The effect of chelating/combustion agent on catalytic activity and magnetic properties of Dy doped Ni–Zn ferrite

The spinel ferrite Ni_0.8Zn_0.2Fe_1.98Dy_0.02O₄ was prepared by sol–gel low temperature autocombustion method using four different chelating/combustion agents: citric acid, tartaric acid, urea and cellulose. Infrared spectroscopy (IR), X-ray diffraction (XRD), scanning electron microscopy (SEM), Brunauer–Emmett–Teller (BET) specific surface area measurement, the catalytic H₂O₂ decomposition and the magnetic behavior were employed to investigate the influence of the combustion agents on structural characteristics, catalytic activity and magnetic properties. Spinel-type phase in the nano-scale domain was accomplished during sol–gel synthesis and was confirmed by XRD and IR. The best catalytic activity is belonging to the sample obtained using urea, which shows the smallest grain size (SEM), the highest specific surface area (BET measurements) and DyFeO₃ phase (XRD), while ferrimagnetic behavior prevails for all the samples independently of fuel agent.     

Multibeam Optical System and Neural Processing for Turbidity Measurement

This paper presents a turbidity measuring system based on a modulated four infrared (IR) light beam architecture with advanced data processing. The turbidity sensing component consists of a pair of IR light-emitting diodes (LEDs) connected to a current drive controlled through the pulsewidth modulated (PWM) outputs of a multifunction input/output board. The scattered and transmitted IR light in the media under test is detected by a two-channel IR photodiode module that includes a set of transimpedance and programmable gain amplifier. The voltages proportional to the detectors' output currents, are acquired using a 12-bit ADC included in a microcontroller and RS232 transmitted to a laptop personal computer (PC) that works as an advanced control and processing unit. Using optimal neural network processing architectures, an accurate extraction of the turbidity information is performed. A practical approach concerning the neural network architectures [multilayer perceptron single-input-single-output (SISO), multiple-input-single-output (MISO)] including neural network training and testing is discussed in the paper. The multi-input architectures prove to be a robust and general solution for the proposed application. Results from a turbidity measuring system that was designed for automated standalone remote operation with sensing channel autocalibration capabilities are presented     

A Digitally Programmable A/D Converter for Smart Sensors Applications

Nonlinear analog-to-digital conversion in smart sensor applications is an important topic since signal digitization and linearization can be performed in a single step near the transducer. In this paper a double pulsewidth modulated (PWM) scheme for nonlinear analog-to-digital conversion is presented. Calibration or auto-calibration data stored in the smart sensor's memory define the nonlinear profile characteristic of the transducer and provide the required data to obtain the inverse function of the analog-to-digital converter (ADC) transfer curve. Basically, as a function of the transducer's nonlinearity degree, the input voltage range of the ADC is segmented in a continuous set of subintervals and, for each of these subintervals, a second-order correction term based on a PWM A/D conversion is used to obtain a linear characteristic for the smart sensor. Additional advantages of this method result from its easy implementation in low-cost microcontrollers that include generally comparator inputs and PWM outputs. A flexible and programmable A/D conversion solution can be dynamically adapted to variations of the transducer's nonlinearity profile, and an increased resolution can be achieved at the expense of a lower conversion rate. Some MATLAB simulations and experimental results obtained with a square-root airflow transducer will be presented in the final part of the paper     

Greater improvement in summer than with light treatment in winter in patients with seasonal affective disorder

OBJECTIVE: The authors sought to compare the degree of mood improvement after light treatment with mood improvement in the subsequent summer in patients with seasonal affective disorder. METHOD: By using the Seasonal Affective Disorder Version of the Hamilton Depression Rating Scale, the authors rated 15 patients with seasonal affective disorder on three occasions: during winter when the patients were depressed, during winter following 2 weeks of light therapy, and during the following summer. They compared the three conditions by using Friedman's analysis of variance and the Wilcoxon signed ranks test. RESULTS: The patients' scores on the depression scale were significantly higher after 2 weeks of light therapy in winter than during the following summer. CONCLUSIONS: Light treatment for 2 weeks in winter is only partially effective when compared to summer. Further studies will be necessary to assess if summer's light or other factors are the main contributors to this difference.     

