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Single-pixel super-resolution with a space–time modulated computational metasurface imager

Photonics Research 2024年第10期12卷 2311-2322页

作者： WENZHI LI JIARAN QI ANDREA ALU Department of Microwave Engineering School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbin 150001China Photonics Initiative Advanced Science Research CenterCity University of New YorkNew YorkNew York 10031USA Physics Program Graduate Center of the City University of New YorkNew YorkNew York 10016USA

Single-pixel imaging is a burgeoning computational imaging technique that utilizes a single detector devoid of spatial resolution to capture an image,offering great potential for creating cost-effective and simplified imaging ***,achieving super-resolution with a single pixel remains a formidable ***,we introduce a single-pixel super-resolution imaging technique based on space–time *** modulation parametrically mixes the incoming signals,enabling the space–time scattered signals of the object carrying finer details to be captured by the single-pixel imaging *** validate our proposed technique,we designed and fabricated a computational metasurface imager that needs only a single transmitting port and a single receiving *** achieved resolution surpasses the Abbe resolution *** principle of our proposed technique is well-suited for low-cost and compact imaging systems.

关键词： resolution utilize details

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Control and Data Integrity are Important Factors of Data Governance Technology

Control and Data Integrity are Important Factors of Data Gov...

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International Conference on ICT For Smart Society (ICISS)

作者： Yakob Utama Chandra Harjanto Prabowo Ford Lumban Gaol Betty Purwandari Information Systems Department BINUS Graduate Program Doctor of Computer Science School of Information Systems Bina Nusantara University Jakarta Indonesia Management Department Doctor of Research Management BINUS Business School Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

The proliferation of data has become an inevitable consequence of the digital age as individuals across all domains increasingly rely on data for various purposes. Both companies and governments have a significant need for data. However, the sheer volume and complexity of this data, along with the potential risks associated with mishandling it, necessitate the establishment of a framework known as data governance. The primary objective of this research paper is to examine the factors that contribute to the effective implementation and utilization of Data governance. This study employs qualitative methods and adopts a systematic literature review approach to address the research inquiry: “What are the important factors that determine the utilization of data governance technology?” Through the systematic literature review, a total of 41 key factors were identified from 20 research literatures that have been found to significantly impact the effective implementation of data governance. There are thirteen primary technological factors that hold significance in the realm of data governance. These factors encompass technology, application and use, storage of data, sharing, archiving, and preservation, big data algorithmic systems, data flow, deletion, diversity of data, mechanisms, size of data, control, and data integrity.

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SPA: Leveraging the SAM with Spatial Priors Adapter for Enhanced Medical Image Segmentation

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Hu, Jihong Li, Yinhao Jain, Rahul Kumar Lin, Lanfen Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Osaka5678570 Japan Ritsumeikan University College of Information Science and Engineering Osaka5678570 Japan Zhejiang University College of Computer Science and Technology Hangzhou310063 China Zhejiang Key Laboratory of Multi-omics Precision Diagnosis and Treatment of Liver Diseases Hangzhou China

The Segment Anything Model (SAM) has gained renown for its success in image segmentation, benefiting significantly from its pretraining on extensive datasets and its interactive prompt-based segmentation approach. Although highly effective in natural (real-world) image segmentation tasks, the SAM model encounters significant challenges in medical imaging due to the inherent differences between these two domains. To address these challenges, we propose the Spatial Prior Adapter (SPA) scheme, a parameter-efficient fine-tuning strategy that enhances SAM's adaptability to medical imaging tasks. SPA introduces two novel modules: the Spatial Prior Module (SPM), which captures localized spatial features through convolutional layers, and the Feature Communication Module (FCM), which integrates these features into SAM's image encoder via cross-attention mechanisms. Furthermore, we develop a Multiscale Feature Fusion Module (MSFFM) to enhance SAM's end-to-end segmentation capabilities by effectively aggregating multiscale contextual information. These lightweight modules require minimal computational resources while significantly boosting segmentation performance. Our approach demonstrates superior performance in both prompt-based and end-to-end segmentation scenarios through extensive experiments on publicly available medical imaging datasets. Performance highlights the potential of the proposed method to bridge the gap between foundation models and domain-specific medical imaging tasks. This advancement paves the way for more effective AI-assisted medical diagnostic systems. © 2013 IEEE.

