In recent years, the foreign fibers in cotton lint significantly affect the quality of the final cotton textile products. It remains a challenging task to accurately distinguish foreign fibers from cotton. This article proposes an embedded system based on field programmable gate array (FPGA) þ digital signal processor (DSP) to recognize and remove foreign fibers mixed in cotton. With substantial tests of this system, we collect massive samples of foreign fibers and fake foreign fibers. Based on these samples, a convolution neural network mode is developed to validate the classification of the suspected targets from the detection subsystem, to improve the detection reliability. After training several model architectures, we find a model with the best balance between performance and computation. The high success rate (up to 96% in the validation set) demonstrates the effectiveness of the model. Moreover, the computation time (5 ms on a single image based on an eightcore DSP) indicates the efficiency of the detection, which ensures the real-time application of the system.
In this paper, we propose an orthogonal frequency division multiplexing (OFDM)-based generalized optical quadrature spatial modulation (GOQSM) technique for multiple-input multiple-output optical wireless communication (MIMO-OWC) systems. Considering the error propagation and noise amplification effects when applying maximum likelihood and maximum ratio combining (ML-MRC)-based detection, we further propose a deep neural network (DNN)-aided detection for OFDM-based GOQSM systems. The proposed DNN-aided detection scheme performs the GOQSM detection in a joint manner, which can efficiently eliminate the adverse effects of both error propagation and noise amplification. The obtained simulation results successfully verify the superiority of the deep learning-aided OFDMbased GOQSM technique for high-speed MIMO-OWC systems.
Planar-mirror-based catadioptric method is one of the most hot topics in recent years. To overcome the disadvantages of the planar-mirror-based catadioptric panoramic camera, described by Nalwa (1996, 2001, 2000), such as the requirement for high-precision optical device designing and the stitching lines in the resulting images, we proposed a planar-mirror-based video image mosaic system with high precision for designing. Firstly, we designed a screw nut on our system, which can be adjusted to locate the viewpoints of the cameras' mirror images at a single point approximately. It provides a method for those who have difficulties in their designing and manufacturing for high precision. Then, after the image distortion correction and cylinder projection transforms, we can stitch the images to get a wide field of view image by template matching algorithm. Finally, nonlinear weighting fusion is adopted to eliminate the stitching line effectively. The experimental results show that our system has good characteristics such as video rate capture, high resolution, no stitching line and without affection by the depth of field of view.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.