Studia Informatica. System and information technology
ISSN: 1731-2264 e-ISSN: 3072-4651 DOI: 10.34739 Points: 20
Wydawnictwo Naukowe Uniwersytetu w Siedlcach
Assesment of the Performance of the Neural Model of the National Electricity Demand System
This paper presents the results of research on the use of a neural model’s capabilities for forecasting national power demand using data from the years 2019–2023. The neural model is a daily MIMO-type model with 48 hourly simultaneous inputs concerning the total electricity generation by the JWCD system and by the nJWCD system, and with 24 hourly simultaneous outputs concerning national electrical power demand shifted by one day ahead relative to the input quantities. The neural model of the National Power Demand System as well as the simulation model built on its basis for investigating model accuracy in the scope of forecasting were designed and implemented in the MATLAB and Simulink environment. Many interesting research results were obtained, particularly in the scope of model quality.
Copyright (c) 2026 Studia Informatica. System and information technology https://creativecommons.org/licenses/by-nc-nd/4.0
Selection of Data for Modeling the Development of the Power System Using a Recurrent Artificial Neural Network
The National Power System in Poland is now already an intelligent system, and the changes increasingly have a hybrid character, that is, structural-parameter in nature. The uniform National Power System is increasingly being supplied with electricity not only from conventional sources but also from renewable sources: solar, wind, and hydro. In addition to producers and consumers of electric power and energy, prosumers are also appearing. The power system in this diversity is equipped with various types of automated devices, which make it increasingly intelligent. In Poland, the total installed capacity of all electricity sources, both conventional and renewable, reached 72.9 GW in April 2025, of which as much as 34.8 GWcomes from renewable sources (over 47.7%), while the rapidly growing number of prosumers makes the NPS system increasingly complex at the system level. The growing complexity and diversity of the National Power System means that controlling the development of the system intelligently involves the necessity of building development models. Thus, there exists a specific research gap between the need to obtain development models of the National Power System for the purpose of development control and the possibilities of obtaining such models using artificial intelligence methods, such as neural methods. In line with research within this research gap, this study attempted to obtain a development model of the National Power System using annual statistical data on the operation of the National Power System from 1990 to 2024. This formulation of the research problem involves, among other things, the selection of data for research experiments in the area of modeling the development of the National Power System, as well as the selection of architecture and learning methods for ANNs. In the present publication, in particular, the research methodology for modeling the development of the National Power System and the obtained research results using Recurrent ANN for modeling are presented.
An Evaluation Methodology for Explanations of Clustering Results in Textual Domain
Explainability has become a must for any AI algorithm acting as a black-box, like Graph Spectral Clustering (GSC). Though a success was achieved in developing respective explanation methodologies, a new challenge has to be faced: the evaluation of the (quality of) explanations. Several evaluation methods for explanations in AI have been developed, but they turn out to have some shortcomings, making them unsuitable for GSC explanation evaluation. In this paper, we recall some of these methods and point out their respective shortcoming. Based on this investigation, we suggest a new explanation methodology oriented towards GSC and similar methods and present a small experiment on the usage of this methodology.
A review and comparison of selected WCAG-based website digital accessibility assessment methodologies
A website accessibility assessment methodology provides a structured process that directs evaluators through a sequence of steps to determine whether a website conforms to established accessibility standards, such as the WCAG guidelines. This article presents a comparative examination of three major web accessibility evaluation frameworks: WCAG-EM (Website Accessibility Conformance Evaluation Methodology) developed by the W3C,RGAA(Référentiel Général d’Amélioration de l’Accessibilité), which is mandatory for public institutions in France, and the Polish Web Accessibility Evaluation Methodology created for use within the Polish public sector. Each of these approaches embodies a distinct perspective on evaluating digital accessibility, shaped by differing legal, technical, and organizational environments. The article contrasts these methodologies with respect to their scope structural composition, integration with legal requirements, degree of detail, testing procedures, and practical usability. The findings underscore the strengths and weaknesses of each framework, offering insights into how evaluation practices may be adapted to national conditions while remaining consistent with international accessibility standards. The study concludes with a comparative analysis of selected characteristics across the examined methodologies.
