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The "Queue Waiting Time Dataset" is a detailed collection of information that records the movement of waiting times in queues. This dataset contains important details such as the time of arrival, the start and finish times, the waiting time, and the length of the queue. The arrival time denotes the moment when customers enter the queue, while the start and finish times track the duration of the service process. The waiting time measures the time spent waiting in the queue, and the queue length shows the number of customers in the queue when a new customer arrives.
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2579 Views
This dataset contains electrical measurements collected in the context of the paper "An Embedded Deep Learning NILM System: A Year-long Field Study in Real Houses".
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587 Views
Dataset Description:
Based on some real-world events, the dataset offers a synthetic representation of 5G network states and metrics during a high traffic event, such as a major sports gathering in a city. Each row corresponds to a unique record capturing the attributes of the network at a particular moment, and each column corresponds to a specific feature or attribute.
Significance:
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2125 Views
Data from NASA Power Project, aiming the study of solar irradiance in the Amazon Basin, focusing 12 cities in the Amazonas State, Brazil. The data is daily basis, the target variable is the solar irradiance, and the input variables are the local temperature, local air humidity, local wind speed at 10m, local wind direction at 10m, percentage of the sky coverture, the total precipitation corrected. The time span covers 2017 to 2023. Deep learning has grown among the prediction tools used within renewable energy options.
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31 Views
A new design and implementation of a control system for an anthropomorphic robotic hand has been developed for the Bioinformatics and Autonomous Learning Laboratory (BALL) at ESPOL. Myoelectric signals were acquired using a bioelectric data acquisition board (CYTON BOARD) with six out of the available eight channels. These signals had an amplitude of 200 [uV] and were sampled at a frequency of 250 [Hz].
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213 Views
The dataset that we published in this data repository can be used to build neural networks-based inverse kinematics for NAO robot arms. This dataset is named ARKOMA. ARKOMA is an acronym for ARif eKO MAuridhi, all of whom are the creators of this dataset. This dataset contains input-output data pairs. In this dataset, the input data is the end-effector position and orientation, while the output data is a set of joint angular positions. For further applications, this dataset was split into the training dataset, validation dataset, and testing dataset.
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608 Views
Extended reality (XR) head-mounted displays (HMDs) are increasingly starting to rely on wireless task
offloading in a bid to allow unobstructed XR user movement, while still rendering high-resolution video on
a remote processing node. An example is the Oculus (Meta) Quest 2. However, congestion and reliability
issues associated with the wireless network can cause high latency and an overall low quality of service (QoS).
Therefore, understanding XR user mobility is of vital importance for supporting XR applications in future
wireless networks.
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179 Views
ommon approaches to stunting prediction, including statistical analysis and machine learning, have poor performance due to shifts in the factors influencing stunting. Causes data cannot be integrated directly when using statistical analysis. At the same time, machine learning causes a decrease in predictive performance down over time. This study proposes a new approach to stunting prediction in infants and toddlers aged 0-5 years using continuous learning methods.
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69 Views
The uploaded dataset appears to be related to various composite materials, and it includes the following columns:
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353 Views
The published Surface Electromyography (sEMG) dataset was captured under the supervision of trained physiotherapists and doctors at Mayo Hospital Lahore and National University of Sciences & Technology by following two months long experimental protocol.
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967 Views