Augmentative and alternative communication (AAC) encompasses a wide range of methods that replace and/or complement speech of individuals with complex communication needs. Predominant AAC methods rely on the interpretation of purposeful gestures; however, such methods limit the solutions in terms of versatility and portability. Moreover, the cost of predominant high-tech AAC systems is generally very high. An alternative AAC solution, based on encoded modulated breathing, is being researched to address the shortfall in this area. The system undergoing development has been validated with the participation of over 39 non-speech disabled participants in two experimental protocols to test modalities of picking up breathing patterns, such as microphones and pressure sensors. The current results show a mean systematic reliability of 93% by utilising machine learning and dynamic programming to learn and recognize the breathing patterns. The results demonstrate that an improved breath-activated AAC solution could be sought in the future.

The control scheme in myoelectric prosthesis includes a pattern recognition section whose task is to decode an input signal and produce a respective actuation signal to drive the motors in the prosthesis limb towards the completion of the user intended gesture motion. The pattern recognition architecture works with a classifier which is typically trained and calibrated offline with a supervised learning framework, this method involves the training of classifiers which form part of the pattern recognition scheme but also induces additional and often undesired lead time in the prosthesis design phase. In this study, a 4 stage identification framework is formulated to design an intelligent system capable of self-learning patterns from bio-signal inputs from Electromyography (neuromuscular) and Electroencephalography (brain wave) biosensors for a Transhumeral amputee case study. The results show that the designed self-learning framework could form an online automated system that will be beneficial in the reduction of lead time involved in the customization and design of the controller for a myoelectric prosthesis.

Nowadays non-healing wounds are diagnosed de facto, when the wound doesn't heal longer than 20 days. This results in subjective wound management and in inappropriate therapy. The diagnosis of non-healing wound is a challenge, because of their unpredictable nature. Moreover, there is no single biomarker for diagnosis of such kind of disease. All this together impedes the development of point-of-care diagnostic systems for the wound. We performed a thorough literature review of proteomic studies of wound exudate, and suggested the investigation object – several low and high abundant proteins, which are typical for healing and non-healing wounds. Based on this choice, we designed prove-of-the-concept studies, in which the first stage is the development of the biosensor-on-chip analytical platform (BioCAP) based on imaging SPR. The development stages of BioCAP include: microstructurization of the SPR substrate by alkanethiols with microcontact printing (μCP) technique forming self-assembled monolayer (SAM) of protein-repellent coatings and deposition of functional SAM of thiols with ion-chelating tris-nitrilotriacetic acid (trisNTA) group; immobilization of antibodies on the same chip with His-tagged protein A (SpA). At the current stage of the research we optimized the conditions for μCP and for the SpA immobilization. Such developed protocols allowed to perform 20 and more SpA re-immobilization cycles with possibility to capture desired antibody depending on the selected antigen using only one chip without sophisticated regeneration of its surface. Futher development of the BioCAP by the integrating of a microfluidic system will allow us to form the micropaterns of at least 4 different antibodies for wound biomarker detection.

In the present work, the surface modification of a carbon screen-printed electrode by electrochemical polymerization of Toluidine blue (TB) for determination of tyramine is described. The electrochemical polymerization of the electrode with TB was done by cyclic voltammetry at a scan rate of 50 mV/s and a potential sweep between ‒0.7 V to 1.0V in presence of 0.5 mM TB in an electrolyte solution. At each cycle, the polymer film started to deposit on the carbon screen-printed electrode which was repeated 20 times. For parameter optimization the electrochemical behaviour of the modified electrode was analysed by amperometric methods such as cyclic voltammetry (CV) and differential pulse voltammetry (DPV). A phosphate buffer solution (PBS) was used as an electrolyte for all the amperometric experiments. The electrochemically modified poly-TB coated carbon screen printed electrode showed an oxidation peak potential of tyramine at 0.67 V. The unmodified carbon screen printed electrode showed the tyramine peak potential at 0.9 V. Based on the voltammetric results, it was found that the poly-TB modified carbon screen printed electrode showed higher sensitivity (1.78 µA nM^-1 cm^-2) than a bare carbon electrode towards tyramine detection. Tyramine in 0.1 M PBS (pH 7.4) was analysed by cyclic voltammetry from the potential of ‒ 0.7 to 1.0 V at a scan rate of 50 mV/s. Poly-TB modified carbon screen printed electrode exhibited a linear response between catalytic peak current and tyramine concentration from 0.02 µM to 270.5 µM with a lower detection limit 0.007 µM (S/N=3).

In this presentation transient absorption, steady-state and time-resolved fluorescence spectroscopy were used to investigate and characterize the photophysical properties of lanthanide complexes, as well as for applications by studying fluorescence quenching process. Also, we used different mechanisms such as dynamic quenching, ground-state complex formation, excited-state reactions, molecular rearrangements, and energy transfer.
We chose to approach the characterization and investigation of lanthanide complexes for the theoretical information, and also sensors for applications resulting from this study.
In order to better characterize the intermediate that appears at longer times, the metal complexes were studied by flash photolysis, with excitation at 355 nm. The lifetime, around 0.0001 s for each metal complexes was calculated by the analysis of the decays with and without oxygen. The measurements for quantum yield and lifetime were in powders, solution and film. The lifetime and quantum yield are dependent on the substitution on metal ions.
Also, a new application of the compounds investigated for the detection of heavy metals from water was obtain.

Acknowledgements
Thanks CNCS-UEFISCDI, PN-III-P1-1.1-MC-2019-0285 for financial support.

