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817 results for “use of devices”
Multimodal video and IMU kinematic dataset on daily life activities using affordable devices (VIDIMU)
<p>Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient tracking solutions for remote daily life activities recognition and kinematic analysis. </p> <p>The VIDIMU dataset includes 54 healthy young adults that were recorded on video and 16 of them were simultaneously recorded using custom IMUs. For each subject, 13 activities were registered using a low-resolution video camera and five Inertial Measurement Units (IMUs). Inertial sensors were placed in the lower or the upper limbs of the subject, respectively for activities that involve movement with the lower or the upper body. Video recordings were postprocessed using the state-of-the-art pose estimator <em>BodyTrack</em> (similar to OpenPose, and included in NVIDIA Maxine-AR-SDK) to provide a sequence of 3D joint positions for each movement. Raw IMU recordings were post-processed to compute joint angles by inverse kinematics with <em>OpenSim</em>. For recordings including simultaneous acquisition of video and IMU data types, these signals were used for data file synchronization. Collected data can be further used in applications related to human activity recognition and biomechanics related experiments in simulated home-like settings.</p> <p> </p> <p> </p>
Moderate associations between the use of levonorgestrel-releasing intrauterine device and metabolomics profile; Supplementary Figures
<p>Supplementary Material for the article "Moderate associations between the use of levonorgestrel-releasing intrauterine device and metabolomics profile", in the Journal of Clinical Endocrinology and Metabolism</p>
Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices
<p>This archive contains the files submitted to the 4th International Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files provided in this package are associated with the paper titled "Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices"</p> <p>With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the ''smartness'' of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: ''What kinds of behaviors do humans expect from their IoT devices?'' The dataset we collected contains the basic information of the IFTTT rules, trigger and action event, and how many people are using each rule.</p> <p>For more detail about this dataset, please refer to the paper listed above.</p>
Dataset for "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices"
<p>Dataset for the paper "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices"</p> <p>The dataset contains localization measurements acquired with UWB devices. We compare the proposed localization method, called FlexTDOA, with a classic TDOA implementation, and with TWR-based localization. For more information about the localization methods, please refer to the paper.</p> <p>The dataset contains the measurements necessary to generate all the plots in the paper. For code examples on how to read and plot the data, please check out the associated Github repository: https://github.com/lauraflu/flextdoa</p> <p>If you find the dataset useful, please consider citing our work:</p> <blockquote> <p>Pătru, G. C., Flueratoru, L., Vasilescu, I., Niculescu, D., & Rosner, D. (2023). FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices. <em>IEEE Access</em>.</p> </blockquote>
Winter sampling microclimatic data using handheld devices at a shrub gradient across Californian drylands.
During a two-week sampling period in February 2023, we collected microclimatic data, including temperature and relative humidity, at shrubs and in the open across 9 sites in California. We collected ambient and ground temperatures.
Profile Data for Stylus Device Calibration using Ball Standard
<p>X and Z profile data for the simulation of Stylus device calibration using Ball standard</p> <p>M1000: Number of profile points -20 means: 20 points at the left of the peak</p>
Operating diagram of hatching module in Zoug jars, this system consists of a 300-litre temperature-controlled isothermal enclosure containing 10 one-litre Zoug jars, each able to accommodate several hundred eggs. An ascending current holds the eggs in suspension and carries the larvae to the surface. Another bottle connected to this device collects the larvae. The water circulating in the jars is independent of that used in the filtration circuit. A cooling unit and UV sterilizer complete the installation. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Operating diagram of hatching module in Zoug jars, this system consists of a 300-litre temperature-controlled isothermal enclosure containing 10 one-litre Zoug jars, each able to accommodate several hundred eggs. An ascending current holds the eggs in suspension and carries the larvae to the surface. Another bottle connected to this device collects the larvae. The water circulating in the jars is independent of that used in the filtration circuit. A cooling unit and UV sterilizer complete the installation.
Operating diagram of IF incubator, two identical incubators were used for eggs that were already sorted. They are made up of an isothermal enclosure and contain three tiers (100 × 60 × 17 cm). The water circulating in each tier comes from the same filtration, cooling and sterilisation device. As a result, the eggs placed in the different tiers are subject to the same temperature regime. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Operating diagram of IF incubator, two identical incubators were used for eggs that were already sorted. They are made up of an isothermal enclosure and contain three tiers (100 × 60 × 17 cm). The water circulating in each tier comes from the same filtration, cooling and sterilisation device. As a result, the eggs placed in the different tiers are subject to the same temperature regime.