PolishWeb Accessibility Evaluation Methodology
Binaural Sound Scenes Creator Supporting The Development Of Spatial Orientation Skills of Blind Individuals
This article introduces an approach to automatic generation of binaural sound scenes BSS that describe in auditory form environments for visually impaired individuals. This audio, with descriptions of the city environment, can be beneficial for developing spatial orientation skills among Blind Individuals (BI), especially those who have recently lost their vision and must adapt to new, difficult living conditions. The idea to solve the problem is that artificially generated binaural scenes can simulate situations in which a BI navigates open spaces, such as a city, or large buildings like train stations, airports, and university campuses. Practising these skills in a simulated environment can be less stressful than real-world training. This work describes the development of a specialised application that facilitates the creation of binaural sound scenes from simple monophonic sounds. Several scenes were subsequently generated and then tested by three visually impaired individuals. This testing aimed to determine whether entirely artificially generated sound scenes are a sufficiently robust approach for constructing more complex simulation environments.
An Interactive AutoML and LLM-Based Platform for Medical Data Analysis
The widespread adoption of machine learning (ML) in healthcare is often limited by the significant technical expertise required to build, optimize and interpret predictive models. This paper presents an interactive platform designed to democratize access to medical data analysis by integrating an Automated Machine Learning (AutoML) engine with a Large Language Model (LLM) conversational assistant. The proposed system enables non-technical users, such as clinicians and biomedical researchers, to upload datasets and generate robust predictive models using AutoGluon without the need for programming. To address the interpretability challenges of complex models, the platformcouples SHAP-based visualizations with an LLM-driven chat interface that explains preprocessing steps, model metrics and feature importance in natural language. We evaluated the platform on eight public biomedical datasets covering both classification and regression tasks. Experimental results demonstrate that the system produces competitive predictive performance relative to established benchmarks while ensuring high usability. By lowering technical barriers and enhancing transparency, this tool empowers domain experts to independently leverage ML for clinical decision support and research.
Searching form the Model of Human Facial Expressions and its Implementation inMATLAB and Simulink Environments
The publication contains selected research results on the creation of a neural model of human facial expressions along with its implementation in the MATLAB and Simulink environments using the library called Deep Learning Toolbox. The model was generated as an object to recognize selected human faces and emotional states based on visual data recorded on the human face in real time. The study was placed against the background of available literature on the analysis of facial expressions and emotion classification methods. In addition to the concept of the original solution, the assumptions of the research experiment were given, a method for measuring facial expressions was selected for the experimental conditions, and a set of data was developed for training a neural model of the facial expression system with their preparation for ANN learning. Ultimately, various artificial neural networks were trained to model the facial expression system and sensitivity and comparative tests were performed in Simulink to, among others: assess the quality of the model in relation to real data. Very high results of ANN training of the facial expression system model were obtained (MSE on the order of 10−14, R close to 1), as well as relatively high model quality relative the facial expression system, measured, among others, average relative error, the value of which was several percent. However, in terms of prediction effectiveness, because of the obtained results, which are not very high for the assumed high measurement accuracies, the research that has been started is continued.
Copyright (c) 2025 Studia Informatica. System and information technology https://creativecommons.org/licenses/by-nc-nd/4.0
Modeling the National Power System Using Artificial Neural Networks
The purpose of this paper is to investigate and demonstrate the potential of modeling the National Power System (NPS) in Poland. The paper presents the results of research modeling of the NPS system using three different models based on Artificial Neural Networks. Data characterizing the NPS were implemented to build the models. The results measured by Mean Squared Error and determination coefficient were presented and analyzed. The research results and conclusions are presented in the summary.
