Raspberry Pi Model Research Articles

Аналіз можливостей використання одноплатних комп’ютерів Raspberry при викладанні розподільчих та паралельних обчислень

The article considers the possibility of using single-board computers in teaching disciplines related to parallel and distributed computing. To conduct practical training in parallel computing, it is a very personal computer with a multi-core processor and an appropriate operating system, but for computational computing practice it is necessary to organize a computing cluster. To develop a cluster, you need a specific software setup that can make it difficult to use a computer class to teach other disciplines. Also, the purchase of personal computers for a computing cluster can lead to significant material costs. Therefore, it is expedient to explore the possibility of using single-board computers to develop a computing cluster and use it in practical work on distributed computing. The analysis of modern single-board computers and various sources for the development of computing clusters has shown that it is suitable for the single-board computer Raspberry Pi 3 Model B +. A comparative analysis of several Raspberry Pi models and, for comparison, one-board computer of another manufacturer – ASUS Tinker Board. It is revealed that Raspberry Pi 3 Model B + characteristics are optimal in the price / quality ratio for forming a learning computing cluster for distributed and parallel computing. Such a cluster has a relatively high price and very good scalability. Also, the problems that need to be solved when designing a computing cluster are found: there is no standardized chassis for placement of boards, the absence of standard heat removal from single-board computers and the need for a non-standard protected power supply. The organization of such a cluster will greatly improve students' skills in developing distributed computing software and build and configure the cluster systems and will allow the development of such competencies of future programmers. Key words. Parallel and distributed computing, single-board computer, computing cluster, Raspberry Pi, software.

Development of a software-hardware complex for studying the process of grinding by a pendulum deformer

The article is devoted to the development of a software and hardware complex for investigating the grinding process on a pendulum deformer. The hardware part of this complex is the Raspberry Pi model 2B platform, to which a contactless angle sensor is connected, which allows to obtain data on the angle of deviation of the pendulum surface, usb-cameras, which allow to obtain grain images before and after grinding, and stepping motors allowing lifting of the pendulum surface and adjust the clearance between the pendulum and the supporting surfaces. The program part of the complex is written in C # and allows receiving data from the sensor and usb-cameras, processing the received data, and also controlling the synchronous-step motors in manual and automatic mode. The conducted studies show that the rational mode is the deviation of the pendulum surface by an angle of 400, and the location of the grain in the central zone of the support surface, regardless of the orientation of the grain in space. Also, due to the non-contact angle sensor, energy consumption for grinding, speed and acceleration of the pendulum surface, as well as vitreousness of grain and the energy consumption are calculated. With the help of photographs obtained from usb cameras, the work of a pendulum deformer based on the Rebinder formula and calculation of the grain area before and after grinding is determined.

Live video monitoring robot controlled by web over internet

Future is all about robots, robot can perform tasks where humans cannot, Robots have huge applications in military and industrial area for lifting heavy weights, for accurate placements, for repeating the same task number of times, where human are not efficient. Generally robot is a mix of electronic, electrical and mechanical engineering and can do the tasks automatically on its own or under the supervision of humans. The camera is the eye for robot, call as robovision helps in monitoring security system and also can reach into the places where the human eye cannot reach. This paper presents about developing a live video streaming robot controlled from the website. We designed the web, controlling for the robot to move left, right, front and back while streaming video. As we move to the smart environment or IoT (Internet of Things) by smart devices the system we developed here connects over the internet and can be operated with smart mobile phone using a web browser. The Raspberry Pi model B chip acts as heart for this system robot, the sufficient motors, surveillance camera R pi 2 are connected to Raspberry pi.

