Abstract
Raman technology offers a cutting-edge approach to measuring glucose solutions, providing precise and non-invasive analysis. By probing the vibrational energy levels of molecular bonds, Raman technology generates a unique spectral fingerprint that allows for the accurate determination of glucose concentrations. This study proposes the use of Raman spectroscopy to identify different glucose concentrations through the detection of Raman fingerprints. As expected, higher concentrations of glucose in the solution conducted to higher peak bands, indicating more glucose molecules interacting with light and consequently increasing the magnitude of inelastic scattering. This non-destructive approach preserves sample integrity and facilitates rapid analysis, making it suitable for various applications in biomedical research, pharmaceutical development, and food science.

Abstract
This work proposes a signal processing algorithm to analyse the optical signal from a Low Coherence Interferometric (LCI) system. The system uses a Mach-Zehnder (MZ) interferometer to interrogate a Fabry-Perot cavity, working as an optical sensor. This algorithm is based on the correlation and convolution operations, which allows the signal to be reconstructed based on itself, as well as, on the linearization of the signal phase, allowing the non-linearities of the actuator incorporated on the MZ interferometer to be compensated. The results show a noise reduction of 30 dB in the signal acquired. As a result, a reduction of 8.2 dB in the uncertainty of the measurement of the physical measurand is achieved. It is also demonstrated that the phase linearization made it possible to obtain a coefficient of determination (namely, R-squared) higher than 0.999.

Abstract
This work presents an implementation of a reference optical cavity based on parasitic cavities on a low coherence interferometric system. This method allows a maximization of the number of sensors to be implemented without occupying additional reading channels.

Abstract
Recent advances in optical trapping have opened new opportunities for manipulating micro and nanoparticles, establishing optical tweezers (OT) as a powerful tool for single-cell analysis. Furthermore, intelligent systems have been developed to characterize these particles, as information about their size and composition can be extracted from the scattered radiation signal. In this manuscript, we aim to explore the potential of optical tweezers for the characterization of sub-micron size variations in microparticles. We devised a case study, aiming to assess the limits of the size discrimination ability of an optical tweezer system, using transparent 4.8 µm PMMA particles, functionalized with streptavidin. We focused on the heavily studied streptavidin-biotin system, with streptavidin-functionalized PMMA particles targeting biotinylated bovine serum albumin. This binding process results in an added molecular layer to the particle’s surface, increasing its radius by approximately 7 nm. An automatic OT system was used to trap the particles and acquire their forward-scattered signals. Then, the signals’ frequency components were analyzed using the power spectral density method followed by a dimensionality reduction via the Uniform Manifold Approximation and Projection algorithm. Finally, a Random Forest Classifier achieved a mean accuracy of 94% for the distinction of particles with or without the added molecular layer. Our findings demonstrate the ability of our technique to discriminate between particles that are or are not bound to the biotin protein, by detecting nanoscale changes in the size of the microparticles. This indicates the possibility of coupling shape-changing bioaffinity tools (such as APTMERS, Molecular Imprinted Polymers, or antibodies) with optical trapping systems to enable optical tweezers with analytical capability. © 2024 SPIE.

Abstract
In recent advancements within sensing technology, driven by the Internet of Things (IoT), significant impacts are observed on health sector applications, notably through wearable electronics like electronic tattoos (e-tattoos). These e-tattoos, designed for direct contact with the skin, facilitate precise monitoring of vital physiological parameters, including body heat, a critical indicator for conditions such as inflammation and infection. Monitoring these indicators can be crucial for early detection of chronic conditions, steering toward proactive healthcare management. This study delves into a thermoelectric sensor e-tattoo designed for detailed skin temperature mapping. Utilizing a novel design, this sensor detects temperature variations across thermoelectric stripes, leveraging screen-printed films of p-type Bi0.35Sb1.65Te3, n-type Bi2Te2.8Se0.2, and poly(vinyl alcohol) (PVA) for enhanced thermoelectric and flexible properties. The application of a prototype printed thermoelectric device on temporary tattoo paper, a pioneering development in wearable health technology is demonstrated. This device, validated through numerical simulations, exhibits significant potential as a non-invasive tool for temperature monitoring, highlighting its value in health diagnostics and management.

Abstract
The domain of Intelligent Transportation Systems (ITS) is becoming a key candidate to enable safer and efficient mobility in IoT enabled smart cities. Several recent research in cooperative autonomous systems are conducted over simulation frameworks as real experiments are still too costly. In this paper, we present a platooning robotic test-bed platform with a 1/10 scale robotic vehicles that functions based on the input front commercially off the shelf technologies (COTS) such as Lidars and cameras. We also present an in-depth analysis of the functionalities and architecture of the proposed system. We also compare the performance of the aforementioned sensors in some real-life emulated scenarios. From our results, we were able to concur that the camera based platooning is able to perform well at partially observable scenarios than its counterpart.