Signal Processing
Visual tracking methods have achieved a successful development in recent years. Especially the Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. The advancement in DCF tracking performance is predominantly attributed to powerful features and sophisticated online learning formulations. However, it would come to some troubles if the tracker learns the samples indiscriminately.
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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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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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