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  • Open access
  • 10 Reads
Digital compasses for orientation-tilt monitoring in offshore deep-sea infrastructures: the KM3NeT case

KM3NeT is a multipurpose observatory being constructed and operated at the abyss of the Mediterranean Sea, housing two large instrumented/detection networks located 40-100 kilometers off the coasts of Toulon (France) and Sicily (Italy), at depths between 2.4-3.4 kilometers, respectively. The cutting-edge technology implemented in the main calibration systems is pointing to an improved performance in time of KM3NeT. In particular, for high-energy neutrino astrophysics, an angular resolution < 0.05º is expected for the sparser detector if synchronization ~ 1 ns, positioning < 20 cm, and orientation < 3º, for the Detection Units, is guaranteed.

The KM3NeT orientation-tilt system known as “Digital Compasses”, is an Attitude and Heading Reference System (AHRS) board coupled to the inner Central Logic Boards of the Detection Units. The AHRS integrates a 3D-magnetometer containing an Anisotropic Magnetoresistive Sensor (AMS) to estimate Earth’s magnetic field, and an 3D-accelerometer equipped with a Micro Electro-Mechanical System (MEMS) that estimates the acceleration field intensity.

The commissioning and performance (onshore, offshore) of the KM3NeT Digital Compasses, together with the reconstruction of orientation-tilt magnitudes and calibration, will be presented and discussed in this contribution.

  • Open access
  • 53 Reads
A Bluetooth 5 Opportunistic Edge Computing System for Vehicular Scenarios.

The limitations of many IoT devices in terms of storage, computing power and energy consumption make them require to be connected to other devices when needing to perform computationally intensive tasks, like it happens with IoT systems based on Edge Computing architectures. However, the lack of wireless connectivity in the places where IoT nodes are deployed or where they move through is still a problem. One of the solutions to mitigate this problem consists in using opportunistic networks, which provide connectivity and processing resources efficiently while reducing the communications traffic with remote clouds. Thus, opportunistic networks are helpful in situations when wireless communication coverage is not available, like it occurs in certain rural areas, during natural disasters, in wars or when other factors cause network disruptions, as well as in other IoT scenarios where the cloud becomes saturated (for example, due to an excessive amount of concurrent communications or when Denial-of-Service (DoS) attacks occur). This article presents the design and initial validation of a novel Opportunistic Edge Computing (OEC) system based on Bluetooth 5 and on the use of low-cost Single-Board Computers (SBCs). After describing the proposed OEC system, several test results are presented for different IoT scenarios where IoT nodes travel inside vehicles. Specifically, latency and packet loss are measured thanks to the use of an experimental testbench made of two separate IoT networks (each one consisted of an IoT node and an OEC gateway): one located in a remote office and another one inside a moving vehicle, which was driven at different vehicular speeds. The obtained results show that the developed system is able to obtain sufficiently low latency values in the selected scenarios and at low-to-medium vehicular speeds, which makes the proposed system useful for OEC applications for urban roads and with low latency requirements.

  • Open access
  • 230 Reads
A Pilot on the Endocrine Effects of Hormonal Replacement Therapy on Menopausal T1 Diabetics Using Wearable Sensors

Menopause is an under-reported and under-researched life stage for women living with Type One Diabetes (T1D) despite it lasting approximately 20 years. Menopause is associated with metabolic dysfunction leading to weight gain, impaired insulin sensitivity, hypertension and hypercholesterolaemia, each of which diminishes longevity for women living with diabetes. Its symptoms, affecting cognition, sleep patterns, mood, cardiac and vascular health, and physical health, are known to impact glucose variability dynamics. Associated vasomotor symptoms of hot-flushes/night-sweats, mood swings, anxiety, depression, and sexual dysfunction make it difficult to differentiate between symptoms of menopause and hypoglycaemia. While it is recognised that transitioning through menopause increases the potential for developing diabetes, there are no recommendations on managing glucose variability or insulin resistance for women with pre-existing diabetes.

