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A Novel End-to-End Framework to image Cortical Networks from EEG

A Novel End-to-End Framework to Image Cortical Networks from...

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IEEE international conference on Computational Intelligence and Virtual Environments for Measurement systems and Applications (CIVEMSA)

作者： Chen, Wanjun Wang, Junpu Yi, Chanlin Li, Fali Xu, Peng Univ Elect Sci & Technol China Sch Life Sci & Technol Chengdu Peoples R China

ISBN: (纸本)9798350322996;9798350323009

Deciphering the intricate causal relationships among cortical sources in the brain's complex systems through a non-invasive and low-cost technique remains a formidable challenge. Electroencephalography (EEG), with its high temporal resolution, offers a promising avenue to unravel the underlying neural mechanisms. To this end, a novel end-to-end model is proposed to capture cortical networks from EEG data based on a temporal convolutional graph neural network (GNN), called TCGNN-BrainNet. The core components of this model are the inverse operator neural network (IONN) and the connectivity estimation neural network (CENN). The IONN transforms the EEG signal into an abstract representation of deep cortical activity, while the CENN constructs and leverages graph encoding for interaction inference. Through extensive training, TCGNN-BrainNet acquires the capability to infer cortical networks directly from EEG data, optimizing accuracy and mitigating error propagation common in conventional methods. Notably, the training strategy concurrently weaves the understanding of physiological cortical features with the implicit relational understanding of supervisory data, through the defined loss function. This enables the neural networks to organically uncover and seize subtle interaction patterns amidst the signals. Extensive simulations were performed to substantiate the efficacy of our model, evidencing the robustness and consistent reliability of TCGNN-BrainNet in accurately constructing cortical networks. These promising outcomes demonstrate the significant potential of TCGNN-BrainNet in practical brain network analyses, paving the way for new frontiers in neuroscientific exploration and advancements in the field.

关键词： cortical network causal inference graph neural network EEG

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The Design of Spectrally-Constrained Sequences with Periodic Zero-Correlation Zone

The Design of Spectrally-Constrained Sequences with Periodic...

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2024 international conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Liu, Shicheng Peng, Xiuping Wang, Shide School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Hebei Province Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

Spectrally-constrained sequences (SCSs) are primarily used in emerging systems operating over non-contiguous spectrum such as cognitive radio and cognitive radar. In order to obtain more SCSs, firstly, a novel type of SCS called periodic zero-correlation zone spectrally-constrained sequence(PZCZ-SCS) is proposed in this paper. Then, based on time-frequency synthesis analysis, the conditions that the PZCZ-SCSs must satisfy in terms of "spectral constraint" are derived. Specifically, it is a divisible difference set with parametersλ1 = k and λ1 > λ2. Finally, the construction of PZCZ-SCSs is presented by using the Chinese remainder theorem. Through this research, a new class of SCS is provided for cognitive radio. © 2025 SPIE.

关键词： image coding

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Deep Learning-based Robust Automatic Modulation Classification Using Higher Order Cumulant Features 15

Deep Learning-Based Robust Automatic Modulation Classificati...

引用

15th international conference on Information technology and Electrical Engineering, ICITEE 2023

作者： Suetrong, Nopparuj Taparugssanagorn, Attaphongse Promsuk, Natthanan Chiang Mai University Faculty of Engineering Department of Computer Engineering Chiang Mai Thailand School of Engineering and Technology Asian Institute of Technology Ict Department Pathum Thani Thailand

ISBN: (纸本)9798350304466

The internet of Things (IoT) represents one of the pivotal technologies in our daily lives. Within these systems, wireless communication plays an indispensable role in connecting IoT devices. Consequently, signal modulation emerges as a technique within wireless communication systems, enabling the transmission of baseband signals at higher frequencies. Given the array of modulation types, modulation classification comes into play to differentiate the modulation type of the received signal. Due to the presence of noise and attenuation, automatic modulation classification (AMC) stands as the primary method for such classification. AMC methods can operate without requiring any prior knowledge regarding the received signal. Multiple research groups have extended the depth of deep learning (DL) models or augmented the neuron count in each layer to enhance performance, albeit at the expense of increased model complexity. To tackle this challenge, we present model that merges the gated recurrent unit (GRU) and long short-term memory (LSTM) architectures. Furthermore, data pre-processing techniques, specifically Min-Max normalization and fourth-order cumulant (FOC), have been employed to enhance classification accuracy. The outcomes underscore that our proposed model surpasses both LSTM and GRU models. Moreover, the classification accuracy of normalized data outperforms that of data without normalization. © 2023 IEEE.

关键词： internet of things

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Visual group target tracking algorithm based on MeanShift-PCA-PF 8

Visual group target tracking algorithm based on MeanShift-PC...

