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SIGVIC: Spatial Importance Guided Variable-Rate image Compression 48

SIGVIC: Spatial Importance Guided Variable-Rate Image Compre...

引用

48th ieee international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Liang, Jiaming Liu, Meiqin Yao, Chao Lin, Chunyu Zhao, Yao Beijing Jiaotong University Institute of Information Science Beijing100044 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

ISBN: (纸本)9781728163277

Variable-rate mechanism has improved the flexibility and efficiency of learning-based image compression that trains multiple models for different rate-distortion tradeoffs. One of the most common approaches for variable-rate is to channel- wisely or spatial-uniformly scale the internal features. However, the diversity of spatial importance is instructive for bit allocation of image compression. In this paper, we introduce a Spatial Importance Guided Variable-rate image Compression (SigVIC), in which a spatial gating unit (SGU) is designed for adaptively learning a spatial importance mask. Then, a spatial scaling network (SSN) takes the spatial importance mask to guide the feature scaling and bit allocation for variablerate. Moreover, to improve the quality of decoded image, Top-K shallow features are selected to refine the decoded features through a shallow feature fusion module (SFFM). Experiments show that our method outperforms other learning- based methods (whether variable-rate or not) and traditional codecs, with storage saving and high flexibility. © 2023 ieee.

关键词： image compression scale factor shallow feature spatial importance variable-rate

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Deep-Learning image Steganalysis based on Generalized Gaussian Distribution Features Clustering 8

Deep-Learning Image Steganalysis Based on Generalized Gaussi...

引用

8th international conference on Intelligent Computing and signal Processing, ICSP 2023

作者： Mo, Chengyu Liu, Fenlin Zhu, Ma Yang, Chunfang Zhengzhou Science and Technology Institute Henan Provincial Key Laboratory of Cyberspace Situational Awareness Zhengzhou China

ISBN: (纸本)9798350302455

The difference in the statistical distribution between the training images and the test images reduces the performance of the steganalysis model, and this sample mismatch phenomenon makes it difficult to improve the accuracy of the detection model. To solve this problem, this paper proposed a deep-learning image steganalysis method based on generalized Gaussian distribution feature clustering. Firstly, a generalized Gaussian distribution is used to fit the coefficients of the image and extract statistical distribution features;Then, the extracted features are clustered to obtain corresponding category labels to achieve sample pre-classification;Finally, different subclasses of samples are used to train the network to achieve more reliable steganalysis. Experimental results show that this scheme can effectively divide images with similar statistical distributions into the same subcategory, reduce the impact of inconsistent statistical characteristics between the training image and the detection image on the steganalysis network, and improve the accuracy of steganalysis. © 2023 ieee.

关键词： image enhancement

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Deep Learning based Channel Estimation for Full-Duplex Backscatter Communication systems 5

Deep Learning based Channel Estimation for Full-Duplex Backs...

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5th international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Jung, Chae Yoon Kang, Jae Mo Kim, Dong In Sungkyunkwan Univ SKKU Dept Elect & Comp Engn Dept Superintelligence Engn Suwon South Korea Kyungpook Natl Univ Dept Artificial Intelligence Daegu South Korea

ISBN: (纸本)9781665456456

A novel deep learning (DL) based channel estimation method is proposed for full-duplex backscatter communication systems to realize the wireless-powered sensor networks (WPSN) for internet of things (IoT). We aim to minimize the power consumption at a sensor node by reflecting the supplied power signal from an access point (AP), which is called backscatter communication. Moreover, by adopting the frequency-shifted modulation technique during backscatter transmission, full-duplex communication is performed between the AP and the sensor node. However, this incurs a problem that the uplink and downlink channels are cascaded, which results in degrading the performance of beamforming. In order to overcome this problem, we propose a novel channel estimation method that extracts separate uplink and downlink channels from the cascaded channels. We formulate the problem for joint channel estimation and pilot optimization, and then design the DL based channel estimator, which is composed of feedforward neural network(FNN) and convolutional neural network(CNN), for compensating non-linearity and non-convexity. Finally, we analyze the performance of the proposed DL based channel estimator compared to the conventional channel estimator.

