370 research outputs found
Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks
This paper proposes three simple, compact yet effective representations of
depth sequences, referred to respectively as Dynamic Depth Images (DDI),
Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images
(DDMNI). These dynamic images are constructed from a sequence of depth maps
using bidirectional rank pooling to effectively capture the spatial-temporal
information. Such image-based representations enable us to fine-tune the
existing ConvNets models trained on image data for classification of depth
sequences, without introducing large parameters to learn. Upon the proposed
representations, a convolutional Neural networks (ConvNets) based method is
developed for gesture recognition and evaluated on the Large-scale Isolated
Gesture Recognition at the ChaLearn Looking at People (LAP) challenge 2016. The
method achieved 55.57\% classification accuracy and ranked place in
this challenge but was very close to the best performance even though we only
used depth data.Comment: arXiv admin note: text overlap with arXiv:1608.0633
A YBCO RF-squid variable temperature susceptometer and its applications
The Superconducting QUantum Interference Device (SQUID) susceptibility using a high-temperature radio-frequency (rf) SQUID and a normal metal pick-up coil is employed in testing weak magnetization of the sample. The magnetic moment resolution of the device is 1 x 10(exp -6) emu, and that of the susceptibility is 5 x 10(exp -6) emu/cu cm
Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks
This paper proposes three simple, compact yet effective representations of
depth sequences, referred to respectively as Dynamic Depth Images (DDI),
Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images
(DDMNI), for both isolated and continuous action recognition. These dynamic
images are constructed from a segmented sequence of depth maps using
hierarchical bidirectional rank pooling to effectively capture the
spatial-temporal information. Specifically, DDI exploits the dynamics of
postures over time and DDNI and DDMNI exploit the 3D structural information
captured by depth maps. Upon the proposed representations, a ConvNet based
method is developed for action recognition. The image-based representations
enable us to fine-tune the existing Convolutional Neural Network (ConvNet)
models trained on image data without training a large number of parameters from
scratch. The proposed method achieved the state-of-art results on three large
datasets, namely, the Large-scale Continuous Gesture Recognition Dataset (means
Jaccard index 0.4109), the Large-scale Isolated Gesture Recognition Dataset
(59.21%), and the NTU RGB+D Dataset (87.08% cross-subject and 84.22%
cross-view) even though only the depth modality was used.Comment: arXiv admin note: text overlap with arXiv:1701.01814,
arXiv:1608.0633
Sonocatalytic Degradation of Rhodamine B in Aqueous Solution in the Presence of Tio2 Coated Activated Carbon
AbstractSynthesis of titanium dioxide coated activated carbon (TiO2/AC) has been undertaken using sol-gel method and its application in Rhodamine B (RB) dye removal has been investigated. The synthesized sonocatalyst (TiO2/AC) was characterized by using SEM and FTIR techniques. The effects of the TiO2/AC on the sonocatalytic degradation of RB dye and the operational parameters such as pH, temperature, ultrasonic frequency with the presence/absence of sonocatalyst of the sonocatalytic degradation of RB were concerned in this study. The degradation efficiency of RB in aqueous solution could be achieved 82.21% with the addition of TiO2/AC at the best conditions. The best conditions for sonocatalytic degradation of RB were found to be pH 6, temperature 50°C, ultrasonic frequency of 30kHz with the presence of sonocatalyst for 60minutes
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
The relational data model was designed to facilitate large-scale data
management and analytics. We consider the problem of how to differentiate
computations expressed relationally. We show experimentally that a relational
engine running an auto-differentiated relational algorithm can easily scale to
very large datasets, and is competitive with state-of-the-art, special-purpose
systems for large-scale distributed machine learning.Comment: ICML 202
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