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Thinly-distributed Cylindrical Array Antenna Optimization Based on Improved Genetic Algorithm

Thinly-distributed Cylindrical Array Antenna Optimization Ba...

引用

IEEE International conference on Signal and image processing (ICSIP)

作者： Shuhan Ye Jinwan Zhang Yuyi Wu Jia Lin Yuxin Yan Electronics and Communication Engineering Sun Yat-sen University Shenzhen China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In response to the urgent demand for lightweight, low-cost, and low-energy-consumption radar systems for unmanned aerial and ground vehicles, this study conducts research on sparse radar array optimization. Based on the relationship between array performance and array structure, an improved genetic algorithm is proposed. This optimization algorithm enhances the traditional genetic algorithm using a harmony search algorithm. By constructing an adaptive function, employing a hybrid crossover strategy, and utilizing perturbation updates, it seeks the optimal solution in a larger search space, optimizing array structure and deployment design. This is achieved by sparsifying part of the array elements from a uniformly spaced full array, forming a non-uniform array with element spacing constrained to integer multiples of half-wavelength. Compared to traditional genetic algorithms and other methods, the proposed approach achieves higher gain with fewer antenna elements, significantly suppresses sidelobes, reduces power consumption and cost, and obtains excellent performance indicators.

关键词： Power demand Array signal processing Perturbation methods Radar Radar imaging Radar antennas Radar signal processing

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A two-channel hybrid convolutional residual network for super-resolution of infrared images

A two-channel hybrid convolutional residual network for supe...

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ACIS International conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Yong Gan Haonan Chen Shaohui Zhou Yuefeng Wang Zhengzhou University of Technology Zhengzhou China College of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

Under specific environmental conditions such as dense fog or high dust levels, conventional RGB imaging technology faces significant challenges in capturing clear images. In contrast, infrared imaging technology, due to its unique characteristics, can effectively acquire images under these adverse conditions. However, the high cost associated with improving image quality through hardware enhancements in infrared imaging makes software-based image quality improvement crucial. Recent studies have demonstrated that deep learning networks hold significant potential for enhancing the quality of super-resolution images. To address the issues of gradient vanishing, insufficient feature utilization, and feature redundancy present in deep learning networks, this paper proposes a dual-channel hybrid convolutional residual network based on CNN with super-resolution of infrared images, which combines dual-feature extraction and dense linking. The network employs channel splitting to effectively reduce feature redundancy and leverages residual and mixed convolution techniques to enhance feature extraction and utilization. This approach efficiently preserves image details while eliminating noise. Comparative analysis using qualitative and quantitative metrics demonstrates the effectiveness of the proposed network for infrared image super-resolution tasks. The effectiveness of the network proposed in this paper in the task of super-resolution of infrared images is demonstrated by comparing it with other methods in terms of qualitative and quantitative metrics.

关键词： Deep learning Measurement image quality Superresolution Noise Termination of employment Infrared imaging

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Text-image Generation Using Visual-Language Transformer parallel Decoding

Text-Image Generation Using Visual-Language Transformer Para...

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6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Peng Fang Fan Wang Chongqing Three Gorges University School of Electronic and Information Engineering Chongqing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Autoregressive generative models have shown their superiority in the fields of vision and language, such as high accuracy, high fidelity, and strong stability. Most existing methods are trained around text-guided image generation tasks and image-guided text generation tasks. However, not only can these two generative tasks be trained on the same dataset, but both require the alignment of visual and textual representations in the same semantic space. In order to be able to learn better generalization of image and text representations, we unify these two generation tasks in an autoregressive generative model and enhance the quality of text and image generation through bidirectional generation goals. In order to take the 2D-structure of the image into account. To this end, we propose a visual-linguistic Transformer autoregressive generative model. The visual-language Transformer model adopts a novel decoding strategy that takes the 2D-structure of the image into account without affecting the generation of the 1D sequence structure of the text. Through improved training strategies, we can not only generate highly accurate text descriptions, but also enhance the model's image understanding and generate high-quality images.

关键词： Training Visualization Accuracy image synthesis Computational modeling Signal processing Transformers Stability analysis Decoding Iterative decoding

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Study on the Absorption Capacity of Distribution Network with distributed Power Supply Based on Improved AFSA 22

Study on the Absorption Capacity of Distribution Network wit...

