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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

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Multiresolution Encoder-Decoder Convolutional Neural Network for Magnetic Resonance image Segmentation

Multiresolution Encoder-Decoder Convolutional Neural Network...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Kishore K. Tarafdar Aaditya Meher Mirat Shah Qutubuddin Saifee Dushyant Kumar Anant V. Nimkar Rama Jayasundar Vikram M. Gadre Department of Electrical Engineering Indian Institute of Technology Bombay Department of Information Technology Sardar Patel Institute of Technology Mumbai LSI India R&D Pvt. Ltd. (Broadcom) Department of NMR All India Institute of Medical Sciences New Delhi Department of Computer Engineering Sardar Patel Institute of Technology Mumbai

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In image segmentation by deep learning, encoder-decoder Convolutional Neural Network (CNN) architectures are fundamental for creating and learning representations. However, with many filters in these models, one often loses the tractability of these models and interprets them as black boxes. In this paper, we describe the design of a multiresolution encoder-decoder CNN having a distinct low-frequency processing encoder and mixed-frequency processing decoder. The encoder of the proposed architecture first creates a localized spatial-frequency representation of the feature space using a framelet system and then learns representations using trainable convolution layers while converging to segment structures. The decoder takes in the encoder output, including distinct low and high-frequency skip connections and reconstructs the Region of Interest (RoI) with accurate boundaries. Spatial attention layers for low frequencies and a wavelet-based self-attention for high frequencies in the decoder successively aid the reconstruction of the desired segment. The described network architecture is overall lossless by design, despite information loss in wavelet pooling layers because of the initial subband decomposition and separate low and high-frequency paths. The model is tested separately using T1-weighted brain Magnetic Resonance (MR) images with two classes and accurately segments large and small RoIs. The trained model also successfully segments MR volumes acquired by our group.

关键词： image segmentation Magnetic resonance Network architecture Brain modeling Decoding Convolutional neural networks Spatial resolution Speech processing signal resolution image reconstruction

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Monitoring Human Muscle signal Using Electromyography Sensor based on internet of Things

Monitoring Human Muscle Signal Using Electromyography Sensor...

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Biomedical Instrumentation and technology conference (IBITeC), international

作者： Roby Hanapi Irmayatul Hikmah Anjar Taufik Hidayat Telecommunication Engineering Faculty of Telecommunication and Electrical Engineering Institut Teknologi Telkom Purwokerto Purwokerto Indonesia Biomedical Engineering Faculty of Telecommunication and Electrical Engineering Institut Teknologi Telkom Purwokerto Purwokerto Indonesia

Muscles serve as a system measure of motion, relaxation, and contraction. The electrical signals generated by muscles during contraction are measured using electrodes connected to electromyography sensors. Electrodes are placed on the surface of the skin to detect electrical signals, which are then used to store condition data based on their placement. Electromyography devices still do not have direct integration through websites or apps, nor are they capable of storing digital patient records. Instead, data collection is done manually or on-site. However, with the internet of Things-based muscle signal detection device, this process becomes easier for medical personnel. Medical personnel can check the patient’s medical record results through a laptop and save the digital record data. The advantage is that the monitoring process can be done online, so that medical personnel can easily see the patient’s progress from day to day through the available digital data records. The electromyography sensor output is processed by the NodeMCU ESP8266 microcontroller and displayed through the blynk platform integrated with the internet. Electromyograph testing was conducted on the gastrocnemius muscle and 5 different subjects. The test results show that the value of muscle tension is greater when the load treatment has been carried out, both in the biceps muscle and in the gastrocnemius muscle with a large average value in the biceps muscle when the muscle extension condition is 9.746 Volt and 17.296 Volt when the muscle flexion condition. And in the gastrocnemius muscle it is 10.685 Volt when the muscle is in extension and 18.028 Volt when the muscle is in flexion. The value of the voltage is influenced by several factors, one of which is muscle strength and body weight.

