Signal Processing
The following dataset consists of utterances, recorded using 24 volunteers raised in the Province of Manitoba, Canada. To provide a repeatable set of test words that would cover all of the phonemes, the Edinburg Machine Readable Phonetic Alphabet (MRPA) [KiGr08], consisting of 44 words is used. Each recording consists of one word uttered by the volunteer and recorded in one continuous session.
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This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/
The DataPort Repository contains the data used primarily for generating Figure 1.
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2339 Views
The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.
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6999 Views
Multimodal sensor fusion has been widely adopted in constructing scene understanding, perception, and planning for intelligent robotic systems. One of the critical tasks in this field is geospatial tracking, i.e., constantly detecting and locating objects moving across a scene. Successful development of multimodal sensor fusion tracking algorithms relies on large multimodal datasets where common modalities exist and are time-aligned, and such datasets are not readily available.
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514 Views
This dataset originates from a longitudinal study examining the factors contributing to the progression of cardiovascular disease. P This particular research employs the unprocessed sequential actigraph recordings collected from an actigraph device. We evaluate sleep quality based on the two indicators as proposed in our previous study [3] which are weekly sleep quality ‘SleepQualWeek’, and sleep consistency ‘SleepCons’. SleepQualWeek and SleepCons are calculated using the pre-processed attribute set derived from the MESA dataset.
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54 Views
The breath rate (BR), heart rate (HR), breathing-breathing interval (BBI) and heart rate variability (HRV) are the critical vital sign parameters. In this article, a novel method named adaptive separation variational mode extraction algorithm (ASVME) is proposed to accurately monitor multi-variable vital signs (MVVS) at the same time with a frequency-modulated continuous wave (FMCW) radar system in practical scenarios.
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The breath rate (BR), heart rate (HR), breathing-breathing interval (BBI) and heart rate variability (HRV) are the critical vital sign parameters. In this article, a novel method named adaptive separation variational mode extraction algorithm (ASVME) is proposed to accurately monitor multi-variable vital signs (MVVS) at the same time with a frequency-modulated continuous wave (FMCW) radar system in practical scenarios.
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13 Views
The dataset was collected in the actual outdoor environment of Dalian, China, with specific areas carefully selected to encompass a diverse range of environmental characteristics. These environments are extensive, including not only the urban center with high-rise office buildings and high population density but also suburban areas with scattered low-rise buildings and relative tranquility; additionally, they encompass major urban arteries with constant traffic flow and urban side streets with lighter traffic.
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174 Views
Deadbands are embedded functions of transducers frequently exploited to remove false alarms. In existing literature, there are two major methods to design alarm deadbands, either based on the estimated probability density function (PDF) of process variables or the estimated PDF of maximum amplitude deviations (MADs). The former method only applies to independently and identically distributed (IID) process variables, while the latter method does not have this restriction. A natural question is: which method should be used for IID process variables?
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50 Views
In the domain of gait recognition, the scarcity of non-simulated, real-world data significantly hampers the performance and applicability of recognition systems. To address this limitation, we present a comprehensive gait recognition dataset - GaitMotion- collected using built-in sensors of Android smartphones in an uncontrolled, real-world environment. This dataset captures the walking activity of 24 subjects (14 females and 10 males) above 18 years old and weighing at least 50 kg.
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124 Views