Abstract The improvement of machine tools, and therefore, of industrial production, requires high accuracy of machining while adapting to the different dimensions of the workpieces and to the machines themselves. As a result, the improvement of testing procedures and the analysis of positioning accuracy results represent an important research task in modern manufacturing engineering. The paper presents the results of the research carried out with the aim of determining the influence of the choice of parameters for standardized testing of positioning accuracy on the measurement results with reference to the characteristics of the machines from the point of view of the size of the workspace and the machines themselves. In this way, it is possible to choose the appropriate test parameters of the machine tools depending on their geometrical characteristics and test conditions and within the existing standards.

Abstract Even in the field of electromagnetic compatibility, low measurement uncertainty means high measurement quality. Although there are standardized procedures for obtaining the uncertainty of such a measurement, which facilitate uncertainty estimation, modern approaches show further reduction possibilities. The paper presents an alternative approach to reducing measurement instrument uncertainty in the case of electromagnetic interference measurement based on many years of our experience and a large number of measurements in this field. In the paper, two different methods of uncertainty reduction are described. The first method is based on a detailed analysis of the sources of uncertainty and the subsequent division of the analyzed frequency band into more subranges. Another method uses the choice of the antenna factor, which also contains information about the test site where the measurement is carried out. In this way, despite a lengthy analysis, it is relatively easy to achieve a measurement instrument uncertainty that is below the maximum measurement uncertainty given by the CISPR standard.

Abstract The intelligent training and assessment of gymnastics movements require studying motion trajectory and reconstructing the character animation. Microsoft Kinect has been widely used due to its advantages of low price and high frame rate. However, its optical characteristics are inevitably affected by illumination and occlusion. It is necessary to reduce data noise via specific algorithms. Most of the existing research focuses on local motion but lacks consideration of the whole human skeleton. Based on the analysis of the spatial characteristics of gymnastics and the movement principle of the human body, this paper proposes a dynamic and static two-dimensional regression compensation algorithm. Firstly, the constraint characteristics of human skeleton motion were analyzed, and the maximum constraint table and Mesh Collider were established. Then, the dynamic acceleration of skeleton motion and the spatial characteristics of static limb motion were calculated based on the data of adjacent effective skeleton frames before and after the collision. Finally, using the least squares polynomial fitting to compensate and correct the lost skeleton coordinate data, it realizes the smoothness and rationality of human skeleton animation. The results of two experiments showed that the solution of the skeleton point solved the problem caused by data loss due to the Kinect optical occlusion. The data compensation time of an effective block skeleton point can reach 180 ms, with an average error of about 0.1 mm, which shows a better data compensation effect of motion data acquisition and animation reconstruction.

Abstract A stable and reliable time keeping system depends on the integrity monitoring of the atomic frequency standard. This paper reports a scheme for autonomous integrity monitoring of multiple atomic clocks, which combines the frequency standard comparison method and the frequency jump detection method. The frequency standard comparison method uses multi-channel synchronous acquisition technology and digital frequency measurement technology to realize the precise measurement of multiple atomic frequency standards. The frequency jump detection method uses adaptive filtering to predict the relative frequency difference and give an accurate and timely alarm for the abnormal of frequency jump. The results show that the noise floor frequency standard comparator is better than 6.5×10−15 s. For a relative frequency deviation of 2.0×10−6 Hz, the probability of anomaly detection is almost 100 %. The system has high frequency measurement resolution and fast alarm of frequency jump, which can meet the real-time requirements of a time keeping system for the integrity monitoring of multiple atomic clocks.

Abstract Down syndrome (DS) or Trisomy 21 is a genetic disorder that causes intellectual and mental disability in fetuses. The most essential marker for detecting DS during the first trimester of pregnancy is nuchal translucency (NT). Effective segmentation of the NT contour from the ultrasound (US) images becomes challenging due to the presence of speckle noise and weak edges. This study presents a Convolutional Neural Network (CNN) based SegNet model using a Visual Geometry Group (VGG-16) for semantically segmenting the NT region from the US fetal images and providing a fast and affordable diagnosis during the early stages of gestation. A transfer learning approach using AlexNet is implemented to train the NT segmented regions for the identification of DS. The proposed model achieved a Jaccard index of 0.96 and classification accuracy of 91.7 %, sensitivity of 85.7 %, and a Receiver operating characteristic (ROC) of 0.95.

