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Journal : International Journal of Reconfigurable and Embedded Systems (IJRES)

FPGA implementation of artificial neural network for PUF modeling Mispan, Mohd Syafiq; Ishak, Mohammad Haziq; Jidin, Aiman Zakwan; Mohd Nasir, Haslinah
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp200-207

Abstract

Field-programmable gate array (FPGA) is a prominent device in developing the internet of things (IoT) application since it offers parallel computation, power efficiency, and scalability. The identification and authentication of these FPGAbased IoT applications are crucial to secure the user-sensitive data transmitted over IoT networks. Physical unclonable function (PUF) technology provides a great capability to be used as device identification and authentication for FPGAbased IoT applications. Nevertheless, conventional PUF-based authentication suffers a huge overhead in storing the challenge-response pairs (CRPs) in the verifier’s database. Therefore, in this paper, the FPGA implementation of the Arbiter-PUF model using an artificial neural network (ANN) is presented. The PUF model can generate the CRPs on-the-fly upon the authentication request (i.e., by a prover) to the verifier and eliminates huge storage of CRPs database in the verifier. The architecture of ANN (i.e., Arbiter-PUF model) is designed in Xilinx system generator and subsequently converted into intellectual property (IP). Further, the IP is programmed in Xilinx Artix-7 FPGA with other peripherals for CRPs generation and validation. The findings show that the Arbiter-PUF model implementation on FPGA using the ANN technique achieves approximately 98% accuracy. The model consumes 12,196 look-up tables (LUTs) and 67 mW power in FPGA.
Home grocery listing hardware system and mobile application with speech recognition feature Faris Eizlan Suhaimi, Mohamad; Zakwan Jidin, Aiman; Mohd Nasir, Haslinah; Haidar Md Hamzah, Mohd; Syafiq Mispan, Mohd
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp109-118

Abstract

A home grocery list is a crucial aspect of household management that ensures sufficient kitchen supplies. The classic pen-and-paper grocery list is ineffective since it is time-consuming and prone to human error. Therefore, in this study, we proposed a microcontroller-based home grocery listing system using a barcode scanner and speech recognition. The proposed system consists of hardware and a mobile application. The main hardware components are the ESP32-S3 microcontroller, MH-ET barcode scanner v3.0, 20×4 LCD, and 2.4 GHz wireless keyboard. The mobile application is developed using the MIT App Inventor. Through the hardware, the system receives user input from barcode scanning or manual data entry using the keyboard. The data captured using a barcode scanner or keyboard is stored in the memory. Subsequently, the data is transmitted to the mobile application of the home grocery listing system via WiFi. Moreover, the mobile application is also equipped with user input via speech recognition and manual data entry using the keyboard. Hence, users have the flexibility to input the grocery list using four methods within the system. The developed home grocery listing system gives a new, satisfying experience to the users and a convenient way for them to make a home grocery list.