WATERMARK DETECTION USING DYNAMIC STOCHASTIC RESONANCE

In this paper, a dynamic stochastic resonance (DSR) based watermark detection technique in discrete wavelet transform (DWT) domain is presented. Pseudo random bit sequence having certain seed value is considered as a watermark. Watermark embedding is done by embedding random bits in spread-spectrum fashion to the significant DWT coefficients. Watermark detection is quantitatively characterized by the value of correlation. The performance of watermark detection is improved by DSR which is an iterative process that utilizes the internal noise present in the image or external noise which is added during attacks. Even under various noise attacks, geometrical distortions, image enhancement and compression attacks, the DSR-based random bits detection is observed to give noteworthy improvement over existing watermark detection techniques. DSR-based technique is also found to give better detection performance when compared with the suprathreshold stochastic resonance-based detection technique.

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LOGO EXTRACTION USING COMBINED DISCRETE WAVELET TRANSFORM AND DYNAMIC STOCHASTIC RESONANCE

International Journal of Image and Graphics ◽

10.1142/s0219467813500046 ◽

2013 ◽

Vol 13 (01) ◽

pp. 1350004

Author(s):

RAJIB KUMAR JHA ◽

PRABIR KUMAR BISWAS ◽

B. N. CHATTERJI

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Stochastic Resonance ◽

Signal Amplitude ◽

Experimental Results ◽

New Method ◽

Discrete Wavelet ◽

Noise Amplitude ◽

Dynamic Stochastic Resonance ◽

Dynamic Stochastic

In this paper, we have introduced a new method for watermark (logo) extraction from distorted watermarked images. The method is based on combined discrete wavelet transform (DWT) and dynamic stochastic resonance (DSR). In this method, the image property such as variance corresponding to the DWT coefficients of the image is tuned with the dynamic stochastic resonance parameters which causes resonance to the DWT coefficients. That is, the signal amplitude enhances and noise amplitude degraded in the DWT coefficients. This approach extracts hided logo from the distorted watermarked image which is almost very similar to the original logo. The experimental results have been compared with the existing techniques and were found to be superior.

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Balanced GHM Mutiwavelet Transform based Contrast Enhancement Technique for Dark Images using Dynamic Stochastic Resonance

Intelligent Automation & Soft Computing ◽

10.31209/2018.100000001 ◽

2018 ◽

pp. 1-14

Author(s):

S. Deivalakshmi ◽

P. Palanisamy ◽

X. Gao

Keyword(s):

Contrast Enhancement ◽

Stochastic Resonance ◽

Enhancement Technique ◽

Dynamic Stochastic Resonance ◽

Dynamic Stochastic

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Enhancement of dark and low-contrast images using dynamic stochastic resonance

IET Image Processing ◽

10.1049/iet-ipr.2012.0114 ◽

2013 ◽

Vol 7 (2) ◽

pp. 174-184 ◽

Cited By ~ 48

Author(s):

Rajlaxmi Chouhan ◽

Rajib Kumar Jha ◽

Prabir Kumar Biswas

Keyword(s):

Stochastic Resonance ◽

Low Contrast ◽

Dynamic Stochastic Resonance ◽

Dynamic Stochastic

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Real-time Mental Stress Detection Technique Using Neural Networks towards Wearable Health Monitor

Measurement Science and Technology ◽

10.1088/1361-6501/ac3aae ◽

2021 ◽

Author(s):

Nilava Mukherjee ◽

Sumitra Mukhopadhyay ◽

Rajarshi Gupta

Keyword(s):

Feature Extraction ◽

Real Time ◽

Mental Stress ◽

Detection Technique ◽

Physiological Signals ◽

Support Vector ◽

Discrete Wavelet ◽

Optimum Number ◽

Stress Detection ◽

Detection Techniques

Abstract Motivation: In recent times, mental stress detection using physiological signals have received widespread attention from the technology research community. Although many motivating research works have already been reported in this area, the evidence of hardware implementation is occasional. The main challenge in stress detection research is using optimum number of physiological signals, and real-time detection with low complexity algorithm. Objective: In this work, a real-time stress detection technique is presented which utilises only photoplethysmogram (PPG) signal to achieve improved accuracy over multi-signal-based mental stress detection techniques. Methodology: A short segment of 5s PPG signal was used for feature extraction using an autoencoder (AE), and features were minimized using recursive feature elimination (RFE) integrated with a multi-class support vector machine (SVM) classifier. Results: The proposed AE-RFE-SVM based mental stress detection technique was tested with WeSAD dataset to detect four-levels of mental state, viz., baseline, amusement, meditation and stress and to achieve an overall accuracy, F1 score and sensitivity of 99%, 0.99 and 98% respectively for 5s PPG data. The technique provided improved performance over discrete wavelet transformation (DWT) based feature extraction followed by classification with either of the five types of classifiers, viz., SVM, random forest (RF), k-nearest neighbour (k-NN), linear regression (LR) and decision tree (DT). The technique was translated into a quad-core-based standalone hardware (1.2 GHz, and 1 GB RAM). The resultant hardware prototype achieves a low latency (~0.4 s) and low memory requirement (~1.7 MB). Conclusion: The present technique can be extended to develop remote healthcare system using wearable sensors.

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