Abstract: Recently, Lidl had to set a recall action due to dangerous pieces of plastic found in the cheese products. The plastic shards, if swallowed, can cut the oral cavity or obstruct breathing. Current inspection techniques in the cheese industry are for the detection of metals using X-ray that does not offer a complete solution as many foreign bodies can go undetected. This paper demonstrates the use of a portable real-time microwave sensing technique for the non-destructive detection of plastic in cheese. The electromagnetic (EM) patch antenna was designed and tested on five Cheddar cheese samples. Different sizes of plastic shards, 1x10mm; 2x15mm and 5x20mm, were inserted into the samples and measurements were taken with and without the foreign objects. The initial results demonstrated that the patch antenna at 4GHz was able to detect and classify Polyvinyl Chloride (PVC) shards with an R2 = 0.95. The initial results are promising and further investigation will be undertaken to detect different shapes and types of foreign objects in food products.

In this work, novel organic-inorganic blend, made from PEGSil (Poly(dimetylsiloksan)-co-[poli(metylohydrosiloksane)-graft-2-winyl-poly(3-heksylthiophene)]-co-[poly(dimetylsiloksane)-graft- metakrylane ethere metylene poly(etylene glicole)]]) mixed with zinc oxide nanomaterial was studied as the sensitive layer for the nitrogen dioxide (NO₂) resistance gas sensor application. Moreover, the PEGSil graft copolymer material was tested in two variants, defined by side-chain length of P3HT: shorter hexane fraction (H) and longer chloroform fraction (CH). Elaborated organic-inorganic blend was deposited on interdigital transducers (Au on Si/SiO2) by drop coating method from chlorobenzene based mixture. Sensor response characteristics to different concentrations of NO₂ (1-10 ppm) in N₂ carrier gas and synthetic air were measured and compared. Measurements were done at room temperature with UV light charge carriers activation. What is more, measurements for low gas concentrations (50-500 ppb) were done and analyzed. Obtained results shows that the sensitivity of fabricated sensors is about 6.8% per 1 ppb for hexane fractions of PEGSil and 9.3% for chloroform fractions in the concentration range from 50 to 200 ppb of NO₂ in N₂ carrier gas. This results show that blend of these materials have a huge potential as a sensing layer for NO_x low concentration sensing.

The evolution of wireless communications has led to the adoption of a wide range of applications not only for the general public, but also including utilities and administrative authorities. Consequently, the huge growth of new city services requires in some specific cases the construction of underground tunnels in order to reduce visual impact within the city center, as well as enabling maintenance and operation works of utilities. One of the main challenges is that inherently, underground service tunnels lack of coverage from exterior wireless systems, such as mobile networks or municipal WLAN networks, which can be potentially dangerous for maintenance personnel working within the tunnels. In this work, wireless channel characterization for urban tunnel scenarios will be analyzed based on the assessment of LoRaWAN and ZigBee technologies operating at 868MHz. For that purpose, a real urban utility tunnel has been modeled and simulated by means of an in-house 3D Ray Launching code and compared with experimental measurements, showing good agreement. The singularity and complexity of the limited tunnel dimensions and the inclusion of additional elements such as service trays, user pathways and handrails have been considered. Results provide an adequate radio planning approach for the deployment of wireless systems in urban utility scenarios, with optimal coverage and enhanced quality of service. Besides, in order to have access to the data obtained by the potential WSN deployed within the tunnels, a solution to store such information in a Cloud is included in the study.

Developing location systems that use mobile device sensors has been a topic of interest to industry and the academy. In this paper, we describe an experiment that was performed for evaluating the feasibility to create a mobile indoor localization model at room level based on data from participatory sensing. In order to achieve it, seven subjects that had a smartphone with magnetometer collected magnetic field information in a building composed by five rooms with different dimensions. The information collected was used to train three machine learning algorithms: k Nearest Neighbors (kNN), decision trees (J48) and Naïve Bayes. The performance of the algorithms was measured through the accuracy and the kappa statistics. Our results show that it is possible to create a mobile indoor localization model at room level using data from participatory sensing. The model with the highest performance was obtained with the kNN algorithm with a value of k = 3, since this offer an accuracy of 97.12% with a concordance level (kappa) of 0.9639 to estimate the localization of individual inside a room at the indoor environment. While the model with the lowest performance was Naïve Bayes, it offers an accuracy of 50.79% with a concordance level of 0.3834.

Wireless networks are technologies with a growing interest in the area of telecommunications, such as the case of MANETs. Despite its advantages, MANETs present several challenges in the transmission of information due to the limited bandwidth, high error rate, energy consumption restriction, and variable topologies. The transmission power can significant influence some of the aforementioned issues. This paper proposes DPCM (Density Power Control Mechanism), which employs a crosslayering approach between AODV (Ad-hoc On-Demand Distance Vector) routing protocol and the physical layer to adapt power transmission. DPCM aims to reduce collisions, maintain or improve the performance of AODV as well as to save power in the nodes.
Our results indicate that our proposal can improve the performance of the network and save power at the same time. Moreover, it is especially useful for low and medium densities scenarios.

With the growing demand of vehicle-mounted sensors over the last years, the amount of critical data communications has increased significantly. Developing applications such as autonomous vehicles, drones or real-time high-definition entertainment, require high data-rates on the order of multiple Gbps. In the next generation of vehicle-to-everything (V2X) networks, a wider bandwidth will be needed, as well as more precise localization capabilities and lower transmission latencies than current vehicular communication systems due to safety application requirements. 5G millimeter wave (mm-wave) technology is envisioned to be the key factor to the development of this next generation of vehicular communications. However, the implementation of mm-wave links arises with difficulties due to blocking effects between mm-wave transceivers, as well as different channel impairments for these high frequency bands. In this work, the mm-wave channel propagation characterization for V2X communications has been performed by means of a deterministic in-house 3D ray launching simulation technique. A complex heterogeneous urban scenario has been modeled to analyze the different propagation phenomena of multiple mm-wave V2X links. Results for large and small-scale propagation effects are obtained for line-of-sight (LOS) and non-LOS (NLOS) trajectories enabling inter-data vehicular comparison. A campaign of measurements has been performed in the real scenario, validating the mm-wave propagation channel characterization. These analyzed results and the proposed methodology can aid in an adequate design and implementation of next generation vehicular networks.

