Signal Processing

It contains two parts of sound and video, and they are in one-to-one correspondence.It is used for emotion recognition for speech and video and contains nine emotions.

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In this paper, we develop an internet of medical things (IoMT)-based electrocardiogram(ECG) recorder for monitoring heart conditions in practical cases. To remove noise from signals recorded by these non-clinical devices, we propose a cloud-based denoising approach that utilizes deep neural network techniques in the time-frequency domain through the two stages. Accordingly, we exploit the fractional Stockwell transform (FrST) to transfer the ECG signal into the time-frequency domain and apply the deep robust two-stage network (DeepRTSNet) for the noise cancellation.

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The data set (induced by different odor types) collected using a Cerebus neural signal acquisition equipment involved thirteen odor stimulating materials, five of which (smelling like rose (A), caramel (B), rotten (C), canned peach (D), and excrement (E)) were selected from the T&T olfactometer (from the Daiichi Yakuhin Sangyo Co., Ltd., Japan) and the remaining eight from essential oils (i.e., mint (F), tea tree (G), coffee (H), rosemary (I), jasmine (J), lemon (K), vanilla (L) and lavender (M)).

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Conventionally, the texture of the object is used for material imaging. However, this method can mistake an image of an object, for the object itself. This dataset furthers a new and more relevant method to classify the material of an object. This data is richer, compared to RGB images, because the time of flight responses correlate with the material property of an object. This makes the features, thus extracted, more suitable to infer the material information.

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Two in-air signature databases were created. Forty participants voluntarily took part in each of the two databases’ construction. Some of them participated in both databases construction. Each participant signs in the air five signatures and imitates five signatures of five other participants.

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Measurement trace from wireless network at Dartmouth College.

This dataset includes measurement trace for over 450 access points and several thousand users at Dartmouth College.

last modified: 2006-11-14

version: 2004-08-05

reason for most recent change: Infocom 2004 trace is added.

short description: Two-year records showing the location (AP association) of each wireless card seen on campus.

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network type: bluetooth

network type: DTN (Delay Tolerant Network)

collection environment:

Four iMote-based experiments were conducted. 

The first included eight researchers and interns working at Intel Research in Cambridge. 

The second obtained data from twelve doctoral students and faculty comprising a research group at the University of Cambridge Computer Lab. 

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This dataset has been taken using the Photonic Mixer Device (PMD) Selene Module. To capture the image, we have constructed a demonstrator setup consisting of five materials (i.e., foam board (location: center), crepe paper (location: top), polystyrene (location: right), bubble wrap (location: left), wax (location: bottom)). Each image has been taken at 5 different distances (uniformly distributed between 82 cm to 47 cm) and at 3 different orientations (uniformly distributed between -10 degree to 10 degree) for each material. To avoid noise, each image has been taken in dark environment.

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The data part is the beneficial supplementary part of the article of Complex Theory and Batch Processing in Mechanical Systemic Data Extraction. It is including 2 parts. One is the about the original designed period. Another is the experimental data from 9 virtual experiments. It serves for the higher efficiency of ABRF.

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In conventional particle beam microscopy, knowledge of the beam current is essential for accurate micrograph formation and sample milling. This generally necessitates offline calibration of the instrument. In this work, we establish that beam current can be estimated online, from the same secondary electron count data that is used to form micrographs.

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