An airborne measurement system with an onboard computer for data processing and recording that does not require constant radio communication for inspection and maintenance is presented. It detects, locates, and quantifies methane leaks using a gimbal-mounted sensor. A model that correlates wind speed with drone attitude is presented and compared to measurements made with an anemometer. The quantification of methane emissions was evaluated in a laboratory setup and at an open-area test site.

A human activity recognition (HAR) system carried by masseurs for controlling a therapy table via different movements of legs or hip was studied. This work starts with a survey on HAR systems using the sensor position "trouser pockets". Afterwards, in the experiments, the impacts of different parameters and configurations on the classification accuracy is examined to get a thorough understanding of the classification process.

This article describes a small prototype sensor designed to sense carbon dioxide (CO₂) levels for indoor air quality monitoring. The device uses a photoacoustic detector fabricated using a wafer-bonding process. This allows for high-volume production of the sensors. The prototype presented is small in size and can detect CO₂ levels as low as 81 ppm with a response time of 53 s. Our results show suitability for application in indoor air quality control systems.

The purpose of our study is to demonstrate the detection of hydrogen in a mixture with natural gas by means of ultrasound measurements. In the coming years, hydrogen will be mixed with natural gas and distributed in the available gas pipeline network; therefore, monitoring the hydrogen content in real time becomes a necessity. We show the implementation of miniature ultrasound transducers (CMUTs) for this purpose by measuring the speed of sound of the mixture.

This study introduces four sensors utilizing multiple fiber Bragg grating constellations embedded in a silicon dioxide single-mode fiber for simultaneous pressure, temperature, and bending curvature measurement. By varying grating constellation, dimensions, and materials, researchers optimized sensor performance. Results indicate that the optimal configuration involves a high cladding-to-coating ratio, less-stiff coating, and the combination of two gratings to form a Fabry–Pérot interferometer.

The paper presents a concept for fabricating a biosensor with enhanced long-term stability and offers new options for designing photonics-based microsystems aimed at analysing biosamples. In order to couple the light used to probe samples, a grating is illuminated from the back side, thus realizing an inverted grating configuration. This offers the possibility of designing microsystems that can be operated more conveniently and with a much extended lifetime.

The temperature dependence of the resonance frequency of quartz resonators can be used for thermal infrared sensors. The quartz chips must be very thin to obtain a good sensor signal. This work describes how to manufacture and package sensors with 5 µm thin chips. Different sensor layouts are ion beam etched; they influence the vibration of the resonators, which is shown by impedance measurements. The temperature coefficient of the resonance frequency is determined to be around 90 ppm K^-1.

A very small, anharmonic but periodic signal is separated from a noise background that is orders of magnitude larger than the pure signal. The approach consists of a sequence of filters and transformations and is demonstrated on an interferometric measurement of the high-temperature chemical expansion of a thin film, containing heat haze, thermal length drift, and parasitic vibrations. The displacement is 38 % larger and the uncertainty 35 % lower than when evaluated with previous approaches.

We present a sensor platform that can detect the oral cancer signature of biomarkers in saliva. This system works by arraying metal–oxide–semiconductor field-effect transistors (MOSFETs) and electrolyte-gated transistors (EGTs), with each targeting a biorecognition element (BRE) from a DNA sequence found in the promoter region of most biomarker genes. This system is also applicable to the simultaneous diagnosis of TNF-α and IL-1β immune-modulator biomarkers.

Continuous quality monitoring has become increasingly important in industrial processes, such as raw-milk monitoring. This can often be achieved by detecting individual process markers in the gas phase, which requires inexpensive analytical systems. Therefore, an easy-to-implement three-step concept has been developed that aims to bring together the chemical–analytical and sensor–technical sides. This concept is designed to be applicable in a wide variety of cases and to a range of sensors.

In the context of climate change adaptation of cities to mitigate heat stress, citizen participation is a promising approach. A densely distributed measurement network of 59 quality-controlled meteorological low-cost sensors was set up in an urban study area in Pune for continuous data collection by engaging local communities. Analyses of measurements show the suitability of the monitoring network for scientific modelling applications and identify a high active interest of involved citizens.

