Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
"Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska" – IAPGOS (“Informatics, Control, Measurement in Economy and Environmental Protection”) is a peer-reviewed, international, multidisciplinary quarterly published in English language by Lublin University of Technology, in an electronic and printed version. The articles presented in this journal concern fields (categories) of automation, biomedical engineering, computer and information sciences, electronic and electrical engineering, information and communication technology, mining and energy. IAPGOS publishes in open access formula under the Creative Commons Attribution licence – CC BY 4.0 Int. (until 2023 inclusive CC BY-SA). Since 2020 the journal's articles are indexed in Scopus database. (more)
Control of the magnetic levitation using a PID controller with adaptation based on linear interpolation logic and genetic algorithm
This paper presents a magnetic levitation system in which the position of a steel ball is obtained at a desired level by controlling the coil current. The object's behavior was approximated using a mathematical model, considering the inductance's dependence on the ball's position. Then, simulations were carried out in the MATLAB/Simulink environment, and the results were compared with data obtained from a physical bench, confirming the accuracy of the simulation model. As part of the work, two variants of the control system were realized: a classic PID controller and an adaptive version, in which the PID parameters are determined using a genetic algorithm. In addition, linear interpolation logic was introduced, enabling smooth adaptation of the settings depending on the current operating conditions. Experimental tests carried out for step and sinusoidal excitation showed a significant improvement in control quality, reduced rise time, and reduced overshoot in the system using the genetic algorithm and linear interpolation logic compared to a classically tuned PID.
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Development of a system for predicting failures of bagging machines
The reliability and effective operation of machines is a pressing problem for every enterprise, which requires labour intensive systematizationof production processes. The goal is to develop an algorithm and a system for predicting failures of packaging machines based on the analysisof operational indicators. The scientific novelty lies in the integration of statistical data to assess the efficiency of machine operation and predict possible failures, which allows significantly improving maintenance processes and reducing the risks of unforeseen breakdowns. The practical valueis the development of a forecasting system that collects the necessary statistical data and performs forecasting. Based on the collected data, an assessment of the efficiency of work and forecasting of possible failures is carried out. The forecasting system is demonstrated on the example of packaging machines LEMO INTERmat ST-SA 850 of "Tatrafan" LLC. Two research methods were used: calculation (mathematical) and forecasting system (least squares method). The forecasting system provides two ways of presenting data: tabular and graphical. Tabular presentation of data allows filtering information according to various criteria, while graphical display is implemented in the form of diagrams showing the operating time and downtime of machines.The main results are the determined range of probable failure of LEMO INTERmat ST-SA 850 packaging machines, which lies in the range from 9090.5to 12736.5 hours of operation and almost coincides with the manufacturer's warranty period. With timely maintenance, it is possible to increase the lower limit of this interval.
Development and verification of a modular object-oriented fuzzy logic controller architecture for customizable and embedded applications
This paper presents an open-source and lightweight, object-oriented fuzzy logic controller architecture designed to overcome key limitations of popular platforms like MATLAB and LabVIEW. These traditional tools, while widely used, are closed-source and license-dependent, creating barriers to portability, system-level integration, and long-term flexibility. This modular framework breaks the controller into reusable components implemented as separate classes, all coordinated by a central controller class that manages the full inference cycle from input fuzzification to output defuzzification. This central controller can be easily customized for different control scenarios, as demonstrated here through validation on both single-input and multi-input cases. The results confirm that the architecture delivers reliable and consistent performance while scaling smoothly to handle increased input complexity without redesigning core components. This approach offers a transparent, maintainable, and flexible alternative that empowers developers with full control over their fuzzy logic implementations and supports integration in a variety of software and embedded environments.
