid of imparting immediate supervising of possessions, people, surroundings and property. This paper deals with the diagram strategy of an embedded real-time surveillance gadget based totally on Raspberry-Pi single board computer (SBC) for intruder detection which is reinforcing technology of surveillance to supply fundamental security to our life and associated control and alert operations. The suggested safety solution is hinging on our novel integration of cameras and action detectors into application of web. Raspberry-Pi is operating and controlling action detectors and video cameras for far flung sensing and surveillance, streams live video and files it for future playback. Also, this paper is focusing on growing a surveillance machine that detects strangers and to response speedily through taking pictures and relaying photos to proprietor based totally wireless module. This Raspberry-Pi based clever surveillance machine presents the concept of monitoring a region in a far-flung area. The suggested solution offers a fee advantageous ubiquitous surveillance solution, environment friendly and convenient to implement. Furthermore, the paper presents the idea of motion detection and tracking using image processing. This type of technology is of great importance when it comes to surveillance and security. Live video streams therefore be used to show how objects can be detected then tracked. The detection and tracking process are based on pixel threshold.

Handheld projective imaging device for near-infrared fluorescence imaging and intraoperative guidance of sentinel lymph node resection.

.We propose a handheld projective imaging device for orthotopic projection of near-infrared fluorescence images onto target biological tissue at visible wavelengths without any additional visual aid. The device integrates a laser diode light source module, a camera module, a projector, an ultrasonic distance sensor, a Raspberry Pi single-board computer, and a battery module in a rugged handheld unit. It is calibrated at the detected working distance for seamless coregistration between fluorescence emission and projective imaging at the target tissue site. The proposed device is able to achieve a projection resolution higher than and a planar projection bias less than 1 mm at a projection field of view of and a working distance of 27 cm. Technical feasibility for projective imaging is verified in an ex vivo model of chicken breast tissue using indocyanine green as a fluorescence agent. Clinical utility for image-guided surgery is demonstrated in a clinical trial where sentinel lymph nodes in breast cancer patients are identified and resected under the guidance of projective imaging. Our ex vivo and in vivo experiments imply the clinical utility of deploying the proposed device for image-guided surgical interventions in resource-limited settings.