Towards Continuous Nano-Plastic Monitoring in Water by High Frequency Impedance Measurement with Nano-Electrode Arrays

We explore the potentiality of high frequency impedance measurements with CMOS nano-electrode arrays for nano-plastic pollutant particles monitoring in water. This technology offers benefits as nano-scale resolution, high parallelization, scalability, label-free single particle detection, and automatic measurements without operator intervention. Simple models are proposed for size and concentration estimation. The former integrates measurements of adjacent electrodes and shows uncertainty comparable to the nominal one with mean prediction error lower than 45 % down to 50 nm radius. The latter accounts for noise in the definition of the sensing volume. We report a worst-case concentration error lower than a factor 1.7 under stationary and continuous flow, which demonstrates the potential of this technology for automated measurements

A transimpedance preamplifier using a feedforward approach for robust rejection of DC photogenerated currents

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

A method for estimating object detection probability, lateral resolution, and errors in 3D-LiDARs

3D-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 point cloud. All other probabilities of interest are derived from such a probability. The proposed method also allows highlighting unexpected errors such as cross-talk. As will be shown by the examples given, due to cross-talk, 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 3D space

Optoelectronic Apparatus Measures Glucose Noninvasively

An optoelectronic apparatus has been invented as a noninvasive means of measuring the concentration of glucose in the human body. The apparatus performs polarimetric and interferometric measurements of the human eye to acquire data from which the concentration of glucose in the aqueous humor can be computed. Because of the importance of the concentration of glucose in human health, there could be a large potential market for instruments based on this apparatus

A Comprehensive Review on Time Sensitive Networks with a Special Focus on Its Applicability to Industrial Smart and Distributed Measurement Systems

The groundbreaking transformations triggered by the Industry 4.0 paradigm have dramati-cally reshaped the requirements for control and communication systems within the factory systems of the future. The aforementioned technological revolution strongly affects industrial smart and distributed measurement systems as well, pointing to ever more integrated and intelligent equipment devoted to derive accurate measurements. Moreover, as factory automation uses ever wider and complex smart distributed measurement systems, the well-known Internet of Things (IoT) paradigm finds its viability also in the industrial context, namely Industrial IoT (IIoT). In this context, communication networks and protocols play a key role, directly impacting on the measurement accuracy, causality, reliability and safety. The requirements coming both from Industry 4.0 and the IIoT, such as the coexistence of time-sensitive and best effort traffic, the need for enhanced horizontal and vertical integration, and interoperability between Information Technology (IT) and Operational Technology (OT), fostered the development of enhanced communication subsystems. Indeed, established tech-nologies, such as Ethernet and Wi-Fi, widespread in the consumer and office fields, are intrinsically non-deterministic and unable to support critical traffic. In the last years, the IEEE 802.1 Working Group defined an extensive set of standards, comprehensively known as Time Sensitive Networking (TSN), aiming at reshaping the Ethernet standard to support for time-, mission-and safety-critical traffic. In this paper, a comprehensive overview of the TSN Working Group standardization activity is provided, while contextualizing TSN within the complex existing industrial technological panorama, particularly focusing on industrial distributed measurement systems. In particular, this paper has to be considered a technical review of the most important features of TSN, while underlining its applicability to the measurement field. Furthermore, the adoption of TSN within the Wi-Fi technology is addressed in the last part of the survey, since wireless communication represents an appealing opportunity in the industrial measurement context. In this respect, a test case is presented, to point out the need for wirelessly connected sensors networks. In particular, by reviewing some literature contributions it has been possible to show how wireless technologies offer the flexibility necessary to support advanced mobile IIoT applications

Comparison of VLP-16 and MRS-1000 LiDAR systems with absolute interferometer

Nowadays, LiDARs hold a relevant place in providing the environmental sensing required by most ADAS. Promoted by such increasing demand, many new manufacturers are emerging and, new LiDARs are continuously made available on the market. If, on the one hand, the availability of LiDARs with increasing performance and reducing cost has brought significant benefits also promoting the spread of such measuring systems in other areas such as industrial controls and agriculture, on the other, it has made it more difficult to extricate in the immense set of LiDARs present on the market today. In response to this growing need for standards and methods capable of comparing the various LiDARs, many international standards and scientific publications are being produced on the subject. In this paper, we continue our work on LiDARs characterization, focusing our attention on comparing the performances of two of the must popular systems - namely, the MRS 1000 by Sick and the VLP 16 by Velodyne. Starting from the analysis of the warm-up time and stability, such a comparison focused on analyzing the axial error of both systems. Such errors have been estimated by exploiting a custom rail system and an absolute interferometer. The obtained results revealed warm-up times of a few tens of minutes and maximum absolute axial errors of a few centimeters in the range [1.5,21] m

A learning model for battery lifetime prediction of LoRa sensors in additive manufacturing

Today, an innovative leap for wireless sensor networks, leading to the realization of novel and intelligent industrial measurement systems, is represented by the requirements arising from the Industry 4.0 and Industrial Internet of Things (IIoT) paradigms. In fact, unprecedented challenges to measurement capabilities are being faced, with the ever-increasing need to collect reliable yet accurate data from mobile, battery-powered nodes over potentially large areas. Therefore, optimizing energy consumption and predicting battery life are key issues that need to be accurately addressed in such IoT-based measurement systems. This is the case for the additive manufacturing application considered in this work, where smart battery-powered sensors embedded in manufactured artifacts need to reliably transmit their measured data to better control production and final use, despite being physically inaccessible. A Low Power Wide Area Network (LPWAN), and in particular LoRaWAN (Long Range WAN), represents a promising solution to ensure sensor connectivity in the aforementioned scenario, being optimized to minimize energy consumption while guaranteeing long-range operation and low-cost deployment. In the presented application, LoRa equipped sensors are embedded in artifacts to monitor a set of meaningful parameters throughout their lifetime. In this context, once the sensors are embedded, they are inaccessible, and their only power source is the originally installed battery. Therefore, in this paper, the battery lifetime prediction and estimation problems are thoroughly investigated. For this purpose, an innovative model based on an Artificial Neural Network (ANN) is proposed, developed starting from the discharge curve of lithium-thionyl chloride batteries used in the additive manufacturing application. The results of experimental campaigns carried out on real sensors were compared with those of the model and used to tune it appropriately. The results obtained are encouraging and pave the way for interesting future developments

LiDARs detected signal and Target distance estimation: measurement errors from Target reflectance and multiple echos

The use of LiDARs in automotive is increasingly widespread. In order to operate in a critical environment such as that of mobility, these systems must offer increasingly high performance. In particular, the ability to estimate the position of objects regardless of their reflectance and presence of diffusing backgrounds is a very sought-after feature by manufacturers. In commercial systems various strategies are used to make the measurement as insensitive as possible to these effects, however, it is not possible to fully compensate for the measurement errors caused by them. In this paper, we propose two simple experimental setups to verify the presence of these measurement errors in two scenarios. Moreover, we report the performance of a commercial LiDAR (MRS 6000 by Sick) using certified reflectance standards (Spectralon (R) Diffuse Reflectance Standards, by Labsphere). For this LiDAR, the results obtained show that a logarithmic variation of the reflectance of the target of 1.26-log at a target distance 2.4 m provides incompatible measurements. Furthermore, the presence of a background at a distance shorter than 11 cm, 12 cm and 13 cm respectively with 50 %, 75 % and 99 % reflectance also provides incompatible measurements for a target distance of 1.3 m from the LiDAR