IoT
We conduct to our knowledge a first measurement study of commercial 5G performance on smartphones by closely examining 5G networks of three carriers (two mmWave carriers, one mid-band 5G carrier) in three U.S. cities. We conduct extensive field tests on 5G performance in diverse urban environments. We systematically analyze the handoff mechanisms in 5G and their impact on network performance, and explore the feasibility of using location and possibly other environmental information to predict the network performance.
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The boring and repetitive task of monitoring video feeds makes real-time anomaly detection tasks difficult for humans. Hence, crimes are usually detected hours or days after the occurrence. To mitigate this, the research community proposes the use of a deep learning-based anomaly detection model (ADM) for automating the monitoring process.
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We propose a blockchain-enabled zero trust information sharing protocol. The proposed protocol supports the filtering of fabricated information, and protect participant privacy during information sharing. We then evaluate its performance using a series of experiments.
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This data set is the result of model test trained on the basis of the Stanford earthquake dataset (stead): a global data set of seismic signals for AI, which can effectively get the seismic signal and the arrival time of seismic phase from the image, so as to prove the effectiveness of this model
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The Widar3.0 project is a large dataset designed for use in WiFi-based hand gesture recognition. The RF data are collected from commodity WiFi NICs in the form of Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). The dataset consists of 258K instances of hand gestures with a duration of totally 8,620 minutes and from 75 domains. In addition, two sophisticated features from raw RF signal, including Doppler Frequency Shift (DFS) and a new feature Body-coordinate Velocity Profile (BVP) are included.
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Open dataset from Machine Learning Repository of Center for Machine Learning and Intelligent Systems at the University of California, Irvine.
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Image : Image was made by me for other International Contest (held by some Medical Institute,USA in the year 2021), 'An intuitive of electromagnetic radiation flowing over epithelial tissue'.
This is an open-access page. All the content can be freely downloaded after sign-up. This webpage contains datasets and models, which are in support of my Research claim/discovery and also used in my Keynote/Featured Speaker Presentations.
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This repository hosts the data and code for studying the UAV positioning problem under obstructive environment. This study focuses on the scenario of the low-altitude UAV to ground communication in a dense urban environment. There could be a lot of local structure, such as buildings and trees, that blocks the communication signal. As a result, the UAV should be optimized to smartly explore a good propagation condition to communicate with the user. At the same time, the UAV also needs to balance the communication link with the BS.
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This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.
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