Mechanical fracture energy and structural-mechanical properties of meat snacks with beekeeping additives
The choice of an effective technological scheme of food production is mainly determined by the need to meet consumer needs, healthy eating requirements, compliance with environmental trends, energy efficiency of the main processes of the technology used. The technology of meat chicken snacks with the addition of such biologically active additives as honey, propolis and bee pollen allows you to meet these factors due to the convenience of consumption, high protein content, ease of storage and preparation. The addition of bee pollen allows you to increase the nutritional value of the product and classify it as a health product. The tests were based on the hypothesis that when the product is destroyed, the force applied to cut the sample is proportional to the structural resistance and energy parameters, which allow you to assess the quality characteristics of snacks through the tenderness or hardness of the finished product. Experimental studies of the force and energy processes of chicken snack processing were carried out separately for a control sample of chicken fillet and spices and samples with a developed recipe, in which, together with the meat base, such beekeeping products as honey, propolis aqueous extract and bee pollen were added. The experiments were conducted to understand the behavior of the product under load, which is important for assessing their quality, safety, as well as adjusting the production processes of meat snacks to ensure high resistance to mechanical loads in practical application conditions. In general, all used samples of meat snacks with beekeeping products demonstrated effective mechanical properties as for the use of a food product; the determined parameters of the critical strength limit allow avoiding structural failure modes under excessive loads. The research results provide opportunities to optimize formulations, justify operating modes to prevent premature material failure under load, improve the structure and efficiency of technological processes based on minimizing energy consumption, preserving structural properties, improving the quality of finished products, and increasing their competitiveness in the market.
Modelling of dynamic processes in a nonholonomic system in the form of Gibbs-Appell equations on the example of a ball mill
In the article the actual scientific and practical problem of creation of the universal mathematical model of dynamic processes in the shockless model of the ball mill is solved. This allowed to determine the technological qualities of the device and to make an effective choice of its rational parameters. Computational studies have been carried out using the developed model by means of computer calculations. As a result, important regularities of the influence of parameters on the technological indicators of the technological device have been established. The result of the study is the first development of a nonholonomic mathematical model of dynamic processes in a ball mill, presented in the form of Gibbs-Appell equations. It is shown that the calculation of the acceleration energy and the formulation of the equations of motion are significantly simplified when quasi-accelerations are used instead of generalised acceleration equations. The developed mathematical model allows to calculate the trajectories of the contact point of the ball with the surface of the spherical chamber, the compressive forces of interaction with the surface of the shell. The results of the mathematical modelling allow efficient selection of rational parameters of the process equipment from the technological point of view.
Real-time Covid-19 diagnosis on embedded IoT platforms
COVID-19 continues to pose a persistent global health challenge, where rapid, accurate, and accessible diagnostic tools are crucial for controlling viral transmission. In this study, we present a non-invasive, embedded diagnostic system for COVID-19 detection based on chest CT scan image analysis. The acquired CT images are preprocessed to align with the input dimensions required by four lightweight convolutional neural network (CNN) architectures – ResNet-18, MobileNet, ShuffleNet, and SqueezeNet – selected for their efficiency and suitability in embedded systems. Among these, SqueezeNet achieved a classification accuracy and an f1-score of 99.1%, delivering performance comparable to the other models while offering superior computational efficiency, making it particularly well-suited for real-time, resource-constrained applications. The optimized model was deployed on an NVIDIA Jetson embedded platform to enable on-device, real-time COVID-19 detection at the edge. Diagnostic results are transmitted to the ThingSpeak cloud platform via the MQTT protocol, facilitating continuous, remote health monitoring. Experimental findings confirm the feasibility, accuracy, and real-time operational capability of the proposed embedded system for COVID-19 detection using chest CT scan images.
Hybrid models for handwriting-based diagnosis of Parkinson's disease
Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily impairs motor functions, leading to symptoms such as tremors and micrographia. Even though early identification of PD is crucial for effective intervention, existing methods of diagnosis are highly invasive and not very sensitive to the early stages of the diseases. The goal of this research is to determine if handwriting could be a non-invasive way to diagnose PD at an early stage. We employed a dataset of 3,264 hand-drawn waves and spirals to evaluate the performance of hybrid machine learning and deep learning models which included Support Vector Machine (SVM), Random Forest (RF), Visual Geometry Group-16 (VGG-16) and MobileNetV2. Combing SVM with VGG-16 for the task reached a stunning 99.00% accuracy for identifying PD, performing the best out of all tested models, demonstrating superior performance in the early identification of PD. The proposed approach not only outperforms existing diagnostic methods but also underscores the transformative potential of handwriting analysis tools in PD diagnosis, aiding in automatic PD detection and enhancing patient outcomes.