analysis of selected sizes of the power system
A Vision-Based System for Rapid DXF Generation in Metalworking: Image Processing Pipeline and Economic Feasibility Case Study
Low-volume and custom production jobs in small and medium-sized metalworking enterprises are often declined due to the high fixed costs and time requirements of manual CAD modeling. This paper presents a practical, production-ready desktop application for the automated generation of DXF files from photographs of physical parts, designed to support rapid laser cutting and prototyping workflows. The system combines a user-friendly GUI with a robust computer vision pipeline including adaptive thresholding, contour extraction, geometric scaling, and vector simplification. Unlike generic raster-to-vector tools, it targets the specific needs of SMEs: offline use, closed contours, minimal training, and compatibility with CAM systems. A real-world case study demonstrates the successful replication of an agricultural machine parts, showing ~90% reduction in preparation time and ~45% profit margin on the order. Interviews with industry stakeholders confirm the potential of the proposed solution to recover previously unviable jobs and increase monthly revenues by an estimated 5-7%. This interdisciplinary contribution integrates computer vision and applied economics, offering a scalable solution to improve productivity and digital inclusion in SMEs.
Functioning of an Intelligent System on the Example of the National Power Demand System
This publication includes a concept and research results regarding the search for regularity in the functioning of an intelligent system using the example of a system for forecasting national power demand. The research employs the ARX regression machine learning method to derive models of the functioning of the electricity forecasting system. The study uses actual values of electricity generated by both uniform and non-uniform domestic electricity systems as input quantities, and forecasted national power demand as the output values from January 2023. The main aim of the research was to obtain several hourly models and several monthly models to demonstrate changes in selected system parameters and to show whether, and what changes occur in the direction of increasing the system’s independence, enhancing the system control level, etc. The research methodology also employs, in addition to the machine learning method, the method of control theory and systems. At the same time, an example of obtaining a model of the national system demand for electric power using the ARX machine learning method, which was transformed into state space model, whose matrix elements were used to interpret the correctness of changes in the intelligent system.
Modern Application Lifecycle Management Approaches
Effective application lifecycle management (ALM) relies on modern tools and practices that streamline development while ensuring security and stability. This article examines key components of a modern ALM strategy, including DevOps culture, CI/CD pipelines, Infrastructure as Code, containerization, orchestration, monitoring, observability, and the growing role of AI. Special attention is given to the integration of security into the CI/CD process, particularly through automated testing techniques such as Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST). These practices help identify vulnerabilities early in the development cycle, reducing risk and enhancing application reliability.Adopting these technologies involves initial investments in licenses, training, and team restructuring. However, these efforts are rewarded through automation of repetitive tasks, accelerated deployment cycles, improved scalability, and proactive issue detection. AI-driven tools further enhance development efficiency through intelligent code suggestions, predictive analytics, and automated bug detection. By embracing these modern approaches, organizations can achieve a more secure, efficient, and agile development process, better equipped to adapt to a rapidly evolving technological landscape.
Application of the genetic algorithm to the estimation of energy consumption in Poland
This work aims to model and estimate electricity consumption in Poland using a genetic algorithm approach. The genetic model was based on socio-economic data such as GDP, imports and exports, and population growth. The data covered the period from 1990 to 2024. Quadratic and exponential models were tested in this study. The exponential model was demonstrated to be the best fit to the data. The best mean absolute error was obtained for the exponential model, MAE = 0.0276, for a population size of 100, a crossover probability of 0.85, and a mutation probability of 0.02. The projection of electricity consumption in Poland was also demonstrated and compared with the data from the literature. The genetic model forecasts an electricity consumption of 211 TWh in 2040.
The architecture of human mind, and non-von Neumann computer architecture
Structural plasticity in the brain (i.e. rewiring the connectome) may be viewed as mechanisms for dynamic reconfiguration of neural circuits. First order computations in the brain are done by static neural circuits, whereas higher order computations are done by dynamic reconfigurations of the links (synapses) between the neural circuits. Static neural circuits correspond to first order computable functions. Synapse creation (activation) between them correspond to the mathematical notion of function composition. Functionals are higher order functions that take functions as their arguments. The construction of functionals is based on dynamic reconfigurations of function compositions. Perhaps the functionals correspond to rewiring mechanisms of the connectome. The architecture of human mind is different than the von Neumann computer architecture. Higher order computations in the human brain (based on functionals) may suggest a non-von Neumann computer architecture, a challenge posed by John Backus in 1977 [7]. The presented work is a substantial extension and revision of [2].
