A Data-Efficient Training Model for Signal Integrity Analysis based on Transfer Learning

Zhang, Tingrui; Chen, Siyu; Wei, Shuwu; Chen, Jienan

doi:10.1109/apccas47518.2019.8953103

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…This has led researchers towards using adaptive methods, such as DNN, for this purpose. Incorporating DNN into the issues of stenography and video watermarking allow a high probability of recovering the watermark and keeping the quality of the watermarked image in high quality [ 38 , 39 , 40 ] by being able to select optimal image composition and decomposition methods as a result of the training process [ 41 , 42 , 43 , 44 , 45 , 46 , 47 ].…”

Section: Literature Reviewmentioning

confidence: 99%

High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

Kaczyński

Piotrowski

Pietrow

2022

Sensors

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This article presents a method for transparent watermarking of high-capacity watermarked video under H.265/HEVC (High-Efficiency Video Coding) compression conditions while maintaining high-quality encoded image. The aim of this paper is to present a method for watermark embedding using neural networks under conditions of subjecting video to lossy compression of the HEVC codec using the YUV420p color model chrominance channel for watermarking. This paper presents a method for training a deep neural network to embed a watermark when a compression channel is present. The discussed method is characterized by high accuracy of the video with an embedded watermark compared to the original. The PSNR (peak signal-to-noise ratio) values obtained are over 44 dB. The watermark capacity is 96 bits for an image with a resolution of 128 × 128. The method enables the complete recovery of a watermark from a single video frame compressed by the HEVC codec within the range of compression values defined by the CRF (constant rate factor) up to 22.

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Section: Literature Reviewmentioning

confidence: 99%

High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

Kaczyński

Piotrowski

Pietrow

2022

Sensors

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“…With respect to the SVR, an active-subspace method has been proposed in Ma et al (2020a, b) to improve the algorithmic performance. Regarding the NN, methods to improve the data efficiency such as transfer learning in Zhang et al (2019) and semi-supervised learning in Chen et al ( 2020) have been deployed.…”

Section: Introductionmentioning

confidence: 99%

AI Models for Supporting SI Analysis on PCB Net Structures: Comparing Linear and Non-Linear Data Sources

Withöft,

John,

Ecik

et al. 2023

Adv. Radio Sci.

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Abstract. Signal integrity (SI) is an essential part in assuring the functionality of microelectronic components on a printed circuit board (PCB). Depending on the complexity of the designed interconnect structure, even the experienced PCB developer might be reliant on multiple design cycles to optimally configure the PCB parameters, which eventually results in a very complex, time-consuming and costly process. Under these aggravating conditions, artificial intelligence (AI) models may have the potential to support and simplify the SI-aware PCB design process by building predictive models and proposing design solutions to streamline the existing workflows and unburden the PCB designer. In this paper, the AI approach is divided into two separate stages consisting of neural network (NN) regression in the first step and parameterization of the PCB net structure in the second step. First, the NN models are applied to learn the relationship between the electrical parameters and the resulting signal quality captured by domain-oriented signal features in the time domain. Second, based on the trained NN models, on the one hand, the k-nearest neighbor (kNN) method is utilized to select solution candidates within the feature space, while on the other hand, genetic algorithms (GA) are applied to directly optimize the parameters of the interconnect structure. Moreover, the influence of the simulation abstraction level is investigated by comparing simulation data originating from linear and I/O buffer information specification (IBIS)-based non-linear modeling of the integrated circuit (IC) characteristics concerning the prediction accuracy and direct transferability. Finally, transfer learning concepts are evaluated to exchange learned knowledge representations between the different modeling of the IC characteristics to improve data efficiency and reduce computational complexity.

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“…However, in the case of deep neural networks, it is possible to use already learned filters from convolutional layers [40][41][42][43][44]. The only problem that remains then is teaching a three-layer neural network, which is usually learned quite quickly in relation to convolutional layers [35,42,[45][46][47]. Another cause for optimism is that modern graphics cards make it possible to reduce the training time of ANN classifiers from several weeks to hours or even minutes (here the leaders in this field are NVIDIA and AMD) [30,31,48].…”

Section: Introductionmentioning

confidence: 99%

Specific Radar Recognition Based on Characteristics of Emitted Radio Waveforms Using Convolutional Neural Networks

Matuszewski

Pietrow

2021

Sensors

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With the increasing complexity of the electromagnetic environment and continuous development of radar technology we can expect a large number of modern radars using agile waveforms to appear on the battlefield in the near future. Effectively identifying these radar signals in electronic warfare systems only by relying on traditional recognition models poses a serious challenge. In response to the above problem, this paper proposes a recognition method of emitted radar signals with agile waveforms based on the convolutional neural network (CNN). These signals are measured in the electronic recognition receivers and processed into digital data, after which they undergo recognition. The implementation of this system is presented in a simulation environment with the help of a signal generator that has the ability to make changes in signal signatures earlier recognized and written in the emitter database. This article contains a description of the software’s components, learning subsystem and signal generator. The problem of teaching neural networks with the use of the graphics processing units and the way of choosing the learning coefficients are also outlined. The correctness of the CNN operation was tested using a simulation environment that verified the operation’s effectiveness in a noisy environment and in conditions where many radar signals that interfere with each other are present. The effectiveness results of the applied solutions and the possibilities of developing the method of learning and processing algorithms are presented by means of tables and appropriate figures. The experimental results demonstrate that the proposed method can effectively solve the problem of recognizing raw radar signals with agile time waveforms, and achieve correct probability of recognition at the level of 92–99%.

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A Data-Efficient Training Model for Signal Integrity Analysis based on Transfer Learning

Cited by 9 publications

References 12 publications

High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

AI Models for Supporting SI Analysis on PCB Net Structures: Comparing Linear and Non-Linear Data Sources

Specific Radar Recognition Based on Characteristics of Emitted Radio Waveforms Using Convolutional Neural Networks

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