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Proactive Transport with High Link Utilization Using Opportunistic Packets in Cloud Data Centers

IEEE Transactions on Mobile Computing 2025年

作者： Hu, Jinbin Huang, Jiawei Li, Zhaoyi Li, Yijun Rao, Shuying Jiang, Wenchao Chen, Kai Wang, Jianxin He, Tian Changsha University of Science and Technology School of Computer Science and Technology Changsha410114 China Central South University School of Computer Science and Engineering Changsha410083 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong University of Minnesota Department of Computer Science and Engineering MinneapolisMN55455 United States

To meet the stringent demanding low latency and high throughput of cloud datacenter applications, recent receiver-driven transport protocols transmit only one packet once receiving each credit packet from the receiver to achieve ultra-low queueing delay. However, the round-trip time variation and the highly dynamic background traffic significantly deteriorate the performance of receiver-driven transport protocols, resulting in under-utilized bandwidth. This paper designs a simple yet effective solution called RPO, which retains the advantages of receiver-driven transmission while efficiently utilizing the available bandwidth. Specifically, RPO rationally uses low-priority opportunistic packets to ensure high network utilization without increasing the queueing delay of high-priority normal packets. Furthermore, to tackle the queueing buildup due to line-rate transmission in the first RTT, we design a selective dropping mechanism called SDM to help the majority of small flows complete within only one RTT by prioritizing the first-RTT bursty packets over the packets triggered by grants. We implement RPO in Linux hosts with DPDK. The experimental results show that RPO significantly improves the network utilization by up to 35% over the state-of-the-art schemes, without introducing additional queueing delay. Moreover, RPO integrated with SDM reduces the AFCT of small flows by up to 45% compared with RPO integrated with Aeolus. © 2002-2012 IEEE.

关键词： Bandwidth

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A Review Analysis on Smart Blind Stick Using IoT 4

A Review Analysis on Smart Blind Stick Using IoT

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Asha, P.K. Shanmugha Priya, R.K. Nagaraj, D. Aswini, S. Wilson, G Preethi Vinoth Kumar, K. New Horizon College of Engineering Department of Chemistry Bengaluru India Jai Shriram Engineering College Avinashipalayam Department of Information Technology Tirupur India Dhanalakshmi College of Engineering Department of Cse Chennai India Bangalore Technological Institute Department of Cse Bengaluru India Jerusalem College of Engineering Department of Computer Science & Business Systems Chennai India New Horizon College of Engineering Department of Eee Bengaluru India

ISBN: (纸本)9798331523923

Millions of individuals worldwide struggle with blindness and other forms of visual impairment. 940 million individuals will suffer some degree of vision loss by 2015, according to the WHO (World Health Organization). 246 million people experience fleeting apparitions, while 39 million people are blind. Most people with bad vision are older than 50 and live in industrialized cultures. The main challenge that blind individuals encounter is mobility. They are cut off from their environment. Since they are oblivious to what is going on around them, they require assistance. Technological advancements have been crucial in increasing the mobility and freedom of those who are blind. One such innovation is the Smart Blind Stick, a high-tech assistance gadget made to improve the safety and navigation of the blind. The goal of this review article is to examine the many facets of Smart Blind Stick technology, including its functionality, benefits, drawbacks, and overall influence on users' life. This review aims to shed light on the current status of Smart Blind Stick technology and its potential future advances by reviewing existing literature and advancements. © 2025 IEEE.

关键词： Obstacle detectors

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Integrating Function and Connection: Two Perspectives to Telepresence Robot Design for Classroom 25

Integrating Function and Connection: Two Perspectives to Tel...

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2025 ACM International Conference on Supporting Group Work, GROUP Companion 2025

作者： Berhane, Roda Akiode, Deo Adejoke Joshi, Swapna Elmimouni, Houda School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Field Robotics Lab Northeastern University BostonMA United States Department of Computer Science University of Manitoba WinnipegMB Canada Information Systems and New Media University of Siegen Siegen Germany

ISBN: (纸本)9798400711879

This study explores robotic telepresence in classrooms, from the perspectives of both the remote students (operators) and their in-person classmates. Analyzing a subset of field study data from 35 participants (22 operators, 13 classmates), we identified differing priorities: operators of the robot emphasize the need for improved functional aspects like camera control, while co-present classmates value social presence, interaction and identification, advocating for features like personalization and effective communication channels. These insights underline the need for telepresence robots that balance functional efficacy with social integration to provide a holistic educational experience. This work, though incomplete, enriches Group and CSCW literature on telepresence robots to facilitate collaborative interactions by highlighting the distinct needs of remote and in-person students, proposing design recommendations for telepresence robots that are technically proficient and socially responsive. © 2025 Copyright held by the owner/author(s).

