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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

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Ice Detection on Edge Device Based on Most Significant Digit First SVM 22

Ice Detection on Edge Device Based on Most Significant Digit...

引用

Proceedings of the 2022 6th international conference on Video and Image processing

作者： Najafi Mohammadreza Saeid Gorgin Danial Javaheri Jeong-A Lee computer engineering Chosun University south korea computer engineering Chosun University south korea and Department of Electrical Engineering and Information Technology IROSt Iran

ISBN: (纸本)9781450397568

Abstract- the present notion of smart cities inspires urban planners and academics to provide citizens with modern, secure, and sustainable infrastructure and a decent standard of living. To meet this demand, video surveillance cameras have been installed to improve the safety and well-being of the populace. Despite technological advances in modern science, detecting anomalous events in surveillance video systems is difficult and takes extensive human effort. Due to the context-dependent nature of the anomalous concept, we identify several objects of interest and freely accessible datasets for anomaly detection. Iced surface in highways is one of the serious anomaly which causes a large number of accidents on the highway every year. CCTVs installed on highways can be a good tool to detect iced surface. Detection is a time-sensitive application of computer vision; hence, this paper has concentrated on investigating iced surface detection using edge devices and methods. We have leveraged Most-Significant Digit First arithmetic to improve the performance and resource utilization of the Support Vector machine. We have applied our proposed method to address the problem of ice detection on highways, where the experimental results indicated a significant improvement in terms of accuracy, speedup, and energy consumption. these metrics are essential for edge computing and real-time intelligent surveillance applications.

关键词： Computer arithmetic FPGA Computer vision machine learning

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Real Time Weather Prediction System Using IOT and machine learning 6

Real Time Weather Prediction System Using IOT and Machine Le...

引用

6th international conference on signal processing and Communication, ICSC 2020

作者： Verma, Gaurav Mittal, Pranjul Farheen, Shaista Jaypee Institute of Information Technology Department of Electronics Communication Noida U.P India Dayananda Sagar College of Engineering Department of Electronics Communication Bangalore Karnataka India

ISBN: (纸本)9781728154930

Today's the focus has been shifted towards intelligent technologies like IoT and machine learning. Many IoT hardware platforms are available for IoT implementations. ESP8266 chip is one of them. this paper implements the real time weather prediction system that can be used in number of applications like homes, industries, agriculture, stadiums etc. for predicting the weather information. the system utilizes a temperature and humidity sensor i.e. DHT11 and a light intensity sensor i.e. LDR. the sensed data from the sensors are uploaded to a thingSpeak cloud server using NodeMCU and ESP8266-01 module. the data is also displayed on a customized HTML webpage for monitoring the real time values. A logistic regression model is used for setting up the machine learning environment. this model is trained using the pre-recorded values of sensor data. Further, NodeMCU records the data from sensors ***, humidity and light intensity and then the values are transferred to the Jupyter notebook that utilizes a python environment. this real time data is used to test the model and prediction is done for a particular value by blinking the led connected to NodeMCU. © 2020 IEEE.

关键词： machine learning

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Device Identification for IoT Security

Device Identification for IoT Security

引用

IEEE international conference on signal and Image processing (ICSIP)

作者： Phornsawan Roemsri Rattikorn Hewett Texas Tech University Lubbock Texas USA

ISBN: (纸本)9781665446006

IoT (Internet of things) devices have become integral parts of our everyday life to function especially in smart homes/offices/cities. However, their huge varieties and scales make it hard to manage the security and the quality of services of IoT networks with generic policies. Furthermore, IoT devices owned by employees can unknowingly endanger the security and integrity of the network from other IoT entities they are connected to. Fortunately, IoT devices of the same type tend to have similar vulnerabilities making attack prevention more manageable. thus, identifying the device types is crucial for IoT security. this paper presents an approach to building a classifier to identify different types of IoT devices using various machine learning techniques. While most existing empirical studies use network traffic data, we use communication protocol data. the paper describes the data collection/treatment, the experiments using four machine learning techniques and compares our proposed approach with an existing work based on the Jaccard similarity measure. the results show that both approaches have competitive accuracy of up to 99% but the Jaccard approach does not scale well (e.g., about 1000 times more than the training time of our approach).

关键词： Training Protocols Image processing machine learning Telecommunication traffic Quality of service Forestry

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Underdetermined Blind Source Separation Source signal Recovery

Underdetermined Blind Source Separation Source Signal Recove...

