Democratizing ocean technology: low-cost innovations in underwater robotics

2020 ◽  
Author(s):  
Allison Chua ◽  
Aaron MacNeill ◽  
Douglas Wallace
Keyword(s):  
Open Source ◽  
Supply And Demand ◽  
Academic Community ◽  
Circuit Board ◽  
Sensor Data ◽  
Raspberry Pi ◽  
Common Source ◽  
Underwater Robotics ◽  
Ocean Science ◽  
Wide Range

<p>In comparison to the ocean’s immense volume and diversity of research areas, the number of sensors required to make the majority of desired measurements is quite small. This inequality of supply and demand elevates prices, adding further barriers for developing nations or fledgling research programs with smaller budgets attempting ocean science. Our work aims to demonstrate the potential of combining commercially available, open-source products to create inexpensive, configurable, and user-friendly platforms that can be adapted for underwater navigation and integration with most commercial oceanographic sensors.</p><p>Specifically, we will highlight modifications made to a Blue Robotics BlueROV2, which we have configured for various missions including vertical profiling of a coastal fjord and three-dimensional mapping of crude oil spills. The BlueROV2 offers an easily modified platform for physical mounting of sensors and streaming of sensor data via its onboard computer, a Raspberry Pi. Our custom circuit board is “sensor-agnostic”, powering sensors from a common source (the ROV battery) and using an Arduino that accepts analog or digital sensor inputs, allowing us to choose from a wide range of sensors. Physical modifications make use of inexpensive, readily available materials, and range from simple plastic brackets for small sensors to a skid for a sensor with half the ROV’s original weight, which utilizes pop bottles for buoyancy.</p><p>While products such as Pixhawk, Raspberry Pi, Arduino, and BlueROV have inspired hobbyists and youth around the world, they paradoxically have not been as widely embraced in the academic community, who perhaps remain unaware of their research potential. Thus, while there has yet to be an analogous push to develop inexpensive, small, power-efficient, and open-source sensors, these platforms offer exciting opportunities to build a new generation of oceanographic tools with measurement abilities far exceeding those of their predecessors. We are at an ocean technology tipping point, and, as MacGyver says, “With a little bit of imagination, anything is possible.”</p>

scholarly journals Elemental: An Open-Source Wireless Hardware and Software Platform for Building Energy and Indoor Environmental Monitoring and Control

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4017 ◽  
Author(s):  
Akram Syed Ali ◽  
Christopher Coté ◽  
Mohammad Heidarinejad ◽  
Brent Stephens
Keyword(s):  
Environmental Monitoring ◽  
Open Source ◽  
Environmental Quality ◽  
Printed Circuit Boards ◽  
Operating Conditions ◽  
Sensor Data ◽  
Raspberry Pi ◽  
Software Platform ◽  
Energy Usage ◽  
Efficient Operation

This work demonstrates an open-source hardware and software platform for monitoring the performance of buildings, called Elemental, that is designed to provide data on indoor environmental quality, energy usage, HVAC operation, and other factors to its users. It combines: (i) custom printed circuit boards (PCBs) with RFM69 frequency shift keying (FSK) radio frequency (RF) transceivers for wireless sensors, control nodes, and USB gateway, (ii) a Raspberry Pi 3B with custom firmware acting as either a centralized or distributed backhaul, and (iii) a custom dockerized application for the backend called Brood that serves as the director software managing message brokering via Message Queuing Telemetry Transport (MQTT) protocol using VerneMQ, database storage using InfluxDB, and data visualization using Grafana. The platform is built around the idea of a private, secure, and open technology for the built environment. Among its many applications, the platform allows occupants to investigate anomalies in energy usage, environmental quality, and thermal performance via a comprehensive dashboard with rich querying capabilities. It also includes multiple frontends to view and analyze building activity data, which can be used directly in building controls or to provide recommendations on how to increase operational efficiency or improve operating conditions. Here, we demonstrate three distinct applications of the Elemental platform, including: (1) deployment in a research lab for long-term data collection and automated analysis, (2) use as a full-home energy and environmental monitoring solution, and (3) fault and anomaly detection and diagnostics of individual building systems at the zone-level. Through these applications we demonstrate that the platform allows easy and virtually unlimited datalogging, monitoring, and analysis of real-time sensor data with low setup costs. Low-power sensor nodes placed in abundance in a building can also provide precise and immediate fault-detection, allowing for tuning equipment for more efficient operation and faster maintenance during the lifetime of the building.

