Performance Evaluation Of Neural Network-Based Channel Detection For STT-MRAM

Nguyen, Cu; Nguyen, Phong Duy; Nguyen, Anh Tuan; Pham, N.; Nguyen, Khoa Dang

doi:10.1109/nics54270.2021.9701555

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of deep space communication, the utilization of the extended Hamming code system is principally showcased through its robust capabilities of error detection and correction. Deep space communication is characterized by extreme distances, substantial signal degradation, and significant environmental noise, leading to a transmission error rate significantly higher than in typical communication scenarios [8]. This necessitates the adoption of a robust coding system to ensure communication reliability, a role fulfilled by the extended Hamming code.…”

Section: Expand Hamming Code Code Systemmentioning

confidence: 99%

Exploring the application and performance of extended hamming code in IoT devices

Shen

2024

ACE

View full text Add to dashboard Cite

This study primarily focuses on the implementation of extended Hamming code within Internet of Things (IoT) devices and examines its impact on device performance, particularly in relation to communication protocols. The research begins by introducing and explaining the essential principles surrounding the extended Hamming code and its system. This introduction is followed by a detailed analysis of its practical application in IoT device communication and the subsequent influence on performance. Additionally, the study explores the potential role of extended Hamming code in strengthening the security measures of IoT devices. Experimental findings indicate that incorporating extended Hamming code can effectively enhance the communication efficiency of IoT devices, ensuring accurate data transmission. It also improves the overall operational efficiency of the devices and fortifies their security framework. Yet, despite these promising outcomes, the real-world application of extended Hamming code presents significant challenges. These hurdles highlight the need for continued research and exploration to maximize the potential of the extended Hamming code in the IoT domain. The study concludes with an optimistic outlook, encouraging ongoing investigation and innovation to further optimize the benefits of this code and drive advancements in IoT technology.

show abstract

Section: Expand Hamming Code Code Systemmentioning

confidence: 99%

Exploring the application and performance of extended hamming code in IoT devices

Shen

2024

ACE

View full text Add to dashboard Cite

show abstract

“…With the advantages of ML and DL, a recurrent neural network (RNN) is applied to many scientific fields as in [24][25][26]. Therefore, in this paper, we proposed a detection model using an RNN to improve the ISI and ITI estimators.…”

Section: Introductionmentioning

confidence: 99%

Interference Estimation Using a Recurrent Neural Network Equalizer for Holographic Data Storage Systems

Nguyen,

Lee

2023

Applied Sciences

View full text Add to dashboard Cite

Holographic data storage (HDS) utilizes the unique properties of light for writing and reading two-dimensional (2D) data from holographic media, providing significantly higher densities and faster data transfer rates than traditional storage media for short-term dependencies. With its ability to store terabytes of data in a single crystal, HDS has garnered attention as a promising candidate for next-generation storage technologies. However, the 2D interference caused by hologram dispersion during the reading process poses a significant obstacle to achieving reliable and efficient HDS systems. This study proposes a method for enhancing the accuracy of estimating the 2D intersymbol interference (ISI) using a recurrent neural network (RNN) equalizer for HDS systems. The proposed method leverages the ability of RNNs to model complex and temporal dependencies in data and more accurately estimate the interference caused by ISI and interchannel interference (ICI) in HDS systems. In addition, to recreate the relationship between the samples in the training process, RNN is applied to fields such as computer vision, natural language process, speech recognition, and so on. We evaluated the performance of our proposed method on a simulation model of HDS system and compared it with the previous studies. In the simulations, the proposed method outperformed the previous schemes in terms of bit error rate, indicating its potential for improving the reliability and efficiency of HDS systems.

show abstract

Neural network detector with sparse codes for spin transfer torque magnetic random access memory

Nguyen

2023

Cogent Engineering

View full text Add to dashboard Cite

Performance Evaluation Of Neural Network-Based Channel Detection For STT-MRAM

Cited by 3 publications

References 16 publications

Exploring the application and performance of extended hamming code in IoT devices

Exploring the application and performance of extended hamming code in IoT devices

Interference Estimation Using a Recurrent Neural Network Equalizer for Holographic Data Storage Systems

Neural network detector with sparse codes for spin transfer torque magnetic random access memory

Contact Info

Product

Resources

About