Increasingly, women using wearable glucose sensor technologies, insulin pumps and artificial pancreas systems, have self-identified increased glucose variability in the data sets provided by wearable digital health technologies. This data, collected in real-time from women outside of traditional research settings, would have been unimaginable 20 years ago. Women highlighting the knowledge deficit are driven to learn and share more about menopause. Their quest for clinician support about the impact of hormone replacement therapies (HRT) on glycaemic variability, the associated risk of developing additional comorbidities further illustrates the importance of this subject.

This work uses datasets from wearable sensors, contributed by women with T1D to inform an understanding of their collective perimenopause and menopause journeys. Glucose readings across a number of weeks, leading up to and immediately following the initiation of prescribed medications, have been analysed to investigate the physiological effects of HRT on the endocrine system of this sample of menopausal T1D women. Self-management and peer support is bridging the research void, which is why there should be more research into this topic.

  • Open access
  • 28 Reads
Optimization of graded arrays of resonators for energy harvesting in sensors as a Markov decision process solved via reinforcement learning

The optimization of a mechanical system is typically tackled via time-consuming heuristic approaches, in which numerical simulations and/or experimental tests are performed to verify the physical understanding of the problem and to tune the design ruling parameters. Both these aspects are proposed to be automatically handled by looking at the optimization task as a Markov decision process, in which states describe specific system configurations, and actions represent the modifications to the current design. The physics-based understanding of the problem is suggested to be exploited to constrain the set of possible modifications to the mechanical system. This formalization of the optimization process is applied to design the grading of an array of resonators for energy harvesting in sensor applications. Specifically, attention is paid to set the resonator heights, possibly removing resonators whenever convenient. Finite elements simulations have been exploited to evaluate the action effects and to inform the reinforcement learning agent. The proximal policy optimization algorithm, one of the latest proposed and most powerful policy gradient algorithms, has been employed to solve the Markov decision problem. The procedure is demonstrated able to automatically exploit the physical principles that guided past design attempts, finally leading to suboptimal configurations enhancing the mechanical system performance with respect to previously proposed configurations. The proposed framework is not limited to the application at hand, but it is generalizable to a large class of problems involving sensor design optimization.

  • Open access
  • 27 Reads
Cobalt detection using fluorescent dye layers

Technological tools that assist in the prevention and detection of ecological disasters are a current priority. Particularly, the detection of contaminants such as cobalt (Co) is important due to their harmful impact on humans and the ecosystem. When cobalt is discharged into aquifers due to anthropogenic activities, it contaminates the marine fauna and flora. In consequence, the potential risk of intoxication in humans and the environment is evident through the food chain. In this paper, we report the preliminary results regarding the fluorescent dye calcein (C30H26N2O13) used as a sensor for the detection of cobalt levels in aqueous solutions. The sensor cell based on Calcein is built by fixed in layers by means of thermoplastic polyurethane (TPU) and adjusted to pH=7. The layer shows a fluorescence emission in the range of λ=545 nm to 570 nm when it is excited by optical fields at a wavelength centered at 465 nm. By the contact of different Cobalt concentrations to the calcein layer structure, quenching of the fluorescence intensity is observed. The results indicate that the sensor exhibits a linear response of the fluorescence quenching related to the cobalt concentration level in the range of 10-5 to 10-3 mol/L. Additionally, the proposed sensor has a simple experimental set-up, low cost, and does not require additional complex instrumentation.

  • Open access
  • 40 Reads
Robust Underwater Image Classification using Image Segmentation, CNN, and dynamic ROI Expansion

Labelled rectangular Regions of Interest (ROI) in underwater images should be automatically detected for underwater inspection. The images contain typical underwater scenes consisting of basically three parts: Background (water); Underwater constructions (e.g., cylindrical piles); Surfaces with and without biological coverage (e.g., pocks).