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

作者： Li, Jianing Tian, Yan Guo, Min Zuo, Kaige Wang, Xin Xi'an Institute of Optics and Precision Mechanics University of Chinese Academy of Sciences Key Laboratory of Space Precision Measurement Technology Xi'an China Xi'an Institute of Optics and Precision Mechanics Key Laboratory of Space Precision Measurement Technology Xi'an China Xi'an Institute of Optics and Precision Mechanics Shaanxi Normal University Key Laboratory of Space Precision Measurement Technology Xi'an China

ISBN: (纸本)9798350302455

In recent years, target tracking has been widely studied and applied in intelligent video surveillance, human-machine interaction, radar early warning and tracking, and other fields. With the emergence of the new combat concept of unmanned aerial vehicle (UAV) swarm operations, and the maturity of large-area, high-resolution visible light COMS imaging device technology, the imaging detection and visualization of UAV swarm targets in large observation fields will greatly improve the efficiency of command systems. Therefore, the research on the group target detection, tracking, and recognition based on video image is very urgent and crucial. Group targets are collections of densely packed multiple objects with certain group characteristics that meet the given distance criterion. Due to the large number of targets, limited image resolution, and waste of communication resources, multiple target tracking techniques are challenging to implement effectively. In this paper, based on the characteristics of group targets, we propose a group target tracking framework based on optical measurement equipment. By dividing groups according to the density feature of group targets, we obtain the structural properties and spatial geometric features of the group and implement tracking using a particle filter. Finally, we validate the effectiveness of the group target tracking technique through simulation experiments in group target tracking. © 2023 IEEE.

关键词： image resolution

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An Efficient Multilevel Threshold image Segmentation Approach based on Rafflesia Optimization Algorithm 1

引用

18th international conference on Intelligent Information Hiding and Multimedia signal Processing, IIH-MSP 2022

作者： Zhou, Jin-Liang Chu, Shu-Chuan Weng, Shaowei Zheng, Wei-Min College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Science and Engineering Flinders University Adelaide Australia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

Segmentation is an important step from image processing to analysis. The thresholding technique based on entropy has become one of the most commonly used techniques in this field. Among the many thresholding techniques based on entropy, the minimum cross-entropy threshold (MCET) has been dramatically developed. The key to multilevel MCET technology is quickly finding the optimal threshold point. MCET is a relatively time-consuming technique, so we use optimization techniques to search for optimal multilevel thresholds. Rafflesia optimization algorithm (ROA) is an optimization algorithm inspired by rafflesia growth theory to solve real-world multi-parameter optimization problems. In this paper, Rafflesia optimization algorithm, particle swarm optimization, gray wolf optimization, whale optimization algorithm, and multi-version optimizer are used to segment six images and the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) as evaluation metrics. The experimental results show that the ROA algorithm can achieve higher-quality image segmentation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Improved Tuna Optimization-based Energy Efficient Clustering Protocol for internet of Things 16

Improved Tuna Optimization-Based Energy Efficient Clustering...

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16th IEEE international conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Rani, S. Jamuna Santhakumar, D. Saveetha University Department of Computer Science and Engineering TamilNadu Chennai602105 India

ISBN: (纸本)9798331505264

In smart applications, internet of Things (IoT) serves as a backbone to facilitate a minimized cost monitoring system due to recent advancements in the technology of sensing and networking. Smart sensors are utilized in diversified number of real-time applications over the recent years to control and monitor physical objects deployed over the implementation environment. This adoption of smart sensors over the real-time applications generates a huge amount of data in the form of text, number, image, and video, which in turn incurs massive amount of energy during the data transmission process. In this context, clustering is ideal for achieving data collection and exchange over the network, and majority of the researchers utilized optimization algorithms for selecting optimized cluster head (CHs) in the network. In this paper, an Improved Tuna Swarm Optimization Algorithm (HSITSOA)-based Clustering Scheme is proposed for CH rotation in the cluster with the objective to reduce algorithmic convergence time which is considered as one of the predominant limitations of the existing clustering approaches. This proposed HSITSOA approach comprises of the processes of cluster construction and CH selection which is established through the balanced trade-off attained during the exploration and exploitation. The simulation conducted using MATLAB R2019a confirmed its efficacy in determining the best CHs on the cluster. It was also identified to improve the packet delivery rate and network lifetime by 23.49% and 27.98%, better than the benchmarked approaches. © 2024 IEEE.

关键词： MATLAB

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Cover selection for image steganography determined by distinct characteristics. 12

Cover selection for image steganography determined by distin...

引用

12th IEEE international conference on Advanced Computing, ICoAC 2023

作者： Jamatia, Rupa Bhuyan, B. North-Eastern Hill University Dept. of Information Technology Shillong India

ISBN: (纸本)9798350318210

Security in digital communication is becoming necessary to protect secret messages during transmission over insecure channels of the internet. Steganography, an ingenious technique, offers a discrete solution to this challenge. It is used as an art of hiding data within a seemingly safe digital medium such as an image, audio, or video file without arousing suspicion if communication is taking place. One of the primary goals of the key aspect of steganography is selecting a medium to embed or hide secret messages in terms of text or images to obtain the effectiveness of data hiding in sensitive communications, generating a new image called a stego-image, which is concerned with maximizing embedding capacity while minimizing being noticeable in the resulting stego-image against steganalysis. In this paper, we mainly focus on finding the characteristics of an image that make it suitable for steganography. We analyze several image features such as color distribution, brightness, contrast, sharpness, entropy, and resolution. We have used Random Pixel Value Insertion (RPVI) to embed the secret information into an image to analyze the performance parameters like PSNR, MSE, RMSE, and SSIM and evaluated their suitability for designing steganographic systems based on the evaluation results which vary with certain image features. © 2023 IEEE.