Computer system for diagnostic and treatment of unilateral neglect syndrome
Unilateral Spatial Neglect (USN) is a neuropsychological disorder commonly resulting from right hemisphere brain damage, leading to impaired awareness of stimuli on the left side of space. It significantly affects patient autonomy and rehabilitation outcomes. Traditional therapies such as Visual Scanning Training (VST) and Prism Adaptation (PA) lack standardization and objective diagnostic tools. This paper presents a computer-based system designed to support both diagnosis and therapy of USN. The system uses a 5-meter LED strip to deliver spatially distributed visual stimuli and records patient responses via a physical button. Reaction times and detection data are stored in a local database, enabling objective assessment and personalized therapy planning. The system architecture is modular, based on Clean Architecture principles, and implemented in C++20 on a Raspberry Pi 5 microcomputer. The graphical interface is built using GTKmm 4.0. Therapy sessions follow VST methodology. They include tracking moving stimuli, using visual anchors, and increasing stimulus density. Preliminary trials confirm the system’s ability to differentiate neglect symptoms and support individualized therapy. Its portability, modularity, and integration of diagnostic and therapeutic functions make it a promising tool for clinical neurorehabilitation. Further validation and development are planned to support broader clinical adoption.
Informatics and measurement in healthcare: deep learning for diabetic patient readmission prediction
Approximately 460 million individuals were living with diabetes globally in 2023. This study explores and contrasts methods for forecasting hospital readmissions among diabetic patients by integrating traditional approaches with modern deep learning frameworks. In this work, a variety of deep learning architectures – including recurrent models like LSTM and GRU, as well as CNNs and Autoencoders – are examined along with conventional machine learning approaches. Four essential metrics – accuracy, precision, recall, and F1-score – were employed to measure and compare the effectiveness of different models. The results revealed that deep neural network methods significantly outperformed classical machine learning algorithms. Among traditional methods, the Decision Tree achieved the highest effectiveness. However, the LSTM network demonstrated superior performance, achieving scores of 0.74 for accuracy, 0.73 for precision, 0.74 for recall, and 0.73 for the F1-score. Additionally, the GRU and Vanilla LSTM models exhibited performance close to the best model, indicating that recurrent networks are more suitable for this problem than traditional methods.
Optimization of non-invasive glucose monitoring accuracy using an optical sensor
The development of non-invasive blood glucose monitoring technologies is progressing steadily, largely due to advancements in optical sensor systems. These systems assess physiological data by analyzing how light interacts with biological tissues. A common technique, near-infrared spectroscopy, typically utilizes wavelengths such as 500 nm, 970 nm, 1400 nm, and 1900 nm, selected for their enhanced responsiveness to glucose levels at varying tissue depths. In experimental settings, this method has demonstrated the ability to generate electrical signals that correlate with glucose concentrations, supporting its potential for accurate glucose tracking. The technique adheres to both detection and precision standards, particularly at higher glucose concentrations, making it a strong candidate for next-generation monitoring solutions. The study also outlines future objectives, including improving sensor accuracy, reducing device size, and enabling seamless integration with both clinical and home-based healthcare systems. Moreover, efforts to minimize interference and signal distortions are explored as part of the broader aim to refine system reliability.
Stochastic multi-objective minimax optimization of combined electromagnetic shield based on three-dimensional modeling of overhead power lines magnetic field
The method of design of combined electromagnetic shield, consisting from active shielding system and electromagnetic passive shield of overhead power lines magnetic field is developed. All designed parameters calculated as multi-objective nonlinear minimax optimization problem solution based on stochastic particles multi-swarm optimization algorithms. Vector nonlinear objective function calculated based on finite element calculations system COMSOL Muliphysics package. Theoretical and experimental studies results of designed combined electromagnetic shield are presented.
