Grinding is one of the most commonly used finishing processes in the manufacture of precision parts. The monitoring of the workpiece surface quality is considered highly complex due to particularities of the cutting tool and material removal mechanism. In this context, the monitoring of the grinding process is very important for the manufacturing industry and of great interest to researchers in the field. A large number of grinding process monitoring studies have been developed aiming at the automation of this process. The objective of this work is to monitor the material removal in the grinding of SAE 4340 steel workpieces by piezoelectric transducers in the emitter and receiver modes along with digital signal processing techniques. The tests were performed in a surface grinding machine equipped with an aluminum oxide grinding wheel. The transducers signals were sampled at a sampling frequency of 2 MHz. The digital signal processing was performed through the spectrum analysis and the application of techniques such as root mean square. The workpiece weight was measured by means of a digital scale before and after the grinding tests. The results show that the monitoring technique proposed in this work is sensitive to the material removal in the grinding process. The appropriate selection of frequency bands allows for the best diagnosis in relation to the events that occur during the grinding process.

The grinding process is usually one of the last stages in the manufacturing process chain since it can provide superior surface finish and closer dimensional tolerances. However, due to peculiarities of the grinding process, workpiece material is susceptible to many problems, what demands a reliable real-time monitoring system. Some grinding monitoring systems have been proposed by means of sensors. However, literature is still scarce in terms of employing time-frequency analysis techniques during grinding of ceramics. Thus, this paper proposes an application of the low-cost piezoelectric transducer (PZT - lead zirconate titanate) in the analysis of the surface quality of ceramic workpieces during grinding process by means of frequency-time domain technique along with the ratio of power parameter (ROP). An integrated high-cost commonly used acoustic emission (AE) sensor was employed in order to compare the results with the low-cost PZT transducer. Tests were performed using a peripheral surface grinding machine. Three values of depth of cut were selected in order to represent slight, moderate and severe grinding conditions. Piezoelectric signals were collected at 2 MHz. The short-time Fourier transform (STFT) was studied in order to obtain the frequency variations over time. An analysis of the ratio of power (ROP) values was performed in order to establish a correlation with the surface roughness. The ROP values are highly desirable for setting a threshold to detect the workpiece surface quality and implementing into a monitoring system. The results of the PZT transducer had a great similarity to those of the AE sensor.

Surface Acoustic Wave (SAW) passive and wireless sensors are a promising solution for condition and structural health monitoring (SHM) applications. They are robust devices, which can withstand extreme operating conditions (very high temperatures, high pressure etc.). They do not require any embedded electronics and can be implanted in various kinds of (dielectric) structures. Moreover, SAW Sensors of the Reflective Delay Line (R-DL) type have built-in RFID capabilities, which can prove extremely useful for various industrial applications.

Our presentation will start with an overview of CTR R&D activities.

We will then present a short review of SAW RFID Sensors for industrial applications, with a focus on implantable devices like torque sensors, pressure sensors, and temperature sensors. Specific examples of applications in the chemical, automotive and steel industries will be provided.

We will then discuss the potential of SAW RFID Sensors for SHM applications. Existing solutions will be described, as well as possible applications based on existing 2.45GHz R-DL devices. As SAW sensors often fail in operation, due especially to housing failure at high temperature, promising ‘package-less’ solutions will be presented. Here, guided modes that propagate in protective multilayer structures are used, instead of surface waves. Several multilayer configurations have been suggested, simulated and/or tested in the past years. Pros & cons of the different configurations will be discussed.

Coarse alignment is the process where the initial transformation matrix between body and navigation frame is determined using inertial sensors output. For low-cost inertial sensors only the accelerometers readings are processed to yield the initial roll and pitch angles. , Since inertial navigation systems (INS) requires the attitude initial condition prior to its operation, the coarse alignment process must be made before. The accuracy of the coarse alignment procedure is vitally important for the navigation solution accuracy due to the navigation solution drift accumulating over time.

