Update about.md

_pages/about.md

@@ -128,7 +128,7 @@ 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>
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-- 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>*
+- 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
 
@@ -142,5 +142,5 @@ Analytical results and numerical results are presented in our papers published o
 
 - Develop the principle of tensor signal filtering with sparse array, thereby enhancing the signal-of-interest with reduced system burden
 - Enhance mainlobe while suppress sidelobes in the beampattern by solving tensor beamformer weight optimization problems
-- Improve output signal-to-interference-plus-noise ratio (SINR) for *<font color=blue>strong anti-interference<\font>*
+- Improve output signal-to-interference-plus-noise ratio (SINR) for *<font color=blue>strong anti-interference</font>*