引用

IEEE international conference on signal and Image processing (ICSIP)

作者： Gangyang Hou Linyu Wang Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9781665446006

At present, most of the UBSS problems are based on compressed sensing(CS), and a premise of using CS theory is to ensure that the source signal is sparse enough. While the signal is not sufficiently sparse, the signal reconstruction accuracy will have a great impact. A sparse dictionary can be used for increase the sparsity of the signal. Compared with the traditional fixed dictionary, the learning dictionary is used to improve the reconstruction accuracy. the learning dictionary can obtain more structured features through training on different signals, and adaptively express different signal features. For the difference in the sparse of representation of each frame of signals, a variable step size adaptive threshold SAMP algorithm (VT-SAMP) based on inner product distribution is proposed, which can solve the accuracy and computational pressure with multiplied candidate atoms increased in the iterative process of the SAMP algorithm. Changing the threshold value can improve the accuracy of atoms selection. this article mainly uses the K-SVD dictionary learning algorithm and the VT-SAMP algorithm to separate the mixed signals to obtain the source signals. Compared with other traditional algorithms, this proposed algorithm has a higher reconstructed accuracy. Finally, it is verified through experiments that this method is superior to traditional signal reconstruction algorithms and has better recovery performance.

关键词： Training Dictionaries signal processing algorithms machine learning signal reconstruction Blind source separation Radar signal processing

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Image Defect Detection and Segmentation Algorithm of Solar Cell Based on Convolutional Neural Network

Image Defect Detection and Segmentation Algorithm of Solar C...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Songsong Tian Weijun Li Shuang Li Guangyan Tian Linjun Sun Xin Ning Chinese Academy of Sciences Institute of Semiconductors Beijing China Institute of Power Source and Ecomaterials Science Hebei University of Technology Tianjin China

ISBN: (纸本)9781665447300

the use of infrared or electroluminescence(EL) images of solar cell modules for defect detection is a very important method in non-destructive testing. Traditionally, this work is done by skilled technicians, which is time-consuming and susceptible to subjective factors. the surface defect detection method of solar cells based on machine learning has become one of the main research directions because of its high efficiency and convenience. For this reason, this paper proposes an improved fusion model based on VGGNet and U-Net++, which is used for defect detection and segmentation of EL images of solar cells. In the defect detection stage, the input image is processed pertinently, and by modifying the convolutional layer and the fully connected layer of the network, while improving the performance of the algorithm, it accelerates the convergence and avoids the phenomenon of over-fitting. In the defect segmentation stage, the defect location is marked based on the public data set, which is used for the training of each segmentation model, and the effect of different segmentation networks is compared to select a reasonable model. the experimental results show that the defect detection accuracy of the improved VGG16 network on the elpv-dataset is 95.2%, and the U-Net++ defect segmentation model has an average MIoU value of 0.955, which is better than other existing methods.

关键词： Training Image segmentation Convolution Photovoltaic cells signal processing algorithms machine learning Data models

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Bayesian network in B2B policy application

Bayesian network in B2B policy application

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Kuotai Tang Yan Guo School of Internet Economics and Business Fujian University of Technology Fuzhou China

ISBN: (纸本)9781665447300

Under the influence of COVID-19, more and more e-commerce companies are in urgent need to understand the causality and impact probability of B2B strategies, and formulate effective cooperative operation strategies. Although recently, few articles believe that B2B strategies are beneficial to improving enterprise profitability. In this paper, data from 362 takeout APP users were surveyed by questionnaire. through machine learning, Bayesian Networks (BNs) algorithm was adopted to analyze the B2B strategy adopted by the takeout industry for the impact of COVID-19. Firstly, BNs analysis structure is designed according to prior knowledge and market information. then the maximum likelihood estimation is used to calculate the conditional dependence probability. Finally, the effectiveness of BNs model is verified by training set and test set. the results show that the causal relationship between B2B strategies is highly concentrated in rider's daily health clock, and this strategy is also dependent on green passcode, highlighting the reliability of the windowing strategy. the contribution of this paper is to introduce BNs to B2B strategies and dependent probabilities, specifically providing a feasible way to find the causal relationship between strategies.

关键词： COVID-19 Industries Training Measurement Uncertainty Profitability machine learning

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Multimodal Wearable Device signal Based Epilepsy Detection with Multi-scale Convolutional Neural Network 8th

Multimodal Wearable Device Signal Based Epilepsy Detection w...

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8th international conference on Cognitive Systems and Information processing, ICCSIP 2023

作者： Ge, Yangbin Hu, Dinghan Cui, Xiaonan Jiang, Tiejia Gao, Feng Jiang, Tao Vidal, Pierre-Paul Cao, Jiuwen Machine Learning and I-health International Cooperation Base of Zhejiang Province Hangzhou Dianzi University Hangzhou310018 China Artificial Intelligence Institute Hangzhou Dianzi University Zhejiang Hangzhou310018 China Department of Neurology Children’s Hospital Zhejiang University School of Medicine Hangzhou310018 China Department of Neurosurgery The First Affiliated Hospital of Anhui Medical University Hefei230002 China Department of Neurosurgery Anhui Public Health Clinical Center Hefei230002 China BioMedTech Facilities Université Paris Cité Paris75270 France

ISBN: (纸本)9789819980208

Seizure detection based on wearable devices has gradually become a popular research direction. the ability of wearable devices to capture signals is also improving, and a variety of physiological signals can be collected. However, current models for wearable devices focus on single-scale analysis and cannot adapt to current multi-modal signals. In this paper, an attention module-based convolutional neural network multi-scale model based on a novel wearable device is proposed to recognize epileptic seizures. the network extracts feature at different scales from multimodal physiological signals, supplemented by an attention module to retain valuable information. Experiments on multimodal physiological data from 13 typical epilepsy patients demonstrated that the proposed model achieves 93.5% sensitivity and 97.3% specificity. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wearable technology

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Intra and Inter Class Consistency Domain Adaptation for Semantic Segmentation

Intra and Inter Class Consistency Domain Adaptation for Sema...

