Low-density parity-check codes with variable node degree two over high order Galois fields, also called non-binary cycle codes, exhibit good error correction performance. In this paper, several efficient encoder struc...
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(纸本)9781424437092
Low-density parity-check codes with variable node degree two over high order Galois fields, also called non-binary cycle codes, exhibit good error correction performance. In this paper, several efficient encoder structures are proposed for non-binary cycle codes with only one optimized group of non-zero entries in GF(q) (q > 2). The computation complexity and storage requirement are compared for these methods. Considering the underlying sparse graph of cycle code, the encoding complexity is quite low with the proposed modifications. In this way, a proper tradeoff between complexity and performance is achieved for non-binary cycle codes.
A switchable-rate quasi-cyclic low-density parity-check (QC-LDPC) coding scheme has been proposed for integration within the legacy and next-generation high-frequency internet protocol (HF-IP) systems. The novelty in ...
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A switchable-rate quasi-cyclic low-density parity-check (QC-LDPC) coding scheme has been proposed for integration within the legacy and next-generation high-frequency internet protocol (HF-IP) systems. The novelty in this work is based upon using a class of switchable-rate short-block-length (<1500 bits) QC-LDPC codes for the HF fading channel modelled by the ITU-R F. 1487 for all latitudes and conditions. The QC-LDPC codes are constructed using a switchable-rate approach based on finite fields which provides the ability to switch among three rates to combat varying channel conditions using a single encoder/decoder structure. The proposed structure enables low-complexity implementation of the low-density parity-check encoder/decoder for use within the existing data link (DL) layer of the standardisation agreement (STANAG) 5066 profile. The performance of the proposed scheme has been evaluated comprehensively for all the HF channel conditions and latitudes. A comparison between the proposed and the current coding scheme in HF-IP systems (based on convolutional coding) shows an improvement in error-rate performance.
From the perspective of detector optimisation, detecting objects using only a one-level feature cannot provide good performance for a wide range of scales. Various complex feature pyramidal structures address this pro...
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From the perspective of detector optimisation, detecting objects using only a one-level feature cannot provide good performance for a wide range of scales. Various complex feature pyramidal structures address this problem using the divide-and-conquer strategy and multi-scale feature fusion. However, this requires adding too many additional convolutional layers and fusion operations. To address the issue, a simple detection part is proposed, which includes three components, namely a one-level feature map for detection, the encoder structure with feedback connection, and a decoupled head. The redesigned encoder and decoupled head can successfully address the performance decline caused by the one-level feature-based detection. Moreover, the proposed method can accelerate the convergence of the detector and achieve a faster inference time. Based on the optimised detection part, an adaptive feedback connection with a single-level feature (AFS) is proposed for object detection. The experiments conducted on the MS COCO 2017 benchmark show that the proposed method can achieve comparable results with its multi-scale pyramid counterpart, You Only Look Once v4 (YOLOv4). In addition, AFS can help the YOLOv4 achieve 44.9 mAP at 27 frame per second and converging 82 epochs earlier under the image size of 608x608, which represents a 42.1% improvements in the convergence speed.
Image semantic segmentation has been applied more and more widely in the fields of satellite remote sensing, medical treatment, intelligent transportation, and virtual reality. However, in the medical field, the study...
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Image semantic segmentation has been applied more and more widely in the fields of satellite remote sensing, medical treatment, intelligent transportation, and virtual reality. However, in the medical field, the study of cerebral vessel and cranial nerve segmentation based on true-color medical images is in urgent need and has good research and development prospects. We have extended the current state-of-the-art semantic-segmentation network DeepLabv3+ and used it as the basic framework. First, the feature distillation block (FDB) was introduced into the encoder structure to refine the extracted features. In addition, the atrous spatial pyramid pooling (ASPP) module was added to the decoder structure to enhance the retention of feature and boundary information. The proposed model was trained by fine tuning and optimizing the relevant parameters. Experimental results show that the encoder structure has better performance in feature refinement processing, improving target boundary segmentation precision, and retaining more feature information. Our method has a segmentation accuracy of 75.73%, which is 3% better than DeepLabv3+.
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