Characterizing soil moisture (SM) around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated by two different microwave imaging techniques based on spatio-temporal measurements at various frequency bands. The study investigates the performance of K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNN) algorithms for moisture classification based on these images in three scenarios: before clutter removal, after clutter removal, and after applying imaging techniques such as back projection and the Born approximation. We also explore the potentials of CNN and KNN for moisture estimation around the plant roots and in the presence of pebbles. The results demonstrate the more accurate moisture estimation using CNN when it is applied after clutter reduction considering back projection algorithm (BPA) as the imaging technique. The results indicate that using the Back Projection technique for image formation, combined with CNN for classification, improves leak detection accuracy by approximately 20% compared to other methods.

Interactions of cells via the extracellular vesicles (EVs), manipulate various actions including cancer initiation and progression, inflammation, anti-tumor signaling and cells migration, proliferation and apoptosis in the tumor microenvironment. EVs as the external stimulus can activate or inhibit some receptor pathways in a way that amplify or attenuate a kind of particle release at target cells. This also could be carried out in a biological feedback-loop where the transmitter is affected by the induced release initiated by the target cell due to the EVs received from the donor cell, to create a bilateral process. In this paper, at first we derive the frequency response of internalization function in the framework of a unilateral communication link. This solution is adapted to a closed-loop system to find the frequency response of a bilateral system. The cells overall release as the combination of the natural release and the induced release are reported at the end of this paper and the results are compared in terms of cells distance and reaction rates of EVs on the cell membranes.