A digital background calibration scheme for non-linearity of SAR ADC using back-propagation algorithm

Lu, Haowei; Yu, Xiaopeng; Wang, Siqi; Liu, Yuyan; Huang, Zhenyan; Lu, Zhenghao; Yeo, Kiat Seng; Chen, Jer‐Ming

doi:10.1016/j.mejo.2021.105113

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…The above equation reveals that for the excitation local gradient δ j ( n ) of the neurons j and its error value e j ( n ) and activation function derivative φ j ′( v j ( n )), their product of values is consistent. Combined with ( 30 ), it can be seen that the error value of excitation e j ( n ) of the neurons j plays a decisive role in the date parameters

[ 26 , 27 ].…”

Section: Improved Generative Adversarial Network By Ccamentioning

confidence: 99%

Optimization of a Deep Learning Algorithm for Security Protection of Big Data from Video Images

Geng¹,

Yan

Lu³

2022

Computational Intelligence and Neuroscience

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With the rapid development of communication technology, digital technology has been widely used in all walks of life. Nevertheless, with the wide dissemination of digital information, there are many security problems. Aiming at preventing privacy disclosure and ensuring the safe storage and sharing of image and video data in the cloud platform, the present work proposes an encryption algorithm against neural cryptography based on deep learning. Primarily, the image saliency detection algorithm is used to identify the significant target of the video image. According to the significant target, the important region and nonimportant region are divided adaptively, and the encrypted two regions are reorganized to obtain the final encrypted image. Then, after demonstrating how attackers conduct attacks to the network under the ciphertext attack mode, an improved encryption algorithm based on selective ciphertext attack is proposed to improve the existing encryption algorithm of the neural network. Besides, a secure encryption algorithm is obtained through detailed analysis and comparison of the security ability of the algorithm. The experimental results show that Bob’s decryption error rate will decrease over time. The average classification error rate of Eve increases over time, but when Bob and Alice learn a secure encryption network structure, Eve’s classification accuracy is not superior to random prediction. Chosen ciphertext attack-advantageous neural cryptography (CCA-ANC) has an encryption time of 14s and an average speed of 69mb/s, which has obvious advantages over other encryption algorithms. The self-learning secure encryption algorithm proposed here significantly improves the security of the password and ensures data security in the video image.

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[ 26 , 27 ].…”

Section: Improved Generative Adversarial Network By Ccamentioning

confidence: 99%

Optimization of a Deep Learning Algorithm for Security Protection of Big Data from Video Images

Geng¹,

Yan

Lu³

2022

Computational Intelligence and Neuroscience

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“…The value of weight vector W is random in the initial case, so the actual calculated output y kr of the network is not high. After the neuron number m is determined in the hidden layer, the error d kr can be reduced by adjusting the W. Back-propagation is along the function e r and adjusts the weight vector with the negative gradient direction [18][19][20]. Then, the correction value of weight vector W is set as ΔW and calculated as DW ¼ À Z @e r @W , where η is the learning rate (0-1).…”

Section: Plos Onementioning

confidence: 99%

Unreferenced English articles’ translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning

2022

PLoS ONE

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Currently, both manual and automatic evaluation technology can evaluate the translation quality of unreferenced English articles, playing a particular role in detecting translation results. Still, their deficiency is the lack of a close or noticeable relationship between evaluation time and evaluation theory. Thereupon, to realize the automatic Translation Quality Assessment (TQA) of unreferenced English articles, this paper proposes an automatic TQA model based on Sparse AutoEncoder (SAE) under the background of Deep Learning (DL). Meanwhile, the DL-based information extraction method employs AutoEncoder (AE) in the bilingual words’ unsupervised learning stage to reconstruct the translation language vector features. Then, it imports the translation information of unreferenced English articles into Bilingual words and optimizes the extraction effect of language vector features. Meantime, the translation language vector feature is introduced into the automatic DL-based TQA. The experimental findings corroborate that when the number of sentences increases, the number of actual translation errors and the evaluation scores of the proposed model increase, but the Bilingual Evaluation Understudy (BLEU) score is not significantly affected. When the number of sentences increases from 1,000 to 6,000, the BLEU increases from 96 to 98, which shows that the proposed model has good performance. Finally, the proposed model can realize the high-precision TQA of unreferenced English articles.

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“…This will confine full-scale and dynamic range of the converter [10,11]. On the other hand, mismatches of capacitor bank and process, voltage and temperature (PVT) sensitivity of DAC should be compensated by hard or soft calibration [12][13][14]. Utilizing calibration circuits increase area and power consumption of the converter.…”

Section: Introductionmentioning

confidence: 99%

Versatile DAC-less successive approximation ADC architecture for medium speed data acquisition

Pourahmad

Dehghani

Mehr

et al. 2022

Microelectronics Journal

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A digital background calibration scheme for non-linearity of SAR ADC using back-propagation algorithm

Cited by 10 publications

References 6 publications

Optimization of a Deep Learning Algorithm for Security Protection of Big Data from Video Images

Optimization of a Deep Learning Algorithm for Security Protection of Big Data from Video Images

Unreferenced English articles’ translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning

Versatile DAC-less successive approximation ADC architecture for medium speed data acquisition

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