关键词： Students

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Comparison and assessment of machine learning approaches in manufacturing applications

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Industrial Artificial Intelligence 2025年第1期3卷 1-16页

作者： Ramesh, K. Indrajith, M. N. Prasanna, Y. S. Deshmukh, Sandip S. Parimi, Chandu Ray, Tathagata Department of Mechanical Engineering BITS Pilani Hyderabad India Birla Institute of Technology and Science-Pilani Hyderabad India Department of Civil Engineering BITS Pilani Hyderabad India Department of Computer Science and Information Systems BITS Pilani Hyderabad India

Machine learning (ML) is increasingly used in industry processes to advance digital technologies for Industry 4.0. This paper comprehensively reviews ML applications in manufacturing, covering supervised, unsupervised, and deep learning (DL) approaches across various industrial processes. The use of ML approaches in manufacturing process planning and control, fault identification/manufacturing/assembly, monitoring in the agricultural industry, quality control, and optimisation of logistics and robots are being investigated. Key highlights include an analysis of 70 primary studies, comparing recent trends in ML for manufacturing, and examining ML training concepts in learning factories. We also use ML techniques to assess the automotive manufacturing industry's architectures, models, and deployment challenges. Furthermore, these notions will be examined and applied to all possible approaches. The improvements in the scope of identification of the proper algorithm for the adequate set of applications will be examined further to ensure the smooth going of the process from training to the testing set.

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Direct Photolithography of WO_(x) Nanoparticles for High‑Resolution Non‑Emissive Displays

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Nano-Micro Letters 2025年第3期17卷 297-309页

作者： Chang Gu Guojian Yang Wenxuan Wang Aiyan Shi Wenjuan Fang Lei Qian Xiaofei Hu Ting Zhang Chaoyu Xiang Yu‑Mo Zhang Laboratory of Optoelectronic Information Technology and Devices Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201People’s Republic of China Hangzhou Bay Laboratory of Advanced Nano‑Optoelectronic Materials and Devices Qianwan Institute of CNITECHNingbo 315336People’s Republic of China Smart Materials for Architecture Research Lab Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314100People’s Republic of China University of Science and Technology of China Hefei 230026People’s Republic of China State Key Lab of Supramolecular Structure and Materials College of ChemistryJilin UniversityChangchun 130012People’s Republic of China

High-resolution non-emissive displays based on electrochromic tungsten oxides(WOx)are crucial for future near-eye virtual/augmented reality interactions,given their impressive attributes such as high environmental stability,ideal outdoor readability,and low energy ***,the limited intrinsic structure of inorganic materials has presented a significant challenge in achieving precise patterning/pixelation at the micron ***,we successfully developed the direct photolithography for WOx nanoparticles based on in situ photo-induced ligand *** strategy enabled us to achieve ultra-high resolution efficiently(line width<4μm,the best resolution for reported inorganic electrochromic materials).Additionally,the resulting device exhibited impressive electrochromic performance,such as fast response(<1 s at 0 V),high coloration efficiency(119.5 cm^(2) C^(−1)),good optical modulation(55.9%),and durability(>3600 cycles),as well as promising applications in electronic logos,pixelated displays,flexible electronics,*** success and advancements presented here are expected to inspire and accelerate research and development(R&D)in high-resolution non-emissive displays and other ultra-fine micro-electronics.

关键词： Electrochromic Direct photolithography WOx nanoparticles In situ photo-induced ligand exchange High-resolution displays

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Mask-guided Multi-scale Spatial-Spectral Transformer for Snapshot Compressive Imaging

Mask-guided Multi-scale Spatial-Spectral Transformer for Sna...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Heyuan Yin Jingxiang Yang Jia Liu Liang Xiao School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Key Laboratory of Analysis of Mathematical Theory and Modeling of Complex Systems The Ministry of Industry and Information Technology China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Effectively reconstructing 3D hyperspectral images (HSIs) from 2D measurements presents a significant challenge in Coded Aperture Snapshot Spectral Imaging (CASSI) systems. While recent transformers exhibit potential in HSI reconstruction, they often suffer from inadequate exploration of multi-scale spatial-spectral self-similarity, leading to mean effects and information loss. Additionally, these methods struggle with insufficient modeling of the degradation inherent in the compressive imaging process. To address these issues, we propose a novel Mask-guided Multi-scale Spatial-Spectral Transformer (MMSST). Specifically, we introduce a Degradation Aware Mask Attention (DAMA) module to incorporate degradation information of the compressive imaging process. Furthermore, MMSST leverages Local-Regional SpAtial attention (LRSA) and Global-Regional SpEctral attention (GRSE) to effectively exploit multi-scale self-similarity across spatial and spectral dimensions. Extensive experimental results demonstrate the effectiveness of our MMSST.