Abstract In information security systems, the algorithm of the Blum-Blum-Shub (BBS) generator, which is based on the use of a one-way function and is a cryptographically secure pseudorandom number generator, became widespread. In this paper, the problem of the analysis of modified algorithms of the BBS generator operation is considered to improve their statistical characteristics, namely, the sequence repetition period. It has been established that in order to improve the characteristics of the classic BBS algorithm, it is necessary to systematize approaches to change the recurrent equation itself, the relationship between the current and the previous members of the sequence. For this purpose, a generalized unified model of the modification of the classical BBS algorithm is derived. The repetition period with computational complexity were analyzed for classical algorithm and 80 proposed modifications. A gain in statistical characteristics is improved with slight increase in the required computing power of the system. The proposed modified BBS pseudorandom sequence generator can be used in training of students when teaching cryptographic stability of information security systems. The study of this generator combines the knowledge of students acquired in both digital electronics and mathematics.

Abstract This work presents distributed predictor and filter without feedback for nonlinear stochastic uncertain system with correlated noises. Firstly, for the problem that the process noise and measurement noise are correlated, the two-step prediction theorem based on projection theorem is used to replace the one-step prediction theorem, and the two-step prediction value of a single sensor is obtained. Secondly, the two-step prediction value of each sensor state is used as the measurement information to modify the distributed fusion predictor to obtain the distributed fusion prediction value. Then, according to the projection theorem, the prediction value of distributed fusion is used as measurement information to modify the filtering value of distributed fusion. Finally, the Cubature Kalman filter (CKF) algorithm is used to implement the algorithm proposed in this paper. By comparison with existing methods, the algorithm proposed in this paper solves the problem that existing methods cannot handle state estimation and prediction problems for nonlinear multi-sensor stochastic uncertain systems with correlated noises.

Abstract The purpose of this study was to examine the accuracy of HR measurements by the PTN-104 sensor in comparison to the fingertip pulse oximeter, which is a photoplethysmographic sensor (PPG). Twelve healthy participants underwent the same protocol during a single visit. Measurements were taken after each participant completed an initial rest period of 5 minutes and after 1-minute of exercising comprising of 30 squats. Each subject had the PTN-104 sensor attached to the index finger and a fingertip pulse oximeter to the opposite one. When examining the data in aggregate, there was a strong correlation between the PTN-104 sensor and PPG for HR (r = 0.988) with a mean bias of −2.55 bpm (95 % LoA +5.0, −10.1). The PTN-104 sensor satisfied validity criteria for HR monitors, however, showed a lower accuracy for measurements at rest, which is surprising. Due to the noticed limitations, this study should be repeated with a larger group of subjects and the PTN-104 sensor should be compared to the gold standard method for measuring HR, which is ECG.

Abstract The method presented in the paper is based on in-the-loop computing applied for impulse response to obtain a spectrum with a much higher frequency resolution than using FFT. Then, higher spectrum frequency resolution results in greater accuracy in estimation of natural frequencies. The frequency resolution of estimated spectrum in this method is completely independent of the length of impulse response and, by extension, the method eliminates the problem of spectral resolution limitation using FFT due to finite length of recorded signals. This fact is very useful and is the main advantage of the proposed method. The results of the method have been shown and compared in quantitative terms to natural frequencies estimated using classical FFT with zero-padding as reference method.

Abstract This paper reports a 3-dimentional (3D) pressure sensor based on surface acoustic wave (SAW) resonators. The SAW resonators were designed and fabricated on 128°Y-X LiNbO3 substrate using the MEMS technology. The pressure sensing structure was 3D-printed using polyactic acid plastic, and two SAW resonators were integrated in the 3D-printed chamber structure for both temperature and pressure sensing. The SAW-based gas pressure sensors demonstrate a sensitivity of 589 ppm/MPa at the pressure range of 100-600 kPa and temperature of 40 °C.