T. Hidvégi

6603684661

Publications - 7

Implementation of a basic flow model in traffic controlling

Sándor Gacsal T. Hidvégi

Publication Name: 9th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics Cinti 2008

Publication Date: 2008-12-01

Volume: Unknown

Issue: Unknown

Page Range: 523-530

Description:

The contribution introduces a basic model, a manner, how to control - using the specific tools - A crossing of a highway and a lower volume road, while we stop the highway's traffic only in a critical case otherwise just slow it down.

Open Access: Yes

DOI: DOI not available

The configurable digital cellular neural - Hopfield network

Tamás Zeffer T. Hidvégi

Publication Name: Ines 2006 10th International Conference on Intelligent Engineering Systems 2006

Publication Date: 2006-12-01

Volume: Unknown

Issue: Unknown

Page Range: 160-164

Description:

A configurable Artificial Neuron Network that is capable of establishing both the emulated digital Cellular Neural Network (CNN) and the Hopfield Network is described. The configurable neural network is designed with the method of modularity where each module is a three weighted input neuron. The network can be optionally large limited only by the gate number available on a chip. Also, the network is reconfigurable during operation. © 2006 IEEE.

Open Access: Yes

DOI: DOI not available

A programmable digital cellular neural network processing on- and off-chip sensory information

Tamás Zeffer T. Hidvégi

Publication Name: Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications

Publication Date: 2006-12-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

In this paper, the architecture of a mixed-signal CNN visual processor with on board photodetecting sensor array analog RAM, and a switching matrix to process off chip sensor signals is proposed. In our design, the CMOS sensor array possesses high performance readout circuitry and embedded data storage capability. For further compactness, a new template memory structure is built that requires about quarter of silicon size compared to previous designs. The vision chip comprises most of the modules of a sensory system and keeps tight relationship between the sensors and the processor. © 2008 IEEE.

Open Access: Yes

DOI: 10.1109/CNNA.2006.341603

The configurable digital neural network with emulated digital cellular neural network cores

Tamás Zeffer T. Hidvégi

Publication Name: 2006 IEEE International Conference on Mechatronics Icm

Publication Date: 2006-12-01

Volume: Unknown

Issue: Unknown

Page Range: 312-315

Description:

A configurable Artificial Neuron Network that is capable of establishing both the emulated digital Cellular Neural Network (CNN) and the static Multilayered Feedforward Neural Network (MFNN) is described. The configurable neural network is designed with the method of modularity where each module is a three weighted input neuron. The network can be optionally large limited only by the gate number available on a chip. © 2006 IEEE.

Open Access: Yes

DOI: 10.1109/ICMECH.2006.252545

Enhanced emulated digital CNN-UM (CASTLE) arithmetic cores

P. Keresztes T. Hidvégi P. Szolgay

Publication Name: Journal of Circuits Systems and Computers

Publication Date: 2003-12-01

Volume: 12

Issue: 6

Page Range: 711-738

Description:

An emulated digital CNN-UM (CASTLE) architecture was published few years ago.1 Different emulated digital CNN-UM architectures are analyzed in the paper. These new modified architectures are optimized according to the silicon area, operating speed or dissipated power. A reconfigurable arithmetic core will also be shown in the paper, by which solution of the neighborhood size can be changed. An advanced CASTLE with pipe-lining is presented. The operation frequency is increased by using this solution in approximately 10 times.

Open Access: Yes

DOI: 10.1142/S0218126603001136

An accelerated digital CNN-UM (CASTLE) architecture by using the pipe-line technique

P. Keresztes T. Hidvégi P. Szolgay

Publication Name: Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications

Publication Date: 2002-01-01

Volume: 2002-January

Issue: Unknown

Page Range: 355-362

Description:

Different CNN-UM architecture implementations, analog and emulated digital, were developed. The emulated digital architecture (CASTLE) is accurate but slower than the analog CNN-UMs. It is generally disadvantageous especially if transient computing is critical. The operation speed of the emulated digital implementations, namely CASTLE, can be increased significantly using the pipeline technique. This solution is analyzed with respect to area, time, etc. These arithmetic cores were tested and simulated using a VIRTEX FPGA development system.

Open Access: Yes

DOI: 10.1109/CNNA.2002.1035070

ModRTU InjectX: A Command Injection Simulation Tool for Industrial Cybersecurity Research

Zoltán Dobrády T. Hidvégi Szilvia Nagy

Publication Name: 60th International Scientific Conference on Information Communication and Energy Systems and Technologies Icest 2025 Proceedings

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

ModRTU_InjectX is a Python-based research tool with a graphical user interface, specifically designed for real-time monitoring, analysis, and command injection simulation within Modbus RTU industrial communication systems. The platform enables users to craft custom data packets and inject them into the serial communication channel using event-driven logic, effectively modelling realistic cyberattack scenarios. All communication is logged and can be exported in structured formats, making the system ideal for creating annotated datasets for training and validating machine learning-based intrusion detection systems. The tool supports parallel injection block configurations, evaluates attack effectiveness, and provides detailed statistical summaries. ModRTU_InjectX serves as a valuable contribution to the research infrastructure for industrial cybersecurity.

Open Access: Yes

DOI: 10.1109/ICEST66328.2025.11098380