A Pathfinding Module for the Indoor Navigation System NaviSecure
NaviSecure is an indoor navigation system developed atUniversity of Siedlce. It utilizes a dedicated hierarchical building map and an infrastructure of Bluetooth Low Energy transmitters, as well as hazard detectors such as smoke, flood, and gas sensors. The system facilitates daily navigation and provides emergency assistance while addressing the needs of individuals with disabilities. In this paper we present the pathfinding module of the NaviSecure system, implemented on top of the Neo4j graph database. We introduce the basic concepts behind our approach, we discuss the system architecture and our custom approach to deal with special needs of the users. Finally, we show the results of our experimental evaluation of several graph algorithms, modified to meet NaviSecure’s specific requirements. The results confirm the efficiency of our approach.
Autonomous Navigation for Unmanned Ground Vehicles in Unstructured Terrain
The problem of autonomous navigation for Unmanned Ground Vehicles (UGVs) in unstructured environments is both challenging and crucial for their deployment in real-world applications. Perception is important, as it provides the necessary information for terrain traversability and environmental awareness. In this article, the developed manned-unmanned vehicle designed to carry out autonomous missions in unstructured terrain is presented, along with the system requirements essential for such operations. Challenges related to environmental perception and navigation in unstructured environments are discussed. Achievements in developing AI models capable of interpreting sensor data are highlighted, demonstrating significant progress in the field of autonomous navigation. However, several gaps remain, particularly in the areas of sensor fusion, real-time decision-making, and adaptability to highly dynamic conditions. Development in the field of autonomous systems allows for a wider expansion of the potential applications ofUGVs in various fields, including disaster response, environmental monitoring, and exploration of hazardous environments.
Analysis of Information Technology Adoption in Polish Agriculture
This study explores the adoption of information technology (IT) in Polish agriculture, focusing on its impact on efficiency, profitability, and farmers’ perceptions of associated costs. A survey conducted among 106 farmers from all Polish provinces assessed their use of IT tools, including farm management software and automatic machine navigation systems. The results highlight the varying degrees of IT adoption based on farm size, with larger farms demonstrating greater integration of these technologies. Farmers’ views on the cost-effectiveness of these systems were mixed, indicating room for improvement in affordability and functionality. The study underscores the potential of IT to enhance agricultural practices but notes the barriers faced by smaller farms in adopting such technologies.
Analysis of the Possibilities of the Evolutionary Algorithm to Improve the Neural Model of the TGE S.A. Day Ahead Market System Using Selected Programming Environments
The article contains selected research results regarding the analysis of the possibility of using the Evolutionary Algorithm to improve neural models of intelligent systems using selected programming environments. Choosing an appropriate programming language is one of the basic activities in the process of implementing complex algorithms, which include methods of artificial neural networks and evolutionary algorithms. Due to the fact that the object of the researchwas an intelligent Day Ahead Market system operating on the Polish Power Exchange and the modeling methods were artificial neural networks and evolutionary algorithms, it was decided to use very high-level programming languages such as Python, Matlab and C# for implementation and associated development environments. It turned out, among other things, that each of these languages and programming environments has its advantages and disadvantages, but all of them are very useful due to their useful syntax and rich included libraries. A thorough analysis of the implementation shows, among other things, that the choice of programming language affects the efficiency, speed and quality of the obtained implementations of system models. Against this background, the advantages and disadvantages of individual programming languages are shown, especially in the context of implementing evolutionary algorithms. The research results indicate directions for selecting an appropriate programming language and the associated programming environment for system modeling using artificial neural networks and evolutionary algorithms. In addition, the method of analysis, as well as the method of modeling and implementation was shown on the example of a specific system, which was the Day Ahead Market system of TGE S.A.
