Singular value of a hermitian matrix are the absolute values of eigenvalues of A I am studying following theorem from Numerical linear Algebra book by Trefethen and Bau.

Theorem : If $A = A^{*}$, then the singular values of A are the absolute values of the eigenvalues of A.
Proof: A Hermitian matrix has a complete set of eigenvectors, and all the  eigenvalues are real. 
$$(1)\quad A = X \Lambda X^{-1}$$ 
An equivalent statement that (1) holds with $X$ equal to some unitary matrix $Q$ and $\Lambda$ a real diagonal matrix. But we can write 
$$ (2) \quad A = Q \Lambda Q^{*}= Q|\Lambda|sign(\Lambda)Q^{*}$$ 
where $|\Lambda|$ and $sign(\Lambda)$ denote the diagonal matrix whose entries are the number $|\lambda_{j}|$ and $sign(\lambda_{j})$, respectively. (We could equally well have put the factor $sign(\Lambda)$ on the left of $|\Lambda|$ instead of right). Since sign($\Lambda)Q^{*}$ is unitary whenever Q is unitary, (2) is an SVD of A, with the singular values equal to the diagonal entries of $|\Lambda|$, $|\lambda_{j}|$. If desired, these numbers can be put into non-increasing order by inserting suitable permutation matrices as factors in the left hand unitary matrix of (2), Q , and the right-hand unitary matrix sign$(\Lambda)Q^{*}$.

I cannot follow the logic in this proof after equation (2). Can any one give an insight on this?
Thanks in advance.
 A: The OP has clarified that he doesn't understand the statement "Since sign(Λ)Q∗ is unitary whenever Q is unitary, (2) is an SVD of A, with the singular values equal to the diagonal entries of |Λ|, |λj|. "
To clarify a small technical point, use the convention that $\mbox{sign}(0)=1$ for this proof.  If you instead use $\mbox{sign}(0)=0$, then the resulting matrices don't turn out to be orthogonal if there are some $0$ eigenvalues of $A$.  
Starting from (2), let 
$W^{*}=\mbox{sign}(\Lambda)Q^{*}$.
So, $W$ is a copy of $Q$, possibly with the signs of some columns multiplied by -1.  $Q$ is a unitary matrix, that is each pair of columns in $Q$ are orthogonal.  Furthermore, each column of $Q$ has length one.  Multiplying some subset of the columns of $Q$ by -1 won't change the orthogonality of the columns.  Furthermore, each column of $W$ has length one.  Thus $W$ is also a unitary matrix.  
The Hermitian of any unitary matrix is also unitary, so $W^{*}$ is a unitary matrix too.  
Then 
$(3)\; A=Q | \Lambda | W^{*}$
where $Q$ and $W$ are unitary matrices and $| \Lambda |$ is a diagonal matrix with nonnegative entries.  This is an SVD of the matrix $A$, with $|\Lambda|$ taking the familiar place of $\Sigma$, $Q$ taking the familiar place of $U$ and $W^{*}$ taking the familiar place of $V^{*}$.
The diagonal elements of $| \Lambda |$ are the singular values in this SVD of $A$.   
A: Here is a much more complicated proof:
The singular values can be defined as follows:
$\sigma_1(A) = \max_{ \|x\| = 1} \|Ax\|$. Choose $v_1 \in \operatorname{argmax}_{ \|x\| = 1} \|Ax\|$. ($v_1 $ is a singular vector
corresponding to $\sigma_1(A)$.)
Suppose we have $\sigma_1(A),...,\sigma_k(A)$ and an orthogonal set of corresponding singular vectors $v_1,...,v_k$, then define
$\sigma_{k+1}(A) = \max_{ \|x\| = 1, x \bot v_1,...,x \bot v_k} \|Ax\|$
and choose $v_{k+1} = \operatorname{argmax}_{ \|x\| = 1, x \bot v_1,...,x \bot v_k} \|Ax\|$.
Now let $A=Q \Lambda Q^*$, where $Q$ is orthogonal and $\Lambda$ is ordered by absolute value.
Note that $\|Ax\| = \|\Lambda Q^* x\|$. Note that if $v_1,...,v_k$ are
an orthonormal collection, then so are $Q^*v_1,...,Q^*v_k$.
Then we see that $\sigma_1(A) = |\lambda_1|$ and we can choose $v_1 = Q e_1$.
Now suppose we have
$\sigma_1(A) = |\lambda_1|,...,\sigma_k(A) = |\lambda_k|$ and corresponding
singular vectors $Qe_1,...,Qe_k$, then
$\sigma_{k+1}(A) = \max_{ \|x\| = 1, x \bot Q e_1,...,x \bot Q e_k} \|\Lambda Q^* x\|$. Since
$\{x | \|x\| = 1, x \bot Q e_1,...,x \bot Q e_k \} = \{ x \in \operatorname{sp} \{ Q e_{k+1},...,Q e_n\} | \|x\| =1 \}$, we see that the value is $|\lambda_{k+1}|$ and
that this is attained with $v_{k+1} = Q e_{k+1}$.