The aim is the development of an automatic bounded region classifier that is at least able to distinguish between background, construction, and construction + coverage classes. The challenge is the low and varying image quality that typically appears in North- and East-sea underwater imaging. The images, typically recorded by a human diver or an AUV, pose low contrast, varying illumination conditions and colours, different viewing angles and spatial orientation and scale, overlaid by mud and bubbles (e.g., from the air supply), and optical focus issues.

We propose and evaluate a hybrid approach with segmented classification using small-scaled CNN classifiers (with less than 1000 hyper parameters) and a reconstruction of labelled ROIs by using an iterative mean and expandable bounding box algorithm. The iterative bounding box algorithm combined with bounding box overlap checking suppress spurious wrong segment classifications and represent the best and most accurate matching ROI for a specific classification label, e.g., surfaces with pocks coverage. The overall classification accuracy (true-positive classification) with respect to a single segments is about 70%, but with respect to the iteratively expanded ROI bounding boxes it is about 90%.

  • Open access
  • 21 Reads
A Sulfo-Cyanine Dye as a Colorimetric Chemosensor for Metal Cation Recognition

Various classic dyes have been applied as chemosensory signaling units, such as coumarin, pyrene, 1,8-naphthalimide, xanthene, boron dipyrromethene difluoride (BODIPY), and cyanine derivatives. Among them, cyanine-based platforms have gained much interest due to their remarkable spectral properties, and particularly an intense π-π* absorption which can be easily tuned from the visible to the near-infrared (NIR) region by structural modifications in the chromophore moiety. Metal-cyanine interactions can produce a color change associated with the conjugation of π-electrons, allowing for easy detection of the intended analyte in solution, making them ideal candidates as colorimetric sensors for metal recognition.

Following the research group’s interest on heterocyclic optical chemosensors for various ions, a pentamethine cyanine dye containing two sulfonic acid groups, for improved solubility in aqueous media, was evaluated as a chemosensor for the recognition of several metal cations with biological and environmental relevance. Chemosensing studies showed that this cyanine displayed a highly sensitive colorimetric response, from blue to colorless, for Cu2+and Fe3+ in acetonitrile solution.

  • Open access
  • 25 Reads
Experiment with Cuffless Determination of Arterial Blood Pressure from PPG Signals Sensed by Wearable Sensors in a Weak Magnetic Field Environment
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We are interested in the analysis of physiological and psychological impacts of vibration and acoustic noise on a person scanned in a weak-field magnetic resonance imager (MRI). The information about the state of the cardio-vascular system of a tested person is mainly acquired by current heart rate and arterial blood pressure (ABP) values. These parameters can be obtained by a photo-plethysmography (PPG) signal with parallel measurement by a blood pressure monitor (BPM). To enable proper and safe function in the weak magnetic field environment of the MRI device, special prototypes of wearable PPG sensors were developed. They consist of non-ferromagnetic materials and are fully shielded against the radiofrequency disturbance. If an external portable BPM device is applied for ABP measurement, the pressure cuff is always placed on the opposite arm than the PPG signal is sensed. From our latest analysis follows that also this measurement instrumentation arrangement has a partial influence on the properties of the sensed PPG wave.

Motivation of the current work was to find a different method to obtain ABP values excluding a BPM device to maintain the quality of the sensed PPG signal. In this paper we use an indirect approach where the ABP values are estimated from the sensed PPG wave. The proposed procedure uses time domain features (first of all the systolic/diastolic pulse times and their widths) extracted from the pre-processed PPG signal. Several different statistical approaches were tested to estimate the ABP value from these features. In the first step, the correctness of the estimated ABP values was confirmed by the BPM values measured in the frame of our previous measurements inside the running MRI device. Next, we plan to use the free access databases MIMIC or PPG-BP to verify the stability and accuracy of the developed ABP estimation method.

