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42 results for “MEMS”
MPU9250 MEMS IMU Sine wave acceleration excitation along the Z axis
<p><strong>MPU9250 MEMS IMU Sine wave acceleration excitation along the Z axis</strong></p> <p>The file Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep_ADC.dump contains a dump of the ADC protbuff messages recorded by the Met4FoF dataaqusition unit during the calibration measurement. The ADC is sampled synchronously to the data ready signals of the MPU9250.</p> <p>The file Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep_Sensor.dump contains a dump of the MPU9250 protbuff messages recorded by the Met4FoF dataaqusition unit during the calibration measurement.</p> <p>Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep.xlsx contains the accelerations recorded by the PTB refferenzsystem for each measurement run. The phase is referred to the analog reference values in the channel Data_11 </p> <p>Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep.csv contains the values from the excel table in panda readable form.</p> <p>1FE4_AC_CAL.zip contains various measurements of the ADC transfer function as JSON files.</p>
Performance of an Electrothermal MEMS Cantilever Resonator with Fano-Resonance Annoyance under Cigarette Smoke Exposure (Data)
<p>Origin projects, figures and LabVIEW software used for the article "Performance of an Electrothermal MEMS Cantilever Resonator with Fano-Resonance Annoyance under Cigarette Smoke Exposure", published in <em>Sensors </em>on 14 Jun 2021.</p>
In-Plane and Out-of-Plane MEMS Piezoresistive Cantilever Sensors for Nanoparticle Mass Detection (Data)
<p>Origin projects, figures and LabVIEW software used for the article "In-Plane and Out-of-Plane MEMS Piezoresistive Cantilever Sensors for Nanoparticle Mass Detection", published in <em>Sensors </em>on 22 Jan 2020.</p>
Phase characteristic optimization of resonant MEMS environmental sensors (Data)
<p>Origin projects and figures used for the article "Phase characteristic optimization of resonant MEMS environmental sensors", published in the proceedings of Sensoren und Messsysteme 2018, 19. ITG/GMA-Fachtagung; 26.06.2018 to 27.06.2018; Nürnberg, Germany.</p>
MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles (Data)
<p>Origin project and figures used for the article "MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles", published in <em>Chemosensors</em> on 04 Aug 2021.</p>
Phase optimization of thermally actuated piezoresistive resonant MEMS cantilever sensors (Data)
<p>Origin projects, figures and COMSOL simulation used for the article "Phase optimization of thermally actuated piezoresistive resonant MEMS cantilever sensors", published in <em>Journal of Sensors and Sensor Systems </em>on 14 Jan 2019.</p>
Enhancement of unsteady frequency responses of electro-thermal resonance MEMS cantilever sensors (Data)
<p>Origin projects and figures used for the article "Enhancement of unsteady frequency responses of electro-thermal resonance MEMS cantilever sensors", published in the proceedings of the 30th Micromechanics and Microsystems Europe Workshop; 18.08.2019 to 20.08.2019; Wolfson College, Oxford, United Kingdom.</p>
Dataset: Matthews Emerging Markets Discovery Active ETF (MEMS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Data and code for: Combining eddy covariance towers, field measurements, and the MEMS 2 ecosystem model improves confidence in the climate impacts of bioenergy with carbon capture and storage
Open the record for dataset details and reuse information.