关键词： Beamforming channel estimation (CE) deep learning (DL) full-duplex backscatter communication internet of things (IoT) wireless-powered sensor networks (WPSN)

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Principal-Minimum Eigenvalue Algorithm for signal Sensing 23

Principal-Minimum Eigenvalue Algorithm for Signal Sensing

引用

23rd ieee international conference on Communication technology, ICCT 2023

作者： Zhong, Yumin Li, Yanhua Beijing Research Institute of Telemetry Beijing China

ISBN: (纸本)9798350325959

This paper proposes a signal sensing method based on principal components to minimum eigenvalue of the received signal. The signal model and theoretical performance analysis of the principal-minimum eigenvalue (PME) algorithm are provided in detail. A major advantage of the PME algorithm is that it does not need any prior information of noise or signal, making it highly promising in scenarios with uncertain noise power. To evaluate the performance of PME algorithm, simulations are made and compared with state-of-the-art algorithms, including energy detection (ED) algorithm, energy to minimum eigenvalue (EME) algorithm, maximum-minimum eigenvalue (MME) algorithm and arithmetic to geometric mean (AGM) algorithm. Simulation results show that the PME algorithm has superior performance under very low signal-noise-ratio (SNR) signals, the performance of PME algorithm is close to the MME algorithm, and has advantages over other methods. © 2023 ieee.

关键词： communications blackout low SNR signal sensing multi-antenna receiver principal-minimum eigenvalue (PME)

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Energy Efficient image Transmission Over Semantic Communication Enabled UAV Swarm Networks 16

Energy Efficient Image Transmission Over Semantic Communicat...

引用

16th international conference on Wireless Communications and signal Processing, WCSP 2024

作者： Feng, Yixiao Wang, Yining Zhang, Bo Dai, Huadong Academy of Military Science Beijing100091 China Advanced Institute of Big Data Beijing100089 China

ISBN: (纸本)9798350390643

In this paper, a semantic communication framework is proposed for unmanned aerial vehicle (UAV) swarm formation flight. In the studied model, a leader extracts semantic information from the aerial image and transmits it to each follower using limited transmission power. The borders of obstacles are referred to semantic information of the aerial image. based on the received semantic information, each follower reconstructs semantic maps for trajectory planning so as to maintain formation and avoid obstacles. We use the metric of Intersection over Union (IoU) to evaluate the effectiveness of the proposed semantic communication network. The problem is formulated as an optimization problem whose goal is to maximize the sum of IoUs of all followers. To solve this problem, a semantic information selection and power allocation (SISPA) algorithm based on reinforcement learning (RL) framework is proposed. The proposed SISPA algorithm enables the leader to evaluate the importance of semantic information, and, thus efficiently allocating transmission power and selecting important semantic information to improve the sum of IoUs. Simulation results demonstrate that, compared with the traditional wireless communication networks without semantic communication techniques, the proposed approach yields a 17.4% improvement in terms of the sum of IoUs. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Vehicle Accident Detection and Prevention using IoT and Deep Learning

Vehicle Accident Detection and Prevention using IoT and Deep...

引用

2022 ieee international conference on signal Processing, Informatics, Communication and Energy systems, SPICES 2022

作者： Lakshmy, S. Gopan, Renjith Meenakshi, M.L. Adithya, V. Elizabeth, Mariya R Mar Baselios College of Engineering and Technology Department of Electronics and Communication Trivandrum India

ISBN: (纸本)9781665449403

Road accidents have become an issue of major concern to the people. This paper presents an accident prevention mechanism developed through alcohol detection using an MQ3 alcohol sensor followed by automatic engine locking. The detection part uses an SW-420 vibration sensor to detect any sort of abnormal vibrations that may occur from a collision. This is accompanied by supervised deep learning CNN algorithms. The accident scene image is captured using a front camera built in the vehicle to be used by the deep learning model for accident prediction. Accident detection is followed by communication to the nearest emergency center using GPS and GSM modules. © 2022 ieee.

关键词： internet of things

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Enhancing Urban Accessibility: Comparative Analysis of Intelligent Traffic signal systems in Major Global Cities

Enhancing Urban Accessibility: Comparative Analysis of Intel...

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2024 international conference on Advanced Technologies for Communications, ATC 2024

作者： Hsiao, Min-Kai Chen, Chien-Min School for Creative Studies Quanzhou University of Information Engineering 249 Bodong Road Fengze District Quanzhou China Software Institute Quanzhou University of Information Engineering 249 Bodong Road Fengze District Quanzhou China International School of Finance Fundan University 220 Handan Road Shanghai China

ISBN: (纸本)9798350353983

As urbanization intensifies, cities worldwide are facing more complex traffic management challenges. The advent of Intelligent Transportation systems (ITS) and internet of Things (IoT) technology offers unparalleled opportunities to improve urban traffic light systems' effectiveness and availability. This paper compares smart traffic signal reforms in Tokyo, Copenhagen, Stockholm, San Francisco, and Hamburg, analyzing their effects on urban accessibility. The study examines three primary perspectives. First, it assesses the influence of intelligent traffic systems on the accessibility of special groups, such as disabled and socially disadvantaged populations, by making precise and responsive adjustments to traffic signals. Second, it investigates enhancements in smart signal control within multi-modal transportation systems, focusing on how algorithms and real-time data processing improve coordination and efficiency. Lastly, it evaluates the overall effectiveness of real-time traffic management implementation. © 2024 ieee.

