Marine bioconstructors are put at risk by natural disturbances caused by climate change and anthropogenic stressors. Given their fundamental importance in marine ecosystems, it is essential to reverse this trend. In this regard, measurement techniques capable of early detecting alterations of marine colonies are essential. Traditional underwater surveys rely on in situ observations performed by divers, and in the last decade, such analysis has been increasingly supported by photogrammetry. Despite the significant benefits brought by photogrammetry and the notable progress of computer vision, to date, no measurement techniques have fully succeeded in the early detection of minor alterations in the state of health. In this regard, fluorescence imaging has the potential to support and improve both ecological and physiological assessments. Nevertheless, up to date, its use in the underwater environment is mainly exploited for qualitative nature photography and much less for quantitative analysis aimed at extracting biological information. One factor limiting the widespread use of quantitative fluorescence imaging to monitor marine bioconstructors is that studies in the literature generally do not allow for metrological traceability. Fluorescence intensity is usually reported in arbitrary units derived from the intensity of the acquired images. This study aims to support the transition from qualitative imagery to quantitative fluorescence imaging. To this end, we proposed introducing a simple and effective traceability obtained by relating the fluorescence intensity recorded by the instrument to the sodium fluorescein concentration that would generate the same intensity, namely, the equivalent fluorescein concentration. This makes measurement results independent of the specific measuring system, enabling research conducted by different groups to be synergised.

Continuous monitoring of blood pCO2 is critical during extracorporeal circulation (ECC) to support clinical decision-making. This study aims to describe and investigate a new, low-cost, disposable fluorescent pCO2 sensor, namely MS2. The performance of MS2 is analysed by comparison with a blood gas analyzer. A significant challenge in developing sensors for in vivo applications is ensuring biocompatibility. In the MS2 sensor, biocompatibility is ensured by using a medical-grade gas-permeable membrane that isolates the sensing chemistry from the patient’s blood. The study also investigates the performance of a commercial optical pCO2 sensor, the PreSens MCR-O1P1C1, which is not approved for use with blood. The aim is to assess the feasibility of employing the PreSens MCR-O1P1C1 for blood monitoring in scenarios where biocompatibility is not a prerequisite, such as in the development stages of biomedical devices that require ex vivo blood testing. The results obtained during a 6.5-hour test with bovine blood demonstrate that both measuring systems can provide valid support for monitoring pCO2 in blood. However, despite its excellent response times, the off-label application of the PreSens MCR-O1P1C1 necessitates an adjustment of the measuring system to prevent significant measurement errors. Thus, these results position MS2 as a promising solution for real-time, in-line blood gas monitoring in ECC procedures.

Adverse weather conditions continue to present a significant challenge for Advanced Driver Assistance Systems (ADAS). Quantification of visibility in fog is typically accomplished by measuring the Meteorological Optical Range (MOR). Despite the MOR's extensive recognition and utilisation across diverse domains, including aviation, navigation, and traffic management, there exists a potential discrepancy between the visibility estimated using the MOR, i.e. the optical path length in the atmosphere necessary to diminish the luminous flux of a collimated beam to 5 percent of its original value, and that actually perceived by drivers or camera-based ADAS systems operating within the visible range. Indeed, the anisotropy of the scattering generated by fog particles, in conjunction with the fact that, in the automotive sector, visibility is generally supported by the headlights and street lighting, can lead to phenomena not considered by the MOR. The present study proposes a measurement method and setup for investigating the degree to which visibility estimated exploiting the MOR represents the visibility perceived by drivers or camera-based ADAS systems. The experimental findings indicate that the lighting angle can substantially influence visibility. Thus, the study will propose a simplified analysis to identify the primary contributors to this discrepancy.

Advanced Driver Assistance Systems (ADAS) have been shown to play a substantial role in reducing both the number and severity of road traffic accidents (RTA). However, adverse weather conditions, one of the situations in which drivers may need the most assistance, continue to present a significant challenge to the functioning of many of the measuring systems that underpin ADAS. The present study investigates the influence of fog on the performance of LiDAR sensors. Fog can substantially degrade the quality of data provided by LiDARs, affecting fundamental perception tasks, including object detection, classification, and tracking. The quality of data provided by LiDARs depends on the characteristics of the echoes received by the LiDAR, and the performance of the receiving hardware. To analyze fog-induced variations in beam intensity and widths, a straightforward experimental approach is proposed. It uses a 1 m sided transparent chamber, which is filled with fog produced by an ultrasonic fog generator. The proposed method thus facilitates the systematic analysis and comparative evaluation of LiDARs performance, circumventing the necessity for substantial financial investment and operational complexity associated with large facilities while overcoming the inherent limitations of mathematical modelling and numerical simulation. To illustrate the types of information that can be extracted using the proposed method, representative results obtained from a widely used LiDAR sensor, the Velodyne VLP 16, are also presented.

