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)
Efficient CNN-based classification of white blood cells: a comparative study of model performance
White blood cells (WBCs) are integral components of the immune system, playing a pivotal role in defending the body against various diseases and infections. Accurate quantitative and qualitative analysis of different WBC types is essential for the effective diagnosis and treatment of numerous medical conditions. Counting and classifying these cells are fundamental steps in blood sample examination and testing. This study focuses on evaluating three convolutional neural network (CNN)-based architectures – ResNet-50, VGG-16, and a classic CNN – for classifying blood cell image categories using a curated dataset from Kaggle. We trained and evaluated each model’s performance using accuracy curves, confusion matrices, and classification reports. Among the tested architectures, the ResNet-50 model achieved the highest validation accuracy of approximately 80%, followed by VGG-16 at 79.6% and the classic CNN at 76.2%. Both VGG-16 and the classic CNN exhibited significant overfitting, with large gaps between training and validation accuracy. These findings highlight the challenges of image classification on imbalanced datasets and suggest directions for future improvements through data augmentation and architectural refinements.
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Automated skin cancer diagnosis using deep learning: a systematic review of state-of-the-art architectures, techniques and performance evaluation
This literature survey offers a comprehensive analysis of deep learning techniques for skin cancer diagnosis. Prompt identification is crucial for improving patient survival rates, and deep learning has demonstrated promising results. The survey examines the fundamentals of skin cancer, various neural network architectures, and their classification efficacy. It investigates the application of deep learning models in clinical decision-making and assesses authentic datasets for evaluating skin cancer detection techniques. Training strategies for enhancing deep learning models are delineated. The survey assesses essential performance indicators, including accuracy, precision, recall, and F1-score. This survey underscores the growing importance of deep learning in skin cancer diagnosis, demonstrating its potential to improve the patient experience and advance clinical practice.
Enhancing driver safety with ECG-based emotion recognition using BiLSTM networks
Emotions critically influence human decision making and behaviour, particularly in safety-sensitive contexts like driving. This study introduces an ECG-based emotion recognition framework suitable for online driver monitoring system that exclusively analyses electrocardiogram (ECG) signals through a Bidirectional Long Short-Term Memory (BiLSTM) network. The framework captures temporal dynamics in physiological features – including heart rate variability and signal entropy – to classify seven emotional states (neutral, happy, sad, angry, fear, surprise, disgust) with high accuracy. Beyond detection, the system incorporates an intelligent recommendation mechanism designed to mitigate emotional distractions demonstrating how emotion predictions could be translated into driver-support feedback. Experimental validation on synthetic ECG data demonstrates robust emotion classification performance in identifying complex emotional patterns from ECG data, outperforming conventional unimodal approaches. By bridging affective computing with intelligent transportation systems, this work advances the development of adaptive Driver Assistance Systems (DAS) that prioritize both road safety and user wellbeing. The proposed system’s real-time capability and nonintrusive design position it as a scalable solution for emotion aware environments, demonstrating the potential of ECG-based emotion recognition as a supporting component for future driver assistance systems. This research contributes to the growing field of affective human-machine interaction while demonstrating practical applications for intelligent transport systems.
An automated system for calibration table calculation of cylindrical horizontal tanks
The article presents the results of the development and implementation of the software "Automated Calculation of Calibration Table" for the calibration of steel cylindrical horizontal tanks obtained using 3D laser scanning. The proposed system automates the processing of 3D point clouds using a hybrid segmentation method (RANSAC and DBSCAN) and improved geometric formulas for calculating a per-millilitre capacity table. The results of calculating volumes and capacity coefficients are compared with alternative enterprise software, which demonstrates an increase in accuracy by 5–15% and a reduction in processing time by 70–80%. The developed software adapts to tank deformations, ensuring reliability in industrial conditions, which is confirmed by data analysis for 10 tanks with a cloud density of 5–15 million points.
