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54 results for “smart devices”
Multi-Sensor Dataset From Android Smart Devices
<p>This dataset contains data acquired on various Android devices, using an Android app called ''Mimir'', developed by the authors. Focus is given on raw GNSS measurements, but other sensors are also logged in the surveys. The dataset is provided under the CC-BY 4.0 license. More information are provided inside the ''readme.md'' provided along the dataset, as well as in our related publication.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 1. The whole-building switch concept for the power lines of standby devices
<p>68 million houses in North America and Europe will be smart by 2019 (Kurkinen, 2016) with a compound annual growth rate of 37 % and 61 %, respectively. The smart equipment is usually installed together with an upgrade (e.g. aluminum wires are replaced by copper ones) of the power grid. In this case, additional power lines for standby devices are cabled, and the WBS concept is applied using one power switch only (see figure 1). For instance, the Songle high-power relay T90 can control the whole building electricity with load up to 30 A using NodeMcu Lua ESP8266 WiFi and/or Arduino Uno / Mega boards.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 3. The unified hardware unit based on NodeMcu Lua ESP8266 WiFi development board, ACS712T ELC-30A current sensor, and relay SRD-05VDC-SL-C
<p>The software consists of two parts, low-level Arduino sketches and high-level C# Windows form appplication. They are connected using the open-source message MQTT broker Mosquitto.11 Every hardware unit has the unique identifier and commands to control the relay. The MQTT topic “/VPP/Relays” is used by subscribers and publishers. The number “50” sent from C# Windows form (it equals number “2” sent from the standard Mosquitto publisher) is a command to switch on the second relay, “51” (“3”) – to switch off, respectively. The prototype was developed with one root controller and two descendant relays. The commands are as follows: “52” (“4”) / “53” (“5”) – to switch on / off the first relay, “54” (“6”) / “55” (“7”) – to switch on / off the third relay, respectively. This solution is similar to the one presented in [22], but ACS712T ELC-30A current sensor and ESP8266WiFi.h library are applied here. In addition, other commands, e.g. “56” (“8”) to get the value of the current in the 3rd segment, are in use as well.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 5. An example of smart lighting using NodeMcu Lua ESP8266 ESP-12 WiFi board
<p> for different purposes together with switching on/off relays, e.g. to control the motors, to acquire the data from sensors. It allows developing multifunctional smart systems. For instance, the smart lighting unit is created using NodeMcu Lua ESP8266 ESP-12 WiFi board, Arduino light sensor, and relay SRD-05VDC-SL-C, which controls the power supply of the lamp. Figure 5 shows a simplified example of smart lighting, where the lamp is represented by eight 5 mm light-emitting diodes (LEDs).</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 4. Screen shot of the C# Windows form app
<p>The screen shot of the C# Windows form app is shown in figure 4. The text field on the left side includes numbers from 2 to 7, which are commands to control the states of relays. </p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 2. An example of smart power grid with hierarchical structure
<p> Figure 2 shows an example of smart power grid with hierarchical structure, where every segment equals a room or office. This approach is similar to the idea presented in Alboteanu et al. (2015), where the connecting / disconnecting of renewable energy sources and consumers are made via the appropriate contactors, automatically (or manually) controlled according to the energy consumption/generation. However, the management of micro smart grid is discussed in Alboteanu et al. (2015) only</p>
Exploring Augmented Reality Privacy Icons for Smart Home Devices and their Effect on Users' Privacy Awareness
<p><strong>Exploring Augmented Reality Privacy Icons for Smart Home Devices and their Effect on Users' Privacy Awareness</strong></p> <p><strong>Authors</strong></p> <p>Kathrin Knutzen, Florian Weidner, Wolfgang Broll</p> <p> </p> <p><strong>About</strong></p> <p>This data represents the supplementary material for the conference paper with above title submitted at ISMAR 2021.</p> <p> </p> <p><strong>Contents</strong></p> <p>The supplementary material contains five files:</p> <ol> <li>The abstraction of paraphrases and transcripts after each condition respectively.<br> According to qualitative content analysis procedure, the conducted interviews were transcribed, paraphrased and subsequently abstracted to generate a category system. Every category is described with a definition and some exemplary quotes. Statements of participants are condensed and abstracted. Number of participants who made statements regarding a category, and most prominent valence are taken as basis to generate tree maps in Figure 5 and 6.<br> Please note that the prevalences represent the views or opinions of the participants on the single categories. Also, the mentioned categories have several subcategories and only the most important regarding privacy awareness are mentioned in the article.<br> <br> Transcripts, audio files and paraphrases are available upon request.</li> <li>The experimental task description. It served as exposition for the task that the participants had to complete.</li> <li>The interview guideline. Please note that this study was part of a larger project that also focused on topics such as usability and immersion, however, the article reports only on privacy awareness.</li> <li>The R script file to generate the tree maps in Figures 5 and 6. The dataset is created using data from the abstraction Excel sheet.</li> <li>A demonstration video of the experimental setup.</li> </ol>
