检索结果-内蒙古大学图书馆

Energy Efficient sparse code-Aided On-Off Division Multiple Access

IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第4期14卷 1254-1258页

作者： Gao, Jingqiu Fan, Pingzhi Li, Ning Jiang, Dexia Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Peoples R China Fuyao Univ Sci & Technol Sch Comp & Artificial Intelligence Fuzhou 350109 Peoples R China

In this letter, we propose an uplink sparse code-aided on-off division multiple access (SC-ODMA) system that enhances energy efficiency and error correction capabilities. In this SC-ODMA system, each user is assigned a predetermined codebook containing M sparse codewords, integrating bit-to-sparse-code mapping and spreading. Specifically, the message of each user undergoes polar encoding and sparse code mapping, then is transmitted in a time-hopping manner based on user-specific on-off patterns. A key contribution of this letter is the integration of sparse code mapping with ODMA, significantly improving performance while maintaining energy efficiency. Simulation results indicate that the proposed SC-ODMA system notably improves energy efficiency and significantly reduces the block error rate (BLER) compared to traditional ODMA systems. Furthermore, the proposed scheme maintains competitive energy efficiency across various conditions, including code rates, user loads, and BLER targets, illustrating its robustness and adaptability in dynamic communication environments.

关键词： codes Encoding Iterative decoding NOMA Energy efficiency Decoding Vectors Uplink Polar codes Multiaccess communication ODMA polar code RA code sparse code

来源：评论

学校读者我要写书评

暂无评论

Speeding Up Distributed Learning via sparse and Flexible coded Computing

引用

IEEE TRANSACTIONS ON INFORMATION THEORY 2025年第4期71卷 3167-3180页

作者： Zhang, Jingzhe He, Xiaofan Dai, Huaiyu Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

Plagued by slow or failing workers (also known as stragglers), the speedup gain assumed by distributed learning often falls short. Although substantial efforts have been devoted to mitigating this straggling effect with coding-theoretic techniques, existing pioneering works often suffer from two issues: dense combination and inflexibility. In particular, a code that involves dense combination of sub-tasks may destroy sparsity and lead to heavy workload. In contrast, an inflexible code that conservatively designs its computation procedure according to the presumed maximum number of stragglers may entail unnecessary redundancy when the actual number of stragglers is small. To this end, a generic framework based on matrix splitting is proposed in this work to construct sparse and flexible codes. Specifically, by splitting an original sparse coding matrix into two sparser sub-matrices, a two-layer coded computation that maintains sparsity can be created accordingly. In the meantime, when the actual number of stragglers is small, the computation may be flexibly terminated at layer-one without executing layer-two, thereby avoiding unnecessary computation. Based on this framework, a novel flexible Bernoulli code is proposed. In addition, by deriving a lower bound in closed-form through the lens of a bipartite graph, its decoding probability is shown to be high and asymptotically one. Moreover, extensive simulations including an application in distributed learning of LeNet are conducted to validate the effectiveness of the proposed scheme.

关键词： codes Encoding sparse matrices Decoding Distance learning Computer aided instruction Polynomials Bipartite graph Prevention and mitigation Machine learning coded computing straggler mitigation sparse code flexible code bipartite graph

来源：评论

学校读者我要写书评

暂无评论

Arrhythmia detection based on the reduced features with K-SVD sparse coding algorithm

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第8期82卷 12337-12350页

作者： Shahsavani, Fatemeh Nasiripour, Reza Shakeri, Reza Gholamrezaee, Alireza Payam & Noor Univ Mashhad Dept Elect & Comp Engn Mashhad Razavi Khorasan Iran Univ Birjand Dept Elect & Comp Engn Birjand Iran Khorasan Univ Mashhad Dept Elect & Comp Engn Mashhad Razavi Khorasan Iran

Arrhythmia is a subnormal heartbeat that causes Tachycardia or Bradycardia. ECG signal is extensively used for the analysis of heart diseases. The ECG is used to measure the rate and regularity of heartbeats. With the help of the study of the ECG signal, the presence of any damage to the heart can be identified. Arrhythmia classification is an important part of the research of medical images and signal processing. This paper aims to apply the K-SVD technique so that both the best features selected, and the accuracy of arrhythmia classification are improved. Additionally, the required space for storage has been reduced. For each ECG, we extracted 100 features of each ECG signal. Afterward, we applied statistical descriptors for extracted features which include Mean, Energy, Variance, Covariance, and Standard Deviation (S.D). The next step is the training process. We applied MLP neural networks. We have used two kinds of datasets in our research paper, UCI Machine Learning Repository and the MIT-BIH dataset. The accuracy rate of the proposed method is 98.90% and 99.14% for the UCI and MIT-BIH Dataset, respectively.

关键词： sparse code ECG K-SVD Feature extraction Back propagation neural network

来源：评论

学校读者我要写书评

暂无评论

AnoCSR-A Convolutional sparse Reconstructive Noise-Robust Framework for Industrial Anomaly Detection 6th

AnoCSR-A Convolutional Sparse Reconstructive Noise-Robust Fr...

