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5G Channel Estimation Kernels on RISC-V Vector Digital Signal Processors

5G Channel Estimation Kernels on RISC-V Vector Digital Signa...

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2024 International Conference on Microelectronics, ICM 2024

作者： Acevedo, Javier Fitzek, Frank H. P. Seeling, Patrick Deutsche Telekom Chair of Communication Networks TU Dresden Germany Germany Central Michigan University Department of Computer Science United States

ISBN: (纸本)9798350379396

Each generation of the wireless communication networks has contiguous demands for higher computing capabilities and lower the power consumption in the integrated circuitry. Thus, the optimization of the base-band signal processing kernels and the hardware architectures plays a pivotal role in the performance, latency, and energy efficiency of the Physical Layer (PHY). Channel Estimation (CE), a computationally intensive task with hard real-time requirements, involves significant amounts of matrix multiplications and inversions, when data flows between transmitter (TX) and receiver (RX). In this work, we present a software implementation of CE for the 5th Generation Cellular networks (5G) New Radio (NR) in the Reduced Instruction Set computer (RISC)-V architecture, showcasing how its vector processing capabilities can satisfy the throughput and delay requirements of 5G use cases. Specifically, we ported two CE kernels, the Least-Squares (LSE) and Minimum Mean Square Error (MMSE), onto a state-of-the-art single core vector processor. By exploiting data vectorization, the ARA core computes matrix arithmetic operations on multiple data sets simultaneously. Our single vector core serial optimization demonstrates that with a 16× 16 matrix window size and 16 vector lanes, the application's performance achieves a speedup of 78.27 when computing the LSE algorithm. When the number of operations per cycle are compared, the vector processor outperforms its scalar counterpart by 181.91 for the MMSE kernel. Our implementation orders the nested loops in a latency-reducing fashion to compute matrix arithmetic operations, resulting in LSE and MMSE estimator computing times of 8.44 μiota s and 12.31 μs, respectively. © 2024 IEEE.

关键词： Mean square error

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Odometry-Less Indoor Dynamic Object Detection and Localization with Spatial Anticipation

Odometry-Less Indoor Dynamic Object Detection and Localizati...

引用

2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Shen, Zhengcheng Gao, Yi Kästner, Linh Lambrecht, Jens Berlin Institute of Technology Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Germany

ISBN: (纸本)9798350312072

The continually evolving image segmentation methods in computer vision can further broaden the cognitive abilities of the robot. As humans, we won't judge if the object is moving by accuracy speed estimation but through semantic information based on visual input. With the existing video segmentation method, the robot can pay more attention to the foreground objects, which are more important for path planning and navigation in most cases. The proposed methods try to deploy the first-person view segmentation method with an RGBD sensor and get the location information and locally egocentric map. The error of the segmentation methods will cause unexpected objects in the egocentric map, and the self-occlusion will influence the location and distance estimation. To further improve the methods, a spatial anticipate unit is embedded into the framework. We improve 5.6% the localization accuracy(RMSE), 12.9% the distance estimation(MAE) with the trained object, and 28.9% the recall for the trained object. The methods also show the margin on the untrained object dataset for similar objects. © 2024 IEEE.

关键词： Semantics

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FGSNet: A Finer-Grained Siamese Network for Industrial Few-Shot Anomaly Detection

FGSNet: A Finer-Grained Siamese Network for Industrial Few-S...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Han, DeLong Xu, Luo Zhou, MingLe Li, Gang Li, Min Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China City University of Macau Faculty of Data Science China

ISBN: (纸本)9781665410205

Image anomaly detection usually relies on a large set of training samples and abnormal samples are relatively rare and challenging to obtain in daily industrial scenarios. In the scenario of Few-Shot Anomaly Detection (FSAD), how to better utilize the fine-grained features of the few images is a key issue. To solve these problems, a Finer-Grained Siamese Network (FGSNet) is proposed in this paper. FGSNet considers the setting of Few-Shot Anomaly Detection (FSAD) and consists of two stages. The first stage is responsible for extracting finer-grained features and roughly aligning the image features, while incorporating an efficient Fine-grained Feature Fusion Module (F3M) to enhance feature representation. Meanwhile, we design a Deep-supervised Loss to improve the level of fine-grained information extraction. The second is primarily used to refine and denoise the fused features, followed by detailed alignment operations for subsequent feature distribution modeling. Compared to most existing FASD models that use single-class training and single-model evaluation, FGSNet is more suitable for existing industrial scenarios. Extensive experiments are conducted on the publicly available MVTec AD and MPDD datasets in this paper. The results show that, for 2, 4 and 8-shot cases, FGSNet can increase the average Image-AUROC on MVTec AD by 0.87%, 1.27% and 1.33% respectively, compared to RegAD. © 2024 IEEE.