TERMINUS WP6: Prototype device for sorting and separation. Reprocessing of materials. TASK 6.2: Performances of packaging in use.
<p>Astrid E. Delorme, Tanja Radusin, Petri Myllytie, Vincent Verney and Haroutioun Askanian: Enhancement of Gas Barrier Properties and Durability of Poly(butylene succinate-co-butylene adipate)-Based Nanocomposites for Food Packaging Applications. Nanomaterials 2022, 12, 978. https://doi.org/10.3390/ nano12060978</p> <p> </p> <p><strong>Abstract</strong></p> <p>Poly(butylene succinate-co-butylene adipate), PBSA, based materials are receiving growing attention in the packaging industry for their promising biodegradability. However, poor gas barrier properties and low durability of biodegradable polymers, such as PBSA, have limited their wide-spread use in food packaging applications. Here we report a scalable solution to improve gas barrier properties and stabilize PBSA against photo-aging, with minimal modifications to the biodegradable polymer backbone by using a commercially available and biocompatible layered double hydroxide, LDH, filler. We investigate and compare the mechanical, gas barrier and photoaging properties of PBSA and PBSA-LDH nanocomposite films produced on pilot scale. An increase in rigidity in the nanocomposite was observed upon addition of LDH fillers to neat PBSA, which direct the application of neat PBSA and PBSA-LDH nanocomposite to different food packaging applications. The addition of LDH fillers into neat PBSA improves the oxygen and water vapour barriers for the PBSA based nanocomposites, which increases the attractiveness of PBSA material in food packaging applications. Through changes in the viscoelastic behaviour, we observe an improved photo-durability of photoaged PBSA-LDH nanocomposites compared to neat PBSA. It is clear from our studies that the presence of LDH enhances the lifetime durability and modulates the photodegradation rate of the elaborated biocomposites.</p> <p> </p> <p><strong>Dataset</strong></p> <p>This dataset contains all the rheological, UV-Vis, DSC, TGA, IR Tensile testing, Gas Barrier and DSC raw data used to generate graphs and discussions in the article “Enhancement of gas barrier properties and durability of Poly(butylene succinate-co-butylene adipate)-based nanocom-posites for food packaging applications” doi . Data are available in a compressed .zip file with 1 folder (Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.zip) containing 7 .xlsx files containing all the rheological, IR, UV-Vis, DSC, TGA, Tensile testing, Gas Barrier raw data, 4 .pdf files one describing the experimental methods and materials, a second .pdf file outlining the metadata and information (this document) and two are the material data sheets for SOBACID®911 and PBSA used in the study, and one .zip file containing the SEM images in .TIF and .JPG formats.</p> <p> </p> <ul> <li>The <em>DSC-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the data of the heating and cooling steps of the DSC experiments performed on PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>Gas-Barrier-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the Oxygen Transmission Rates (OTR) and Water Vapour Transmission Rates (WVTR) of PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>IR-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the FT-IR data used to create the IR-spectra of aged and unaged PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>Rheology-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the melt rheology data of unaged and aged PBSA and PBSA-LDH nanocomposite films used for Cole-Cole plots and extrapolation of zero shear viscosity.</li> <li>The <em>Tensile-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the tensile testing data of PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>TGA-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the TGA data of PBSA and PBSA-LDH nanocomposite films.</li> <li>The<em> UV-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the UV-Vis absorption data of aged and unaged PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>PBSA-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.pdf</em> is the product sheet of PBSA used in this study.</li> <li>The <em>SORBACID-911-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.pdf</em> is the product sheet of SORBACID<sup>®</sup> 911 used in this study.</li> <li>The <em>SEM-images-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.zip</em> file contains for SEM images (.jpg and .tif) corresponding to neat PBSA film and PBSA-LDH nanocomposite films with 2 wt%, 5 wt% and 8 wt% LDH loadings.</li> <li>The <em>Materials_and_experimental_method-D6-2-Packaging-performances-PBSA-LDH.pdf </em>file details the experimental method and conditions for the data acquisition presented in the Laccase-thermostability-DES_v1.0_TER_WP4_D4-2.xlsx. Guidance is also provided on how to use the data to calculate the laccase activity and thermostability.</li> <li>The <em>Metadata_information-D6-2- Packaging-performances-PBSA-LDH.pdf</em> file includes more detailed metadata information for the datasets represented in here.</li> </ul>
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>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 8. Functions of the mobile devices used in the practices (Note: Each case could involve the use of more than one function.)
