Molecular communication uses molecules or other nanoscale particles to transmit data in scenarios where conventional communication techniques are not feasible. In previous work a testbed using superparamagnetic iron oxide nanoparticles (SPIONs) as information carriers in a fluid transmission channel with constant background flow was proposed. The SPIONs are detected at a receiver as change of a coils inductance. We now improve the testbed by using a piezoelectric micropump as transmitter, making amplitude modulation (AM) with di?erent injection volumes possible. Machine learning is employed at the receiver to di?erentiate between six di?erent amplitude levels and grey code is used to reduce bit errors. With AM and the designed coding scheme, the achievable e?ective data rate was doubled to 4.45 bit/s.

Testbeds are required to assess concepts and devices in the context of molecular communication. These allow the observation of real-life phenomena in a controlled environment and therefore present the basis of future work. A testbed using superparamagnetic iron oxide nanoparticles (SPIONs) as information carriers was constructed with regard to this context and requires a sensitive receiver for the detection of SPIONs. This paper focusses on the comparison between a newly presented device (inductance sensor), a previously constructed SPION sensor (resonance bridge), and a commercial susceptometer as reference. The new inductance sensor is intended to improve on a low sensitivity achieved with the previous device and restrictions with respect to sample rate and measurement aperture encountered with the susceptometer. The signal-to-noise ratio (SNR) for each device is assessed at a variety of SPION concentrations. Furthermore, the sensors bit error rates (BER) for a random bit sequence are determined. The results show the device based on an inductance sensor to be the most promising for further investigation as values both for BER and SNR exceed those of the resonance bridge while providing a su?ciently high sample rate. On average the SNR of the new device is 13 dB higher while the BER for the worst transmission scenario is 9% lower. The commercial susceptometer, although returning the highest SNR, lacks adaptability for the given use case.

In this paper, the design of a microwave based noninvasive glucose sensor at the operating frequency of 19 GHz is presented. Worldwide, about 451 million adults suffer from diabetes. For optimal therapy, people with diabetes should monitor their blood glucose level continuously over the day. For that purpose non-invasive microwave based glucose sensors are under research. Non-invasive microwave based glucose sensors must automatically perform continuous measurements with a high and stable accuracy. However, there exists no reliable noninvasive microwave based glucose sensor up to now. The proposed sensor is designed based on a two-port microstripline (MSL) network. Water with various glucose concentration from 0 to 500 mg/dl is placed in a slot between the two ports. The simulation results show that the phase change in the reflection coefficient for the various glucose concentration depends on the matching. The measurement of the glucose concentration 10 mg/dl can be realized by applying a taper ground plane as a matching technique decreasing reflections at the interface between the microstripline substrate and the water. The proposed sensor achieves a sensitivity of 2 ° phase change per 10 mg/dl glucose concentration variation. In the future, the simulation results should be validated in an experimental setup.

Intracardiac impedance (ICI) has been proposed as an indicator of cardiac status in heart failure patients. We introduce a biophysical model of the measurement setup and apply it to the movement of ICD leads reconstructed from clinical-routine X-ray recordings in a study population of 12 patients. Tilting of the right ventricular lead is found to be a major determinant of ICI changes during cardiac contraction with a mean contribution of 42±23%. The relative position between right and left ventricular lead is the second major contributor (40±22%). However, the contributions of the components of movement strongly differ between the patients. The proposed method provides means for a better interpretation of ICI measurements and for an improvement of its performance for monitoring heart failure status.

Classification of physical activity based on the k-NN algorithm is assessed with different combinations of sensors (from accelerometer, gyroscope, barometer) with respect to classification accuracy, power consumption and computation time. For that purpose, a wearable sensor platform is proposed and a study with 20 subjects is conducted. The combination of accelerometer and barometer is found to provide the best trade-off for the three criteria: It provides an F1 score of 94.96 ± 1.73 %, while computation time and power consumption are reduced by 45 % and 88 %, respectively, compared to the full sensor set.