Properties of Tactile Sensors Based on Resistive Ink and the Dimension of Electrodes

This chapter presents the ongoing research, which aims to select suitable electrodes for their use in the pressure distribution measurement system Plantograf. In our research, we examine more materials, especially Yokohama conductive rubber CS57-7RSC and also conductive inks, which are represented type DZT-3 K, Graphit 33 and mixture of Loctite-Henkel conductive inks type Loctite 7004Hr and Loctite NCI 7002EC. All materials can be used as a converter between pressure and electrical quantities in the design of planar pressure transduces. We build on our previous works, where were examined the properties of conductive rubber, conductive inks and electrodes. Next part is focused on the newest results of our research. Due to the still incomplete results in the given issue, we decided to perform an extensive and original measurement of a total of 172 combinations of different electrode sizes, the ratio of conductive ink mixtures and the thickness of the applied ink layer. Thanks to this, it will be possible in the future to select a suitable combination of electrodes and inks when designing tactile pressure sensors for industrial or medical applications without the need to perform time-consuming preparatory measurements and exclude unsuitable ink-electrode combinations.

Estimation of Rheological Effects in Cantilever Concrete Bridges on the Basis of a Span's Deflection Line

A characteristic feature of bridges as large span objects made using cantilever concreting technology are their excessive deflections, which are a result of rheological processes in concrete and pre-stressing steel. These deflections can be caused by the destruction of the material, e.g., concrete cracking, as well as the changing of the static scheme of the bridge structure, such as the subsidence of supports. The purpose of the work is to determine internal forces based on the deformation of a span. An algorithm for the correction of the deflection function, which is determined from geodetic measurements with a low accuracy, was proposed. It is characterized by a marked improvement in the results of calculations and, to a small extent, leads to the smoothing of the original measurement results. The algorithm is adapted to the analysis of a selected fragment of the structure, e.g., spans with the largest length and can be useful for monitoring bridge structures.

Measurement problems of miniature electrical machines

The article presents the issue related to experimental research on the determination of static and dynamic properties of miniature electrical machines. The tested objects are, for example, micromotors with body diameters in the order of single millimetres, or linear drives with millimetre dimensions. The main measurement problems were presented and the specificity of these measurements was characterized. First of all, electromechanical time constants have small values due to small values of masses or mass moments of inertia. The force quantities (forces and torques) generated by such actuators also require the use of unconventional measuring transducers. The research may concern the identification of static and dynamic characteristics, but also methods of dynamic measurement of the quantities used in the control of such micromachines are presented. Practical examples of original measurement methods and systems are presented.

Denoising MAX6675 reading using Kalman filter and factorial design

This paper aims to tune the Kalman filter (KF) input variables, namely measurement error and process noise, based on two-level factorial design. Kalman filter then was applied in inexpensive temperature-acquisition utilizing MAX6675 and K-type thermocouple with Arduino as its microprocessor. Two levels for each input variable, respectively, 0.1 and 0.9, were selected and applied to four K-type thermocouples mounted on MAX6675. Each sensor with a different combination of input variables was used to measure the temperature of ambient-water, boiling water, and sudden temperature drops in the system. The measurement results which consisted of the original and KF readings were evaluated to determine the optimum combination of input variables. It was found that the optimum combination of input variables was highly dependent on the system's dynamics. For systems with relatively constant dynamics, a large value of measurement error and small value of process noise results in higher precision readings. Nevertheless, for fast dynamic systems, the previous input variables' combination is less optimal because it produced a time-gap, which made the KF reading differ from the original measurement. The selection of the optimum input combination using two-level factorial design eased the KF tuning process, resulting in a more precise yet low-cost sensor.

High-pass NGD characterization of resistive-inductive network based low-frequency circuit

Purpose The purpose of this paper is to provide the high-pass (HP) negative group delay (NGD) circuit based (RL) network. Synthesis and experimental investigation of HP-NGD circuit are developed. Design/methodology/approach The research work methodology is organized in three phases. The definition of the HP-NGD ideal specifications is introduced. The synthesis method allowing to determine the RL elements is developed. The validation results are discussed with comparison between the calculated model, simulation and measurement. Findings This paper shows a validation of the HP-NGD theory with responses confirming NGD optimal frequency, value and attenuation of about (9 kHz, −1.12 µs, −1.64 dB) and (21 kHz, −0.92 µs, −4.81 dB) are measured. The tested circuits have experimented NGD cut-off frequencies around 5 and 11.7 kHz. Research limitations/implications The validity of the HP-NGD topology depends on the coil self-inductance resonance. The HP-NGD effect is susceptible to be penalized by the parasitic elements of the self. Practical implications The NGD circuit is usefully exploited in the electronic and communication system to reduce the undesired delay effect context. The NGD can be used to compensate the delay in any electronic devices and system. Social implications Applications based on the NGD technology will be helpful in the communication, transportation and security research fields by reducing the delay inherent to any electronic circuit. Originality/value The originality of the paper concerns the synthesis formulations of the RL elements in function of the expected HP-NGD optimal frequency, value and attenuation. In addition, an original measurement technique of HP-NGD is also introduced.

