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Polar codes have emerged as a focal point in the field of error-correcting codes, owing to their remarkable capacity-achieving characteristics and their relevance in various modern communication systems. The basic successive cancellation (SC) approach is not optimal to use in terms of the trade-off between performance and decoding complexity. SC-Creeper algorithm performs better with about the same low complexity as the SC version of the algorithm. However, the SC-Creeper algorithm did not have the ability to use the candidate list as a measure to improve performance and refine the search for the true codeword. To compare with successive cancellation list (SCL) approach and the ability to use more computing memory, the SCL-Creeper method was developed, using two additional lists. This method can also be used as a development of Fano algorithms for polar codes (mainly, Fano decoding in polar decoding does not use lists). This paper addresses the challenge of computational complexity in polar code decoding by integrating a list structure with the SC-Creeper algorithm. Building on prior research that introduced the concept of SC-Creeper, the study focuses on enhancing error correction performance while mitigating computational burden. The first chapters describe the polar encoding process and basic decoding technologies, then discuss the basic Creeper algorithm. In the following chapters, the authors describe a modified version of the two-list Creeper approach (that is, the SCL-Creeper version of the algorithm). Extensive simulations and numerical analysis presented in the paper underscore the tangible advantages of this novel decoding strategy. Leveraging the basic list algorithm, renowned for its superior error correction capabilities, the research explores the integration of Creeper to systematically prune unnecessary decoding paths. The resulting SCL-Creeper hybrid approach aims to strike a balance between error correction efficiency and computational complexity. Finally, the optimal selection of parameters for the SCL-Creeper approach and future directions in the research of the list version of the fast Creeper algorithm are discussed.

Protecting ownership of digital content is challenging in today’s online world, especially when sharing content through social networks and instant messengers. One possible solution is the use of watermarking; however, if the watermarking method is not robust enough, the watermark can get damaged or erased during transmission. This study introduces a template-based watermarking method with neural network-based extraction, strengthened by error-correcting polar codes, that is designed to work well on major social networks like Facebook, Facebook Messenger, VK, Telegram, Snapchat, Pinterest, and WhatsApp. The method can embed imperceptible (PSNR=39.66) watermarks ranging from 650 to 1600 bits for high-definition images (1920-by-1080 pixels) with small probability (0.01) of erroneous extraction after being transferred through social networks. The key feature of the method is its ability to work with real communication channels, as shown during its testing.

A large amount of text data flows through the web every day in the context of various comments and reactions of users to social network content. Such information tokens can act as quality metrics for content, a group, or an industry. This information can be valuable for decision makers and content managers of higher education institutions (HEIs) as well as researchers studying the marketing activities of HEIs. Topic modelling, as one of the methods for analysing textual information, can help practitioners and researchers create analytical models that allow them to make important management decisions based on audience reactions to content. As part of this task, the goal of this research is to develop a topic model based on LDA to uncover key topics of posts in 15 university groups on the “VK” social network of five leading universities located in Saint Petersburg, Russia. For each university, posts from three groups were collected: “Overhead,” “Main” and “Enrolment.” In total, 247,633 posts were collected over the 2018–2023 period, resulting in more than 6 million tokens. During the application of the methodology, large clusters of posts on social networks were identified. Using analytics of reaction metrics and feedback from subscribers of university groups, recommendations were made for managing community content, and a convenient decision-making matrix was developed based on the bubble “Comments-Likes” diagram. These results can be used by decision makers to improve university brands and increase engagement in social networks.

Linearization is a powerful tool that enables the analysis and control of nonlinear systems by simplifying their dynamics and allowing the use of well-established linear control techniques. This paper presents a new methodology for the linearization of dynamic nonlinear systems that can be applied without the need to know their internal structure and parameters solely from measured input/output data. The proposed technique, based on fuzzy logic, simplifies a higher-order nonlinear system into a first-order nonlinear fuzzy system. This enables the design of control systems based on an inverse fuzzy model in the vicinity of the chosen operating point. The properties of the proposed methodology were first verified for less complex systems by simulation in MATLAB. This paper presents the application of this method for torque control of an induction motor, which is a complex nonlinear system of the fifth order. The results were experimentally validated by measurements using an IC inverter-induction motor system. The experimental measurements confirmed the correctness and quality of the proposed methodology. Compared to traditional methods, it does not require any knowledge of the mathematical model of the nonlinear system, use of complex differential algebra, or other additional transformations. The proposed non-analytical linearization technique is primarily designed for a class of mechatronic systems with electric drives. Its simple structure and design methodology minimize the complexity of implementation, thereby enabling it to be widely used in industrial practice for other nonlinear systems with comparable structures.

The steady downsizing of semiconductor technology nodes in recent years has led to a rapid increase in the density of power consumption on chips which, in turn, renders temperature a major issue for many-core systems, adversely affecting their performance, reliability, leakage, cost, etc. In this context, task migration is a powerful technique that is widely used for controlling the temperature profile of many-core systems under dynamic workloads with the goal to improve their performance, utilization, reliability, etc. In this paper, we present a task migration policy for thermal-aware performance optimization in heterogeneous many-core systems. The proposed policy is developed based on an analytical and thermally safe power-budgeting scheme and uses Dynamic Voltage and Frequency Scaling (DVFS) for power and thermal management of the system. Our migration policy aims at maximizing the system’s performance and, at the same time, proactively enforcing thermal safety using DVFS. To that end, it iteratively adapts the distribution of active cores in the system (through proper migration decisions) to maximize the thermally safe power budget of active cores and, thereby, enable them to operate on higher frequencies without violating their safe thermal threshold. Experimental results demonstrate that the proposed policy offers $2\times $ higher performance gain in comparison to existing approaches which aim at greedily reducing the average, variance, or gradient of temperature as an indirect means to enhance performance.

