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212 results for “Measurement Systems”
Review of Recent Trends in Measuring the Computing Systems Intelligence-Figure 4. Intelligent robots (accessed 01.11.2017). 4.1. Erica, a humanoid robot (https://www.tech-review.com/erica-is-the-latest-japanese-robot-with-human-appearance.html). 4.2. Atlas, a bipedal humanoid robot developed by Boston Dynamics (https://en.wikipedia.org/wiki/Atlas_(robot))
<p>One of the most highly quoted and interesting definitions of machine intelligence was presented by Alan Turing (1950). Turing considered a computing system intelligent if a human assessor could not decide the nature of the system (being human or artificial) based on questions asked from a room hidden from a human assessor. Until recently there were performed different discussions and comments on the Turing test. Hernández-Orallo (2000) presents an interesting study related to the Turing Test. Dowe and Hajek, (1998) propose a computational extension of the Turing Test. The design and development of intelligent systems are historically very recent. But, even if the advance of hardware and software is very fast, it will take a longer time until the artificial computing systems will attain a similar intelligence with the humans. Based on this fact, we consider that is not appropriate to formulate the problem of the direct comparison at a general level of human intelligence with the machine intelligence. Different definitions were proposed for the intelligence of the agents (Russell, & Norvig, 2003; Iantovics, & Zamfirescu, 2013). Many authors (Russell, & Norvig, 2003; Iantovics, 2005) argue that the intelligence of the agents cannot be defined universally. The impossibility to give a universal definition to the human intelligence is based mostly on the enormous complexity of the human brain and complexity of the human thinking and decision making. Similarly, we may consider the impossibility of universal definition of intelligence of the agents based on the very large variety (by type and complexity) of intelligent agents. The machine intelligence frequently is defined based on different abilities such as (Iantovics, 2005; Sharkey, 2006): autonomous learning, self-adaptation, and evolution. These principles of considering the intelligence are inspired by biological life forms able to learn autonomously during their life cycle, to adapt to the environment and to evolve during more generations. We would like to outline that not all the designed agents are intelligent. There is not a required property of an agent to be intelligent.</p>
Review of Recent Trends in Measuring the Computing Systems Intelligence-Figure 5. What machine intelligence is (accessed 01.11.2017) http://www.ibmbigdatahub.com/blog/measuring-artificial-intelligence-quotient)
<p>There are many developed cooperative systems composed of very simple agents that at the system’s level are considered intelligent. Yang, Galis, Guo, and Liu (2003) presented an intelligent cooperative mobile multiagent system composed of simple reactive agents. The mobile agents are specialized in a computer network administration. They are endowed with knowledge retained as a set of rules which describe network administration tasks. The multiagent system could be considered intelligent based on the fact that it simulates the behavior of a human network administrator. In some cooperative systems, the member agents can organize themselves into cooperative coalitions/groups. Each coalition being able to solve cooperatively problems. Iantovics and Zamfirescu (2013) presented such an adaptive cooperative multiagent system, able to reorganize autonomously the coalitions in order to solve more intelligently problems. The biological and artificial intelligence are by a completely different type (Figure 5). Recently, the biological intelligence is the source of inspiration for the development of many intelligent artificial systems and different problem-solving algorithms.</p>
Data and results for manuscript: "Imaging and functional characterization of crop root systems using spectroscopic electrical impedance measurements"
<p>This package contains measured raw EIT data, electrical imaging results, spectral results from the Debye decomposition, and the Python scripts used to generate the plots in the manuscript titled:<br> <br> Imaging and functional characterization of crop root systems using spectroscopic electrical impedance measurement</p>
Fig. 2. Measurement system for cervical vertebrae. A in The first discovery of pterosaurs from the Upper Cretaceous of Mongolia
Fig. 2. Measurement system for cervical vertebrae. A. Atlas−axis complex in anterior (A), right lateral (A), ventral (A), and posterior (A) views. B. 3rd 1 2 3 4 cervical in anterior (B1), posterior (B2), right lateral (B3), dorsal (B4), and ventral (B5) views. C. Posterior part of a middle cervical in dorsal (C1), ventral (C2), and posterior (C3) views. Specific measurements are as follows: 1, total length; 2, length of centrum; 3, width of anterior part; 4, width of middle point of centrum; 5, width of posterior part with postzygapophyses; 6, width of posterior part of centrum; 7, width including postexapophyses; 8, height of neural arch on posterior part.
