Raspberry Pi Card Research Articles

Cost-Efficient Multi-GNSS Station with Real-Time Transmission for Geodynamics Applications

GNSS is a standard tool for monitoring and studying the Earth’s dynamic environment. However, the development of dense GNSS measurements remains limited in many experiments by the cost of high-class geodetic equipment to achieve the high precision required by many applications. Recently, multi-constellation, multi-frequency, low-power and, above all, less expensive GNSS electronic chips have become available. We present a prototype of a low-cost, open-source multi-GNSS station. Our prototype comprises a dual-frequency GNSS chip, a calibrated antenna, a Raspberry Pi card and a 4G key for data transmission. The system is easy to deploy in the field and allows precise positioning in real-time and post-processing. We assess the performance of our prototype in terms of raw data quality, and quality of the obtained high rate and daily position one-year-long time series. Our results demonstrate a quality equivalent to high-class geodetic equipment and better quality than other low-cost systems proposed so far.

Machine Design Automation Model for Metal Production Defect Recognition with Deep Graph Convolutional Neural Network

Error detection has a vital function in the production stages. Computer-aided error detection applications bring significant technological innovation to the production process to control the quality of products. As a result, the control of product quality has reached an essential point because of computer-aided image processing technologies. Artificial intelligence methods, such as Convolutional Neural Network (CNN), can detect and classify product errors. However, detecting acceptable and small defects on base parts cannot be done with a high rate of accuracy. At this point, it is possible to detect such minor errors with the help of the graph convolutional network, which has emerged as a new method. In this study, the defect elements on the surfaces of metal nut parts are determined through the graph convolutional network, and quality control is ensured. First, the surface images of the metal nut parts are captured. For this, a python-based Raspberry pi card and a modified camera system were installed. Adapters with three different zoom options are used on the camera system, depending on the part to be captured. The images obtained in the second step are sent to the other computer, which is used for image processing via the local server. In the third stage, image transformations are obtained by graphically separating the obtained images in white and black color tones on the second computer, and histogram maps of these images are drawn. Value ranges of these maps are determined and classified according to the value ranges obtained from the images of the defective parts. As a result, nine different models were analyzed. According to the analysis results, the graph convolutional neural network method gives 2.9554% better results than conventional methods.

Face Mask Detection Using Lightweight Deep Learning Architecture and Raspberry Pi Hardware: An Approach to Reduce Risk of Coronavirus Spread While Entrance to Indoor Spaces

The COVID-19 pandemic continues to spread around the world at full speed, threatening public health. In response, the World Health Organization recommends various preventive measures to reduce the spread of the COVID-19 virus. Wearing a mask is one of the preventive measures to reduce the contagion of the disease, and many governments around the world advise people to wear masks. One of the prominent symptoms of coronavirus is high fever. A person with a fever above normal is likely to have contracted the corona virus. This requires the identification of people with a high fever in order to prevent the epidemic in the public arena. This situation has caused people who want to enter public places to need masks and officers who control their body temperature. The aim of this study is to detect people who do not wear masks or do not wear them properly, and also to detect people with high fever through a system. The proposed system is designed as a system that can be integrated into automatic door systems. The system was basically implemented by running Mobile Net, one of the deep learning models, on the Raspberry Pi card. In the proposed method, 97.0% accuracy was obtained. Experimental results show that the proposed method can effectively recognize face masks and whether people have a high fever. This work is necessary for many closed areas that will make masks and fever control in public areas.

Artificial Neural Networks, Support Vector Machine And Energy Detection For Spectrum Sensing Based On Real Signals

A Cognitive Radio (CR) is an intelligent wireless communication system, which is able to improve the utilization of the spectral environment. Spectrum sensing (SS) is one of the most important phases in the cognitive radio cycle, this operation consists in detecting signals presence in a particular frequency band. In order to detect primary user (PU) existence, this paper proposes a low cost and low power consumption spectrum sensing implementation. Our proposed platform is tested based on real world signals. Those signals are generated by a Raspberry Pi card and a 433 MHz Wireless transmitter (ASK (Amplitude-Shift Keying) and FSK (Frequency-Shift Keying) modulation type). RTL-SDR dongle is used as a reception interface. In this work, we compare the performance of three methods for SS operation: The energy detection technique, the Artificial neural network (ANN) and the support vector machine (SVM). So, the received data could be classified as a PU or not (noise) by the ED method, and by training and testing on a proposed ANN and SVM classification model. The proposed algorithms are implemented under MATLAB software. In order to determine the best architecture, in the case of ANN, two different training algorithms are compared. Furthermore, we have investigated the effect of several SVM functions. The main objective is to find out the best method for signal detection between the three methods. The performance evaluation of our proposed system are the probability of detection and the false alarm probability . This Comparative work has shown that the SS operation by SVM can be more accurate than ANN and ED.