Control of water–diesel emulsion stability using turbidity measurements
This paper describes a technical solution for emulsion homogeneity monitoring. It consists of turbidity sensors mounted in existing emulsion storage tanks, an electronic module, and corresponding measuring instruments. An experimental setup was developed to test the metrological characteristics of the turbidity sensors using standard kaolin suspension samples. Experiments have shown that turbidity sensors have an extensive measuring range, high sensitivity, and a close-to-linear transfer function. Using turbidity sensors allows for monitoring the stages of emulsion breakdown, such as sedimentation, coalescence, and complete separation into two liquids. Detecting the moment of emulsion breakdown in real-time makes it possible to apply three different modes to restore emulsion homogeneity by repeated mixing at different time intervals. An automated system for monitoring emulsion homogeneity, consisting of turbidity sensors, an electronic module, and a programmable logic controller, is proposed. Monitoring the emulsion destabilization process in real time makes it possible to use three different modes to restore its homogeneity by repeated mixing at different time intervals. This provides a significant reduction in the time to restore the homogeneity of the water-fuel emulsion. The proposed system can be integrated into most existing installations for the production and storage of water-fuel emulsions.
Improving the induction motor starting mode under a voltage drop conditions
Asynchronous motors are the most common electric motors used to drive work machines. This is due to their high structural reliability and other significant advantages. During operation, they are exposed to a variety of operational influences. One of the most frequent of these is a voltage dip. As a result of this influence, the performance of the electric motor deteriorates both in steady-state and starting modes. A voltage drop is especially dangerous when starting an electric motor, as it leads to an increase in starting time and a decrease in starting torque. The thermal effect of inrush currents during a voltage dip can lead to motor failure. Therefore, the article proposes to increase the voltage in one of the phases of an induction motor during the startup period under conditions of voltage dip. To assess this technical impact, a methodology has been developed for studying the starting torque and starting time of an induction motor under conditions of rated voltage, voltage dip, and voltage asymmetry. It takes into account the voltage reduction factor, the voltage ratios of the forward and reverse sequences, and the motor load factor. The evaluation criteria are the starting torque of the motor and its starting time. The paper compares these criteria according to the developed methodology in the following cases: nominal mains voltage, mains voltage dip, mains voltage dip with an artificial increase in voltage in one of the induction motor phases. The results of the comparison in these cases showed the effectiveness of artificial voltage asymmetry for the period of starting the motor during a voltage dip. To implement the proposed idea, a block diagram of a device for improving the starting mode of an induction motor under voltage dip conditions was drawn up.
Modelling of dynamic modes in a DC motor for electric vehicle
The paper investigates mathematical models of a DC electric motor with independent excitation for electric vehicles and PWM as a speed control method. Differential equations of electromechanical state are written in normal Cauchy form. This representation simplifies the computational process, since integration is carried out by the explicit numerical method of Runge-Kutta of the fourth order, which is simpler than implicit and more accurate than single-step explicit methods. The symbolic programming language Force 2.0, which is a variant of the Fortran language, is used for modelling. Compared to mathematical packages of simulation modelling, it is convenient in terms of low time costs for compiling the program itself, since the program includes model equations together with initial conditions, a numerical method, and an integration procedure. The developed models take into account electromagnetic couplings of the motor's electrical circuits and make it possible to simulate dynamic operating modes. Such models can be used to analyse the operation of motors both autonomously and as an element of an electromechanical system, including valve converters. The operation and transient modes of a DC motor are simulated, the simulation results are given, and their analysis is presented. The results confirmed the correctness of the chosen approach to modelling and numerical methods, as well as compliance with the classical theory of electric machines.
Development and analysis of power grid failure scenarios using ontology, power flow model, and knowledge graph
Power grids are complex systems that play an important role in many processes and are essential for the functioning of key services to society. Component failure can lead to cascading effects, equipment damage, and potential blackouts. In this paper, an ontology is used to formalize knowledge about the subject area, draw new conclusions, and build failure scenarios. The software simulates the operation of the power grid based on power flow, combining computer simulation of power grid failure scenarios, a semantic model of the subject area, and builds a knowledge graph, which is analysed using centrality metrics and used to draw conclusions about the scenarios of grid failure development. The ontology and knowledge graph can be supplemented with new knowledge and used for further analysis using other models or approaches, as the data is easily interpreted and described using the concepts of the subject area.
