Updated username

.gitmodules

@@ -18,10 +18,10 @@
 	url = https://github.com/nothings/stb.git
 [submodule "vendor/librtlsdr"]
 	path = vendor/librtlsdr
-	url = https://github.com/FiendChain/librtlsdr_cmake.git
+	url = https://github.com/williamyang98/librtlsdr_cmake.git
 [submodule "vendor/viterbi_decoder"]
 	path = vendor/viterbi_decoder
-	url = https://github.com/FiendChain/ViterbiDecoderCpp.git
+	url = https://github.com/williamyang98/ViterbiDecoderCpp.git
 [submodule "vendor/argparse"]
 	path = vendor/argparse
 	url = https://github.com/p-ranav/argparse.git

README.md

@@ -1,7 +1,7 @@
 # Introduction
-[![x86-windows](https://github.com/FiendChain/DAB-Radio/actions/workflows/x86-windows.yml/badge.svg)](https://github.com/FiendChain/DAB-Radio/actions/workflows/x86-windows.yml)
-[![x86-linux](https://github.com/FiendChain/DAB-Radio/actions/workflows/x86-linux.yml/badge.svg)](https://github.com/FiendChain/DAB-Radio/actions/workflows/x86-linux.yml)
-[![x86-macos](https://github.com/FiendChain/DAB-Radio/actions/workflows/x86-macos.yml/badge.svg)](https://github.com/FiendChain/DAB-Radio/actions/workflows/x86-macos.yml)
+[![x86-windows](https://github.com/williamyang98/DAB-Radio/actions/workflows/x86-windows.yml/badge.svg)](https://github.com/williamyang98/DAB-Radio/actions/workflows/x86-windows.yml)
+[![x86-linux](https://github.com/williamyang98/DAB-Radio/actions/workflows/x86-linux.yml/badge.svg)](https://github.com/williamyang98/DAB-Radio/actions/workflows/x86-linux.yml)
+[![x86-macos](https://github.com/williamyang98/DAB-Radio/actions/workflows/x86-macos.yml/badge.svg)](https://github.com/williamyang98/DAB-Radio/actions/workflows/x86-macos.yml)
 
 An implementation of a DAB (digital audio broadcasting) radio using software defined radio. 
 
@@ -39,12 +39,12 @@ For those who are interested only in parts of the implementation refer to the fo
 
 Refer to ```src/examples/README.md``` for other example applications.
 
-If you can't find any DAB ensembles in your area, then you can download binary files from the Releases page [here](https://github.com/FiendChain/DAB-Radio/releases/tag/raw-iq-data). These contain raw IQ values as well as pre-demodulated OFDM digital frames. You can read in these files with the applications described in <code>src/examples/README.md</code>
+If you can't find any DAB ensembles in your area, then you can download binary files from the Releases page [here](https://github.com/williamyang98/DAB-Radio/releases/tag/raw-iq-data). These contain raw IQ values as well as pre-demodulated OFDM digital frames. You can read in these files with the applications described in <code>src/examples/README.md</code>
 
 # Building programs
 Clone the repository using the command
 
-```git clone https://github.com/FiendChain/DAB-Radio.git --recurse-submodules -j8```
+```git clone https://github.com/williamyang98/DAB-Radio.git --recurse-submodules -j8```
 
 Refer to ```./toolchains/*/README.md``` to build for your platform.
 
@@ -59,7 +59,7 @@ The continuous integration (CI) scripts are in <code>.github/workflows</code> if
 ## Build notes (Read this if you get illegal instructions)
 SIMD instructions are used for x86 and ARM cpus to speed up computation heavy code paths.
 
-Refer to [this github issue](https://github.com/FiendChain/DAB-Radio/issues/2#issuecomment-1627787907) explaining how to modify the build for **older CPUs**. 
+Refer to [this github issue](https://github.com/williamyang98/DAB-Radio/issues/2#issuecomment-1627787907) explaining how to modify the build for **older CPUs**. 
 
 ```./toolchains/windows/README.md``` has steps for configuring the right files to build for older CPUs.
 

src/ofdm/dsp/chebyshev_sine.h

@@ -3,7 +3,7 @@
 #include "detect_architecture.h"
 #include "simd_flags.h"
 
-// SOURCE: https://gist.github.com/FiendChain/7aca0ca0f1978c7374a66002892e0d8a
+// SOURCE: https://gist.github.com/williamyang98/7aca0ca0f1978c7374a66002892e0d8a
 //         Chebyshev polynomial that approximates f(x) = sin(2*pi*x) accurately within [-0.5,+0.5]
 // STEPS:  1. settings are { grad_t=double, TOTAL_COEFFICIENTS=6, SINE_ROOT=0.5, ...defaults }
 //         2. train with   { coefficient_t=double, TOTAL_SAMPLES=128 }