scholarly journals Light-weight Electrophysiology Hardware and Software Platform for Cloud-Based Neural Recording Experiments

2021 ◽  
Author(s):  
Kateryna Voituik ◽  
Jinghui Geng ◽  
Matthew G. Keefe ◽  
David F. Parks ◽  
Sebastian E. Sanso ◽  
...  
Keyword(s):  
Neural Activity ◽  
User Interaction ◽  
Neural Recording ◽  
Circuit Board ◽  
Raspberry Pi ◽  
Development Organization ◽  
Human Neurons ◽  
Wide Range ◽  
Processing Device

Objective. Neural activity represents a functional readout of neurons that is increasingly important to monitor in a wide range of experiments. Extracellular recordings have emerged as a powerful technique for measuring neural activity because these methods do not lead to the destruction or degradation of the cells being measured. Current approaches to electrophysiology have a low throughput of experiments due to manual supervision and expensive equipment. This bottleneck limits broader inferences that can be achieved with numerous long-term recorded samples. Approach. We developed Piphys, an inexpensive open source neurophysiological recording platform that consists of both hardware and software. It is easily accessed and controlled via a standard web interface through Internet of Things (IoT) protocols. Main Results. We used a Raspberry Pi as the primary processing device and Intan bioamplifier. We designed a hardware expansion circuit board and software to enable voltage sampling and user interaction. This standalone system was validated with primary human neurons, showing reliability in collecting real-time neural activity. Significance. The hardware modules and cloud software allow for remote control of neural recording experiments as well as horizontal scalability, enabling long-term observations of development, organization, and neural activity at scale.

scholarly journals Pengembangan Sistem Streaming Sensor Data Temperatur dan Kelembapan di Ruang Laboratorium BBTA3 Melalui Teknologi Open Source

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Arizal Akbar Zikri
Keyword(s):  
Open Source ◽  
Streaming Data ◽  
Sensor Data ◽  
Raspberry Pi ◽  
Data Streaming ◽  
Application Development ◽  
Web Browser ◽  
Streaming Application ◽  
To Receive ◽  
Data Reading

AbstractDevelopment of temperature and humidity data streaming Application from sensors in the server room is necessary to enable the operator to monitor the real-time air condition in the server room, without having to enter the room. This application development utilizes open source technology to make developers more independent and able to interact easily within the community without license hassle.Sensor data reading is done by Raspberry Pi, assigned as a producer in sending data to Kafka Cluster. Kafka is an open source technology used as tools for streaming data distributedly. One node in a cluster is set to receive sensor data, known as consumer, executes python service to handle requests from users through Server Sent Event (SSE) in form of REST API.This application is called TempHum and can be executed on Desktop (Windows, Linux, Mac OS), web browser, and smartphone (Android and iOS). Hence, the application can serve many clients in monitoring air condition in realtime.Keywords: open source, cluster, raspberry pi, kafka, python.AbstraksAplikasi streaming data sensor berupa temperatur dan kelembapan di ruang server perlu dikembangkan, sehingga memudahkan bagi operator untuk memantau kondisi udara terkini secara dinamis di ruang server tanpa harus masuk kedalam ruang tersebut. Pengembangan aplikasi dilakukan menggunakan teknologi open source agar memudahkan pengembang untuk mandiri dan berinteraksi dalam komunitas tanpa terikat dengan permasalahan lisensi.Pembacaan data sensor dilakukan oleh Raspberry Pi dan dijadikan sebagai producer untuk mengirimkan data tersebut ke Kafka Cluster. Kafka merupakan teknologi open source yang digunakan sebagai alat untuk streaming data terdistribusi. Satu node dalam cluster digunakan untuk menerima kiriman data atau dikenal sebagai consumer sekaligus menjalankan python servis untuk menangani permintaan dari pengguna aplikasi melalui Server Sent Event (SSE) dalam bentuk REST API.Aplikasi ini diberi nama TempHum dan dapat dijalankan di Desktop (Windows, Linux, Mac OS), web browser, dan smartphone (Android dan iOS), sehingga aplikasi ini dapat melayani banyak pengguna dalam memantau kondisi ruang server secara dinamis.Kata Kunci : open source, cluster, raspberry pi, kafka, python. 