Kinetics of grain material drying in installations with intermittent energy supply by microwave and infrared radiation
In both microwave and infrared grain treatment methods, selective heating of the moist material occurs since moisture (water) components selectively absorb energy with greater intensity, ensuring targeted energy delivery, as only the water content heats up rather than the structural matrix of the material. Since moisture is evenly distributed throughout the material volume, heating occurs uniformly throughout the body. Based on the analysis of typical generalized equations of coupled heat and mass transfer, mathematical models of the grain material drying process in the microwave electromagnetic field and infrared radiation were formulated. Using experimental data on grain drying, parametric identification of the developed mathematical models for the drying process dynamics under intermittent MW and IR energy supply was performed. An algorithm for calculating unsteady drying regimes was developed. The MW-IR dryer ensures environmentally friendly grain drying with increased energy efficiency. The influence of operating parameters on the drying kinetics in a dryer with MW and IR energy supply was determined. Rational parameters of microwave-IR treatment: microwave power – 600 W/dm3, IR: 940 W/m2 – for the first period; 1130 W/m2 – for the second period; 1210 W/m2 – for the third period. Experimental studies confirmed the high efficiency of electromagnetic energy supply to the drying material – the intensity of moisture removal was more than twice as high compared to convective drying. It is established that the use of a combined energy supply in the process of grain material drying reduces specific energy consumption and intensifies moisture removal without overheating the material.
Smartphone shell temperature controller automatic tuning method
The performance of mobile Systems-on-Chip is frequently limited by elevated shell temperatures that degrade user comfort. This paper presents a PID-based thermal controller that co-manages central and graphics processing units’ frequencies, featuring a novel auto-tuning framework to optimize its parameters for any given device. The principal result is a universal tuning formula, derived by simulating diverse hardware thermal models to establish a polynomial relationship. This formula maps easily measurable system characteristics from an on-device relay experiment to optimal PID parameters, ensuring controller stability across different hardware configurations, with average gain margin of 2.14, and phase margin of 78°. Validation on a commercial smartphone confirmed the formula's accuracy, yielding PID parameters with less than 5% deviation from the theoretical optimum. When benchmarked against the device's default hysteresis governor, our controller demonstrated significant gains, simultaneously increasing sustained performance by up to 9% while reducing thermal limit violations by up to 2.6 times. This work provides a validated, practical framework for automatic tuning of control systems, that maximize mobile device performance within user comfort constraints.
Using FPGA for modelling and generating chaotic processes
This work presents a comprehensive approach to the implementation of two chaotic dynamic systems – Nose–Hoover and Rikitake – on an FPGA platform. Initially, the systems were modeled in a Python environment using the Euler method, which allowed for the verification of chaotic behavior and an assessment of the impact of the integration step on stability. A comparison of time series and phase portraits confirmed the persistent chaotic nature of both systems within a defined parameter range. Next, a Verilog-based implementation using fixed-point arithmetic (Q16.16) was developed and tested in ModelSim. Simulation results showed a close match with the Python models, indicating a correct choice of bit width and Euler method settings. For real-time data transmission, UART modules and intermediate converters were used to scale Q16.16 outputs to an 8-bit format. This approach enables tracking and recording of computations on a personal computer while maintaining real-time chaotic dynamics. The experimental results confirm that the Nose–Hoover and Rikitake systems can be successfully implemented on an FPGA with limited bit width without significant loss of chaotic properties. This approach has potential applications in cryptography, secure communication, and high-performance pseudorandom signal generators, where flexibility, energy efficiency, and real-time processing are essential.
Simulation and electronic design of a chaotic 5d artificial neural network
This study investigates the complex dynamics of a five-dimensional artificial neural network (ANN) system with a hyperbolic tangent activation function. The objective is to analyze the chaotic behavior and validate multi-platform implementation of the proposed system. The methodology involves numerical simulations in MATLAB-Simulink and LabVIEW, followed by circuit design in Multisim for hardware feasibility verification. Through systematic parameter variation, the system exhibits rich dynamical regimes, including periodic oscillations and chaos. Lyapunov exponent analysis reveals a positive value (LE1 = 0.035) and Kaplan-Yorke dimension DKY = 2.488, confirming chaotic dynamics with fractional attractor geometry. Bifurcation diagrams demonstrate transitions between periodic and chaotic states as parameter b varies. The Multisim circuit simulation results demonstrate excellent agreement with MATLAB-Simulink and LabVIEW outputs, confirming the system's robustness and practical implementability across different platforms for potential applications in secure communications and cryptography.
