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Hybrid-Field Channel Estimation for Massive MIMO Systems based on OMP Cascaded Convolutional Autoencoder

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Hybrid-Field Channel Estimation for Massive MIMO Systems bas...

IEEE Conference on Vehicular technology (VTC)

作者： Hasan Nayir Erhan Karakoca Ali Görçin Khalid Qaraqe Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Department of Electronics and Communication Engineering Istanbul Technical University İstanbul Turkey Communications and Signal Processing Research (HİSAR) Lab. TÜBİTAK BİLGEM Kocaeli Turkey Department of Electronics and Communication Engineering Yildiz Technical University İstanbul Turkey

ISBN: (纸本)9781665454698

Frequency scarcity implies the utilization of higher frequencies for wireless communications; however, spreading loss becomes a dominating issue as the frequency increases to the level of and beyond millimeter waves. To this end, massive multiple-input multiple-output structures introduce mitigation alternatives. However, to make these solutions possible, the channel estimation approach strives to be modified: since Rayleigh distance is very short for conventional systems, the only far-field channel is examined in that context. On the other hand, the implementation of massive antenna arrays in high frequencies increases Rayleigh distance; thus, both near-field and far-field analyses become necessary. Instead of a dual estimation process, it would be effective and efficient to develop hybrid-field channel estimation techniques. Therefore, in this study, a new channel estimation method which is based on convolutional autoencoder (CAE) and orthogonal matching pursuit (OMP) approach, is proposed for hybrid channel estimation. The results indicate that the proposed OMP-CAE method has much better error performance when compared to the conventional OMP algorithm, especially at low signal-to-noise ratio regimes.

关键词： Wireless communication Vehicular and wireless technologies Convolution Simulation Channel estimation Matching pursuit algorithms Ray tracing

New Optimized Time Window Technique Based MFCC Feature Extraction for Text-Independent Speaker Verification

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SSRN

SSRN 2022年

作者： Yessad, Dalila Amrouche, Abderrahmane Faculty of Sciences and Technology Abdelhafid Boussouf University Mila43000 Algeria LASS Analysis of Signals and Systems Lab Faculty of Technology Mohamed Boudiaf University P.O. Box 166 Msila28000 Algeria LCPTS Speech Communication and Signal Processing Lab Faculty of Electronics and Computer Sciences USTHB P.O. Box 32 El Alia Bab Ezzouar Alger16111 Algeria

In this paper, we suggest a new method named Optimized Time Windowing (OTWin) technique aims to optimize features computation methods. We focus on the popular feature extractor Mel Frequency Cepstral Coefficients (MFCC) widely used in speech recognition and audio applications, in order to extract essential information from human voice which is by nature a nonstationary signal, to ensure its stationarity the audio signal is broken into short overlapping frames, windowed with a windowing function applied for each frame, to damp out the Gibbs phenomenon resulting from splitting signal. Our study investigate a new window technique OTWin as a step of MFCC feature extraction to improve speaker verification accuracy, versus MFCC using classical windows functions. The experimental results showed the effectiveness of our proposed method that achieved a high verification accuracy based novel OTWin Blackman window. This result confirm that MFCC based Efficient Optimized Time Windowing technique can be an alternative to the traditional MFCC for automatic speaker verification. © 2022, The Authors. All rights reserved.

关键词： Feature extraction

Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features

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arXiv 2024年

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is availab.e at this url. © 2024, CC BY.

关键词： Semantics

Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques

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arXiv 2024年

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Yang, Zhiyong Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is availab.e at this url. © 2024, CC BY.

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Design and Implementation of a Course Answering System Based on Error Correction and Trie Tree Language Model

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Design and Implementation of a Course Answering System Based...

作者： Danyang Li Yahui Zhao Mengjiang Luo Rongyi Cui Intelligent Information Processing Lab. Dept.of Computer Science & TechnologyYanbian University

With the rapid growth of network information,the accuracy of input text affects the retrieval results,text proofreading technology emerges as the times require in order to avoid the situation of "wrong answers" caused by wrong questions when searching for *** the course question answering system,we also need to consider the query speed and *** order to avoid the trouble of manual proofreading,this paper proposes a system which can automatically correct wrong questions in the professional field of ***,used the edit distance method for fuzzy matching of error strings;then,used trie tree language model to store data to improve query ***,compared the proofreading effect under different text similarity thresholds,and selected the best value for the *** experimental analysis and comparison,the best result is selected when the text similarity is 0.5,the accuracy rate is 77.91%,the recall rate is 67%,and the F value is 72.04%.Experiments show that the system designed in this paper can effectively correct the wrong text in the field of computer.

