Signal Processing
Tactile perception of the material properties in real-time using tiny embedded systems is a challenging task and of grave importance for dexterous object manipulation such as robotics, prosthetics and augmented reality [1-4] . As the psychophysical dimensions of the material properties cover a wide range of percepts, embedded tactile perception systems require efficient signal feature extraction and classification techniques to process signals collected by tactile sensors in real-time.
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The training, validation, and test set used for Deep Xi (https://github.com/anicolson/DeepXi).
Training set:
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This is the noisy-speech test set used in the original Deep Xi paper: https://doi.org/10.1016/j.specom.2019.06.002. The clean speech and noise used to create the noisy-speech set are also available.
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Noisy-speech set used to test Deep Xi (https://github.com/anicolson/DeepXi). The clean speech and noise used to create the noisy-speech set are also available. The clean-speech recordings are from Librispeech test-clean (http://www.openslr.org/12/).
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The dataset consists of two populations of fetuses: 160 healthy and 102 Late Intra Uterine Growth Restricted (IUGR). Late IUGR is an adverse pathological condition encompassing chronic hypoxia as a consequence of placental insufficiency, resulting in an abnormal rate of fetal growth. In standard clinical practice, Late IUGR diagnosis can only be suspected in the third trimester and ultimately confirmed at birth. This data collection comprises of a set of 31 Fetal Heart Rate (FHR) indices computed at different time scales and domains accompanied by the clinical diagnosis.
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The present database contains records of underwater sounds produced by dolphins of the species Tursiops Truncatos. The dolphins live in Dolphinarium Varna (Varna, Bulgaria) and are a family of five
individuals. The records was made in the autumn 2019 by the “SigNautic Lab” crew, using measurement equipment of the world’s leading suppliers Bruel&Kjaer and National Instruments. The database
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BCI-Double-ErrP-Dataset is an EEG dataset recorded while participants used a P300-based BCI speller. This speller uses a P300 post-detection based on Error-related potentials (ErrPs) to detect and correct errors (i.e. when the detected symbol does not match the user’s intention). After the P300 detection, an automatic correction is made when an ErrP is detected (this is called a “Primary ErrP”). The correction proposed by the system is also evaluated, eventually eliciting a “Secondary ErrP” if the correction is wrong.
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In the present article we analyze data from two temperature sensors of the Curiosity rover, which has been active in Mars since August 2012. Temperature measurements received from the rover are noisy and must be processed and validated before being delivered to the scientific community. Currently, a simple moving average filter is used to perform signal denoising. The application of this basic algorithm is based on the assumption that the noise is stationary and statistically independent from the underlying structure of the signal, an arguable assumption in this kind of harsh environment.
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CUPSNBOTTLES is an object data set, recorded by a mobile service robot. There are 10 object classes, each with a varying number of samples. Additionally, there is a clutter class, containing samples where the object detector failed.
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A Traffic Light Controller PETRI_NET (Finite State Machine) Implementation.
An implementation of FSM approach can be followed in systems whose tasks constitute a well-structured list so all states can be easily enumerated. A Traffic light controller represents a relatively complex control function
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