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Overview of Automatic Modulation Recognition Methods

Overview of Automatic Modulation Recognition Methods

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), ieee international conference on

作者： Shaoxiong Xie Jun Ye School of Cyberspace Security Hainan University Key Laboratory of Internet Information Retrieval of Hainan Province Haikou China

Blind detection and identification processing such as communication signal detection, modulation identification, and individual radiation source identification under non-cooperative reception conditions is an important basis for radio spectrum monitoring and battlefield communication reconnaissance and confrontation. However, due to adverse factors such as strong interference and fast-changing electromagnetic environment during non-cooperative reception, complex and diverse communication signal systems, and inability to fully grasp signal prior information, blind detection and identification of communication signals are extremely challenging. This article comprehensively sorts out the modulation recognition algorithm. The DL-based AMR algorithm is systematically reviewed from the three levels of the data set, network model, and final performance. Finally, the existing problems, potential research directions, and conclusions of AMR are outlined.

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Visual malware classification and detection method based on convolutional neural network

Visual malware classification and detection method based on ...

引用

ieee international conference on Data Science in Cyberspace (DSC)

作者： Hua Huang Hua Sheng Xingwang Han Wenbin Zhang Zilin Zhao Information and Telecommunications Company State Grid Shandong Electric Power Company Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the rapid development of internet technology, leading to the evolution of malware variants, and malicious threats are also increasing. Therefore, rapid and accurate classification of malware becomes crucial. In response to this challenge, this paper proposes a malware classification method based on deep learning and visualization techniques. The method first extracts sample features, then converts malware binary samples into image format, thus transforming abstract binary data into images with visual information. Subsequently, color mapping technique is applied to convert the processed images into color images. Furthermore, to address imbalanced datasets, this paper introduces data augmentation methods to ensure more balanced and comprehensive training of the model across different sample categories. Finally, an improved convolutional neural network architecture is employed for the classification detection of malware families, achieving accurate classification and identification of different malware families through learning and analysis of sample features. To evaluate the effectiveness of the proposed method, experiments are conducted on the Google Code Jam (GCJ) benchmark dataset. The experimental results demonstrate an accuracy rate of 99.29% on the GCJ dataset. Additionally, we also compared the performance of the improved method before and after, and found that the proposed method can provide more comprehensive feature representation and significantly improve classification accuracy and detection efficiency.

关键词： Deep learning Training Visualization Accuracy Color Static analysis Feature extraction Malware internet Convolutional neural networks

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Mental Disorder Detection using Non-Orthogonal Adaptive Constrained IVABMGGMM

Mental Disorder Detection using Non-Orthogonal Adaptive Cons...

引用

international conference on Advanced Technologies for signal and image Processing (ATSIP)

作者： Ali Algumaei Muhammad Azam Nizar Bouguila Concordia Institute for Information Systems Engineering Concordia University Quebec Montreal Canada School of Computer Science and Technology Algoma University Sault Ste. Marie Ontario Canada

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

The constrained independent component analysis (cICA) algorithm has proven remarkable effectiveness in integrating prior information into the framework of independent component analysis (ICA). However, the necessity of orthogonality for decoupling the constraints on individual sources restricts the optimization space and consequently the separation performance of cICA algorithms. To overcome this limitation, we propose a decoupling method called non-orthogonal adaptive constrained independent vector analysis with a bounded multivariate generalized Gaussian mixture model (non-orthogonal acIVABMGGMM), designed to address the drawbacks of independent component analysis in modeling multivariate data. This structure has the capability to incorporate prior knowledge regarding the sources or the mixing coefficients into the cost function of IVA. A robust decoupling strategy is needed to enable this integration. We also identify the brain regions impacted by Alzheimer’s disease by using the extracted components from our non-orthogonal acIVABMGGMM model. We demonstrate the superior performance of non-orthogonal acIVABMGGMM over the basic IVA model through experiments conducted on both simulated and real medical imaging data.