MEMS-cochlea: Dataset for publication
<p>This is the dataset to the publication " Neuromorphic acoustic sensing using an adaptive microelectromechanical cochlea with integrated feedback" by Lenk et al. (DOI will follow soon).</p> <p>Explanation of data:</p> <p>1.) 'MEMS cochlea response to natural sound' (dataset for fig 2 in publication): In this dataset, the file "natural sound dateset" was used to drive a loudspeaker. Its given in wav-format. File named timeseries..." give the data of the response of two different sensors as well as a measurement microphone (named "input") to the wav-file. First column time in sec, second column sensor signal in V. Files named "powerspectra..." give the power spectra data of the three time series, first column frequency in Hz, second column power in absolute values not dB.</p> <p>2.) 'Sensor response in dependence of feedback' (dataset for fig 3 in publication): The dataset includes the sensor signal amplitudes in mV (2nd column) as function of sound pressure amplitudes in Pa (1st column) in files with name starting "fig3a..." for different feedback strengths a_f given by the filename. Files, whose names start with "fig3b+c", give the gain (sensor amplitude active, i.e. a_f>0, divided by sensor amplitude passive, i.e. a_f=0) in the 2nd column as a function of the feedback strength a_f (1st column). In files named "fig3e_sensamp...", the sensor signal amplitude in V (2nd column) is given in dependence of the feedback strength a_f (1st column) for different driving voltages of the loudpseaker, given by the number after "loud" in the filename. If the filename says "negafnegDC", the feedback strength a_f is negativ. If it says "posafnegDC", the feedback strength was positive. The DC voltage of the feedback was always -200mV. From the dependence of sensor signal amplitude on the driving signal amplitude (both in mV), the sensitivity is extracted as the slope of the curve in mV/mV. The sensitvity is given in files, named "fig3e_sensitvity..." in the 2nd column as function of feedback strength a_f (first column).</p> <p>3.) 'Comparison experiment vs model' (dataset for fig 4 in publication): These files give the values plotted in the graphs. The files, named "acrit..." contain the values of a_crit (feedback strength at bifurcation, 2nd column of file) as function of bias voltage u_DC in mV (first column of file) obtained either from experiment or from the formula (last equation in methods part). The file, named "sensitivity...", has the feedback strength a_f in the 1st column and the sensitivity in nm/Pa, obtained frome xperiments, in the 2nd column. The files, named "effective_Q_factor...", have the feedback strength a_f in 1st column and the effective Q factor, obtained from simulations, in the 2nd column. </p> <p>4.) 'Two coupled sensors' (dataset for fig 5 in publication): The files contain the frequency response, i.e. power spectral density in dB (2nd column) as function of frequency in kHz (1st column), of two different sensors for different values of the coupling strength, given by the value after "b" in the filename.</p> <p>5.) 'Dynamic_adaptation_with_code' (dataset for fig 6 in publication): This dataset contains files, names starting with "timeseries...", which give the time series (sensor signal in mV vs. time in sec) for two different driving voltages of the loudspeaker (given by the value after "sound" in the filename), which are shown in fig. 6b in the publication. Files, named "envelope...", give the extracted envelope of the modelled sensor signal in V (2nd column) as function of time in sec (1st column) for different modelled sound inputs, as shown in fig 6c. The envelope was extracted with the program "env.m", written in Matlab. The program "spice_sim" is used to start the LTSpice simulations for adaptation with different parameters. The files in the zip-archive "adapt_,8_,5_natelec" incorporates the necessary files for the LTSPice simulation of the adaptation process. </p> <p> </p>
Investigation of adhesion layers for the deposition of magnetostrictive CoxFe(1-x) films on ScyAl(1-y)N films for magnetoelectric MEMS sensors
<p>For magnetoelectric film stacks, Co/Fe multilayers are deposited on piezoelectric (Sc)AlN films on Si/SiO2 substrates and subsequently annealed by rapid thermal annealing (RTA) to create a Co-Fe alloy phase. In this study, the influence of an optional adhesion layer of 5 nm Cr or Zr on the phase transformation and interlayer properties was investigated. The dataset provided contains logfiles from the RTA process, raw data of X-ray diffraction (XRD), enery dispersive X-ray spectroscopy (EDS) measurements and scanning electron microscope (SEM) images of focused ion beam (FIB) prepared cross-sections. </p> <p> </p> <p>Results published in:</p> <p>H. Honig<span>, </span>H. Töpfer<span>, </span>P. Schaaf; Adhesion layers between piezoelectric and magnetostrictive layers in a MEMS magneto-sensor stack: Influence on the phase transformation of deposited Co/Fe multilayers to magnetostrictive Co<sub><em>x</em></sub>Fe<sub>1−<em>x</em></sub> phase. <em>J. Vac. Sci. Technol. A</em> 1 December 2024; 42 (6): 063405. <a href="https://doi.org/10.1116/6.0004071" target="_blank" rel="noopener">https://doi.org/10.1116/6.0004071</a></p>
A Multi-site Double-blind Placebo-controlled Trial of Memantine Versus Placebo in Children With Autism (MEM)
ClinicalTrials.gov study NCT01372449. IPD Sharing: NO. Countries: 1. Publications: 2.
Study to Evaluate the Safety and Efficacy of MEM 3454 as Adjunctive Treatment in Combination With a Preexisting Antipsychotic in Patients With Cognitive Impairment Associated With Schizophrenia
ClinicalTrials.gov study NCT00604760. IPD Sharing: Not stated. Countries: 1. Publications: 2.