引用

6th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Zhong, Jie Tu, Xiaotong Huang, Yue Ding, Xinghao Xiamen Univ Sch Informat Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Peoples R China

ISBN: (纸本)9789819984688;9789819984695

Industrial anomaly detection involves the identification and localization of abnormal regions in images, of which the core challenge is modeling normal data in appropriate ways. Inspired by dictionary learning, we propose a convolutional sparse reconstructive noise-robust framework, named AnoCSR. The proposed convolutional sparse encoding block (CSE-Block) in AnoCSR treats the convolutional dictionary as learnable parameters, where the convolutional kernels serve as the atoms. By training on normal samples, we optimize the parameters of the CSE-Block to extract optimal sparse codes. The CSE-Block stacks to form a convolutional sparse reconstructive network (CSR-Net) to progressively extract the sparse code and reconstruct the input image inversely. This enables effective reconstruction of normal samples while inadequately reconstructing abnormal regions, thereby facilitating anomaly detection and localization. The CSR-Net is parallelly connected with the downstream Localizer, forming an end-to-end framework. Our experimental results demonstrate that AnoCSR achieves performance comparable to state-of-the-art image reconstruction-based methods on the MVTecAD dataset. Moreover, considering that noise may affect the modeling of normal data and the discrimination of anomalies, we conduct the simulated noise resistance experiment. The result demonstrates that AnoCSR significantly outperforms other similar methods, indicating its strong noise robustness in addition to its outstanding performance.

关键词： Anomaly Detection sparse code Dictionary Learning

来源：评论

学校读者我要写书评

暂无评论

sparse code LBP and SIFT Features Together for Scene Categorization 4

Sparse Code LBP and SIFT Features Together for Scene Categor...

引用

International Conference on Audio, Language and Image Processing

作者： Bai, Shuang Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9781479939039

Local descriptors, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT), are widely used in various computer applications. They emphasize different aspects of image contents. In this paper, we propose to sparse code them together for categorizing scene images. We first regularly extract LBP and SIFT features from the images. Then, corresponding to each feature, a visual word codebook is constructed by using training images. For creating a representation for an image, the LBP and SIFT features extracted from the same position of the image are encoded together based on sparse coding. Specifically, we combine the obtained LBP codebook and SIFT codebook as a two dimensional table. In this table, each entry corresponds to a LBP visual word and a SIFT visual word. Therefore, the encoding values of the entries depend on both features. After all features of the input image are processed, the spatial max pooling is adopted to determine the image representation. Obtained image representations are classified by utilizing SVM classifiers. Finally, we conduct extensive experiments on datasets scene categories 8 and MIT 67 indoor scene to evaluate the proposed method. Obtained results demonstrate that combining features in the proposed manner has improved the scene categorization performance significantly. In addition, the results of the proposed method are comparable to other state of the art works.

关键词： sparse code SIFT LBP Feature combination Scene categorization

来源：评论

学校读者我要写书评

暂无评论

Approach to model human appearance based on sparse representation for human tracking in surveillance

引用

IET IMAGE PROCESSING 2020年第11期14卷 2383-2394页

作者： Damotharasamy, Sangeetha Govt Coll Technol Dept ECE Coimbatore Tamil Nadu India

In human tracking, sparse representation successfully localises the human in a video with minimal reconstruction error using target templates. However, the state-of-the-art approaches use colour and local appearance of a human to discriminate the human from the background regions, and hence fail when the human is occluded and appears in the varying illumination environment. In this study, a robust tracking algorithm is proposed that utilises gradient orientation and fine and coarse sparse representation of the target template. sparse representation-based human appearance model utilises weighted gradient orientation that is insensitive to illumination variation. Coarse and fine representation of sparse code facilitates tracking under varying scales. Subspace learning from image gradient orientation is enforced with occlusion detection during the dictionary updation stage to capture the visual characteristics of the local human appearance that supports tracking under partial occlusion with lesser tracking error. The proposed human tracking algorithm is evaluated on various datasets and shows efficient human tracking performance when compared to the other state-of-the-art approaches. Furthermore, the proposed human tracking algorithm is suitable for surveillance applications.

关键词： object detection learning (artificial intelligence) image reconstruction gradient methods image colour analysis face recognition image sequences image motion analysis feature extraction object tracking video signal processing image representation minimal reconstruction error target template local appearance varying illumination environment robust tracking algorithm fine representation sparse code image gradient orientation local human appearance human tracking algorithm sparse representation-based human appearance model

来源：评论

学校读者我要写书评

暂无评论

A survey of encoding techniques for reducing data-movement energy

引用

JOURNAL OF SYSTEMS ARCHITECTURE 2019年 97卷 373-396页

作者： Mittal, Sparsh Nag, Subhrajit IIT Hyderabad Dept Comp Sci & Engn Hyderabad Telangana India

In modern processors, data-movement consumes two orders of magnitude higher energy than a floating-point operation and hence, data-movement is becoming the primary bottleneck in scaling the performance of modern processors within the fixed power budget. Intelligent data-encoding techniques hold the promise of reducing the data-movement energy. In this paper, we present a survey of encoding techniques for reducing data-movement energy. By classifying the works on key metrics, we bring out their similarities and differences. This paper is expected to be useful for computer architects, processor designers and researchers in the area of interconnect and memory system design.