The latest Augmented Reality (AR) systems are able to provide immersive and innovative methods for user interaction, but their full potential can only be achieved when they are able to exchange bidirectional information with the physical world that surround them, including the objects that belong to the Internet of Things (IoT). The problem is that elements like AR display devices or IoT sensors/actuators often use heterogeneous technologies that make it difficult to intercommunicate them in an easy way, thus requiring a high degree of specialization to carry out such a task. This paper presents an open-source framework that eases the integration of AR and IoT devices as well as the transfer of information among them, both in real time and in a fluid way. The proposed framework makes use of widely used standard protocols and open-source tools like MQTT, HTTPS or Node-Red. In order to illustrate the operation of the framework, this paper presents the implementation and evaluation of a practical home automation example: an AR application for energy consumption monitoring that allows for using a pair of Microsoft HoloLens AR glasses to interact with a smart power outlet.

Chemical oxygen demand (COD) is a widely used parameter in analysing and controlling the degree of pollution in water. Methods of analysis based on electrochemical sensors are increasingly being used for COD quantitation, because they could be simple, accurate, sensitive and environmentally friendly. Electro-oxidizing the organic contaminants to completely transform them into CO₂ and H₂O is considered the best method for COD estimation using sensors. In this sense, copper electrodes have been reported based on the fact that copper in alkaline media acts as a powerful electrocatalyst for oxidation of aminoacids and carbohydrates, which are believed to be the major culprits for organic pollution. In this work, three kinds of copper/copper oxide electrodes were studied employing the cyclic voltammetry technique: electrodeposited copper nanoparticle electrode, copper nanoparticle-graphite composite electrode and copper oxide nanoparticle-graphite composite electrode. Actual COD estimations are based on the measurement of oxidation currents of organic compounds. Glucose, potassium hydrogen phthalate and ethylene glycol were chosen to be the standard substances to observe the responses, and correlate current intensity vs. COD values. The performed calibrations showed that glucose and ethylene glycol can be oxidized by these three electrodes, as the current intensity increased along with increasing concentrations. However, only the electrodeposited copper nanoparticle electrode showed ability to oxidize potassium hydrogen phthalate. Besides, the obtained voltammetric profiles presented different shapes with the tested organic compounds, suggesting this can be used as a potential fingerprint for distinguishing the organic compounds. Ongoing work is focused on optimizing measuring condition and detecting the COD values of real samples.

Capsaicin, a lipophilic alkaloid, is responsible for the main reason of hotness in chili peppers. The presented research focuses on the development of an electrochemical sensor based on epoxy-graphite composite with the modification of Titanium dioxide (TiO₂) nanoparticles for the quantification of capsaicin. The measurements were carried out in glycine buffer at pH 2.5 using the cyclic voltammetry technique.

It is observed that carbon based electrodes lead to fouling effect in the cyclic voltammetric measurements of capsaicin. This fouling results in unstable baselines with decreasing oxidation peak current. In order to overcome this problem, extensive search of electrode modifiers, mainly of nano-technological origin, has been made. From this we observed good behavior in TiO₂ modified electrode. TiO₂ nanoparticles were incorporated into the sensor by adding the nanoparticles into the mixture of epoxy graphite composite during the fabrication of the sensor.

When performing the calibration of the developed sensors, two linear concentration ranges were obtained from 6 to 75 μM (R = 0.99) and 12 to 138 µM; the detection limit was estimated as 5.34 µM and 11.3 μM capsaicin for 1st and 2nd oxidation peak, respectively. The main advantage of the developed sensor is its repeatability with a relative standard deviation (RSD) value of 2.5% after 10 repeated measurements. To the best of our knowledge, our proposed sensor is the first sensor to be developed which does not show fouling effect with capsaicin, this is accomplished through the use of chemical cleaning of the electrode surface which simply involved rinsing the surface in specific media (50% ethanol). This voltammetric sensing platform has successfully been applied to quantify capsaicin in various real samples such as sauce and pharmaceuticals.

Multiphase gravity separators are widely used in the petroleum industry to separate the produced stream of oil, water and natural gas into pure (single phase) streams. These equipment work based on the density differences of each fluid which tend to settle into layers when dwelled for some time in the separator. It is fundamental to monitor the levels of these phases inside the vessel, so that this information can be used in control strategies in order to increase efficiency and safety of the process. There are different sensing technologies for multiphase level monitoring, based, for instance, on ultrasound, ionizing radiation and electrical impedance. However, none can be seen as general and each solution present some disadvantage. In this work, we present a novel multiphase level sensor based on capacitance and inductance measurements of planar multi-electrodes and multi-coils. The sensor is low-cost, fast and does not apply ionizing radiation, being therefore simple to operate. The prototype sensor was constructed in standard PCB technology and highly sensitive capacitance and inductance measurements are acquired with modern ICs. Capacitive sensor highly depends on the water salinity and therefore we include inductance measurements to compensate such dependence. We have tested the prototype at varying water salinity and for different water/oil emulsions. Therefore, the novel sensor is well able to differentiate each one of the produced fluids, i.e. salty water, oil and gas, as well as the interfaces between them.