T-Mem GEne in Atherosclerosis
ClinicalTrials.gov study NCT06230406. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Measure of Pharmacokinetic Parameters and Adherence With MEMS in Naive HIV Infected Patients Treated With Reyataz Once Daily Combined With Norvir and Truvada
ClinicalTrials.gov study NCT00528060. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Single-Center, Double-Blind (DB) Study of MEM 3454 on P50 Sensory Gating and Mismatch Negativity in Schizophrenia Patients
ClinicalTrials.gov study NCT00725855. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: Spatial detection of outlier loci with Moran eigenvector maps (MEM)
The spatial signature of microevolutionary processes structuring genetic variation may play an important role in the detection of loci under selection. However, the spatial location of samples has not yet been used to quantify this. Here, we present a new two-step method of spatial outlier detection at the individual and deme levels using the power spectrum of Moran eigenvector maps (MEM). The MEM power spectrum quantifies how the variation in a variable, such as the frequency of an allele at a SNP locus, is distributed across a range of spatial scales defined by MEM spatial eigenvectors. The first step (Moran spectral outlier detection: MSOD) uses genetic and spatial information to identify outlier loci by their unusual power spectrum. The second step uses Moran spectral randomization (MSR) to test the association between outlier loci and environmental predictors, accounting for spatial autocorrelation. Using simulated data from two published papers, we tested this two-step method in different scenarios of landscape configuration, selection strength, dispersal capacity and sampling design. Under scenarios that included spatial structure, MSOD alone was sufficient to detect outlier loci at the individual and deme levels without the need for incorporating environmental predictors. Follow-up with MSR generally reduced (already low) false-positive rates, though in some cases led to a reduction in power. The results were surprisingly robust to differences in sample size and sampling design. Our method represents a new tool for detecting potential loci under selection with individual-based and population-based sampling by leveraging spatial information that has hitherto been neglected.
Evaluation of MEMS NIR spectrometers for on-farm analysis of raw milk composition
<p>Today, measurement of raw milk quality and composition relies on Fourier-transform infrared spectroscopy to monitor and improve dairy production and cow health. However, these laboratory analyzers are bulky, expensive and can only be used by experts. Moreover, the sample logistics and data transfer delay the information on product quality, and the measures taken to optimize the care and feeding of the cattle rendering them less suitable for real-time monitoring. An on-farm spectrometer with compact size and affordable cost could bring a solution for this discrepancy. This paper evaluates the performance of Micro-Electro-Mechanical-System (MEMS) based near-infrared (NIR) spectrometers as on-farm milk analysers. These spectrometers use Fabry-Pérot interferometers for wavelength tuning, giving them the advantage of very compact size and affordable price. This study discusses the ability of MEMS spectrometers to reach the accuracy limits set by ICAR for at-line analyzers of the milk content regarding fat, protein and lactose. According to achieved results, the transmission measurements with the NIRONE2.5 spectrometer perform best, with an acceptable root mean squared error of prediction (RMSEP=0.21% w/w) for the measurement of milk fat and excellent performance (RMSEP≤0.11% w/w) for protein and lactose. In addition, the transmission measurements using the NIRONE2.0 module give similar results for fat and lactose (RMSEP of 0.21 and 0.10% w/w respectively), while the prediction of protein is slightly deteriorated (RMSEP=0.15% w/w). These results show that the MEMS spectrometers can reach sufficient accuracy for at-line and in-line fat, protein and lactose prediction.</p>
Supplementary material 1 from: Costa AF, Siciliano S, Emin-Lima R, Martins BML, Sousa MEM, Giarrizzo T, Silva Júnior JS (2017) Stranding survey as a framework to investigate rare cetacean records of the north and north-eastern Brazilian coasts. ZooKeys 688: 111-134. https://doi.org/10.3897/zookeys.688.12636
S1 Movie : Explanation note: Dolphins trapped at Praia da Corvina, Salinópolis (MOV).
Supplementary material 3 from: Costa AF, Siciliano S, Emin-Lima R, Martins BML, Sousa MEM, Giarrizzo T, Silva Júnior JS (2017) Stranding survey as a framework to investigate rare cetacean records of the north and north-eastern Brazilian coasts. ZooKeys 688: 111-134. https://doi.org/10.3897/zookeys.688.12636
S3 Movie : Explanation note: Dolphin stranded at Praia da Travosa, Santo Amaro (MOV)
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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.