Scaling XML Using a Beowulf Cluster

2018 ◽  
Author(s):  
John J. Chelsom ◽  
Jay H. Chelsom
Keyword(s):  
Open Source ◽  
Low Cost ◽  
Data Access ◽  
Raspberry Pi ◽  
Optimal Size ◽  
Database Access ◽  
Xml Databases ◽  
Beowulf Cluster ◽  
Cost Efficient ◽  
And Performance

We describe a series of experiments which test the performance and scalability of an XML records system deployed on a Beowulf cluster of open source XML databases. Using the open source cityEHR health records system as an example, we first ran experiments to determine the feasibility and optimal size of database instances running on Raspberry Pi and low-cost Intel computers. We describe the implementation of a Data Access Layer for create, read, query and delete operations, using XForms submissions, which encapsulates all database access. We then present the results of testing the scalability and performance of this implementation on clusters of one to sixteen physical database nodes. We conclude that Beowulf clustering provides an effective and cost-efficient mechanism for scaling XML records systems.

scholarly journals Open-Source Electronics Platforms as Enabling Technologies for Smart Cities: Recent Developments and Perspectives

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 404 ◽  
Author(s):  
Daniel Costa ◽  
Cristian Duran-Faundez
Keyword(s):  
Open Source ◽  
Smart City ◽  
Smart Cities ◽  
Low Cost ◽  
Raspberry Pi ◽  
Computational Power ◽  
Different Types ◽  
Recent Developments ◽  
Programmable Devices ◽  
Hardware Platforms

With the increasing availability of affordable open-source embedded hardware platforms, the development of low-cost programmable devices for uncountable tasks has accelerated in recent years. In this sense, the large development community that is being created around popular platforms is also contributing to the construction of Internet of Things applications, which can ultimately support the maturation of the smart-cities era. Popular platforms such as Raspberry Pi, BeagleBoard and Arduino come as single-board open-source platforms that have enough computational power for different types of smart-city applications, while keeping affordable prices and encompassing many programming libraries and useful hardware extensions. As a result, smart-city solutions based on such platforms are becoming common and the surveying of recent research in this area can support a better understanding of this scenario, as presented in this article. Moreover, discussions about the continuous developments in these platforms can also indicate promising perspectives when using these boards as key elements to build smart cities.

Prototyping and Simulated Analysis of Autonomous Swarm-Bots

2018 ◽  
Author(s):  
Maulikkumar Dhameliya ◽  
Sidharth Sher ◽  
Souma Chowdhury
Keyword(s):  
Low Cost ◽  
Raspberry Pi ◽  
System A ◽  
Simulated Environment ◽  
3D Printed ◽  
Modular Platform ◽  
And Performance ◽  
Small Form Factor ◽  
Rate Of Success ◽  
Simulated Analysis

Teams of small (mm-to-cm scale) robots, often known as swarm-bots, can provide unique functionality owing to their small form factor, distributed sensing capabilities, resilience to disruptions and agent-loss, and likely low cost. Such swarm-bots are being increasingly touted to support various indoor surveillance, hazard detection, and search and rescue missions. This paper presents the conceptual design, fabrication, and testing of a new cm-scale wheeled swarm-bot. Simulated investigation of a simple particle-swarm-inspired approach to coordinated path planning for these swarm-bots is also presented. The swarm bot is developed around a modular platform, comprising snap-on (3D printed) structural components, a stepper-motor actuated wheel system, a Raspberry Pi computing node, a wireless radio module, a Lipo battery, and proximity sensors; all components are readily detachable, thereby allowing reconfiguration flexibility. Through three design generations, a stable prototype offering >20cm/s speed and ∼50 min endurance, was developed, assembled and tested. A virtual simulated environment is developed by combining MATLAB-based modules and a V-Rep environment, in order to simulate the coordinated operation of these swarm-bots. A 78% rate of success in completing target (light source) search missions was observed during the numerical experiments, and performance robustness was observed to improve with increasing swarm size.