关键词： Medical imaging

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Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models

arXiv

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arXiv 2024年

作者： Hu, Zheng Li, Zhe Jiao, Ziyun Nakagawa, Satoshi Deng, Jiawen Cai, Shimin Zhou, Tao Ren, Fuji School of Computer Science and Engineering University of Electronic Science and Technology of China China Graduate School of Information Science and Technology University of Tokyo Japan Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

In recent years, knowledge graphs have been integrated into recommender systems as item-side auxiliary information, enhancing recommendation accuracy. However, constructing and integrating structural user-side knowledge remains a significant challenge due to the improper granularity and inherent scarcity of user-side features. Recent advancements in Large Language Models (LLMs) offer the potential to bridge this gap by leveraging their human behavior understanding and extensive real-world knowledge. Nevertheless, integrating LLM-generated information into recommender systems presents challenges, including the risk of noisy information and the need for additional knowledge transfer. In this paper, we propose an LLM-based user-side knowledge inference method alongside a carefully designed recommendation framework to address these challenges. Our approach employs LLMs to infer user interests based on historical behaviors, integrating this user-side information with item-side and collaborative data to construct a hybrid structure: the Collaborative Interest Knowledge Graph (CIKG). Furthermore, we propose a CIKG-based recommendation framework that includes a user interest reconstruction module and a cross-domain contrastive learning module to mitigate potential noise and facilitate knowledge transfer. We conduct extensive experiments on three real-world datasets to validate the effectiveness of our method. Our approach achieves state-of-the-art performance compared to competitive baselines, particularly for users with sparse interactions. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Multi-channel Sliced Deep RCNN with Residual Network for Text Classification

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Chinese Journal of Electronics 2020年第5期29卷 880-886页

作者： ZHOU Chuanhua ZHOU Jiayi YU Cai ZHAO Wei PAN Ruilin School of Management Science and Engineering Anhui University of Technology Graduate School of Information Production and Systems Waseda University School of Computer Science and Technology Anhui University of Technology

We propose a multi-channel sliced deep Recurrent convolutional neural network(RCNN) with a residual network. We expand the RCNN into a deep neural network. Our proposed model can directly learn to extract bigram features and other features from sentences where other machine learning methods cannot. The experimental results indicate that our model outperforms the traditional methods.

关键词： Text classification Deep neural network Bigram residual network Residual network text analysis deep recurrent convolutional neural network Multichannel text classification recurrent neural nets feature extraction convolutional neural nets RCNN deep neural network pattern classification learning (artificial intelligence) multichannel sliced deep RCNN

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Feasibility study of MPTCP ACK via alternative path in real network environment

Feasibility study of MPTCP ACK via alternative path in real ...

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2021 International Symposium on Grids and Cloud, ISGC 2021

作者： Koibuchi, Hiroyuki Abe, Hirotake Kato, Kazuhiko Graduate School of System and Information Engineering University of Tsukuba Japan Department of Computer Science University of Tsukuba Japan

Multi-path TCP (MPTCP) is a network protocol that has the potential to enhance network performance, which is crucial in grid computing systems to improve the overall system's performance. It utilizes multiple network paths between machines to achieve a high network performance. This protocol can also improve the connectivity of multiple servers that may be in different data centers by combining multiple network paths. One of the main issues of MPTCP is the path for ACK (acknowledgment) packets, which take the same path where original packets come from. When networks have different round trip times (RTTs), MPTCP may dispatch the ACK packet through a longer path, which is inefficient. HayACK solves this issue by selecting the path with the least RTTs. However, HayACK may suffer from inconsistent packets because of middleboxes, which are intermediary devices between the source and the destination that process network packets. These middleboxes may discard MPTCP packets because of inconsistent sequence numbers of HayACK packets, which is a product of HayACK's path selection. This study aims to investigate the usefulness of HayACK for high-performance data transfer when using multiple paths in real network environments and the influence of middleboxes on HayACK. Our investigation was conducted using various network paths by preparing many client hosts with different IP addresses and one server. The influence of middleboxes is determined by checking the probe packets passed through, changed, or discarded. Assuming the HayACK response, we crafted packets such as TCP SYN packets and TCP ACK packets. © Copyright owned by the author(s) under the terms of the Creative Commons

关键词： Network performance

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Testing Facility Location with Constrained Queue Time Problem: A Case Study in Florida, USA 24

Testing Facility Location with Constrained Queue Time Proble...

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Proceedings of the Winter Simulation Conference

作者： Almir Antonio Monteiro Junior Rafael Schneider Vanessa de Almeida Guimarães Pedro Henrique González System Engineering and Computer Science Program Federal University of Rio de Janeiro BRAZIL COPPEAD Graduate School of Business Federal University of Rio de Janeiro BRAZIL

ISBN: (纸本)9798331534202

This study addresses the Testing Facility Location with Constrained Queue Time Problem. This optimization problem focuses on determining the best places to deploy testing sites and their available testers for infectious diseases, while constraining the maximum time in the queue with a given probability. An integer programming model is introduced and applied to the three biggest counties, in terms of population, of Florida, United States. Moreover, the Monte Carlo method is used to evaluate the model's output, aiming to check if the queueing time constraint is being satisfied. Through the experiments, a testing facility deployment plan can be determined for each county and further validated by the simulation. The results show that the solutions returned by the model behaved successfully when submitted to the Monte Carlo method, not exceeding the time in the queue in more than the predefined probability.

