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Corporate Network Segmentation to Security Level Improving

The aim of the bachelor thesis is Corporate Network Segmentation to Security Level Improving. During the audit of the company's network, several problems were identified, such as a single network, weak nodes, no backup channel, a separate strand with Lithuanian servers, HQ branch without a backup channel. The most popular network security threats and attacks were reviewed, DiD strategies and measures were introduced, an audit of the existing computer network was performed, and the new network topology was drawn. Based on the Cisco PPDIOO model, a plan for gradual restructuring in a new network has been drawn up. A central device was installed in the DC of Latvia, an IPsec tunnel was agreed with the DC of Lithuania, tests were carried out. All branches were provided with backup channels, which made it possible to test a new network first through the backup channel and only when all the tests were performed and were positive, the main channel could easily be connected as well. The backup channel routers and branch routers are configured according to a single template to allow easy interchangeability. Configured access to branch switches and backup routers from both channels: primary channel and backup channel. The network has been tested and is working.

Author: Igors Manžurcevs

Supervisor: Elena Revzina

Degree: Bachelor

Year: 2024

Work Language: English

Improving the accuracy of optical character recognition of stone engravings using image pre-processing methods

This study focuses on the development of preprocessing methods to improve the accuracy of Optical Character Recognition (OCR) for stone engravings. The primary goal is to enhance the precision of widely used OCR tools, particularly for texts engraved on stone surfaces, which present unique challenges that differ from traditional OCR applications. Emphasis is placed on developing image preprocessing methods as a software product. Customized image manipulation scripts were used to improve recognition accuracy and address issues such as contrast, alignment, noise, and resolution. The preprocessing stage was integrated into the workflow designed for image transformation before OCR processing. Subsequently, the recognition improvements were evaluated based on text similarity metrics analysis. Iterative text recognition and repeated recognition of images after applying preprocessing demonstrated significant improvements in OCR accuracy. This work provides a solid foundation for further enhancement of OCR workflows by employing adaptable preprocessing techniques specifically designed for particular problem areas, achieving higher precision in text recognition.

Author: Romans Urbans-Orbans

Supervisor: Aleksandrs Grakovskis

Degree: Bachelor

Year: 2024

Work Language: Latvian

Study programme: Computer Science

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CNN-Based pipeline-related artifact and damage recognition in IHC staining as preprocessing step for pathological analysis

This work proposes automated solution for artifact and damage segmentation in biomedical images using machine learning algorithms. The development process includes data preprocessing, label classification using a clustering algorithm and segmentation model. CNN architectures like YOLO and U-NET are utilized for segmentation, and K-Means and DBSC algorithms are evaluated for clustering. The outcomes include a set of data preprocessing precodures, clustering algorithm testing and results analysis, segmentation model and recommendations for further development.

Author: Taisija Kožarina

Supervisor: Jeļena Kijonoka

Degree: Bachelor

Year: 2024

Work Language: English

Study programme: Computer Science

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