关键词： Design and Implementation of a Course Answering System Based on Error Correction and Trie Tree Language Model

VG-Swarm: A Vision-based Gene Regulation Network for UAVs Swarm Behavior Emergence

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arXiv 2022年

作者： Cai, Yuwei Li, Huanlin Hong, Juncao Xu, Peng Cheng, Hui Zhu, Xiaomin Hu, Bingliang Hao, Zhifeng Fan, Zhun Key Lab of Digital Signal and Image Processing of Guangdong Province College of Engineering University of Shantou Guangdong China School of Computer Science and Engineering University of Sun Yat-Sen Guangdong China College of Systems Engineering National University of Defense Technology Hunan Changsha China Xi’an Institute of Optics and Precision Mechanics Shanxi China

Unmanned Aerial Vehicles (UAVs) dynamic encirclement is an emerging field with great potential. Researchers often get inspiration from biological systems, either from macro-world like fish schools or bird flocks etc, or from micro-world like gene regulatory networks (GRN). However, most swarm control algorithms rely on centralized control, global information acquisition, and communications among neighboring agents. In this work, we propose a distributed swarm control method based purely on vision and GRN without any direct communications, in which swarm agents of e.g. UAVs can generate an entrapping pattern to encircle an escaping target of UAV based purely on their installed omnidirectional vision sensors. A finite-state-machine (FSM) describing the behavioral model of each drone is also designed so that a swarm of drones can accomplish searching and entrapping of the target collectively in an integrated way. We verify the effectiveness and efficiency of the proposed method in various simulation and real-world experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Drones

Automatic diagnosis of disc herniation based on DenseNet fusion model 8

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Automatic diagnosis of disc herniation based on DenseNet fus...

8th International Conference on Digital Home, ICDH 2020

作者： Wang, Zimin Qin, Junyan Huang, Jinrong Wang, Yumeng Li, Ji Guilin University of Electronic Technology Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Image and Graphics Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9781728192345

Spine MRI images generally have the characteristics of low contrast and much noise. Because the variable shape of the spine edge, the traditional spine image segmentation method requires a lot of preprocessing and can not perform image segmentation automatically, which is inefficient and cannot compliant the clinical work requirements. So as to solve the above problems, this paper use deep learning algorithms to identify the spine images, and proposed a disc herniation recognition method for spinal MRI images which based on DenseNet fusion model. Use DenseNet and Inception-Resnet-V2 model fusion to get more image features and improve the image recognition accuracy. Experiments prove that this method can be used to identify the herniation of spinal intervertebral disc with high accuracy and real-time. © 2020 IEEE.

关键词： Image segmentation

An Audio-Based Deep Learning Framework For BBC Television Programme Classification

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arXiv 2021年

作者： Pham, Lam Baume, Chris Kong, Qiuqiang Hussain, Tassadaq Wang, Wenwu Plumbley, Mark Center for Digital Safety & Security Austrian Institute of Technology Austria Center for Vision Speech and Signal Processing University of Surrey United Kingdom BBC Research and Development BBC United Kingdom ByteDance AI Lab. ByteDance United States

This paper proposes a deep learning framework for classification of BBC television programmes using audio. The audio is firstly transformed into spectrograms, which are fed into a pre-trained Convolutional Neural Network (CNN), obtaining predicted probabilities of sound events occurring in the audio recording. Statistics for the predicted probabilities and detected sound events are then calculated to extract discriminative features representing the television programmes. Finally, the embedded features extracted are fed into a classifier for classifying the programmes into different genres. Our experiments are conducted over a dataset of 6,160 programmes belonging to nine genres lab.lled by the BBC. We achieve an average classification accuracy of 93.7% over 14-fold cross validation. This demonstrates the efficacy of the proposed framework for the task of audio-based classification of television programmes. Copyright © 2021, The Authors. All rights reserved.

关键词： Support vector machines

An effective coaxiality measurement for twist drill based on line structured light sensor

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arXiv 2021年

作者： Cheng, Ailing Ye, Jiaojiao Yang, Fei Lu, Shufang Gao, Fei The Laboratory of Graphics & Image Processing College of Computer Science and Technology Zhejiang University of Technology Zhejiang Province Hangzhou310023 China The Research Center of Intelligent Computing Software Zhejiang Lab. Zhejiang Province Hangzhou311121 China

Aiming at the accurate and effective coaxiality measurement for twist drill with irregular surface, an optical measurement mechanism is proposed in this paper. First, A high-precision rotation instrument based on four core units is designed, which can obtain the 3-D point cloud data of full angle for the twist drill. Second, in the data processing stage, an improved robust Gaussian mixture model is established for accurate and rapid blade back segmentation. To improve measurement efficiency, a rapid reconstruction method of the twist drill axis based on orthogonal synthesis is provided to locate the axial position of the maximum deviation from the benchmark by utilizing the extracted blade back data. Finally, by calculating the maximum radial Euclidean distance from the benchmark, the coaxiality error of the twist drill is obtained. Comparing with other measurement methods, experimental results show that our proposed method is effective with high precision of 3 um and high efficiency of less than 3 s/pc. The result demonstrate that the proposed method is effective, robust and automatic, it can be applied in many actual industrial scene. © 2021, CC BY.

关键词： Drills