16,246 research outputs found
Independent component approach to the analysis of EEG and MEG recordings
Multichannel recordings of the electromagnetic fields
emerging from neural currents in the brain generate large amounts
of data. Suitable feature extraction methods are, therefore, useful
to facilitate the representation and interpretation of the data.
Recently developed independent component analysis (ICA) has
been shown to be an efficient tool for artifact identification and
extraction from electroencephalographic (EEG) and magnetoen-
cephalographic (MEG) recordings. In addition, ICA has been ap-
plied to the analysis of brain signals evoked by sensory stimuli. This
paper reviews our recent results in this field
Wireless industrial monitoring and control networks: the journey so far and the road ahead
While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks
TasNet: time-domain audio separation network for real-time, single-channel speech separation
Robust speech processing in multi-talker environments requires effective
speech separation. Recent deep learning systems have made significant progress
toward solving this problem, yet it remains challenging particularly in
real-time, short latency applications. Most methods attempt to construct a mask
for each source in time-frequency representation of the mixture signal which is
not necessarily an optimal representation for speech separation. In addition,
time-frequency decomposition results in inherent problems such as
phase/magnitude decoupling and long time window which is required to achieve
sufficient frequency resolution. We propose Time-domain Audio Separation
Network (TasNet) to overcome these limitations. We directly model the signal in
the time-domain using an encoder-decoder framework and perform the source
separation on nonnegative encoder outputs. This method removes the frequency
decomposition step and reduces the separation problem to estimation of source
masks on encoder outputs which is then synthesized by the decoder. Our system
outperforms the current state-of-the-art causal and noncausal speech separation
algorithms, reduces the computational cost of speech separation, and
significantly reduces the minimum required latency of the output. This makes
TasNet suitable for applications where low-power, real-time implementation is
desirable such as in hearable and telecommunication devices.Comment: Camera ready version for ICASSP 2018, Calgary, Canad
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