This contribution deals with the exploration of human–robot collaboration using an infrared sensor and artificial intelligence (AI) to improve the safety and efficiency of the collaboration. The main contribution is the unique thermal dataset WLRI-HRC generated in a manufacturing environment and the evaluation of the dataset using different AI network methods.

Some air pollution gases, such as nitrogen dioxide, are toxic at very low concentrations, highlighting the need for extensive research on materials with high sensitivity. Carbon nanotubes are nanomaterials capable of sensing toxic gases in very low concentrations. Their integration into sensors remains challenging, due, in part, to their small dimensions. This work represents a study that tests a method of safely and efficiently localizing carbon nanotubes for their integration into sensor devices.

The gas-sensing performance of the CuO/Cu₂O functionalized with Au NPs towards CO₂, CO, and HC_Mix was investigated by resistance measurement. The type of ligands has an impact on the sensing behaviour of CuO/Cu₂O sensors, and the highest response (39 %) for CO₂ is achieved by Au NPs stabilized with citrate. These differences in behaviour of the films depending on their ligands could be due to insufficient thermal degradation of the ligands.

Microelectromechanical systems mirrors are used in applications such as projection, biological imaging, and light detection and ranging due to their compact size, affordable price, and low power consumption. Piezoelectric actuation with lead zirconate titanate offers high force and fast response but raises environmental concerns due to its lead content. Potassium sodium niobate is a promising lead-free alternative, exhibiting high performance on 200 mm silicon substrates.

Ferromagnetic materials change their magnetic properties under load, enabling the implementation of a force sensor. The magnetic field emerging from such a sensor can be measured by secondary sensors to approximate the load acting on the sensor. A test setup simulating a potential application environment is described and its measurement results are presented. Furthermore, relevant magnetic material properties of an exemplarily chosen cold working steel are discussed.

A simple physically motivated model for measurements obtained by nondestructive testing and evaluation using eddy-current methods is created by using equivalent circuit networks that include all relevant effects. The transfer function of a network is derived and its physical meaning discussed. For use in process control, a sensor system with low-latency parameter identification is integrated into a forming process, and its abilities to monitor microstructure changes are demonstrated.

Continuous odor monitoring is becoming increasingly important. Therefore, odor monitoring systems are needed that are less expensive than conventional laboratory equipment and do not require trained personnel. Here, a gas chromatography selective odorant measurement sensor array (GC-SOMSA) system is used as part of the development concept. It correlates a sensor response to a mass spectrometer signal and an odor impression. This allows for a fast and inexpensive characterization suitable for many applications.

To improve the accuracy of polarization random noise signal recognition and the noise suppression effect, a polarization random method of noise suppression in a two-dimensional force sensor based on random forest is proposed.

Photo-thermal actuation and laser Doppler vibrometry enable fast, contactless characterization of MEMS (microelectromechanical system) resonators already before electrical connections are made. This paper details a tailored setup combining a precision stage, vacuum chamber, laser diode, and vibrometer to analyze silicon MEMS devices on a wafer level. Tests reveal how silicon device layer thickness affects resonance frequency and identify dominant loss mechanisms in different vibrational modes.

A simple and cost-effective MIP-based sensing system for the colorimetric detection of 2-methoxphenidine (2-MXP) was demonstrated. Unlike typical colorimetric systems that rely on spectrometers, this system utilizes an RGB sensor and an LED. Constructed from readily available commercial components, it can be manufactured in a standard lab environment. The system detects 2-MXP in aqueous solutions at concentrations below 1 mM and can distinguish between concentrations of 1, 0.5, 0.25, and 0.1 mM.

We developed a measurement system to improve sand core production in foundries. Sand cores are essential for creating complex shapes in metal casting. Our system uses ultrasound waves to monitor the curing process, indicating how much of the catalyst remains in the exhaust gas. This real-time monitoring allows manufacturers to optimize the process, reducing waste and enhancing product quality. We tested the system on a lab-scale core-making machine and demonstrated its effectiveness.

Selecting the best model for monitoring industrial machines is challenging due to limited data and varying conditions. While deep learning (DL) is often considered ideal, it does not always perform well. We compared DL with traditional methods focused on feature extraction and classification. Our tests on seven datasets revealed that traditional methods are more reliable when machine conditions change, highlighting the importance of simpler, interpretable models for industrial monitoring.