Digital signal processing
Dataset used for "A Machine Learning Approach for Wi-Fi RTT Ranging" paper (ION ITM 2019). The dataset includes almost 30,000 Wi-Fi RTT (FTM) raw channel measurements from real-life client and access points, from an office environment. This data can be used for Time of Arrival (ToA), ranging, positioning, navigation and other types of research in Wi-Fi indoor location. The zip file includes a README file, a CSV file with the dataset and several Matlab functions to help the user plot the data and demonstrate how to estimate the range.
- Categories:
We conducted an undersea magnetic induction (MI) communication experiment in the South China Sea to demonstrate the feasibility of a rotating permanent magnet transmitter. The rotating permanent magnet transmitter is placed on the floating platform for generating the inductive magnetic field, and a ferrite-rod coil with the glue-filled waterproof seal is hung in the seawater as a receiving antenna. This data is a received magnetic signal at a depth of 30 m in seawater.
- Categories:
This dataset accompanies a paper titled "Detection of Metallic Objects in Mineralised Soil Using Magnetic Induction Spectroscopy".
- Categories:
This paper presents a novel implementation scheme
of the essential circuit blocks for high performance, full-precision
Booth multipliers leveraging a hybrid logic style. By exploiting
the behavior of parasitic capacitance of MOSFETs, a carefully
engineered design style is employed to reduce dynamic power dissipation
while improving the glitch immunity of the circuit blocks.
The circuit-level techniques along with the proposed signal-flow
optimization scheme prevent the generation and propagation
- Categories:
This paper applies AI (artificial intelligence) technology to analyze low-dose HRCT (High-resolution chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training.
- Categories:
Dataset asscociated with a paper in IEEE Transactions on Pattern Analysis and Machine Intelligence
"The perils and pitfalls of block design for EEG classification experiments"
DOI: 10.1109/TPAMI.2020.2973153
If you use this code or data, please cite the above paper.
- Categories:
This work develops a novel power control framework for energy-efficient powercontrol in wireless networks. The proposed method is a new branch-and-boundprocedure based on problem-specific bounds for energy-efficiency maximizationthat allow for faster convergence. This enables to find the global solution forall of the most common energy-efficient power control problems with acomplexity that, although still exponential in the number of variables, is muchlower than other available global optimization frameworks.
- Categories:
This dataset is composed of 4-Dimensional time series files, representing the movements of all 38 participants during a novel control task. In the ‘5D_Data_Extractor.py’ file this can be set up to 6-Dimension, by the ‘fields_included’ variable. Two folders are included, one ready for preprocessing (‘subjects raw’) and the other already preprocessed ‘subjects preprocessed’.
- Categories:
This dataset is associated with the paper entitled "DeepWiPHY: Deep Learning-based Receiver Design and Dataset for IEEE 802.11ax Systems", accepted by IEEE Transactions on Wireless Communications. It has synthetic and real-word IEEE 802.11ax OFDM symbols. The synthetic dataset has around 110 million OFDM symbols and the real-world dataset has more than 14 million OFDM symbols. Our comprehensive synthetic dataset has specifically considered typical indoor channel models and RF impairments. The real-world dataset was collected under a wide range of signal-to-noise ratio (SNR) levels and at va
- Categories: