Signal Processing

The dataset is an extensive collection of labeled high-frequency Wi-Fi Radio Signal Strength (RSS) measurements corresponding to multiple hand gestures made near a smartphone under different spatial and data traffic scenarios. We open source the software code and an Android app (Winiff) to create this dataset, which is available at Github (https://github.com/mohaseeb/wisture). The dataset is created using an artificial traffic induction (between the phone and the access point) approach to enable useful and meaningful RSS value

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This is the Smulation Data for Power System State Estimation.

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Speech detection systems are known as a type of audio classifier systems which are used to recognize, detect or mark parts of audio signal including human speech. Here, a novel robust feature named Long-Term Spectral Pseudo-Entropy (LTSPE) is proposed to detect speech and its purpose is to improve performance in combination with other features, increase accuracy and to have acceptable performance. Experimental results show that if LTSPE is combined with other features, performance of the detector is improved.

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In order to discriminate and mark audio signal segments which include normal human speech and discriminate segments which do not include speech (like silence, music and noise), Speech/Music Discrimination (SMD) systems are used. Using this definition, SMD systems can be considered as a specific or accurate type of speech activity detection system.

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Recognition of human activities is one of the most promising research areas in artificial intelligence. This has come along with the technological advancement in sensing technologies as well as the high demand for applications that are mobile, context-aware, and real-time. We have used a smart watch (Apple iWatch) to collect sensory data for 14 ADL activities (Activities of Daily Living). 

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This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/

The DataPort Repository contains the data used primarily for generating Figure 1.

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This is the data competion hosted by the IEEE Machine Learning for Signal Processing (MLSP) Technical Committee as part of the 27th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2017), Tokyo, Japan. This year the competion is based on a dataset kindly provided Petroleum Geo-Systems (PGS), on source separation for seismic data acquistion. 

Last Updated On: 
Tue, 05/01/2018 - 15:07
Citation Author(s): 
IEEE MLSP Technical Committee

The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.

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The aim of the database consists of providing the researchers with a collection of power quality real-life impulsive events to test experiments and measurement instruments. The dataset provides signals recordings from the power network of the University of Cádiz during the last five years (electrical network according to the UNE-EN-50160: 2011).

The dataset offers a diversity of real impulsive events, which are specifically acquired in order to test Power Quality Instruments according to the UNE-IEC 61000-4-11: 2005.

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Costas arrays are permutation matrices that meet the added Costas condition that, when used as a frequency-hop scheme, allow at most one time-and-frequency-offset signal bin to overlap another.  Databases to various orders have been available for many years.  Here we have a database that is far more extensive than any available before it.  A very powerful and easy-to-use Windows utility with a GUI accompanies the database.

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