Selected Aspects of Neural-Evolutionary Modeling of Prices on the Day Ahead Market of TGE S.A.
Modeling in the context of Artificial Intelligence (AI) is using mathematics to describe, analyze, and predict real-world systems. Building models that can simulate or predict various aspects of reality is a key issue that is the subject of many studies. The quality of models depends on many elements, starting from the architecture of the neural network itself, through the selection of teaching data in terms of the size of the sets, and the number of factors influencing the choice of the network itself. Modifications of the network training methods themselves also play an important role, e.g. through the use of Evolutionary Algorithms (AE). The paper focuses on several selected aspects related to the quality of modeling based on prices on the Day Ahead Market (DAM). The influence of network architecture factors, network type, number of training data, and Evolutionary Algorithms on the improvement of the model quality measured by the Mean Squared Error (MSE) and the coefficient of determination (R2) were considered.
Data Collection and Analysis in Social Anxiety VR Therapies
Virtual reality (VR) therapy has emerged as a promising approach for treating social anxiety disorder by allowing controlled exposure to anxiety-provoking social situations in a safe virtual environment. Collecting and analyzing physiological data from participants during VR therapy sessions is crucial for monitoring their anxiety levels, tailoring the therapy, and assessing treatment outcomes. This article discusses the challenges and solutions for collecting and analyzing rapidly generated physiological data, such as galvanic skin response, heart rate, and respiration rate, in the context of social anxiety VR therapies. It explores techniques for real-time data streaming, storage in efficient file formats like Apache Parquet, and subsequent analysis of the collected data. The article also examines the role of data analysis in improving therapy protocols, personalizing treatment, and advancing our understanding of the physiological responses associated with social anxiety. By leveraging modern data engineering practices, VR therapy platforms can unlock valuable insights and enhance the effectiveness of exposure-based interventions for social anxiety disorder.
Supporting the Development of Spatial Orientation Skills of Blind People Using Binaural Sounds in VSR Game
Spatial orientation skills are crucial for the comfortable everyday functioning of blind individuals and affect their physical and mental well-being. Developing spatial orientation is usually carried out through traditional training courses without the support of electronic tools. Training them in a natural city environment, even under the supervision of an instructor, is often a very stressful process for the student. This paper proposes a solution for a simplified city space simulator created as a game using 3-dimensional binaural sounds. This simulator can be used at home to develop basic spatial orientation skills for blind people. While feedback from testing by three blind users was positive, the limited scope of this initial evaluation underscores the need for developing more advanced applications supporting the teaching of spatial orientation to blind users.
This paper offers a detailed and comprehensive analysis of the various cyber threats that target machine learning models and applications. It begins by characterizing basic classifiers and exploring the objectives of intentional attacks on typical classifiers, providing a foundational understanding of the threat landscape. The paper then thoroughly examines the vulnerabilities that machine learning systems face, alongside the methods for detecting, countering, and responding to these cyber threats. Special attention is given to specific types of threats, including attacks on machine learning models, adversarial attacks, poisoning attacks, and backdoor attacks. The paper also addresses critical issues such as attacks on data protection mechanisms, replay attacks, denial of service attacks, learning model theft, malware, and breaches in data privacy. Each of these threats is analyzed in detail, with a focus on their potential impact and the strategies that can be employed to mitigate them. In its conclusion, the paper provides recommendations on regulatory measures and best practices to safeguard machine learning models and applications against these evolving cyber threats. These recommendations emphasize the necessity for a robust regulatory framework to ensure the security, reliability, and integrity of machine learning systems in an increasingly digital and interconnected world.
