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A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection
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作者 Zhong Qu Guoqing Mu Bin Yuan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第7期255-273,共19页
Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of cr... Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage space.This limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile devices.To solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion.Firstly,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of images.In addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context information.Finally,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map.We evaluate our method on three public crack datasets:DeepCrack,CFD,and Crack500.Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight crack detectionmodel,the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M.This advancement also facilitates technical support for portable scene detection. 展开更多
关键词 Shallow feature extraction module large kernel atrous convolution dual encoder lightweight network crack detection
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Remaining Useful Life Prediction of Rail Based on Improved Pulse Separable Convolution Enhanced Transformer Encoder
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作者 Zhongmei Wang Min Li +2 位作者 Jing He Jianhua Liu Lin Jia 《Journal of Transportation Technologies》 2024年第2期137-160,共24页
In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is di... In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set. 展开更多
关键词 Equipment Health Prognostics Remaining Useful Life Prediction Pulse Separable Convolution Attention Mechanism Transformer encoder
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Feature Enhanced Stacked Auto Encoder for Diseases Detection in Brain MRI
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作者 Umair Muneer Butt Rimsha Arif +2 位作者 Sukumar Letchmunan Babur Hayat Malik Muhammad Adil Butt 《Computers, Materials & Continua》 SCIE EI 2023年第8期2551-2570,共20页
The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)... The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)images.These techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain diseases.However,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques.This paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain diseases.The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy.Therefore,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman image.The proposed model consists of four stages.First,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature extraction.Second,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and Channelization.Third,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’s.Finally,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes. 展开更多
关键词 Brain diseases deep learning feature enhanced stacked auto encoder stack auto encoder
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Multi-scale attention encoder for street-to-aerial image geo-localization 被引量:2
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作者 Songlian Li Zhigang Tu +1 位作者 Yujin Chen Tan Yu 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第1期166-176,共11页
The goal of street-to-aerial cross-view image geo-localization is to determine the location of the query street-view image by retrieving the aerial-view image from the same place.The drastic viewpoint and appearance g... The goal of street-to-aerial cross-view image geo-localization is to determine the location of the query street-view image by retrieving the aerial-view image from the same place.The drastic viewpoint and appearance gap between the aerial-view and the street-view images brings a huge challenge against this task.In this paper,we propose a novel multiscale attention encoder to capture the multiscale contextual information of the aerial/street-view images.To bridge the domain gap between these two view images,we first use an inverse polar transform to make the street-view images approximately aligned with the aerial-view images.Then,the explored multiscale attention encoder is applied to convert the image into feature representation with the guidance of the learnt multiscale information.Finally,we propose a novel global mining strategy to enable the network to pay more attention to hard negative exemplars.Experiments on standard benchmark datasets show that our approach obtains 81.39%top-1 recall rate on the CVUSA dataset and 71.52%on the CVACT dataset,achieving the state-of-the-art performance and outperforming most of the existing methods significantly. 展开更多
关键词 global mining strategy image geo-localization multiscale attention encoder street-to-aerial cross-view
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Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis 被引量:1
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作者 Qiankun Zuo Junhua Hu +5 位作者 Yudong Zhang Junren Pan Changhong Jing Xuhang Chen Xiaobo Meng Jin Hong 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第12期2129-2147,共19页