Development of Conventional Controller Based on Image Processing for Monitoring and Controlling Burning Zone Temperature in a Cement Plant in Rotary Kiln Process Through IOT

In the cement factories, a rotary kiln is a pyro-processing device that is used to raise the temperature of the materials in a continuous process. Temperature monitoring is an essential process in the rotary kiln to yield high quality clinker and it has been implemented using various image processing techniques. In this paper we are measuring and controlling the temperature of rotational kiln in cement industry to get proper clinker ouput. Burning zone flame images are captured using CCD(Charge Coupled Device) camera and are processed using image processing with PID(Proportion Integration and Derivative) controller and which are programmed on raspberry pi card with the help of python language, also the captured images and attributes are transferred to authorized mobile/pc through Raspberry PI by selecting the IP address of mobile or PC. All the attributes received in the mobile in the form of web page the according to the object following data temperature controlled and object is ceaselessly followed to get the proper clinker output. Picture handling calculation with Open cv, as indicated by the calculation the edge estimation of the camera is settled. The frame value of the camera is set. Conversion from RGB color space to HSV color space is achieved and the reference color threshold value is determined. The range esteem estimated by the camera is contrasted and the reference esteem. In this study temp of rotational kiln is measured effectively using PID controller, this controller continuously control the temperature of revolving kiln by varying the i/p images of burning zone at finally fix one flame which is giving 1400degc.

A Formal Testing Model for Operating Room Control System using Internet of Things

Technological advances in recent years have significantly changed the way an operating room works. This work aims to create a platform to solve the problems of operating room occupancy and prepare the rooms with an environment that is favorable for all operations. Using this system, a doctor can control all operation rooms, especially before an operation, and monitor their temperature and humidity to prepare for the operation. Also, in the event of a problem, an alert is sent to the nurse responsible for the room and medical stuff so that the problem can be resolved. The platform is tested using a Raspberry PI card and sensors. The sensors are connected to a cloud layer that collects and analyzes the temperature and humidity values obtained from the environment during an operation. The result of experimentations is visualized through a web application and an Android application. The platform also considers the security aspects such as authorization to access application functionalities for the Web and the mobile applications. We can also test and evaluate the system’s existing problems and vulnerabilities using the IEEE and owasp IoT standards. Finally, the proposed framework is extended with a model based testing technique that may be adopted for validating thesecurity aspects.

Modeling Design and Implementation of an Embeds System Real Time Over a Network of Wireless Sensors to Environmental Monitoring

Artificial Neurons Network (ANN) is used in the decision and control of dynamic systems which can be with a lack of superfluous information.it forces the use of fuzzy logic.
 
 For this reason, several methods and monitoring techniques have been implemented. This article presents a technique based on artificial neural networks implanted at the level of a multisensor surveillance system. It is a statistical learning method that displays optimal training and generalization performance in several domains, including the recognition domain of forms.
 
 In this case ANN based on raspberry PI card for decision node and arduino for the input and hidden nodes, in order to develop a complete platform environmental monitoring system. and hence enhance multi-Sensor wireless signals aggregation via multi-bit decision fusion. 
 
 The back-propagation algorithm generates a weight for all nodes in the networks, with aim of minimizing absolute error committed in fusion data and economics of electrical energy using artificial intelligence techniques. This algorithm is more efficient than the human being since it can reason and learn from its errors so as not to repeat them.
 
 Its main applications include a variety of data monitoring parameters (such as : temperature, humidity, gas sensor, … etc), that can be found in factory automation, for instance : home automation, remote monitoring and home device control, or it may be used in environment to make an exact decision in short time.

