A film stress measurement system applicable for hyperbaric environment was developed to characterize stress evolution in a physical simulation test of a gas-solid coupling geological disaster. It consists of flexible film pressure sensors, a signal conversion module, and a highly-integrated acquisition box which can perform synchronous and rapid acquisition of 1 kHz test data. Meanwhile, we adopted a feasible sealing technology and protection method to improve the survival rate of the sensors and the success rate of the test, which can ensure the accuracy of the test results. The stress measurement system performed well in a large-scale simulation test of coal and gas outburst that reproduced the outburst in the laboratory. The stress evolution of surrounding rock in front of the heading is completely recorded in a successful simulation of the outburst which is consistent with the previous empirical and theoretical analysis. The experiment verifies the feasibility of the stress measurement system as well as the sealing technology, laying a foundation for the physical simulation test of gas-solid coupled geological disasters.

Liquid-gas flows in pipelines appear in many industrial processes, e.g. in the nuclear, mining, and oil industry. The gamma-absorption technique is one of the methods that can be successfully applied to study such flows. This paper presents the use of the gamma-absorption method to determine the water-air flow parameters in a horizontal pipeline. Three flow types were studied in this work: plug, transitional plug-bubble, and bubble one. In the research, a radiometric set consisting of two Am-241 sources and two NaI(TI) scintillation detectors have been applied. Based on the analysis of the signals from both scintillation detectors, the gas phase velocity was calculated using the cross-correlation method (CCM). The signal from one detector was used to determine the void fraction and to recognise the flow regime. In the latter case, a Multi-Layer Perceptron-type artificial neural network (ANN) was applied. To reduce the number of signal features, the principal component analysis (PCA) was used. The expanded uncertainties of gas velocity and void fraction obtained for the flow types studied in this paper did not exceed 4.3% and 7.4% respectively. All three types of analyzed flows were recognised with 100% accuracy. Results of the experiments confirm the usefulness of the gamma-ray absorption method in combination with radiometric signal analysis by CCM and ANN with PCA for comprehensive analysis of liquid-gas flow in the pipeline.

In this paper a new method of frequency jumps detection in data from atomic clock comparisons is proposed. The presented approach is based on histogram analysis for different time intervals averaging phasetime data recorded over a certain period of time. Our method allows identification of multiple frequency jumps for long data series as well to almost real-time jump detection in combination with advanced filtering. Several methods of preliminary data processing have been tested (simple averaging, moving average and Vondrak filtration), to achieve flexibility in adjusting the algorithm parameters for current needs which is the key to its use in determining ensemble time scale or to control systems of physical time scales, such as UTC(PL). The best results have been achieved with the Vondrak filter.

Falls are one of the leading causes of disability and premature death among the elderly. Technical solutions designed to automatically detect a fall event may mitigate fall-related health consequences by immediate medical assistance. This paper presents a wearable device called TTXFD based on MPU6050 which can collect triaxial acceleration signals. We have also designed a two-step fall detection algorithm that fuses threshold-based method (TBM) and machine learning (ML). The TTXFD exploits the TBM stage with low computational complexity to pick out and transmit suspected fall data (triaxial acceleration data). The ML stage of the two-step algorithm is implemented on a server which encodes the data into an image and exploits a fall detection algorithm based on convolutional neural network to identify a fall on the basis of the image. The experimental results show that the proposed algorithm achieves high sensitivity (97.83%), specificity (96.64%) and accuracy (97.02%) on the open dataset. In conclusion, this paper proposes a reliable solution for fall detection, which combines the advantages of threshold-based method and machine learning technology to reduce power consumption and improve classification ability.

Whatever the type of surgery related to inner organs, traditional or robotic, the contact with them during surgery is a key moment for pursuing the intervention. Contacts by means of surgery instruments namely scalpels, staples, clamps, graspers, etc. are decisive moments. False, and erroneous touching and manoeuvring of organs operated on can cause irreversible damage as regard morphological aspects (outer impact) and physiological aspects (inner impact). The topic is a great challenge in the effort to measure and characterize damages. In general, electrical instruments for surgery employ the following technologies: ultrasound, radiofrequency (monopolar, and bipolar), and laser. They all result in thermal damages difficult to evaluate. The article proposes a method for a pre-screening of organ features during robotic surgery sessions by pointing out mechanical and thermal stresses. A dedicated modelling has been developed based on experimental activities during surgery session. The idea is to model tissue behaviour from real images to help surgeons to be aware of handling during surgery. This is the first step for generalization by considering the type of organ. The measurement acquisitions have been performed by means of an advanced external camera located over the surgery quadrant. The modelling and testing have been carried out on kidneys. The modelling, carried out through Comsol Multiphysics, is based on the bioheat approach. A further comparative technique has been implemented. It is based on computer vision for robotics. The findings of human tissue behavior exhibit reliable results.

