Auto-encoder based deep learning for surface electromyography signal processing

© 2018 Advances in Science, Technology and Engineering Systems. All Rights Reserved. Feature extraction is taking a very vital and essential part of bio-signal processing. We need to choose one of two paths to identify and select features in any system. The most popular track is engineering handcrafted, which mainly depends on the user experience and the field of application. While the other path is feature learning, which depends on training the system on recognising and picking the best features that match the application. The main concept of feature learning is to create a model that is expected to be able to learn the best features without any human intervention instead of recourse the traditional methods for feature extraction or reduction and avoid dealing with feature extraction that depends on researcher experience. In this paper, Auto-Encoder will be utilised as a feature learning algorithm to practice the recommended model to excerpt the useful features from the surface electromyography signal. Deep learning method will be suggested by using Auto-Encoder to learn features. Wavelet Packet, Spectrogram, and Wavelet will be employed to represent the surface electromyography signal in our recommended model. Then, the newly represented bio-signal will be fed to stacked autoencoder (2 stages) to learn features and finally, the behaviour of the proposed algorithm will be estimated by hiring different classifiers such as Extreme Learning Machine, Support Vector Machine, and SoftMax Layer. The Rectified Linear Unit (ReLU) will be created as an activation function for extreme learning machine classifier besides existing functions such as sigmoid and radial basis function. ReLU will show a better classification ability than sigmoid and Radial basis function (RBF) for wavelet, Wavelet scale 5 and wavelet packet signal representations implemented techniques. ReLU will illustrate better classification ability, as an activation function, than sigmoid and poorer than RBF for spectrogram signal representation. Both confidence interval and Analysis of Variance will be estimated for different classifiers. Classifier fusion layer will be implemented to glean the classifier which will progress the best accuracies' values for both testing and training to develop the results. Classifier fusion layer brought an encouraging value for both accuracies either training or testing ones

Do microfinance institutions reach the poorest?

The question raised in the title is an important one to the microfinance sector, especially since the Microcredit Summit held in Washington, DC, in 1997. In order to gain more transparency on the depth of poverty outreach, the Consultative Group to Assist the Poorest (CGAP) supported research at IFPRI during 1999 and 2000 to design and test a simple, low-cost operational tool to measure the poverty level of MFI clients relative to nonclients. This policy brief informs about the results from recent case studies on the poverty outreach of four selected microfinance institutions. The case studies were conducted for four MFIs world-wide: MFI A (Central America), MFI B (East Africa), MFI C (Southern Africa), and MFI D (South Asia).Microenterprises Finance. ,Finance Developing countries. ,Financial institutions. ,Finance Southern Africa. ,Finance Central America. ,Finance South Asia. Finance Africa, East. ,

The role of HLA-DP mismatches and donor specific HLA-DP antibodies in kidney transplantation : a case series

BACKGROUND: The impact of HLA-DP mismatches on renal allograft outcome is still poorly understood and is suggested to be less than that of the other HLA loci. The common association of HLA-DP donor-specific antibodies (DSA) with other DSA obviates the evaluation of the actual effect of HLA-DP DSA. METHODS: From a large multicenter data collection, we retrospectively evaluated the significance of HLA-DP DSA on transplant outcome and the immunogenicity of HLA-DP eplet mismatches with respect to the induction of HLA-DP DSA. Furthermore, we evaluated the association between the MFI of HLA-DP antibodies detected in Luminex assays and the outcome of flowcytometric/complement-dependent cytotoxicity (CDC) crossmatches. RESULTS: In patients with isolated pretransplant HLA-DP antibodies (N = 13), 6 experienced antibody-mediated rejection (AMR) and 3 patients lost their graft. In HLAMatchmaker analysis of HLA-DP mismatches (N = 72), HLA-DP DSA developed after cessation of immunosuppression in all cases with 84DEAV (N = 14), in 86% of cases with 85GPM (N = 6/7), in 50% of cases with 56E (N = 6/12) and in 40% of cases with 56A mismatch (N = 2/5). Correlation analysis between isolated HLA-DP DSA MFI and crossmatches (N = 90) showed negative crossmatch results with HLA-DP DSA MFI <2000 (N = 14). Below an MFI of 10,000 CDC crossmatches were also negative (N = 33). Above these MFI values both positive (N = 35) and negative (N = 16) crossmatch results were generated. CONCLUSIONS: Isolated HLA-DP DSA are rare, yet constitute a significant risk for AMR. We identified high-risk eplet mismatches that can lead to HLA-DP DSA formation. We therefore recommend HLA-DP typing to perform HLA-DP DSA analysis before transplantation. HLA-DP DSA with high MFI were not always correlated with positive crossmatch results

Intelligent optical performance monitor using multi-task learning based artificial neural network

An intelligent optical performance monitor using multi-task learning based artificial neural network (MTL-ANN) is designed for simultaneous OSNR monitoring and modulation format identification (MFI). Signals' amplitude histograms (AHs) after constant module algorithm are selected as the input features for MTL-ANN. The experimental results of 20-Gbaud NRZ-OOK, PAM4 and PAM8 signals demonstrate that MTL-ANN could achieve OSNR monitoring and MFI simultaneously with higher accuracy and stability compared with single-task learning based ANNs (STL-ANNs). The results show an MFI accuracy of 100% and OSNR monitoring root-mean-square error of 0.63 dB for the three modulation formats under consideration. Furthermore, the number of neuron needed for the single MTL-ANN is almost the half of STL-ANN, which enables reduced-complexity optical performance monitoring devices for real-time performance monitoring

