Air quality in kitchens and adjoining rooms plays an important role for well-being at home. Through the incorporation of olfactory information, the automation of kitchen ventilation systems may be achieved. Considering cost constraints in consumer applications, Volatile Organic Compounds (VOCs) sensors are preferred over expensive gas sensors as a more cost-effective option. Since no comparable dataset for this application existed, a custom dataset was recorded using a specifically developed data logger and labeling tool. A sensor node combines six different sensor types which were used to record cooking situations in eight apartment kitchens with a total of 18 GB of measurement data (VOC, temperature, relative humidity, scattered light). As an initial attempt, a simple Artificial Intelligence (AI) odor classifier was developed. It can distinguish between four different odors by using 1D-Convolutional Neural Networks (CNNs) on time series. The model was quantized to 8 Bit after training and was ported to an Arm Cortex-M4 microcontroller platform. It performs with an F1-score of over 55% (median) in unseen kitchens, requires 183 KB of RAM memory and takes 35 s of inference time.

The design, fabrication and testing of a low-cost portable medical device for the detection and quantification of exosomes is presented in this paper. The portable medical device comprises a sensor array that can detect the presence of exosomes and quantify its concentration, a micro fluidic device that handles the human serum containing the exosomes, and all the necessary readout and control electronics. Measurement results performed with exosomes showed that the portable medical device can detect exosomes with a concentration of $2.5\mathrm{x}10^{8}/\mu \mathrm{L}$ thus paving the way to a wide range of diagnostic applications.

Calcium (Ca2+) elevation is an essential secondary messenger in many cellular processes, including disease progression and adaptation to external stimuli, e.g., gravitational load. Therefore, mapping and quantifying Ca2+ signaling with a high spatiotemporal resolution is a key challenge. However, particularly on microgravity platforms, experiment time is limited, allowing only a small number of replicates. Furthermore, experiment hardware is exposed to changes in gravity levels, causing experimental artifacts unless appropriately controlled. We introduce a new experimental setup based on the fluorescent Ca2+ reporter CaMPARI2, onboard LED arrays, and subsequent microscopic analysis on the ground. This setup allows for higher throughput and accuracy due to its retrograde nature. The excellent performance of CaMPARI2 was demonstrated with human chondrocytes during the 75th ESA parabolic flight campaign. CaMPARI2 revealed a strong Ca2+ response triggered by histamine but was not affected by the alternating gravitational load of a parabolic flight.

This paper presents an IoT sensing system for agricultural applications, which can monitor at real time the moisture content of grains stored in silo bags located in remote locations thus allowing to avoid costly grain losses and to optimize the logistics of post-harvesting operations. The system comprises a customized gateway that collects the sensor data and sends it to a satellite network as well as multiple sensing units, which can measure both the grain moisture content with a resolution of 0.1% and the air temperature and air humidity inside the silo bags that are used together to assess the quality and condition of grains. The details about the modeling, design and fabrication of the proposed system together with the experimental results obtained during field tests are reported.

The design, fabrication and testing of a wearable medical device for remote monitoring the health of elderly people at home are presented in this paper. The proposed wearable medical device comprises a wide range of sensors, innovative sensor fusion algorithms and flexible electrodes that make possible to continuously run analyses such as hydration assessment, stress assessment, body temperature assessment, fall detection and heart monitoring. In-vivo measurement results showed the excellent performance of this wearable medical device in real-life scenarios.