Advanced energy management strategies for AC/DC microgrids
This article investigates the active and reactive power dynamics of a hybrid AC/DC microgrid, with a particular focus on coordinated energy management among photovoltaic (PV) systems, synchronous generators, fuel cells, batteries, and supercapacitors. The novelty of this work lies in the integrated evaluation of bidirectional power flow, transient response, and voltage stability, which are addressed simultaneously through detailed simulations. Unlike existing studies that mainly highlight steady-state operation or isolated component performance, our analysis provides a holistic view of component interaction under dynamic conditions. The PV unit is modeled with a triangular power profile, peaking at 7 kW within 200 seconds, thereby capturing realistic variability in solar generation. The synchronous generator consistently delivers around 5 kW, adapting its output to compensate for load fluctuations ranging from 5.5 to 9.5 kW. The fuel cell, with a minimum output of 2 kW, ensures continuous supply during low renewable periods. Energy storage elements demonstrate complementary roles: the battery alternates between charging and discharging cycles, while the supercapacitor mitigates fast power deviations in the range of −0.05 kW to 0.3 kW. On the reactive power side, the load demands between −0.95 kvar and −0.5 kvar, with the synchronous generator contributing −0.65 kvar and the voltage source converters (VSC1 and VSC2) providing additional dynamic support. Importantly, the battery and ultracapacitor maintain voltage stability at 431.665 V and 249.5 V, respectively. Overall, the findings validate the robustness and efficiency of the proposed hybrid microgrid strategy, highlighting its contribution to enhancing grid resilience, stability, and reliable energy flow management.
Experimental study of a multi-stage converter circuit
An experimental analysis was carried out on a multi-stage energy conversion system configured in a solar panel–converter–load structure. Multi-stage converters are operated according to fundamental energy transfer principles, enabling the conversion of solar radiation into usable electrical power for consumer applications. The performance of the system was evaluated under different load scenarios, with variations in efficiency and output voltage behavior analyzed. The experimental results indicate that the integration of multiple converter stages leads to enhanced productivity of photovoltaic power systems. These findings emphasize opportunities for the optimization of photovoltaic technology and the advancement of energy conversion designs intended for automotive applications.
Deep learning-based prediction of structural parameters in FDTD-simulated plasmonic nanostructures
The research creates a new approach to estimate essential dimensions of plasmonic nanoparticles that use the Finite-Difference Time-Domain (FDTD) simulation program. The research team uses EfficientNetB0 alongside ResNet50 and VGG16 deep learning models to obtain quick and exact simulations parameter predictions from simulation image data. The developed dataset consists of dielectric and magnetic field images that stem from FDTD simulated fields through representative materials MgF₂, Au, and glass. The preparation process for the dataset includes a systematic variation of 38 structural parameters for achieving sufficient coverage of potential configurations. VGG16 proved to be the most effective model from the testing group because it attained a training loss 0.1592, validation loss of 0.1607, and test loss 0.1625. The outstanding result shows deep learning techniques can be effectively used to boost nanophotonic device design speeds and optimization processes. The methodology developed in this work has the potential to reduce substantially the computational expenses together with simulation duration for nanostructure engineering processes.
Development of an algorithm for calculating ion exchange processes using the Python ecosystem
Ion exchange plays a key role not only in eliminating water hardness and reducing the concentration of unwanted ions, but also in protecting the environment. Due to its versatility, it is an indispensable tool in modern environmental technologies. Calculation methods are an important component of the implementation of ion exchange technologies, as they play a crucial role in their effective operation. There are calculation methods used in the design of ion exchange systems, but similar methods have not yet been developed to determine operating parameters during operation. This complicates the optimization of the process in real conditions and requires further research and development. The calculation method should be easy to use, but at the same time provide high accuracy of results, taking into account such important factors as water composition, filtration rate, temperature and type of ion exchange resins. It is especially important that the method be developed based on open Python libraries to ensure accessibility and versatility. The differential mathematical approach using Python provides significantly higher accuracy, efficiency and flexibility in calculations. Therefore, the development of an algorithm for solving systems of nonlinear equations by Newton's method will allow achieving maximum accuracy of results. The article develops an algorithm for solving a nonlinear system of equations by Newton's method, which allows determining the distribution of ions in the working zone of the filter in both liquid and solid phases. The implementation of the developed algorithm is carried out using the Python ecosystem. Based on the calculation results, a graph of the output curve is constructed and a complex software module is developed that allows controlling the most important operating parameters of ion-exchange filters. Also, the use of a complex software module will contribute to reducing the operating costs of ion-exchange filters and increasing the efficiency of processes.