scholarly journals COMPARISON OF POINT CLOUD REGISTRATION ALGORITHMS FOR BETTER RESULT ASSESSMENT – TOWARDS AN OPEN-SOURCE SOLUTION

2018 ◽  
Vol XLII-2 ◽  
pp. 551-558 ◽  
Author(s):  
E. Lachat ◽  
T. Landes ◽  
P. Grussenmeyer
Keyword(s):  
Open Source ◽  
Laser Scanning ◽  
Quality Criteria ◽  
Point Clouds ◽  
Academic Community ◽  
Multiple Point ◽  
Common Reference ◽  
Global Registration ◽  
Wide Range ◽  
First Results

Terrestrial and airborne laser scanning, photogrammetry and more generally 3D recording techniques are used in a wide range of applications. After recording several individual 3D datasets known in local systems, one of the first crucial processing steps is the registration of these data into a common reference frame. To perform such a 3D transformation, commercial and open source software as well as programs from the academic community are available. Due to some lacks in terms of computation transparency and quality assessment in these solutions, it has been decided to develop an open source algorithm which is presented in this paper. It is dedicated to the simultaneous registration of multiple point clouds as well as their georeferencing. The idea is to use this algorithm as a start point for further implementations, involving the possibility of combining 3D data from different sources. Parallel to the presentation of the global registration methodology which has been employed, the aim of this paper is to confront the results achieved this way with the above-mentioned existing solutions. For this purpose, first results obtained with the proposed algorithm to perform the global registration of ten laser scanning point clouds are presented. An analysis of the quality criteria delivered by two selected software used in this study and a reflexion about these criteria is also performed to complete the comparison of the obtained results. The final aim of this paper is to validate the current efficiency of the proposed method through these comparisons.

scholarly journals VAGADRONE: Intelligent and Fully Automatic Drone Based on Raspberry Pi and Android

2021 ◽  
Vol 11 (7) ◽  
pp. 3153
Author(s):  
Saifeddine Benhadhria ◽  
Mohamed Mansouri ◽  
Ameni Benkhlifa ◽  
Imed Gharbi ◽  
Nadhem Jlili
Keyword(s):  
Remote Control ◽  
Autonomous Navigation ◽  
Object Identification ◽  
External Control ◽  
Raspberry Pi ◽  
Optimal Trajectories ◽  
Live Streaming ◽  
Wide Range ◽  
Fully Automatic ◽  
Direct Use

Multirotor drones are widely used currently in several areas of life. Their suitable size and the tasks that they can perform are their main advantages. However, to the best of our knowledge, they must be controlled via remote control to fly from one point to another, and they can only be used for a specific mission (tracking, searching, computing, and so on). In this paper, we intend to present an autonomous UAV based on Raspberry Pi and Android. Android offers a wide range of applications for direct use by the UAV depending on the context of the assigned mission. The applications cover a large number of areas such as object identification, facial recognition, and counting objects such as panels, people, and so on. In addition, the proposed UAV calculates optimal trajectories, provides autonomous navigation without external control, detects obstacles, and ensures live streaming during the mission. Experiments are carried out to test the above-mentioned criteria.

scholarly journals The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin Pullinger ◽  
Jonathan Kilgour ◽  
Nigel Goddard ◽  
Niklas Berliner ◽  
Lynda Webb ◽  
...  
Keyword(s):  
Survey Data ◽  
Energy Demand ◽  
Secondary Data ◽  
Hot Water ◽  
Power Measurement ◽  
Sensor Data ◽  
Energy Awareness ◽  
Household Energy ◽  
Wide Range ◽  
The Ideal

AbstractThe IDEAL household energy dataset described here comprises electricity, gas and contextual data from 255 UK homes over a 23-month period ending in June 2018, with a mean participation duration of 286 days. Sensors gathered 1-second electricity data, pulse-level gas data, 12-second temperature, humidity and light data for each room, and 12-second temperature data from boiler pipes for central heating and hot water. 39 homes also included plug-level monitoring of selected electrical appliances, real-power measurement of mains electricity and key sub-circuits, and more detailed temperature monitoring of gas- and heat-using equipment, including radiators and taps. Survey data included occupant demographics, values, attitudes and self-reported energy awareness, household income, energy tariffs, and building, room and appliance characteristics. Linked secondary data comprises weather and level of urbanisation. The data is provided in comma-separated format with a custom-built API to facilitate usage, and has been cleaned and documented. The data has a wide range of applications, including investigating energy demand patterns and drivers, modelling building performance, and undertaking Non-Intrusive Load Monitoring research.

