Volume 32 (2022): Edizione 1 (March 2022)

The global stability of discrete-time nonlinear systems with descriptor positive linear parts, positive scalar feedbacks and interval state matrices is addressed. Sufficient conditions for the global stability of this class of nonlinear systems are established. The effectiveness of these conditions is illustrated using numerical examples.

Fast and smooth trajectory planning is crucial for modern control systems, e.g., missiles, aircraft, robots and AGVs. However, classical spline based trajectory planning tools introduce redundant constraints and parameters, leading to high costs of computation and complicating fast and smooth execution of trajectory planning tasks. A new tool is proposed that employs truncated power functions to annihilate some constraints and reduce the number of parameters in the optimal model. It enables solving a simplified optimal problem in a shorter time while keeping the trajectory sufficiently smooth. With an engineering background, our case studies show that the proposed method has advantages over other solutions. It is promising in regard to the demanding tasks of trajectory planning.

Travel time is a fundamental measure in any transportation system. With the development of technology, travel time can be automatically collected by a variety of advanced sensors. However, limited by objective conditions, it is difficult for any sensor system to cover the whole transportation network in real time. In order to estimate the travel time of the whole transportation network, this paper gives a system of linear equations which is constructed by the user equilibrium (UE) principle and observed data. The travel time of a link which is not covered by a sensor can be calculated by using the observed data collected by sensors. In a typical transportation network, the minimum number and location of sensors to estimate the travel time of the whole network are given based on the properties of the solution of a systems of linear equations. The results show that, in a typical network, the number and location of sensors follow a certain law. The results of this study can provide reference for the development of transportation and provide a scientific basis for transportation planning.

In a distributed denial of service (DDoS) attack, the attacker gains control of many network users through a virus. Then the controlled users send many requests to a victim, leading to its resources being depleted. DDoS attacks are hard to defend because of their distributed nature, large scale and various attack techniques. One possible mode of defense is to place sensors in a network that can detect and stop an unwanted request. However, such sensors are expensive, as a result of which there is a natural question as to the minimum number of sensors and their optimal placement required to get the necessary level of safety. Presented below are two mixed integer models for optimal sensor placement against DDoS attacks. Both models lead to a trade-off between the number of deployed sensors and the volume of uncontrolled flow. Since the above placement problems are NP-hard, two efficient heuristics are designed, implemented and compared experimentally with exact mixed integer linear programming solvers.

The paper concerns the design of a framework for implementing fault-tolerant control of hybrid assembly systems that connect human operators and fully automated technical systems. The main difficulty in such systems is related to delays that result from objective factors influencing human operators’ work, e.g., fatigue, experience, etc. As the battery assembly system can be considered a firm real-time one, these delays are treated as faults. The presented approach guarantees real-time compensation of delays, and the fully automated part of the system is responsible for this compensation. The paper begins with a detailed description of a battery assembly system in which two cooperating parts can be distinguished: fully automatic and semi-automatic. The latter, nonderministic in nature, is the main focus of this paper. To describe and analyze the states of the battery assembly system, instead of the most commonly used simulation, the classic max-plus algebra with an extension allowing one to express non-deterministic human operators’ work is used. In order to synchronize tasks and schedule (according to the reference schedule) automated and human operators’ tasks, it is proposed to use a wireless IoT platform called KIS.ME. As a result, it allows a reference model of human performance to be defined using fuzzy logic. Having such a model, predictive delays tolerant planning is proposed. The final part of the paper presents the achieved results, which clearly indicate the potential benefits that can be obtained by combining the wireless KIS.ME architecture (allocated in the semi-automatic part of the system) with wired standard production networks.

Alpha-Theory was introduced in 1995 to provide a simplified version of Robinson’s non-standard analysis which overcomes the technicalities of symbolic logic. The theory has been improved over the years, and recently it has been used also to solve practical problems in a pure numerical way, thanks to the introduction of algorithmic numbers. In this paper, we introduce Alpha-Theory using a novel axiomatic approach oriented towards real-world applications, to avoid the need to master mathematical logic and model theory. To corroborate the strong link of this Alpha-Theory axiomatization and scientific computations, we report numerical illustrative applications never carried out by means of non-standard numbers within a computer, i.e., the computation of the eigenvalues of a non-Archimedean matrix, some computations related to non-Archimedean Markov chains, and the Cholesky factorization of a non-Archimedean matrix. We also highlight the differences between our numerical routines and pure symbolic approaches: as expected, the former scales better when the dimension of the problem increases.

