ESammon: A computationaly enhanced sammon mapping based on data density

<div>This paper presents the first experimental demonstration</div><div>of a ternary memristor-CMOS logic family. We systematically</div><div>design, simulate and experimentally verify the primitive</div><div>logic functions: the ternary AND, OR and NOT gates. These are then used to build combinational ternary NAND, NOR, XOR and XNOR gates, as well as data handling ternary MAX and MIN gates. Our simulations are performed using a 50-nm process which are verified with in-house fabricated indium-tin-oxide memristors, optimized for fast switching, high transconductance, and low current leakage. We obtain close to an order of magnitude improvement in data density over conventional CMOS logic, and a reduction of switching speed by a factor of 13 over prior state-of-the-art ternary memristor results. We anticipate extensions of this work can realize practical implementation where high data density is of critical importance.</div>

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Interpretation of the Chemical and Physical Time-Series Retrieved from Sentik Glacier, Ladakh Himalaya, India

Journal of Glaciology ◽

10.3189/s0022143000008509 ◽

1984 ◽

Vol 30 (104) ◽

pp. 66-76 ◽

Cited By ~ 2

Author(s):

Paul A. Mayewski ◽

W. Berry Lyons ◽

N. Ahmad ◽

Gordon Smith ◽

M. Pourchet

Keyword(s):

Time Series ◽

Chemical Species ◽

Data Sets ◽

Reactive Iron ◽

Physical Time ◽

Ladakh Himalaya ◽

Data Density ◽

Mass Circulation ◽

The Himalaya ◽

Analysis Of Time Series

AbstractSpectral analysis of time series of a c. 17 ± 0.3 year core, calibrated for total ß activity recovered from Sentik Glacier (4908m) Ladakh, Himalaya, yields several recognizable periodicities including subannual, annual, and multi-annual. The time-series, include both chemical data (chloride, sodium, reactive iron, reactive silicate, reactive phosphate, ammonium, δD, δ(18O) and pH) and physical data (density, debris and ice-band locations, and microparticles in size grades 0.50 to 12.70 μm). Source areas for chemical species investigated and general air-mass circulation defined from chemical and physical time-series are discussed to demonstrate the potential of such studies in the development of paleometeorological data sets from remote high-alpine glacierized sites such as the Himalaya.

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A Summary of F-Transform Techniques in Data Analysis

Electronics ◽

10.3390/electronics10151771 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1771

Author(s):

Ferdinando Di Martino ◽

Irina Perfilieva ◽

Salvatore Sessa

Keyword(s):

Data Analysis ◽

Analysis Data ◽

High Dimensional ◽

Sufficient Data ◽

Fuzzy Transform ◽

Transform Method ◽

Data Density ◽

Fuzzy Partitions ◽

Definition Of ◽

Performance Results

Fuzzy transform is a technique applied to approximate a function of one or more variables applied by researchers in various image and data analysis. In this work we present a summary of a fuzzy transform method proposed in recent years in different data mining disciplines, such as the detection of relationships between features and the extraction of association rules, time series analysis, data classification. After having given the definition of the concept of Fuzzy Transform in one or more dimensions in which the constraint of sufficient data density with respect to fuzzy partitions is also explored, the data analysis approaches recently proposed in the literature based on the use of the Fuzzy Transform are analyzed. In particular, the strategies adopted in these approaches for managing the constraint of sufficient data density and the performance results obtained, compared with those measured by adopting other methods in the literature, are explored. The last section is dedicated to final considerations and future scenarios for using the Fuzzy Transform for the analysis of massive and high-dimensional data.