The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders.The brain functional network is suitable to bridge the correlat... The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders.The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders.However,it is challenging to access considerable amounts of brain functional network data,which hinders the widespread application of data-driven models in dementia diagnosis.In this study,a novel distribution-regularized adversarial graph auto-Encoder(DAGAE)with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset,improving the dementia diagnosis accuracy of data-driven models.Specifically,the label distribution is estimated to regularize the latent space learned by the graph encoder,which canmake the learning process stable and the learned representation robust.Also,the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain regions.The typical topological properties and discriminative features can be preserved entirely.Furthermore,the generated brain functional networks improve the prediction performance using different classifiers,which can be applied to analyze other cognitive diseases.Attempts on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that the proposed model can generate good brain functional networks.The classification results show adding generated data can achieve the best accuracy value of 85.33%,sensitivity value of 84.00%,specificity value of 86.67%.The proposed model also achieves superior performance compared with other related augmentedmodels.Overall,the proposedmodel effectively improves cognitive disease diagnosis by generating diverse brain functional networks. 展开更多
关键词 Adversarial graph encoder label distribution generative transformer functional brain connectivity graph convolutional network DEMENTIA
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从隐空间理解编码器(Encoder)
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作者 高焕堂 《电子产品世界》 2023年第6期5-7,共3页
1前言当我们在阅读关于AIGC的文章时,常常会看到Encoder和Decoder名词。它们是AI(即ML)的核心模型,如果能深入理解它们的涵意和功能,就能更流畅地理解相关文章的内容,以及图示。例如,关于Diffusion(扩张模型)的文章里常看到如图1。
关键词 encoder 编码器 扩张模型 核心模型
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A New Speech Encoder Based on Dynamic Framing Approach
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作者 Renyuan Liu Jian Yang +1 位作者 Xiaobing Zhou Xiaoguang Yue 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第8期1259-1276,共18页
Latent information is difficult to get from the text in speech synthesis.Studies show that features from speech can get more information to help text encoding.In the field of speech encoding,a lot of work has been con... Latent information is difficult to get from the text in speech synthesis.Studies show that features from speech can get more information to help text encoding.In the field of speech encoding,a lot of work has been conducted on two aspects.The first aspect is to encode speech frame by frame.The second aspect is to encode the whole speech to a vector.But the scale in these aspects is fixed.So,encoding speech with an adjustable scale for more latent information is worthy of investigation.But current alignment approaches only support frame-by-frame encoding and speech-to-vector encoding.It remains a challenge to propose a new alignment approach to support adjustable scale speech encoding.This paper presents the dynamic speech encoder with a new alignment approach in conjunction with frame-by-frame encoding and speech-to-vector encoding.The speech feature fromourmodel achieves three functions.First,the speech feature can reconstruct the origin speech while the length of the speech feature is equal to the text length.Second,our model can get text embedding fromspeech,and the encoded speech feature is similar to the text embedding result.Finally,it can transfer the style of synthesis speech and make it more similar to the given reference speech. 展开更多
关键词 Speech synthesis dynamic framing convolution network speech encoding
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ASP源代码加密程序Script Encoder算法研究 被引量:2
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作者 陈莲娜 《中国计量学院学报》 2001年第3期66-70,共5页
微软为了保护脚本代码的安全性 ,以 COM组件的形式提供了一种对脚本代码进行编码加密的技术 .但其安全性到底如何呢 ?本文将通过“反编译”的方式对其加密。
关键词 ASP SCRIPT encoder 加密算法 反编译
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基于ENCODER_ATT机制的远程监督关系抽取
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作者 王健 郑七凡 +1 位作者 李超 石晶 《广西师范大学学报(自然科学版)》 CAS 北大核心 2019年第4期53-60,共8页
在信息抽取中,关系抽取是一项准确识别自然语言中实体间关系的关键技术。针对关系抽取模型中容易丢失关键语义特征问题及远程监督的基本假设容易引入噪声数据的问题,本文提出一种基于远程监督的ENCODER_ATT关系抽取模型。基于循环神经... 在信息抽取中,关系抽取是一项准确识别自然语言中实体间关系的关键技术。针对关系抽取模型中容易丢失关键语义特征问题及远程监督的基本假设容易引入噪声数据的问题,本文提出一种基于远程监督的ENCODER_ATT关系抽取模型。基于循环神经网络构造的ENCODER模型在以词级别进行特征记忆提取,并在句子层面进行语义特征信息整合,保证不遗失关键语义特征的同时去除冗余特征。然后在句子层面引入了注意力机制来降低噪声数据对实验结果的影响。在真实的数据集上进行实验,并绘制准确率-召回率曲线,实验结果表明ENCODER_ATT模型对比同类型的关系抽取方法有明显的提升。 展开更多
关键词 关系抽取 远程监督 encoder 注意力机制
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在Flash中基于Adobe Media Encoder组件的视频导入与应用方法
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作者 张佳丽 《电脑知识与技术》 2018年第7Z期215-216,共2页
