From 4aed0635bd1c74a8db7ae0c86c1ce0355881d96d Mon Sep 17 00:00:00 2001 From: Hang Zheng <54340561+HangZheng98@users.noreply.github.com> Date: Sun, 17 Sep 2023 18:59:35 +0800 Subject: [PATCH] Update about.md --- _pages/about.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/_pages/about.md b/_pages/about.md index 31184a36fa6..cf8785b6cf2 100644 --- a/_pages/about.md +++ b/_pages/about.md @@ -127,10 +127,12 @@ I am an active reviewer for IEEE TSP, IEEE TAES, IEEE TVT, IEEE SPL, Signal Proc <div class='paper-box'><div class='paper-box-image'><div><img src='images/DOA.png' alt="sym" width="100%"></div></div> <div class='paper-box-text' markdown="1"> -- Derive the high-order co-array tensor from sub-Nyquist tensor signals, and achieve co-array tensor-based direction-of-arrival estimation with *high accuracy*, *super resolution* and *increased degrees-of-freedom* +- Derive the high-order co-array tensor from sub-Nyquist tensor signals, and achieve co-array tensor-based direction-of-arrival estimation with <font color=blue>*high accuracy*</font>, <font color=blue>*super resolution*</font> and <font color=blue>*increased degrees-of-freedom*<\font> - Optimize the uniqueness condition of co-array tensor decomposition, and identify multiple sources that exceed the number of physical array sensors - Complete the coarray tensor with missing slices to exploit the full co-array information Analytical results and numerical results are presented in our papers published on IEEE TSP, IEEE TAES, and IEEE ICASSP. </div> </div> + +