Palaniappan Ramu

ECG Steganography provides secured transmission of secret information such as patient personal information through ECG signals. This paper proposes an approach that uses discrete wavelet transform to decompose signals and singular value decomposition (SVD) to embed the secret information into the decomposed ECG signal. The novelty of the proposed method is to embed the watermark using SVD into the two dimensional (2D) ECG image. The embedding of secret information in a selected sub band of the decomposed ECG is achieved by replacing the singular values of the decomposed cover image by the singular values of the secret data. The performance assessment of the proposed approach allows understanding the suitable sub-band to hide secret data and the signal degradation that will affect diagnosability. Performance is measured using metrics like Kullback–Leibler divergence (KL), percentage residual difference (PRD), peak signal to noise ratio (PSNR) and bit error rate (BER). A dynamic location selection approach for embedding the singular values is also discussed. The proposed approach is demonstrated on a MIT-BIH database and the observations validate that HH is the ideal sub-band to hide data. It is also observed that the signal degradation (less than 0.6 %) is very less in the proposed approach even with the secret data being as large as the sub band size. So, it does not affect the diagnosability and is reliable to transmit patient information.

Background and objective Pedicle screw instrumentation is widely used in the treatment of spinal disorders and deformities. Currently, the surgeon decides the holding power of instrumentation based on the perioperative feeling which is subjective in nature. The objective of the paper is to develop a surrogate model which will predict the pullout strength of pedicle screw based on density, insertion angle, insertion depth and reinsertion. Methods A Taguchi's orthogonal array was used to design an experiment to find the factors effecting pullout strength of pedicle screw. The pullout studies were carried using polyaxial pedicle screw on rigid polyurethane foam block according to American society for testing of materials (ASTM F543). Analysis of variance (ANOVA) and Tukey's honestly significant difference multiple comparison tests were done to find factor effect. Based on the experimental results, surrogate models based on Krigging, polynomial response surface and radial basis function were developed for predicting the pullout strength for different combination of factors. An ensemble of these surrogates based on weighted average surrogate model was also evaluated for prediction. Results Density, insertion depth, insertion angle and reinsertion have a significant effect (p < 0.05) on pullout strength of pedicle screw. Weighted average surrogate performed the best in predicting the pull out strength amongst the surrogate models considered in this study and acted as insurance against bad prediction. Conclusions A predictive model for pullout strength of pedicle screw was developed using experimental values and surrogate models. This can be used in pre-surgical planning and decision support system for spine surgeon.

Connected health enables patient centric interventions resulting in better healthcare and hence better living. In order to accomplish this, bio-signals, medical and diagnosis information are shared and accessed by multiple actors and it is important to protect the privacy of patient data. Steganography is widely used to protect patient data by hiding it in the medical information. Current work investigates ECG steganography using Discrete Wavelet Transform (DWT) and Quick Response (QR) code. Steganography deteriorates the ECG signal and it is important to minimize this deterioration to preserve diagnosability. 1D ECG signal is converted to 2D ECG image and decomposed into sub-bands by subjecting it to DWT. The novelty of the proposed approach lies in converting the patient data into QR code and using it as watermark in ECG steganography. The QR code is embedded in the 2D image using additive quantization scheme. The performance of proposed method is measured using Peak Signal to Noise Ratio, Percentage Residual Difference and Kullback–Leibler distance. These metrics are used as a measure of imperceptibility while the data loss during retrieval is measured by Bit Retrieval Rate. The proposed method is demonstrated on normal ECG signals obtained from MIT-BIH database for different QR code versions. Metrics reveal that imperceptibility decreased for increasing patient data size and increasing scaling factors. Metrics were independent of the sub-band and the proposed method allows reliable patient data protection with full retrieval ability.

Current study presents fluid flow analysis using CFD and a surrogate based framework for design optimization of Savonius wind turbines. The CFD model used for the study is validated with results from a physical model in water tunnel experiment. Four variables that best define blade geometry are considered and a feasible design space consisting of different combinations of these variables that provide positive overlap ratio is identified. The feasible space is then sampled with Latin hyper cube design of experiment. Numerical simulations utilizing K-epsilon turbulence model are performed at each point in the Design of Experiments to obtain coefficient of performance and weighted average surrogate (WAS) is fitted to them. Novelty of the current work is the use of WAS for design of savonius turbine. The WAS is an ensemble of surrogates that consists of polynomial response surface, kriging and radial basis functions. Error metrics reveal that WAS performs better compared to any surrogate individually thus avoiding misleading optima and eliminates surrogate dependent optima. WAS is used to explore the design space and perform optimization with limited number of CFD analyses. It is observed that at the optimal profile, there is more power on the rotors and primary recirculation in the immediate downstream of rotor is high, enforcing maximum momentum on turbine.

Biomedi cal signals transmitted over the internet are usually tagged with patient information. Data hiding techniq ues such as steganography ensures the security of such data by hiding the data into signa ls. However, data hiding results in signal deterioration that might affect diag nosability. A novel technique which uses curvelet transforms to hide patient information into their ECG signal is presented. Curvelet transform decomposes the ECG signal into frequency sub-bands. A quantisation approach is used to embed patient data into coeffi cients whose values are around zero, in the high-frequency subband. Performance metrics provide the measure of watermar k imperceptibility of the proposed approach. BER is used to m easure the ability to extract patient data. The proposed approach is dem onstrated on the MIT-BIH database and the observations validate that its performance is superior compared with the random locations approach. Although the performance of the proposed approach decreases as patient information size increases, the peak signal-to-noise ratio values are high. Therefore, the proposed approach can be used for the safe transfer of patient data.