Artificial Neural Networks, Support Vector Machine And Energy Detection For Spectrum Sensing Based On Real Signals

A Cognitive Radio (CR) is an intelligent wireless communication system, which is able to improve the utilization of the spectral environment. Spectrum sensing (SS) is one of the most important phases in the cognitive radio cycle, this operation consists in detecting signals presence in a particular frequency band. In order to detect primary user (PU) existence, this paper proposes a low cost and low power consumption spectrum sensing implementation. Our proposed platform is tested based on real world signals. Those signals are generated by a Raspberry Pi card and a 433 MHz Wireless transmitter (ASK (Amplitude-Shift Keying) and FSK (Frequency-Shift Keying) modulation type). RTL-SDR dongle is used as a reception interface. In this work, we compare the performance of three methods for SS operation: The energy detection technique, the Artificial neural network (ANN) and the support vector machine (SVM). So, the received data could be classified as a PU or not (noise) by the ED method, and by training and testing on a proposed ANN and SVM classification model. The proposed algorithms are implemented under MATLAB software. In order to determine the best architecture, in the case of ANN, two different training algorithms are compared. Furthermore, we have investigated the effect of several SVM functions. The main objective is to find out the best method for signal detection between the three methods. The performance evaluation of our proposed system are the probability of detection and the false alarm probability . This Comparative work has shown that the SS operation by SVM can be more accurate than ANN and ED.

Secured cloud computing for medical data based on watermarking and encryption

In this study, the authors propose a hardware implementation of a local cloud for storing, sharing and archiving patient's folders (health report and medical imaging) based on raspberry Pi card and a local cloud (hard disc) while exchanging with other distant public cloud (google drive, azure cloud, ...). Securing the medical data is ensured by a proposed encrypting method for the watermarked digital imaging and communications in medicine imaging which contains the patient information (the watermark) and the advanced encryption standard is used to crypt the text files (health reports). The imaging encryption method is based on the exoring of the i th block to be encrypted and the i th block key. The watermarking method is based on the insertion of the watermark in the least significant bit. Finally, encryption and watermarking keys are encrypted and stored in the database. A real-time implementation on raspberry Pi and android smartphone is proposed for the whole system using, respectively, PHP and android studio. Experimental results demonstrate that the proposed security method is robust against different types of attacks and can achieve high security with a good performance.

A facial expression controlled wheelchair for people with disabilities

Background and ObjectivesIn order to improve assistive technologies for people with reduced mobility, this paper develops a new intelligent real-time emotion detection system to control equipment, such as electric wheelchairs (EWC) or robotic assistance vehicles. Every year, degenerative diseases and traumas prohibit thousands of people to easily control the joystick of their wheelchairs with their hands. Most current technologies are considered invasive and uncomfortable such as those requiring the user to wear some body sensor to control the wheelchair. MethodsIn this work, the proposed Human Machine Interface (HMI) provides an efficient hands-free option that does not require sensors or objects attached to the user's body. It allows the user to drive the wheelchair using its facial expressions which can be flexibly updated. This intelligent solution is based on a combination of neural networks (NN) and specific image preprocessing steps. First, the Viola-Jones combination is used to detect the face of the disability from a video. Subsequently, a neural network is used to classify the emotions displayed on the face. This solution called "The Mathematics Behind Emotion" is capable of classifying many facial expressions in real time, such as smiles and raised eyebrows, which are translated into signals for wheelchair control. On the hardware side, this solution only requires a smartphone and a Raspberry Pi card that can be easily mounted on the wheelchair. ResultsMany experiments have been conducted to evaluate the efficiency of the control acquisition process and the user experience in driving a wheelchair through facial expressions. The classification accuracy can expect 98.6% and it can offer an average recall rate of 97.1%. Thus, all these experiments have proven that the proposed system is able of accurately recognizing user commands in real time. Indeed, the obtained results indicate that the suggested system is more comfortable and better adapted to severely disabled people in their daily lives, than conventional methods. Among the advantages of this system, we cite its real time ability to identify facial emotions from different angles. ConclusionsThe proposed system takes into account the patient's pathology. It is intuitive, modern, doesn't require physical effort and can be integrated into a smartphone or tablet. The results obtained highlight the efficiency and reliability of this system, which ensures safe navigation for the disabled patient.

