A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications

Krishna, Sabbi Vamshi; An, Jianing; Zheng, Lianxi

doi:10.1109/inec.2013.6465961

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…We employed the Multi-layer perceptron (MLP) in this research. It is a feed-forward artificial neural network model that maps sets of input data into a set of target outputs [36,37]. MLP is a multiple layer system (input layer, hidden layer(s) and output layer) where each layer contains several nodes (cell).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Non-Invertible Online Signature Biometric Template Protection via Shuffling and Trigonometry Transformation

LaythMalallah¹,

Rahman²,

Adnan³

et al. 2014

IJCA

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This paper describes a novel approach to a cancelable template protection scheme that secures online handwritten signature samples in the reference database of a biometric verification system. We propose a renewable-noninvertible transformation process named Bio-Trigono comprising two consecutive steps. First, a shuffling scheme is applied to a signature sample to attain the renewability property for template protection. This is followed by the deployment of a cosine function for which its periodic characteristic is exploited to achieve a much desired non-invertible property for additional security. The overall template protection scheme was tested rigorously on signature samples of a SIGMA database through an online signature verification system. Its verification utilized the Principal Component Analysis (PCA) for features' extraction and the Artificial Neural Network (ANN) for user reference modeling and classification processes. Results demonstrated an effective cancelable template protection scheme whereby the best averaged error rates were 10.3, 10.5 and 14.1% for untransformed first and second transformed signature templates, respectively.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Non-Invertible Online Signature Biometric Template Protection via Shuffling and Trigonometry Transformation

LaythMalallah¹,

Rahman²,

Adnan³

et al. 2014

IJCA

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“…In our study, we employed an artificial neural network (ANN), [140][141][142] activation functions, respectively, and Levenberg Marquardt algorithm (LMA) was the back propagation algorithm adopted for the training process. 143,144 The data used for classification was split into train (for network learning) and test data in the ratio 3:2, and 10 validation checks were performed. Various functions in MATLAB were used to perform different steps of classification, of which, the most important ones have been listed in Table 3.1.…”

Section: Discrimination Of Such Response Data Obtained From Differentmentioning

confidence: 99%

Carbon nanotube based electronic detection of biomolecules and chemicals

Krishna¹

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First and foremost, I would like to thank my ex-supervisor, Prof. Zheng Lianxi, for giving me an opportunity to embark on this journey of PhD in the area of nanotechnology. These four years have undoubtedly been the toughest phase of my life as I kept encountering several hurdles both personally and academically. Nevertheless, every time I look up to Prof. Zheng, he has given me priceless suggestions, constant encouragement, and timely guidance, which have largely been the source of my strength during these strenuous phases. Prof. Zheng has been a true role model for me for his simple attitude towards work and life. I am deeply indebted to him, and without his guidance, I would not have been able to improve in various aspects of research and life. I also sincerely thank my current supervisor Prof. Wong Chee How, who has been instrumental in my thesis submission process following Prof. Zheng's departure from NTU. He has been kind and I am extremely grateful to him for lending a helping hand in my troubled times.

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Online Handwritten Signature Recognition by Length Normalization using Up-Sampling and Down-Sampling

Malallah¹

2015

IJCSDF

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A neural network approach to classify inversion regions of high mobility ultralong channel single walled carbon nanotube field-effect transistors for sensing applications

Cited by 3 publications

References 8 publications

Non-Invertible Online Signature Biometric Template Protection via Shuffling and Trigonometry Transformation

Non-Invertible Online Signature Biometric Template Protection via Shuffling and Trigonometry Transformation

Carbon nanotube based electronic detection of biomolecules and chemicals

Online Handwritten Signature Recognition by Length Normalization using Up-Sampling and Down-Sampling

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