Single-Pixel Moving Object Classification with Differential Measuring in Transform Domain and Deep Learning

Due to limited data transmission bandwidth and data storage space, it is challenging to perform fast-moving objects classification based on high-speed photography for a long duration. Here we propose a single-pixel classification method with deep learning for fast-moving objects. The scene image is modulated by orthogonal transform basis patterns, and the modulated light signal is detected by a single-pixel detector. Thanks to the property that the natural images are sparse in the orthogonal transform domain, we used a small number of basis patterns of discrete-sine-transform to obtain feature information for classification. The proposed neural network is designed to use single-pixel measurements as network input and trained by simulation single-pixel measurements based on the physics of the measuring scheme. Differential measuring can reduce the difference between simulation data and experiment data interfered by slowly varying noise. In order to improve the reliability of the classification results for fast-moving objects, we employed a measurement data rolling utilization approach for repeated classification. Long-duration classification of fast-moving handwritten digits that pass through the field of view successively is experimentally demonstrated, showing that the proposed method is superior to human vision in fast-moving digit classification. Our method enables a new way for fast-moving object classification and is expected to be widely implemented

Single-Pixel Moving Object Classification with Differential Measuring in Transform Domain and Deep Learning

Due to limited data transmission bandwidth and data storage space, it is challenging to perform fast-moving objects classification based on high-speed photography for a long duration. Here we propose a single-pixel classification method with deep learning for fast-moving objects. The scene image is modulated by orthogonal transform basis patterns, and the modulated light signal is detected by a single-pixel detector. Thanks to the property that the natural images are sparse in the orthogonal transform domain, we used a small number of basis patterns of discrete-sine-transform to obtain feature information for classification. The proposed neural network is designed to use single-pixel measurements as network input and trained by simulation single-pixel measurements based on the physics of the measuring scheme. Differential measuring can reduce the difference between simulation data and experiment data interfered by slowly varying noise. In order to improve the reliability of the classification results for fast-moving objects, we employed a measurement data rolling utilization approach for repeated classification. Long-duration classification of fast-moving handwritten digits that pass through the field of view successively is experimentally demonstrated, showing that the proposed method is superior to human vision in fast-moving digit classification. Our method enables a new way for fast-moving object classification and is expected to be widely implemented

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Supplementary Materia

Interfacial Reaction Dependent Performance of Hollow Carbon Nanosphere – Sulfur Composite as a Cathode for Li-S Battery

Lithium-sulfur (Li-S) battery is a promising energy storage system due to its high energy density, cost effectiveness and environmental friendliness of sulfur. However, there are still a number of technical challenges, such as low Coulombic efficiency and poor long-term cycle life, impeding the commercialization of Li-S battery. The electrochemical performance of Li-S battery is closely related with the interfacial reactions occurring between hosting substrate and active sulfur species which are poorly conducting at fully oxidized and reduced states. Here, we correlate the relationship between the performance and interfacial reactions in the Li-S battery system, using a hollow carbon nanosphere (HCNS) with highly graphitic character as hosting substrate for sulfur. With an appropriate amount of sulfur loading, HCNS/S composite exhibits excellent electrochemical performance because of the fast interfacial reactions between HCNS and the polysulfides. However, further increase of sulfur loading leads to increased formation of highly resistive insoluble reaction products (Li2S2/Li2S) which limits the reversibility of the interfacial reactions and results in poor electrochemical performances. These findings demonstrate the importance of the interfacial reaction reversibility in the whole electrode system on achieving high capacity and long cycle life of sulfur cathode for Li-S batteries