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arXiv 2024年

作者： Yuan, Xinyu Qiao, Yan Wei, Zhenchun Zhang, Zeyu Li, Minyue Zhao, Pei Hu, Rongyao Li, Wenjing School of Computer Science and Information Engineering Hefei University of Technology China Faculty of Information Technology Macau University of Science and Technology China State key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Although some methods proposed in recent years allowed estimating network traffic from partial flow-level or link-level measurements, they often perform poorly for traffic matrix estimation nowadays. Despite strong assumptions like low-rank structure and the prior distribution, existing techniques are usually task-specific and tend to be significantly worse as modern network communication is extremely complicated and dynamic. To address the dilemma, this paper proposed a diffusion-based traffic matrix analysis framework named Diffusion-TM, which leverages problem-agnostic diffusion to notably elevate the estimation performance in both traffic distribution and accuracy. The novel framework not only takes advantage of the powerful generative ability of diffusion models to produce realistic network traffic, but also leverages the denoising process to unbiasedly estimate all end-to-end traffic in a plug-and-play manner under theoretical guarantee. Moreover, taking into account that compiling an intact traffic dataset is usually infeasible, we also propose a two-stage training scheme to make our framework be insensitive to missing values in the dataset. With extensive experiments with real-world datasets, we illustrate the effectiveness of Diffusion-TM on several tasks. Moreover, the results also demonstrate that our method can obtain promising results even with 5% known values left in the datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Street traffic control

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Few-Shot Object Segmentation with a New Feature Aggregation Module

SSRN

引用

SSRN 2023年

作者： Liu, Kaijun Lyu, Shujing Shivakumara, Palaiahnakote Lu, Yue Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Faculty of Computer Science and Information Technology University of Malaya Malaysia

The success of convolutional neural network for object segmentation depends on a large amount of training data and high-quality samples. But annotating such high-quality training data for pixel-wise segmentation is labor-intensive. To reduce the massive labor work, few-shot learning has been introduced to segment objects, which uses a few samples for training without compromising the performance. However, the current few-shot models are biased towards the seen classes rather than being class-irrelevant due to lack of global context prior attention. Therefore, this study aims at proposing a few-shot object segmentation model with a new feature aggregation module. Specifically, the proposed work develops a detail-aware module to enhance the discrimination of details with diversified attributes. To enhance the semantics of each pixel, we propose a global attention module to aggregate detailed features containing semantic information. Furthermore, to improve the performance of the proposed model, the model uses support samples that represents class-specific prototype obtained by respective category prototype block. Next, the proposed model predicts label of each pixel of query sample by estimating the distance between the pixel and prototypes. Experiments on standard datasets demonstrate significance of the proposed model over SOTA in terms of segmentation with a few training samples. © 2023, The Authors. All rights reserved.

关键词： Pixels

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A Novel Circularly Polarized Filtering Reflectarray by Manipulating the Multimode Reflective Phase of Orthogonal Polarizations

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IEEE Transactions on Antennas and Propagation 2025年

作者： Fang, Chang Zhang, Xiao Li, Yin Zhu, Lei Wong, Sai-Wai Quan, Zhi China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Peng Cheng Laboratory Shenzhen518038 China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering Macau China

This paper presents a novel design methodology for circularly polarized (CP) filtering reflectarray (RA). Unlike conventional filtering techniques based on magnitude response, the proposed filtering RA solely manipulates the phase responses of the reflected wave under the total reflection condition. An isosceles triangle patch radiator loaded with slots is employed as the unit cell. Two sets of orthogonal resonant modes of the triangular patch are exploited to produce a phase difference between the u- and v-polarized components. By adopting slot loading, first- and third-order resonant states are induced in the two orthogonal directions. Next, the phase difference is fully controlled by adjusting the slope of the reflection phase. In the passband, the phase difference is maintained at 180°, while outside the band, it rapidly rolls down (up) to 0° (360°), enabling steep band edges. Finally, a CP filtering RA using the optimized element is constructed for the first time, which verifies the validity of the proposed method. The fabricated RA exhibits satisfactory performance, including a 3-dB gain bandwidth of 25.7%, 3-dB axial ratio (AR) bandwidth of 29.8%, and the out-of-band suppression levels of 33.5 dB (22.8 dB) in the low (high) frequency band. © 1963-2012 IEEE.