关键词： Image enhancement

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Depicting Instant Messaging Encrypted Traffic Characteristics through an Empirical Study 32

Depicting Instant Messaging Encrypted Traffic Characteristic...

引用

32nd International Conference on computer Communications and networks, ICCCN 2023

作者： Erdenebaatar, Zolboo Alshammari, Riyad Nandy, Biswajit Seddigh, Nabil Elsayed, Marwa Zincir-Heywood, Nur Dalhousie University Faculty of Computer Science Halifax Canada Solana Networks Ottawa Canada

ISBN: (纸本)9798350336184

Instant Messaging Applications (IMAs), such as Discord and WhatsApp, have become one of the main communication tools for mobile device users. Network traffic analysis is a method of monitoring network activity to identify operational and security issues. There is limited research on network traffic analysis of IMAs on mobile devices due to the challenges of end-to-end encryption, user privacy, and dynamic port usage. In this paper, we design, develop and evaluate a framework to generate end-to-end IMA traffic on mobile devices, employ feature selection and conduct traffic analysis that can cope with encrypted traffic while identifying different IMAs. Results show a performance evaluation workbench as well as highlight the key characterictis of six popular IMAs. © 2023 IEEE.

关键词： Message passing

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Instant Messaging Application Encrypted Traffic Generation System 36

Instant Messaging Application Encrypted Traffic Generation S...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Erdenebaatar, Zolboo Nandy, Biswajit Seddigh, Nabil Alshammari, Riyad Elsayed, Marwa Zincir-Heywood, Nur Dalhousie University Faculty of Computer Science Halifax Canada Solana Networks Ottawa Canada

ISBN: (纸本)9781665477161

Instant Messaging Applications (IMAs) have become the leading communication tool for smartphone users. While it is insightful for network operators and security researchers to monitor and analyze the network traffic of their organization, there is a lack of research on IMA encrypted traffic analysis. In a companion work [1], we introduced a flow-based encrypted IMA traffic analysis using a data driven approach. Given the lack of publicly available data in this area, a new encrypted IMA traffic generation system is designed and implemented to automatically generate and label encrypted IMA traffic including Discord, Facebook Messenger, Signal, Microsoft Teams, Telegram, and WhatsApp. The new system utilizes a combination of open-source tools to emulate user behavior, to capture, filter and label the resulting traffic directly on an Android device. This demonstration shows the functionality of the proposed system via data generation, capture, and analysis of the six IMAs. © 2023 IEEE.

关键词： Machine learning

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Analyzing Traffic Characteristics of Instant Messaging Applications on Android Smartphones 36

Analyzing Traffic Characteristics of Instant Messaging Appli...

引用

36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Erdenebaatar, Zolboo Alshammari, Riyad Zincir-Heywood, Nur Elsayed, Marwa Nandy, Biswajit Seddigh, Nabil Dalhousie University Faculty of Computer Science Halifax Canada Solana Networks Ottawa Canada

ISBN: (纸本)9781665477161

Instant Messaging Applications (IMAs), such as WhatsApp and Messenger, have become one of the main communication tools for smartphone users. However, there is limited research analyzing the nature of encrypted network traffic produced by IMAs. In this paper, we employ a data driven approach using machine learning classification models to analyze and identify encrypted traffic from six different IMAs. Our results show that it is possible to distinguish the behaviour of different IMAs with high F1 scores. © 2023 IEEE.

关键词： Machine learning

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Developing Mask R-CNN Framework for Real-Time Object Detection 6

Developing Mask R-CNN Framework for Real-Time Object Detecti...

引用

6th International Conference on Advanced Communication Technologies and Networking, CommNet 2023

作者： Oussama, Hmidani Mehdi, Ismaili Alaoui El Moulay Ismail University Computer Networks and Systems Laboratory Faculty of Science Meknes Morocco

ISBN: (纸本)9798350329391

In the field of computer vision, achieving real-time object detection through deep learning holds significant importance. Notable strides have been made in real-time object detection methods, particularly due to the rapid progress of deep convolutional neural networks (CNNs) compared to traditional approaches. It has been observed that existing real-time deep CNN-based object detectors face performance limitations, primarily stemming from the architecture of the underlying base network. This study introduces an improved framework for real-time object detection based on the Mask R-CNN model. To address the challenge of enhancing performance under stricter localization criteria, we replace the original Mask R-CNN's Region of Interest Align (RoIAlign) with spatial interpolation. Additionally, in the final phase of the Mask R-CNN framework, we utilize the depthwise separable convolution architecture from EfficientNet-B7 to construct a classifier for proposal categorization and to adjust bounding boxes for detected objects. Experimental findings on both the COCO dataset and the ImageNet dataset demonstrate that our proposed approach surpasses the original Mask R-CNN in terms of detection accuracy and inference speed. Categorically, our method outperforms the original Mask R-CNN framework by 51.5% on the COCO test set and 46.2% on the ImageNet test set. © 2023 IEEE.