Intelligent model for reliability control and safety in urban transport systems
This paper proposes an intelligent model for controlling the reliability and safety of urban transport systems, integrating fuzzy cognitive maps (FCMs) with the Bellman-Zadeh decision-making principle. This approach enables the consideration of complex interactions between evaluation criteria and controllable variables, which are characteristic of modern urban environments. To harmonise the modelling outcomes, a membership function to the ideal solution is introduced, allowing for the aggregation of the criteria vector through the intersection of corresponding fuzzy sets. A generalised algorithm is developed for forecasting reliability and safety parameters under the influence of multiple factors, facilitating multi-criteria selection of alternatives in dynamic and uncertain conditions. Special attention is given to the model’s applicability in road safety audits, where both technical and behavioural risk factors must be considered. The proposed framework supports scenario analysis, enabling the simulation of various event developments, the assessment of their implications for transport safety, and the formulation of adaptive response strategies. The integration of FCMs with the Bellman-Zadeh principle formalises the evaluation of safety scenarios, ranks critical factors, and supports decision-making for the optimisation of transport infrastructure. The approach can be adapted to various types of transport systems and utilised to enhance risk management, environmental safety, and strategic planning in urban contexts.
Analysis of the interaction of components of a modular parcel storage system using UML diagrams
The article studies the interaction of components of a modular parcel storage system using UML diagrams. The key aspects of modeling the structural and dynamic interaction of components, such as physical elements (cells, scanners, conveyors) and software modules (control system, database), to ensure flexibility, scalability and automation of logistics processes are considered. The author presents the use of class diagrams, sequences, precedents, and activities to detect logical errors, predict potential failures such as overload or out-of-sync, and optimize system performance. Particular attention is paid to the roles of actors (client, manager, designer, logistician, etc.) and their interaction with the system, which allows for a clear definition of functional requirements and use cases. The modeling results demonstrate the possibility of improving the efficiency of processing, tracking, and delivery of parcels, as well as providing convenient access for users. The data obtained can be used to improve modern solutions in the field of automated parcel management systems, contributing to the creation of reliable and adaptive logistics platforms that can meet the growing demands of the market.
Evaluating modified pairing insertion heuristics for efficient dial-a-ride problem solutions in healthcare logistics
A subset of the Vehicle Routing Problem (VRP), the Dial-a-Ride Problem (DARP) is concerned with the effective route planning of cars employed to pick up and deliver passengers to designated destinations. For the transportation of elderly or incapacitated patients in Apizaco, Tlaxcala, Mexico, this research suggests using DARP. We propose to tackle this problem, a mathematical programming model and an insertion heuristic as a solution method. The objective is to optimize the trip time while adhering to the problem constraints. We ran a number of trials in different settings, accounting for different model parameter values. The outcomes demonstrate notable progress, with each of the generated routes having the least trip times. For instance, in a scenario with 20 patients, increasing vehicle speed from 30 km/h to 60 km/h reduced the total travel time from 30.66 minutes to 15.11 minutes. This significant improvement underscores the computational efficiency and practical applicability of the proposed heuristic approach in patient transportation systems.
Analysis of modern tools, methods of audit and monitoring of database security
The mismatch between modern technological innovations and traditional approaches to information security can significantly affect the effectiveness of database monitoring systems. This paper examines the ethical and legal aspects of database monitoring, taking into account current regulatory requirements and the importance of maintaining data confidentiality. The study also evaluates the use of each tool to determine their effectiveness in real-world conditions, the possibility of improving the functional characteristics of monitoring systems, their adaptation to new technologies and increasing the overall level of information protection.