High-speed serial standards are rapidly developing, and with a requirement for effective compliance and characterization measurement methods. Jitter decomposition consists in troubleshooting steps based on jitter components from decomposition results. In order to verify algorithms with different deterministic jitter (DJ) in actual circuits, jitter generation model by cross-point calibration and timing modulation for jitter decomposition is presented in this paper. The generated jitter is pre-processed by cross-point calibration which improves the accuracy of jitter generation. Precisely controllable DJ and random jitter (RJ) are generated by timing modulation such as data-dependent jitter (DDJ), duty cycle distortion (DCD), bounded uncorrelated jitter (BUJ), and period jitter (PJ). The benefit of the cross-point calibration was verified by comparing generation of controllable jitter with and without cross-point calibration. The accuracy and advantage of the proposed method were demonstrated by comparing with the method of jitter generation by analog modulation. Then, the validity of the proposed method was demonstrated by hardware experiments where the jitter frequency had an accuracy of 20 ppm, the jitter amplitude ranged from 10 ps to 8.33 ns, a step of 2 ps or 10 ps, and jitter amplitude was independent of jitter frequency and data rate.

In order to find the defects in ferromagnetic materials, a non-contact harmonic detection method is proposed. According to the principle of frequency modulated carrier wave, a tunnel magneto resistance harmonic focusing vector array detector was designed which radiates lower and higher frequency electromagnetic waves through the coil array to the detection targets. We use bistable stochastic resonance to enhance the energy of collected weak target signal and apply quantum computation and a Sobol low deviation sequence to improve genetic algorithm performance. Then we use the orthogonal phase-locked loop to eliminate the intrinsic background excitation field and tensor calculations to fuse the vector array signal. The finite element model of array detector and the magnetic dipole harmonic numerical model were also established. The simulation results show that the target signal can be identified effectively, its focusing performance is improved by 2 times, and the average signal-to-noise ratio is improved by 9.6 times after the algorithm processing. For the experiments, we take Q235 steel pipeline as the object to realize the recognition of three defects. Compared with the traditional methods, the proposed method is more effective for ferromagnetic materials defects detection.

This paper presents a method of optical fluorescence analysis for the evaluation of homogeneity of multicomponent grain mixtures. This method is based on the evaluation of the content of fluorescent marker. Maize with two degrees of fineness d1 = 1:25 mm and d2 = 2:00 mm was used as a tracer. Maize was covered with Rhodamine B, which emits red light under the influence of ultraviolet radiation. The tracer was introduced into the mixture before the mixing process began. Nine multicomponent grain mixtures were used. The proportion of fluorescent maize was evaluated on the basis of computer image analysis. Additionally, the fraction of the tracer was evaluated using a control method (validation of the accuracy of the proposed method). The results indicate that the degree of the tracer’s fineness influences the results obtained. The use of fluorescent maize with particle size d2 = 2:00 mm allowed to obtain results which differed less from the control method. The average size of the difference in results ranged from 0.20–0.38 for the 2.00 mm tracer and 0.38–1.34 for the 1.25 mm tracer.

Active thermography is an efficient tool for defect detection and characterization as it does not change the properties of tested materials. The detection and characterization process involves heating a sample and then analysing the thermal response. In this paper, a long heating pulse was used on samples with a low thermal diffusivity and artificially created holes of various depths. As a result of the experiments, heating and cooling curves were obtained. These curves, which describe local characteristics of the material, are recognized using a classification tree and divided into categories depending on the material thickness (hole depths). Two advantages of the proposed use of classification trees are: an in-built mechanism for feature selection and a strong reduction in the dimensions of the pattern. Based on the experimental study, it can be concluded that classification trees are a useful tool for the thinning detection of homogeneous material.