The use of LiDARs to support machine driving is generally widely recognized as a valuable ally. However, the conditions in which human drivers would need more support are often those characterized by limited or poor visibility, conditions in which even LiDARs may find themselves in great difficulty. In this regard, in recent years, more and more studies have begun to investigate the performance of LiDARs in adverse weather conditions such as fog, heavy rain, or heavy snowfall. In this study, we proposed a test bench to analyze performance in the presence of dust. Although perhaps rarer in road applications, the presence of dust is unfortunately ubiquitous in the offroad. It is a significant criticality in the use of LiDARs in offhighway applications. The proposed method is based on a dust chamber, that is a transparent chamber inside which the dust conditions to be analyzed are created. To provide an example of the information that can be obtained, the proposed method has been exploited to analyze one of the most widespread LiDARs, the Velodyne VLP-16. The results obtained have shown that when the Meteorological Optical Range, MOR, drops below about 5 m, the VLP-16 is not only no longer able to detect any targets inside the dust but the dust cloud itself is erroneously detected as a target.

The human respiratory Central Pattern Generator (CPG) is a complex and tightly regulated network of neurons responsible for the automatic rhythm of breathing. Among the brain nuclei involved in respiratory control, excitatory neurons within the PreBotzinger Complex (PreBötC) are both necessary and sufficient for generating this rhythmic activity. Although several models of the PreBötC circuit have been proposed, a comprehensive analysis of network behavior in response to physiologically relevant external inputs remains limited. In this study, we present a computational model of the PreBötC consisting of 1000 excitatory neurons, divided into two functional subgroups: the rhythm-generating population and the pattern-forming population. To enable real-time closed-loop simulations, we employed parallelized multi-process computing to accelerate network simulation. The network, composed of asynchronous neurons, could produce bursting activity at a eupneic breathing frequency of 0.22 Hz, which could also reproduce the rapid and stable chemoreception of breathing activated in response to hypercapnia. Additionally, it successfully replicated rapid and stable respiratory responses to elevated carbon dioxide levels (hypercapnia), mediated through simulated chemoreception. External inputs from a carbon dioxide sensor were used to modulate the network activity, allowing the implementation of a real-time respiratory control system. These results demonstrate that a network of asynchronous, non-bursting neurons can emulate the behavior of the respiratory CPG and its modulation by external stimuli. The proposed model represents a step toward developing a closed-loop controller for breathing regulation.

Since LiDARs usually exploit near-infrared (NIR) radiation, we often do not even notice them. Nonetheless, they have become an almost constant presence that accompanies us most of the day. If, on the one hand, this has brought us advantages and simplifications, on the other hand, it will require ever greater attention to all aspects related to safety. Indeed, the LASER sources exploited by LiDARs can potentially pose risks to people. Since the received power decreases as the distance to the target increases, the farther away the objects to detect can be, the more powerful, and thus dangerous, the LASER source/s may need to be. In this scenario, 3D scanning-LiDARs for automotive applications are probably among the most powerful LASER sources that any of us can come across in our daily lives. For our safety, compliance with the limits imposed by the safety standards must be guaranteed not only when the LiDAR, or the product including it, leaves the production line but throughout the entire operational life. Unfortunately, measuring the energy of each beam emitted by a 3D scanning-LiDAR to verify its compliance with the limits imposed by the safety standards can be far from trivial. In this study, we exploit the peculiarities of 3D scanning-LiDARs to propose a simplified method for testing the compliance of the emitted radiation with the limits for class 1 LASERs imposed by International Electrotechnical Commission (IEC) 60825 standards. The method is primarily intended for automotive LiDARs but can be applied to most 3D scanning-LiDARs.

