IEEE Xplore Digital Library

sEMG data, from male and female, for 8 movements of the shoulder. Used for classification. All details about acquisition and preliminary results are published in : D. Rivela, A, Scannella, E. Pavan, C. Frigo, P. Belluco, G. Gini, “Processing of surface EMG through pattern recognition techniques aimed at classifying shoulder joint movements”, 37 annual international conference of the IEEE engineering in medicine and biology society, Milan August 2015, p 2107-2110.

A dataset of metainformation of benign and malware Android samples used in the paper Martín, A., Calleja, A., Menéndez, H. D., Tapiador, J., & Camacho, D. (2016, December). ADROIT: Android malware detection using meta-information. In Computational Intelligence (SSCI), 2016 IEEE Symposium Series on (pp. 1-8). IEEE.

This dataset is partly associated to the "Hand Tremor Based Biometric Recognition Using Leap Motion Device" paper (doi: 10.1109/ACCESS.2017.2764471 ). If you think this new dataset is useful for your studies please cite our paper above. Objective is to investigate whether hand jitter can be treated as a new behavioral biometric recognition trait in the filed od security so that imitating and/or reproducing artificially can be avoided.Dataset contains five subjects. 1024 samples each subject's spatiotemporal hand tremor signals as a time series data were acquired via leap motion device. Features are X, Y, Z and Mixed (Average) channels. Channel represents displacement value of adjacent frames (difference between current and previous positions) and finally the last item is class label having value from 1 to 5.lease read the "Hand Tremor Based Biometric Recognition Using Leap Motion Device" paper for more details and feature extraction methods. If you have any questions related to the preprocessing and/or processing the dataset please do not hesitate to contact with me via e-mail: hakmesyo@gmail.com . It should be noted that, data acquisition software was implemented in Java (Netbeans) and I utilized Processing, Open Cezeri Library and Weka tools alongside.

SARBake is an algorithm described in my first article "Convolutional Neural Networks for SAR Image Segmentation" co-authored by Morten Nobel-Jørgensen. The algorithm converts 3D CAD models of objects in to a label mask given the specific details about SAR viewing angles. The mask defines for every pixel whether the radar wave illuminated the object, the background or was in shadow. A good segmentation of a SAR image is very relevant as it simplifies image information. For example can object height above ground be estimated from the length of an objects shadow. An annotated image also enables the possibility of using Supervised Machine Learning techniques to create a segmentation mask automatically. If used in scientific publications we kindly ask for a citation of our article, @article{cnnforsegmentation, author = {David Malmgren-Hansen and Morten Nobel-Jørgensen}, title = {Convolutional Neural Networks for SAR Image Segmentation}, journal = {IEEE International Symposium on Signal Processing and Information Technology}, year = 2015 }

Current pulses with the shape of 8/20 were injected into the sample (MOV). The changes in the I-V characteristics of low voltage ZnO varistor after sequences of high current stress were measured.

This data sets includes 216 news on 240 wind turbine accidents between the years 1980 and 2013. The analysis of this data set and the insights obtained are reported in the following research paper: Asian, S., Ertek, G., Haksoz, C., Pakter, S., Ulun, S. “Wind Turbine Accidents: A Data Mining Study”. IEEE Systems Journal, vol: PP, issue: 99, Pages: 1 - 12, 2016. DOI: 10.1109/JSYST.2016.2565818. Please refer to the following web page for detailed explanation of the data, together with images: http://ertekprojects.com/wind-turbine-accidents/data/

One of the challenges faced by our research was the unavailability of reliable training datasets. In fact, this challenge faces any researcher in the field. However, although plenty of articles about predicting phishing websites using data mining techniques have been disseminated these days, no reliable training dataset has been published publically, maybe because there is no agreement in literature on the definitive features that characterize phishing websites, hence it is difficult to shape a dataset that covers all possible features. In this dataset, we shed light on the important features that have proved to be sound and effective in predicting phishing websites. In addition, we proposed some new features, experimentally assign new rules to some well-known features and update some other features.

This data presents a PSIM files simulation of the paper "Proposal and implementation of a novel perturb and observe algorithm using embedded software". https://www.researchgate.net/publication/301912126_Proposal_and_implementation_of_a_novel_perturb_and_observe_algorithm_using_embedded_software

Understanding group activities is an essential step towards studying complex crowd behaviours in video. However, such research is often hampered by the lack of a formal definition of a group, as well as a dearth of datasets that concentrate specifically on Atomic Group Actions. We introduce the Atomic Group Actions Dataset, a set of 200 videos that concentrate on the atomic group actions of objects in video, namely the group-group actions of formation, dispersal, and movement of a group, as well as the group-person actions of person joining and person leaving a group. In addition, we make the full dataset (the videos, along with their associated tracks and ground truth) publicly available to the research community for free use and extension at .