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Equilibrium vapor pressure and surface tension from cluster data: Density functional results

The Journal of Chemical Physics ◽

10.1063/1.2996180 ◽

2008 ◽

Vol 129 (15) ◽

pp. 154507 ◽

Cited By ~ 1

Author(s):

Ismo Napari

Keyword(s):

Surface Tension ◽

Vapor Pressure ◽

Density Functional ◽

Functional Results ◽

Cluster Data ◽

Data Density ◽

Equilibrium Vapor Pressure

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On the Use of a Modified Intersection of Confidence Intervals (MICIH) Kernel Density Estimation Approach

Athens Journal of Sciences ◽

10.30958/ajs.8-4-4 ◽

2021 ◽

Vol 8 (4) ◽

pp. 309-332

Author(s):

Efosa Michael Ogbeide ◽

Joseph Erunmwosa Osemwenkhae

Keyword(s):

Confidence Intervals ◽

Density Estimation ◽

Kernel Density Estimation ◽

Mean Squared Error ◽

Kernel Density ◽

Window Size ◽

National Bureau ◽

Squared Error ◽

Data Density ◽

Adaptive Kernel

Density estimation is an important aspect of statistics. Statistical inference often requires the knowledge of observed data density. A common method of density estimation is the kernel density estimation (KDE). It is a nonparametric estimation approach which requires a kernel function and a window size (smoothing parameter H). It aids density estimation and pattern recognition. So, this work focuses on the use of a modified intersection of confidence intervals (MICIH) approach in estimating density. The Nigerian crime rate data reported to the Police as reported by the National Bureau of Statistics was used to demonstrate this new approach. This approach in the multivariate kernel density estimation is based on the data. The main way to improve density estimation is to obtain a reduced mean squared error (MSE), the errors for this approach was evaluated. Some improvements were seen. The aim is to achieve adaptive kernel density estimation. This was achieved under a sufficiently smoothing technique. This adaptive approach was based on the bandwidths selection. The quality of the estimates obtained of the MICIH approach when applied, showed some improvements over the existing methods. The MICIH approach has reduced mean squared error and relative faster rate of convergence compared to some other approaches. The approach of MICIH has reduced points of discontinuities in the graphical densities the datasets. This will help to correct points of discontinuities and display adaptive density. Keywords: approach, bandwidth, estimate, error, kernel density

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First year of the new Arctic AWIPEV-COSYNA cabled Underwater Observatory in Kongsfjorden, Spitsbergen

10.5194/os-2016-52 ◽

2016 ◽

Cited By ~ 4

Author(s):

Philipp Fischer ◽

Max Schwanitz ◽

Reiner Loth ◽

Uwe Posner ◽

Markus Brand ◽

...

Keyword(s):

Statistical Power ◽

Single Species ◽

First Year ◽

Total Abundance ◽

Digital Sampling ◽

Scientific Analysis ◽

Data Density ◽

Species Abundances ◽

Sampling Procedures ◽

Total Species

Abstract. A combined year round assessment of selected oceanographic data and a macrobiotic community assessment was performed from October 2013 to November 2014 in the littoral zone of the polar fjord systems Kongsfjorden on the west coast of Svalbard (Norway). A state of the art remote controlled cabled underwater observatory technology was used for daily vertical profiles of temperature, salinity and turbidity together with a stereo-optical assessment of the macrobiotic community, including fish. The results reveal a distinct seasonal cycle in total species abundances with a significantly higher total abundance and species richness during the polar winter when no light is available under water compared to the summer months when 24-h light is available. During the winter months, a temporally highly segmented community was observed with respect to species occurrence with single species dominating the winter community for restricted times. In contrast, the summer community showed an overall lower total abundance, as well as a significantly lower number of species. The study clearly demonstrates the high potential of cable connected remote controlled digital sampling devices, especially in remote areas, such as the polar fjord systems, with harsh environmental conditions and limited accessibility. A smart combination of such new digital “sampling” methods with classic sampling procedures can provide a possibility to significantly extend the sampling time and frequency especially in remote and difficult to access areas. This can help to provide a sufficient data density and therefore statistical power for a sound scientific analysis without increasing the invasive sampling pressure in ecologically sensitive environments.

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