Improving underwater visuals by fusion of Deep-Retinex and GAN for enhanced image quality in subaquatic environments
The enhancement of subaquatic images is crucial for various applications such as marine resource development, underwater photography, surveillance, and scientific imaging. However, the underwater environment presents challenges like color distortion, low contrast, and poor visibility, which traditional image processing techniques struggle to address effectively. In response, this study proposes an innovative approach named Deep‑Retinex‑GAN, which integrates Deep Retinex preprocessing and Generative Adversarial Networks (GANs) to refine underwater images. Initially, the subaquatic images are processed using Deep Retinex to separate them into reflectance and illumination components, reducing color distortion and enhancing contrast. Subsequently, the reflectance‑enhanced images are used as conditional inputs for a GAN model, allowing it to learn the mapping to a target domain with improved illumination, texture, and sharpness. Experimental evaluations conducted on both synthetic and real‑world underwater image datasets demonstrate the superior performance of the proposed method compared to existing techniques, achieving a PSNR of 34.741 dB, an SSIM of 0.978, and a CF(ΔE) of 8.2, as well as noticeable artifact reduction. Qualitative assessments further highlight the method’s ability to produce visually pleasing and realistic results. The proposed approach shows strong potential for a broad range of underwater applications, including photography, surveillance, exploration, and scientific research, by significantly enhancing the quality and interpretability of underwater imagery across diverse domains.
The mathematical method for assessing the cybersecurity state of cloud services
This study presents the developed mathematical method for assessing the cybersecurity state of cloud services of information infrastructure facilities. The developed assessment method consists of 11 assessment stages, which provide a comprehensive assessment of 5 types of cloud services. The developed approach to assessing the cybersecurity state of cloud services can become the basis for further research in the field of cybersecurity by expanding tools for analyzing risks in the cloud environment and will help increase confidence in the use of cloud services in the face of modern information challenges.
Evaluation of the performance of LLMs deployments in selected cloud-based container services
The growing adoption of serverless container services has created challenges in selecting optimal cloud platforms for production LLM deployments, yet comparative performance evaluations remain limited. This study evaluates AWS Fargate and Azure Container Apps for LLM deployments, investigating whether architectural differences cause substantial performance variations under diverse load patterns. We conducted systematic experiments using containerized Llama 3.2:1b across multiple scenarios: baseline measurements, inference tests with varying prompt lengths, streaming API performance, and concurrent load testing with progressive scaling. Each scenario was executed on both standard and auto-scaled infrastructure with 10 runs per configuration to ensure statistical reliability. Key findings reveal distinct platform characteristics: AWS Fargate demonstrates superior baseline API response times and time-to-first-token performance, while Azure Container Apps consistently outperforms AWS in inference processing for short and medium prompts with better consistency across test runs. Streaming performance shows platform-specific trade-offs, with AWS achieving lower initial latency but Azure providing superior token generation consistency. Under concurrent loads, both platforms maintain full capacity at lower concurrency levels, but AWS exhibits exponential response time degradation at higher loads while Azure shows more linear, predictable scaling behavior. Statistical analysis confirms significant performance differences across all metrics, validating that platform architecture fundamentally impacts LLM deployment performance. These findings indicate platform selection should align with specific workload requirements: AWS Fargate for latency-critical applications with steady loads, and Azure Container Apps for inference-intensive workloads requiring robust scaling and consistency. This study offers crucial benchmarking data for businesses deploying production-grade AI services on serverless container platforms.
Implementing traits in C# using Roslyn Source Generators
The paper presents an approach to implementing traits functionality in the C# programming language through the use of Roslyn Source Generators, a compile-time metaprogramming tool introduced in .NET5. Traits, which are composable units of fine-grained reuse, can benefit the C# language by bringing a mechanism for behavior reuse in the context of single inheritance. The article describes the design and implementation of a source generator that allows to annotate classes with metadata attributes, automatically injecting the corresponding member definitions at compile time. To achieve better code generation performance at design time, Incremental Source Generators were used. The composition of trait members is described in detail, focusing on name conflict resolution and type safety by utilizing interfaces and the override mechanism. Defining non-public members in a context of a trait is also discussed and implemented, thus allowing for better code reusability. The article concludes that language-level features such as traits can be implemented in the C# language by using Roslyn Source Generators, thus demonstrating the potential of such an instrument and metaprogramming in general.