关键词： Adaptation models Analytical models Mental disorders image processing Independent component analysis Brain modeling Vectors

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Real-Time Wildfire Detection Using ResNet50V2

Real-Time Wildfire Detection Using ResNet50V2

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Innovations in High Speed Communication and signal Processing (IHCSP), international conference on

作者： Vijay Madaan Neha Sharma R Archana Reddy Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

This research provides a convolutional neural network (CNN) model for feature recognition between real and bogus ones. The model was trained across ten epochs with a batch size of 64 and a learning rate of 0.001. The design includes many convolutional layers after pooling layers and activation functions (ReLU) to aid in feature extraction. Flexibility of the model was improved by use of data augmentation methodologies. The training approach includes 2,278 photographs for validation and a set of 6,799 images for training. On the validation set, the model demonstrated to be 98.5% accurate, thereby proving its capacity to distinguish between true and spurious attributes. The end loss evaluated at 0.05 reflects a well-fitted model since the loss throughout training fell considerably. These results illustrate how successfully CNNs might manage face classification.

关键词： Training Technological innovation Accuracy Wildfires Transfer learning Data augmentation Real-time systems Convolutional neural networks image preprocessing image classification

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Blind Detection of Communication signals based on Improved YOLO3 6

Blind Detection of Communication Signals Based on Improved Y...

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6th ieee international conference on Intelligent Computing and signal Processing, ICSP 2021

作者： Li, Rundong Hu, Jianhao Li, Shaoqian Chen, Shaohe He, Peng University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications Chengdu China Southwest Electronics and Telecommunication Technology Research Institute Chengdu China

ISBN: (纸本)9780738143705

Blind detection of communication signals is a challenging task. In this paper, a general and novel blind detection method is proposed based on the similarity between communication signal detection and image object detection. We designed an improved YOLO3 model to detect the communication signals contained in the 2D wide-band spectrograms, the main innovates are as follows: 1) in order to reduce the burden of spectrograms labeling, an ingenious and automatic signal object labeling method is proposed;2) in view of the fact that the communication signals are long and narrow objects in the spectrograms, the corresponding prior anchors are designed to improve the detection probability;3) in order to improve the training efficiency and detection accuracy, the CIOU cost function and DIOU-NMS inference algorithm are introduced to achieve high-precision signal detection. The simulation results demonstrate that the proposed method can effectively detect the continuous and burst signals in wide-band communication signal data, and its performance is better than the traditional energy detection method. © 2021 ieee.

关键词： signal detection

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The Watchlist Imbalance Effect in Biometric Face Identification: Comparing Theoretical Estimates and Empiric Measurements 18

The Watchlist Imbalance Effect in Biometric Face Identificat...

引用

ieee/CVF international conference on Computer Vision (ICCVW)

作者： Drozdowski, Pawel Rathgeb, Christian Busch, Christoph Hsch Darmstadt Da Sec Biometr & Internet Secur Res Grp Darmstadt Germany

ISBN: (纸本)9781665401913

Recently, different research groups have found that the gallery composition of a face database can induce performance differentials to facial identification systems in which a probe image is compared against up to all stored reference images to reach a biometric decision. This negative effect has been referred to as "watchlist imbalance effect" by the researchers and exhibits high relevance in real applications of biometrics, most prominently in identification searches against criminal databases and blacklists. In this work, we conduct a detailed analysis of said effect. In particular, we compare empiric observations with theoretical estimates, based on the verification performance across demographic groups and the composition of the used gallery. The experimental evaluations are conducted by systematically varying the size and demographic composition of a cleaned subset of the academic MORPH database and utilising the state-of-the-art open-source ArcFace face recognition system.

关键词： Computer vision Databases Face recognition conferences Probes Open source software

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A Low-Voltage Wide Swing image Sensor with Simultaneous Energy Harvesting and Imaging Modes

A Low-Voltage Wide Swing Image Sensor with Simultaneous Ener...

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ieee international conference on Electronics, Circuits and systems (ICECS)

作者： Zhipeng Li Jian Guan Haoning Sun Yuqi Lin Wenji Mo Jingjing Liu School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China Fu Foundation School of Engineering and Applied Science Columbia University New York U.S.A

This paper proposes a CMOS image sensor that can achieves imaging and energy harvesting simultaneously without introducing additional P-N junctions in the pirel array. The proposed pixel utilizes the vertical N+P-well/DNW/P-sub structures as photodiodes based on a standard 180 nm CMOS mixed-signal process. The N+P-well is used for imaging, while the P-well/DNW and DNW/P-sub are used for energy harvesting with shorting P-well and P-sub together. Moreover, the traditional 4 T pirel has been improved by using CMOS pairs as the switches and zero-threshold NMOS as the source follower. The rail-to-rail pixel output swing can be achieved. Simulation results show that the dynamic range is increased by 13.4 dB compared with the traditional 4 T pirel. Single pixel occupies an area of $11 \times 13~\mathrm{mm}^image$ with a fill factor of 72%. An image sensor with 32 × 32 proposed pixel array and a dual-channel PWM quantizer is designed. Simulation results show that the average power consumption of the image sensor is approximately 6.7$\mu$W@2MHz.