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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

作者： Wanjun Chen Junpu Wang Chanlin Yi Fali Li Peng Xu School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350322996

ISBN: (纸本)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： Training Virtual environments Transforms Network analyzers Brain modeling Electroencephalography Graph neural networks

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2021 ieee 2nd international conference on Big Data, Artificial Intelligence and internet of Things Engineering, ICBAIE 2021

2021 IEEE 2nd International Conference on Big Data, Artifici...

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2nd ieee international conference on Big Data, Artificial Intelligence and internet of Things Engineering, ICBAIE 2021

ISBN: (纸本)9780738131221

The proceedings contain 229 papers. The topics discussed include: an adaptive accuracy threshold aggregation strategy based on federated learning;unknown radar signal recognition technology based on ds evidence theory;analysis and discussion of component in the ecosphere of Hadoop from HuaWei'FusionInsight;self-supervised domain adaptation for satellite imagery classification via representation learning;whole slide images based cervical cancer classification using self-supervised learning and multiple instance learning;EfficientNet-based model with test time augmentation for cancer detection;deep compression of neural networks for fault detection on Tennessee Eastman chemical processes;and design and implementation of intelligent feeding system based-on the oneM2M.

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Intrusion Detection Method based on Sparse Autoencoder 3

Intrusion Detection Method Based on Sparse Autoencoder

引用

3rd international conference on Computer Communication and the internet, ICCCI 2021

作者： Li, Yuqi Gao, Pan Wu, Zhijun College of Electronic Information and Automation Civil Aviation University of China Tianjin China

ISBN: (纸本)9781728176185

In view of the low detection rate and high false alarm rate in the current imbalance of classification in intrusion detection, an intrusion detection algorithm based on sparse autoencoder is proposed. This method uses sparse autoencoder technology to build a classified model to complete the detection of different types of data labels, and to improve the detection effect by adjusting the parameters. The experiment uses the UNSW-NB15 data set to test the algorithm. The experimental results show that the algorithm has a higher detection rate and a lower false positive rate. © 2021 ieee.

关键词： signal encoding

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An Adaptive EEG signal Filter based on Discrete Wavelet Transform and Least Mean Square

An Adaptive EEG Signal Filter Based on Discrete Wavelet Tran...

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ieee international conference on signal and image Processing (ICSIP)

作者： Haiying Yuan Cheng Shi Minghao Li Mengfan Dai Faculty of Information Technology Beijing University of Technology Beijing PR China

Electroencephalogram (EEG) contains a lot of physiological characteristics and disease information, and the existence of various artifacts is a major obstacle in EEG analysis. Artifacts are mostly generated by the unconscious eye activity and muscle activity of the human body and the most serious effect is the strong electrooculogram (EOG) generated by eye movement. The existing removal of EOG requires various pretreatment and calibration procedures, which are inconvenient and time-consuming. Therefore, for complex EOG artifacts, we proposed a method combining the discrete wavelet transform (DWT) and the least mean square (LMS) adaptive filter. The EOG component decomposed by DWT is used as the reference signal of the adaptive filter to estimate EOG artifacts. The experimental results show that the average mean square error (MSE) of the subjects is reduced by 79.58% and the average signal-to-noise ratio (SNR) is increased by 143.6S%. We also implement the proposed filtering algorithm in hardware and finally form a mature EEG filtering system for the brain-computer interface.

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Web Page Classification Using Convolutional Neural Network (CNN) towards Eliminating internet Addiction 7

Web Page Classification Using Convolutional Neural Network (...

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7th international conference on Software Engineering and Computer systems and 4th international conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Apandi, Siti Hawa Sallim, Jamaludin Mohamed, Rozlina Madbouly, Araby Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan Malaysia Muscat College Department of Business and Accounting Muscat Oman

ISBN: (纸本)9781665414074

In the modern world, everyone has access to the internet as a source of information by surfing the web pages. The most popular web page surf is on Game and Online Video Streaming. Users who are spending too much time on these kinds of web pages may lead to a negative impact on internet addiction. To overcome the internet addiction problem, access to Game and Online Video Streaming web pages needs to be restricted. Thus, a mechanism that can classify the category of the incoming web page based on the web page content is needed. This paper is proposing a web page classification model using a Convolutional Neural Network (CNN) to classify the web page, then identify whether it is a Game or Online Video Streaming based on the pattern of words in the word cloud image taken from the web page text content. The proposed web page classification model has achieved 82.22 % accuracy to detect the pre-classifled web pages. © 2021 ieee.

关键词： Convolutional neural networks

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An Interference Detection Method for Satellite Communications based on Maximum Between-Class Variance of Spectral Histogram

An Interference Detection Method for Satellite Communication...