在flash cs6的默认情况下,Flash cs6只支持flv和f4v格式的视频.如果不是这种格式的视频,我们可以使用Flash cs6自带的视频转换组件Adobe Media Encoder将其他视频格式转换成FLV和F4V格式.本文主要讲解如何使用flash自带的Adobe Media En... 在flash cs6的默认情况下,Flash cs6只支持flv和f4v格式的视频.如果不是这种格式的视频,我们可以使用Flash cs6自带的视频转换组件Adobe Media Encoder将其他视频格式转换成FLV和F4V格式.本文主要讲解如何使用flash自带的Adobe Media Encoder组件进行视频文件的转换,导入和使用. 展开更多
关键词 FLASH ADOBE MEDIA encoder组件 视频
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基于改进Encoder-Decoder模型的新闻摘要生成方法 被引量:5
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作者 李晨斌 詹国华 李志华 《计算机应用》 CSCD 北大核心 2019年第S02期20-23,共4页
针对通过Extractive方式实现自动文摘而存在文本连贯性欠缺和出现未登录词问题,提出一种基于改进Encoder-Decoder模型的新闻摘要生成方法。首先,在数据预处理的过程中融入额外的语言特征,如词语的词性和TF-IDF,使词语具有多维度的含义;... 针对通过Extractive方式实现自动文摘而存在文本连贯性欠缺和出现未登录词问题,提出一种基于改进Encoder-Decoder模型的新闻摘要生成方法。首先,在数据预处理的过程中融入额外的语言特征,如词语的词性和TF-IDF,使词语具有多维度的含义;其次,采用Decoder/Pointer机制在摘要中指向原文本中的位置对低频词进行处理;最后,采用注意力机制来协助模型记忆输入数据并确定其注意程度。在News2016zh数据集上进行实验,结果表明基于改进Encoder-Decoder模型与基线Encoder-Decoder相比,ROUGE-1、ROUGE-2和ROUGE-L值分别提高了32.1%、30.5%和32.5%,在摘要连贯性方面也得到了较好提升。 展开更多
关键词 摘要生成 注意力机制 未登录词 数据预处理 encoder 输入数据 自动文摘 低频词
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A correction method of encoder bias in satellite laser ranging system 被引量:3
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作者 Wang Peiyuan Zhu Wei +1 位作者 Zou Tong Guo Tangyong 《Geodesy and Geodynamics》 2013年第3期61-64,共4页
In a satellite laser ranging telescope system, well-aligned encoders of the elevation and azimuth axes are essential for tracking objects. However, it is very difficult and time-consuming to correct the bias between t... In a satellite laser ranging telescope system, well-aligned encoders of the elevation and azimuth axes are essential for tracking objects. However, it is very difficult and time-consuming to correct the bias between the absolute-position indices of the encoders and the astronomical coordinates, especially in the absence of a finder scope for our system. To solve this problem, a method is presented based on the phenomenon that all stars move anti-clockwise around Polaris in the northern hemisphere. Tests of the proposed adjustment procedure in a satellite laser ranging (SLR)system demonstrated the effectiveness and the time saved by using the approach, which greatly facilitates the optimization of a trackin~ svstem. 展开更多
关键词 CORRECTION encoder index TELESCOPE satellite laser ranging
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Determination of optimal period of absolute encoders with single track cyclic gray code 被引量:1
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作者 张帆 朱衡君 《Journal of Central South University》 SCIE EI CAS 2008年第S2期362-366,共5页
Low cost and miniaturized rotary encoders are important in automatic and precise production. Presented here is a code called Single Track Cyclic Gray Code (STCGC) that is an image etched on a single circular track of ... Low cost and miniaturized rotary encoders are important in automatic and precise production. Presented here is a code called Single Track Cyclic Gray Code (STCGC) that is an image etched on a single circular track of a rotary encoder disk read by a group of even spread reading heads to provide a unique codeword for every angular position and features such that every two adjacent words differ in exactly one component, thus avoiding coarse error. The existing construction or combination methods are helpful but not sufficient in determining the period of the STCGC of large word length and the theoretical approach needs further development to extend the word length. Three principles, such as the seed combination, short code removal and ergodicity examination were put forward that suffice determination of the optimal period for such absolute rotary encoders using STCGC with even spread heads. The optimal periods of STCGC in 3 through 29 bit length were determined and listed. 展开更多
关键词 ROTARY encoder ABSOLUTE encoder single track GREY code CYCLIC reliability
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Application of Instantaneous Rotational Speed to Detect Gearbox Faults Based on Double Encoders 被引量:1
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作者 Lin Liang Fei Liu +2 位作者 Xiangwei Kong Maolin Li Guanghua Xu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2019年第1期54-64,共11页
Considerable studies have been carried out on fault diagnosis of gears, with most of them concentrated on conventional vibration analysis. However, besides the complexity of gear dynamics, the diagnosis results in ter... Considerable studies have been carried out on fault diagnosis of gears, with most of them concentrated on conventional vibration analysis. However, besides the complexity of gear dynamics, the diagnosis results in terms of vibration signal are easily misjudged owing to the interference of sensor position or other components. In this paper, an alternative gearbox fault detection method based on the instantaneous rotational speed is proposed because of its advantages over vibration analysis. Depending on the timer/counter-based method for the pulse signal of the optical encoder, the varying rotational speed can be obtained e ectively. Owing to the coupling and meshing of