Intelligent DL-SCH/PDSCH processing chain in 5G with adaptive HARQ mechanism
The article presents the design and analysis of an intelligent processing chain for the Downlink Shared Channel (DL-SCH) and the Physical Downlink Shared Channel (PDSCH) with an adaptive Hybrid Automatic Repeat Request (HARQ) mechanism for fifth-generation (5G) wireless networks. The study integrates machine learning techniques to optimize the HARQ retransmission process, thereby enhancing system throughput, latency, and energy efficiency. Experimental results compare the proposed system with a conventional HARQ mechanism across various signal-to-noise ratio (SNR) levels, demonstrating throughput improvements of up to 72.5% and latency reduction of up to 23% under low-to-moderate SNR conditions. The findings highlight the potential of adaptive HARQ schemes for future fifth- and sixth-generation (5G and 6G) communication systems. System performance was evaluated in terms of bit error rate (BER), error vector magnitude (EVM), modulation error ratio (MER), and power efficiency, with an emphasis on real-time adaptability and system-level quality of service (QoS) requirements.
Exploring generative models for remote sensing: a comprehensive review
Remote sensing enables systematic observation of the Earth’s surface through aerial and satellite imagery, supporting a wide range of applications from environmental monitoring to urban planning. Recent advances in deep learning models have significantly enhanced the analysis of remote sensing data; however, these models often require large volumes of high-quality labelled data, which are difficult and costly to obtain due to limitations in sensor resolution and data acquisition. Generative Adversarial Networks (GANs), a class of deep generative models, have emerged as a transformative solution by synthesizing realistic data and improving model robustness in data-scarce environments. In addition to data augmentation, GANs facilitate critical remote sensing tasks such as image super-resolution, cloud removal, cross-modal translation, and change detection. Their ability to model complex data distributions and perform adversarial training makes them particularly effective for addressing challenges such as noise, resolution gaps, and domain discrepancies. This review provides a comprehensive overview of GAN architectures, explores their diverse applications in remote sensing, discusses relevant evaluation metrics, and highlights key challenges and opportunities for future research.
Ensemble noise-aided bit flipping decoding of low-density parity-check codes
Moderate-length low-density parity-check codes are incorporated to increase the data transmission reliability and the energy gain in the next generation wireless communication systems. The paper proposes ensemble decoding based on reduced-complexity noise-aided gradient descent bit flipping algorithm. Block diagram and pseudocode of this approach were presented. The decoding by each constituent noise-aided bit flipping decoder is performed independently and in parallel mode. The ensemble size and the chosen noise scale values are key parameters of this decoding framework. The simulation results confirmed the efficiency of the proposed noise-aided ensemble decoder. It was shown that the increasing of ensemble size leads to improve the error performance. The average number of decoding iterations is acceptable in high signal-to-noise ratio region. The application of the presented decoding algorithm will improve the data transmission reliability under low-latency requirements in the next generation wireless technologies.
