Signal Processing involves the manipulation, analysis, and interpretation of signals. Signals can take various forms, including electrical signals in electronic systems, acoustic signals in audio processing, and visual signals in image and video processing. The primary goal of signal processing is to extract useful information from signals or to modify signals to meet specific criteria.

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Vibration-Based Status Identification of Power Transmission Poles

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Among the power transmission infrastructures, the low-voltage overhead power lines are specifically critical, due to the complicated roadside environments and the significant number of connections to the end utility users. Maintaining of such a large size grid with mostly wood poles is a challenging task and knowing the operating status and its structural integrity drastically speeds up the routine inspection. Applying a data-driven approach using accelerometer data to analyze the power line-induced vibration to classify different poles within different operational conditions is proposed.Feature creation is the important aspect to improve an accuracy of data-driven algorithms. For this purpose, a time-frequency domain classifier is developed, based on the data collected from two tri-axial accelerometers installed on the wood poles before and after streetlights are on. Data are explored both in time and frequency domain using techniques such as data augmentation and segmentation, averaging, filtering, and principal component analysis. Results of the machine learning classifier clearly shows distinct characteristics among the data collected from different work conditions and different poles. Further exploration of the applied algorithm will be pursued to construct more sophisticated features based on supervised learning to enhance the identification accuracy.

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Time Series Prediction and Classification using Silicon Photonic Neuron with Self-Connection

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We experimentally demonstrated the real-time operation of a photonic neuron with a self-connection, a prerequisite for integrated recurrent neural networks (RNNs). After studying two applications, we propose a photonics-assisted platform for time series prediction and classification.

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DAVID: Dual-Attentional Video Deblurring

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Blind video deblurring restores sharp frames from a blurry sequence without any prior. It is a challenging task because the blur due to camera shake, object movement and defocusing is heterogeneous in both temporal and spatial dimensions. Traditional methods train on datasets synthesized with a single level of blur, and thus do not generalize well across levels of blurriness. To address this challenge, we propose a dual attention mechanism to dynamically aggregate temporal cues for deblurring with an end-to-end trainable network structure. Specifically, an internal attention module adaptively selects the optimal temporal scales for restoring the sharp center frame. An external attention module adaptively aggregates and refines multiple sharp frame estimates, from several internal attention modules designed for different blur levels. To train and evaluate on more diverse blur severity levels, we propose a Challenging DVD dataset generated from the raw DVD video set by pooling frames with different temporal windows. Our framework achieves consistently better performance on this more challenging dataset while obtaining strongly competitive results on the original DVD benchmark. Extensive ablative studies and qualitative visualizations further demonstrate the advantage of our method in handling real video blur.

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