The paper discusses a non-deterministic model for data association tasks in visual surveillance of crowds. Using detection and tracking of crowd components (i.e., individuals and groups) as baseline tools, we propose a simple algebraic framework for maintaining data association (continuity of labels assigned to crowd components) between subsequent video-frames in spite of possible disruptions and inaccuracies in tracking/detection algorithms. Formally, two alternative schemes (which, in practice, can be jointly used) are introduced, depending on whether individuals or groups can be prospectively better tracked in the current scenario. In the first scheme, only individuals are tracked, and the continuity of group labels is inferred without explicitly tracking the groups. In the second scheme, only group tracking is performed, and associations between individuals are inferred from group tracking. The associations are built upon non-deterministic estimates of memberships (individuals in groups) and estimates obtained directly from the baseline detection and tracking algorithms. The framework can incorporate any detectors and trackers (both classical or DL-based) as long as they can provide some geometric outlines (e.g., bounding boxes) of the crowd components. The formal analysis is supported by experiments in exemplary scenarios, where the framework provides meaningful performance improvements in various crowd analysis tasks.

The paper is concerned with clustering with respect to the shape and size of 2D contours that are boundaries of cross-sections of 3D objects of revolution. We propose a number of similarity measures based on combined disparate Procrustes analysis (PA) and dynamic time warping (DTW) distances. A motivation and the main application for this study comes from archaeology. The computational experiments performed refer to the clustering of archaeological pottery.

The visual appearance of outdoor captured images is affected by various weather conditions, such as rain patterns, haze, fog and snow. The rain pattern creates more degradation in the visual quality of the image due to its physical structure compared with other weather conditions. Also, the rain pattern affects both foreground and background image information. The removal of rain patterns from a single image is a critical process, and more attention is given to remove the structural rain pattern from real-time rain images. In this paper, we analyze the single image deraining problem and present a solution using the dual stage deep rain streak removal convolutional neural network. The proposed single image deraining framework primarily consists of three main blocks: a derain streaks removal CNN (derain SRCNN), a modified residual dense block (MRDB), and a six-stage scale feature aggregation module (3SFAM). The ablation study is conducted to evaluate the performance of various modules available in the proposed deraining network. The robustness of the proposed deraining network is evaluated over the popular synthetic and real-time data sets using four performance metrics such as the peak signal-to-noise ratio (PSNR), the feature similarity index (FSIM), the structural similarity index measure (SSIM), and the universal image quality index (UIQI). The experimental results show that the proposed framework outperforms both synthetic and real-time images compared with other state-of-the-art single image deraining approaches. In addition, the proposed network takes less running and training time.

Mobile edge computing (MEC) is one of the key technologies to achieve high bandwidth, low latency and reliable service in fifth generation (5G) networks. In order to better evaluate the performance of the probabilistic offloading strategy in a MEC system, we give a modeling method to capture the stochastic behavior of tasks based on a multi-source fluid queue. Considering multiple mobile devices (MDs) in a MEC system, we build a multi-source fluid queue to model the tasks offloaded to the MEC server. We give an approach to analyze the fluid queue driven by multiple independent heterogeneous finite-state birth-and-death processes (BDPs) and present the cumulative distribution function (CDF) of the edge buffer content. Then, we evaluate the performance measures in terms of the utilization of the MEC server, the expected edge buffer content and the average response time of a task. Finally, we provide numerical results with some analysis to illustrate the feasibility of the stochastic model built in this paper.

The COVID-19 pandemic changed the lives of millions of citizens worldwide in the manner they live and work to the so-called new norm in social standards. In addition to the extraordinary effects on society, the pandemic created a range of unique circumstances associated with cybercrime that also affected society and business. The anxiety due to the pandemic increased the probability of successful cyberattacks and as well as their number and range. For public health officials and communities, location tracking is an essential component in the efforts to combat the disease. The governments provide a lot of mobile apps to help health officials to trace the infected persons and contact them to aid and follow up on the health status, which requires an exchange of data in different forms. This paper presents the one-time stamp model as a new cryptography technique to secure different contact forms and protect the privacy of the infected person. The one-time stamp hybrid model consists of a combination of symmetric, asymmetric, and hashing cryptography in an entirely new way that is different from conventional and similar existing algorithms. Several experiments have been carried out to analyze and examine the proposed technique. Also, a comparison study has been made between our proposed technique and other state-of-the-art alternatives. Results show that the proposed one-time stamp model provides a high level of security for the encryption of sensitive data relative to other similar techniques with no extra computational cost besides faster processing time.

The paper presents the concept of using color template charts for the needs of telemedicine, particularly telediagnosis of the stoma. Although the concept is not new, the current popularity and level of development of digital cameras, especially those embedded in smartphones, allow common and reliable remote advice on various medical problems, which can be very important in the case of limitations in a physical contact with a doctor. The article focuses on the initial stages of photo processing for the needs of telemedicine, i.e., on the assumptions and the process of designing the appropriate template and detecting it in photos for stoma telediagnosis. Research on the developed algorithms for the location of fiducial markers and reference color fields, carried out on the basis of over 2,000 photos, showed a very high tolerance to scene exposure, lighting conditions and the camera used. The obtained results allowed the initial image intensity normalization of the stoma area as well as correct localization and measurement of changes detected on the skin and the mucosa, which, in the opinion of doctors, significantly increased the diagnostic value of the photographs.