Your ICASSP paper, "TRLS: A TIME SERIES REPRESENTATION LEARNING FRAMEWORK VIA SPECTROGRAM FOR MEDICAL SIGNAL PROCESSING," was very interesting in addressing problems in medical time-series data. I tried to find your email address online but had no luck. Therefore, I am leaving a message here, and hopefully, it does not distract other people from your great work and the great repository that you are currently working on!
In your paper's abstract, you mentioned "We will open-source our code when the paper is accepted." I wonder if you are willing to share your TRLS framework on your GitHub page. Your paper is very useful to us because we are doing similar research on detecting cardiovascular disease using the time-frequency spectrogram derived from ECG and PPG (photoplethysmography). Your novel TFRblock and TFRNN could potentially help us improve our detection results. We will properly cite your work if you are willing to share your code.
Dear Luyuan,
Your ICASSP paper, "TRLS: A TIME SERIES REPRESENTATION LEARNING FRAMEWORK VIA SPECTROGRAM FOR MEDICAL SIGNAL PROCESSING," was very interesting in addressing problems in medical time-series data. I tried to find your email address online but had no luck. Therefore, I am leaving a message here, and hopefully, it does not distract other people from your great work and the great repository that you are currently working on!
In your paper's abstract, you mentioned "We will open-source our code when the paper is accepted." I wonder if you are willing to share your TRLS framework on your GitHub page. Your paper is very useful to us because we are doing similar research on detecting cardiovascular disease using the time-frequency spectrogram derived from ECG and PPG (photoplethysmography). Your novel TFRblock and TFRNN could potentially help us improve our detection results. We will properly cite your work if you are willing to share your code.
Thank you very much for your time!
Sincerely, Dong Han