PPT-ECG ARTIFACT REMOVAL FROM SINGLE-CHANNEL SURFACE EMG USING FULLY CONVOLUTIONAL NETWORKS

Presenter Cheng Lun Hsieh Email p12922006ntuedutw Phone 0933280509 Outline Background and motivation The proposed method Materials Results and discussion Conclusion 1. The dark channel prior is a kind of s tatistics of the hazefree outdoor images It is based on a key observation most local patches in hazefree outdoor images contain some pixels which have very low intensities in at least one color channel Using th ABSTRACT From the desire to update the maximum road speed data for navigation devices a speed sign recognition and detection system is proposed This system should prevent accidental speeding at roads where the map data is incorrect for example due t RECOGNITION. does size matter?. Karen . Simonyan. Andrew . Zisserman. Contents. Why I Care. Introduction. Convolutional Configuration . Classification. Experiments. Conclusion. Big Picture. Why I . care. 1. RRAT 2014. 9 feet deep. 1,500 feet long. Navigation Channel Design. During . low water periods required to maintain a 9 feet deep and a minimum of . 300 feet wide navigation channel, with additional width in bends. Neural . Network Architectures:. f. rom . LeNet. to ResNet. Lana Lazebnik. Figure source: A. . Karpathy. What happened to my field?. . Classification:. . ImageNet. Challenge top-5 error. Figure source: . Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . By, . . Sruthi. . Moola. Convolution. . Convolution is a common image processing technique that changes the intensities of a pixel to reflect the intensities of the surrounding pixels. A common use of convolution is to create image filters. Reading Assignment. Sections . 4.1-4.5. Alternative: relevant papers. . References provided. Overview. Separate the routing decisions from implementation. Choice of path vs. . Performance. E.g., buffering, route computation implementation. Last time. Linear classifiers on pixels bad, need non-linear classifiers. Multi-layer . perceptrons. . overparametrized. Reduce parameters by local connections and shift invariance => Convolution. Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations. Honglak. Lee, Roger Grosse, Rajesh . Ranganath. , Andrew Y. Ng. Playing Atari with Deep Reinforcement Learning. . Convolutions. Reduce parameters. Capture shift-invariance: location of patch in image should not matter. Subsampling. Allows greater invariance to deformations. Allows the capture of large patterns with small filters. Convolutional Codes COS 463 : Wireless Networks Lecture 9 Kyle Jamieson [Parts adapted from H. Balakrishnan ] So far, we’ve seen block codes Convolutional Codes: Simple design, especially at the transmitter person. grass. trees. motorbike. road. Evaluation metric. Pixel classification!. Accuracy?. Heavily unbalanced. Common classes are over-emphasized. Intersection over Union. Average across classes and images. Kannan . Neten. Dharan. Introduction . Alzheimer’s Disease is a kind of dementia which is caused by damage to nerve cells in the brain and the usual side effects of it are loss of memory or other cognitive impairments..