Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?

Medscape

Automated medical image analysis is an emerging field of research that identifies the disease with the help of imaging technology. Diabetic retinopathy (DR) is a retinal disease that is diagnosed in diabetic patients. Deep neural network (DNN) is widely used to classify diabetic retinopathy from fundus images collected from suspected persons. The proposed DR classification system achieves a symmetrically optimized solution through the combination of a Gaussian mixture model (GMM), visual geometry group network (VGGNet), singular value decomposition (SVD) and principle component analysis (PCA), and softmax, for region segmentation, high dimensional feature extraction, feature selection and fundus image classification, respectively. The experiments were performed using a standard KAGGLE dataset containing 35,126 images. The proposed VGG-19 DNN based DR model outperformed the AlexNet and spatial invariant feature transform (SIFT) in terms of classification accuracy and computational time. Utilization of PCA and SVD feature selection with fully connected (FC) layers demonstrated the classification accuracies of 92.21%, 98.34%, 97.96%, and 98.13% for FC7-PCA, FC7-SVD, FC8-PCA, and FC8-SVD, respectively.

Fundus Image Classification Using VGG-19 Architecture with PCA and SVD

Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are used to help diagnose heart disease by recording the heart’s activity. However, automated medical-aided diagnosis with computers usually requires a large volume of labeled clinical data without patients' privacy to train the model, which is an empirical problem that still needs to be solved. To address this problem, we propose a generative adversarial network (GAN), which is composed of a bidirectional long short-term memory(LSTM) and convolutional neural network(CNN), referred as BiLSTM-CNN,to generate synthetic ECG data that agree with existing clinical data so that the features of patients with heart disease can be retained. The model includes a generator and a discriminator, where the generator employs the two layers of the BiLSTM networks and the discriminator is based on convolutional neural networks. The 48 ECG records from individuals of the MIT-BIH database were used to train the model. We compared the performance of our model with two other generative models, the recurrent neural network autoencoder(RNN-AE) and the recurrent neural network variational autoencoder (RNN-VAE). The results showed that the loss function of our model converged to zero the fastest. We also evaluated the loss of the discriminator of GANs with different combinations of generator and discriminator. The results indicated that BiLSTM-CNN GAN could generate ECG data with high morphological similarity to real ECG recordings.

/pdf/electrocardiogram-generation-with-a-bidirectional-lstm-cnn-2toolbk7an.pdf

Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network.

[서평]「Chaos and Fractals : New Frontiers of Science」

Performance Study of Different Denoising Methods for ECG Signals

Drowsy Driving Detection by EEG Analysis Using Wavelet Transform and K-means Clustering☆

This paper presents a method for lane boundaries detection which is not affected by the shadows, illumination and un-even road conditions. This method is based upon processing grayscale images using local gradient features, characteris-tic spectrum of lanes, and linear prediction. Firstly, points on the adjacent right and left lane are recognized using the local gradient descriptors. A simple linear prediction model is deployed to predict the direction of lane markers. The contribution of this paper is the use of vertical gradient image without converting into binary image(using suitable thre-shold), and introduction of characteristic lane gradient spectrum within the local window to locate the preciselane marking points along the horizontal scan line over the image. Experimental results show that this method has greater tolerance to shadows and low illumination conditions. A comparison is drawn between this method and recent methods reported in the literature.

/pdf/robust-lane-detection-in-shadows-and-low-illumination-32yez46e76.pdf

Robust Lane Detection in Shadows and Low Illumination Conditions using Local Gradient Features

Diabetic retinopathy may potentially lead to blindness without early detection and treatment In this research, an approach to automate the identification of the presence of diabetic retinopathy from color fundus images of the retina has been proposed Classification of an input fundus image into one of the three classes, healthy/normal, Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR) has been achieved Blood vessel segmentation from the input image is achieved by Gaussian filtering An adaptive, input — driven approach is considered for the mask generation and thresholding is accomplished using local entropy The processed image obtained is characterized by second order textural feature, contrast, in four different orientations- 0°, 45°, 90° and 135° and structural features namely, fractal dimension and lacunarity The research incorporates a three layered artificial neural network (ANN) and support vector machines (SVM) to classify the retinal images The efficiency of the proposed approach has been evaluated on a set of 106 images from the DRIVE and DIARETB1 databases The experimental results indicate that this method can produce a 972% and 981% classification accuracy using ANN and SVM respectively invariant of rotation, translation and scaling in input retinal images as opposed to a fixed mask based on the matched filter method

/pdf/machine-learning-identification-of-diabetic-retinopathy-from-4mj0lrvk7k.pdf

Machine learning identification of diabetic retinopathy from fundus images

This paper presents a wireless channel characterization for a short-range, temperate, medium density forest environment. This environment and frequency band have not been thoroughly characterized. We conducted measurements at a center frequency of 5.12 GHz with a 50MHz bandwidth signal and obtained impulse response estimates. Measured results for delay spread are presented; mean root-mean square delay spread values range from 64 to 87 ns, for link distances up to approximately 110 m. Maximum delay spreads were approximately 360 ns. The presented models should be useful for wireless system design and optimization in the 5 GHz band forest setting.

H. Bryan Riley

Papers

Performance Study of Different Denoising Methods for ECG Signals

Drowsy Driving Detection by EEG Analysis Using Wavelet Transform and K-means Clustering☆

Robust Lane Detection in Shadows and Low Illumination Conditions using Local Gradient Features

Machine learning identification of diabetic retinopathy from fundus images

Short range forest channel modeling in the 5 GHz band