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IEEE international conference on signal and Image processing (ICSIP)

作者： Wang Yichao Tian Lihua Zhang Menghao Li Chen Wei Tingting Xi’an Jiaotong University School of Software Engineering Xi’an China

ISBN: (纸本)9781665446006

We consider the problem of unsupervised domain adaptation for the task of semantic segmentation. Existing methods mainly focus on matching the marginal distributions between two domains from the global level through adversarial learning. However, this global alignment method does not consider the class-level joint distribution, which will increase the domain invariance of class features but weaken the discriminability. In this paper, we propose a novel category-level domain adaptation network for semantic segmentation, which explicitly reduces the divergence of intra-class features and enhances the separability of inter-class features. Extensive experiments verify the effectiveness of our proposed IaICC model on challenging unsupervised domain adaptation tasks, i.e., GTA5-Cityscapes.

关键词： Image segmentation Adaptation models conferences Semantics Adversarial machine learning Task analysis

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A machine learning method based on lesion segmentation for quantitative analysis of CT radiomics to detect COVID-19 6

A machine learning method based on lesion segmentation for q...

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6th international conference on signal processing and Intelligent Systems (ICSPIS)

作者： Rezaeijo, Seyed Masoud Ghorvei, Mohammadreza Alaei, Mohammad Tarbiat Modares Univ Dept Med Phys Tehran Iran Tarbiat Modares Univ Dept Elect & Comp Engn Tehran Iran Iran Univ Sci & Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9781728186290

Coronavirus disease (COVID-19) since late December 2019 became an epidemic all over the world so that widely spread throughout. Computed tomography (CT) imaging can be effective in isolating the infected persons and controlling this epidemic. Radiomics is an image quantitative analysis procedure that can quantify imaging by extracting specific features from CT images. We aimed to develop a machine learning (ML) method based lesion segmentation for quantitative analysis of CT radiomics to detect COVID-19. the current study was carried out on two groups of patients including 98 patients with confirmed COVID-19 and 96 with suspected COVID-19. A total of 755 radiomics features were extracted, including 594 gray level co-occurrence matrix features (GLCM), 56 intensity direct features, 49 intensity histogram features, 33 gray level run length matrix features (GLRLM), 18 shape features, and 5 neighbor intensity difference features. Two feature selection procedures including Pearson Correlation (PC) and Recursive Feature Elimination (RFE) were used. As well as, we examined three classifiers including Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbor (KNN). the performance of the feature selection and classification procedures was obtained using 6 criteria. We have obtained the best performance as the accuracy of 98%, recall of 99%, and the area under the curve (AUC) value of 100% for the feature selection procedure RFE and RF classifier. As a result, it can be concluded that the radiomics features of the lung lesions based on ML can be used to differentiate COVID-19 patients.

关键词： machine learning lesion segmentation CT radiomics COVID-19

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A HT Detection and Diagnosis Method for Gate-level Netlists based on machine learning

A HT Detection and Diagnosis Method for Gate-level Netlists ...

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IEEE international conference on signal and Image processing (ICSIP)

作者： Maofan Du Zhao Huang Yin Chen Liang Li Quan Wang Jinhui Liu School of Cyber Engineering Xidian University Xi'an China School of Computer Science and Technolgoy Xidian University Xi'an China Graduate School of Media and Governance Keio University Kanagawa Japan

ISBN: (纸本)9781665446006

the risk of hardware Trojan (HT) attack in the design step of the integrated circuit (IC) development process has become a “hot spot” in hardware security. Despite some solutions presented in the literature, there are still limitations such as high time complexity, low HT detection accuracy, and the inability to locating HTs. this paper presents a novel HT detection and diagnosis method based on machine learning (ML) to identify the HT-related gates/nets maliciously inserted into the IC gate-level netlists at design stage. We partition the circuit into n sectors and extract five HT-related features from the netlists of each sector to construct a five-dimensional vector of that sector. then, these n five-dimensional feature vectors are trained using support vector machine (SVM) to obtain the learned model. We have evaluated our method on the ISCAS'85 benchmark circuits. the experimental results show that our method can achieve 100% true positive rate (TPR), more than 90% true negative rate (TNR), complete 95% average precision and F-measure, average accuracy exceeds 80%. Moreover, it can also report the possible implantation location of a Trojan instance.

关键词： Support vector machines conferences machine learning Logic gates Feature extraction Hardware Trojan horses

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