关键词： Circular polarization

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Wideband Reconfigurable Instantaneous Microwave Multi-Frequency Measurement System Without Mirror Image Blurring Based on an Optical Frequency Shifter and Optical Frequency Comb

SSRN

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SSRN 2024年

作者： Zhang, Jiahong Ji, Qian Zhang, Jing Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Kunming650500 China

In this paper, a wideband reconfigurable instantaneous microwave multi-frequency measurement system without mirror image blurring has been proposed, designed and investigated. In this proposed scheme, an optical frequency shift is employed to shift the optical carrier of the input dual-parallel Mach-Zehnder modulator (DPMZM), aligning it with the first comb line of the optical frequency comb. Simultaneously, the central frequencies of the two demultiplexers are designed to ensure that the optical signals output after coupling through a 90° optical hybrid coupler contain only one local oscillator optical carrier. Furthermore, the local oscillator optical carrier is positioned at the initial location of the channel. This configuration allows for the determination of an unknown signal with only one beat frequency, effectively resolving the issue of ambiguous mirror frequencies. Simulation experiments reveal that when the channel bandwidth is set to 2 GHz, the system frequency measurement range is 0.01-50 GHz, with an error of less than ±5 MHz. By adjusting the channel bandwidth and the spacing of the optical frequency comb, the system measurement range can be extended to 0.01-100 GHz, with a frequency measurement error of 5-14.6 MHz. This approach offers advantages such as a wide measurement range, reconfigurability, and low error. © 2024, The Authors. All rights reserved.

关键词： Mirrors

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Incentive-driven Federated Learning in Mobile Edge Networks

Incentive-driven Federated Learning in Mobile Edge Networks

引用

International Conference on Distributed Computing Systems Workshop

作者： Yanlang Zheng Huan Zhou Liang Zhao Shouzhi Xu Victor C. M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Federated Learning (FL) is proposed as a privacy-preserving distributed learning methodology that can better protect the privacy and reduce communication costs. To stimulate sufficient User Equipments (UEs) to participate in FL, proper incentives need to be designed for FL. Existing incentive mechanisms do not jointly consider UE selection and local learning accuracy optimization to reduce the training expenditure. This paper designs a reverse auction-based incentive mechanism for FL to minimize the training expenditure of Base Station (BS). To this end, we first propose a Greedy Winner Determination (GWD) algorithm to select UEs with the minimum bidding prices. Then, we incorporate the Particle Swarm Optimization (PSO)-based local learning accuracy optimization into UE selection to further reduce the training expenditure of BS. In addition, we design a Vickrey Clarke Groves (VCG)-based payment rule to determine the payment to each participating UE. The simulation experiments show that our proposed PSO with Winner Determination (PSOWD) algorithm is superior to other existing methods in different scenarios.

关键词：

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

引用

International Journal of computer vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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A New Simulation Tool for Sensor Networks Based on an Energy-Efficient and Fault-Tolerant Methodology 1

引用

17th International Conference on Software Technologies, ICSOFT 2022

作者： Rouainia, Hanene Grichi, Hanen Kahloul, Laid Khalgui, Mohamed Faculty of Sciences of Tunis El-Manar University Tunis Tunisia University of Carthage Bizerte Tunisia School of Electrical and Information Engineering Jinan University Zhuhai China LINFI Laboratory Computer Science Department Biskra University Biskra Algeria INSAT Institute University of Carthage Tunis Tunisia

