The rounding errors of floating-point operations are inevitable in computers or microprocessors, and this issue will make the calculation results inaccurate, unreliable, or even completely incorrect. For this purpose, this paper proposes to replace floating-point operations with integer operations to improve the operation precision. The key lies in not only controlling the variable type as the integer to avoid the automatic conversion of intermediate operation results into floating-point numbers but also converting floating-point operations in the operation process into integer operations using some numerical calculation methods. Lock-in amplifier is one of the most widely used instruments in the field of weak signal detection. This paper only takes the digital lock-in amplifier (DLIA) as an example for detailed analysis and proposes a DLIA based on integer calculation. The experimental results show that replacing floating-point operations with integer operations can obtain higher operation precision without "wasting" memory, and the improvement will be more significant as the calculation amount increases. The research will help to further improve the calculation accuracy of digital signal processing and other scientific computations in computers or microprocessors.
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http://dx.doi.org/10.1063/5.0026078 | DOI Listing |
Food Res Int
January 2025
Tea Research Institute of Shandong Academy of Agricultural Sciences, Jinan 250100, China; College of Mechanical and Electronic Engineering, Shihezi University, Shihezi 832000, China. Electronic address:
Tea may be mixed with impurities during picking and processing, which can lower their quality. At present, the sorting of impurities in premium green tea mainly relies on manual labor, which is inefficient. In response to the technical challenges in this industry, this article uses deep learning technology to detect impurities in premium green tea.
View Article and Find Full Text PDFArtif Intell Med
January 2025
Koç University, Department of Physics, Electrical and Electronics Engineering, Istanbul, Turkiye. Electronic address:
Deep neural networks have significantly advanced medical image classification across various modalities and tasks. However, manually designing these networks is often time-consuming and suboptimal. Neural Architecture Search (NAS) automates this process, potentially finding more efficient and effective models.
View Article and Find Full Text PDFRev Sci Instrum
January 2025
School of Mechanical Engineering, Southwest Petroleum University, Chengdu, Sichuan 610500, China.
Efficient identification of the flocculation state of waste drilling fluid remains a significant challenge. This study proposes an improved You Only Look Once version 8 nano-algorithm (YOLOv8n), specifically optimized for real-time monitoring of drilling fluid flocculation under field conditions. The algorithm employs MobileNetV3 as the backbone network to minimize memory usage, improve detection speed, and reduce computational requirements.
View Article and Find Full Text PDFBMC Med Imaging
January 2025
Department of Information, Third Affiliated Hospital of Naval Medical University, No. 225 Changhai Road, Yangpu District, 200438, Shanghai, China.
Purpose: To develop an end-to-end convolutional neural network model for analyzing hematoxylin and eosin(H&E)-stained histological images, enhancing the performance and efficiency of nuclear segmentation and classification within the digital pathology workflow.
Methods: We propose a dual-mechanism feature pyramid fusion technique that integrates nuclear segmentation and classification tasks to construct the HistoNeXt network model. HistoNeXt utilizes an encoder-decoder architecture, where the encoder, based on the advanced ConvNeXt convolutional framework, efficiently and accurately extracts multi-level abstract features from tissue images.
Sensors (Basel)
December 2024
Department of Automation, North China Electric Power University, Baoding 071003, China.
To address the difficulty in detecting workers' violation behaviors in electric power construction scenarios, this paper proposes an innovative method that integrates knowledge reasoning and progressive multi-level distillation techniques. First, standards, norms, and guidelines in the field of electric power construction are collected to build a comprehensive knowledge graph, aiming to provide accurate knowledge representation and normative analysis. Then, the knowledge graph is combined with the object-detection model in the form of triplets, where detected objects and their interactions are represented as subject-predicate-object relationship.
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