This publication was prepared as a permanent record of research findings
presented by doctoral students and academics representing diverse research
backgrounds and schools. The focus is on interdisciplinary solutions, combining
engineering, computer science, and mathematical methods with approaches relevant
to management science and socio-economic applications.
The International Interdisciplinary PhD Workshop (IIPhDW) is a cyclical
and international conference, and its primary function is to provide a platform for
research presentations, knowledge exchange, and collaboration between young
scientists from various disciplines. The 2025 edition featured a particularly strong
technical component, encompassing artificial intelligence, computer science, automation
and control, robotics and mechatronics, telecommunications, signal
processing, as well as mechanical and production engineering. At the same time,
the inclusion of topics in economics and management confirmed the workshop’s
broad scope and its ability to integrate research perspectives relevant to contemporary
technological and organizational challenges.
A significant group of publications includes works on process and biomedical
tomography, as well as image reconstruction methods using machine learning
and deep learning, including approaches combining physical models with neural
network architectures. Examples include research on image reconstruction in
electrical impedance tomography, the integration of tomography with neural networks
for industrial process monitoring, and the use of ultrasound tomography
in measurement and reconstruction analysis. This theme is further reinforced by
works on hybrid tomography systems, process monitoring using mixed reality
technology, and applications in the areas of physiological parameter monitoring
and non-invasive diagnostics.
The second recognizable axis is artificial intelligence in IT and cyberphysical
systems, encompassing both the construction of predictive and classification
models and their implementation in industrial, medical, and service environments.
This trend includes work related to the application of machine learning
methods in system and network security, solutions based on LLM agents in
project team workflows, processing unstructured data using OCR and language
models, and multimodal analysis in intelligent customer service systems. This
perspective highlights the contemporary trend of convergence of AI techniques
with data engineering, software engineering, and systems integration, which has
direct implications for the design of scalable implementation solutions.The third thematic area covers embedded systems, communication, and signal
processing, along with elements of computational resource optimization. The
monograph includes papers on, among other things, phase shift estimation in
noisy environments, pseudorandom sequence generation, acoustic feature detection,
and the efficiency of machine learning applications at the network edge in
the context of Kubernetes scheduling heuristics. In the monograph, this strand
serves a methodological purpose, providing signal analysis tools and computational
mechanisms that form the foundation for many AI and measurement system
applications.
A significant complement to the technical perspective are works in the areas
of management and organizational and economic analysis, which address the
need to understand the determinants of technology implementation and the conduct
of innovative projects. The publications address, among other things, the
predictors of success in startup management and the analysis of organizational
improvements in public institutions. Their presence strengthens the interdisciplinary
nature of the monograph by demonstrating that the effectiveness of engineering
solutions depends not only on the quality of algorithms and devices, but
also on the organizational, process, and decision-making context.
The monograph is intended as a reference for academics and doctoral students,
particularly those seeking examples of research that combines theory with
application. The collected papers offer a comprehensive overview of research activities
typical of early careers in science, from conceptual studies and method
analysis, through device and software architecture prototyping, to experiments
and evaluation of the effectiveness of proposed solutions. At the same time, the
publication allows for the identification of common methodological denominators,
such as the growing importance of measurement data, simulation, deep
learning, systems integration, and the pursuit of real-time operation in industrial
and biomedical environments.
The introduction, on the one hand, contextualizes the monograph within the
mission of IIPhDW as a workshop supporting researcher development and the
internationalization of research. On the other hand, it organizes the chapter topics
in the perspective of dominant technological trends and application needs that
permeate various fields. Consequently, the monograph can be viewed as a synthetic
overview of current research directions for doctoral students and young
academics, as well as an inspiration for undertaking work combining artificial
intelligence methods, measurement systems, software engineering, and management
analyses within modern interdisciplinary projects.
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