In this study, we present a low-cost, disposable fluorescent pCO(2) sensor designed for real-time blood pCO(2) monitoring in extracorporeal circulation (ECC) treatments. The sensor consists of a sensitive element enclosed inside a disposable cuvette. The sensor is interrogated by an optical head that, not coming into contact with the blood, can be non-disposable. The heart of the sensor is a gas filter composed of gas-permeable fibers, specifically developed for this application, designed to be low-cost and disposable, ensuring both affordability and convenience for real-time blood-pCO(2) monitoring. The blood in extracorporeal circulation is made to flow through the fibers that, being gas permeable, allow the exchange of gas with the measuring chamber, i.e., the space inside the cuvette outside the fibers, while preventing the exchange of anything that is not gaseous. Inside the measuring chamber, CO2 exchanged with the blood reacts and modifies the pH of the measuring chamber; this variation is measured thanks to a ratiometric pH-sensitive fluorophore inside the chamber. The fibers used are custom-made polypropylene porous hollow fibers produced using the TIPS technique (thermally induced phase separation) and have a unique structure that allows blood to flow through them. Before being inserted into the cuvette to make the sensor, the produced fibers were fully characterized by optical and scanning electron microscopy (SEM) and their permeability and porosity were also verified. The measuring system thus obtained was verified by simulating a 7-hour extracorporeal circulation treatment using bovine blood and comparing the measurements obtained with those provided by a bench-top blood gas analyzer.

LiDARs hold promise for various automotive applications, but their performance in adverse weather conditions remains a severe limitation. Indeed, fog can compromise the ability to perform fundamental tasks such as detection, classification, and tracking. The success of these tasks depends on the quality of the data provided by the LiDAR, i.e., the point cloud, PC, and the algorithms used to analyse that PC. Some previous studies exploited large and sophisticated facilities filled with fog to analyse LiDARs in fog. However, such facilities are intrinsically highly complex and costly. To overcome these limitations, we propose a much less expensive method based on a fog chamber, a 1 m side transparent chamber to be placed between the LiDAR and the targets, then filled with fog. The proposed method allows for the analysis and comparison of the performance of both LiDARs and processing algorithms while avoiding the cost and complexity of large facilities and the limitations intrinsic to mathematical modelling and numerical simulation. To provide examples of the information that is obtainable using the proposed method, the results from a popular LiDAR and processing algorithm, namely, the Velodyne VLP 16, and the MATLAB (R) Computer Vision Toolbox, are also reported.

Marine communities are facing both natural disturbances and anthropogenic stressors. Bioconstructor species are endangered by multiple large-scale and local pressures and the early identification of impacts and damages is a primary goal for preserving coral reefs. Taking advantage of the recent development in underwater photogrammetry, the use of photogrammetry and fluorimetry was coupled to design, test and validate in laboratory a multi-sensor measuring system that could be potentially exploited in open water by SCUBA divers for assessing the health status of corals and detecting relevant biometric parameters with high accuracy and resolution. The approach was tested with fragments of the endemic coral Cladocora caespitosa, the sole zooxanthellate scleractinian reef-builder in the Mediterranean. The most significant results contributing to the scientific advancement of knowledge were: 1) the development of a cost-effective, flexible and easy-to-use approach based on emerging technologies; 2) the achievement of a sub-centimetric resolution for measuring relevant biometric parameters (polyp counting, colony surface areas and volumes); 3) set up of a reliable and repeatable strategy for multi-temporal analyses capable of quantifying changes in coral morphology with sub-centimeter accuracy; 4) detect changes in coral health status at a fine scale and under natural lighting through autofluorescence analysis. The novelty of the present research lies in the coupling of emerging techniques that could be applied to a wide range of 3D morphometrics, different habitats and species, thus paving the way to innovative opportunities in ecological research and more effective results than traditional in-situ measurements. Moreover, the possibility to easily modify the developed system to be installed on an underwater remotely operated vehicle further highlights the possible concrete impact of the research for ecological monitoring and protection purposes.

