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  A Technique for Controlling the Proportion of Information in the Sonification of Complex Time Series Data 

  Roddy, Stephen (2022)

  This paper presents a technique for controlling the proportion of information present in parameter mapping sonifications which use time-series data. It suggests treating parameter mapping sonification as the addition of a ...
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  Creating Signal to Noise Loops v4 

  Roddy, Stephen (2022)

  Signal to Noise Loops v4 is a data-driven audiovisual piece. It is informed by principles from the fields of IoT, Sonification, Generative Music, and Cybernetics. The piece maps data from noise sensors placed around Dublin ...
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  FPGA-based Deep-Learning Accelerators for Energy Efficient Motor Imagery EEG classification 

  Shanker, Shreejith (2022)

  In recent years, Deep Learning has emerged as a powerful framework for analysing and decoding bio-signals like Electroencephalography (EEG) with applications in brain computer interfaces (BCI) and motor control. Deep ...
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  IMEC: A Memory-Efficient Convolution Algorithm For Quantised Neural Network Accelerators 

  Shanker, Shreejith (IEEE, 2022)

  Quantised convolution neural networks (QCNNs) on FPGAs have shown tremendous potential for deploying deep learning on resource constrained devices closer to the data source or in embedded applications. An essential building ...
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  Deep Learning-based embedded Intrusion Detection Systems for CAN bus in Automotive Networks 

  Shanker, Shreejith; Khandelwal, Shashwat; Wadhwa, Eashan (IEEE, 2022)

  Rising complexity of in-vehicle electronics is enabling new capabilities like autonomous driving and active safety. However, rising automation also increases risk of security threats which is compounded by lack of in-built ...

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