The macro-scale molecular communication (MC) recently received considerable attention because of its potential applications. Since most of the experimental research in MC focuses on the micro-scale cases, it is necessary to study and implement experiments to investigate the concept’s feasibility as well as to validate the models and parameters. In this paper, a macro-scale flow-based MC platform with fluidic medium is developed, in a semi-cylindrical channel with laminar flow condition. The transmission medium we consider is water in the plexi pipe, a transmitter releases Hydrochloric acid molecules into this pipe and a chemical sensor is used as the receiver. We propose an LTI model for the pH-meter and obtain its parameters based on the experimental results. Next, we design the appropriate encoding-decoding schemes and implement the optimum receiver for the validated model and obtained parameters. To increase the rate, an ISI mitigating method is implemented with adaptive thresholds. In particular, an end-to-end theoretical model is proposed for this system which is confirmed by experiments as well as simulation results using COMSOL software platform. We validate our proposed model and derive related parameters both with the experimental results as well as the simulation results. The performance of the system is analyzed in terms of the bit error probability and the communication rate.

Reliable detection and localization of humans through structural obstructions is critical for time-sensitive applications such as disaster response. This paper presents a novel ultra-wideband (UWB) radar-based framework for multi-person localization and breathing rate estimation in obstructed environments. To overcome the challenges posed by signal attenuation, multipath interference, and limited spatial resolution, we propose a synthetic aperture radar (SAR) system that combines linear translation and on-axis rotation of a single radar. By fusing intensity maps from multiple radar viewpoints, the system achieves enhanced localization of human targets. A signal quality assessment pipeline, including SNR-based filtering, dominance ratio analysis, and periodicity checks, is used to extract clean respiratory signals from selected range bins. The estimates are further integrated into a spatial breathing-rate matrix, which is fused with radar motion maps to suppress false detections and improve reliability. The proposed algorithm is validated experimentally under line-of-sight, through-wall, and emulated rubble conditions. Results demonstrate significant improvements in localization accuracy and robustness of respiration-based human presence detection. The proposed post-processing filtering algorithms reduce the 80^th percentile Chamfer distance by 1.53×, while the maximum Chamfer distance is reduced by $\sim 3\times$. The estimated breathing rate correlates strongly with ground truth while achieving a Pearson correlation of 0.79.