Angle calibrations are widely used in various fields of science and technology, while in the high-precision angle calibrations, a complete closure method which is complex and time-consuming is common. Therefore, in order to improve the measurement efficiency and maintain the accuracy of the complete closure method, an improved calibration method was proposed and verified by the calibration of a high-precision angle comparator with sub-arc-second level. Firstly, a basic principle and algorithm of angle calibration based on complete closure and symmetry connection theory was studied. Then, depending on the pre-established calibration system, the comparator was respectively calibrated by two calibration methods. Finally, by comparing En values of two calibration results, the effectiveness of the improved method was verified. The calibration results show that the angle comparator has a stable angle position error of 0:1700 and a measurement uncertainty of 0:0500 (k = 2). Through method comparisons, it was shown that the improved calibration method can greatly reduce calibration time and improve the calibration efficiency while ensuring the calibration accuracy, and with the decrease of measurement interval, the improvement of calibration efficiency was more obvious.

Low-cost Micro-Electromechanical System (MEMS) gyroscopes are known to have a smaller size, lower weight, and less power consumption than their more technologically advanced counterparts. However, current low-grade MEMS gyroscopes have poor performance and cannot compete with quality sensors in high accuracy navigational and guidance applications. The main focus of this paper is to investigate performance improvements by fusing multiple homogeneous MEMS gyroscopes. These gyros are transformed into a virtual gyro using a feedback weighted fusion algorithm with dynamic sensor bias correction. The gyroscope array combines eight homogeneous gyroscope units on each axis and divides them into two layers of differential configuration. The algorithm uses the gyroscope array estimation value to remove the gyroscope bias and then correct the gyroscope array measurement value. Then the gyroscope variance is recalculated in real time according to the revised measurement value and the weighted coefficients and state estimation of each gyroscope are deduced according to the least square principle. The simulations and experiments showed that the proposed algorithm could further reduce the drift and improve the overall accuracy beyond the performance limitations of individual gyroscopes. The maximum cumulative angle error was - 2:09 degrees after 2000 seconds in the static test, and the standard deviation (STD) of the output fusion value of the proposed algorithm was 0.006 degrees/s in the dynamic test, which was only 1.7% of the STD value of an individual gyroscope.

In recent years, assessing supply system impedance has become crucial due to the concerns on power quality and the proliferation of distributed generators. In this paper, a novel method is shown for passive measurement of system impedances using the gapless waveform data collected by a portable power quality monitoring device. This method improves the overall measurement accuracy through data regrouping. Compared with the traditional methods that use the consecutive measurement data directly, the proposed method regroups the data to find better candidates with less flotation on the system side. Simulation studies and extensive field tests have been conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method can serve as a useful tool for impedance measurement tasks performed by utility companies.

The advent of language implementation tools such as PyPy and Truffle/Graal have reinvigorated and broadened interest in topics related to automatic compiler generation and optimization. Given this broader interest, we revisit the Futamura Projections using a novel diagram scheme. Through these diagrams we emphasize the recurring patterns in the Futamura Projections while addressing their complexity and abstract nature. We anticipate that this approach will improve the accessibility of the Futamura Projections and help foster analysis of those new tools through the lens of partial evaluation.

Post-Quantum Cryptography (PQC) attempts to find cryptographic protocols resistant to attacks by means of for instance Shor's polynomial time algorithm for numerical field problems like integer factorization (IFP) or the discrete logarithm (DLP). Other aspects are the backdoors discovered in deterministic random generators or recent advances in solving some instances of DLP. The use of alternative algebraic structures like non-commutative or non-associative partial groupoids, magmas, monoids, semigroups, quasigroups or groups, are valid choices for these new kinds of protocols. In this paper, we focus in an asymmetric cipher based on a generalized ElGamal non-arbitrated protocol using a non-commutative general linear group. The developed protocol forces a hard subgroup membership search problem into a non-commutative structure. The protocol involves at first a generalized Diffie-Hellman key interchange and further on the private and public parameters are recursively updated each time a new cipher session is launched. Security is based on a hard variation of the Generalized Symmetric Decomposition Problem (GSDP). Working with GF(2518) a 64-bits security is achieved, and if GF(25116) is chosen, the security rises to 127-bits. An appealing feature is that there is no need for big number libraries as all arithmetic if performed in Z251 and therefore the new protocol is particularly useful for computational platforms with very limited capabilities like smartphones or smartcards.