In this paper, instead of using traditional approaches, we propose using machine learning (ML) approaches to conduct the coarse alignment procedure. To that end, a new methodology for the alignment process is proposed, based on state-of-the art ML approaches, including Random Forests (RF) and more advanced boosting methods such as gradient tree XGBoost and Light Boost. Results from simulated alignment of stationary INS scenarios are presented. The results are compared with the traditional coarse alignment methods in terms of time to convergence and accuracy performance. Using the proposed approach, results show a significance improvement of the accuracy and time required for alignment process of an INS for autonomous platforms. The results show that RF performs consistently good followed by boosted trees. The contribution of the proposed approach to CA performance will enable enhanced performance of autonomous platforms operation.

The Al doped silicon–silicon oxide–hafnium oxide–silicon oxide–silicon capacitor device (hereafter Al-SOHOS) could be a candidate for blue light radiation total dose (hereafter TD) nonvolatile sensor. The UV radiation induces a significant increase in the threshold voltage V_T of the Al-SOHOS capacitor, and the change in V_T for Al-SOHOS capacitor also has a correlation to UV TD. The experimental results indicate that UV TD radiation-induced increase of V_T in Al-SOHOS capacitor under gate positive bias stress (hereafter PVS) is significant. Moreover, the V_T retention loss of the nonvolatile Al-SOHOS capacitor device after 10 years retention is good. The UV TD data can be permanently stored and accumulated in the non-volatile Al-SOHOS capacitor device. Furthermore, the UV TD data in the Al-SOHOS capacitor devices can be erased to original null state by opposite charges injection under gate negative bias stress (hereafter NVS). The Al-SOHOS capacitor device in this study has demonstrated the feasibility of non-volatile UV TD radiation sensing.

Tears are exceptionally reach sources of information on the health status of the eyes, as well as of the whole body functionality, due to the presence of a large variety of salts and organic components that can be altered by pathologies, eye's diseases and/or inflammatory processes. Surface enhanced Raman spectroscopy (SERS) provides an unique methods for analyzing low concentration organic fluids as tears. In this work, home made SERS substrates were prepared by using gold nanoparticles for investigating fluid samples excited by a HeNe laser (λ= 633 nm). The method was preliminary tested on Rhodamine 6G aqueous solutions at different concentrations, proving the possibility to sense substance concentration as low as the levels of the main organic components of the human tear. Tears were extracted from informed healthy patients by means of a not-invasive methods based on a direct collection of the tear fluid by means of a suitable micro-capillary tube. A clear SERS response was obtained for human tear samples. The SERS spectra were analyzed by using a “wavelet” based algorithm and a deconvolution procedure in terms of Lorentzian curves, in order to identify the contributions of biochemical components. The SERS spectra analysis allowed a preliminary insight on tear composition.

The DYNAMAP project is aimed at implementing a dynamic noise mapping system able to determine the acoustic impact of road infrastructures in real-time, due to the basis settled by the European Noise Directive 2002/49/EC. A Wireless Acoustic Sensor Network is used to collect the measurements in two pilot areas: in the city of Milan (urban) and in the A90 motorway around Rome (suburban). For a proper evaluation of the road infrastructure noise level, the anomalous noise events (ANE) unrelated to traffic noise (e.g. sirens, horns, speech, doors…) should be removed before updating the noise maps. For this purpose, an anomalous noise events detector (ANED) has been designed using Mel Frequency Cepstral Coefficients (MFCC) and Gaussian Mixture Models (GMM), and trained using data from a real-life recording campaign in the A90. In this work, the preliminary version of the ANED algorithm is adjusted to conform to the requirements of the final 19-node WASN deployed in the suburban environment. The study pays special attention to analyze the characteristics of the acoustic data in real-life conditions and their differences between the 19-nodes in the Rome pilot, in order to adapt the ANED to the entire WASN.

The energy consumption in wireless sensor networks is the critical concern of different studies, especially because of the great effort, or even the impossibility, to replace the battery of their motes. Consequently, it is fundamental to investigate and evaluate the energy spent by every individual task executed by the motes in order to provide an efficient use of their batteries. In this work, we employ different metrics to present a thorough study of how the use of multiple transmission power levels affects multihop wireless sensor networks. This work is motivated by the current employment of the multiple transmission power levels, on both academic works and commercial solutions, which is a novel feature of some radio transceivers commonly used in wireless sensor network motes. Aiming reliable and extensive analysis, this study employs simulations in different scenarios and models of commonly employed electronic components. The contribution of this works is a detailed investigation of the impact caused by the use of different transmission power levels employing different metrics, offering a wide perspective on the subject. In general, the results of this study indicate that the use of multiple power levels grants both positive and negative results, according to the scenario and metrics analyzed. Consequently, the analysis, results and remarks are presented in a didactic manner in order to provide valuable information and to support other works regarding this topic.

