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7th International Conference on Image and Graphics processing, ICIGP 2024

作者： Yang, Rui Wang, Xiao Xu, Xin Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology China School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan China

ISBN: (纸本)9798400716720

Magnetic Resonance Imaging (MRI) is an invaluable tool for brain tumor segmentation. However, in clinical practice, certain modalities might be unavailable, leading to potential performance degradation in prediction tasks. According to current implementations, different modalities are treated as independent entities during the feature extraction phase, ignoring their inherent complementary nature. In this paper, inspired by knowledge distillation techniques, we introduce the Fusion-Inspired Modality Distillation (FIMD) framework. FIMD leverages the output from a fused multi-modal model as a teacher to guide the training of individual modalities. Each modality benefits from the collective knowledge of all modalities, enhancing its performance. Our FIMD method, devoid of specific architectural constraints, seamlessly integrates into existing multi-modal brain tumor segmentation frameworks. Notably, extensive experiments on BraTS2020, BraTS2018, and BraTS2015 datasets indicate that FIMD can enhance the performance of current state-of-the-art (SOTA) algorithms on Dice by an average of 0.55-1.60%. Especially in the case of missing modalities, the effectiveness of the FIMD framework in enhancing the robustness and accuracy of brain tumor segmentation is demonstrated. © 2024 ACM.

关键词： Semantic Segmentation

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SCENERY:a lightweight block cipher based on Feistel structure

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Frontiers of computer Science 2022年第3期16卷 193-202页

作者： Jingya FENG Lang LI Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang 421002China College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China College of Computer Science and Technology Hengyang Normal UniversityHengyang 421002China

In this paper,we propose a new lightweight block cipher called *** main purpose of SCENERY design applies to hardware and software *** is a 64-bit block cipher supporting 80-bit keys,and its data processing consists of 28 *** round function of SCENERY consists of 84×4 S-boxes in parallel and a 32× 32 binary matrix,and we can implement SCENERY with some basic logic *** hardware implementation of SCENERY only requires 1438 GE based on 0.18 um CMOS technology,and the software implementation of encrypting or decrypting a block takes approximately 1516 clock cycles 0118-bit microcontrollers and 364 clock cycles on 64-bit *** with other encryption algorithms,the performance of SCENERY is well balanced for both hardware and *** the security analyses,SCENERY can achieve enough security margin against known attacks,such as differential cryptanalysis,linear cryptanalysis,impossible differential cryptanalysis and related-key attacks.

关键词： lightweight block cipher feistel structure cryptanalysis internet of things

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Side-channel analysis attacks based on deep learning network

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Frontiers of computer Science 2022年第2期16卷 35-45页

作者： Yu OU Lang LI Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang 421002China College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China College of Computer Science and Technology Hengyang Normal UniversityHengyang 421002China

There has been a growing interest in the sidechannel analysis(SCA)field based on deep learning(DL)*** DL network or model has been developed to improve the efficiency of ***,few studies have investigated the impact of the different models on attack results and the exact relationship between power consumption traces and intermediate *** on the convolutional neural network and the autoencoder,this paper proposes a Template Analysis Pre-trained DL Classification model named TAPDC which contains three *** TAPDC model detects the periodicity of power trace,relating power to the intermediate values and mining the deeper features by the multi-layer convolutional *** implement the TAPDC model and compare it with two classical models in a fair *** evaluative results show that the TAPDC model with autoencoder and deep convolution feature extraction structure in SCA can more effectively extract information from power consumption ***,Using the classifier layer,this model links power information to the probability of intermediate *** completes the conversion from power trace to intermediate values and greatly improves the efficiency of the power attack.

