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[IEEE TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) - Kochi, India (2019.10.17-2019.10.20)] TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) - Speech Enabled Visual Question Answering using LSTM and CNN with Real Time Image Capturing for assisting the Visually Impaired
摘要： The proposed work benefits visually impaired individuals in identifying objects and visualizing scenarios around them independent of any external support. In such a situation, the surrounding and ask an open-ended question, classification question, counting question or yes/no question to the application by speech input. The proposed application uses Visual Question Answering (VQA) to integrate image processing and natural language processing which is also capable of speech to text translation and vice versa that helps to identify, recognize and thus obtain details of any particular image. The work uses a classical CNN-LSTM model where image features and language features are computed separately and combined at a later stage using image features and word embedding obtained from the question and runs a multilayer perceptron on the combined features to obtain the results. The model achieves an accuracy of 57 per cent. The model can also be utilized to develop cognitive interpretation better in kids. As the application is speech enabled it is best suited for the visually impaired with an easy to use GUI.
关键词： VGG16,Visually Impaired,Keras Neural Network Library,ImageNet,gTTS,Feature extraction,Image Recognition,VQA,Word2Vec,Speech Recognition,Glove vector,CNN,Multi Layer Perceptron,LSTM
更新于2025-09-16 10:30:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Barrage Jamming Detection and Classification Based on Convolutional Neural Network for Synthetic Aperture Radar
摘要： Suppression technology of barrage jamming is an important approach to ensure the normal operation of the synthetic aperture radar (SAR) system. The detection and classification of jamming is a necessary procedure in this technology. Unsuitable thresholds set in the traditional methods may reduce the detection accuracy. In order to avoid it, this paper proposes a new method of barrage jamming detection and classification for SAR based on convolutional neural network (CNN). The signal model is constructed based on the statistical characteristics of the SAR echo signal. Based on this, a data set containing echo signals and interference signals is generated by simulation. Finally, the convolution neural network VGG16 is used to detect whether the signals in the dataset is contaminated by barrage jamming and identify the type of the interference. The experiment result illustrates that the VGG16 network trained by the frequency domain signals can effectively detect and classify the jamming signals.
关键词： convolutional neural network,jamming detection,VGG16,Barrage jamming
更新于2025-09-10 09:29:36