Finding software vulnerabilities in concurrent programs is a challenging task due to the size of the state-space exploration, as the number of interleavings grows exponentially with the number of program threads and statements. We propose and evaluate EBF (Ensembles of Bounded Model Checking with Fuzzing) – a technique that combines Bounded Model Checking (BMC) and Gray-Box Fuzzing (GBF) to find software vulnerabilities in concurrent programs. Since there are no publicly-available GBF tools for concurrent code, we first propose OpenGBF – a new open-source concurrency-aware gray-box fuzzer that explores different thread schedules by instrumenting the code under test with random delays. Then, we build an ensemble of a BMC tool and OpenGBF in the following way. On the one hand, when the BMC tool in the ensemble returns a counterexample, we use it as a seed for OpenGBF, thus increasing the likelihood of executing paths guarded by complex mathematical expressions. On the other hand, we aggregate the outcomes of the BMC and GBF tools in the ensemble using a decision matrix, thus improving the accuracy of EBF. We evaluate EBF against state-of-the-art pure BMC tools and show that it can generate up to 14.9% more correct verification witnesses than the corresponding BMC tools alone. Furthermore, we demonstrate the efficacy of OpenGBF, by showing that it can find 24.2% of the vulnerabilities in our evaluation suite, while non-concurrency-aware GBF tools can only find 0.55%. Finally, thanks to our concurrency-aware OpenGBF, EBF detects a data race in the open-source wolfMqtt library and reproduces known bugs in several other real-world programs, which demonstrates its effectiveness in finding vulnerabilities in real-world software.

In the modern high-performance drive applications, a high-precision and efficient control of the induction motor depends on the accuracy of parameter values. However, the motor parameters may change due to the winding temperature fluctuations, flux saturation and skin effect. Any discrepancy between the values of the motor's actual parameters and the ones used for the design of the controllers may result in degradation of the drive performance. In this work, a new identification method of hardly measurable internal quantities of the induction motor, such as components of the magnetic flux vector and electromagnetic torque, is outlined. Commonly, the measurable quantities of the induction motor like stator currents, stator voltage frequency and mechanical angular speed are used to determine a feedback effect of the rotor flux vector on the vector of the stator currents of induction motor. Based on this feedback, it is also possible to identify the actual value of the rotor resistance, which may alter during the induction motor operation. This has a significant impact on the precision of the identified quantities as well as on the master control of the induction motor. Stability of the identification structure is guaranteed by the position of roots of characteristic equation of its linear transfer function. Simulation and experimental results are given to highlight the quality, effectivity, feasibility, and robustness of the proposed identification method, which is working reliably within the whole range of the motor angular speed.

We present a new method for the automated synthesis of digital controllers with formal safety guarantees for systems with nonlinear dynamics, noisy output measurements, and stochastic disturbances. Our method derives digital controllers such that the corresponding closed-loop system, modeled as a sampled-data stochastic control system, satisfies a safety specification with probability above a given threshold. Our technique uses a fast solver and an optimization method to search for candidate controllers, which are then formally evaluated in closed-loop with the system in question by a verified solver. Unstable candidate controllers are discarded by efficiently checking a sufficient condition for Lyapunov stability of sampled-data nonlinear systems. We evaluate our technique on three case studies: an artificial pancreas model, a powertrain control model, and a quadruple-tank process.

Sand nourishment is widely adopted as an effective soft approach to provide long-term coastal safety, protect the ecology environment, and promote tourism and recreation. With the increase in frequency and expenses in beach nourishment worldwide, an adequate prediction of morphology evolution is greatly desired for coastline management. Based on detailed monitoring data of the mega-nourishment Sand Engine, this article integrates a convolutional neural network (CNN) and long short-term memory (LSTM) to predict the nourishment morphology evolution. The historical surveyed data are transformed into sequence grids, which are input into the CNN to obtain the spatial features of beach nourishment. The CNN is constructed by performing the convolutional and pooling operation on the historical data, which can extract actual spatial features and reduce network complexity. The output of the CNN is input to LSTM to learn the temporal relationship to predict future nourishment terrain using past time-series features. Finally, the LSTM output is decoded by the fully connected layer to obtain the prediction result. The complex spatiotemporal correlations among the input data are identified through effective training of the proposed model. The major contribution of this article is to propose a data-driven model that combines CNN and LSTM for the morphology evolution prediction of beach nourishment, and validate the effectiveness of the proposed model by comparing with the performances of other popular methods in predicting the nourishment changes.

Ensuring healthy lives and promoting well-being for all, at all ages, is one main objective for sustainable development proposed by the United Nations. The concept of connected health (CH) has been proposed to achieve that goal by connecting all the stakeholders through enabling Telehealth technologies. This paper has first presented an overview of the whole picture of CH along with the data collection process in CH. In the whole picture of CH, translational medicine (TM), as a rapidly growing discipline in biomedical research, aims to expedite the discovery of new diagnostic tools and treatments by using a multi-disciplinary and highly collaborative approach. It has been introduced to bridge the technique gap between the clinics and data scientists, particularly targeting on health related data analysis and evidence medicine. What clinicians are expecting and what researchers can offer will/should all be defined and clarified through TM. To further facilitate the communication between the clinicians and the researchers, electronic health records (EHRs) are often applied in place. This paper first reviews the evolution history of EHR and its current status and standards. Then a detailed and comprehensive discussion on data analysis techniques applied in TM through both quantitative and qualitative approaches is elaborated. We reveal that future work in TM should put an emphasis on data oriented qualitative analysis, using advanced techniques from the artificial intelligence domain to predict health risk, such as heart attacks and early stages of cancers. Multidisciplinary research in the Internet of Medical Things across health science, data science, and engineering will be the main challenge in TM.