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3rd Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2022

作者： Deng, Panpan Leshan Vocational and Technical College Leshan China

ISBN: (纸本)9781450395786

In order to study the absorption capacity of distribution network access DG, the improved AFSA proposed in this paper adjusts the view and step size, and maps multiple independent ponds in N parallel space-time, so as to balance the global search ability and convergence speed well, and improve the adaptive ability and optimization accuracy of the algorithm. Based on the improved AFSA, this paper studies the absorption capacity of distribution network access DG. In view of the current radial distribution of distribution network, weak branch feeder, long power supply radius of rural power network, low voltage and other practical situations, the absorption capacity is studied and judged, so as to reasonably plan the reasonable distribution of DG in each region and the optimal operation state. Finally, IEEE33 nodes and actual engineering cases are used for example analysis and verification. The results show that the fast convergence speed and adaptive ability have strong engineering practice guidance value for DG installed capacity configuration, site selection, operation state location, etc. © 2022 ACM.

关键词： Site selection

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Temporal Clustering and Temporal Reference Based Specular Detection For 1-MS Visual Feedback System

Temporal Clustering and Temporal Reference Based Specular De...

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IEEE International conference on image processing

作者： Tingting Hu Ryuji Fuchikami Shigekiyo Nosaka Panasonic Connect Co. Ltd. Research and Development Division Fukuoka-shi Japan

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The 1-ms visual feedback system is critical for seamless actuation in robotics, as any delay affects its performance in handling dynamic situations. Specular reflections cause problems in many visual technologies, making specular detection crucial in 1-ms visual feedback systems. However, existing real-time methods, which target Neumann architecture, fail to achieve the 1-ms delay due to spatial memory paths resulting from extensive frame-based processing. This research aims to develop a 1-ms specular detection system from both algorithm and architecture perspectives, proposing 1) temporal clustering and temporal reference based specular detection method, which leverages temporal domain information to address the requirements of frame-based processing; and 2) global-local integrated specular detection architecture, which enables the coexistence of local and global processing within a 1-ms stream-based architecture. The proposed methods are implemented on FPGA. The evaluation shows that the proposed system supports sensing and processing a $1000-\mathrm{fps}$ sequence with a delay of $0.941 \mathrm{~ms} /$ frame.

关键词： Visualization parallel processing Robot sensing systems Real-time systems Reflection Delays Systems support

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NATURAL SCENE STATISTICS AND CNN BASED parallel NETWORK FOR image QUALITY ASSESSMENT

NATURAL SCENE STATISTICS AND CNN BASED PARALLEL NETWORK FOR ...

引用

IEEE International conference on image processing (ICIP)

作者： Jain, Parima Shikkenawis, Gitam Mitra, Suman K. Indian Inst Technol Jammu Jammu India CR Rao Adv Inst Math Stat & Comp Sci Hyderabad India Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar India

ISBN: (纸本)9781665441155

image Quality Assessment (IQA) tasks have increasing importance in today's world due to the widespread use of imaging devices and social media. Statistical studies proved that naturalness measures are good discriminators for evaluating image quality. Convolutional neural networks (CNN) based IQA models gained popularity in recent years due to their enhanced performance. In this article, we present a no-reference image quality assessment method that integrates natural image statistics (NSS) with CNN. The proposed approach extracts NSS features from the image, integrates them with the CNN features to predict the quality score. Our experimental results show that the performance of the proposed method is competitive against the existing methods of image quality assessment. Cross database testing on Live in the Wild (LIVE-itW) and Smartphone Photography Attribute and Quality (SPAQ) databases shows excellent generalization.

关键词： No-reference image Quality Assessment Natural Scene Statistics Convolutional Neural Networks

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image Super-resolution Reconstruction Based on Multi-layer parallel Residual Network 2

Image Super-resolution Reconstruction Based on Multi-layer P...

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2nd International conference on Computers, Information processing and Advanced Education, CIPAE 2021

作者： Wang, Huifeng Xu, Yan Wei, Yiming Lanzhou Jiaotong University School of Electronic and Information Engineering Gansu China

ISBN: (纸本)9781665426657

The existing methods can improve the overall visual effect of the image, but the reconstructed image is still lack of high-frequency information, leading to texture blur. To solve these problems, this paper proposes an image super-resolution reconstruction algorithm based on multi-layer parallel convolution and residual network. Taking the multilayer parallel structure as the whole framework, firstly, different convolution combinations are used to enrich the feature information of the parallel structure, and the jump connection is added to further enrich the feature and fuse the output to extract more high-frequency information. Secondly, an adaptive residual network is introduced to supplement information and optimize network performance. Finally, perceptual loss is used to improve the overall quality of the restored image. Experimental results show that the proposed algorithm performs well for the reconstructed image, and the PSNR and structural similarity are significantly improved in the objective evaluation. © 2021 IEEE.