Knowledge sharing in Independent Deep Q-Network
This paper investigates knowledge sharing mechanisms in weakly coupled multi-agent reinforcement learning systems based on Independent Deep Q-Networks (IDQN). Although parallel agents can accelerate data collection, their learning processes typically remain isolated, resulting in suboptimal use of collective experience. To address this limitation, the study proposes two complementary methods: (1) a teacher-selection mechanism that identifies the most efficient agent based on episodic performance, and (2) a dynamic control mechanism that adjusts the intensity of knowledge transfer according to the performance gap between teacher and student. The experiments were conducted in the OpenAI Gym CartPole-v1 and LunarLander-v3 environments using three independent agents, to validate the effectiveness across tasks with different reward structures, dynamics, and difficulty levels. All agents were trained with Batch TD(0) at the end of each episode, using a replay. Knowledge transfer was implemented through policy distillation on pseudo-labeled transitions sampled from the teacher’s experience buffer. The number of distillation epochs was dynamically determined using a nonlinear scaling function bounded by predefined minimum and maximum values. Results demonstrate that the proposed mechanisms consistently accelerate learning and improve stability compared to baseline DQN configurations without knowledge sharing. Systems employing teacher selection outperform random teacher choice and all-to-all sharing. Dynamic intensity adjustment proves more effective than constant-intensity distillation. Normalized AUC analysis further confirms statistically significant improvements in both maximum and average episodic returns, indicating faster convergence of the best agent as well as more uniform progress across all agents. The findings show that knowledge sharing with informed teacher selection and adaptive transfer strength provides a robust and scalable approach for improving the efficiency of independent agents in stationary environments. These mechanisms are compatible with common DQN extensions and can serve as a foundation for future research on adaptive multi-agent knowledge exchange strategies.
Detection of humans in drone images using deep learning techniques
Interest in creating reliable and fast human detection systems has increased due to the growing use of Unmanned Aerial Vehicles (UAVs) in a variety of applications, including search and rescue operations and surveillance. The YOLOv8 deep learning algorithm, specially designed for drone imagery, is used in this paper's real-time human recognition method. Because it is effective and precise at identifying people in complicated backdrops and with different lighting conditions, YOLOv8, an enhanced version of the You Only Look Once (YOLO) object detection algorithm, is used. The YOLOv8 model is trained on a large dataset of annotated drone images, encompassing diverse scenarios and environments to facilitate robust human detection. This study uses a UAV Human Dataset encompassing various environmental conditions, ensuring robust performance in challenging scenarios. This study contributes to advancing drone-based technologies for human detection, offering a scalable and efficient solution for real-world deployment. The YOLOv8 model achieved a precision of 88.83%, recall of 71%, and mAP score of 74%.
Comparative analysis of DeepSORT, ByteTrack and StrongSORT algorithms for multi-object tracking in UAV-based video surveillance
This paper presents a comparative analysis of state-of-the-art multi-object tracking algorithms applied in UAV-based video surveillance systems. The performance results of three advanced tracking methods – DeepSORT, ByteTrack, and StrongSORT – integrated with the YOLOv8 object detector are presented. A mathematical description and experimental simulations were conducted to evaluate the accuracy, stability, and computational performance of the algorithms in dynamic and complex scenes. The obtained results indicate that the StrongSORT + YOLOv8 combination provides the best balance between accuracy and robustness, whereas the ByteTrack method demonstrates high track continuity in high-density environments. The proposed approach can be utilized to enhance the efficiency of UAV-based autonomous monitoring systems.
Highly efficient approaches to processing complex visual data in decision support systems
Modern decision support systems (DSS) increasingly must analyze large-scale, complex, and heterogeneous data streams in real time. High-performance, AI-driven processing methods – particularly deep neural networks – offer effective solutions. This study examines the integration of contemporary architectures – YOLOv8, ResNet-50, EfficientNet-B3, and the Vision Transformer (ViT) – to enhance DSS capabilities. The models are benchmarked on a representative image-classification task using the COCO dataset for training and evaluation. Empirical results indicate that the transformer-based model (ViT) attains the highest accuracy, whereas the one-stage architecture (YOLOv8) achieves the fastest inference. EfficientNet-B3 and ResNet-50 exhibit intermediate trade-offs between accuracy and speed. Deployment considerations across DSS scenarios are outlined: YOLOv8 is appropriate for real-time, resource-constrained environments; ResNet-50 provides balanced performance; EfficientNet-B3 offers strong accuracy with moderate computational demand; and ViT delivers the best accuracy when ample data and computational resources are available. The findings are discussed in the context of DSS workflows, illustrating how the model outputs can directly inform and improve decision-making processes.