ECG steganography allows secured transmission of patient data that are tagged to the ECG signals. Signal deterioration leading to loss of diagnosis information and inability to retrieve patient data fully are the major challenges with ECG steganography. In this work, an attempt has been made to use curvelet transforms which permit identifying the coefficients that store the crucial information about diagnosis. The novelty of the proposed approach is the usage of curvelet transform for ECG steganography, adaptive selection of watermark location and a new threshold selection algorithm. It is observed that when coefficients around zero are modified to embed the watermark, the signal deterioration is the least. In order to avoid overlap of watermark, an n × n sequence is used to embed the watermark. The imperceptibility of the watermark is measured using metrics such as Peak Signal to Noise Ratio, Percentage Residual Difference and Kullback-Leibler distance. The ability to extract the patient data is measured by the Bit Error Rate. Performance of the proposed approach is demonstrated on the MIT-BIH database and the results validate that coefficients around zero are ideal for watermarking to minimize deterioration and there is no loss in the data retrieved. For an increased patient data size, the cover signal deteriorates but the Bit Error Rate is zero. Therefore the proposed approach does not affect diagnosability and allows reliable steganography.

Treatment of uncertainties in structural design involves identifying the boundaries of the failure domain to estimate reliability. When the structural responses are discontinuous or highly nonlinear, the failure regions tend to be an island in the design space. The boundaries of these islands are to be approximated to estimate reliability and perform optimization. This work proposes Alpha (α) shapes, a computational geometry technique to approximate such boundaries. The α shapes are simple to construct and only require Delaunay Tessellation. Once the boundaries are approximated based on responses sampled in a design space, a computationally efficient ray shooting algorithm is used to estimate the reliability without any additional simulations. The proposed approach is successfully used to decompose the design space and perform Reliability based Design Optimization of a tube impacting a rigid wall and a tuned mass damper.

ECG Steganography ensures protection of patient data when ECG signals embedded with patient data are transmitted over the internet. Steganography algorithms strive to recover the embedded patient data entirely and to minimize the deterioration in the cover signal caused by the embedding. This paper presents a Continuous Ant Colony Optimization (CACO) based ECG Steganography scheme using Discrete Wavelet Transform and Singular Value Decomposition. Quantization techniques allow embedding the patient data into the ECG signal. The scaling factor in the quantization techniques governs the tradeoff between imperceptibility and robustness. The novelty of the proposed approach is to use CACO in ECG Steganography, to identify Multiple Scaling Factors (MSFs) that will provide a better tradeoff compared to uniform Single Scaling Factor (SSF). The optimal MSFs significantly improve the performance of ECG steganography which is measured by metrics such as Peak Signal to Noise Ratio, Percentage Residual Difference, Kullback-Leibler distance and Bit Error Rate. Performance of the proposed approach is demonstrated on the MIT-BIH database and the results validate that the tradeoff curve obtained through MSFs is better than the tradeoff curve obtained for any SSF. The results also advocate appropriate SSFs for target imperceptibility or robustness.

The performance of a path generating linkage is measured in terms of the error in the generated path. The probability of producing its intended path is its reliability. Tighter tolerances in link lengths and joint clearances result in higher reliability but incur more costs. Therefore, it is desirable to understand the trade-off relationship between the costs and reliability. In the current work, a genetic algorithm is used to construct the Pareto trade-off front between cost and reliability by solving a bi-criterion optimisation problem. Statistical moments required to estimate reliability are computed by combining an approximate cumulative density function of error and a 3-point approximation technique. This approach uses a fraction of the samples compared to crude Monte Carlo simulation. The proposed approach is demonstrated on a four bar mechanism tracing a straight line and a closed path. It is observed that the Pareto front generated using the proposed approach with fewer samples compares well with the one generated with crude Monte Carlo simulation with a large sample set, thus offering enormous gains in computational efficiency.

Participatory approaches elicit information from multiple stakeholders while planning and implementing resource management systems. Such elicited information is often associated with significant variability. Public participation geographical information science (GIS) (PP-GIS) solutions can reduce this variability by helping stakeholders to measure the factors involved and provide the elicited information. We propose a ‘Quality Function Deployment’-based participatory framework for developing such PP-GIS solutions. It is demonstrated using a case study to enhance an existing PP-GIS into a solution for rainwater harvesting systems in Indian villages. The novelty of the proposed framework is that it identifies metrics and carries out comparative analysis of three existing solutions: participatory rural appraisal, participatory mapping and PP-GIS. In the case study, PP-GIS scored less than participatory mapping as it scored less on usability and affordability. To improve PP-GIS in these aspects, an easy-to-use mobile and web based, free and open source PP-GIS solution, Watershed GIS, was developed. It scored better than the three existing solutions and its usage resulted in substantial reduction of variability in criteria values and thus better ranking of alternatives, with the average coefficient of variation decreasing from 0.12 to 0.05.