Safe and sustainable mobility in urban and rural regions can be fostered by developing intelligent road infrastructure to avoid accidents by warning drivers about approaching vulnerable traffic participants. Radar sensors accurately detect different objects, however the reliable classification still remains challenging. In this paper, a new approach to extract and interpret unique spectral signatures of pedestrians and cyclists is proposed. Moreover, this approach can be also extended to any moving object including wild animals. This method uses Doppler-Range measurements in real time which result from the local dynamic of the moving parts in order to extract statistical parameters of the movement pattern. The movement pattern is represented by a time dependent velocity distribution which can be further analyzed by conventional signal processing techniques. In this work, the intensity-normalized average velocity calculations are based on a probabilistic approach of the detection. Such a velocity series can be further analyzed by applying Fast-Fourier-Transform in order to extract spectral information of the movement. In case of more than one detected object, a spectrogram can be built, therewith it is possible to determine average velocities as well as periodicities of the measured movement patterns. Machine learning algorithms can be also applied to the discussed time series in order to automate the classification.

The growth of urban areas in recent years has motivated a large amount of new sensor applications in smart cities. At the centre of many new applications stands the goal of gaining insights into human activity. Scalable monitoring of urban environments can facilitate better informed city planning, efficient security, regular transport and commerce. A large part of monitoring capabilities have already been deployed, however, most rely on expensive motion imagery and privacy invading video cameras. It is possible to use a low-cost sensor alternative which enables deep understanding of population behaviour such as the Global Positioning System (GPS) data. However, the automated analysis of such low dimensional sensor data, requires new flexible and structured techniques that can describe the generative distribution and time dynamics of the observation data, while accounting for external contextual influences such as time of day or the difference between weekend/weekday trends. We propose a novel time series analysis technique that allows for multiple different transition matrices depending on the data’s contextual realisations all following shared adaptive observational models that govern the global distribution of the data given a latent sequence. The proposed approach, which we name Adaptive Input Hidden Markov model (AI-HMM) is tested on two datasets from different sensor types: GPS trajectories of taxis and derived vehicle counts in populated areas. We demonstrate that our model can group different categories of behavioural trends and identify time specific anomalies.

Given the boom linked to smart mobility, transport systems require increasingly precise and relevant navigation applications to offer optimized journeys in terms of time and energy consumption, such as for HEV. Most of these navigation applications are based on the processing of 2D digital road maps while taking into account the GNSS location of vehicles. These localization systems also integrate sensors such as accelerometers and gyroscopes to overcome the well-known problems of GPS positioning even if the current limited introduction of IoT in the transport industry has made it possible to develop new aided-GPS method such as geofencing. This paper focuses on one important parameter in journey optimization of land vehicles: the road slope. We propose a method to estimate the roads inclination parameters by fusing GNSS, INS, OSM and ASTER GDEM data through a nonlinear filter. The incremental estimate of the slope will complement the 2D modeling of the roads already available in OpenStreetMap and could be used in route planning optimization. The scientific novelty lies more specifically in the statistical map-matching approaches that we develop both for OSM and DEM data. Estimation results of the roads slopes are shown in experimental conditions.

A new method and related sensory system used to determine the surface tension (γ) of a liquid investigated based on the increase of the area (A) of a drop vibrated sinusoidally is presented. The materialization of the method consists in specific device placed on the table of a microscope or a stereomicroscope, both based on the principle of light reflection. A drop of the analyzed liquid deposited with a dispenser on a metal plate, vibrated electrodynamically under the action of a sinusoidal oscillation, of constant frequency and amplitude, increases its surface wetted on the metal plate with each applied sinusoidal oscillation. At each magnification of the droplet surface area, an image acquisition takes place through the optoelectronic system of the microscope or stereomicroscope, the frequency of the oscillations being strictly correlated with the acquisition frequency of the images. At a predetermined number of images / oscillations, using specific software, both the images of the droplets and the graph containing the number of pixels inside the outline of each image and the current number of vibration corresponding to that image are displayed. The surface tension is automatically expressed by the growth speed of the drops surface, speed given by the curve slope of the pixels number of the drops according to the current number of the oscillation. A collateral application, is the possibility of using the sensory system and specialized software for rapid determination of solutions concentration, measuring the surface tension using the Szyszkowski relation.

This article presents a new metrological atomic force microscope (MAFM) with a homodyne interferometer and a tilt measuring system by position sensitive device (PSD). The combination allows the simultaneous three-dimensional detection of the tip displacement by capturing the position, bending and torsion of a reflecting surface of the cantilever realized with one laser beam. Based on an existing interferometric detection head of a micro-tactile 3D probe, the sensor head was revised and adapted for atomic force microscopy. The new measuring system uses two tiltable plane mirrors to adjust direction and position of a focused laser beam. With this adjustment unit, the focused laser beam can be steered perpendicular to the reflecting backside of the cantilever. Regarding the probe system, the optical design of the measuring head has been reengineered to reduce the disturbing interference on the PSD. A simulation applying the optical design program OpticStudio from Zemax shows that the integration of two wedge plates with a wedge angle of 0.5° reduces the disturbing interference significantly. After manufacturing initial measurement results are presented to verify the functionality.