关键词： Adaptive systems Convolution Superresolution Reconstruction algorithms Feature extraction Visual effects Nonhomogeneous media

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Contour-Weighted Loss For Class-Imbalanced image Segmentation

Contour-Weighted Loss For Class-Imbalanced Image Segmentatio...

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IEEE International conference on image processing

作者： Zhengyong Huang Yao Sui National Institute of Health Data Science Peking University Beijing China Institute of Medical Technology Peking University Beijing China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

image segmentation is critically important in almost all medical image analysis for automatic interpretations and processing. However, it is often challenging to perform image segmentation due to data imbalance between intra- and inter-class, resulting in over- or under-segmentation. Consequently, we proposed a new methodology to address the above issue, with a compact yet effective contour-weighted loss function. Our new loss function incorporates a contour-weighted cross-entropy loss and separable dice loss. The former loss extracts the contour of target regions via morphological erosion and generates a weight map for the cross-entropy criterion, whereas the latter divides the target regions into contour and non-contour components through the extracted contour map, calculates dice loss separately, and combines them to update the network. We carried out abdominal organ segmentation and brain tumor segmentation on two public datasets to assess our approach. Experimental results demonstrated that our approach offered superior segmentation, as compared to several state-of-the-art methods, while in parallel improving the robustness of those popular state-of-the-art deep models through our new loss function. The code is available at https://***/ huangzyong/Contour-weighted-Loss-Seg.

关键词： image segmentation image analysis Codes Computational modeling Brain modeling Robustness Tumors

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MLPs: Efficient Training of MiniGo on Large-scale Heterogeneous Computing System

MLPs: Efficient Training of MiniGo on Large-scale Heterogene...

引用

International conference on parallel and distributed Systems (ICPADS)

作者： Peng Qiao Zhouyu He Rongchun Li Jingfei Jiang Yong Dou Dongsheng Li Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

Deep Reinforcement Learning has been successfully applied in various applications and achieved impressive performance compared with previous traditional methods but suffers from high computation cost and long training time. MLPerf takes deep reinforcement learning as one of the benchmark tracks and provides a single node training version of MiniGo as a reference. A key challenge is to achieve efficient MiniGo training on a large-scale computing system. According to the training computation pattern in MiniGo and the characteristics of our large-scale heterogeneous computing system, we propose a MultiLevel parallel strategy, MLPs, including task-level parallelism between nodes, CPU-DSP heterogeneous parallelism, and DSP multi-core parallelism. The proposed method reduces the overall execution time from 43 hours to 16 hours while scaling the node size from 1067 to 4139. The scaling efficiency is 69.1%. According to our fitting method, the scaling efficiency is 46.5% when scaling to 8235 nodes. The experimental results show that the proposed method achieves the efficient training of MiniGo on the largescale heterogeneous computing system.

关键词： Training Deep learning Neural networks Memory management Reinforcement learning parallel processing Programming

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FEDKA: Federated Knowledge Augmentation for Multi-Center Medical image Segmentation on non-IID Data

FEDKA: Federated Knowledge Augmentation for Multi-Center Med...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yuhao Zhang Shaoming Duan Xinyu Zha Jinhang Su Peiyi Han Chuanyi Liu School of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China Peng Cheng Laboratory Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen China

Federated learning (FL) allows decentralized medical institutions to collaboratively learn a shared global model without breaching data privacy. However, in the context of medical image segmentation, data distributions across centers may vary a lot due to the diverse imaging protocols, vendors and partial annotation, which usually hampers the optimization convergence and the performance of FL. In this paper, we propose a novel approach called federated knowledge augmentation (FedKA) to address the non-IID (non-independent and identically distributed) problem in medical image segmentation within FL. FedKA first designs a pixel-wise knowledge augmentation method to preserve the knowledge of globally labeled regions for the local model during training, and augments each local feature statistical knowledge based on a mixture of Gaussian distribution. Our experiments on public datasets show the superiority of FedKA over the state-of-the-art methods in test performance.

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