Embedded Systems: Fundamentals, Design and Practical Applications, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: closed (15 January 2026) | Viewed by 577

Special Issue Editors


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Guest Editor
Institute of Metrology, Electronics and Computer Science, University of Zielona Gora, 65-417 Zielona Gora, Poland
Interests: logic synthesis; FSM design; FPGA; ASIC; telecommunications; antenna arrays; hardware-software co-design; CAD of VLSI-based digital systems
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Guest Editor
Department of Technology, The Jacob of Paradies University, ul. Fryderyka Chopina 52/b.7, 66-400 Gorzow Wielkopolski, Poland
Interests: distributed embedded systems; embedded system design; IoT; hardware synthesis; process control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The role of embedded systems in our world has massively expanded in recent years. Now, about 99% of all manufactured microprocessors are used in embedded systems. The development of the Internet of Things (IoT), the Internet of Industrial Things (IIoT), the Internet of Battle Things (IoBT), the growing automation of production, and the increasing utilization of various robots is impossible without the wide application of embedded systems. Modern embedded systems are implemented using both fairly simple microcontrollers and very sophisticated application-specific standard products (ASSP) consisting of billions of transistors. In many cases, embedded systems are real-time cyber–physical systems that monitor and control complex physical objects. Such systems should be sufficiently reliable and secure. Failures in the operation of such systems should not lead to fatal consequences associated with a threat to life and/or the environment. In response to increasingly frequent hacking attacks, such systems should have strong protection against unauthorized access. The solution to this problem requires the development of implemented cryptographic protection methods, for example, in embedded systems. In the case of distributed embedded systems, effective methods of interaction for their components are of paramount importance. The incredible complexity of modern embedded systems requires the development of effective methods for specification and automation of the design process. Modern embedded systems are deeply influenced by the artificial intelligence (AI). Such AI-based embedded systems are smart, as they are able to make decisions in real-time, thanks to highly advanced algorithms and developments in neural networks. Such intelligent systems will have a huge effect on a huge number of applications and industries. Here, we have listed only a small number of the existing problems associated with the development and implementation of embedded systems. These problems provide a wide field for the theoretical research and practical implementations of embedded systems.

The aim of this Special Issue is to collate scientific manuscripts on the practical and theoretical aspects associated with theoretical and practical aspects of implementing efficient embedded systems. The key focus is to present theoretical advances, as well as new specification, design, and verification methods, in order to improve the main characteristics of embedded systems.

Topics may include, but are not limited to, the following:

  1. Theoretical foundations of embedded systems;
  2. Using AI in embedded systems;
  3. Development of embedded systems focused on the Internet of Industrial Things (IIoT).
  4. Real-time embedded systems and real-time operating systems (RTOS);
  5. Scheduling algorithms in embedded systems;
  6. Techniques of low-power design in embedded systems;
  7. Improving the security and dependability of embedded systems;
  8. Hardware–software co-design: methods and CAD tools;
  9. Design of embedded systems based on microcontrollers;
  10. User interfaces in embedded systems;
  11. Improving communication channels and communication protocols;
  12. Using embedded systems in Internet of Things and Internet of Battle Things applications;
  13. Using models of finite state machines in design of embedded systems.
  14. Methods of the implementation of smart mobile and autonomous embedded systems;
  15. Development of standard hardware platforms for implementing embedded systems (including reprogrammable devices, e.g., FPGAs);
  16. Soft and hard IP cores in embedded system design;
  17. Dynamic voltage and frequency scaling, HW and SW dynamic power management;
  18. Approximate computing, low-power arithmetic;
  19. Design space exploration techniques, with special emphasis on power/energy estimations and power minimization methodologies;
  20. High-level synthesis and HW/SW co-design techniques for low-power digital systems;
  21. Cybersecurity aspects in embedded systems;
  22. Graphical modeling of embedded systems (including Petri nets, UML, etc.);
  23. Verification, analysis, and validation techniques of embedded systems.
  24. Using augmented and virtual reality in prototyping the embedded systems.
  25. Using RISC-V architectures in the development of embedded systems.
  26. Deploying neural networks on microcontrollers. 

Prof. Dr. Alexander Barkalov
Prof. Dr. Larysa Titarenko
Dr. Kazimierz Krzywicki
Guest Editors

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Keywords

  • embedded systems
  • hardware-software co-design
  • microcontrollers
  • dependability
  • security and cybersecurity
  • distributed embedded systems
  • ASIC-based design of embedded systems
  • FPGA-based design of embedded systems
  • mobile and autonomous embedded systems
  • optimization of power consumption communication channels

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Published Papers (1 paper)

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23 pages, 13345 KB  
Article
Neural-Based Controller on Low-Density FPGAs for Dynamic Systems
by Edson E. Cruz-Miguel, José R. García-Martínez, Jorge Orrante-Sakanassi, José M. Álvarez-Alvarado, Omar A. Barra-Vázquez and Juvenal Rodríguez-Reséndiz
Electronics 2026, 15(1), 198; https://doi.org/10.3390/electronics15010198 - 1 Jan 2026
Viewed by 295
Abstract
This work introduces a logic resource-efficient Artificial Neural Network (ANN) controller for embedded control applications on low-density Field-Programmable Gate Array (FPGA) platforms. The proposed design relies on 32-bit fixed-point arithmetic and incorporates an online learning mechanism, enabling the controller to adapt to system [...] Read more.
This work introduces a logic resource-efficient Artificial Neural Network (ANN) controller for embedded control applications on low-density Field-Programmable Gate Array (FPGA) platforms. The proposed design relies on 32-bit fixed-point arithmetic and incorporates an online learning mechanism, enabling the controller to adapt to system variations while maintaining low hardware complexity. Unlike conventional artificial intelligence solutions that require high-performance processors or Graphics Processing Units (GPUs), the proposed approach targets platforms with limited logic, memory, and computational resources. The ANN controller was described using a Hardware Description Language (HDL) and validated via cosimulation between ModelSim and Simulink. A practical comparison was also made between Proportional-Integral-Derivative (PID) control and an ANN for motor position control. The results confirm that the architecture efficiently utilizes FPGA resources, consuming approximately 50% of the available Digital Signal Processor (DSP) units, less than 40% of logic cells, and only 6% of embedded memory blocks. Owing to its modular design, the architecture is inherently scalable, allowing additional inputs or hidden-layer neurons to be incorporated with minimal impact on overall resource usage. Additionally, the computational latency can be precisely determined and scales with (16n+39)m+31 clock cycles, enabling precise timing analysis and facilitating integration into real-time embedded control systems. Full article
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