Quantum Inspired Evolutionary Algorithm based on Day Ahead Market of the Polish Electricity Power Exchange
The paper discusses the essence of the method and selected results of the quantum-inspired implementation of the so-called System Evolutionary Algorithm on the example of the neural model of the Day-Ahead Market of the Polish Electricity Exchange Market S.A. First, the appropriate Perceptron Artificial Neural Network was designed and implemented in the MATLAB and Simulink environment, in which the Day-Ahead Market model was taught. Then it was assumed that the obtained parameters of the neural model, i.e. weights and biases, are quantum-encoded numbers, the values of whichwere corrected by the quantum-inspired Evolutionary Algorithm. Finally, a hybrid model was obtained in the form of an Artificial Neural Network with weights and biases corrected by a quantum inspired Evolutionary Algorithm. As a result of the conducted research, the relative error improvement was obtained from the level for different hours of the day from -0.11% ÷ 0.12% to the level from 0.04% ÷ 0.05%, i.e. by an order of magnitude. Moreover, the improvement of the quantum-inspired evolutionary algorithm measured by the fitness metric formulated as MSE error was achieved from a value of 0.990366 to a value of 0.990375.
Dependence of Spectrogram from Graph Spectral Clustering in Text Document Domain onWord Distribution Models
Analysis of the shape of a Laplacian spectrogram is a new line of research used in graph spectral clustering. More precisely, we observed that (properly normalized) plots of the eigenvalues of sub-Laplacians characterizing different groups of documents differ in their shape. Thus, by computing the distance between these plots, we can solve the problem of clustering and classifying new observations. This idea is developed in a number of our papers and as such, can be considered a pioneering approach to cluster analysis. In an attempt to answer why it is so useful, in this paper we consider the hypothesis that the shape of a spectrogram could be attributed to the writing style of the authors of the document group in the cluster. We explore this hypothesis for several models of word distribution. In particular, we assume that the writing style is reflected in the word distribution of texts of an author or a group of them. We check if changing of distribution parameters of a widely accepted log-normal word distribution model changes in fact the Laplacian eigenvalue spectrogram in such a way as to distinguish between document groups. We found that in fact variation of each of the distribution parameters leads to distinct groups of documents. These findings justify the usage of Laplacian spectrograms to distinguish (cluster or classify) groups of documents.
Artificial Text Generation from Simple Language Model
A modification of the Gale-Shapley algorithm used to assign Polish pupils to schools
Allocation algorithms based on rankings are utilized in various situations, such as selecting appropriate tasks for workers, scheduling doctors’ appointments, or assigning children to schools.Amodified version of the standard Gale-Shapley algorithm is introduced and employed in Poland’s secondary school recruitment process. This algorithm relies on students’ school results and their priority lists. During the recruitment process, students are assigned to a school on the basis of their performance and preference list. However, this approach disadvantages weaker students, as they are only considered towards the end of the process. Consequently, they are often assigned to schools with a lower priority on their lists or, in some cases, remain unassigned. The proposed modification aims to improve opportunities for students to plan their educational future more fairly, considering their individual achievements.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
Scheduling using Convolutional Neural Network in GPU environment
Graphics processing units (GPU) have become the foundation of artificial intelligence. Machine learning was slow, inaccurate, and inadequate for many of today’s applications. The inclusion and utilization of GPUs made a remarkable difference in large neural networks. The numerous core processors on a GPU allow machine learning engineers to train complex models using many files relatively quickly. The ability to rapidly perform multiple computations in parallel is what makes them so effective; with a powerful processor, the model can make statistical predictions about very large amounts of data. GPUs are widely used in machine learning because they offer more power and speed than CPUs. In this paper, we show the use of GPU for solving a scheduling problem. The results show that this idea is useful, especially for large optimization problems.
Selected applications of models based on quantum computing
The work discusses mathematical models using paradigms developed for quantum phenomena, including the vector-matrix Hilbert space and quantum gates for linear transformations of states to build a day-ahead market model (DAM). The article presents the most popular directions in the development of models using tools used in the description of quantum phenomena to present the methodology of conducting this type of calculation. Mathematical structures such as Hilbert space and operations on this space were used to build a quantum-inspired Artificial Neural Network. The work presents the proposition of implementation of DAM system to build a Neural Network Model with usage quantum phenomena based on Hilbert space. The concept of quantum processing based on quantum circuits was also noticed due to its large use and the development of implementing tools as a supplement to the entire area of development of building models based on quantum computing.