关键词： Object detection

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Covert Channel Detection and Generation Techniques: A Survey 3

Covert Channel Detection and Generation Techniques: A Survey

引用

3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Al-Khulaidi, Nesmah A. Zahary, Ammar T. Hazaa, Muneer A.S Nasser, Adel A. Yemen Academy for Graduate Studies Department of Computer Networks Sana'a Yemen University of Science and Technology Faculty of Computing and It Department of Computer Science Sana'a Yemen Thamar University Faculty of Computer and Information System Department of Computer Science Thamar Yemen Sa'adah University Faculty of Sciences Department of Information Systems and Computer Science Sa'adah Yemen

ISBN: (纸本)9798350305333

The use of covert communications has become more widespread recently as a solution to the information security issue. Information security can be partially solved by the discovery and creation of covert routes. Covert channel use is still in its early phases, even though information security concerns are growing as a result of quick technology advancements. As a result, several problems and concerns need to be resolved, and the future plan is foggy. This paper seeks to give a thorough comparison of earlier and more recent attempts in this field. It offers a survey of the literature on the drawbacks and gaps in conventional detection techniques, emphasizing the most recent techniques for discovering and creating these covert channels while carefully considering machine learning and deep learning-based detection techniques. Additionally, it broadens the topic to cover the crucial function that these channels play in technology. Timing channels, storage channels, and hybrid channels are just a few of the ways covert channels may be utilized to transfer data. They can be used to get around security measures like intrusion detection systems and firewalls. Additionally, they may be used to steal confidential data from a system, including passwords, credit card numbers, and intellectual property. System security is seriously threatened by covert channels, and more study on covert channel production and detection is required to stay up with the changing threat environment. © 2023 IEEE.

关键词： Intrusion detection

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A Cross-Modal Interactive Memory Network Based on Fine-Grained Medical Feature Extraction for Radiology Report Generation

A Cross-Modal Interactive Memory Network Based on Fine-Grain...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ma, Xitong Kuang, Yuansen Yuan, Lin Tian, Cheng Zeng, Yijie Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Radiology report generation is an essential task in the medical field, which aims to automate the generation of medical terminology descriptions of radiology images. However, this task currently suffers from several problems: 1) existing methods need to manually build knowledge graphs or templates (consuming time and effort) to introduce medical or prior knowledge to assist in report generation;2) previous models cannot handle the problem of data bias well (anomaly reports and anomaly descriptions make up only a tiny portion of the dataset), causing the models to ignore the learning of anomaly descriptions easily;3) existing approaches cannot robustly supervise the model, resulting in incomplete and inconsistent reports being generated. To address these issues, we propose a cross-modal interactive memory network based on fine-grained medical feature extraction. In our model, we design a cross-modal interactive memory network to automatically store and remember the required medical text knowledge and use this medical knowledge to help generate reports. Furthermore, we design an abnormal medical knowledge enhancement module to enhance the learning of abnormal fine-grained knowledge through the interaction of disease topics and their states to interact with text features. In addition, we design a cross-modal joint semantic loss unit to reduce semantic differences between different features and improve the visual representation ability of the model. We experimented and evaluated our model on MIMIC-CXR and IU-Xray datasets to compare with other baseline models. © 2024 IEEE.

关键词： Radiology

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An Entity Relation Extraction Framework Based on Large Language Model and Multi-Tasks Iterative Prompt Engineering

An Entity Relation Extraction Framework Based on Large Langu...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Geng, Haibin Shi, Chenglong Jiang, Xuesong Kong, Zan Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Document-level entity relation extraction is an important task in the field of natural language processing, which plays an important role in semantic understanding and knowledge graph construction. However, existing deep neural networks and graph neural networks models are limited by their performance and parameters number, which can not capture global semantics and have poor generalization ability. Furthermore, existing methods employing large language model for entity relation extraction do not establish good relationships among multi-tasks of entity relation extraction task, resulting in more information can not be effectively shared and transmitted between tasks. In addition, previous approaches can not effectively eliminate false entities and relationships. To solve these problems, we propose an entity relation extraction framework based on large language model and multi-tasks iterative prompt engineering. In our model, we design an iterative prompt engineering, which can better establish the relationship among multi-tasks, and ensure every task to obtain the optimal results. Moreover, we design semantic merging, group disambiguation and self-verification modules to eliminate the false entity relations and noise nodes. Additionally, we design summary prompts to provide sufficient global semantics for better text segmentation. Finally, we evaluated our model on wikiann, wikineural, ACE2005, CoNLL2003, CoNLL2004, and SciERC datasets and compared it with other baseline models. © 2024 IEEE.

关键词： Graph neural networks

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