Anti-aliasing method for second-order curves on a hexagonal raster
This paper presents a method for anti-aliasing of second-order curves using method of additional lines. The proposed method refers to supersampling methods, which allow achieving a high level of smoothing even for complex contours. For each type of second-order curves, the method is implemented using an evaluation function. The algorithm by which the method is implemented consists of six main sides. Direct formation of the internal and external additional lines of pixels is implemented in the fourth block of the algorithm. The pixels of both the internal and external additional lines are painted with a colour intensity of 50% of the colour intensity of the pixels of the main contour of the curve. This creates the appearance of a smooth transition and smoothes the steps. This allows reducing the aliasing effect on the boundaries of graphic objects of high curvature. The method significantly increases the speed of anti-aliasing implementation, due to the possibility of using it when constructing the main contour of the curve on a hexagonal raster. The method of additional lines for anti-aliasing of second-order curves on a hexagonal raster is equally effective for all types of second-order curves.
Method for assessing the risk of user compromise based on individual security profile
This article presents a method for assessing the risk of user compromise in corporate information systems caused by social engineering cyberattacks. The approach integrates an evaluation of the individual security profile of each user – based on psychological, organizational, technical, and information influence factors – with graph-based modelling of internal interactions within the organization. Unlike most existing methods that focus solely on isolated user characteristics or assume a single-stage (direct) cyberattack scenario, the proposed method accounts for the propagation of cyberattacks through multi-stage trajectories in communication networks. A formalized four-phase model of social engineering cyberattack implementation is developed, with each phase described as a function of interacting factors. Conditional probabilities are estimated using dynamic coefficients that reflect both the base value and the contextual impact of modifying factors. A graph-based procedure is introduced to calculate the probability of multi-stage compromise based on the structure and intensity of user interactions (e.g., project participation, communication frequency, hierarchical relationships, and shared access to information assets). The proposed method was validated through individual user security profiling, followed by scenario-based modeling of multi-stage social engineering attack propagation on a representative subset. Results show that users with strong individual protection can remain vulnerable due to their position in critical communication chains. The visualization of trajectories exceeding a defined probability threshold supports the identification of high-risk paths and intermediary nodes that require prioritized protection measures. The main scientific contribution lies in combining individualized risk assessment with system-level propagation modelling using interpretable and adaptable mathematical constructs. The method does not require large volumes of empirical data, which ensures its practical applicability even in conditions of limited access to internal information. Future research will focus on automating data collection for factor assessment, adapting the model for real-time operation, and extending it through advanced modelling of behavioral attack scenarios.
Positional coding method in differential wave space
The study addresses the development of intelligent services for the transmission of infrared (IR) imagery, with a focus on the generation and exploitation of semantic metadata that reflects the informativeness and spatial structure of image regions. Such metadata facilitates classification of object types and states and supports the selection of informative fragments within IR frames. These functionalities are particularly relevant in applications involving unmanned onboard systems, where tasks such as dynamic object tracking and autonomous navigation impose stringent requirements on the timeliness and fidelity of visual data. With the increasing complexity of operational scenarios, the demands for high information completeness – such as higher frame rates, improved resolution, and reduced distortion – place significant burdens on infocommunication subsystems. This induces a fundamental conflict between the need for low-latency transmission and the preservation of the structural integrity of IR data. To address this issue, the paper formulates a new scientific and applied objective: to enhance the quality of IR-based intelligent services through the design of a positional coding method in differential wave space. The proposed method operates by hierarchically segmenting IR images to localize structurally homogeneous regions, applying differential wave transformations to capture local contrast dynamics, and encoding positional information relative to wavelet-domain variations. This positional representation enables spectral-differential group coding, which effectively reduces bit volume while preserving spatial-semantic features. An experimental evaluation using the Open Turbulent Image Set (OTIS) – consisting of PNG-encoded IR images with varying levels of informativeness – demonstrated an average bit volume reduction of approximately 37%. Comparative analysis with JPEG2000 encoding of 16-bit images revealed a 25% gain in compression ratio while maintaining equivalent PSNR values.
