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1

Universal structural map for indoor navigation in university campus

Świtalski Piotr, Salamończyk Andrzej

Studia Informatica : systems and information technology

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2023

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Vol. 2(29)

69--79

EN

Mapmodeling is an important aspect of indoor navigation. It involves creating a digital map of the indoor environment that can be used for navigation purposes. The map can be created using various techniques such as laser scanning, photogrammetry, and computer vision. Once the map is created, it can be used to develop navigation algorithms that can help users navigate the indoor environment. In this paper, we propose a solution based on CAD files. These models can be used for a variety of purposes, including indoor navigation. There are many CAD applications available, including AutoCAD, SketchUp, and SolidWorks, among others. It is a relatively cheap method to model any indoor environment from scans of plans or CAD files. CAD files are the most accurate way to build a digital indoor map because these files can include 2D or 3D designs and usually contain important location information (e.g. floor level) within the layer properties. Moreover, we can map the CAD annotation to the following feature classes if they conform to the indoors model: types of rooms, types of doors, etc. We propose a solution based on DXF format files. We developed the parser to transfer the necessary data from the CAD files to the navigation system. It consists of processing existing maps from the CAD format to the appropriate structure, supplementing it with the data of BLE transmitters, and saving it as a graph suitable for determining routes and guiding the user along the route. We use our method in a system that supports navigation and user safety, emphasizing users with special needs, which we are implementing on our academic campus.

2

Firefly algorithm applied to the job-shop scheduling problem

Świtalski Piotr, Bolesta Arkadiusz

Studia Informatica : systems and information technology

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2021

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Vol. 1-2(25)

87--100

EN

The job shop scheduling problem (JSSP) is one of the most researched scheduling problems. This problem belongs to the NP-hard class. An optimal solution for this category of problems is rarely possible. We try to find suboptimal solutions using heuristics or metaheuristics. The firefly algorithm is a great example of a metaheuristic. In this paper, this algorithm is used to solve JSSP. We used some benchmarking JSSP datasets for experiments. The experimental program was implemented in the aitoa library. We investigated the optimal parameter settings of this algorithm in terms of JSSP. Analysis of the experimental results shows that the algorithm is useful to solve scheduling problems.

3

Krótko o krótkich rurociągach

Świtalski Piotr

Pompy, Pompownie

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2021

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nr 2

8--9

PL

Przyjmując zaszczytną propozycję wystąpienia w charakterze eksperta, złożoną mi przez redakcję, zamiast rozważań na tematy podnoszone w publikacjach problemowych, podzielę się kilkoma pytaniami. Pragnę pobudzić krytyczne spojrzenie na powszechnie stosowane rozwiązania w technice pompowej i oczekiwać dyskusji.

4

Machine Learning Methods in E-mail Spam Classification

Świtalski Piotr, Kopówka Mateusz

Studia Informatica : systems and information technology

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2019

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Vol. 1-2(23)

57--76

EN

Increasing number of unwanted e-mails has influence on users’ security in the Internet. Today spam e-mails can store potential malicious messages which e.g. can redirect user to fake sites. These messages recently appeared in social media. Filtering of this content is important due to minimize financial and branding costs. Traditional methods of spam filtering cannot be sufficient for present threats. We required new methods for constructing more dependable and robust antispam filters. Machine learning recently becomes very popular technique in classification methods. It has been successfully used in spam classification. In this paper we present some methods of machine learning for spam detecting. We would also like to introduce ways to solve the spam classification problem. We show that these methods can be useful in classification of malicious messages. We also compared developed methods and presented results in the experimental section.