gears in transmission, the excitations are the same for the instantaneous rotational speed of the input and output shafts. Thus, the di erential signal of instantaneous rotational speeds can be adopted to eliminate the e ect of the interference excitations and extract the associated feature of the localized fault e ectively. With the experiments on multistage gearbox test system, the di erential signal of instantaneous speeds is compared with other signals. It is proved that localized faults in the gearbox generate small angular speed fluctuations, which are measurable with an optical encoder. Using the di erential signal of instantaneous speeds, the fault characteristics are extracted in the spectrum where the deterministic frequency component and its harmonics corresponding to crack fault characteristics are displayed clearly. 展开更多
关键词 Instantaneous ROTATIONAL speed Optical encoder Localized fault MULTISTAGE GEARBOX
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Absolute multi-pole encoder with a simple structure based on an improved gray code to enhance the resolution 被引量:1
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作者 刘勇 《Journal of Chongqing University》 CAS 2009年第3期181-187,共7页
We developed a novel absolute multi-pole encoder structure to improve the resolution of the multi-pole encoder, realize absolute output and reduce the manufacturing cost of the encoder. The structure includes two ring... We developed a novel absolute multi-pole encoder structure to improve the resolution of the multi-pole encoder, realize absolute output and reduce the manufacturing cost of the encoder. The structure includes two ring alnicos defined as index track and sub-division track, respectively. The index track is magnetized based on the improved gray code, with linear halls placed around the track evenly. The outputs of linear halls show the region the rotor belongs to. The sub-division track is magnetized to N-S-N-S (north-south-north-south), and the number of N-S pole pairs is determined by the index track. Three linear hall sensors with an air-gap of 2 mm are used to translate the magnetic filed to voltage signals. The relative offset in a single N-S is obtained through look-up. The magnetic encoder is calibrated using a higher-resolution incremental optical encoder. The pulse output from the optical encoder and hall signals from the magnetic encoder are sampled at the same time and transmitted to a computer, and the relation between them is calculated, and stored in the FLASH of MCU (micro controller unit) for look-up. In the working state, the absolute angle is derived by looking-up with hall signals. The structure is simple and the manufacturing cost is very low and suitable for mass production. 展开更多
关键词 signal encoding magnetic encoder improved gray code absolute output
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Design and implementation of LDPC encoder based on FPGA 被引量:1
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作者 WANG Guodong LI Jinming +1 位作者 ZHENG Zhiwang TIAN Denghui 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2021年第1期12-19,共8页
A low density parity check(LDPC)encoder with the codes of(8176,7154)and encoding rate of 7/8 under CCSDS standard for near space communication is designed.Based on LDPC encoding theory,the FPGA-based coding algorithm ... A low density parity check(LDPC)encoder with the codes of(8176,7154)and encoding rate of 7/8 under CCSDS standard for near space communication is designed.Based on LDPC encoding theory,the FPGA-based coding algorithm is designed.Based on the characteristics of LDPC generating matrix,the cyclic shift register is introduced as the core of the encoding circuit,and the shift-register-Adder-Accumulator(SRAA)structure is adopted to realize the fast calculation of matrix multiplication,so as to construct the encoding module with partial parallel encoding circuit as the core.In addition,the serial port input and output module,RAM storage module and control module are also designed,which together constitute the encoder system.The design scheme is implemented by FPGA hardware and verified by simulation and experiment.The results show that the test results of the designed LDPC encoder are consistent with the theoretical results.Therefore,the coding system is practical,and the design method is simple and efficient. 展开更多
关键词 low-density parity check(LDPC) encoder parallel encoding field-programmable gate array(FPGA) shift register
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System-Level Performance Evaluation of Very High Complexity Media Applications : A H264/AVC Encoder Case Study 被引量:1
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作者 Hajer Krichene Zrida Abderrazek Jemai +1 位作者 Ahmed C Ammari Mohamed Abid 《International Journal of Communications, Network and System Sciences》 2011年第7期436-446,共11页
