Signal Processing

Magnetotellurics forward modeling synthesizing time series

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5G-NR is beginning to be widely deployed in the mmWave frequencies in urban areas in the US and around the world. Due to the directional nature of mmWave signal propagation, improving performance of such deployments heavily relies on beam management and deployment configurations.

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Please cite the following paper when using this dataset:

N. Thakur and C.Y. Han, “An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection,” Journal of COVID, 2022, Volume 5, Issue 3, pp. 1026-1049

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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 EegDot data set  (EEG data evoked by Different Odor Types established by Tianjin University) 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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Syslog, SNMP, and tcpdump data for 3 years or more from wireless network at Dartmouth College.

This dataset includes syslog, SNMP, and tcpdump data for 3 years or more, for over 450 access points and several thousand users at dartmouth college.

Note: This dataset has multiple versions.  The dataset file names of the data associated with this version are listed below, under the 'Traceset' heading and can be downloaded under 'Dataset Files' on the right-hand side of the page.

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Traces of Bluetooth sightings by groups of users carrying small devices (iMotes) for a number of days.

This data includes a number of traces of Bluetooth sightings by groups of users carrying small devices (iMotes) for a number of days - in office environments, conference environments, and city environments.

All versions of this dataset, oldest to newest: v. 2006-01-31,  v. 2006-09-15,  v. 2009-05-29.

network type: bluetooth

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