ISBN: (数字)9783031372315

ISBN: (纸本)9783031372308

Recently, reconfigurable wireless sensor networks (RWSNs) have attracted a lot of attention in research and industrial communities. They became more complex and dynamic systems which led to the emergence of many challenges. The lack of energy, real-time constraints, and software and hardware failures are the most important challenges in RWSNs. Indeed, several solutions have proposed to come up with these challenges. To avoid huge costs in terms of money, time, and effort of real experimentation, networks’ simulation tools have become an essential necessity to study the impact of the proposed solutions. In this work, we propose a new energy-efficient and fault-tolerant methodology that composed of a set of solutions summarized in the use of mobile sink nodes (MSNs), application of the mobility, resizing, and test packet technique using a multi-agent architecture and an energy-efficient routing protocol. Moreover, we propose a new discrete-event simulation tool named RWSNSim designed for sensor networks (WSNs & RWSNs). We present its description, modeling, and provided services. The proposed simulation tool allows simulating sensor networks with and without application of the proposed methodology. Finally, we simulate a case study using RWSNSim in a 3D environment which proves the effectiveness of the proposed methodology and demonstrate the efficiency of the suggested simulation tool. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Discrete event simulation

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Wearable Fall Detection Based on Motion Signals Using Hybrid Deep Residual Neural Network 1

引用

15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Mueang Phayao Thailand Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

There have been several approaches for wearable fall detection devices during the last twenty years. The majority of technologies relied on machine learning. Although the given findings appear that the issue is practically addressed, critical problems remain about feature extraction and selection. In this research, the constraint of machine learning on feature extraction is addressed by including a hybrid convolutional operation in our proposed deep residual network, called the DeepFall model. The proposed network automatically generates high-level motion signal characteristics that can be utilized to track falls and everyday activities. FallAllD dataset, a publicly available standard dataset for fall detection that gathered motion signals of falls and other events, was utilized to analyze the proposed network. We performed investigations using a 5-fold cross-validation technique to determine overall accuracy and F-measure. The experimental outcomes show that the proposed DeepFall performs better accuracy (95.19%) and F-measure (92.79%) than the state-of-the-art baseline deep learning networks. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fall detection

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Recognizing Driver Activities Using Deep Learning Approaches Based on Smartphone Sensors 1

引用

15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

Human motion detection based on smartphone sensors has gained popularity for identifying everyday activities and enhancing situational awareness in pervasive and ubiquitous computing research. Modern machine learning and deep learning classifiers have been demonstrated on benchmark datasets to interpret people’s behaviors, including driving activities. While driving, driver behavior recognition may assist in activating accident detection. In this paper, we investigate driving behavior detection using deep learning techniques and smartphone sensors. We proposed the DriveNeXt classifier, which employs convolutional layers to extract spatial information and multi-branch aggregation transformation. This research evaluated the proposed model using a publicly available benchmark dataset that captures four activities: a driver entering/exiting and sitting/standing out of a vehicle. Classifier performance was evaluated using two common HAR indicators (accuracy and F1-score). The recommended DriveNeXt outperforms previous baseline deep learning models with the most fantastic accuracy of 96.95% and the highest F1-score of 96.82%, as shown by many investigations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smartphones

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Edge-aware Feature Aggregation Network for Polyp Segmentation

arXiv

引用

arXiv 2023年

作者： Zhou, Tao Zhang, Yizhe Chen, Geng Zhou, Yi Wu, Ye Fan, Deng-Ping PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering NPU Xi’an China School of Computer Science and Engineering Southeast University Nanjing China Computer Vision Lab ETH Zürich Zürich Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp boundaries, it is still a challenging task to achieve satisfactory segmentation performance with different scales and shapes. In this study, we present a novel Edge-aware Feature Aggregation Network (EFA-Net) for polyp segmentation, which can fully make use of cross-level and multi-scale features to enhance the performance of polyp segmentation. Specifically, we first present an Edge-aware Guidance Module (EGM) to combine the low-level features with the high-level features to learn an edge-enhanced feature, which is incorporated into each decoder unit using a layer-by-layer strategy. Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation. Further, a Cross-level Fusion Module (CFM) is proposed to effectively integrate the cross-level features, which can exploit the local and global contextual information. Finally, the outputs of CFMs are adaptively weighted by using the learned edge-aware feature, which are then used to produce multiple side-out segmentation maps. Experimental results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and effectiveness. Our implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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