Managing Safety and Security in the Smart City: A Machine Learning Approach to Predicting Airborne Disease Transmission

In this article, we explored the concept of encoding information using odor signals. We discussed how binary symbols can be represented by assigning unique intensity levels to each symbol, and how these symbols can be emitted in a successive manner to form an odor signal over space-time. We also highlighted the importance of understanding the relationship between odor intensity and concentration, as outlined in the Weber-Fechner law.
To illustrate this concept, we presented simulations that generated probability density functions (PDFs) for the odor intensities at different concentrations. These PDFs were shown graphically in Figure 4, with magenta and red dashed lines denoting the appropriate concentration ranges for the random variables respectively as in Table II.
By using these concentration ranges, we can encode information into the odor signal by varying the intensity of the symbols according to a modulation scheme. This scheme involves assigning different intensity levels to each symbol based on their position in the sequence, with the first symbol having the highest intensity and subsequent symbols decreasing in intensity as they move further along the sequence.
Overall, this article provides a fascinating glimpse into the world of odor encoding and decoding, demonstrating how complex information can be conveyed through the sense of smell using simple binary symbols and their corresponding intensity levels. By understanding the relationships between odor intensity, concentration, and modulation, we can unlock the potential of this powerful communication channel to transmit information in a unique and innovative way.