关键词： side-channel analysis template attack machine learning deep learning

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Balancing the reception performance of ONUs in CDM Coherent PON downstream signaling by Hadamard Transform Pre-coding

Balancing the reception performance of ONUs in CDM Coherent ...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Zhang, Luxiao Mao, Meng Sun, Lin Liu, Gordon Ning Li, Zhaohui School of Electronic and Information Engineering Soochow University Suzhou China Sun Yat-sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China

We observed and investigated the inconsistency of the reception performance deviation of ONUs in CDM coherent PONs by experiments. We employed the Hadamard transform pre-coding for balancing the performance of CDM-ass... 详细信息

ISBN: (纸本)9798350312614

关键词： CDM coherent PON Hadamard transform

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GPMN: Human Pose Explicit Modeling Network based on Graph Model 7

GPMN: Human Pose Explicit Modeling Network based on Graph Mo...

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7th IEEE information technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Zhang, Yuru Zhao, Jiayuan Su, Xiaodong Xu, Hongyan Harbin University of Commerce College of Computer and Information Engineering Harbin China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin China

ISBN: (纸本)9798350334197

The human pose estimation task requires the use of visual cues and anatomical relationships between joints to locate key points. Due to the structural dependencies between human joints, it is difficult to model the dependencies between joints with a convolutional neural network-based approach. In this paper, we propose a graph pose modeling network (GPMN) based on a graph model, which constructs a structural graph model between joints and then uses a convolutional neural network to explicitly model the dependencies between joints. Specifically, this method extracts features through a backbone network and uses a graph model to obtain proximity joint dependencies. At the same time, a cross-cross-attention mechanism is used to obtain the distant joint dependencies for reinforcing challenging key points. To solve the problem that the challenging key points of the network are weakened during the training process, this paper uses a focal loss function to make the network focus more on the challenging key points, such as knees and ankles. Under the same experimental conditions, HRNet, the highest accuracy feature extraction network, and ResNet, the classical feature extraction network, are used as the backbone network. The accuracy of the algorithm with HRNet network as the backbone is improved by 2.6% compared with the original network. © 2023 IEEE.

关键词： Extraction

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Transformer-Based Deep Hashing Method for Multi-Scale Feature Fusion 48

Transformer-Based Deep Hashing Method for Multi-Scale Featur...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： He, Chao Wei, Hongxi Inner Mongolia University School of Computer Science Hohhot China Provincial Key Laboratory of Mongolian Information Processing Technology Hohhot China National and Local Joint Engineering Research Center of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9781728163277

The deep image hashing aims to map the input image into simply binary hash codes via deep neural networks. Motivated by the recent advancements of Vision Transformers (ViT), many deep hashing methods based on ViT have been proposed. Nevertheless, the ViT has enormous number of model parameters and high computational complexity. Moreover, the last layer of the ViT outputs only the classification tokens as image feature vectors, while the rest of the vectors are discarded. This results in the inefficiency of the model computation and the neglect of useful image information. Therefore, this paper proposes a Transformer-based deep hashing method for multi-scale feature fusion (TDH). Specifically, we use a hierarchical Transformer backbone to capture both global and local features of images. The hierarchical Transformer utilizes a local self-attention mechanism to process image blocks in parallel, which reduces computational complexity and promotes computational efficiency. Multi-scale feature fusion module captures all image feature vectors of the hierarchical Transformer output to obtain more enriched image feature information. We perform comprehensive experiments on three widely-studied datasets: CIFAR-10, NUS-WIDE and IMAGENET. The experimental results demonstrate that the proposed method in this paper indicates superior results compared to the existing state-of-the-art work. Source code is available https://***/shuaichaochao/TDH. © 2023 IEEE.

关键词： deep hashing hash code image retrieval multi-scale feature fusion Transformer

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Metal Surface Defect Detection Algorithm Based on Improved YOLOv5 7

Metal Surface Defect Detection Algorithm Based on Improved Y...