Smart Village as an action system supporting people with special information and communication needs
The paper presents selected results of theoretical research regarding the concept of a smart village as an intelligent operating system oriented to supporting people with special information and communication needs. Such a system is a developing system of socio-technical nature, which in its area enables the cooperation of its inhabitants as a local society. The presented idea of Smart Village is therefore based on the combination of modern tools and information and communication technologies to improve the quality of life and raise the standard of public services for citizens with much better use of resources and with less negative impact on the environment. It was pointed out that before proceeding with the design of the Smart Village as an intelligent operating system, it is important to identify the needs and capabilities of subsystems in a specific village and commune, and then, on this basis, design a model of an operating system supporting people with special information and communication needs as a hybrid system. The concept of Smart Village as a system of action is therefore very important for the development of rural areas, both because of new opportunities to create new jobs, as well as from the point of view of the quality of life and work in the countryside. More and more attention is paid to these problems, especially in terms of practical aid programs, but so far no one has defined a smart village system. For these reasons, this study is pioneering and awaited due to the need to develop a modeling method and possible subsequent implementation of the Smart Village system model in system practice.
Comparative Analisis of Digital Filters for Proper Positioning of User Mobile Device Using BLE Transmitters and Multilateration Technique
Nowadays, traveling without systems supporting navigation and guidance to the destination is hard for us to imagine. The outdoor positioning techniques used in these solutions have been mastered mainly by global positioning systems. However, assisting the navigation of users inside large public buildings still needs to be improved. It matters even more for persons with special needs, such as visually impaired, seniors, or people in wheelchairs. Determining the correct position of the user inside the complex space of a multi-floor building is a big challenge for such persons. Several methods can help in this matter. For example, technologies such as RFID, WIFI networks, image recognition, or lidar are used. However, the best solution to this problem is using infrastructures of low energy transmitters (BLE), called beacons. Then, construct an appropriate map to determine the user’s position to help guide the user to a destination. Nevertheless, to designate such a position, we need to know each transmitter’s signal strength and coordinates. Because of the physical properties of radio waves, the data collected from such transmitters are often inaccurate. This paper compares two methods, the Kalman filter and particle filter, to improve the quality of signal strength data received from BLE transmitters. As a result, the recommendation of the Kalman filter as the best method to improve the quality of these data and use it in the developed applications supporting indoor navigation in large buildings is provided.
Spectral Clustering Versus Watanabe Theorem
We devote this paper to a special case of Graph Spectral Clustering of graphs with identical distances between nodes. This study is motivated by the special theorem presented by Watanabe which claims that given all derivable attributes are taken into account, all distinct objects are at the same distance. As the multi-view clustering becomes popular, the mentioned Watanabe theorem may imply serious problems for recovering the intrinsic structure of the collection of objects. We show that Graph Spectral Clustering should not be affected in the most favourable case that is block structure of similarity matrix in theory, but in practice the underlying k-means algorithm introduces up to 20% error rate in assignment of elements to clusters.
Forearm vein pattern recognition using features from visible and NIR images
Forearm vein recognition is one of many available methods used for identification. However, forearm veins can be considered more secure compared to other biometric traits because the veins are inside the human body and therefore not easily manipulated. Veins possess several properties that make a good biometric feature for personal identification: 1) they are difficult to damage and modify; 2) they are difficult to simulate using a fake template; and 3) vein information can represent the liveness of person. Features were extracted from each pair of visible and NIR images. For the visible images, feature extraction was done using the Gabor filter. For the NIR forearm images, a crossing number was used to extract properties of the veins e.g. bifurcation. We present the results of the recognition of forearm veins patterns that show the suitability of the method for biometric identification purposes.