Body worn camera

A body worn camera is small video camera worn on the body, typically used by police officers to record arrests, evidence from crime scenes. It helps preventing and resolving complaints brought by members of the public; and strengthening police transparency, performance, and accountability. The main constants of this type of the system are video format, resolution, frames rate, and audio quality. This system records the video in .mp4 format with 1080p resolution and 30 frames per second. One more important aspect to while designing this system is amount of power the system requires as battery management becomes very critical. The main design challenges are Size of the Video, Audio for the video. Combining both audio and video and saving it in .mp4 format, Battery, size that is required for 8 hours of continuous recording, Security. For prototyping this system is implemented using Raspberry Pi model B.

Intra-Minute Cloud Passing Forecasting Based on a Low Cost IoT Sensor-A Solution for Smoothing the Output Power of PV Power Plants.

Clouds moving at a high speed in front of the Sun can cause step changes in the output power of photovoltaic (PV) power plants, which can lead to voltage fluctuations and stability problems in the connected electricity networks. These effects can be reduced effectively by proper short-term cloud passing forecasting and suitable PV power plant output power control. This paper proposes a low-cost Internet of Things (IoT)-based solution for intra-minute cloud passing forecasting. The hardware consists of a Raspberry PI Model B 3 with a WiFi connection and an OmniVision OV5647 sensor with a mounted wide-angle lens, a circular polarizing (CPL) filter and a natural density (ND) filter. The completely new algorithm for cloud passing forecasting uses the green and blue colors in the photo to determine the position of the Sun, to recognize the clouds, and to predict their movement. The image processing is performed in several stages, considering selectively only a small part of the photo relevant to the movement of the clouds in the vicinity of the Sun in the next minute. The proposed algorithm is compact, fast and suitable for implementation on low cost processors with low computation power. The speed of the cloud parts closest to the Sun is used to predict when the clouds will cover the Sun. WiFi communication is used to transmit this data to the PV power plant control system in order to decrease the output power slowly and smoothly.

A Telepresence Mobile Robot Controlling and Real Time Detection using Internet of Things

Objective: Today, the field of versatile mechanical tele presence is in fast development, with an expanding measure of business frameworks and quick extensions. Methods: There are two techniques to control the robot in which one is a wired and the other is wireless. Customarily, they are called as LAN and IOT. An advanced mobile phone individual hotspot is used to control and screen the robot. Consequently, the Raspberry Pi board and PC are associated with advanced mobile phone. Findings: With a specific end goal to upgrade the client's capacity to remotely convey, control and feel present in a remote domain, the emphasis is more on 1. Semi-independent robot control 2. Situation mindfulness by ongoing face identification 3. People following or individual’s collaboration along these lines utilizing Raspberry Pi model B as a part of which a robot is controlled through wired innovation and web of things which has boundless scope territory and the robot can be controlled more than a few million kilometers furthermore offloads some calculation on the cloud. Applications: The clients may be able to influence remote regions such that the robot position, activities, developments and so forth are detected, and transmitted.

Raspberry Pi as a low-cost data acquisition system for human powered vehicles

Abstract The presented work was conducted to test the hypothesis that a Raspberry Pi Model B single board computer can be applied as a riding dynamics data acquisition system for use on human powered vehicles. The research of the subject was followed by the development, which led to a working prototype of such a data acquisition system. The procedure included the system component integration, system software setup and data acquisition software development. The prototype system was tested in laboratory conditions, and in the field using a bicycle as the test vehicle. The results prove the initial hypothesis and confirm that the performance of the developed system is comparable to some higher-priced and less portable data acquisition systems. Other possibilities of application of the developed system and intended future improvements are discussed in the final part of the paper.

Implementación y evaluación de un PBX IP usando raspberry pi

En un escenario en el que hay un número reducido de usuarios de telefonía IP, hay varias alternativas para prestar el servicio de forma que no se incurra en la subutilización de los recursos. Una de estas alternativas es utilizar como servidor, un ordenador de bajo costo como la raspberry pi, el cual tiene la ventaja de presentar un bajo consumo de potencia. Este trabajo muestra el procedimiento, y los resultados, de una evaluación de rendimiento realizada a un servidor de telefonía IP, el cual tiene la funcionalidad de un PBX IP, usando como plataforma una tarjeta raspberry pi modelo B. Dicha evaluación tiene como finalidad determinarlas características de desempeño básicas de la alternativa teniendo en cuenta aspectos como: el consumo de potencia y la calidad del servicio. Como resultado se tiene que la alternativa presentada en este artículo, es una buena opción en escenarios donde sea suficiente un servidor con capacidad para 7 llamadas concurrentes.