Information as a Substitute for Bailouts in Sovereign Debt Markets

This paper argues that multilateral financial institutions (MFIs), such as the International Monetary Fund, play an important informational role in international financial markets. By providing low-cost and high quality information, that is otherwise very costly for private lenders to obtain, the MFI allows a private lender to form a more accurate estimate of the credit-worthiness of a sovereign borrower. This creates a positive externality for private lenders and for sovereign borrowers with low risk credit ratings that are revealed by the provision of MFI information. The MFI can choose to internalize the negative externality created for sovereign borrowers who are revealed to be a higher credit risk by providing stand-by commitments to the sovereign. We construct a formal model of the private lenders decision to purchase costly information about the sovereign borrower. The model suggests that the free provision of MFI information has greater positive effects on financial markets the less risk-averse the private lender, the less information the private lender already has, the greater the size of the loan, and the smaller the expected default probability of the sovereign borrower.information default bailouts IMF

Solar Sources of Interplanetary Magnetic Clouds Leading to Helicity Prediction

This study identifies the solar origins of magnetic clouds that are observed at 1 AU and predicts the helical handedness of these clouds from the solar surface magnetic fields. We started with the magnetic clouds listed by the Magnetic Field Investigation (MFI) team supporting NASA's WIND spacecraft in what is known as the MFI table and worked backwards in time to identify solar events that produced these clouds. Our methods utilize magnetograms from the Helioseismic and Magnetic Imager (HMI) instrument on the Solar Dynamics Observatory (SDO) spacecraft so that we could only analyze MFI entries after the beginning of 2011. This start date and the end date of the MFI table gave us 37 cases to study. Of these we were able to associate only eight surface events with clouds detected by WIND at 1 AU. We developed a simple algorithm for predicting the cloud helicity which gave the correct handedness in all eight cases. The algorithm is based on the conceptual model that an ejected flux tube has two magnetic origination points at the positions of the strongest radial magnetic field regions of opposite polarity near the places where the ejected arches end at the solar surface. We were unable to find events for the remaining 29 cases: lack of a halo or partial halo CME in an appropriate time window, lack of magnetic and/or filament activity in the proper part of the solar disk, or the event was too far from disk center. The occurrence of a flare was not a requirement for making the identification but in fact flares, often weak, did occur for seven of the eight cases.Comment: 18 pages, 8 figures, 2 table

Comparison of product quality estimation of propylene polymerization in loop reactors using artificial neural network models

One of the major challenges in polymerization industry is the lack of online instruments to measure polymer end-used properties such as xylene soluble, particle size distribution and melt flow index (MFI). As an alternative to the online instruments and conventional laboratory tests, these properties can be estimated using model based-soft sensor. This paper presents models for soft sensors to measure MFI in industrial polypropylene loop reactors using artificial neural network (ANN) model, serial hybrid neural network (HNN) model and stacked neural network (SNN) model. All models were developed and simulated in MATLAB. The simulation results of the MFI based on the ANN, HNN, and SNN models were compared and analyzed. The MFI was divided into three grades, which are A (10-12g/10 min), B (12-14g/10 min) and C (14-16 g/10 min). The HNN model is the best model in predicting all range of MFI with the lowest root mean square error (RMSE) value, 0.010848, followed by ANN model (RMSE=0.019366) and SNN model (RMSE=0.059132). The SNN model is the best model when tested with each grade of the MFI. It has shown lowest RMSE value for each type of MFI (0.012072 for MFI A, 0.017527 for MFI B and 0.015287 for MFI C), compared to HNN model (0.014916 for MFI A, 0.041402 for MFI B and 0.046437 for MFI C) and ANN model (0.015156 for MFI A, 0.076682 for MFI B, and 0.037862 for MFI C)

Comparison of different methods for the calculation of the microvascular flow index

The microvascular flow index (MFI) is commonly used to semiquantitatively characterize the velocity of microcirculatory perfusion as absent (0), intermittent (1), sluggish (2), or normal (3). There are three approaches to compute MFI: (1) the average of the predominant flow in each of the four quadrants (MFI by quadrants), (2) the direct assessment during the bedside video acquisition (MFI point of care), and (3) the mean value of the MFIs determined in each individual vessel (MFI vessel by vessel). We hypothesized that the agreement between the MFIs is poor and that the MFI vessel by vessel better reflects the microvascular perfusion. For this purpose, we analyzed 100 videos from septic patients. In 25 of them, red blood cell (RBC) velocity was also measured. There were wide 95% limits of agreement between MFI by quadrants and MFI point of care (1.46), between MFI by quadrants and MFI vessel by vessel (2.85), and between MFI by point of care and MFI vessel by vessel (2.56). The MFIs significantly correlated with the RBC velocity and with the fraction of perfused small vessels, but MFI vessel by vessel showed the best R 2. Although the different methods for the calculation of MFI reflect microvascular perfusion, they are not interchangeable and MFI vessel by vessel might be better.Facultad de Ciencias Médica