In the automotive industry's future, the advent of autonomous driving poses a significant challenge. A key component in this transformation is LiDAR (Light Detection and Ranging), which plays a fundamental role in environmental sensing. Over recent years, several new LiDAR systems have emerged on the market, reflecting the growing importance of this technology. Consequently, there is an urgent demand for tools to analyze and compare LiDAR systems specifically tailored to meet the automotive industry's needs. In recent years, researchers have proposed studies for analysing and benchmarking the performance of commercial LiDARs. In particular, three key aspects have been analysed for their importance: i) object detection capability and range, ii) robustness to different optical properties of objects, and iii) robustness to adverse weather conditions. Researchers have presented studies to characterize these aspects of an automotive LiDAR by proposing different approaches, setups and analyses. This paper provides a review of recent measurement approaches focusing on detection and ranging capability, noise resilience and adaptation to adverse weather conditions. Finally, the paper concludes with a discussion of proposed measurement methodologies and offers insights for potential future analyses in LiDAR assessment.

Nowadays, most of the LiDARs used in the automotive sector are in scanning technology. Such implies that the acquisition of the surrounding environment takes place sequentially. If there is relative motion between the vehicle - the LiDAR - and the surrounding environment, the acquired 3D image is distorted. Theoretically, knowing the scanning frequency and the displacement vectors, such a distortion could be compensated. Nonetheless, as experienced by anyone who has analyzed point clouds (PCs) acquired from moving LiDARs, the distortion observed is often more severe and seemingly unpredictable than expected from the LiDAR scanning frequency and the displacement vectors. Thus, the in-motion performance analysis of LiDARs is significant for automotive applications. In-motion characterization and comparison are challenging. In this paper, we present a testbed for repeatable LiDAR in-motion characterization. The proposed test setup is composed of a track and a cart moving along it, at which the LiDAR is fixed. Since the cart speed is known and the surrounding environment is controlled, it is possible to estimate all the deformations introduced by the relative motion. The experimental examples obtained by analyzing a commercial LiDAR, the VLP 16 by Velodyne, demonstrate how the deformations obtained can be more significant than expected from a simple geometric analysis based on relative motion.

Given the neuroprotective, anti-inflammatory, and analgesic properties of cannabidiol (CBD), many countries have recently legalized the use of fiber-type Cannabis products, including those known as “Cannabis light”. Nonetheless, in freely commercialized products, it is not uncommon to find $\Delta^{9}$ -tetrahydrocannabinol (THC), the principal psychoactive constituent of cannabis, in concentrations exceeding the legal limit. To determine whether a product is commercially viable, the THC/CBD ratio is typically analyzed using chromatographic techniques. However, chromatographic techniques have costs, complexity, and response times that prevent their in-situ use, making control actions much more expensive and ineffective. In this work, we report our preliminary activities aimed at verifying the possibility of performing in-situ analysis of cannabinoids in cannabis-derived products using an ad-hoc designed measuring system based on screen-printed electrodes modified with carbon black. The results obtained from preliminary tests comparing fiber cannabis (legal THC concentration) and recreational cannabis (illegal THC concentration) suggest that the proposed system can allow the effective and efficient in-situ analysis of cannabis-derived products.

LiDARs are crucial for fully autonomous driving and have greatly improved in performance and availability on the market, leading to a need for methods and tools to compare them. Most of the studies in this field consider only tests in optimal meteorological conditions. Otherwise, it is relevant to investigate the performance of these devices in adverse weather conditions. In this study, we extend our work regarding the characterization of LiDARs in a foggy environment. In particular, we introduce a new measurement method aimed at estimating the minimum fog concentration for which a LiDAR detects the fog as a target, precluding the possibility of detecting any other objects that may be present inside the bank of fog. This method, together with the previously presented method, has been used to characterize and compare two commercial LiDARs - the MRS 6000 by Sick and the VLP 16 by Velodyne. These analyses were achieved through a custom setup extremely simple to be implemented. Thanks to the proposed methods, it has been possible to discover significant differences in the performance of the two LiDARs in foggy environments. For example, the VLP 16 showed good performance starting from optical visibility of about 3 m, while, for the MRS 6000, a visibility of about 40 m is required.