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Design of Echo Information Processing Platform based on VPX Bus

Design of Echo Information Processing Platform Based on VPX ...

引用

2021 CIE international conference on Radar, Radar 2021

作者： Xiao, Wenguang Jin, Dongyong School of the Internet of Things Technology Wuxi Vocational Institute of Commerce Wuxi China East China Research Institute of Electronic Engineering Hefei China

ISBN: (纸本)9781665498142

The echo information processing platform is an important part of the phased array radar system. With the increasing demands of the airborne phased array radar mission on bus transmission bandwidth, computational processing speed, system response time, and data processing capability, the traditional echo information processing platform has been difficult to meet the requirements of technological development. based on the advantages of high transmission bandwidth, rich I/O resources and strong scalability of VPX bus, this paper proposes a design scheme of echo information processing platform based on VPX bus, focusing on the composition of information processing platform and the design principle of each functional module. based on the idea of dataflow driving, a new peer-to-peer backplane architecture is adopted. The test results show that the information processing platform only needs 20.2s to complete 32K∗32K complex image processing. The performance is superior, stable and reliable. © 2021 ieee.

关键词： dataflow driven signal processing VPX bus

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Optimizing Multibit Spread Spectrum Audio Watermarking for internet of Things 12

Optimizing Multibit Spread Spectrum Audio Watermarking for I...

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12th international conference on ICT Convergence (ICTC) - Beyond the Pandemic Era with ICT Convergence Innovation

作者： Alief, Revin Naufal Lee, Jae Min Kim, Dong-Seong Kumoh Natl Inst Technol Dept IT Convergence Engn Gumi South Korea

ISBN: (纸本)9781665423830

The development of internet has lead the technology to internet of Things (IoT). As the internet of Things is used for sending many types of data through many interconnected devices, the risk of stealing the data owner's right has been increased. But, even though this risk comes up, the high demand for sending data through IoT is still high. Thus, watermarking schemes is used for preventing this problem. Watermarking can be used for protecting the owner's rights in the form of a digital image, and this will be useful to be implemented in IoT. In this paper, an enhancement of Spread Spectrum (SS) based audio watermarking system against MP3 Compression attacks using Genetic Algorithm process is proposed. The main idea of the system will be using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) as the pre-processing method and then using Multibit Spread Spectrum to embed the watermark. The simulation results show that the Genetic Algorithm could optimize between imperceptibility and robustness resulting a good result against the MP3 Compression attack.

关键词： Genetic Algorithm Spread Spectrum Water-marking

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image aesthetic assessment based on emotion-assisted multi-task learning network 21

Image aesthetic assessment based on emotion-assisted multi-t...

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6th international conference on Multimedia systems and signal Processing, ICMSSP 2021

作者： Chen, Yunlong Pu, Yuanyuan Zhao, Zhengpeng Xu, Dan Man Qian, Wenhua School of Information Yunnan University China School of Information Yunnan University Key Laboratory of Technology and Application of Internet of Things in Colleges Universities of Yunnan Province China

ISBN: (纸本)9781450390378

image emotion recognition and image aesthetic assessment are recent research hotspots in user perception of image content. However, for the study of image aesthetics and image emotion, the vast majority of studies are separated from the relationship between the two, respectively. But in fact, there is a potential and explicit connection between image aesthetics and image emotion. In this paper, we use a multi-task convolutional neural network. To achieve the purpose of this experiment, for the first time, we propose a strictly labeled dataset, which contains multi-person annotated aesthetic scores and eight types of emotion distributions. With the help of this dataset, on the basis of aesthetic assessment based on scene and object branch, the method of an emotion-assisted multi-task learning network is adopted to improve the performance of aesthetic assessment. The experimental results show that our network structure achieves excellent performance in many indexes of image aesthetic assessment when comparing several state-of-the-art aesthetic assessment algorithms and convolution neural networks with strong performance. © 2021 ACM.

关键词： Convolutional neural networks

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