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Superparamagnetic iron oxide nanoparticles (SPIONs) have recently been introduced as information carriers in a testbed for molecular communication (MC) in duct flow. Here, a new receiver for this testbed is presented, based on the concept of a bridge circuit. The capability for a reliable transmission using the testbed and detection of the proposed receiver was evaluated by sending a text message and a 80 bit random sequence at a bit rate of 1/s, which resulted in a bit error rate of 0 %. Furthermore, the sensitivity of the device was assessed by a dilution series, which gave a limit for the detectability of peaks between 0.1 to 0.5 mg/mL. Compared to the commercial susceptometer that was previously used as receiver, the new detector provides an increased sampling rate of 100 samples/s and flexibility in the dimensions of the propagation channel. Furthermore, it allows to implement both single-ended and differential signaling in SPION-bases MC testbeds.

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.<br>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.<br>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.

Diabetes is a chronic and, according to the state of the art, an incurable disease. Therefore, to treat diabetes, regular blood glucose monitoring is crucial since it is mandatory to mitigate the risk and incidence of hyperglycemia and hypoglycemia. Nowadays, it is common to use blood glucose meters or continuous glucose monitoring via stinging the skin, which is classified as invasive monitoring. In recent decades, non-invasive monitoring has been regarded as a dominant research field. In this paper, electrochemical and electromagnetic non-invasive blood glucose monitoring approaches will be discussed. Thereby, scientific sensor systems are compared to commercial devices by validating the sensor principle and investigating their performance utilizing the Clarke error grid. Additionally, the opportunities to enhance the overall accuracy and stability of non-invasive glucose sensing and even predict blood glucose development to avoid hyperglycemia and hypoglycemia using post-processing and sensor fusion are presented. Overall, the scientific approaches show a comparable accuracy in the Clarke error grid to that of the commercial ones. However, they are in different stages of development and, therefore, need improvement regarding parameter optimization, temperature dependency, or testing with blood under real conditions. Moreover, the size of scientific sensing solutions must be further reduced for a wearable monitoring system.

Millimeter-wave sensing using automotive radar imposes high requirements on the applied signal processing in order to obtain the necessary resolution for current imaging radar. High-resolution direction of arrival estimation is needed to achieve the desired spatial resolution, limited by the total antenna array aperture. This work gives an overview of the recent progress and work in the field of deep learning based direction of arrival estimation in the automotive radar context, i.e. using only a single measurement snapshot. Additionally, several deep learning models are compared and investigated with respect to their suitability for automotive angle estimation. The models are trained with model-and databased approaches for data generation, including simulated scenarios as well as real measurement data from more than 400 automotive radar sensors. Finally, their performance is compared to several baseline angle estimation algorithms like the maximum-likelihood estimator. All results are discussed with respect to the estimation error, the resolution of closely spaced targets and the total estimation accuracy. The overall results demonstrate the viability and advantages of the proposed data generation methods, as well as superresolution capabilities of several architectures.