Internet of Things (IoT) applications in smart environments demand challenging requirements to the wireless networks in terms of security, coverage, availability, power consumption, and scalability. The technologies employed so far to cope with IoT scenarios are not yet able to manage simultaneously all these demanding requirements, but recent solutions like Low-Power Wide Area Networks (LP-WANs) have emerged as a promising alternative to provide low-cost and low-power consumption connectivity to nodes spread throughout a wide area. Specifically, the Long-Range Wide Area Network (LoRaWAN) standard is one of the most successful developments, receiving attention from both industry and academia. This work presents a comprehensive case study of LoRaWAN under a realistic scenario within a smart city: a smart campus. Such a medium-scale scenario has been implemented through the in-house developed 3D Ray Launching radio planning simulator including traffic lights, vehicles, people, buildings and urban furniture and vegetation. The developed tool is able to provide accurate radio propagation estimations within the smart campus scenario in terms of coverage,
capacity and energy efficiency of the network. These results are compared with an empirical validation in order to assess the operating conditions and the system accuracy. We further present an analysis of the key findings in order to provide some guidelines for IoT vendors, network operators, and city planners to investigate further deployments of LoRaWAN for large-scale smart city applications.

In the last years, it has been a growing interest in the development of biosensors for different applications in medicine, environmental sensing, food testing, etc. [1] Bridging the gap between the laboratory status of the biosensors to applications is expensive and time-consuming. Besides their design, manufacture and biofunctionalization, an extensive experimental testing work is required. One way to reduce the testing time is reusing the biosensors. With this aim, different regeneration strategies have been explored by different groups [2-4]. However, most of the protocols for regeneration require to remove the transducers from the experimental system and to reassemble the system again.

In this work, we pursue a strategy to reuse silicon-based photonic biosensors functionalized with molecular beacon (MB) probes immobilized for multiple detection of target microRNAs. This strategy aims at performing a so-called online regeneration, which not only allows saving time, but also reducing the sensor-to-sensor variance in the experimental sensing results, what is specially useful when testing similar levels of analyte. Chemical regeneration based on formamide (FA) was the strategy explored in this study. FA is a denaturing agent for nucleic acids, it is commonly used in DNA solutions [5]. However, little is known about FA as denaturing agent for microRNA bounded to MB probes immobilized on silicon surfaces as in the case we are interested in [6]. Our study consisted of, after running a typical microRNA sensing experiment, flowing FA in water to dehybridize the probes and regenerate the sensor for performing further experiments. Several tests were carried out and finally a regeneration protocol based on FA was successfully developed.

References:

[1] P. J. Conroy, S. Hearty, P. Leonard, and R. J. O’Kennedy, Seminars in Cell&Development Biology, 2009, 20, 10-26.

[2] Stephane Leahy, and Yongjun Lai, Sensing and Bio-Sensing Research, 2015, 6, 24-27.

[3] J. A. Goode, J. V. Rushworth, and P. A. Millner, Langmuir, 2015, 31, 6267-6276.

[4] Saumya Joshi, Vijay Deep Bhatt, Andreas Märtl, Markus Becherer, and Paolo Lugli, Biosenors, 2018, 8, 9.

[5] Julia Fuchs, Daniel Dell’Atti, Arnaud Buhot, Roberto Calemczuk, Marco Macini, Thierry Livache, Anal. Biochem, 2010, 397, 132-134

[6] Ángela Ruiz-Tórtola, Francisco Prats-Quílez, Daniel González-Lucas, María-José Bañuls, Ángel Maquieira, Guy Wheeler, Tamas Dalmay, Amadeu Griol, Juan Hurtado, and Jaime García-Rupérez. Biomed. Opt. Express 2018, 9(4), 1717-1727.