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Collaborative funded project with IRRAS Inc and AU Hospital (May, 2022)

Our group will partner with IRRAS Inc and AU Hospital to develop a novel technology to quantify brain compliance using the IRRAflow system. The combination of compliance calculations and IRRAflow treatment may provide real-time data that determines effectiveness and completion of therapeutic treatment. More details on this link.

Article Update (March, 2022)

Our recent article, published in IEEE Transactions on Emerging Topics in Computational Intelligence, proposes a novel motor imagery EEG signal classification method based on variational mode decomposition algorithm. Here is the link to the article.

Article Update (November, 2020)

Our recent research article published in IEEE Transactions on Signal Processing presents a new signal denoising method designed specifically for multichannel or multivariate data sets. The method is unique in that it fully utilizes inter-channel correlations within multiple variates of the input data set. Given the ubiquity of multichannel data sets in disparate application areas, owing to the rapid recent developments in sensor technologies, specialized multichannel algorithms have gained vital importance. Here is the link to the paper.

Article Update (August, 2020)

Our article titled “FPGA-Based Design for Online Computation of Multivariate Empirical Mode Decomposition” has been published in IEEE Transactions on Circuits and Systems I: Regular Papers. The article proposes a novel FPGA based architecture of a popular nonstationary signal processing algorithm, multivariate empirical mode decomposition (MEMD). The architecture would pave the way towards the utility of the MEMD algorithm in many real-life online applications where the algorithm has already proven its prowess e.g., biomedical engineering, condition monitoring, signal denoising etc. Here is the web link.

Article Update (January, 2020)

Our recent publication in IEEE Transactions on Signal Processing, titled Multivariate Variational Mode Decomposition, introduces a generic extension of the variational mode decomposition (VMD) algorithm to multichannel data sets. The main feature of the algorithm is its ability to not only decompose multichannel data into its inherent principal modulated oscillations, but to also align common frequency modes present across multiple channels. That greatly facilitates subsequent signal processing on the decomposed components in many real-world applications e.g., data fusion, biomedical signal classification etc.