  • Open access
  • 21 Reads
Success and Failure in Antibody Recognition by Surface-type Sensors: Essential Prerequisites

The identification of low molecular weight analytes (LMWRs) in biological fluids is a rather complex analytical task both in terms of selectivity and detection limits. The most practical competitive assays require selective receptors (e.g. antibodies) against the analyte. Since LMWR themselves do not cause an immune response, conjugates of LMWR with a protein are used to obtain antibodies. As shown in this work, the method of creating such a conjugate as well orientation of immobilized analyte’s analog has a dramatic effect on the efficiency of analyte detection. We demonstrate the validity of this requirement using the example of SPR detection of 17β-Estradiol (E2), one of the most important steroid hormones widely spread in environments. An immunospecific detection of E2 following the competitive inhibition format uses E2 analog directly immobilized on the gold surface through the specific spacer providing both optimal detection conditions, stability of interfacial architecture and low level of non-specific sorption. Comprehensive studies have shown that the efficiency of the system strongly depends on which fragment of E2 is attached to the surface. If antibodies obtained against conjugates with E2 bound in the 17th position of the steroid ring were used, then the antibodies did not recognize the immobilized E2 analogue. However, serum obtained using a conjugate where the BSA-carrier protein is attached to the 3rd position of E2 instead of the 17th showed a high affinity for surface-bound E2. Using an illustrative example of E2 detection, we conduct a comparative analysis of the elements of success and failure of the LMWR analytical analysis in terms of the spatial structure of sensitive zones and various biochemical information contained in certain fragments of steroid hormones important for the formation of unique antibody recognition centers.

  • Open access
  • 28 Reads
Highly selective electrochemical profiling of heroin in street samples
Published: 01 November 2022 by MDPI in 9th International Electronic Conference on Sensors and Applications session Posters

Trafficking and consumption of drugs of abuse are a global concern that threatens social structures and jeopardizes the security of nations [1]. Particularly, heroin use still accounts for the largest share of drug-related harms [2]. Thus, effective, rapid, low-cost and selective analytical methods are vital to hinder drug trafficking, and prevent its availability in the drug market [3]. This way, chemical color tests and sophisticated spectroscopic instrumentation are often the first choice. However, significant drawbacks should be considered e.g. the inaccuracy of the color tests or the high cost and low portability of the spectroscopic devices. Interestingly, electrochemical sensors proved to be the solution for the on-site detection of illicit drugs due to their balance between affordability and analytical performance [4,5].
The present study reports on an improved method for the on-site profiling of heroin. The principle is based on two-peak recognition i.e. from heroin and its main metabolite 6-monoacetylmorphine (6-MAM) at basic pH. Unfortunately, paracetamol, which is the most used cutting agent in heroin seizures, overlaps completely 6-MAM peak at unmodified electrodes, hindering its potential use to selective detect heroin. As a result, a rapid and smart electrochemical pretreatment is presented to overcome this masking phenomena. Besides, a customized script is integrated to enhance peak-to-peak separation and enlighten the full composition of heroin samples.
Overall, the proposed strategy paves the way to a rapid, user-friendly and low-cost on-site detection of heroin in real scenarios by law enforcement officers: (i) analysis of suspicious powders in the street; and (ii) rapid screening of cargos in border settings (e.g. airports, harbors).

[1] European Monitoring Centre for Drugs and Drug Addiction, EU Drug Markets Report 2020, 2020.
[2] European Monitoring Centre for Drugs and Drug Addiction, EU Drug Markets Report 2019, 2019.
[3] W.R. de Araujo, T.M.G. Cardoso, R.G. da Rocha, M.H.P. Santana, R.A.A. Muñoz, E.M. Richter, T.R.L.C. Paixão, W.K.T. Coltro, Anal. Chim. Acta. 2018, 1034, 1.
[4] H. Teymourian, M. Parrilla, J.R. Sempionatto, N. Felipe Montiel, A. Barfidokht, R. Van Echelpoel, K. De Wael, J. Wang, ACS Sensors. 2020, 5, 2679.
[5] F. Truta, A. Florea, A. Cernat, M. Tertis, O. Hosu, K. de Wael, C. Cristea, Front. Chem. 2020, 8.

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 833787, BorderSens.