关键词： Steganography

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An Efficient Maximum Subcarrier Power Detection Scheme for OFDM-IM systems 19

An Efficient Maximum Subcarrier Power Detection Scheme for O...

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19th IEEE international Wireless Communications and Mobile Computing (IEEE IWCMC)

作者： Lian, Yuhao Ying, Mingjun Wang, Shuyu Wang, Yuhua Chongqing Univ Posts & Telecommun Chongqing Sch Commun & Informat Engn Chongqing 400065 Peoples R China

ISBN: (纸本)9798350333398

As a promising technology, Orthogonal Frequency Division Multiplexing-Index Modulation (OFDM-IM) has received significant attention in wireless communication systems. However, as the number of subcarriers increases, the complexity of the Maximum Likelihood (ML) detector grows exponentially. In this paper, we propose a novel detection method, referred to as the Maximum Subcarrier Power (MSP) detection algorithm. The MSP algorithm leverages the power information of each subcarrier to detect its activation status and employs a power threshold to determine the appropriate modulation method. Specifically, the MSP algorithm switches from SIPM-OOFDM to OFDM-IM in low signal-to-noise ratio (SNR) scenarios. In comparison to ML detection and Log-Likelihood Ratio (LLR) detection techniques based on optimum transceiver design in OFDM-IM, the proposed MSP detection algorithm has lower complexity and is more robust in terms of Bit Error Rate (BER) performance at varying noise levels. Additionally, the MSP algorithm effectively reduces the cost of Digital signal Processing (DSP) and the detection time. This consequently leads to enhanced communication efficiency, demonstrating great potential for future low-latency B5G/6G wireless networks.

关键词： OFDM-IM ML detection MSP detection signal processing Robustness

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6th international conference on Information and Communication technology for Intelligent systems, ICTIS 2021

6th International Conference on Information and Communicatio...

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6th international conference on Information and Communication technology for Intelligent systems, ICTIS 2021

ISBN: (纸本)9789811935701

The proceedings contain 149 papers. The special focus in this conference is on Information and Communication technology for Intelligent systems. The topics include: COVID-19 Triggers a Paradigm Shift in technology for Insurance Industry;internet of Things in Saudi Arabia Universities: State of the Art, Future Opportunities, and Open Challenges;study of Security in Cryptocurrency;study and Analysis of Network Steganography Methods;preface;Review of Different ML Techniques Used for Heart Disease Prediction;survey on Driver Fatigue Detection Using Sensors, Big Data Analytics and Machine Learning Techniques;on Characteristics of κ–Q–Fuzzy Translation and κ–Q–Multiplication in T-Ideal in T-Algebras;blockchain-driven Smart Healthcare System: Challenges, Technologies and Future Research;a Future to the Blockchain technology and Its Concepts;Impacts of DDoS Attacks in Software-Defined Networks;ioT-based Agriculture Trend Prediction based on Weather Data;EEG in Optic Nerves Disorder based on FSVM Using Kernel Membership Function;DDoS Detection in ONOS SDN Controller Using Snort;classification of Keratoconus Using Corneal Topography Pattern with Transfer Learning Approach;intelligent Heuristic Keyword-based Search Methodologies Applied to Cryptographic Cloud Environment;image Enhancement by Various Segmentation Techniques Using Machine Learning;Surakhsha Kavach: ML-based Cross-platform Application for COVID-19 Vulnerability Detection;Malware Family Categorization Using Genetic Algorithm-CNN-based image Classification Technique;master Data Management Maturity Evaluation: A Case Study in Educational Institute;malware Family Classification Using Music Information Retrieval Techniques;text-based Prediction of Heart Disease Doctor Chatbot Using Machine Learning;intelligent Proctoring System;project-based Learning: A Contemporary Approach to Blend Theory and Practical Knowledge of Database Management Course;student’s Employability Concern for Colleges and Universities.

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Edge-based Intrusion Detection using Machine Learning over the IoT Network 11

Edge-Based Intrusion Detection using Machine Learning over t...

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11th international conference on Emerging Trends in Engineering and technology - signal and Information Processing, ICETET - SIP 2023

作者： Wadate, Akash J. Deshpande, S.P. DCPE HVPM PG Department of Computer Science Maharashtra Amravati India

ISBN: (纸本)9798350348422

Edge systems supported & assisted using IoT at a large scale called edge-assisted IoT. For large computational tasks, the need for edge servers has come into existence. Security of IoT is a difficult task to secure IoT information transmission to the edge. The proposed method shows an attack intrusion detection system (IDS) using back propagation (BP) as well as a basis function Radial (BFR) neural network. Attack intrusion detection function found using BP network and for more attack detection BFR has been used. © 2023 IEEE.

关键词： internet of things

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