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international conference on signal Processing Proceedings (ICSP)

作者： Tao Guan Kaili Cui Lin Yang Yufeng Zhang Peng Wei Xin Ma The Sixty-Third Research Institute National University of Defense Technology Nanjing China Nanjing University of Information Science & Technology Nanjing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Interference detection is one of the critical prerequisites for avoiding and suppressing interference in satellite communication applications. In this article, we propose to treat the frequency-domain interference detection as a threshold segmentation problem and develop an interference detection method from a novel perspective. First, to deeply analyze the characteristic differences between interference and background noise from a statistical perspective, a Spectral Histogram of mixed signals is introduced. Then, a frequency-domain preprocessing method, which combines a morphological filter and a mean filter, is designed to reduce the impact of background noise. Inspired by the Otsu adaptive thresholding method in the field of image processing, an adaptive interference detection method based on a Maximum Between-Class Variance (MBCV) criterion is finally proposed to detect the interference in a wideband spectrum environment. Simulation experiments have been conducted on interference signals with different types and parameters, and the detection results validate the effectiveness of the proposed method.

关键词： Histograms Thresholding (Imaging) Satellites Frequency-domain analysis Interference signal processing Background noise Satellite communications Wideband

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DC Terminal Impedance Modeling and Stability Analysis for VSG-Controlled Grid-Connected Inverter

DC Terminal Impedance Modeling and Stability Analysis for VS...

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DC Technologies and systems (DCTS), ieee international conference on

作者： Xinyang Su Minghao Wang Ying Xue Junyu Chen Wenzhuo Shi Zhao Xu Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong SAR China Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City The University of Macau Macau SAR China School of Electric Power Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350379464

ISBN: (纸本)9798350379471

As a kind of grid-forming control method that can effectively provide virtual inertia for the power grid, the virtual synchronous generator (VSG) control has received increasing attention in recent years. However, the VSG control will extract energy from the DC side during a grid frequency drop, which brings a burden on the stability of the DC side of a VSG. To address this problem, this paper establishes a fully analytical small-signal model of the VSG DC terminal impedance, incorporating both the inertia mechanism and the DC side dynamics. The accuracy of the model is validated through comparison with the sweep frequency results of the simulation. Finally, the Nyquist criterion is employed to analyze the DC stability of the VSG, and the simulation results match well with the theoretical predictions.

关键词： Analytical models Simulation Stability criteria Power system dynamics Microgrids Power system stability Synchronous generators Impedance Testing Grid forming

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BCACP-IoE: A Novel Blockchain-based Security Access Control Protocol for internet of Energy

BCACP-IoE: A Novel Blockchain-Based Security Access Control ...

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ieee international conference on Information Communication and signal Processing (ICICSP)

作者： Yanchi Chen Haoxiang Luo Qi Huang Jian Luo Glasgow College University of Electronic Science and Technology of China Chengdu China Key Laboratory of Optical Fiber Sensing and Communications (Ministry of Education) University of Electronic Science and Technology of China Chengdu China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Meishan Branch State Grid Corporation of China Meishan China

Thanks to the development of information technology, our energy system is gradually becoming connected and intelligent. The deep integration of the mature internet of Things and various fields of energy forms the internet of Energy (IoE), which can make an important contribution to improving energy efficiency and reducing carbon dioxide emissions. However, the continuous deepening of information technology in IoE, it also brings a lot of network security problems, because the essence of IoE information exchange is wireless communication. Such network security problems may result in the disclosure of private information of the power user side (PUS) to other unauthorized parties, or in the acquisition of trade secrets of the power supply side (PSS) by competitors. Therefore, we need to develop a specific data access protocol to ensure the communication security of IoE. This paper proposes a blockchain-based access control protocol for IoE, namely “BCACP-IoE”. As most grid companies (GC) are run by governments, BCACP-IoE treats them as trusted entities and designs secure access protocols for PSS/PUS-GC communication. Meanwhile, we use Ripple, a vote-based blockchain consensus algorithm, to validate transactions and add them to the blockchain. Through various security analyses, we prove the security of the BCACP-IoE against different types of potential attacks. In addition, it also has lower communication and computing costs, and its performance is better than other existing schemes.

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