Given the substantially increasing complexity of embedded systems, the use of relatively detailed clock cycle-accurate simulators for the design-space exploration is impractical in the early design stages. Raising the... Given the substantially increasing complexity of embedded systems, the use of relatively detailed clock cycle-accurate simulators for the design-space exploration is impractical in the early design stages. Raising the abstraction level is nowadays widely seen as a solution to bridge the gap between the increasing system complexity and the low design productivity. For this, several system-level design tools and methodologies have been introduced to efficiently explore the design space of heterogeneous signal processing systems. In this paper, we demonstrate the effectiveness and the flexibility of the Sesame/Artemis system-level modeling and simulation methodology for efficient peformance evaluation and rapid architectural exploration of the increasing complexity heterogeneous embedded media systems. For this purpose, we have selected a system level design of a very high complexity media application;a H.264/AVC (Advanced Video Codec) video encoder. The encoding performances will be evaluated using system-level simulations targeting multiple heterogeneous multiprocessors platforms. 展开更多
关键词 System-Level Performance Evaluation Embedded Systems DESIGN Space Exploration Tools the Sesame/Artemis DESIGN Tool a Parallel H.264/AVC Video encoder
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Application of deep autoencoder model for structural condition monitoring
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作者 PATHIRAGE Chathurdara Sri Nadith LI Jun +2 位作者 LI Ling HAO Hong LIU Wanquan 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2018年第4期873-880,共8页
Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the hea... Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion. 展开更多
关键词 auto encoder non-linear regression deep auto en-coder model damage identification VIBRATION structural health monitoring
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Real-time Video Encoder of Digital HDTV
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作者 梁昌钛 《High Technology Letters》 EI CAS 1998年第2期78-82,共5页
On the basis of considering the present condition of China and referring the methods of the West, a real time video encoder of digital HDTV based on the MPEG 2 Standard is developed. In this paper, the division and ... On the basis of considering the present condition of China and referring the methods of the West, a real time video encoder of digital HDTV based on the MPEG 2 Standard is developed. In this paper, the division and the realization of every function unit of HDTV are discussed, and the test results of this system are given. 展开更多
关键词 DIGITAL High Definition TV MPEG 2 VIDEO encoder
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Low Complexity Encoder with Multilabel Classification and Image Captioning Model
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作者 Mahmoud Ragab Abdullah Addas 《Computers, Materials & Continua》 SCIE EI 2022年第9期4323-4337,共15页
Due to the advanced development in the multimedia-on-demandtraffic in different forms of audio, video, and images, has extremely movedon the vision of the Internet of Things (IoT) from scalar to Internet ofMultimedia ... Due to the advanced development in the multimedia-on-demandtraffic in different forms of audio, video, and images, has extremely movedon the vision of the Internet of Things (IoT) from scalar to Internet ofMultimedia Things (IoMT). Since Unmanned Aerial Vehicles (UAVs) generates a massive quantity of the multimedia data, it becomes a part of IoMT,which are commonly employed in diverse application areas, especially forcapturing remote sensing (RS) images. At the same time, the interpretationof the captured RS image also plays a crucial issue, which can be addressedby the multi-label classification and Computational Linguistics based imagecaptioning techniques. To achieve this, this paper presents an efficient lowcomplexity encoding technique with multi-label classification and image captioning for UAV based RS images. The presented model primarily involves thelow complexity encoder using the Neighborhood Correlation Sequence (NCS)with a burrows wheeler transform (BWT) technique called LCE-BWT forencoding the RS images captured by the UAV. The application of NCS greatlyreduces the computation complexity and requires fewer resources for imagetransmission. Secondly, deep learning (DL) based shallow convolutional neural network for RS image classification (SCNN-RSIC) technique is presentedto determine the multiple class labels of the RS image, shows the novelty ofthe work. Finally, the Computational Linguistics based Bidirectional EncoderRepresentations from Transformers (BERT) technique is applied for imagecaptioning, to provide a proficient textual description of the RS image. Theperformance of the presented technique is tested using the UCM dataset. Thesimulation outcome implied that the presented model has obtained effectivecompression performance, reconstructed image quality, classification results,and image captioning outcome. 展开更多
关键词 Image captioning unmanned aerial vehicle low complexity encoder remote sensing images image classification
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