Saliency Detection Using Sparse and Nonlinear Feature Representation by Shahzad Anwar, Qingjie Zhao, Muhammad Farhan Manzoor, Saqib Ishaq Khan

“…An important aspect of visual saliency detection is how features that form an input image are represented. …”
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MSFFNet: A Multilevel Sparse Feature Fusion Network for Infrared Dim Small Target Detection by Xiangyang Ren, Boyang Jiao, Zhenming Peng, Renke Kou, Peng Wang, Mingyuan Li

“…To tackle this issue, this article proposes a novel multilevel sparse feature fusion network for detecting infrared dim small targets. …”
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A Sparse Feature-Based Mixed Signal Frequencies Detecting for Unmanned Aerial Vehicle Communications by Yang Wang, Yongxin Feng, Fan Zhou, Xi Chen, Jian Wang, Peiying Zhang

“…In this paper, we propose a mixed-signal frequency detection method based on the reconstruction of sparse feature signals. …”
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DSAT: a dynamic sparse attention transformer for steel surface defect detection with hierarchical feature fusion by Shouluan Wu, Hui Yang, Liefa Liao, Chao Song, Yating Fang, Jianglong Fu, Tan Li

“…These defects exhibit diverse morphological characteristics and complex patterns, which pose substantial challenges to traditional detection models, particularly regarding multi-scale feature extraction and information retention across network depths. …”
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An improved EAE-DETR model for defect detection of server motherboard by Jian Chi, Mingke Zhang, Puhon Zhang, Guowang Niu, Zhihao Zheng

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SDPSNet: An Efficient 3D Object Detection Based on Spatial Dynamic Pruning Sparse Convolution and Self-Attention Feature Diffusion by Meng Wang, Qianlei Yu, Haipeng Liu

“…To address these issues, this paper proposes a novel 3D object detection model, SDPSNet, which combines spatial dynamic pruning and self-attention feature diffusion to reduce data redundancy and improve the representation of central features. …”
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Medical Image Fusion Based on Feature Extraction and Sparse Representation by Yin Fei, Gao Wei, Song Zongxi

“…SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. …”
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R-Sparse R-CNN: SAR Ship Detection Based on Background-Aware Sparse Learnable Proposals by Kamirul Kamirul, Odysseas A. Pappas, Alin M. Achim

“…Weintroduce R-Sparse R-CNN, a novel pipeline for oriented ship detection in Synthetic Aperture Radar (SAR) images that leverages sparse learnable proposals enriched with background contextual information, termed background-aware proposals (BAPs). …”
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PAMFPN: Position-Aware Multi-Kernel Feature Pyramid Network with Adaptive Sparse Attention for Robust Object Detection in Remote Sensing Imagery by Xiaofei Yang, Suihua Xue, Lin Li, Sihuan Li, Yudong Fang, Xiaofeng Zhang, Xiaohui Huang

“…To address these limitations, we propose a Dynamic Multi-Kernel Position-Aware Feature Pyramid Network (PAMFPN), which integrates adaptive sparse position modeling and multi-kernel dynamic fusion to achieve robust feature representation. …”
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Analysis of super-long and sparse feature in pseudo-random sequence based on similarity by Chun-jie CAO, Jing-zhang SUN, Zhi-qiang ZHANG, Long-juan WANG, Meng-xing HUANG

“…Similarity analysis of pseudo-random sequence in wireless communication networks is a research hotspot problem in the domain of information warfare.Based on the difficulties in super-long sequence,extremely sparse feature,and futilities in engineering application for real-time processing exist in similarity analysis of sequence in wireless net-work,a method of similarity analysis of sequence in a certain margin of misacceptance probability was proposed.Firstly,the similarity probability distribution of real-random sequence was theoretically analyzed.Secondly,according to the standard of NIST SP 800-22,the randomness of pseudo-bitstream was analyzed and the validity of pseudo-bitstream was judged.Finally,similarity was analyzed and verified by combining super-long pseudo-random sequence in real wireless communication networks.The results indicate that the lower bound of similarity value is 0.62 when misacceptance prob-ability uncertainty at about 1%.Above conclusion is considerable importance from the significance and theoretical values in network security domains,such as protocol analysis,traffic analysis,intrusion detection and others.…”
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Evaluating Sparse Feature Selection Methods: A Theoretical and Empirical Perspective by Monica Fira, Liviu Goras, Hariton-Nicolae Costin

“…The mathematical foundations of feature selection methods inspired by compressed detection are presented, highlighting how the principles of sparse signal recovery can be applied to identify the most relevant features. …”
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Harnessing feature pruning with optimal deep learning based DDoS cyberattack detection on IoT environment by Eunmok Yang, Sooyong Jeong, Changho Seo

“…This manuscript proposes an effective Feature Pruning with Optimal Deep Learning-based DDoS Attack Detection (FPODL-DDoSAD) technique in the IoT framework. …”
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Infrared Small Target Detection Based on Compound Eye Structural Feature Weighting and Regularized Tensor by Linhan Li, Xiaoyu Wang, Shijing Hao, Yang Yu, Sili Gao, Juan Yue

“…This paper proposes a low-rank and sparse decomposition method based on bio-inspired infrared compound eye image features for small target detection. …”
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Sleep Posture Recognition Method Based on Sparse Body Pressure Features by Changyun Li, Guoxin Ren, Zhibing Wang

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Music Emotion Detection Using Hierarchical Sparse Kernel Machines by Yu-Hao Chin, Chang-Hong Lin, Ernestasia Siahaan, Jia-Ching Wang

“…For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. …”
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Multi-Scale Feature Fusion and Context-Enhanced Spatial Sparse Convolution Single-Shot Detector for Unmanned Aerial Vehicle Image Object Detection by Guimei Qi, Zhihong Yu, Jian Song

Subjects: “…UAV image object detection…”
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Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms by Lal Hussain, Shahzad Ahmad Qureshi, Amjad Aldweesh, Jawad ur Rehman Pirzada, Faisal Mehmood Butt, Elsayed Tag eldin, Mushtaq Ali, Abdulmohsen Algarni, Muhammad Amin Nadim

“…The hybrid features with RICA were found to yield the highest detection performance. …”
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A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM by Xin-Sheng Zhang

“…Then we designed the sparse feature learning based MCs classification algorithm using twin support vector machines (TWSVMs). …”
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Compositional transformations can reasonably introduce phenotype-associated values into sparse features by George I. Austin, Tal Korem

“…While we do not intend to address other concerns regarding tumor microbiome analyses, validate Poore et al.’s results, or evaluate batch-correction pipelines, we conclude that because phenotype-associated features that were initially sparse can be created by a sample-wise transformation that cannot artifactually inflate machine learning performance, their detection is not independently sufficient to demonstrate information leakage in machine learning pipelines. …”
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Hierarchical Sampling Representation Detector for Ship Detection in SAR Images by Ming Tong, Shenghua Fan, Jiu Jiang, Chu He

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