To enhance the decoding efficiency of Globally Coupled (GC) LDPC codes, we incorporated Early Termination (ET) and Forced Convergence (FC) into the local/global two-phase decoding algorithm to expedite the decoding process. The two-phase decoding scheme integrates the ET technique to halt unnecessary iterations in the local decoding phase while employing the FC technique to accelerate convergence in the global phase decoding. The application of ET technology in the local decoding of GC-LDPC codes will not cause performance loss as in traditional block codes and will cause considerable complexity gains. For a longer code length and larger convergence differences between nodes' global codes, the FC technique operates more efficiently in global code than local code. Two variants are proposed for the ET scheme in the local decoding, namely ET-1 and ET-2. The initial variant, ET-1, predicts whether local decoding can be successful according to data characteristics and stop the local decoding iteration that is not expected to be successful in time. In the case of ET-2, the saved local iterations are transformed to global decoding equally. The results show that ET-1 saves considerable decoding time complexity and ET-2 improves the performance of the GC-LDPC code with the same decoding time complexity. The combined approach of ET-1 with FC reduces the decoding time complexity up to 42% at a low Signal Noise Rate region while maintaining its performance; ET-2-FC two-phase decoding saves approximately 25% decoding time complexity while improving the BER by about 0.18 dB and FER by about 0.23 dB.
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http://dx.doi.org/10.3390/s24216893 | DOI Listing |
Front Neurorobot
December 2024
Department of Fine Arts, Bozhou University, Bozhou, Anhui, China.
Introduction: Segmentation tasks in computer vision play a crucial role in various applications, ranging from object detection to medical imaging and cultural heritage preservation. Traditional approaches, including convolutional neural networks (CNNs) and standard transformer-based models, have achieved significant success; however, they often face challenges in capturing fine-grained details and maintaining efficiency across diverse datasets. These methods struggle with balancing precision and computational efficiency, especially when dealing with complex patterns and high-resolution images.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Mechanical Engineering Department, Tianjin University, No. 135, Yaguan Road, Haihe Education Park, Jinnan District, Tianjin City, 300350, China.
The hybrid CNN-transformer structures harness the global contextualization of transformers with the local feature acuity of CNNs, propelling medical image segmentation to the next level. However, the majority of research has focused on the design and composition of hybrid structures, neglecting the data structure, which enhance segmentation performance, optimize resource efficiency, and bolster model generalization and interpretability. In this work, we propose a data-oriented octree inverse hierarchical order aggregation hybrid transformer-CNN (nnU-OctTN), which focuses on delving deeply into the data itself to identify and harness potential.
View Article and Find Full Text PDFFront Bioeng Biotechnol
December 2024
School of Information Engineering, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China.
Introduction: Accurate image segmentation is crucial in medical imaging for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods often fall short in integrating global and local features in a meaningful way, failing to give sufficient attention to abnormal regions and boundary details in medical images. These limitations hinder the effectiveness of segmentation techniques in clinical settings.
View Article and Find Full Text PDFProteins
January 2025
Department of Biotechnology, Maulana Abul Kalam Azad University of Technology, Haringhata, India.
The structural plasticity of proteins at the molecular level is largely dictated by backbone torsion angles, which play a critical role in ligand recognition and binding. To establish the anion-induced cooperative arrangement of the main-chain (mc) torsion, herein, we analyzed a set of naturally occurring CαNN motifs as "static models" for their anion-binding competence through docking and molecular dynamics simulations and decoded its torsion angle influenced mc-driven anion recognition potential. By comparing a pool of 20 distinct sets of CαNN motif with identical sequences in their "anion bound/present, aP" and "anion free/absent, aA" versions, we could discern that there exists a positive correlation between the "difference of anion residence time (ΔR)" and "difference among the main-chain torsion angle" of the aP and aA population.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Engineering Management, University of Antwerp, Antwerp, Belgium.
Self-motion is an essential but often overlooked component of sound localisation. As the directional information of a source is implicitly contained in head-centred acoustic cues, that acoustic input needs to be continuously combined with sensorimotor information about the head orientation in order to decode to a world-centred frame of reference. When utilised, head movements significantly reduce ambiguities in the directional information provided by the incoming sound.
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