Machine Learning
Mirror arrays have been applied to indoor visible light communication (VLC) as a passive the reconfigurable intelligent surface (RIS) which has no signal processing to solve the problem of indoor visible light line-of-sight obstruction, however, after channel modeling, it is found that the reflected channel in this scenario has a serious multipath effect, to this end, we introduce deep learning techniques into channel estimation of VLC systems with mirror arrays for the first time, and propose a hybrid new model of Transformer and the bidirectional longshort-term memory model (Transformer-B
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This is a pump fillage time series data set, consisting of 8 time series. The data is sourced from actual production data during the operational process of an oil field. It includes data from 8 oil wells, with measurements collected every half hour between July 22, 2022, and August 16, 2022. The pump fillage is extracted from the operational process of an oil field. The pump fillage data for each well is sorted in chronological order to obtain the pump fillage time series for each well. The data set had varying numbers of cards due to potential communication issues, rangin
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This is a pump fillage time series data set, consisting of 8 time series. The data is sourced from actual production data during the operational process of an oil field. It includes data from 8 oil wells, with measurements collected every half hour between July 22, 2022, and August 16, 2022. The pump fillage is extracted from the operational process of an oil field. The pump fillage data for each well is sorted in chronological order to obtain the pump fillage time series for each well. The data set had varying numbers of cards due to potential communication issues, rangin
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5 Views
A key challenge in cybersecurity is the absence of a large-scale network dataset that accurately captures modern traffic patterns, diverse intrusion types, and comprehensive network activity. Existing benchmark datasets such as KDDCup99, NSL-KDD, GureKDD, and UNSW-NB15 require updates to reflect contemporary cyberattack signatures effectively.
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This dataset contains electromagnetic field (EMF) intensity measurements recorded at half-hour intervals. The dataset spans a continuous timeline, capturing variations in electric field strength in volts per meter (V/m). It serves as a valuable resource for environmental monitoring, predictive modeling, and studying the impacts of EMF exposure. Applications include urban planning, public health assessments, and advanced regression or machine learning modeling.
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Pacemaker use due to conduction abnormalities is a common complication following surgical aortic valve replacement (AVR). Heart rate variability (HRV) is associated with sinus node dysfunction and significant dysrhythmias. However, its predictive value for postoperative electrical pacing requirements after AVR remains unclear. This retrospective study reviewed pre-registered electrical records from 194 adult patients who underwent isolated AVR. HRV parameters in both time and frequency domains were obtained prior to anesthesia induction and before initiating cardiopulmonary bypass.
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The data was collected by a tester holding a Xiaomi 13 smartphone while walking and collecting data in an underground parking lot covering a 16x70m area. The data includes 5G radio features and geomagnetic field information.
Collection Time: From 09:58 AM to 10:34 AM on July 13, 2024.
Total Samples: 12,800
Training Set (including validation set): 10,240
Test Set: 2,560
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30 Views
This MATLAB script implements a reinforcement learning (RL) approach to optimize IRS phase configurations in a MIMO wireless system. The implementation features a basic MIMO setup with a 16-element IRS operating at 12 GHz (mid-band frequency). Using the policy gradient method with a two-layer neural network, it learns optimal phase shifts while considering user mobility and Rician fading channels. The system models both direct and IRS-reflected paths, incorporating realistic path loss and channel conditions.
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66 Views
In this dataset, a human detecting model using with UWB radar technology is presented. Two distinct datasets were created using the UWB radar device, leveraging its dual features. Data collection involved two main scenarios, each containing multiple sub-scenarios. These sub-scenarios varied parameters like the position, distance, angle, and orientation of the human subject relative to the radar. Unlike conventional approaches that rely on signal processing or noise/background removal, this study uniquely emphasizes analyzing raw UWB radar data directly.
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The JU-Impact Radiomap Dataset is a comprehensive dataset designed for research and development in indoor positioning systems. It comprises 5431 instances characterized by readings from 105 static Wi-Fi Access Points (APs) and spans 152 distinct virtual grids. Each virtual grid represents a 1x1 square meter area, derived by dividing a physical floor of a university building into reference coordinate points (x, y). The dataset was collected over a period of 21 days using four mobile devices: Samsung Galaxy Tab, Moto G, Redmi Note 4, and Google Pixel.
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