Building Monitoring and Diagnostics: A Web-Based Approach

2016 ◽  
Vol 861 ◽  
pp. 556-563 ◽  
Author(s):  
Matthias Schuss ◽  
Stefan Glawischnig ◽  
Ardeshir Mahdavi
Keyword(s):  
Low Cost ◽  
Raspberry Pi ◽  
Data Repository ◽  
Multiple Sources ◽  
Data Logger ◽  
Present Contribution ◽  
Web Based ◽  
Building Monitoring ◽  
And Performance ◽  
Set Up

Efforts toward optimized building management and operation require monitoring data from multiple sources. Experiences from previous research projects underline the need for an easily adaptable, low-cost, and easy to set up monitoring infrastructure that could provide data for modeling and performance evaluation. The increasing availability of small and powerful development boards (e.g. Raspberry Pi BeagleBoard or Arduino) facilitates the implementation of a cost-efficient infrastructure for data collection and building monitoring. For the purpose of the present contribution, the Arduino Yún was used to create a data logger that obtains data from wireless sensors, stores it locally, and syncs it with a data repository. Toward this end, we have developed a web-based user interface that enables the user to evaluate various aspects of the monitored building's performance. The communication between the software components is implemented via RESTful interfaces and enables the user to integrate also other data sources such as web services. The paper includes an actual implementation of the above approach. Thereby, we illustrate how the constitutive system components can be integrated in terms of a versatile monitoring system with multiple utilities in terms of building performance assessment and building diagnostics.

scholarly journals AutoTriage - An Open Source Edge Computing Raspberry Pi-based Clinical Screening System

2020 ◽  
Author(s):  
Chaitra Hegde ◽  
Zifan Jiang ◽  
Pradyumna Byappanahalli Suresha ◽  
Jacob Zelko ◽  
Salman Seyedi ◽  
...  
Keyword(s):  
Open Source ◽  
Low Cost ◽  
Characteristic Curve ◽  
Rapid Assessment ◽  
Infrared Camera ◽  
Visible Spectrum ◽  
Wait Time ◽  
Far Infrared ◽  
Raspberry Pi ◽  
Body Parts

AbstractWith the recent COVID-19 pandemic, healthcare systems all over the world are struggling to manage the massive increase in emergency department (ED) visits. This has put an enormous demand on medical professionals. Increased wait times in the ED increases the risk of infection transmission. In this work we present an open-source, low cost, off-body system to assist in the automatic triage of patients in the ED based on widely available hardware. The system initially focuses on two symptoms of the infection fever and cyanosis. The use of visible and far-infrared cameras allows for rapid assessment at a 1m distance, thus reducing the load on medical staff and lowering the risk of spreading the infection within hospitals. Its utility can be extended to a general clinical setting in non-emergency times as well to reduce wait time, channel the time and effort of healthcare professionals to more critical tasks and also prioritize severe cases.Our system consists of a Raspberry Pi 4, a Google Coral USB accelerator, a Raspberry Pi Camera v2 and a FLIR Lepton 3.5 Radiometry Long-Wave Infrared Camera with an associated IO module. Algorithms running in real-time detect the presence and body parts of individual(s) in view, and segments out the forehead and lip regions using PoseNet. The temperature of the forehead-eye area is estimated from the infrared camera image and cyanosis is assessed from the image of the lips in the visible spectrum. In our preliminary experiments, an accuracy of 97% was achieved for detecting fever and 77% for the detection of cyanosis, with a sensitivity of 91% and area under the receiver operating characteristic curve of 0.91. Heart rate and respiratory effort are also estimated from the visible camera.Although preliminary results are promising, we note that the entire system needs to be optimized before use and assessed for efficacy. The use of low-cost instrumentation will not produce temperature readings and identification of cyanosis that is acceptable in many situations. For this reason, we are releasing the full code stack and system design to allow others to rapidly iterate and improve the system. This may be of particular benefit in low-resource settings, and low-to-middle income countries in particular, which are just beginning to be affected by COVID-19.

scholarly journals One Metre Plus (1M+): A Multifunctional Open-Source Sensor for Bicycles Based on Raspberry Pi

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5812
Author(s):  
Andres Henao ◽  
Philippe Apparicio ◽  
David Maignan
Keyword(s):  
Knowledge Sharing ◽  
Open Source ◽  
Scientific Community ◽  
Low Cost ◽  
Video Recording ◽  
Raspberry Pi ◽  
Essential Tool ◽  
Geographical Position ◽  
Motorized Vehicles ◽  
Comparability Of Results

During the last decade, bicycles equipped with sensors became an essential tool for research, particularly for studies analyzing the lateral passing distance between motorized vehicles and bicycles. The objective of this article is to describe a low-cost open-source sensor called one metre plus (1m+) capable of measuring lateral passing distance, registering the geographical position of the cyclist, and video-recording the trip. The plans, codes, and schematic design are open and therefore easily accessible for the scientific community. This study describes in detail the conceptualization process, the characteristics of the device, and the materials from which they are made. The study also provides an evaluation of the product and describes the sensor’s functionalities and its field of application. The objective of this project is to democratize research and develop a platform/participative project that offers tools to researchers worldwide, in order to standardize knowledge sharing and facilitate the comparability of results in various contexts.

scholarly journals A New Seven-Segment Profile Algorithm for an Open Source Architecture in a Hybrid Electronic Platform

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 652 ◽  
Author(s):  
José R. García-Martínez ◽  
Juvenal Rodríguez-Reséndiz ◽  
Edson E. Cruz-Miguel
Keyword(s):  
Velocity Profile ◽  
Open Source ◽  
Low Cost ◽  
High Stress ◽  
Rate Of Change ◽  
Open Architecture ◽  
Raspberry Pi ◽  
Field Programmable ◽  
Acceleration And Deceleration ◽  
S Curve

The velocity profiles are used in the design of trajectories in motion control systems. It is necessary to design smoother movements to avoid high stress in the motor. In this paper, the rate of change in acceleration value is used to develop an S-curve velocity profile which presents an acceleration and deceleration stage smoother than the trapezoidal velocity profile reducing the error at the end of the duty-cycle pre-established in one degree of freedom (DoF) application. Furthermore, a new methodology is developed to generate a seven-segment profile that works with negative velocity and displacement constraints applying an open source architecture in a hybrid electronic platform compounded by a system on a chip (SoC) Raspberry Pi 3 and a field programmable gate array (FPGA). The performance of the motion controller is measured through the comparison of the error obtained in real-time application with a trapezoidal velocity profile. As a result, a low-cost platform and an open architecture system are achieved.

scholarly journals Low-Cost, Open Source IoT-Based SCADA System Design Using Thinger.IO and ESP32 Thing

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 822 ◽  
Author(s):  
Lawrence Oriaghe Aghenta ◽  
Mohammad Tariq Iqbal
Keyword(s):  
Internet Of Things ◽  
Open Source ◽  
Supervisory Control ◽  
Low Cost ◽  
Raspberry Pi ◽  
Voltage Sensors ◽  
Scada System ◽  
Iot Platform ◽  
Field Instrumentation ◽  
Distributed Processes

Supervisory Control and Data Acquisition (SCADA) is a technology for monitoring and controlling distributed processes. SCADA provides real-time data exchange between a control/monitoring centre and field devices connected to the distributed processes. A SCADA system performs these functions using its four basic elements: Field Instrumentation Devices (FIDs) such as sensors and actuators which are connected to the distributed process plants being managed, Remote Terminal Units (RTUs) such as single board computers for receiving, processing and sending the remote data from the field instrumentation devices, Master Terminal Units (MTUs) for handling data processing and human machine interactions, and lastly SCADA Communication Channels for connecting the RTUs to the MTUs, and for parsing the acquired data. Generally, there are two classes of SCADA hardware and software; Proprietary (Commercial) and Open Source. In this paper, we present the design and implementation of a low-cost, Open Source SCADA system by using Thinger.IO local server IoT platform as the MTU and ESP32 Thing micro-controller as the RTU. SCADA architectures have evolved over the years from monolithic (stand-alone) through distributed and networked architectures to the latest Internet of Things (IoT) architecture. The SCADA system proposed in this work is based on the Internet of Things SCADA architecture which incorporates web services with the conventional (traditional) SCADA for a more robust supervisory control and monitoring. It comprises of analog Current and Voltage Sensors, the low-power ESP32 Thing micro-controller, a Raspberry Pi micro-controller, and a local Wi-Fi Router. In its implementation, the current and voltage sensors acquire the desired data from the process plant, the ESP32 micro-controller receives, processes and sends the acquired sensor data via a Wi-Fi network to the Thinger.IO local server IoT platform for data storage, real-time monitoring and remote control. The Thinger.IO server is locally hosted by the Raspberry Pi micro-controller, while the Wi-Fi network which forms the SCADA communication channel is created using the Wi-Fi Router. In order to test the proposed SCADA system solution, the designed hardware was set up to remotely monitor the Photovoltaic (PV) voltage, current, and power, as well as the storage battery voltage of a 260 W, 12 V Solar PV System. Some of the created Human Machine Interfaces (HMIs) on Thinger.IO Server where an operator can remotely monitor the data in the cloud, as well as initiate supervisory control activities if the acquired data are not in the expected range, using both a computer connected to the network, and Thinger.IO Mobile Apps are presented in the paper.