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Scattering Guided Image Despeckling : A Score-Based Diffusion Approach in Frequency Domain

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IEEE Geoscience and Remote Sensing Letters 2025年 22卷

作者： Dong, Sihao Xiao, Xiayang Pan, Mingjun Inner Mongolia University of Science and Technology School of Automation and Electrical Engineering Inner Mongolia Baotou014010 China School of Information Science and Technology Shanghai200433 China China Mobile Internet Company Limited Guangzhou510030 China Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen China

When it comes to deep learning-based methods for despeckling Synthetic Aperture Radar (SAR) images, maintaining spatial structure, ensuring process stability as well as achieving fast inference are still major challenges. To address these issues, a novel method named SGID (Scattering Guided Image Despeckling), which is based on the wavelet - transform - conditioned diffusion model for SAR despeckling, has been proposed. First, the core component of this approach is the Adaptive Scattering Extraction Module (ASEM), which is designed to preserve the spatial structure of images effectively. It achieves the preservation of spatial structure by leveraging SAR imaging principles to adaptively extract scattering points, thereby retaining critical spatial information. Second, considering that speckle noise in SAR images is mainly concentrated in the high-frequency region, this study has designed a High-Frequency Despeckling Module (HFDM). This module adopts a direction-aware adaptive weight allocation strategy, aiming to precisely regulate the weight allocation of high-frequency components in all directions during the processing, so as to maximize the coherence and consistency of the high-frequency detail structures in the images. At last, the entire processing workflow is conducted in the frequency domain, which contributes significantly to the improvement of inference speed. Experimental results demonstrate that the proposed method achieves state-of-the-art (SOTA) performance in both spatial structure preservation and despeckling quality. Besides, its inference speed is 0.19 seconds, which is 67 times faster than that of comparable diffusion model-based methods. The code for this study will be publicly available at https://***/SihaoDong/SGID. © 2004-2012 IEEE.

关键词： Speckle

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Rotational Regrasping For a Stick-type Object Using Inertial Force Based on High-speed Swing-up Motion

Rotational Regrasping For a Stick-type Object Using Inertial...

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IEEE International Conference on Robotics and Biomimetics

作者： Taku Senoo Atsushi Konno Hayato Otsubo Idaku Ishii Course of Systems Science and Informatic Graduate School of Information Science and Technology Hokkaido University Sapporo Hokkaido Japan Smart Innovation Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan

In this paper, robotic regrasping is considered with the goal of achieving dexterous manipulation. The strategy using quick wrist snap is based on human regrasping, and involves rotating the grasped object due to the inertial force while maintaining the contact state. The posture of the grasped object is measured by high-speed vision in real time and is used for posture control. Experimental results are shown where regrasping of a stick-type object in the direction contrary to gravity can be achieved by a high-speed robot hand.

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Energy Efficient Transmission Parameters Selection Method Using Reinforcement Learning in Distributed LoRa Networks

arXiv

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arXiv 2024年

作者： Airiyoshi, Ryotai Hasegawa, Mikio Ohtsuki, Tomoaki Li, Aohan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan Department of Electrical Engineering Tokyo University of Science Tokyo Japan Department of Information and Computer Science Keio University Yokohama Japan

With the increase in demand for Internet of Things (IoT) applications, the number of IoT devices has drastically grown, making spectrum resources seriously insufficient. Transmission collisions and retransmissions increase power consumption. Therefore, even in long-range (LoRa) networks, selecting appropriate transmission parameters, such as channel and transmission power, is essential to improve energy efficiency. However, due to the limited computational ability and memory, traditional transmission parameter selection methods for LoRa networks are challenging to implement on LoRa devices. To solve this problem, a distributed reinforcement learning-based channel and transmission power selection method is proposed, which can be implemented on the LoRa devices to improve energy efficiency in this paper. Specifically, the channel and transmission power selection problem in LoRa networks is first mapped to the multiarmed-bandit (MAB) problem. Then, an MAB-based method is introduced to solve the formulated transmission parameter selection problem based on the acknowledgment (ACK) packet and the power consumption for data transmission of the LoRa device. The performance of the proposed method is evaluated by the constructed actual LoRa network. Experimental results show that the proposed method performs better than fixed assignment, adaptive data rate low-complexity (ADR-Lite), and ϵ-greedybased methods in terms of both transmission success rate and energy efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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