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. 



This dataset accompanies a paper titled "Detection of Metallic Objects in Mineralised Soil Using Magnetic Induction Spectroscopy". 


Every sweep of the detector over an object is contained in a different file, with the following file naming convention being used: ___.h5, where is globally unique identifier for the file. Each file is a HDF5 file generated using Pandas, containing a single DataFrame. The DataFrame contains 8 columns. The first three correspond to the x-, y- and z-position (in cm) relative to an arbitrary datum. The arbitrary datum stays constant for all sweeps over all objects in a given combination of soil and depth. The other 5 columns contain the complex transimpedance values as measured by the MIS system, after calibration against the ferrite piece. Due to experimental constraints, there is no data for one of the rocks buried at 10 cm depth in "Rocky" soil.


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


It is the HDL files with a submisstion to the IEEE journal.

Last Updated On: 
Fri, 05/01/2020 - 05:55

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.


This tool model propose a Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT architecture. Based on autoencoder of Mask-RCNN for area mark feature maps objection detection for the identification of COVID-19 pneumonia have very serious pathological and always accompanied by various of symptoms. We collect a lot of lung x-ray images were be integrated into DICM style dataset prepare for experiment on computer on vision algorithms, and deep learning architecture based on autoencoder of Mask- RCNN algorithms are the main technological breakthrough.


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.


See the paper "The perils and pitfalls of block design for EEG classification experiments" on IEEE Xplore.

DOI: 10.1109/TPAMI.2020.2973153

Code for analyzing the dataset is included in the online supplementary materials for the paper.

The code and the appendix from the online supplementary materials are also included here.

If you use this code or data, please cite the above paper.


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.


This package only contains data which belongs to the work "A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks" (B. Matthiesen, A. Zappone, K.-L. Besser, E. Jorswieck, and M. Debbah, IEEE Transactions on Signal Processing, vol. 68, pp. 3887-3902).To use it, the source code included with this data set is required. The most recent version is available on GitHub.


The "results" directory contains the trained models.

  • "final4users" contains the big model used for the numerical evalution in the paper cited above.

  • "final16" is the smaller model analyzed in the last subsection of the numerical evaluation.

  • "final7users" contains the models of the 7 user scenario.


The "data" directory contains the channel data.

  • "channels-4.h5": Channels used for training and validation for 4 users

  • "channels-7.h5": Channels used for training and validation for 7 users


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’.


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


Detailed documentations and instructions will be found in the uploaded files.


This dataset aims at providing a toy demo for the comparison between the doubly orthogonal matching pursuit (DOMP) and its predecessor, the OMP.


File descriptions:

- toydemo.m: Main file. It provides a comparison of the algorithms by loading the measurement  and performing a behavioral model of the PA.

- demo_meas.mat: example of the input and output measurements of a power amplifier (PA) working under a 15-MHz LTE signal sampled at a sampling rate of 92.16MHz.

- model_gmp_domp_omp.m: generates the model structure and calls the pruning techniques.

- omp_domp.m: executes the OMP and DOMP techniques.