scholarly journals Implementation of a Six-Layer Smart Factory Architecture with Special Focus on Transdisciplinary Engineering Education

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2944
Author(s):  
Benjamin James Ralph ◽  
Marcel Sorger ◽  
Benjamin Schödinger ◽  
Hans-Jörg Schmölzer ◽  
Karin Hartl ◽  
...  
Keyword(s):  
Open Source ◽  
User Interfaces ◽  
Industrial Revolution ◽  
Low Cost ◽  
Material Behavior ◽  
Sensor Data ◽  
Management Tool ◽  
Special Focus ◽  
Programming Environments ◽  
Metal Processing

Smart factories are an integral element of the manufacturing infrastructure in the context of the fourth industrial revolution. Nevertheless, there is frequently a deficiency of adequate training facilities for future engineering experts in the academic environment. For this reason, this paper describes the development and implementation of two different layer architectures for the metal processing environment. The first architecture is based on low-cost but resilient devices, allowing interested parties to work with mostly open-source interfaces and standard back-end programming environments. Additionally, one proprietary and two open-source graphical user interfaces (GUIs) were developed. Those interfaces can be adapted front-end as well as back-end, ensuring a holistic comprehension of their capabilities and limits. As a result, a six-layer architecture, from digitization to an interactive project management tool, was designed and implemented in the practical workflow at the academic institution. To take the complexity of thermo-mechanical processing in the metal processing field into account, an alternative layer, connected with the thermo-mechanical treatment simulator Gleeble 3800, was designed. This framework is capable of transferring sensor data with high frequency, enabling data collection for the numerical simulation of complex material behavior under high temperature processing. Finally, the possibility of connecting both systems by using open-source software packages is demonstrated.

scholarly journals BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2144
Author(s):  
Stefan Reitmann ◽  
Lorenzo Neumann ◽  
Bernhard Jung
Keyword(s):  
Point Clouds ◽  
Training Data ◽  
Sensor Data ◽  
Generation Process ◽  
Data Generation ◽  
Depth Sensor ◽  
Depth Sensing ◽  
Depth Sensors ◽  
3D Point Clouds ◽  
Wide Range

Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the training data generation process for a wide range of domains, we have developed the BLAINDER add-on package for the open-source 3D modeling software Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniques Light Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within the BLAINDER add-on, different depth sensors can be loaded from presets, customized sensors can be implemented and different environmental conditions (e.g., influence of rain, dust) can be simulated. The semantically labeled data can be exported to various 2D and 3D formats and are thus optimized for different ML applications and visualizations. In addition, semantically labeled images can be exported using the rendering functionalities of Blender.

scholarly journals Monitoring System for Tracking a PV Generator in an Experimental Smart Microgrid: An Open-Source Solution

2021 ◽  
Vol 13 (15) ◽  
pp. 8182
Author(s):  
José María Portalo ◽  
Isaías González ◽  
Antonio José Calderón
Keyword(s):  
Open Source ◽  
Monitoring System ◽  
Smart Grids ◽  
Raspberry Pi ◽  
Operator Interface ◽  
Open Source Hardware ◽  
Real Time Information ◽  
And Storage ◽  
Time Information ◽  
Pv Generator

Smart grids and smart microgrids (SMGs) require proper monitoring for their operation. To this end, measuring, data acquisition, and storage, as well as remote online visualization of real-time information, must be performed using suitable equipment. An experimental SMG is being deployed that combines photovoltaics and the energy carrier hydrogen through the interconnection of photovoltaic panels, electrolyser, fuel cell, and load around a voltage bus powered by a lithium battery. This paper presents a monitoring system based on open-source hardware and software for tracking the temperature of the photovoltaic generator in such an SMG. In fact, the increases in temperature in PV modules lead to a decrease in their efficiency, so this parameter needs to be measured in order to monitor and evaluate the operation. Specifically, the developed monitoring system consists of a network of digital temperature sensors connected to an Arduino microcontroller, which feeds the acquired data to a Raspberry Pi microcomputer. The latter is accessed by a cloud-enabled user/operator interface implemented in Grafana. The monitoring system is expounded and experimental results are reported to validate the proposal.

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