关键词： Data movement Encoding technique Energy saving sparse code Limited weight coding Value locality Value prediction

来源：评论

学校读者我要写书评

暂无评论

Automated paper impurities evaluation using feature representations based on ADMM sparse codes

Automated paper impurities evaluation using feature represen...

引用

International Conference on Telecommunication, Power Analysis and Computing Techniques

作者： Huangpeng, Qizi Huang, Wenwei Shi, Hanyi Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Nine Changsha Hunan Peoples R China Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha Hunan Peoples R China

To automatic detect and characterize paper impurities with computer vision, we present a novel two parts evaluation procedure with feature representations using Alternating Direction Method of Multipliers (ADMM) sparse codes. The method is based on an offline training step to obtain sparse coefficients and codebooks via learning extracted features with ADMM optimization, followed by an online detection step to use linear SVM classifier to assess defective paper samples from non-defective ones. Our approach bridges the gap between paper impurities evaluation and sparse feature representations, taking advantages of existing ADMM algorithms to handle sparse codes problem. We compare different feature descriptors and sparse code methods to implement the procedure and experimentally validate it on a dataset of 11 paper classes. Experiment results show that the proposed method is competitive and effective in terms of evaluation accuracy and speed.

关键词： Paper impurities evaluation feature representation sparse code ADMM

来源：评论

学校读者我要写书评

暂无评论

Low-rank sparse coding and region of interest pooling for dynamic 3D facial expression recognition

引用

SIGNAL IMAGE AND VIDEO PROCESSING 2018年第8期12卷 1611-1618页

作者： Zarbakhsh, Payam Demirel, Hasan Eastern Mediterranean Univ Elect & Elect Engn Dept Via Mersin 10 Famagusta Northern Cyprus Turkey Cyprus Int Univ Fac Engn Via Mersin 10 Nicosia Northern Cyprus Turkey

In this paper, we propose a dynamic three-dimensional facial expression recognition using low-rank sparse codes pooled from automatically detected regions of interests. Low-rank sparse coding has been applied for the first time in dynamic facial expression recognition and a novel region-based pooling is suggested. Twelve regions of interests are defined using detected landmarks based on facial expression activation dynamics. Landmarks are tracked in subsequent frames using multi-point tracker. Temporal segmentation is utilized by using geometric information extracted from detected landmarks. LBP-TOP feature descriptors are extracted from cuboids inside spatiotemporal regions of interests in both texture and depth sequences. Texture and depth features are then fused to form the feature matrix. Low-rank sparse coding is utilized to obtain sparse codes from feature descriptors. Finally, hidden-state conditional random fields are employed to classify six basic expressions. Experimental results and comparison with recent studies verify that proposed method improves the accuracy of dynamic facial expression recognition. The average accuracy of recognition of six basic expressions is improved by the proposed system from 83.07 to 85.12% on 95 subjects in BU-4DFE data set.

关键词： Facial landmark detection Landmark tracking Low rankness sparse code Region of interest

来源：评论

学校读者我要写书评

暂无评论

Subject-based discriminative sparse representation model for detection of concealed information

引用

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017年 143卷 25-33页

作者： Akhavan, Amir Moradi, Mohammad Hassan Vand, Safa Rafiei Amirkabir Univ Technol Dept Biomed Engn Tehran Iran

Background and objectives: The use of machine learning approaches in concealed information test (CIT) plays a key role in the progress of this neurophysiological field. In this paper, we presented a new machine learning method for CIT in which each subject is considered independent of the others. The main goal of this study is to adapt the discriminative sparse models to be applicable for subject-based concealed information test. Methods: In order to provide sufficient discriminability between guilty and innocent subjects, we introduced a novel discriminative sparse representation model and its appropriate learning methods. For evaluation of the method forty-four subjects participated in a mock crime scenario and their EEG data were recorded. As the model input, in this study the recurrence plot features were extracted from single trial data of different stimuli. Then the extracted feature vectors were reduced using statistical dependency method. The reduced feature vector went through the proposed subject-based sparse model in which the discrimination power of sparse code and reconstruction error were applied simultaneously. Results: Experimental results showed that the proposed approach achieved better performance than other competing discriminative sparse models. The classification accuracy, sensitivity and specificity of the presented sparsity-based method were about 93%, 91% and 95% respectively. Conclusions: Using the EEG data of a single subject in response to different stimuli types and with the aid of the proposed discriminative sparse representation model, one can distinguish guilty subjects from innocent ones. Indeed, this property eliminates the necessity of several subject EEG data in model learning and decision making for a specific subject. (C) 2017 Elsevier B.V. All rights reserved.

关键词： sparse representation classifier Discriminative dictionary learning sparse code Reconstruction error Concealed information test

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：