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7th IEEE information technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Su, Xiaodong Zhang, Fengchun Zhang, Yurong Xu, Hongyan Chen, Xu Harbin-University of Commerce College of Computer And-Information:Engineering Harbin China Heilongiiang Provincial Key:Laboratory of Electronic Commerce and Information Processing Harbin China

ISBN: (纸本)9798350334197

Aiming at the problem of model misdetection and missed detection caused by the small defect and unclear features of industrial metal surfaces, this paper studies a large number of metal surface defects, and proposes a metal surface micro defect detection algorithm based on improved YOLOv5. First, this paper uses the FenceMask data enhancement method for regularization to solve the problem of few sample images. Then an enhanced multi-scale feature fusion pyramid network DS-FPN is proposed by introducing depthwise separable convolution, dilated convolution and spatial attention mechanism, so that the model can improve the ability to extract key information of images without adding additional parameters and calculations. An adaptive channel spatial attention mechanism SCBAM is also proposed, which adds non-local information to the original interaction with only local information, improving the feature expression ability of the model. Finally, through experimental verification, the detection accuracy of the model in this paper on the public data set GC10-DET has reached 74.9%, which is 4.7% higher than the original YOLOv5 model. © 2023 IEEE.

关键词： Convolution

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A video archive management method based on blockchain technology 11

A video archive management method based on blockchain techno...

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Optoelectronic Imaging and Multimedia technology XI 2024

作者： Wu, Rongteng College of Computer and Big Data Minjiang University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

ISBN: (纸本)9781510682061

Video archives play a crucial role in various industries, from media and entertainment to security and surveillance. Managing these archives efficiently and securely is a growing challenge due to the increasing volume of video data being generated. In this paper, a video archive management method is presented to solve security and sensibility of the video files on blockchain. By encrypting video files and generating cryptographic hashes, the presented method can protect the integrity of the data and prevent unauthorized modifications. The method based on blockchain technology offers a compelling solution to the challenges faced in managing video archives. By combining the inherent security and transparency of blockchain with innovative data management algorithms, organizations can ensure the integrity, security, and accessibility of their video archives. © 2024 SPIE.

关键词： Data integrity

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GM-HRNet: Human Pose Estimation Based on Global Modeling 7

GM-HRNet: Human Pose Estimation Based on Global Modeling

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7th IEEE information technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Su, Xiaodong Xu, Hongyan Zhao, Jiayuan Zhang, Fengchun Chen, Xu Harbin University of Commerce College of Computer and Information Engineering Harbin China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin China

ISBN: (纸本)9798350334197

Convolutional neural networks have been widely used for human pose estimation tasks, but with some issues. It is limited to local receptive fields and it is difficult to capture global information. To address this problem, we propose the GM-HRNet network. The network aggregates the multi-stage feature information of HRNet, and makes full use of the criss-cross attention and channel attention to obtain the context information in the space and channel dimensions, and realizes the modeling of the global relationship, so as to effectively locate the keypoints of the human body. In this paper, HRNet is used as a benchmark to conduct experiments on MPII dataset. Experimental results show that the accuracy of GM-HRNet is 1.3 percent higher than that of HRNet under the same experimental conditions, which validates the effectiveness of the GM-HRNet model. © 2023 IEEE.

关键词： Convolutional neural networks

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NCCNet: Arbitrary Neural Style Transfer with Multi-channel Conversion 12th

NCCNet: Arbitrary Neural Style Transfer with Multi-channel ...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Xiang, Jin Zhao, Huihuang Lin, Mugang Liu, Qingyun College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9783031463136

Arbitrary style transfer aims to create a novel image from a content image and a style image, stylizing the content image with the style of the style image. However, recent algorithms are prone to unnatural output due to focusing on the statistical distribution of deep features and ignoring the relationship between channels in feature maps. In this paper, we devise a novel Arbitrary Neural Style Transfer Network called NCCNet, which is useful for artistic style transfer. Specifically, NCCNet creates good visual effects by fusing different layers of textures. Moreover, we design an Effective Channel Conversion Modules (ECCM) which uses a multi-channel matrix to rearrange style feature maps to the appropriate range by analyzing the relationship between channels. Then, we can obtain a covariance based on the rearranged style feature map. At the same time, we design a decoder named Self-Adaptive Decoder (SA-Decoder) that conforms to our algorithm. Moreover, we apply our algorithm to the video style transfer and get good results. Qualitative and quantitative evaluations demonstrate that NCCNet performs well in arbitrary video and image style transfer tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Textures

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