<p>Figure 8 captures the functions of mobile devices used in the practices. The results show that tailor-made applications for specific practices were most common (74%), followed by the use of a speaker (32%) and a camera (26%), where learners had to listen to audio materials using speakers or access online information by scanning QR-codes through cameras. In the various practices, other functions were also used, such as messaging (16%) for interacting with diverse parties and GPS (14%) for outdoor learning activities. For the practices using older models of mobile devices without cameras, tools such as an RFID reader (8%) were used for accessing information via communication tags.</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 6. Purposes of using mobile devices for learning
<p>Figure 6 shows the purposes of mobile and ubiquitous learning in the studies. A majority of the cases were multi-purpose (54%). Those with a single specific purpose were focused on “practice or revision” (34%) and “knowledge acquisition” (10%). There was a case where the application was to help new students to become familiar with the campus and teach them about the use of the facilities.</p>
Data - The Benefits of Neurofeedback Training for Alpha Enhancement and Cognitive Performance - a Single-Blind, Sham-Feedback Study Using a Low-Prized EEG Device
<p>This data set includes the minimal data set, which was used to obtain the results in Naas, Rodrigues, Knirsch, & Sonderegger (2019, doi: http://dx.doi.org/10.1101/527598).</p>
Data for device simulation in the article "Analysing the impact of the hole transport layer on the space charge distribution and hysteresis in perovskite solar cells using capacitance-voltage profiling"
<p>This repository contains the data used to perform device simulation in the article "Analysing the impact of the hole transport layer on the space charge distribution and hysteresis in perovskite solar cells using capacitance-voltage profiling", submitted in September 2024 to the journal Sustainable Energy and Fuels.</p> <p><br>The authors of this data and the article are E. Regalado-Pérez, Evelyn B. Díaz-Cruz, and J. Villanueva-Cab </p> <p><br>The scripts (.m) and input files (.csv) hosted here are based on the files created by the authors of the Driftfusion code, which can be found in the GitHub repository "barnesgroupICL/Driftfusion" at https://github.com/barnesgroupICL/Driftfusion.</p> <p> </p>
Database of RF fingerprinting on use case IoT devices
<p>This document is a dataset of radiofrequency signals. It is composed of 1000 signals coming emitted by 10 different devices. This dataset was developped for benchmarking machine learning methods on an Internet of Things classification task: recognizing which device emitted a signal.</p> <p>This dataset is in an adaptation of the dataset collected by Basak et al. in “Drone classification from RF fingerprints using deep residual nets” (IEEE COMSNETS conference, 2021).</p> <p>Basak et al. collected signals from six commercial drones, three drone radio-controllers and one WiFi router. The conducted the measurements in an anechoic chamber, using a universal software radio peripheral (USRP X310) placed seven meters apart from the devices . The signals were all in the 2.4 GHz ISM band and the whole 100 MHz band was received instantaneously using a receiving sampling rate of 100 MSps (i.e. the system down-converted the signal frequencies to the 0-100 MHz band to sample them correctly).</p> <p>While the original dataset by Basak et al. consisted in spectrograms of 256 frequency bins by 256 time frames, we have converted in into averaged spectra of 256 frequency bins. Furthermore, while Basak et al. have considered several noise levels, here we only consider the lowest noise level available (-60 dBm).</p> <p>The database is stored in an h5 file, a format adapted to databases. Inside the file there are two datasets: the signals (‘Signals’) and the targets (‘Targets’). The targets correspond to the ten different classes of signals: Parrot Disco (0), Q205 (1), Tello (2), MultiTx (3), Nine Eagles (4), Spektrum DX4e (5), Spectrum DX6i (6), Wltoys (7), S500 (8) and Linkys router (9).</p> <p>This dataset corresponds to the Deliverable D6.2 of the RadioSpin EU funded project.</p>
APNIWAVE: A Dataset Collected Using a Radar-Based Sleep-Apnea Screening Device for Use at Home
