217 research outputs found
Histograms of Points, Orientations, and Dynamics of Orientations Features for Hindi Online Handwritten Character Recognition
A set of features independent of character stroke direction and order
variations is proposed for online handwritten character recognition. A method
is developed that maps features like co-ordinates of points, orientations of
strokes at points, and dynamics of orientations of strokes at points spatially
as a function of co-ordinate values of the points and computes histograms of
these features from different regions in the spatial map.
Different features like spatio-temporal, discrete Fourier transform, discrete
cosine transform, discrete wavelet transform, spatial, and histograms of
oriented gradients used in other studies for training classifiers for character
recognition are considered. The classifier chosen for classification
performance comparison, when trained with different features, is support vector
machines (SVM).
The character datasets used for training and testing the classifiers consist
of online handwritten samples of 96 different Hindi characters. There are 12832
and 2821 samples in training and testing datasets, respectively.
SVM classifiers trained with the proposed features has the highest
classification accuracy of 92.9\% when compared to the performances of SVM
classifiers trained with the other features and tested on the same testing
dataset. Therefore, the proposed features have better character discriminative
capability than the other features considered for comparison.Comment: 21 pages, 12 jpg figure
A Classifier Using Global Character Level and Local Sub-unit Level Features for Hindi Online Handwritten Character Recognition
A classifier is developed that defines a joint distribution of global
character features, number of sub-units and local sub-unit features to model
Hindi online handwritten characters. The classifier uses latent variables to
model the structure of sub-units. The classifier uses histograms of points,
orientations, and dynamics of orientations (HPOD) features to represent
characters at global character level and local sub-unit level and is
independent of character stroke order and stroke direction variations. The
parameters of the classifier is estimated using maximum likelihood method.
Different classifiers and features used in other studies are considered in this
study for classification performance comparison with the developed classifier.
The classifiers considered are Second Order Statistics (SOS), Sub-space (SS),
Fisher Discriminant (FD), Feedforward Neural Network (FFN) and Support Vector
Machines (SVM) and the features considered are Spatio Temporal (ST), Discrete
Fourier Transform (DFT), Discrete Cosine Transform (SCT), Discrete Wavelet
Transform (DWT), Spatial (SP) and Histograms of Oriented Gradients (HOG). Hindi
character datasets used for training and testing the developed classifier
consist of samples of handwritten characters from 96 different character
classes. There are 12832 samples with an average of 133 samples per character
class in the training set and 2821 samples with an average of 29 samples per
character class in the testing set. The developed classifier has the highest
accuracy of 93.5\% on the testing set compared to that of the classifiers
trained on different features extracted from the same training set and
evaluated on the same testing set considered in this study.Comment: 23 pages, 8 jpg figures. arXiv admin note: text overlap with
arXiv:2310.0822
Effect of Ni-doping on magnetism and superconductivity in Eu0.5K0.5Fe2As2
The effect of Ni-doping on the magnetism and superconductivity in
Eu0.5K0.5Fe2As2 has been studied through a systematic investigation of magnetic
and superconducting properties of Eu0.5K0.5(Fe1-xNix)2As2 (x = 0, 0.03, 0.05,
0.08 and 0.12) compounds by means of dc and ac magnetic susceptibilities,
electrical resistivity and specific heat measurements. Eu0.5K0.5Fe2As2 is known
to exhibit superconductivity with superconducting transition temperature Tc as
high as 33 K. The Ni-doping leads to a rapid decrease in Tc; Tc is reduced to
23 K with 3% Ni-doping, and 8% Ni-doping suppresses the superconductivity to
below 1.8 K. In 3% Ni-doped sample Eu0.5K0.5(Fe0.97Ni0.03)2As2
superconductivity coexists with short range ordering of Eu2+ magnetic moments
at Tm ~ 6 K. The suppression of superconductivity with Ni-doping is accompanied
with the emergence of a long range antiferromagnetic ordering with TN = 8.5 K
and 7 K for Eu0.5K0.5(Fe0.92Ni0.08)2As2 and Eu0.5K0.5(Fe0.88Ni0.12)2As2,
respectively. The temperature and field dependent magnetic measurements for x =
0.08 and 0.12 samples reflect the possibility of a helical magnetic ordering of
Eu2 moments. We suspect that the helimagnetism of Eu spins could be responsible
for the destruction of superconductivity as has been observed in Co-doped
EuFe2As2. The most striking feature seen in the resistivity data for x = 0.08
is the reappearance of the anomaly presumably due to spin density wave
transition at around 60 K. This could be attributed to the compensation of
holes (K-doping at Eu-site) by the electrons (Ni-doping at Fe site). The
anomaly associated with spin density wave further shifts to 200 K for x = 0.12
for which the electron doping has almost compensated the holes in the system.Comment: 9 pages, 10 figure
Sarcomatoid Carcinoma Metastasis to the Colon from a Small Renal Mass: Case Report with Review of Literature
A third of patients with renal cell carcinoma (RCC) present with metastatic disease. Metastasis in RCC from small renal mass (SRM) (≤4 cm) is rare. We report a case of stage cT1a clear-cell RCC with low-risk features on pathology presenting with disproportionately large synchronous solitary metastasis to the transverse colon. He underwent resection of the mass with the involved transverse colon and adjoining mesocolon. Intestinal continuity was restored, following which partial nephrectomy was performed for the right renal tumor. Final pathology of the right renal mass confirmed clear-cell RCC. The large mass after immunohistochemistry profile confirmed metastasis from the renal tumor
Group-Level Emotion Recognition Using a Unimodal Privacy-Safe Non-Individual Approach
This article presents our unimodal privacy-safe and non-individual proposal
for the audio-video group emotion recognition subtask at the Emotion
Recognition in the Wild (EmotiW) Challenge 2020 1. This sub challenge aims to
classify in the wild videos into three categories: Positive, Neutral and
Negative. Recent deep learning models have shown tremendous advances in
analyzing interactions between people, predicting human behavior and affective
evaluation. Nonetheless, their performance comes from individual-based
analysis, which means summing up and averaging scores from individual
detections, which inevitably leads to some privacy issues. In this research, we
investigated a frugal approach towards a model able to capture the global moods
from the whole image without using face or pose detection, or any
individual-based feature as input. The proposed methodology mixes
state-of-the-art and dedicated synthetic corpora as training sources. With an
in-depth exploration of neural network architectures for group-level emotion
recognition, we built a VGG-based model achieving 59.13% accuracy on the VGAF
test set (eleventh place of the challenge). Given that the analysis is unimodal
based only on global features and that the performance is evaluated on a
real-world dataset, these results are promising and let us envision extending
this model to multimodality for classroom ambiance evaluation, our final target
application
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