Research on wind field characteristics measured in inland city by Lidar

In order to study the wind characteristics in inland cities, a Wind3D 6000 Lidar which has many advantages in measuring wind parameters was placed on a campus in Xi’an city. Then, through filtering the original measurement data, the wind speed, wind direction, wind attack angle and turbulence intensity were analyzed using the data of nearly four months ranging from 64m to 610m. It is found that during the field measurement period, there is a linear correlation and Lorentz density function distribution between wind speed and height, but the power index law is not obvious. The wind direction changes obviously with the time and season but less along the height. The probability density of wind attack angle presents Gauss distribution, with an average value of about 1.35°, which is negatively linear correlated with height and wind speed. The probability density of turbulence shows the distribution characteristic of LogNormal density function, which increases first and then decreases with the height, and has a positive linear correlation with the wind attack angle.

Analog-to-Information Conversion with Random Interval Integration

A novel method of analog-to-information conversion—the random interval integration—is proposed and studied in this paper. This method is intended primarily for compressed sensing of aperiodic or quasiperiodic signals acquired by commonly used sensors such as ECG, environmental, and other sensors, the output of which can be modeled by multi-harmonic signals. The main idea of the method is based on input signal integration by a randomly resettable integrator before the AD conversion. The integrator’s reset is controlled by a random sequence generator. The signal reconstruction employs a commonly used algorithm based on the minimalization of a distance norm between the original measurement vector and vector calculated from the reconstructed signal. The signal reconstruction is performed by solving an overdetermined problem, which is considered a state-of-the-art approach. The notable advantage of random interval integration is simple hardware implementation with commonly used components. The performance of the proposed method was evaluated using ECG signals from the MIT-BIH database, multi-sine, and own database of environmental test signals. The proposed method performance is compared to commonly used analog-to-information conversion methods: random sampling, random demodulation, and random modulation pre-integration. A comparison of the mentioned methods is performed by simulation in LabVIEW software. The achieved results suggest that the random interval integration outperforms other single-channel architectures. In certain situations, it can reach the performance of a much-more complex, but commonly used random modulation pre-integrator.

A Deep Learning Approach for the Photoacoustic Tomography Recovery From Undersampled Measurements

Photoacoustic tomography (PAT) is a propitious imaging modality, which is helpful for biomedical study. However, fast PAT imaging and denoising is an exigent task in medical research. To address the problem, recently, methods based on compressed sensing (CS) have been proposed, which accede the low computational cost and high resolution for implementing PAT. Nevertheless, the imaging results of the sparsity-based methods strictly rely on sparsity and incoherence conditions. Furthermore, it is onerous to ensure that the experimentally acquired photoacoustic data meets CS’s prerequisite conditions. In this work, a deep learning–based PAT (Deep-PAT)method is instigated to overcome these limitations. By using a neural network, Deep-PAT is not only able to reconstruct PAT from a fewer number of measurements without considering the prerequisite conditions of CS, but also can eliminate undersampled artifacts effectively. The experimental results demonstrate that Deep-PAT is proficient at recovering high-quality photoacoustic images using just 5% of the original measurement data. Besides this, compared with the sparsity-based method, it can be seen through statistical analysis that the quality is significantly improved by 30% (approximately), having average SSIM = 0.974 and PSNR = 29.88 dB with standard deviation ±0.007 and ±0.089, respectively, by the proposed Deep-PAT method. Also, a comparsion of multiple neural networks provides insights into choosing the best one for further study and practical implementation.

Microwave Method for Measuring Electrical Properties of the Materials

The article is about the development of a new non-destructive microwave method for measuring the electrophysical properties of materials, based on the method of transmission and reflection of a plane monochromatic wave through layered materials. The method has been verified by the results of numerical and field experiments. A data processing technique is described for obtaining complex values of dielectric and magnetic permeability based on an original measurement scheme. Based on the results of mathematical calculations, the laboratory model was created using an ultra-wideband antenna and a parabolic mirror. The optimal distance of the antenna from the parabolic mirror for focusing the electromagnetic field has been determined based on the simulation. Testing was carried out in the frequency range 3–13 GHz on two samples of materials (plexiglass and textolite) with known electrophysical properties. The obtained results showed the reliability of the developed method and its applicability. The measurement error was less than 2%.

Internal construct validity of the Brazilian version of a tool for assessing the population’s knowledge of human papillomavirus

ABSTRACT: Objective: To verify the internal construct validity of the Brazilian Portuguese version of a tool for measuring the general population’s knowledge of human papillomavirus (HPV). Materials and methods: A cross-culturally adapted Brazilian Portuguese version of a measurement tool originally designed for English speaking populations was administered to 330 adults in Tubarão, Santa Catarina, Southern Brazil. After examining the overall suitability of the method, we performed investigations based on the item response theory and exploratory factor analysis. Results: Ten of the 29 items presented a low contribution to the construct and were excluded from subsequent analysis. The factor analysis yielded three factors, which explained approximately 51% of the variance variability. A different arrangement from the original measurement tool was found: general HPV knowledge, with six items; HPV vaccination knowledge, with five items; HPV transmission and testing knowledge, with eight items. Conclusion: The Brazilian Portuguese version under study presented a different behavior from the original measurement tool, but proved to be a reliable and valid instrument in assessing the Brazilian population’s knowledge about HPV.