In this work, a new dual-frequency imaging framework of capacitively coupled electrical impedance tomography (CCEIT) is presented. Unlike conventional singlefrequency imaging, the dual-frequency imaging adopts two different working frequencies to obtain the real part and the imaginary part of the impedance respectively. With the real part image and the imaginary part image at the two frequencies, the framework further introduces image fusion to obtain the fused image. This work focuses on optimization of the two working frequencies. To achieve the optimal selection of the two frequencies, data collection in a wide range frequency is carried out. The multifrequency data is then analyzed in depth and investigated from several aspects, including the measurement data, the sensitivity distribution and the imaging quality. Experiment was carried out with a 12-electrode CCEIT sensor, an impedance analyzer, and a computer to obtain the real part and imaginary part measurements of the impedance. Research results show that a low working frequency is recommended for the real part, while a relatively high working frequency is recommended for the imaginary part. Within the investigated frequency range of 200 kHz -20 MHz, the optimized frequencies for the real part and the imaginary part are 1 MHz and 15 MHz respectively. Results of verification experiment shows the superior performance of the two selected frequencies. Additionally, this paper demonstrates the advantages of the dual-frequency imaging framework. Compared with CCEIT in individual frequencies, CCEIT based on dualfrequency imaging with the two optimized frequencies has much better imaging performance.

More than 96% of steel in the world is produced via the method of continuous casting. The flow condition in the mould, where the initial solidification occurs, has a significant impact on the quality of steel products. It is important to have timely, and perhaps automated, control of the flow during casting. This work presents a new concept of using contactless inductive flow tomography (CIFT) as a sensor for a novel controller, which alters the strength of an electromagnetic brake (EMBr) of ruler type based on the reconstructed flow structure in the mould. The method was developed for the small-scale Liquid Metal Model for Continuous Casting (mini-LIMMCAST) facility available at the Helmholtz-Zentrum Dresden-Rossendorf. As an example of an undesired flow condition, clogging of the submerged entry nozzle (SEN) was modelled by partly closing one of the side ports of the SEN; in combination with an active EMBr, the jet penetrates deeper into the mould than when the EMBr is switched off. Corresponding flow patterns are detected by extracting the impingement position of the jets at the narrow faces of the mould from the CIFT reconstruction. The controller is designed to detect to undesired flow condition and switch off the EMBr. The temporal resolution of CIFT is 0.5 s.

Landmines are often be made out of plastic with almost no metallic components which makes detection difficult. A plausible solution is to detect superficial buried plastic objects using planar array Electrical Capacitance Tomography (ECT). Distance detection is a big limiting factor of planar array ECT. Given the ill-posedness and loss of sensitivity with depth, regularization and optimal selection of reconstruction parameters are required for detection. In this work we propose an 'Automatic Parameter Selection' (APS) method for image reconstruction algorithms that selects optimal parameters based on the input data based on a 3 step process. The aim of the first 2 steps is to provide an approximate estimate of the parameters so that future reconstructions can be performed quickly in step 3. To optimise the reconstruction parameters the APS method uses the following metrics. Front Surface Distance Detection (FSDD) is a method of determining an accurate distance measurement from sensor head to object surface in low resolution image reconstructions using interpolation between voxels and Otsu thresholding. Cross-Section Reconstruction Score (CSRS) is a simple binary image comparison method which calculates a ratio of expected image to reconstructed image. An initial set of capacitance data was taken for an object at various distances and used to train the APS method by finding the best reconstruction parameters for each distance. Then another set of capacitance data was taken for a new object at different distances than before and reconstructed using the parameters selected by the APS method. The results of this showed that the APS method was able to select unique parameters for each reconstruction which produced accurate FSDDs and consistent CSRSs. This has taken away the need for an expert to manually select parameters for each reconstruction and sped up the process of reconstructions after training. The introduction of FSDD and CSRS is useful as they accurately describe how reconstructions were score and will allow future work to compare results effectively.

Electrical impedance tomography (EIT) is a promising technique for large area tactile sensing for robotic skin. This study presents a novel EIT-based force and touch sensor that features a latex membrane acting as soft skin and an ionic liquid domain. The sensor works based on fringing field EIT where the touch or force leads to a deformation in the latex membrane causing detectable changes in EIT data. This article analyses the performance of this electronic skin in terms of its dynamical behaviour, position accuracy and quantitative force sensing. Investigation into the sensor’s performance showed it to be hypersensitive, in that it can reliably detect forces as small as 64 mN. Furthermore, multi-touch discrimination and annular force sensing is displayed. The hysteresis in force sensing is investigated showing a very negligible hysteresis. This is a direct result of the latex membrane and the ionic liquid-based domain design compared to more traditional fabric-based touch sensors due to the reduction in electromechanical coupling. A novel test is devised that displayed the dynamic performance of the sensor by showing its ability to record a 1 Hz frequency, which was applied to the membrane in a tapping fashion. Overall, the results show a considerable progress in ionic liquid EIT-based sensors. These findings place the EIT-based sensors that comprise a liquid domain, at the forefront of research into tactile robotic skin.

This study addresses the issue of energy optimization by investigating solutions for the reduction of energy consumption in the diagnostics and monitoring of technological processes. The implementation of advanced process control is identified as a key approach for achieving energy savings and improving product quality, process efficiency, and production flexibility. The goal of this research is to develop a cost-effective system with a minimal number of ultrasound sensors, thus reducing the energy consumption of the overall system. To accomplish this, a novel method for obtaining high-resolution reconstruction in transmission ultrasound tomography (t-UST) is proposed. The method involves utilizing a convolutional neural network to take low-resolution measurements as input and output high-resolution sinograms that are used for tomography image reconstruction. This approach allows for the construction of a super-resolution sinogram by utilizing information hidden in the low-resolution measurement. The model is trained on simulation data and validated on real measurement data. The results of this technique demonstrate significant improvement compared to state-of-the-art methods. The study also highlights that UST measurements contain more information than previously thought, and this hidden information can be extracted and utilized with the use of machine learning techniques to further improve image quality and object recognition.