The employees of the Chair of Information Systems Applications investigate broadly understood issues of data and signal processing with the use of information methods to control automatic machines, industrial processes and work support by voice interface. These topics include methods of artificial intelligence, group intelligence and deep neural networks used for speech recognition, image processing and recognition, classification, biometric identification of people, and robotic automation of tasks and optimization processes.
A globally unique approach developed at the Department is the method of motivated learning in embodied artificial intelligence systems and related problems such as self-organizing goal-driven learning, associative spatial-temporal memories, motivated mechanisms of sensory-motor interactions, event prediction, robotic perception and building environmental models.
Scientific research projects financed from external sources:
Subject: The technology of learning digital twins in the process of increasing employee performance
Project Manager: Arkadiusz Lewicki, PhD firstname.lastname@example.org
The main objective of the research project is to test the feasibility of applying the concept of digital replicas of employees on selected workstations. This requires the acquisition of real-time data representing the characteristics of the tasks performed, job position, competencies, knowledge and experience of the employee. Additionally, the project plans to analyze data acquired from wearable devices. This data will represent the psychophysical parameters of employees and the characteristics of the physical environment in which they perform their work. The purpose of this analysis is to investigate the factors that have a significant impact on increasing employee productivity while enhancing employee satisfaction and experience. In addition, the project will validate the concept of using digital communication between twins performing tasks with similar characteristics. This research task aims to find out the additional factors that can optimizing the performance of a specific task.
Period of implementation: 06.2021–02.2022
Financing: Podkarpackie Centrum Innowacji (PCI)
Subject: Development of effective mechanisms for robot perception using motivated learning and self-organizing associative memory
Project manager: Prof. Janusz Starzyk, Ph.D. email@example.com
The main aim of the research proposed in the project is to develop new effective perception mechanisms using the generalized idea of Motivated Learning (ML) and new associative learning and reasoning mechanisms. The research results achieved under this project will allow to build modern cognitive systems which, based on specific needs, are conditionally and intelligently capable of defining associations and forming the knowledge needed to achieve the set goals.
Period of implementation: 15.03.2017–14.09.2020
Financing: National Science Center OPUS program
Subject: IVA service platform of virtual voice agents for emergency call hotlines automation
Project manager on the side of UITM: Leszek Gajecki, Ph.D., Eng. firstname.lastname@example.org
The research team from UITM, together with the Poznań Supercomputing and Networking Center, participated in the Haxon Telecom research and development project. Its main aim on the side of the UITM team was to improve the quality of speech recognition by the latest computational techniques.
Period of implementation: 2017–2018
Financing: National Center for Research and Development (NCBiR)
Scientific research projects financed from the Ministry:
Subject: Advanced methods of Artificial Intelligence application
Project Manager: Leszek Gajecki, Ph.D email@example.com
Main aims of conducted research are:
– Development of new association mechanisms of learning and inference.
– Semantic analysis application for improvement of speech recognition system performance, application i.e. call center support
– Advanced methods of Artificial Intelligence application for Big Data real sets analysis
Implementation period: 2019-2022