关键词： Degradation Three-dimensional displays Computational modeling Imaging Transformer cores Signal processing Transformers Speech processing Image reconstruction Hyperspectral imaging

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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Combining Instance Segmentation and Background Subtraction Models for Small Object Detection

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International Journal of Automation technology 2025年第3期19卷 237-247页

作者： Dhammatorn, Wisan Ito, Seiya Kaneko, Naoshi Sumi, Kazuhiko Graduate School of Science and Engineering Aoyama Gakuin University 5-10-1 Fuchinobe Chuo-ku Kanagawa Sagamihara252-5258 Japan Tokyo Japan Department of Information Systems and Multimedia Design School of Science and Technology for Future Life Tokyo Denki University Tokyo Japan Department of Integrated Information Technology College of Science and Engineering Aoyama Gakuin University Sagamihara Japan

Object detection is a fundamental problem in computer vision that has been extensively investigated over the past decades. Although deep neural networks (DNNs) improve object detection, they cannot effectively rec-ognize small objects. Detecting small objects remains challenging owing to several factors, such as low res-olution and scale variance. These challenges are par-ticularly evident in object detection using surveillance cameras, where small objects typically appear in clut-tered environments and at varying distances. In object detection using surveillance cameras, the static nature of the background has not been fully exploited in DNN-based object detection methods, although it can be an important cue for detection. In this study, we propose a simple yet effective method to enhance object detection in areas of small objects not detectable by state-of-the-art DNN-based instance segmentation methods. The proposed method extracts foreground regions using a background subtraction model and classifies them, thereby enabling the identification of small objects. In our experiments, we evaluate two real-world scenarios: detecting a person walking on campus and identifying vehicles in road-surveillance footage. The results show that our method improves the detection of small objects and performs better than baseline methods. © Fuji technology Press Ltd.

关键词： Vehicle detection

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An Energy Efficient Hybrid Communication Protocol for Large Area Wireless Sensor Networks

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Procedia Computer science 2025年 252卷 985-994页

作者： Shreyas Karthigeyan Sampark Bhol Akshat Jain Karthik Yadav Akash Sinha Padma Priya R School of Computer Science and Information Systems Vellore Institute of Technology Vellore Campus Vellore Tamil Nadu 632014 India School of Computer Science and Engineering Vellore Institute of Technology Vellore Campus Vellore Tamil Nadu 632014 India Department of Computer Science and Engineering Maulana Azad National Institute of Technology Bhopal Madhya Pradesh 462003 India

Energy conservation is an indispensable aspect of the protocols designed for Wireless Sensor Networks (WSNs). The communication protocols for WSN fall mainly under two categories: centralized and distributed. Centralized protocols are not feasible for real-world WSNs as the network can span across large areas where most of the nodes cannot directly communicate with the base station (BS). The routing issues in such networks can be addressed using multi-hop protocols. However, the process of BS guided clustering in such methods cannot be performed. Distributed clustering methods that minimize the intervention of BS in clustering can provide feasible solutions in such cases, but the energy expenditure is considerably high. This paper, hence, proposes a hybrid clustering based routing protocol for large WSN that employs certain aspects of centralized as well as that of distributed protocols. Experimental evaluation of the proposed protocol in terms of network lifetime gives promising results that clearly indicate the energy efficiency and consistency of the proposed work in different scenarios. The proposed work can prove beneficial in real-life scenarios where the deployed nodes are far away from the BS.

关键词： WSN Hybrid Cluster Multi-Hop Energy

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ImprovNet: Generating Controllable Musical Improvisations with Iterative Corruption Refinement

arXiv

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arXiv 2025年

作者： Bhandari, Keshav Chang, Sungkyun Lu, Tongyu Enus, Fareza R. Bradshaw, Louis B. Herremans, Dorien Colton, Simon Electronic Engineering and Computer Science Department Queen Mary University of London United Kingdom Information Systems Technology and Design Singapore University of Technology and Design Singapore

Deep learning has enabled remarkable advances in style transfer across various domains, offering new possibilities for creative content generation. However, in the realm of symbolic music, generating controllable and expressive performance-level style transfers for complete musical works remains challenging due to limited datasets, especially for genres such as jazz, and the lack of unified models that can handle multiple music generation tasks. This paper presents ImprovNet, a transformer-based architecture that generates expressive and controllable musical improvisations through a self-supervised corruption-refinement training strategy. ImprovNet unifies multiple capabilities within a single model: it can perform cross-genre and intra-genre improvisations, harmonize melodies with genre-specific styles, and execute short prompt continuation and infilling tasks. The model’s iterative generation framework allows users to control the degree of style transfer and structural similarity to the original composition. Objective and subjective evaluations demonstrate ImprovNet’s effectiveness in generating musically coherent improvisations while maintaining structural relationships with the original pieces. The model outperforms Anticipatory Music Transformer in short continuation and infilling tasks and successfully achieves recognizable genre conversion, with 79% of participants correctly identifying jazz-style improvisations. Our code and demo page can be found at https://***/keshavbhandari/improvnet. © 2025, CC BY.

关键词： Deep learning

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