Modified UNet Model for Brain Stroke Lesion Segmentation on Computed Tomography Images

The task of segmentation of brain regions affected by ischemic stroke is help to tackle important challenges of modern stroke imaging analysis. Unfortunately, at the moment, the models for solving this problem using machine learning methods are far from ideal. In this paper, we consider a modified 3D UNet architecture to improve the quality of stroke segmentation based on 3D computed tomography images. We use the ISLES 2018 (Ischemic Stroke Lesion Segmentation Challenge 2018) open dataset to train and test the proposed model. Interpretation of the obtained results, as well as the ideas for further experiments are included in the paper. Our evaluation is performed using the Dice or f1 score coefficient and the Jaccard index. Our architecture may simply be extended to ischemia segmentation and computed tomography image identification by selecting relevant hyperparameters. The Dice/f1 score similarity coefficient of our model shown 58% and results close to ground truth which is higher than the standard 3D UNet model, demonstrating that our model can accurately segment ischemic stroke. The modified 3D UNet model proposed by us uses an efficient averaging method inside a neural network. Since this set of ISLES is limited in number, using the data augmentation method and neural network regularization methods to prevent overfitting gave the best result. In addition, one of the advantages is the use of the Intersection over Union loss function, which is based on the assessment of the coincidence of the shapes of the recognized zones.     

Empirical Mode Decomposition and Principal Component Analysis implementation in processing non-invasive cardiovascular signals

Biomedical signals are relentlessly superimposed with interferences. The nonlinear processes which generate the signals and the interferences regularly exclude or limit the usage of classical linear techniques, and even of wavelet transforms, to decompose the signal.Empirical Mode Decomposition (EMD) is a nonlinear and adaptive technique to decompose data. Biomedical data has been one of its most active fields. EMD is fully data-driven, thus producing a variable number of modes. When applied to cardiovascular signals, the modes expressing cardiac-related information vary with the signal, the subject, and the measurement conditions. This makes problematic to reconstruct a noiseless signal from the modes EMD generates.To synthesize and recompose the results of EMD, Principal Component Analysis (PCA) was used. PCA is optimal in the least squares sense, removing the correlations between the modes EMD discovers, thus generating a smaller set of orthogonal components. As EMD-PCA combination seems profitable its impact is evaluated for non-invasive cardiovascular signals: ballistocardiogram, electrocardiogram, impedance and photo plethysmogram.These cardiovascular signals are very meaningful physiologically. Sensing hardware was embedded in a chair, thus acquiring also motion artefacts and interferences, which EMD-PCA aims at separating. EMD is seen to be important, because of its data adaptability, while PCA is a good approach to synthesize EMD outcome, and to represent only the cardiovascular portion of the signals.     

The metal-overlap laterally-diffused (mold) Schottky diode

A novel uniform-field/breakdown structure is described consisting of a planar metal-overlap laterally-diffused (MOLD) Schottky diode. This structure has been demonstrated to be free of edge breakdown by numerical two-dimensional calculations. Experimentally obtained aluminum-silicon MOLD Schottky diodes have shown near-ideal characteristics in terms of breakdown voltage, reverse I–V characteristics and forward I–V characteristics. The barrier height determined from forward current measurements (zero-volts intercept and activation-energy plot) is 0.74 V.     

Integration of Ge nanowire arrays in glass micro-fibers

We report on a technological route for the integration of large arrays of Ge nanowires (NWs) in a human-hair-like glass micro-fiber, the length of the micro-fiber reaching one meter. The route comprises (a) the formation of semiconductor microwire in glass insulation by capillary drawing from the bottom of a glass tube softened by a conducting Ge melt drop levitating in the high-frequency electromagnetic induction field; (b) mechanical assembly of a bundle from equal-length cut microwires which are distributed in a two-dimensional quasi-hexagonal densely packed lattice encircled by a joint glass envelope; (c) stretching of the obtained preform under proper heating conditions to reduce the diameters of the stacked together microwires; (d) repeating the cut-assembly-stretching processes for the purpose of further decreasing in transverse dimensions of constituents down to 150 nm. The fascinating incorporation of huge amounts of Ge nanowires in glass micro-fibers opens new possibilities for the development of highly integrated photonic and quantumelectronic systems.