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The embedding systems play an inevitable role in healthcare informatics and medical diagnosis. This article presents the design of a microcontroller ($\mu C$)-based online parameter detection and diagnosis system using real-time nerve signals. In developing the system, a low cost and low power battery-based voltage controlled neurostimulator is designed to stimulate the targeted sites of underlying nerve. In the second phase, a signal conditioning circuit is designed to accurately process and obtain the recorded signals for parameter extraction. In offline mode, an optimum neural network structure is determined from MATLAB simulation using prediagnosed nerve signal dataset through cross-validation technique which is then implemented in programmable interface controller (PIC) $\mu C$ (PIC18F45K22) for online estimation of parameter and diagnosis. Afterwards, nerve signals are collected from various subjects in laboratory following the guidelines of standard nerve conduction study (NCS) which are processed through embedded programmed $\mu C$. Experimental results and subsequent comparison with the state-of-the-art designs indicate that the proposed system is promising and reliable for online detection of the NCS parameters and subsequent diagnosis. [ABSTRACT FROM AUTHOR]

This paper presents the real-time implementation of a feature fusion based learning using multidomain discriminant correlation analysis (MDCA) for accurate diagnosis of nonlinear processes. The algorithm is also implemented in a 8-b PIC-microcontroller (PIC18F45k22) from the perspective of online applications. In the MDCA, a set of multiple features is evaluated in direct and wavelet domain for each process employing large-volume available signals. Features are subjected for correlation analysis to demonstrate the variation of features and to extract low-order statistics that represent underlying phenomena. The locally evaluated statistics are fused using MDCA and obtained discriminant features are subjected to linear transformation. Finally, a set of efficient key statistics are derived for accurate characterization of various processes. The algorithm is validated statistically and integrated with decision models-k-nearest neighbor, discriminant analysis, and neural network for real-time process diagnosis. The method achieves accuracy in the range of 98.75–100%. Results and comparison analysis show the effectiveness and reliability of the proposed model. [ABSTRACT FROM AUTHOR]

Fusion of large-scale information is the key strategy for the complete understanding of many nonlinear and complicated industrial and medical processes. This paper presents the proposed multiview (MV) feature fusion-based learning generalizing discriminant correlation analysis (DCA) for assessment and diagnosis of nonlinear processes. The core focus of this algorithm is to explore the effectiveness of MV information embedding in learning models and viable implementation in real-time applications. Our method is capable of incorporating high-dimensional information inherent in MV features generated from available inputs using the proposed DCA-based scheme. The algorithm is tested with two real-time electromyogram data sets, which include three categories of nonlinear data-amyotrophic lateral sclerosis, myopathy, and healthy control subjects. A set of MV features is generated in both the time and the wavelet domain for all of the study groups. The features are subjected to DCA projection and optimization and obtained low-order features are concatenated using DCA-based fusion scheme. The discriminant features are applied for the statistical analysis and the model validation. The model achieves an accuracy of 99.03% with a specificity of 99.58% and sensitivities of 98.50% and 97.59%. However, the accuracy over the second data set is 100% with sensitivities of 100% and the specificity of 100%. Results are further compared with the state-of-the-art methods. The proposed scheme is promising and outperforms many state-of-the-art methods, and thus it ensures the faithfulness for industrial applications. [ABSTRACT FROM AUTHOR]

This paper describes a method for developing a novel, low-cost, hand-held electronic nose (E-Nose) for black tea flavor estimation using metal–oxide–semiconductor gas sensors. In this research, a first-level experimentation has been conducted with a computer-assisted-technology-based system for sensor selection and validation. In the second level, a novel hand-held E-Nose is designed and developed for generation of tea aroma, capturing the response patterns and signal processing in a microcontroller (PIC 18F45K22) for tea flavor grading. The results of the first-level experimentation provide the sensor selection, where only four out of 12 sensors were selected based on their performance. Moreover, the classification network was predetermined for implementation in the microcontroller. Experiments conducted on the developed hand-held E-Nose show the classification on eight different grades of tea (each with ten samples) varying from 70% to 100%, revealing an average classification efficiency of 90%. The repeatability of the system was also found to be 80%. The developed system also overcomes the problem of heating the tea sample, since the volatile organic compounds of tea are generated due to the emitted heat of the sensors in the proposed system. [ABSTRACT FROM PUBLISHER]