关键词： Traffic signals

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Intelligent Speech Enhancement Algorithm Model based on Recorded Classroom Audio

Intelligent Speech Enhancement Algorithm Model Based on Reco...

引用

2024 ieee international conference on Cognitive Computing and Complex Data

作者： Zhang, Xinyun Shen, Li Zhou, Li Luo, Dawei Dejiangmo Luo, Xiaonan Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin Peoples R China Gannan Normal Univ Sch Phys & Elect Informat Ganzhou Peoples R China Northwest Minzu Univ Sch Math & Comp Sci Lanzhou Peoples R China

ISBN: (纸本)9798350352900;9798350352894

With advancements in communication technology, there is increasing demand for high-quality voice communication and accurate speech *** the complex acoustic environment of a recording classroom, noise, reverberation and other interferences can seriously affect the quality and intelligibility of the picked-up target speech signal, which in turn affects the effectiveness of the subsequent speech communication and recognition, so it is crucial to enhance the target speech signal in order to satisfy people's demand for clear and recognizable speech information. In this paper, based on the network model of Deep Complex Convolutional Recurrent Network (DCCRN), we introduce the F-T-LSTM structure to more accurately describe the correlation between the time and frequency domains of speech, and at the same time, we integrate the channel attention mechanism and the spatial attention mechanism to carry out a more comprehensive feature extraction of the speech features in the complex domain, and we skillfully concentrate the computational power in the area with the most feature-rich information, so that the quality and intelligibility of speech signals under the complex domain are significantly improved. significantly improve the overall performance of speech enhancement networks in the complex domain.

关键词： Intelligent Speech Enhancement F-T-LSTM DCCRN channel attention spatial attention

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Multi-Objectives based Reliable Routing Algorithm for IoT Health Care

Multi-Objectives Based Reliable Routing Algorithm for IoT He...

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2023 international conference on Smart systems for Applications in Electrical Sciences, ICSSES 2023

作者： Raghavendra, M. Sunitha, N.R. Siddaganga Institute of Technology Department of Cse Tumkur India

ISBN: (纸本)9798350347296

Efficient routing techniques play an important role in IoT healthcare. Existing Multi-objectives based routing is done based on optimal route delay, energy, distance, overhead, QOS, trust and security. Since the speed and Received signal Strength (RSSI) are very important parameters in the critical IoT health care, routes will have major impact. The proposed approach considers these two additional parameters, which uses clustering technique to organize the IoT network. The nodes are grouped together to from a cluster and cluster head is selected. The data will be sent to the cluster head. The next forwarding cluster head is selected based on above said parameters. and data will finally reach the Gateway. The simulation results shows that proposed algorithm - Multi-Objectives based Reliable Routing Algorithm (MOBRRA) for IoT Health Care, performs better compared to the existing algorithm w.r.t. the energy, delivery probability, latency, network load, network life time and throughput. © 2023 ieee.

关键词： internet of things

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image Security Barrier (ISB): Hide valuable information in image using machine learning 10

Image Security Barrier (ISB): Hide valuable information in i...

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10th international conference on Emerging Trends in Engineering and technology - signal and Information Processing, ICETET-SIP-22 2022

作者： Maske, Pranay Pardhi, Kunal Hatzade, Shubham Sharma, Rakesh Wazalwar, Sampada Center of Excellence Information Security G H Raisoni College of Engineering Nagpur India

ISBN: (纸本)9781665467414

People have a addictions of sharing their daily activities on story, posts, chatting or snap in form of an image on social media. Sometimes image content information includes the date of birth, addresses, phone no, Aadhar no, PAN no, or QR *** someone shares too much information, then hackers can harvest this information and use it for illegal purposes and like government and Businesses secret information leak, due to lack of security or maliciously, causes big costs in money, a distinction of business, and national security. In this paper, we present software/tool for hiding such kind sensitive/private data of users while shearing on the world of internet. ISB is a software/tool that creates a barrier between hackers and public it recognizes our sensitive information from image/document and hide or encrypt them. An ISB provides secured image with 95.5% of accuracy The final output is completely secure for sharing anywhere on internet/social media platforms. © 2022 ieee.

关键词： image recognition Computer hacking Social networking (online) Machine learning Media Market research internet

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