In this paper, we describe secure gateway for Internet of Things (IoT) devices with internal AAA mechanism, implemented to connect IoT sensors with Internet users. Secure gateway described in this paper allows to (1) authenticate each connected device, (2) authorise connection or reconguration performed by the device and (3) account each action. The same applies to Internet users who want to connect, download data from or upload data to an IoT device. Secure Gateway with internal AAA mechanism could be used in Smart Cities environments and in other IoT deployments where security is a critical concern. The mechanism presented in this paper is a new concept and has been practically validated in Polish national research network PL-LAB2020.

In this paper we propose right-angled Artin groups as a platform for secret sharing schemes based on the efficiency (linear time) of the word problem. Inspired by previous work of Grigoriev-Shpilrain in the context of graphs, we define two new problems: Subgroup Isomorphism Problem and Group Homomorphism Problem. Based on them, we also propose two new authentication schemes. For right-angled Artin groups, the Group Homomorphism and Graph Homomorphism problems are equivalent, and the later is known to be NP-complete. In the case of the Subgroup Isomorphism problem, we bring some results due to Bridson who shows there are right-angled Artin groups in which this problem is unsolvable.

We demonstrate a modularity bug in the interface system of Java 8 on the practical example of a textbook design of a modular interface for vector spaces. Our example originates in our teaching of modular object-oriented design in Java 8 to undergraduate students, simply following standard programming practices and mathematical denitions. The bug shows up as a compilation error and should be xed with a language extension due to the importance of best practices (design delity).

SQL Injection is one of the vulnerabilities in OWASP's Top Ten List forWeb Based Application Exploitation. These type of attacks take place on Dynamic Web applications as they interact with databases for various operations. Current Content Management System like Drupal, Joomla or Wordpress have all information stored in their databases. A single intrusion into these type of websites can lead to overall control of websites by an attacker. Researchers are aware of basic SQL Injection attacks, but there are numerous SQL Injection attacks which are yet to be prevented and detected. Over here, we present the extensive review for the Advanced SQL Injection attack such as Fast Flux SQL Injection, Compounded SQL Injection and Deep Blind SQL Injection. We also analyze the detection and prevention using the classical methods as well as modern approaches. We will be discussing the Comparative Evaluation for prevention of SQL Injection.

The paper concerns a risk assessment and management methodology in critical infrastructures. The aim of the paper is to present researches on risk management within the experimentation tool based on the OSCAD software. The researches are focused on interdependent infrastructures where the specific phenomena, like escalating and cascading effects, may occur. The objective of the researches is to acquire knowledge about risk issues within interdependent infrastructures, to assess the usefulness of the OSCAD-based risk manager in this application domain, and to identify directions for further R&D works. The paper contains a short introduction to risk management in critical infrastructures, presents the state of the art, and the context, plan and scenarios of the performed validation experiments. Next, step by step, the validation is performed. It encompasses two collaborating infrastructures (railway, energy). It is shown how a hazardous event impacts the given infrastructure (primary and secondary eects) and the neighbouring infrastructure. In the conclusions the experiments are summarized, the OSCAD software assessed and directions of the future works identified.

The availability of cheap and widely applicable person identification techniques is essential due to a wide-spread usage of online services. The dynamics of typing is characteristic to particular users, and users are hardly able to mimic the dynamics of typing of others. State-of-the-art solutions for person identification from the dynamics of typing are based on machine learning. The presence of hubs, i.e., few instances that appear as nearest neighbours of surprisingly many other instances, have been observed in various domains recently and hubness-aware machine learning approaches have been shown to work well in those domains. However, hubness has not been studied in the context of person identification yet, and hubnessaware techniques have not been applied to this task. In this paper, we examine hubness in typing data and propose to use ECkNN, a recent hubness-aware regression technique together with dynamic time warping for person identification. We collected time-series data describing the dynamics of typing and used it to evaluate our approach. Experimental results show that hubness-aware techniques outperform state-of-the-art time-series classifiers.