Nonlinear signal processing
The measured dataset in [1] contains an orthogonal frequency division multiplexing signal with quadrature phase-shift keying modulation and 10 MHz bandwidth. The dataset size is 20480.
[1]A. Balatsoukas-Stimming, “Nonlinear digital self-interference cancellation for in-band full-duplex radios using neural networks,” in Proc. IEEE 19th Int. Workshop Signal Process. Adv. Wireless Commun. Workshop, 2018, pp. 1–5.
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At present, big data technology is booming, and it plays an extremely important role in all aspects of our lives, from healthcare and financial services to smart city construction and personalized recommendations, big data applications are everywhere. Through the analysis and processing of massive data, big data technology not only improves the accuracy of decision-making, but also provides strong support for optimizing resource allocation, improving operational efficiency and achieving innovation. The use of big data to prevent and control heart disease is on the rise
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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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This is a dataset: a database of radar cross sections of models; The characteristics of the data set include radar frequency, polarization mode (currently only the co-polarized VV and HH), radar Angle of sight (pitch and azimuth), and target characteristics RCS. According to these input feature information, the highly nonlinear feature rcs of the predicted target is estimated. Prediction is actually an interpolation method, which can be divided into two kinds, interpolation and extrapolation. We can try different methods according to the data set provided, please show your skills!
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This dataset contains the impedance and frequency response test results of commercial LED lamp beads of different brands in multiple configurations. The dataset is divided into two parts: one is a single test of LED lamp beads of different brands, covering their impedance and frequency response characteristics; the other is a test of LED lamp beads with different array configurations (such as 1x3, 4x4, 2x1, etc.) to show their performance in the array structure.
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Experiment Details:
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This is the supporting data sets for the paper entitled “Low-Complexity Gradient-Based Algorithm for Phase-Only Pattern Synthesis”, which introduce a gradient-based algorithm for phased array pattern synthesis with element phase only. The algorithm can be applied to linear and planar arrays, obtaining the required element phases for arbitrary patterns with desired tolerance, and minimizing nulls or sidelobe levels (SLL). The file contains element phases for the results demonstrated in the paper and a script to read the element phases to regenerate the array patterns.
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This graph illustrates the visualization trend of a subset of the dataset I have uploaded, which comprises 6500*9 data points. The dataset consists of nine columns representing underwater speed (UWS), underwater course (UWC), depth below the surface (DBS), rate of change in speed (RCS), rate of change in course (RCC), rate of change in depth (RCD), trend A and B of vibrational signals (TVS_A, TVS_B) and electromagnetic noise trend (TEN) recorded by the AUV.
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This dataset includes results of simulation and experiment for tuning of bumpless feedforward controller. The tuninig of FF and the simultaneous tuning of FF and DOB are selected as comparsion group. Their results are also included in this dataset. For each method, two parameters are chosen to be tuned in their inverse uniform model or inverse sub-model. The results of each iteration for every method and every trajectory are also included. In simuation file, there are five group of result. For each group, results of three methods are included.
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This letter presents an effective digital passive intermodulation (PIM) cancellation technique for multi-band multiple-input multiple-output frequency-division duplexing radios for 5G and beyond. We begin by introducing a universal PIM signal model for multi-band radios with an arbitrary number of transmit--receive antennas, which enables the calculation of basis functions at all intermodulation frequencies. After establishing this signal model, a comparative analysis of state-of-the-art behavioral models is conducted in the context of PIM modeling.
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