Electrical capacitance tomography (ECT) is a non-invasive imaging technique that aims at visualization of the cross-sectional permittivity distribution of a dielectric object based on the measured capacitance. In this paper, we propose a modified iterative Landweber method to accelerate the convergence rate and enhance the quality of the reconstructed image. In doing so, an additional acceleration term is added to the conventional iterative Landweber method and the optimal step length is determined analytically.

This work is concerned with the generation of sensitivity maps in electrical capacitance tomography based on the concepts of electrical field centre lines. Electrical capacitance tomography systems are normalized at the upper and lower permittivity values for image reconstruction. Conventional normalization assumes the distribution of materials in parallel and results in normalized capacitance as a linear function of measured capacitance. A recent approach is the usage of a series sensor model which results in normalized capacitance as a nonlinear function of measured capacitance. In this study different forms of normalizations are combined with sensitivity maps based on electrical field centre lines and it is shown that a mix of two normalization models improves the reconstruction performance.

Potholes, a kind of road defect, can damage vehicles and negatively affect drivers’ safe driving, and in severe cases can lead to traffic accidents. Efficient and preventive management of potholes in a complex road environment plays an important role in securing driver safety. It is also expected to contribute to the prevention of traffic accidents and the smooth flow of traffic. In the past, pothole detection was mainly performed via visual inspection by human experts. Recently, automated pothole-detection methods apply various technologies that converge basic technologies such as sensors and signal processing. The automated pothole-detection methods can be classified into three types according to the technology used in the pothole-recognition process: a vision-based method, a vibration-based method, and a 3D reconstruction-based method. In this paper, three methods are compared, and the strengths and weaknesses of each method are summarized. The detection process and technology proposed in the latest research related to automated pothole detection are described for each method. The development plans of future technology that is connected with those studies are also presented in this paper.

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