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>
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