Add license | Update readme

LICENSE

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+MIT License
+
+Copyright (c) 2023 Piotr Baryczkowski
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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-# preprocessor-based-on-neural-networks
-CMOS preprocessor design based on neural networks for detecting heart rate.
+# Heart rate detection with CMOS preprocessor based on neural network
+
+## Table of Contents
+
++ [About](#about)
++ [Getting Started](#getting-started)
+  + [Prerequisites](#prerequisites)
++ [Installing the requirements](#installing)
++ [Running the code](#running-the-code)
++ [Todo](#todo)
++ [License](#license)
+
+## About
+
+Heart rate detection with neural network-based CMOS preprocessor. It involves developing a heart rate detection model using TinyML techniques on the PhysioNet dataset and parsing it on a CMOS preprocessor written in Spice.
+
+## Getting Started
+
+These instructions will get you a copy of the project up and running on your local machine.
+
+### Prerequisites
+
+Python libraries that you need to use this software:
+
++ WFDB
++ Tensorflow
+
+CMOS simulation tools, that are required:
+
++ Eldo
++ EZwave
+
+### Installing
+
+A step by step series of examples that tell you how to get a development env running.
+
+## Running the code
+
+In order to run the code, you will only need to prepare two files, multiplication coefficients grid and model file.
+
+**prepare_dataset_pipeline.py**
+
+```bash
+$ python prepare_dataset.py -h
+usage: prepare_dataset.py [-h] --ds-dir DS_DIR --output-file OUTPUT_FILE --sample-length SAMPLE_LENGTH --sample-freq SAMPLE_FREQ [--download]
+
+options:
+  -h, --help            show this help message and exit
+  --ds-dir DS_DIR       Directory with ECG data from physionet (default: None)
+  --output-file OUTPUT_FILE
+                        File in which the dataset will be saved (default: None)
+  --sample-length SAMPLE_LENGTH
+                        The length of a sample`s window (default: None)
+  --sample-freq SAMPLE_FREQ
+                        Number of sampled points from a window (default: None)
+  --download            Whether to download database or not (default: False)
+```
+
+**parse_model.py**
+
+Now, in order to run the code you can call the parse_model.py directly.
+
+```bash
+$ python parse_model.py -h
+usage: parse_model.py [-h] --model MODEL --grid GRID
+
+options:
+  -h, --help     show this help message and exit
+  --model MODEL  Path to file, that contains TensorFlow model (default: None)
+  --grid GRID    Path to file, that contains weight grid (default: None)
+```
+
+## Authors
+
++ **Piotr Baryczkowski** - *ANN to CMOS preprocessor parser implementation* - [Piotr45](https://github.com/Piotr45)
++ **Sebastian Szczepaniak** - *Implementation of dataset pipeline and training ANN* - [D3nz13](https://github.com/D3nz13)
+
+See also the list of [contributors](https://github.com/Piotr45/cmos-preprocessor/graphs/contributors) who participated in this project.
+
+## TODO
+
+- [ ] clean parser code
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details