To measure salinity in solutions, many applications make use of inductive sensors. Compared to electrode-based conductive sensors, inductive sensors are less prone to biofouling and polarization. Although inductive sensors are well suited for long time operations, distributed monitoring applications, such as low-cost sensor drifter, suffer under high costs. Industrial standard for inductive sensors is the transformer type sensor. An alternative approach is a design based on the eddy current effect, which is different by using magnetic flux through the water. However, the research presented until now is mostly of empirical nature. This paper presents a new theoretical description for inductive eddy current sensors. The fundamental functionality is based on Maxwell’s equations and allows an equivalent electrical RLC-circuit representation. The derived model proves that rather than a changing permeability of the fluid, the damping effect by the eddy currents determines the behavior of the sensor. For model validation, magnetic FEM-Simulations and practical experiments with prototypes were conducted. The results confirmed the modeling approach. With the aim to provide fundamentals for future development of more cost-efficient and smaller sensors, this paper gives a better understanding of the physical effects of this sensor type.

Integrated environmental sensing for personal health care monitoring is a topic of increasing interest. Optical measurement approaches could provide intrinsic selectivity and the sensitivity, required for the development of integrated gas sensors. In an ongoing project, we work towards a Si-photonics non-dispersive infrared gas sensor and are investigating the possibility of the incorporation of IR-active plasmonic materials, which could allow to increase sensitivities and reduce size of such sensors. We will first present the overall idea of combining pillar photonic crystal waveguides with plasmonic elements to provide maximal interaction with gaseous analytes, which was proposed, recently. Then, we describe the characterization of the very first test structures, which were fabricated. Reflectivity measurements on grating structures allow the detailed characterization of the plasmon resonances, which can also be related to theoretical estimations and FEM simulations. The simulation results predicted narrow line widths of only a few wavenumbers for Ag coated gratings, which were, indeed, observed in reflectance measurements with a quantum cascade laser at 4.26 µm. We hope that approaches incorporating plasmonic structures will significantly extend the range of possibilities in the field of integrated infrared sensors.

The detection, manipulation and tracking of magnetic nanoparticles is of major importance in the fields of biology, biotechnology, biomedical applications as labels as well as in drug delivery, (bio-)detection and tissue engineering. In this regard, the trend goes towards improvements of existing state-of-the-art methodologies in the spirit of timesaving, high-throughput analysis at ultra-low volumes. Here, microfluidics offers vast advantages to address these requirements as it deals with the control and manipulation of liquids in confined microchannels. This conjunction of microfluidics and magnetism, namely micro-magnetofluidics, is a dynamic research field, which requires novel sensor solutions to boost the detection limit of tiny quantities of magnetized objects. We present a sensing strategy relying on planar Hall Effect (PHE) sensors in droplet-based micro-magnetofluidics for the detection of a multiphase liquid flow, i.e. superparamagnetic aqueous droplets in an oil carrier phase. The high resolution of the sensor allows the detection of nanoliter-sized superparamagnetic droplets with a concentration of 0.58 mg cm⁻³, even when they are biased in a geomagnetic field only. The limit of detection can be boosted another order of magnitude reaching 0.04 mg cm⁻³ (1.4 million particles in a single 100 nL droplet) when a magnetic field of 5 mT is applied to bias the droplets. With this performance, our sensing platform outperforms the state-of-the-art solutions in droplet-based micro-magnetofluidics by a factor of 100. This allows us to detect ferrofluid droplets in clinically and biologically relevant concentrations and even below without the need of externally applied magnetic fields.

The aim of this paper is the development of a sensor for the quantification of nickel ions in food raw materials and foods. It seems that about 15% of the human population suffers from nickel allergy. In addition to digestive manifestations, food intolerance to nickel may also have systemic manifestations, such as: diffuse dermatitis, diffuse itching, fever, rhinitis, headache, altered general condition. Therefore, it is necessary to control this content of nickel ions for the health of the human population by developing this new method that brings advantages such as: it is fast, not expensive, in situ and provides accurate analysis. For this purpose, bismuth oxide –SPEs (screen-printed electrodes) and graphene modified SPEs were used with very small amount of dimethylglyoxime and amino acid L-histidine which were deposited. A potentiostat which displays the response in the form of a cyclic voltammogram was used to study the electrochemical properties of nickel standard solution with different concentration. The results were compared and the most sensitive sensor proved to be bismuth oxide –SPEs with dimethylglyoxime (Bi₂O₃/C - dmgH₂) with a linear response over a wide range (0.1–10 ppm) of nickel concentrations. Furthermore, the sensor shows excellent selectivity in the presence of common interfering species. The Bi₂O₃/C - dmgH₂ sensor showed good viability for nickel analysis in food samples (cocoa, spinach, cabbage and red wine) and demonstrated significant advancement in sensor technology for practical applications.

In order to obtain a high microscopic and spectral resolution, both for the microscopic study and for the spectrometric analysis carried out simultaneously at the same area on the sample, an adaptive optoelectronic system for Raman spectromicroscopes with the near-infrared excitation light source is designed. The current system and its working mode have a major disadvantage due to the fact the sample is moved several times to and from the focusing lens of the excitation radiation in the search for the focal point, in order to ensures the maximum spectral resolution. In this process, the peak height for the Stokes spectrum is monitored and the focal point is considered achieved when the peak heghit reaches its maximum. Due to the high energy density in a focal point, repeated searches of this point may lead to the modification of the chemical composition of the investigated material and, in some cases, even to the decomposition of some of its components.The paper presents an advanced technical solution that allows the microscopic study of the sample in the focal point of the visible spectrum, as well as the rapid and automatic search of the focal point in the Raman spectral analysis, at the 1064 nm wavelength in the near-infrared spectral domain, without thermally affecting the sample.