scholarly journals Environmental Impacts of Distributed Manufacturing from 3-D Printing of Polymer Components and Products

2013 ◽  
Vol 1492 ◽  
pp. 85-90 ◽  
Author(s):  
Megan Kreiger ◽  
Joshua M. Pearce
Keyword(s):  
Environmental Impact ◽  
Open Source ◽  
Large Scale ◽  
Low Cost ◽  
Acrylonitrile Butadiene Styrene ◽  
Mass Scale ◽  
Embodied Energy ◽  
Scale Production ◽  
Distributed Manufacturing ◽  
And Performance

ABSTRACTAlthough additive layer manufacturing is well established for rapid prototyping the low throughput and historic costs have prevented mass-scale adoption. The recent development of the RepRap, an open source self-replicating rapid prototyper, has made low-cost 3-D printers readily available to the public at reasonable prices (<$1,000). The RepRap (Prusa Mendell variant) currently prints 3-D objects in a 200x200x140 square millimeters build envelope from acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA). ABS and PLA are both thermoplastics that can be injection-molded, each with their own benefits, as ABS is rigid and durable, while PLA is plant-based and can be recycled and composted. The melting temperature of ABS and PLA enable use in low-cost 3-D printers, as these temperature are low enough to use in melt extrusion in the home, while high enough for prints to retain their shape at average use temperatures. Using 3-D printers to manufacture provides the ability to both change the fill composition by printing voids and fabricate shapes that are impossible to make using tradition methods like injection molding. This allows more complicated shapes to be created while using less material, which could reduce environmental impact.As the open source 3-D printers continue to evolve and improve in both cost and performance, the potential for economically-viable distributed manufacturing of products increases. Thus, products and components could be customized and printed on-site by individual consumers as needed, reversing the historical trend towards centrally mass-manufactured and shipped products. Distributed manufacturing reduces embodied transportation energy from the distribution of conventional centralized manufacturing, but questions remain concerning the potential for increases in the overall embodied energy of the manufacturing due to reduction in scale. In order to quantify the environmental impact of distributed manufacturing using 3-D printers, a life cycle analysis was performed on a plastic juicer. The energy consumed and emissions produced from conventional large-scale production overseas are compared to experimental measurements on a RepRap producing identical products with ABS and PLA. The results of this LCA are discussed in relation to the environmental impact of distributed manufacturing with 3-D printers and polymer selection for 3-D printing to reduce this impact. The results of this study show that distributed manufacturing uses less energy than conventional manufacturing due to the RepRap's unique ability to reduce fill composition. Distributed manufacturing also has less emissions than conventional manufacturing when using PLA and when using ABS with solar photovoltaic power. The results of this study indicate that open-source additive layer distributed manufacturing is both technically viable and beneficial from an ecological perspective.

scholarly journals MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms

2021 ◽  
Vol 12 (1) ◽  
pp. 89
Author(s):  
Ruiqi Chen ◽  
Tianyu Wu ◽  
Yuchen Zheng ◽  
Ming Ling
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Low Cost ◽  
Raspberry Pi ◽  
Future Research ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Field Programmable ◽  
Ip Cores ◽  
Cost Efficient

In Internet of Things (IoT) scenarios, it is challenging to deploy Machine Learning (ML) algorithms on low-cost Field Programmable Gate Arrays (FPGAs) in a real-time, cost-efficient, and high-performance way. This paper introduces Machine Learning on FPGA (MLoF), a series of ML IP cores implemented on the low-cost FPGA platforms, aiming at helping more IoT developers to achieve comprehensive performance in various tasks. With Verilog, we deploy and accelerate Artificial Neural Networks (ANNs), Decision Trees (DTs), K-Nearest Neighbors (k-NNs), and Support Vector Machines (SVMs) on 10 different FPGA development boards from seven producers. Additionally, we analyze and evaluate our design with six datasets, and compare the best-performing FPGAs with traditional SoC-based systems including NVIDIA Jetson Nano, Raspberry Pi 3B+, and STM32L476 Nucle. The results show that Lattice’s ICE40UP5 achieves the best overall performance with low power consumption, on which MLoF averagely reduces power by 891% and increases performance by 9 times. Moreover, its cost, power, Latency Production (CPLP) outperforms SoC-based systems by 25 times, which demonstrates the significance of MLoF in endpoint deployment of ML algorithms. Furthermore, we make all of the code open-source in order to promote future research.

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