Impact of customizable orchestrator scheduling on machine learning efficiency in edge environments
This article explores the impact of custom scheduling strategies on the performance of machine learning workflows at the edge by using the case of Kubernetes scheduling. Optimizing machine learning (ML) workloads on resource-constrained edge devices has become a significant scientific challenge addressed by multiple studies. The severe limitations of edge systems in processing power, memory, and energy render conventional cloud-native schedulers ineffective, leading to poor resource utilization and degraded performance. While numerous advanced, data-driven solutions have been proposed for large-scale systems, their complexity and overhead are often impractical for edge deployments. In contrast, this work investigates a simpler, lightweight scheduling mechanism for CPU-based workloads that provides efficient and predictable performance without relying on historical data, making it well-suited for the unique requirements of the edge.Using a lightweight K3s cluster integrated with Kubeflow Pipelines, we investigate how varying binpacking functions influence resource allocation and training efficiency of a CNN model on the MNIST dataset. Our experiments demonstrate that tailored scheduling configurations can lead to noticeable improvements in training times and hardware utilization across different edge cluster sizes. The results offer actionable insights for optimizing AI workloads in resource-constrained edge environments.
Reconfigured CoARX architecture for implementing ARX hashing in microcontrollers of IoT systems with limited resources
This paper presents CoARX – a reconfigurable coarse-grained architecture tailored for the efficient implementation of ARX-based hash functions in resource-constrained IoT microcontrollers. The architecture is designed to support various cryptographic algorithms based on addition, rotation, and XOR operations, offering a balance between performance, energy efficiency, and hardware simplicity. CoARX employs configurable processing elements with local memory, adaptive routing, and support for both 32-bit and 64-bit operations, making it suitable for implementing lightweight hash functions such as Skein-512, BLAKE-256, and ChaCha20. The study introduces a set of configuration templates enabling dynamic switching between cryptographic modes without modifying the hardware structure. For each algorithm, specific optimization strategies are proposed, including staggered round scheduling for Skein, SIMD-based vector masks for BLAKE, and memoryless route switching for ChaCha20. The dataflow between clusters is optimized using parallel graph decomposition and cluster overlap to minimize routing delays. Experimental results demonstrate that CoARX achieves up to 35% higher performance compared to FPGA-based solutions and over twofold improvement compared to STM32 implementations. The architecture also reduces energy consumption by up to 3× and minimizes memory footprint by 30–60%, depending on the algorithm. These advantages make CoARX a promising platform for secure data processing in embedded and real-time IoT systems operating under limited energy and memory constraints.
Integral assessment of the spring water quality with the use of fuzzy logic toolkit
Water, as an important strategic resource, ensures the livelihood of people and the economic development of society. The effective use of water resources is one of the main goals of sustainable development. The possibility of using a particular water body depends on the degree of water pollution. The drinking water quality is characterized by the content of a significant number of chemical elements, while not only instrumental and analytical methods are used to determine certain criteria, but also methods in which parameters are assessed by experts, in particular organoleptic indicators. Accordingly, considering the use of the survey for expert assessment, the integral indicator of the drinking spring water quality is determined using the fuzzy logic toolkit. For the integral assessment of the drinking water quality, indicators are divided into three groups: organoleptic indicators of the water quality, general physical and chemical indicators of the water quality and quality indicators of the content of inorganic chemical elements. A universal set and corresponding terms are determined for the factors of each group as linguistic variables. For each linguistic variable, fuzzy sets are determined, a knowledge base is formed, and fuzzy logic equations are constructed. The results of the work are checked with the help of the Matlab Fuzzy Logic Toolbox package by obtaining the corresponding models. The models developed by the fuzzy logic toolkit will make it possible to assess the possibility of using drinking water sources and make informed decisions when using them for industrial and domestic needs.
Selected issues concerning fibre-optic bending sensors
This paper presents a comprehensive review of bending sensors and their classifications. The main focus is on advancements in the design of optical fibre bending sensors based on fibre Bragg gratings (FBGs). Various measurement principles employed in optical fibre bending sensors are analysed, highlighting their respective advantages and limitations. Particular attention is given to sensors utilising long period gratings (LPGs), Tilted fibre Bragg gratings (TFBGs), and multicore (MCF) fibre structures, which demonstrate significant potential for the development of highly sensitive and compact bending sensing systems.