On Goodput and Energy Measurements of Network Coding Schemes in the Raspberry Pi

Given that next generation networks are expected to be populated by a large number of devices, there is a need for quick deployment and evaluation of alternative mechanisms to cope with the possible generated traffic in large-scale distributed data networks. In this sense, the Raspberry Pi has been a popular network node choice due to its reduced size, processing capabilities, low cost and its support by widely-used operating systems. For information transport, network coding is a new paradigm for fast and reliable data processing in networking and storage systems, which overcomes various limitations of state-of-the-art routing techniques. Therefore, in this work, we provide an in-depth performance evaluation of Random Linear Network Coding (RLNC)-based schemes for the Raspberry Pi Models 1 and 2, by showing the processing speed of the encoding and decoding operations and the corresponding energy consumption. Our results show that, in several scenarios, processing speeds of more than 80 Mbps in the Raspberry Pi Model 1 and 800 Mbps in the Raspberry Pi Model 2 are attainable. Moreover, we show that the processing energy per bit for network coding is below 1 nJ or even an order of magnitude less in these scenarios.

Benchmarking Further Single Board Computers for Building a Mini Supercomputer for Simulation of Telecommunication Systems

Parallel Discrete Event Simulation (PDES) with the conservative synchronization method can be efficiently used for the performance analysis of telecommunication systems because of their good lookahead properties. For PDES, a cost effective execution platform may be built by using single board computers (SBCs), which offer relatively high computation capacity compared to their price or power consumption and especially to the space they take up. A benchmarking method is proposed and its operation is demonstrated by benchmarking ten different SBCs, namely Banana Pi, Beaglebone Black, Cubieboard2, Odroid-C1+, Odroid-U3+, Odroid-XU3 Lite, Orange Pi Plus, Radxa Rock Lite, Raspberry Pi Model B+, and Raspberry Pi 2 Model B+. Their benchmarking results are compared to find out which one should be used for building a mini supercomputer for parallel discrete-event simulation of telecommunication systems. The SBCs are also used to build a heterogeneous cluster and the performance of the cluster is tested, too.

MARX-TV: An affordable mobile solution to adjust and monitor DVB-T reception antennas

Competitiveness and customer care are key factors that influence success or failure in any business. Therefore, to survive and succeed, the entrepreneurs tend to concentrate efforts on optimize working methods and procedures towards efficiency. However, the possible changes must not neglect personal security by any circumstance. This paper presents and proposes a low-cost prototype solution capable of increasing the technical work efficiency on installing TV reception antennas, which also reduces the risks associated to works at heights (on roofs). The solution provides monitoring and adjustment of TV antennas without the need to carry any heavy equipment, thus leading to a higher mobility for the technicians. The system proposed consists at a microcontroller based hardware and an application for mobile devices. The hardware solution (HMARX-TV) uses a Raspberry Pi model B minicomputer (ARM, 32 bit architecture), an Arduino Uno board (ATmega328, 8 bit) and a USB DVB-T receiver; the software application (AppMARX-TV) is supported in Android 4.x.x OS and runs on common mobile devices (e.g. smartphones or tablet PCs). The AppMARX-TV connects to the HMARX-TV hardware via a wireless link (IEEE 802.11b/g/n) and takes control of its operation fully. It also allows to adjust a programmable band-pass filter thus improving tuning and reception quality. Upon conformity, a detailed report is issued containing the most important parameters associated to the installation. This allows both, the technician and the customer, to become aware on the correctness and conformity of the work, thus strengthening customer relations and technical prestige. To evaluate the overall prototype performance, several relevant reception parameters were measured and compared with the results obtained with laboratory and certified commercial equipment. The functional tests conducted and performed on site, for several scenarios, demonstrate the conformity in operation for the system prototype.