Dataset for the publication "The Use of Voltage Transformers for the Measurement of Power System Subharmonics in Compliance With International Standards"
<p>This is dataset for paper published:</p> <p>G. Crotti, G. D’Avanzo, P. S. Letizia and M. Luiso, "The Use of Voltage Transformers for the Measurement of Power System Subharmonics in Compliance With International Standards," in <em>IEEE Transactions on Instrumentation and Measurement</em>, vol. 71, pp. 1-12, 2022, Art no. 9005912, doi: 10.1109/TIM.2022.3204318.</p> <p> </p>
Combined measures of mimetic fidelity explain imperfect mimicry in a brood parasite–host system
<p>The persistence of imperfect mimicry in nature presents a challenge to mimicry theory. Some hypotheses for the existence of imperfect mimicry make differing predictions depending on how mimetic fidelity is measured. Here, we measure mimetic fidelity in a brood parasite–host system using both trait-based and response-based measures of mimetic fidelity. Cuckoo finches <em>Anomalospiza</em> <em>imberbis</em> lay imperfectly mimetic eggs that lack the fine scribbling characteristic of eggs of the tawny-flanked prinia <em>Prinia</em> <em>subflava</em>, a common host species. A trait-based discriminant analysis based on Minkowski functionals—that use geometric and topological morphometric methods related to egg pattern shape and coverage—reflects this consistent difference between host and parasite eggs. These methods could be applied to quantify other phenotypes including stripes and waved patterns. Furthermore, by painting scribbles onto cuckoo finch eggs and testing their rate of rejection compared to control eggs (i.e. a response-based approach to quantify mimetic fidelity), we show that prinias do not discriminate between eggs based on the absence of scribbles. Overall, our results support relaxed selection on cuckoo finches to mimic scribbles, since prinias do not respond differently to eggs with and without scribbles, despite the existence of this consistent trait difference.</p>
Multiparameter Water Quality Monitoring System for Continuous Monitoring of Fresh Waters Calibration and Measurement Data Set
<p>This data set contains calibration data for all sensors incorporated in the sensor node. It provides comparison measurements of TPL fluorescence taken by the node and reference spectrofluorimeter. Initial test measurements, as well as site measurements, are also provided. Finally, data from a heuristic method of TPL detection in the presence of algae and mud are also given.</p>
Data associated with the following publication: "A fully automated measurement system for the characterization of micro thermoelectric devices near room temperature"
<p>Data associated with the following publication: "A fully automated measurement system for the characterization of micro thermoelectric devices near room temperature" (DOI: <a href="https://doi.org/10.1016/j.applthermaleng.2023.120111">10.1016/j.applthermaleng.2023.120111</a>).</p>
Metadata with submitted Emission Control Science and Technology Journal manuscript Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems
<p>Metadata with submitted Emission Control Science and Technology Journal <em>Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems</em></p> <p>Link to article: https://link.springer.com/article/10.1007/s40825-025-00260-z</p>
Figure 1 in On the occurrence of the Synodontis eupterus (Mochokidae) in the Adriatic drainage system of Croatia: a case of an introduced aquarium species and suggestions for alien species detection measures
Figure 1. – Synodontis eupterus (from Mala Neretva River) (TL = 193 mm) (catalogue number SE-IOR 8112017).
Combined measures of mimetic fidelity explain imperfect mimicry in a brood parasite–host system
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Data for: Constitutive expression of the Type VI secretion system carries no measurable fitness cost in Vibrio cholerae
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Denitrification losses in response to N fertiliser rates - integrating high temporal resolution N2O, in-situ 15N2O and 15N2 measurements and fertiliser 15N recoveries in intensive sugarcane systems
Denitrification is a key process in the global nitrogen (N) cycle, causing both nitrous oxide (N2O) and dinitrogen (N2) emissions. However, estimates of seasonal denitrification losses (N2O+N2) are scarce, reflecting methodological difficulties in measuring soil-borne N2 emissions against the high atmospheric N2 background and challenges regarding their spatio-temporal upscaling. This study investigated N2O+N2 losses in response to N fertiliser rates (0, 100, 150, 200 and 250 kg N ha-1) on two intensively managed tropical sugarcane farms in Australia, by combining automated N2O monitoring, in-situ N2 and N2O measurements using the 15N gas flux method and fertiliser 15N recoveries at harvest. Dynamic changes in the N2O/(N2O+N2) ratio (< 0.01 to 0.768) were explained by fitting generalised additive mixed models (GAMMs) with soil factors to upscale high temporal-resolution N2O data to daily N2 emissions over the season. Cumulative N2O+N2 losses ranged from 12 to 87 kg N ha-1, increasing non-linearly with increasing N fertiliser rates. Emissions of N2O+N2 accounted for 31–78% of fertiliser 15N losses and were dominated by environmentally benign N2 emissions. The contribution of denitrification to N fertiliser loss decreased with increasing N rates, suggesting increasing significance of other N loss pathways including leaching and runoff at higher N rates. This study delivers a blueprint approach to extrapolate denitrification measurements at both temporal and spatial scales, which can be applied in fertilised agroecosystems. Robust estimates of denitrification losses determined using this method will help to improve cropping system modelling approaches, advancing our understanding of the N cycle across scales.
Data from: Unmanned aerial systems measure structural habitat features for wildlife across multiple scales
1.Assessing habitat quality is a primary goal of ecologists. However, evaluating habitat features that relate strongly to habitat quality at fine-scale resolutions across broad-scale extents is challenging. Unmanned aerial systems (UAS) provide an avenue for bridging the gap between relatively high spatial resolution, low spatial extent field-based habitat quality measurements and lower spatial resolution, higher spatial extent satellite-based remote sensing. Our goal in this study was to evaluate the potential for UAS structure from motion (SfM) to estimate several dimensions of habitat quality that provide potential security from predators and forage for pygmy rabbits (Brachylagus idahoensis) in a sagebrush-steppe environment. 2.At the plant and patch scales, we compared UAS-derived estimates of vegetation height, volume (estimate of food availability), and canopy cover to estimates from ground-based terrestrial laser scanning (TLS), and field-based measurements. Then, we mapped habitat features across two sagebrush landscapes in Idaho, USA, using point clouds derived from UAS SfM. 3.At the individual plant scale, the UAS-derived estimates matched those from TLS for height (r2 = 0.85), volume (r2 = 0.94), and canopy cover (r2 = 0.68). However, there was less agreement with field-based measurements of height (r2 = 0.67), volume (r2 = 0.31), and canopy cover (r2 = 0.29). At the patch scale, UAS-derived estimates provided a better fit to field-based measurements (r2 = 0.51-0.78) than at the plant scale. Landscape-scale maps created from UAS were able to distinguish structural heterogeneity between key patch types. 4.Our work demonstrates that UAS was able to accurately estimate habitat heterogeneity for a key terrestrial vertebrate at multiple spatial scales. Given that many of the vegetation metrics we focus on are important for a wide variety of species, our work illustrates a general remote sensing approach for mapping and monitoring fine-resolution habitat quality across broad landscapes for use in studies of animal ecology, conservation, and land management.
Dataset: Measurements and simulations of rate coefficients for the deuterated forms of the H2+ + H2 and H3+ + H2 reactive systems at low temperature
<p>Raw measurement data and processign scripts used to produce the reaction rate results in the paper: "Measurements and simulations of rate coefficients for the<br>deuterated forms of the H2+ + H2 and H3+ + H2 reactive systems at low temperature" by Miguel Jiménez-Redondo, Olli Sipilä, Pavol Jusko, and Paola Caselli;</p> <p>DOI: <a href="https://doi.org/10.1051/0004-6361/202451757">10.1051/0004-6361/202451757</a></p>
Dataset for "A low-cost and low-power continuous measurement system for atmospheric carbonyl sulfide concentration"
<p>The dataset used in the manuscript "A low-power continuous measurement system for atmospheric carbonyl sulfide concentration" by Kamezaki et al. The dataset contains COS concentrations at Tsukuba in Japan. Additionally, the data used in the paper are also included. Raw data are available upon request from the author.</p>
Power, performance and system measures of HPC benchmarks on multiple hardware
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Data for Development of LOS Analysis Procedures and Performance Measurement Systems for Parking (UTEP Year 4 Project)
<p>These Excel files consists of the data in the tables and figures in the final report. They appear in Chapters 3 to 6 in the final report. Each file is for one chapter. Each table or figure is listed as an Excel worksheet. The name of the worksheet is the figure or table number in the final report.</p>
Comparing black-carbon- and aerosol-absorption-measuring instruments – a new system using lab-generated soot coated with controlled amounts of secondary organic matter
<p>A preprint of the publication can be found here: <a href="https://amt.copernicus.org/preprints/amt-2021-214/">AMTD - Response of black carbon and aerosol absorption measuring instruments to laboratory-generated soot coated with controlled amounts of secondary organic matter (copernicus.org)</a> (doi.org/10.5194/amt-2021-214).</p> <p>The files correspond to the raw data sets used for Figures 3 and 4 of the aforementioned publication.</p> <p>The date and start/stop time of the measurements are listed in the file "overview_measurements".</p>
Assimilation of NASA's Airborne Snow Observatory snow measurements for improved hydrological modeling: A case study enabled by the coupled LIS/WRF-Hydro system
<p>Data Analysis Scripts and Post-Processed Model Data for a case study using assimilation of ASO Snow Data into the NASA LIS/WRF-Hydro Model. </p> <p>Manuscript Citation:</p> <p>Lahmers T. M., S. V. Kumar, D. Rosen, A. L Dugger, D. Gochis, J. A. Santanello, C. Gangodagamage<sup>,</sup> and R. Dunlap,<strong> </strong>2020: Assimilation of NASA’s Airborne Snow Observatory snow measurements for improved hydrological modeling: A case study enabled by the coupled LIS/WRF-Hydro system,<em>Water Resour. Res.,</em></p>
ScienceDex guides
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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.