Under physiological conditions, the human body maintains blood pH within [7.36, 7.44] pH. Small deviations from this range can reveal the onset of pathological states and worsen the patient’s condition. This article reports the performance analysis of a real-time, noninvasive pH-measuring system for extracorporeal circulation (ECC). In particular, this study focuses on the analysis of the effects that the measurand temperature may have on the error in estimating blood pH. Even if the blood temperature in ECC is often thermostated at 37 °C, there are treatments in which the blood temperature is varied by a few Celsius degrees, and the exploited measurement principle—fluorescence—is known to be affected by temperature. First, we verified that the temperature-induced error could exceed the maximum permissible measurement error of ±0.04 pH. Hence, a linear-correction factor for temperature compensation was proposed. The results obtained showed how the simple addition to the measuring system of a temperature sensor and the use of a linear-correction factor can effectively allow maintaining the measurement error within the ±0.04-pH range, even when the fluid—phosphate buffer saline (PBS) and blood—temperature is varied in the range [30 °C, 39 °C].

The preamplifier proposed in this paper is designed to extract weak variable photogenerated signals from a high-level continuous background ensuring low noise and high transimpedance gain. An efficient cancellation of the DC component directly at the photodetector output, exploiting a feedforward approach, allows us to properly amplify the variable signal components of interest avoiding saturation of the preamplifier. Furthermore, the large transimpedance gain allows for minimizing the effects of the noise introduced by the following stages on the signal processing chain. In the paper, we present the proposed approach and a possible circuit realization with a signal AC/DC ratio as small as 1/1000 ensuring low noise, high gain, and a considerable bandwidth. The realized preamplifier offers a Noise Equivalent Power NEP ≃ 1.12 nW, an in-band transimpedance gain of 4.4 MΩ, and a wide bandwidth from about 1 Hz up to 100 kHz, making it suitable for use in several applications both in biomedical and industrial fields.

The 3-D light detection and rangings (LiDARs) are nowadays used for many applications, the success of which certainly depends on the processing of the LiDAR output-the point cloud (PC)-but it also inexorably depends on the quality of the PC data. In this study, we propose an experimental method aimed at allowing estimating the errors and deformations that will statistically affect the LiDAR output-the PC. Taking advantage of the fact that LiDARs sample the surrounding space by observing it along divergent lines, hereinafter referred to as rays, this study proposes a simple method based on the experimental determination of the ray detection probability-the probability that a single ray detects the hit object, or a fraction of it, by adding a point in the PC. All other probabilities of interest are derived from such a probability. The proposed method also allows highlighting unexpected errors, such as crosstalk. As will be shown by the examples given, due to crosstalk, small objects may be deformed and enlarged on a significantly greater number of points in the PC. Likewise, objects angularly separated by an angle greater than the angular resolution declared by the manufacturer may unexpectedly result in a continuum of points. Such errors may compromise the ability to perform very important tasks, such as detection, classification, and tracking of dynamic and static objects, as well as the partition of the scene into drivable and non-drivable regions and the path planning around generic obstacles in 3-D space.

The vast multitude of LiDAR systems currently available on the market makes the need for methods to compare their performances increasingly high. In this study, we focus our attention on the development of a method for the analysis of the effects induced by the fog, one of the main challenges for Advanced Driver Assist Systems (ADASs) and autonomous driving. Large experimental setups capable of reconstructing adverse weather conditions on a large scale in a controlled and repeatable way are certainly the best test conditions to analyze and compare LiDARs performances in the fog. Nonetheless, such large plants are extremely expensive and complex, therefore only available in a few sites in the world. In this study, we thus propose a measurement method, a data analysis procedure and, an experimental setup that are extremely simple and inexpensive to implement. The achievable results are reasonably less accurate than those obtainable with large plants. Nevertheless, the proposed method can allow to easily and quickly obtain a preliminary estimate of the performance in the presence of fog and a rapid benchmarking of different LiDAR systems.

The development of ever intelligent systems in various application fields is nowadays a hot research topic. Artificial Intelligence (AI) techniques become a key enabler of the transition between classical static, hard-coded algorithms and innovative, flexible ones. Actually, the automotive sector can undoubtedly benefit from the usage of the aforementioned techniques, aiming at building a novel smart automotive industry. Indeed, the application of AI spreads all round the automotive sector, ranging from on–board measuring systems to customer satisfaction analysis and demand prediction. This paper aims to review the possible applications of Artificial Intelligence techniques to the automotive sector, with a special focus on innovative measurement systems and metrology. Indeed the focus will be, between others, on Advanced Driver Assistance Systems (ADAS), in-vehicle IoT systems and intelligent industrial measuring systems, thus allowing to both increase road safety and design accurate predictive maintenance, additive manufacturing systems and, in substance, to build the smart automotive factory of the future.