A Real-Time Palm Dorsa Subcutaneous Vein Pattern Recognition System Using Collaborative Representation-Based Classification

This paper describes the development of a real-time system for the recognition of a real human subject using the palm dorsa subcutaneous vein pattern (PDSVP) as a physiological biometric feature. The system has been developed using a low-cost, single board computer, called the Raspberry Pi Model B, in conjunction with an infrared sensitive camera, called the Raspberry Pi No Infrared camera, and other components. The camera is sensitive to near infrared (NIR) radiations and this acquisition property has been used to acquire the pattern of vascular structure present in the subcutaneous layer of the dorsum of the human palm. Moreover, an automatic two-axis pan-tilt mechanism has been developed on which the camera is mounted. This is a completely novel mechanism that has been developed so that the data acquisition is independent of the position where the palm dorsum is positioned, as an automatic palm dorsum self-locating strategy is developed using the two-axis pan-tilt mechanism. Now, the NIR images of the PDSVP acquired, in the aforementioned methodology, do not represent the vein pattern with appreciable clarity and discernibility. Therefore, each image acquired undergoes few steps of image preprocessing, to extract the vein pattern, before they are subjected to testing conditions or they are incorporated into the training database. The recognition strategy has been developed using the collaborative representation-based classification. In this paper, we have emphasized upon the most severe case of small sample size, which is single sample per person-based training data set creation. The proposed method is tested on a well-structured database, of NIR images of the PDSVP, JU-NIR-V1: NIR Vein Database, developed in the Electrical Instrumentation and Measurement Laboratory, Electrical Engineering Department, Jadavpur University, Kolkata, India. Subsequently, through extensive experimentation it has been proven that the proposed strategy attains substantially high and stable recognition rate. Moreover, the performance of the recognition strategy is highly robust even in the presence of artifacts, such as angular displacement and scaling, that corrupt the NIR images acquired during data acquisition.

Reducing the energy consumption of distributed computer systems with energy-restriced measurement modules

Development and growth of the electronics that use microcontrollers in different sites of application systems, such as distributed information-measuring systems, cause the search for more effective solutions to reduce energy consumption as well as modules usually consume from autonomous energy sources. In the article the problem of reducing the energy consumption of distributed computer systems with energy-restriced measurement modules based on microcontrollers through optimal scaling CPU frequency measurement of the module is considered. A mathematical model of this system was created, which considers energy restrictions of systems. For power optimization algorithm was used EDF. Performance of the system was experimentally verified on the basis of the measuring unit , which consists of selected piezoceramic element TsTS-19 , Microstick for dsPIC33F Development Board the controller , Raspberry Pi Model B.

Iridis-pi: a low-cost, compact demonstration cluster

In this paper, we report on our cluster, which consists of 64 Raspberry Pi Model B nodes each equipped with a 700 MHz ARM processor, 256 Mbit of RAM and a 16 GiB SD card for local storage. The cluster has a number of advantages which are not shared with conventional data-centre based cluster, including its low total power consumption, easy portability due to its small size and weight, affordability, and passive, ambient cooling. We propose that these attributes make Iridis-Pi ideally suited to educational applications, where it provides a low-cost starting point to inspire and enable students to understand and apply high-performance computing and data handling to tackle complex engineering and scientific challenges. We present the results of benchmarking both the computational power and network performance of the Iridis-Pi. We also argue that such systems should be considered in some additional specialist application areas where these unique attributes may prove advantageous. We believe that the choice of an ARM CPU foreshadows a trend towards the increasing adoption of low-power, non-PC-compatible architectures in high performance clusters.