<p>The "APNIWAVE: A Dataset Collected Using a Radar-Based Sleep-Apnea Screening Device for Use at Home" dataset was created by collecting data from 11 patients with Obstructive Sleep Apnea and Hypopnea Syndrome (OSAHS) using an UWB sensor, which was the X4M200 UWB radar sensor by Novelda and a Raspberry Pi 3 device, for the purposes of APNIWAVE project (EIT Health RIS Scheme, Project ID: Project ID 2021-RIS_Innovation-066). The dataset comprises 1,011 selected samples of 10 sec each (a total of about 3 hours), from three main events that are the most frequent during sleep:</p> <ul> <li><em>Normal breathing</em> – periodic movement of the patient’s torso as a result of the inhale and exhale. (label in "<em>Labels Three Events.csv</em>" file equal to 0)</li> <li><em>Apnea event</em> – based on the Apnea type, e.g. central Apnea or hypopnea, no torso movement or a periodic torso movement but weaker than normal breathing, respectively. (label in "<em>Labels Three Events.csv</em>" file equal to 1)</li> <li><em>Other event</em> – events that may occur during data collection, such as change of posture, leaving the bedroom etc. (label in "<em>Labels Three Events.csv</em>" file equal to 2)</li> </ul> <p>Data from five distances are included for each 10 sec sample. More specifically, the central distance is the distance of the user from the radar; on top of that we added two more discrete distance steps (as designated by the radar range resolution) in front of and another two behind the user, thus totaling five distances from the radar. These five distances were selected from a set of 165 distance steps (from 0.5m to 9 m) considering a distance step of about 0.05144 m and a sampling rate of 17 Hz. As a consequence, the size of each sample (of 10 sec duration) is 5 x 170, which leads to a total size of the uploaded "<em>Multiple Distances Three Events.csv</em>" file of 5 x 171870.</p>
Predicting time of failure of Internet of Things devices using Bayesian workflow
<p>Repository includes the environment details in ```energies_iot_env.yml``` file.</p> <p>All code for model analysis is included in the ```iot_tests_refactor.ipynb``` notebook.</p> <p>Code for computing simulation based calibration is in the ```compute_sbc.py``` file, and can be run by ```just_csv.ipynb``` notebook.</p> <p> </p> <p>The data set was created in the project NCN OPUS "Process Fault Prediction and Detection" (UMO-2021/41/B/ST7/03851)</p>
Bipolar device fabrication using a scanning tunnelling microscope
<p>Hydrogen resist lithography with the tip of a scanning tunneling microscope (STM) can be used to fabricate atomic-scale dopant devices in silicon substrates and could potentially be used to build a dopant-based quantum computer. However, all devices fabricated so far have been based on the n-type dopant precursor phosphine. Here, we show that diborane can be used as p-type dopant precursor, allowing p-type and bipolar dopant devices to be created. Characterisation of diborane <span class="math-tex">\(\delta\)</span>-layers reveals that similar mobilities and densities can be achieved as for phosphine, with sheet resistivities as low as 300 <span class="math-tex">\(\Omega\)</span>. STM imaging and transport measurements of a 5.5 nm-wide p-type dopant nanowire give an estimated upper bound of 2 nm for the lithographic resolution of the p-type dopant profiles.<br> By combining our p-type doping approach with established phosphine-based n-type doping, we fabricate a 100 nm wide pn-junction and show that its electrical behaviour is similar to that of an Esaki diode.</p>
Physical-Layer Fingerprinting of LoRa devices using Supervised and Zero-Shot Learning
<p>This dataset contains all raw signals (complex float I/Q samples) used in the LoRa fingerprinting experiments of the paper entitled "Physical-Layer Fingerprinting of LoRa devices using Supervised and Zero-Shot Learning". There are 4 databases included: lora1msps, lora2msps, lora5msps, and lora10msps. Each document in the databases is a symbol extracted from a 4-byte random payload LoRa frame, transmitted by a RN2483 radio and received by a USRP B210 sampling at a rate corresponding to the database name. A total of 22 different transmitters were used. For more information, please consult the paper. The document structure is as follows:</p> <ul> <li>_id: Unique MongoDB document ID</li> <li>chirp: Base 64 encoded binary float complex I/Q data</li> <li>field: Symbol location inside a LoRa frame</li> <li>tag: Name of the device that sent the frame</li> <li>date: Time and date of reception</li> <li>fn: Frame number</li> <li>rand: Random number for sorting</li> </ul> <p><strong>How to import</strong></p> <p>Extract the tar archive. Inside the directory, run the following command to import the lora2msps database:</p> <p><em>mongorestore --gzip -d lora2msps ./lora2msps</em></p> <p>This process can be repeated for each dataset. Alternatively, all datasets can be imported automatically by executing:</p> <p><em>mongorestore --gzip . </em></p> <p><strong>How to use</strong></p> <p>After the data has been imported, an experiment can be run by simply providing the corresponding config file to tf_train (see https://github.com/rpp0/lora-phy-fingerprinting), e.g.:</p> <p><em>./tf_train.py train conf/experiment_lora2msps_mlp.conf</em></p>
Ultrafast Vibrational Control of Organohalide Perovskite Optoelectronic Devices Using Vibrationally Promoted Electronic Resonance
<p>2D PC/PL-VIPER Maps, and results of molecular dynamics simulations for the above titled paper</p>
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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.