HPM meters are required for the assessment of fields generated by sources of high-power microwaves. Finding the inverse calibration curves for such instruments is important for ensuring accuracy. The procedure is relatively simple for meters consisting of linear devices but there can also be hardware solutions implementing nonlinear ones. The objective of the present work was to develop a convenient procedure to allow finding such a curve when the meter uses a D-dot probe and a power detector. For that purpose, the results of low voltage measurements describing the properties of the detector were first analysed. Then a software code was developed to estimate the RMS value of an incident field based on measured output and frequency response. The response was estimated with very low electric field. And finally, the performance of the proposed procedure was verified by tests conducted with high electric field in a TEM cell. High conformity of the output of the meter with fields of known values was demonstrated. The maximum error related to the meter range did not exceed 4%.

Shaft steelwork is a component of critical infrastructure in underground mines. It connects the mining areas to the surface and enables the transport of personnel, equipment, and raw materials. Its failure or malfunction poses a threat to people and causes economic losses. Shaft steelwork is an exceptional engineering structure exposed to dynamic loads from large masses moving at high speeds and is subject to intensive deterioration resulting from corrosion and geological or mining-induced deformations. These issues cause shaft steelwork to be subject to high structural safety requirements, design oversizing, demanding maintenance procedures, and costly replacement of corroded members. The importance and unique working conditions of shaft steelwork create practical design and maintenance problems that are of interest to engineers and scientists. This paper reviews publications on the structural safety of rigid shaft steelwork and summarises the range of research from the detection of guide rail failures through the assessment of load effects and guide resistance, to the evaluation of structural reliability. The effects of guide rail failures on guiding forces, models of the conveyance-steelwork interaction, the load-carrying capacity of shaft steelwork under advanced corrosion, and the probabilistic assessment of structural reliability are presented. Significant advances in understanding the mechanical behaviour of shaft steelwork and assessing its properties have been reported. This review summarises the current state of research on shaft steelwork structural safety and highlights key future development directions.

With development of medical diagnostic and imaging techniques the sparing surgeries are facilitated. Renal cancer is one of examples. In order to minimize the amount of healthy kidney removed during the treatment procedure, it is essential to design a system that provides three-dimensional visualization prior to the surgery. The information about location of crucial structures (e.g. kidney, renal ureter and arteries) and their mutual spatial arrangement should be delivered to the operator. The introduction of such a system meets both the requirements and expectations of oncological surgeons. In this paper, we present one of the most important steps towards building such a system: a new approach to kidney segmentation from Computed Tomography data. The segmentation is based on the Active Contour Method using the Level Set (LS) framework. During the segmentation process the energy functional describing an image is the subject to minimize. The functional proposed in this paper consists of four terms. In contrast to the original approach containing solely the region and boundary terms, the ellipsoidal shape constraint was also introduced. This additional limitation imposed on evolution of the function prevents from leakage to undesired regions. The proposed methodology was tested on 10 Computed Tomography scans from patients diagnosed with renal cancer. The database contained the results of studies performed in several medical centers and on different devices. The average effectiveness of the proposed solution regarding the Dice Coefficient and average Hausdorff distance was equal to 0.862 and 2.37 mm, respectively. Both the qualitative and quantitative evaluations confirm effectiveness of the proposed solution.

Minimally invasive procedures for the kidney tumour removal require a 3D visualization of topological relations between kidney, cancer, the pelvicalyceal system and the renal vascular tree. In this paper, a novel methodology of the pelvicalyceal system segmentation is presented. It consists of four following steps: ROI designation, automatic threshold calculation for binarization (approximation of the histogram image data with three exponential functions), automatic extraction of the pelvicalyceal system parts and segmentation by the Locally Adaptive Region Growing algorithm. The proposed method was applied successfully on the Computed Tomography database consisting of 48 kidneys both healthy and cancer affected. The quantitative evaluation (comparison to manual segmentation) and visual assessment proved its effectiveness. The Dice Coefficient of Similarity is equal to 0.871 ± 0.060 and the average Hausdorff distance 0.46 ± 0.36 mm. Additionally, to provide a reliable assessment of the proposed method, it was compared with three other methods. The proposed method is robust regardless of the image acquisition mode, spatial resolution and range of image values. The same framework may be applied to further medical applications beyond preoperative planning for partial nephrectomy enabling to visually assess and to measure the pelvicalyceal system by medical doctors.

Voice acoustic analysis can be a valuable and objective tool supporting the diagnosis of many neurodegenerative diseases, especially in times of distant medical examination during the pandemic. The article compares the application of selected signal processing methods and machine learning algorithms for the taxonomy of acquired speech signals representing the vowel a with prolonged phonation in patients with Parkinson’s disease and healthy subjects. The study was conducted using three different feature engineering techniques for the generation of speech signal features as well as the deep learning approach based on the processing of images involving spectrograms of different time and frequency resolutions. The research utilized real recordings acquired in the Department of Neurology at the Medical University of Warsaw, Poland. The discriminatory ability of feature vectors was evaluated using the SVM technique. The spectrograms were processed by the popular AlexNet convolutional neural network adopted to the binary classification task according to the strategy of transfer learning. The results of numerical experiments have shown different efficiencies of the examined approaches; however, the sensitivity of the best test based on the selected features proposed with respect to biological grounds of voice articulation reached the value of 97% with the specificity no worse than 93%. The results could be further slightly improved thanks to the combination of the selected deep learning and feature engineering algorithms in one stacked ensemble model.

Finger tapping is one of the standard tests for Parkinson's disease diagnosis performed to assess the motor function of patients' upper limbs. In clinical practice, the assessment of the patient's ability to perform the test is carried out visually and largely depends on the experience of clinicians. This article presents the results of research devoted to the objectification of this test. The methodology was based on the proposed measurement method consisting in frame processing of the video stream recorded during the test to determine the time series representing the distance between the index finger and the thumb. Analysis of the resulting signals was carried out in order to determine the characteristic features that were then used in the process of distinguishing patients with Parkinson's disease from healthy cases using methods of machine learning. The research was conducted with the participation of 21 patients with Parkinson's disease and 21 healthy subjects. The results indicate that it is possible to obtain the sensitivity and specificity of the proposed method at the level of approx. 80 %. However, the patients were in the so-called ON phase when symptoms are reduced due to medication, which was a much greater challenge compared to analyzing signals with clearly visible symptoms as reported in related works.