Communications
Data and codes for journal paper "MmWave Vehicular Beam Training with Situational Awareness Using Machine Learning" submitted to IEEE Access.
The code assumes Python 3.
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1. Figure S1 shows the plasma frequency profile of the two-layer analytical model of the ionosphere, see Eq. (9) of the main text.
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A spectrally encapsulated orthogonal frequency division multiplexing (SE-OFDM) precoding scheme for short packet transmission that is able to suppress the out-of-band emission (OoBE) while maintaining the advantage of thecyclic prefix (CP)-OFDM is proposed. The SE-OFDM symbol consists of a prefix, an information (I)-symboland a suffix that are generated by head, center and tail matrices, respectively.
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HTTP and DNS sample traffic traces for experimenting of protocol reverse engineering methods.
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The dataset is used in the paper entitled "A distributed Fog node assessment model by using Fuzzy rules learned by XGBoost" as fuzzy rules extracted by XGboost
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Our goal is to find whether a convolutional neural network (CNN) performs better than the existing blind algorithms for image denoising, and, if yes, whether the noise statistics has an effect on the performance gap. We performed automatic identification of noise distribution, over a set of nine possible distributions, namely, Gaussian, log-normal, uniform, exponential, Poisson, salt and pepper, Rayleigh, speckle and Erlang. Next, for each of these noisy image sets, we compared the performance of FFDNet, a CNN based denoising method, with noise clinic, a blind denoising algorithm.
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Full Duplex Sensor array
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Our Signing in the Wild dataset consists of various videos harvested from YouTube containing people signing in various sign languages and doing so in diverse settings, environments, under complex signer and camera motion, and even group signing. This dataset is intended to be used for sign language detection.
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This dataset was generated on a small-scale process automation scenario using MODBUS/TCP equipment, for research on the application of ML techniques to cybersecurity in Industrial Control Systems. The testbed emulates a CPS process controlled by a SCADA system using the MODBUS/TCP protocol. It consists of a liquid pump simulated by an electric motor controlled by a variable frequency drive (allowing for multiple rotor speeds), which in its turn controlled by a Programmable Logic Controller (PLC).
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Device identification using network traffic analysis is being researched for IoT and non-IoT devices against cyber-attacks. The idea is to define a device specific unique fingerprint by analyzing the solely inter-arrival time (IAT) of packets as feature to identify a device. Deep learning is used on IAT signature for device fingerprinting of 58 non-IoT devices. We observed maximum recall and accuracy of 97.9% and 97.7% to identify device. A comparitive research GTID found using defined IAT signature that models of device identification are better than device type identification.
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