Update README.md

README.md

@@ -58,7 +58,7 @@ This may be made more efficient and robust (accomodating any image width) if by
 We use two streams of data in each process with it's own processing unit in our IP , which can be schematically represented in:
 <image for our Implementation>
 
-In the background extraction technique we use a modified form of the watershed algorithm to suit different layers of the image with a similar range and intensity of pixels, so we have a customizable tayer to extract.
+In the background extraction technique we use a modified form of the watershed algorithm to suit different layers of the image with a similar range and intensity of pixels, so we have a customizable layer to extract.
 <Image modified watershed>
 
 # Tasks
@@ -83,6 +83,7 @@ The Tasks and maximum estimated time:
 7. AXI4 : https://ch.mathworks.com/help/hdlcoder/ug/getting-started-with-axi4-stream-interface-in-zynq-workflow.html
 8. Interface of Streaming : https://www.xilinx.com/html_docs/xilinx2020_2/vitis_doc/managing_interface_synthesis.html#ariaid-title34
 9. Database in FPGA : https://dspace.mit.edu/bitstream/handle/1721.1/91829/894228451-MIT.pdf, https://www.xilinx.com/publications/events/developer-forum/2018-frankfurt/accelerating-databases-with-fpgas.pdf, https://www.xilinx.com/html_docs/xilinx2020_2/vitis_doc/vitis_hls_process.html#djn1584047476918
+10. Muxed Stream : https://liu.diva-portal.org/smash/get/diva2:1057270/FULLTEXT01.pdf
 
 #### Future scope
 The image processing can serve a stepping stone for controlling multi-agent systems. Where each streaming interface can be used for instruction input and output for each agent. Instead of using RTOS in each bot we can have multiple datastreams from each bots being processed in an IP designed to emulate a FSM for each agent and decide their action. This can lead to higher robustness and fault tolerance and lower costs.