Implementación de un prototipo para captura y procesamiento digital de imágenes térmicas adquiridas desde un UAV

El presente trabajo se enfoca en el desarrollo de un prototipo para captura y procesamiento de imágenes térmicas desde un UAV (Vehículo Aéreo no Tripulado). El sistema está compuesto por dos partes: una etapa “aire” instalada en un dron DJI Phantom 3 Standard; y, otra programada en un PC receptor denominada “tierra”. El sistema aire permite la adquisición de imágenes con el uso de 3 elementos: una cámara térmica Flir Lepton, una tarjeta Raspberry Pi y un módulo GPS (para georreferenciación), mientras que para la transmisión de las imágenes a tierra se hace uso de una red Ad-Hoc. En la PC se efectúa un análisis de la información mediante el uso de histogramas y la detección de bordes (algoritmo de Canny), lo que desemboca en la generación de un filtro que permitirá discriminar en qué fotografías se puede definir claramente la ubicación de puntos de calor, evitando así el desperdicio de tiempo y potencia de procesamiento en la detección de falsos positivos en las imágenes. Finalmente, y para la comprobación de la correcta operación del sistema, el prototipo fue probado en condiciones climáticas adversas (neblina) en el sector del Volcán Pululahua.

Implementación de sistemas distribuidos de bajo costo bajo norma IEC-61499, en la estación de clasificación y manipulación del MPS 500

<p>El desarrollo de las tecnologías, los requerimientos de nuevas prestaciones y las limitantes dadas por los fabricantes de software y hardware para la automatización ha hecho que nuevos estándares sean desarrollados. El presente trabajo tiene como propósito la implantación de un sistema distribuido de bajo costo, usando la norma IEC-61499 en la estación de clasificación y manipulación del MPS-500. En estos módulos se pueden dar problemas de aplicaciones industriales reales. IEC-61499 estandariza un entorno de programación para sistemas distribuidos y tiene un alto nivel de versatilidad para el diseño de sistemas, pues combina software y hardware de manera independiente. Se utilizó el <em>softwar</em>e 4DIAC y se crearon nuevos bloques de función (FB) para el control del proceso y el <em>runtime</em> FORTE fue ejecutado en una tarjeta Raspberry Pi. La norma IEC-61499 permite al diseñador del sistema crear un modelo de ejecución de bloques de función conducido por eventos, de esta manera, puede dar prioridad a la orden de ejecución y modificar fácilmente algún parámetro de la planta.</p>

The Monitoring of Monthly, Seasonal and Yearly Optimum Tilt Angles by Raspberry Pi Card for Bilecik City, Turkey

Solar energy is extracted from photovoltaic (PV) panels by semiconductor materials used in the system. However, the PV panel efficiency is low. Moreover, inaccurate determination of optimum fixed tilt angle of the panels, some environmental factors such as dirt and dust and solar radiation level and temperature variations depending on the seasonal changes decrease the panel efficiency. In the study, PV panels were placed at 10°, 20°, 30°, 40°, 50° and 60° tilt angles and the monthly, seasonally and yearly optimum tilt angles for Bilecik city were determined. Therefore, it was determined at which tilt angle the maximum power is extracted from PV panels. The record and the storage of the voltage, current and the power data obtained from PV panels is provided by Raspberry Pi card. Moreover, the obtained data is open to access on the internet by a web server set up on Raspberry Pi. Therefore, the data can be watched live in any computer with an internet access. Annual measurements were performed by the designed system and the data was recorded with the date and time data.

A smart city application: a fully controlled street lighting isle based on Raspberry-Pi card, a ZigBee sensor network and WiMAX.

A smart city application has been realized and tested. It is a fully remote controlled isle of lamp posts based on new technologies. It has been designed and organized in different hierarchical layers, which perform local activities to physically control the lamp posts and transmit information with another for remote control. Locally, each lamp post uses an electronic card for management and a ZigBee tlc network transmits data to a central control unit, which manages the whole isle. The central unit is realized with a Raspberry-Pi control card due to its good computing performance at very low price. Finally, a WiMAX connection was tested and used to remotely control the smart grid, thus overcoming the distance limitations of commercial Wi-Fi networks. The isle has been realized and tested for some months in the field.