R. Ananda Natarajan

Performance Analysis of Sculptured Diaphragm for Low Pressure MEMS Sensors

Sculptured or Bossed diaphragm is a specialized geometry with rigid center or boss. This paper presents the outcome of design approaches of sculptured diaphragm structure for low pressure applications. The simulation results are obtained using Intellisuite MEMS CAD design tool. The results indicate that sculptured diaphragm are designed with minimum thickness, compensating the large (a/h) ratio with local stiffening by means of rigid center and better linearity. Further, the maximum stress regions are analyzed for fixing the position of the piezoresistor. Finally, the sensitivity is improved by using the Silicon-On-Insulator (SOI) diaphragms.

Non-negative matrix factorization algorithm for the deconvolution of one dimensional chromatograms

In chromatogram analysis, overlapped chromatograms are difficult to analyze if they are not resolved. The conventional multivariate resolution techniques do not give accurate results when the chromatograms are severely overlapped. In this work, ML-NMFdiv, modified non-negative matrix factorization (NMF) with divergence objective algorithm has been proposed for the separation of severely overlapped chromatograms of acetone and acrolein mixture. Before applying NMF, principal component analysis (PCA) is applied to determine number of components in the mixture taken. Most of the NMF algorithms used so far for chromatogram separation do not converge to a stable limit point and no uniqueness in the results. To get unique results, instead of random initialization, three different initialization methods namely, Robust initialization, NNDSVD (Non-Negative Double Singular Value Decomposition) based initialization and EFA (Evolving Factor Analysis) based initializations, have been used in this work and the performances are compared. The multiplicative update of already existing NMFdiv algorithm has been modified and proposed in this work as ML-NMFdiv (NMFdiv with modified multiplicative update) for overlapped chromatogram separation to improve the convergence. The proposed ML-NMFdiv algorithm is applied on the simulated and experimental chromatograms obtained for acetone and acrolein mixture. The results of proposed ML-NMFdiv are compared with existing Multivariate Curve Resolution-Alternating Least Square (MCR-ALS) method.

An algorithm for overlapped chromatogram separation

Non-negative matrix factorization (NMF) is a recently developed method for real time data analysis. In the past it has been used for facial recognition and spectral data analysis. Most of the NMF algorithms do not converge to a stable limit point and uniqueness in results is also a problem in NMF. To improve the convergence, a new NMF algorithm with modified multiplicative update (ML-NMFmse) has been proposed in this work for strongly overlapped and embedded chromatograms separation. To get same results for all the runs, instead of random initialization, three different initialization methods have been used namely, ALS–NMF (robust initialization), NNDSVD based initialization and EFA based initializations. The proposed ML-NMFmse algorithm is applied on the simulated and experimental overlapped chromatograms obtained for acetone and acrolein mixture, using Gas Chromatography–Flame Ionization Detector. Before applying NMF, Principal Component Analysis (PCA) was applied to determine number of components in the mixture taken. The result of proposed ML-NMFmse is compared with that of existing Multivariate Curve Resolution-Alternating Least Squares method in optimal conditions for both the algorithms. In the case of embedded chromatogram, the proposed ML-NMFmse with Robust method (ALS-NMF) of initialization performs better than all other methods. For a resolution of severely overlapped chromatograms, the proposed ML-NMFmse with NNDSVD method of initialization outperforms all other methods.

An Automated Segmentation Algorithm for Detection of MENINGIOMA and GBM using Reformed Self-Organizing Maps

The MRI Brain tumor segmentation can be performed by different algorithms that are based on a wide range of principles. In case of a high manual interaction, the process is time consuming and it introduces a high intra-subject and inter-subject variability due to the personal subjectivity. Tumor segmentation from MRI images is a challenging task. Due to the complex overlay of images in MRI, an automated segmentation and decision based on segmentation would be difficult. We propose an automated segmentation algorithm for MRI images based on typical radiological signs using Reformed Self-Organizing Maps (RSOM). The performance of the automated segmentation method was evaluated using seven performance indices and we found our RSOM proposed method was having high J coefficient, high F measure, better Accuracy & better Quality of segmentation.

Retinal Layer Segmentation in Pathological SD-OCT Images Using Boisterous Obscure Ratio Approach and its Limitation

Optical Coherence Tomography (OCT) imaging technique is a precise and prominent approach in retinal diagnosis on layers level. The pathological effect in retina, challenges a computational segmented approach in the boundary layer level for evaluating and identification of defect. The segmentation of layers and boundary edging process is misguided by the noise in the computation method. In these criteria, a novel algorithm of segmentation with the base of denoising techniques is required. In this work, Robust Outlyingness Ratio (ROR) algorithm is a noise detective operation which is applied in edge direction with gradient deformable contour model for layers detection. This Boisterous Obscure Ratio (BOR) computation procedure is derived. BOR is an image segmentation process with connectivity of eight formed layers in retinal SD-OCT images. The validation is done by comparing with the prior demonstration method. The highlighting feature of the BOR method is that it is time consuming and the results produced are highly substantial and effective. Keyword: Image analysis; Noise in imaging systems; Image detection systems; Transforms; Optical coherence tomography; Ophthalmology.

Microcontroller based non invasive diagnosis of knee joint diseases

This paper deals with the monitoring of joint diseases status under the influence of high frequency electrical signals. The response of the system for applied electrical signal was acquired, processed and displayed using four surface electrodes, Bio signal amplifier, Analogue to digital converter and PIC microcontroller. The electrical property of the tissue alters whenever the tissue surface is applied with any external electrical energy at very high frequency in the range of kHz. These electrical conductivity changes vary with healthy joint and disease affected joints. On the basis of changes in the electrical conductivity changes under the existence of high frequency signal a non invasive system has been implemented to monitor the disease status. The conductivity changes were monitored in terms of voltage drop across the joint region. Based on the voltage value varying from IV to 3V and its corresponding impedance value joint diseases were diagnosed. During the proposed research microcontroller was utilized for processing and displaying real time voltage signal acquired from knee joint surface using metal surface electrodes. These displayed real time voltage values were stored in local PC using microcontroller and MAX-232 IC for serial communication.

Diagnosis and Estimation of Sensor Bias in Dynamic Systems Using Adaptive Techniques

The estimation and detection of sensor bias fault in a dynamic system is discussed. The development of more general methodology for diagnosing sensor bias faults in a dynamic system which depends on the state of the system is established. The non linearity in the system nominal model is assumed to be the function of input and output only, this note used the adaptive technique of model based system and Kaczmarz’s Projection algorithm. An Observer is designed to generate residual of the system which indicates the presence of fault and initiates to estimate unknown sensor bias in the presence of model uncertainties. The tank level system is considered for simulative example is presented to illustrate the methodology. The robustness, sensitivity and stability properties of the dynamic system were analyzed.

An Integrated Fault Detection and Diagnosis Using Kaman Filter and Eigen Structure Assignment - Application to Three Tank System

An integrated fault detection and diagnosis approach against partial actuator failure for discrete-time stochastic systems has been proposed. When the dynamics of the process under control are changed due to the component (actuator) faults, the performance of the system degrades. The aim of proposed scheme is to detect and diagnoses the fault present and also to predict the effectiveness of the actuator. The scheme is based on a kalman filter for the parameter estimation via the Eigen structure assignment. This method also guarantees the stability of the system by analyzing the cost effectiveness factor of the actuator. The proposed approach it is demonstrated in a three tank benchmark system and the result illustrates its robustness.