Digital Signal Processing (DSP)
4800 + 2400 chipless RFID measurements of a population of 16 tags. Magnitude and phase are phase to allow DSP in the time domain. The measurement are made in a monostatic configuration with linearly-polarized antennas. The population of tags include circular ring resonators and square ring resonators. The dataset is used to trained convolutional neural networks. The inputs of the CNN is the continuous wavelet transform of the signals. The CWT is get from a previously selected portion of th signal in time domain.
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Noise recognition plays an essential role in human-computer interaction and various technological applications. However, identifying individual speakers remains a significant challenge, especially in diverse and acoustically challenging environments. This paper presents the Enhanced Multi-Layer Convolutional Neural Network (EML-CNN), a novel approach to improve automated speaker recognition from audio speech. The EML-CNN architecture features multiple convolutional layers and a dense block, finely tuned to extract unique voice signatures from English speech samples.
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The password for the zip files are in the articles which cite this dataset.
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It is suggested to use zero crossing detectors to build a high-precision power-factor meter. Low pass filters are suggested to stop this error source after the influence of input signal distortion is examined. Based on the measurement of voltage, current, and power factor, this system is also proposed as a new type of power standard meter.
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We discuss the use of multi-rate FIR filters in radio frequency (RF) transient spectroscopy as well as the implementation challenges these multi-rate filters face when used in this application to reduce the sampling rate (decimation) and raise the sampling rate (interpolation). On a Texas Instruments TMS320-C30 DSP processor, all implementation measurements given here were carried out.
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