Web platform with Checkbox support: aspects of fiscal accounting, reporting, and interaction with tax authorities
The article examines the process of developing a web platform for an online store with functionality for online fiscalisation of receipts. The implementation of such solutions is becoming increasingly relevant in the context of business digital transformation and legislative changes requiring entrepreneurs to fiscalise online sales. The main focus is on the use of software-based cash register systems, particularly the Checkbox service, to automate reporting and interaction with tax authorities. Key advantages of software-based cash register systems are highlighted, including flexibility, resource savings, and the ability to integrate via REST API. The article discusses the methodology of platform development, including the integration of fiscalisation services, automated generation of X and Z reports, and the use of modern web technologies. The study provides an analysis of existing fiscalisation services, system architecture considerations, and practical implementation aspects such as route organization, business logic, and shopping cart optimization. The proposed approaches help minimize administrative costs, enhance reporting accuracy, and adapt the system to legislative changes.
Comparative analysis of web development frameworks in PHP: Codeigniter, Cakephp and Yii
This study presents a comparative analysis of three PHP frameworks: CodeIgniter, Yii, and CakePHP, focusing on performance, reliability, resource efficiency, stability, and community support. Three identical applications performing CRUD (Create, Read, Update, Delete) operations on a database were developed for testing. The evaluation metrics included request handling speed, CPU and memory usage, error rates under varying workloads (10 to 16,000 virtual users), and framework popularity within the developer community. Experiments were conducted using Apache JMeter and PerfMon in a controlled testing environment. The results indicate that CodeIgniter outperformed Yii and CakePHP in most scenarios, demonstrating the lowest memory consumption, fastest response times, and greater stability under high concurrency. Its lack of an ORM (Object-Relational Mapping), replaced by a procedural QueryBuilder, contributed to this performance advantage. By contrast, Yii and CakePHP, which rely on ORM, required more resources and showed greater CPU instability, particularly under heavy load.
Crop price forecasting using a Temporal Fusion Transformer for Krishna district of Andhra Pradesh
Indian farmers experience ongoing income volatility from fluctuating market prices, which erodes their financial health and long-term sustainability. To resolve this issue, a revamped Temporal Fusion Transformer (TFT), a state-of-the-art deep learning model is proposed for time-series forecasting with applications in agricultural price prediction. The TFT takes into account critical factors, including rainfall and temperature, previous price trends and market pressure in giving accurate, actionable forecasts to farmers. The model performed well when trained on a large database from Krishna district, Andhra Pradesh, India between January 2017 and September 2024. The TFT model achieved a RMSE of 99.13, MAPE of 2.16%, MAE of 72.08 and an accuracy rate of 93.24%. The system also allows the farmer to compare the forecast with the MSP and give them a very precise suggestion to maximise their revenue. It allows for farmers to take proactive decisions and supports an aware decision making approach by mitigating price volatility and stabilizing income.
Model of packet transmission of text data using SDR in the GNU Radio Companion environment
This paper outlines the design, implementation, and testing of a digital transmission system capable of sending text data in packetized form using QPSK modulation. The setup leverages Software-Defined Radio (SDR) hardware in combination with GNU Radio Companion software. The system operates by initiating transmission from a configured source, transmitting the data wirelessly, and recovering the signal at the receiver end for decoding and analysis. Two configurations were tested: one using a pseudo-random bit sequence and another using actual file input with both source and channel coding mechanisms. Signal behavior was examined at various stages in the processing chain, including modulation, filtering, over-the-air transmission, synchronization, equalization, and phase correction. Analytical tools such as constellation plots, IQ diagrams, and spectral analysis were employed to evaluate system performance and signal integrity. The model successfully demonstrated reliable data transmission and reconstruction, making it a useful platform for both educational purposes and further exploration in the area of digital communications.
Modelling of a pull-flow production system with dynamic buffer stock control
In modern industrial logistics, efficient production relies on synchronizing raw material supply with real-time demand to minimize waste and improve performance. This paper proposes a modelling approach for production systems based on a pull flow strategy, which controls the replenishment of raw materials in response to real-time demand. The production system is represented in Simulink as a discrete manufacturing unit supported by a buffer stock. A feedback control loop governs the flow of materials from the warehouse to the buffer, using demand-driven trigger points to prevent shortages and maintain stable machine throughput. The objective is to ensure uninterrupted production while optimizing stock levels. Simulation results highlight the effectiveness of the proposed approach in synchronizing supply with consumption, enhancing overall system responsiveness and operational efficiency.