This work presents the design of a cost-effective online wireless monitoring system of two Linear Fresnel solar plants (LFR) located in two different countries. The first LFR plant is installed in SEKEM medical center near Belbis city, Egypt, while the second one is installed in the campus of the University of Palermo, Italy. The proposed system is a standalone system that reduces the interaction of labors as it offers online wireless monitoring for important parameters of the LFR such as solar irradiance, ambient temperature, outlet and inlet collector temperature and heat transfer fluid flow. For that purpose, a Wireless Sensor Network (WSN) based on Arduino Mega boards coupled with XBee modules are used. The ZigBee XBee modules operate at 2.4 GHz, which have the advantages of being low cost and relatively low power consumption. The wireless nodes are supplied by solar paneled power banks, and send the data to a Cloud in order to monitor both LFR plants remotely. The proposed system has been implemented and tested successfully before the future deployment on the LFR plants.

In this paper, we present a synthetic wide-bandwidth radar system using USRP devices. Normally, USRP devices have tens of MHz bandwidth, and cannot generate large bandwidth sweeps to achieve cm level range resolution. By using a synthetic wide-bandwidth approach, we can generate frequency sweeps up to 5 GHz bandwidth and obtain high-resolution range profiles. We will first summarize the mathematical details of the proposed approach, then present a pure Python based solution using the UHD library, and a GNU radio + Octave based implementation, and finally present experimental results for two different test cases. The developed code is available on a public GitHub repo. Compared to the FMCW radars with a voltage controlled oscillator, the sweep time or the experiment duration is longer, but very large bandwidth sweeps can be realized easily by using low-cost USRP devices, and sweeps are more accurate. All of our experimental results indicate the effectiveness of the proposed radar system.

The role of plasma functionalization of multi-walled carbon nanotubes for ammonia gas sensors was investigated. Plasma functionalization of multi-walled carbon nanotubes with maleic anhydride was carried out with various durations. The sensing properties were tested at room temperature. Active material of gas sensor was investigated by scanning electron microscopy, energy-dispersive X-ray spectroscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy. It has been shown that formation of functional groups on the surface of carbon nanotubes led to the increase in ammonia sensor response by 2-4 times. The formation of plasma coating on the surface of multi-walled carbon nanotubes is also accompanied with growth of sensor resistance. The increase of functionalization duration induces the rise of O:C from 0.28 to 0.335 and distortion of shape of I-V curves of active material of ammonia gas sensor.

Human periodontal ligament (PDL) is a membrane-like connective tissue interposed between the tooth root and the alveolar bone of which the main component is represented by collagen fibers. This tissue has an important role in supporting the tooth in the bone socket of the jaw and also in maintaining homeostasis of the surrounding tissues, such as alveolar bone and cementum. During the early stage of the application of orthodontic forces, different changes occur in PDL. For this reason, its characterization with conventional and non-conventional techniques can be extremely interesting. We investigated samples of PDL of orthodontic patients, aged between 13 and 21 years, treated with extraction of upper and/or lower premolars using two different vibrational techniques: Fourier Transform Infrared (FT-IR) spectroscopy and Raman microspectroscopy (m-RS). FT-IR spectra were obtained with a Perkin Elmer Spectrum One FT-IR spectrometer in a transmission geometry using KBr pellets. m-RS spectra were obtained with a He-Ne laser and a Jobin-Yvon TriAx 180 monochromator, equipped with a liquid N₂ cooled CCD and a grating of 1800 grooves/mm.FT-IR and m-RS spectra were analyzed in terms of convoluted peak functions to determine the basic vibrational modes that contribute to the FT-IR and Raman signal by using a best-fit peak fitting routine of GRAMS software based on the Levenberg-Marquardt nonlinear least square method. Lorentzian-Gaussian and Lorentzian curves were used for infrared and Raman spectra, respectively. Peaks constituting the spectrum were manually selected in order to define the starting conditions for the best-fit procedure. The best fit was then applied to determine the optimized intensity, position, and width of the peaks. The performance of the procedure was evaluated by means of the chi-2 parameter. Biochemical characterization of PDL tissues with clear evidence of contributions from collagen, lipid, and other protein was obtained. The analysis of Amide I and Amide III components was also performed giving an indication about the protein secondary structure. In addition, morphological characterization of PDL samples was carried out by using a Supra 40 Zeiss Field-emission scanning electron microscope.

Water-oil separation is important in the oil industry, as the incorrect classification of oil can lead to losses in the production and environmental impact. This paper proposes the use of fiber Bragg grating (FBG) temperature sensor array to identify the oil in water-emulsion-oil systems, using only the temperature responses for oil classification results in operational and economic benefits. To demonstrate the possibility of using the FBG temperature sensor to classify oil level, the temperature distribution of an oil storage tank, with 2 m height and 0.8 m in diameter, is simulated using thermal distribution models. Then, the temperature effect in a 2 m long FBG array with different number and distribution of FBGs is simulated using the transfer matrix method. In each case, we extract the wavelength shift, total width at half the maximum (FWHM) and the location of the FBG in the fiber. For the oil classification, we dichotomized the fluids into oil and not oil (water and emulsion). Due to the low variability of the classes, the Random Forest algorithm was chosen for classification. Starting with 200 FBG equidistant sensors and decreasing to 6, with different distributions along the fiber. As expected, the highest accuracy occurs with the 200 FBGs array (96%). However, it was possible to classify the oil with an accuracy of 94.89% with only 8 FBGs, using Tests for Two Proportions (with a significance of 5%), the accuracy for 8 FBGs is the same of 50 FBGs.