Support data for conference paper "The Concept of Efficient Utilization of the Uplink Frequency Resource of a Smart Factory 5G Cluster by IIoT Devices"
<p>upport data for conference paper<br>Kovtun, and O. Kovtun, “The Concept of Efficient Utilization of the Uplink Frequency Resource of a Smart Factory 5G Cluster by IIoT Devices.” In Proc. 8th International Conference on Computational Linguistics and Intelligent Systems. Volume I: Machine Learning Workshop, CEUR-WS, vol. 3664, 2024; pp. 273-283.<br>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p>
Data from: AudioMoth: evaluation of a smart open acoustic device for monitoring biodiversity and the environment
1. The cost, usability and power efficiency of available wildlife monitoring equipment currently inhibits full ground-level coverage of many natural systems. Developments over the last decade in technology, open science, and the sharing economy promise to bring global access to more versatile and more affordable monitoring tools, to improve coverage for conservation researchers and managers. 2. Here we describe the development and proof-of-concept of a low-cost, small-sized and low-energy acoustic detector: 'AudioMoth'. The device is open-source and programmable, with diverse applications for recording animal calls or human activity at sample rates of up to 384kHz. We briefly outline two ongoing real-world case studies of large-scale, long-term monitoring for biodiversity and exploitation of natural resources. These studies demonstrate the potential for AudioMoth to enable a substantial shift away from passive continuous recording by individual devices, towards smart detection by networks of devices flooding large and inaccessible ecosystems. 3. The case studies demonstrate one of the smart capabilities of AudioMoth, to trigger event logging on the basis of classification algorithms that identify specific acoustic events. An algorithm to trigger recordings of the New Forest cicada (Cicadetta montana) demonstrates the potential for AudioMoth to vastly improve the spatial and temporal coverage of surveys for the presence of cryptic animals. An algorithm for logging gunshot events has potential to identify a shotgun blast in tropical rainforest at distances of up to 500 m, extending to 1km with continuous recording. 4. AudioMoth is more energy efficient than currently available passive acoustic monitoring (PAM) devices, giving it considerably greater portability and longevity in the field with smaller batteries. At a build cost of ~US$43 per unit, AudioMoth has potential for varied applications in large-scale, long-term acoustic surveys. With continuing developments in smart, energy-efficient algorithms and diminishing component costs, we are approaching the milestone of local communities being able to afford to remotely monitor their own natural resources.
Dataset for ISSTA'22 Understanding Device Integration Bugs in Smart Home System
<p>This is the dataset for the ISSTA`22 submission "Understanding Device Integration Bugs in Smart Home System". It contains 330 device integration bugs collected from the most popular open source SmartHome system, i.e., Home Assistant.</p>
Multi-Sensor Dataset in outdoor and indoor environment from Android Smart Devices and ULISS
<p>This dataset contains data acquired on various Android smart devices, i.e., smartphone and smartwatches (Mimir, TAU) and ULISS devices (AME-GEOLOC). The surveys have been performed in multiple environment (open-sky, urban canyon, light indoor, deep indoor), in different carrying mode (texting, swinging, pocket). The raw sensor data logged are : GNSS raw measurements and position fix, accelerometer, gyroscope, magnetometer, barometer, step counter/detector. The dataset is provided under the CC-BY 4.0 license. More information are provided inside the ''notes.txt' provided along the dataset, as well as in our related publication.</p>
Usability of SMART ANGEL Medical Device to Record and Transmit Health Data From Patient's Home Following Outpatient Surgery
ClinicalTrials.gov study NCT03464721. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Utilizing Smart Devices to Identify New Phenotypical Characteristics in Movement Disorders
ClinicalTrials.gov study NCT03638479. IPD Sharing: NO. Countries: 1. Publications: 2.
Fetal Life: Smart-Device Based Uterine Activity Monitoring
ClinicalTrials.gov study NCT03940365. IPD Sharing: NO. Countries: 1. Publications: 2.
'Smart Reminder': a Feasibility Pilot Study on the Effects of a Wearable Device Treatment
ClinicalTrials.gov study NCT05878132. IPD Sharing: NO. Countries: 1. Publications: 3.
Validation of INSPiRED Innovative Smart Diagnostic Devices for the Detection of Parasites Infections.
ClinicalTrials.gov study NCT04505046. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Exploring the Effects of Sleep Patterns and Physical Activity on Asthma in Adolescents With Wrist-worn Smart Devices
ClinicalTrials.gov study NCT02556567. IPD Sharing: NO. Countries: 1. Publications: 7.
Research on the Early Warning Model of Children Asthma Acute Attack Based on Wearable Wrist Smart Device of Huami
ClinicalTrials.gov study NCT05243667. IPD Sharing: NO. Countries: 1. Publications: 3.
Home Rehabilitation Using Smart Wearable Exercise and Electrical Stimulation Device After Anterior Cruciate Ligament Reconstruction
ClinicalTrials.gov study NCT04079205. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Home-based Walking Program With Smart Devices
ClinicalTrials.gov study NCT04113057. IPD Sharing: NO. Countries: 1. Publications: 17.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.