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Exploring a Discriminative Metric Network for Few-Shot SAR Target Classification

Exploring a Discriminative Metric Network for Few-Shot SAR T...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Jia Zheng Ming Li Peng Zhang Yan Wu Dazhi Xu Xinyue Xin National Key Laboratory of Radar Signal Processing Xidian University Xi'an China School of Electronic Engineering Xidian University Xi'an China

Few-shot learning aims to learn a classifier for image classification when only a few training samples are available. Recent researches have shown that using local descriptors instead of holistic image representations can provide more informative features. However, local descriptors only capture local information within an image while disregarding global contextual information, leading to decreased accuracy in synthetic aperture radar (SAR) target classification. To address this issue, we propose a novel discriminative metric network (DMnet) for few-shot SAR target classification. the DMnet comprises global-level metric relationship and local-level metric relationship. Among them, the local-level metric relation is computed by an image-to-class metric module that measures the similarity of local descriptors. Simultaneously, we introduce global pooling to obtain the global information for each local descriptor, reducing the influence of local background noise. Subsequently, a prototype-based metric module is utilized to calculate the similarity of descriptors. To better integrate these two types of metric relations, we design an adaptive metric fusion module to achieve more discriminative metric performance. Experimental results on the MSTAR dataset demonstrate the effectiveness of the proposed DMnet.

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Bangla Document Classification Based on machine learning and Explainable NLP

Bangla Document Classification Based on Machine Learning and...

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international conference on Electrical Information and Communication Technology (EICT)

作者： Md. Habibullah Md. Shymon Islam Fatima Tuz Jahura Jyoti Biswas Department of Computer Science and Engineering North Western University Khulna Bangladesh Computer Science and Engineering Discipline Khulna University Khulna Bangladesh

Massive digital texts are now accessible, thanks to technological advancement. Any amount of disorganized writing is useless. A high-quality representative corpus of any particular language is essential for research in computational linguistics and natural language processing (NLP). Bangla NLP research is still in its infancy because of the dearth of high-quality public corpus. this paper proposed a newly produced corpus consists of 1,30,307 documents covering 10 categories collected from 11 websites, having 2,94,80,828 tokens and 17,59,085 unique tokens. Seven supervised machine learning methods are explored in this work. Furthermore, Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive explanations (SHAP) are also examined to explain about different model performance. the obtained results show that the Random Forest (RF), Decision Tree (DT) and Support Vector machine (SVM) outperform other models. RF classifier achieves the highest accuracy 99.91% which is better than the existing state-of-the-art methods.

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A simple yet efficient unsupervised anomaly detection network for industrial machine

A simple yet efficient unsupervised anomaly detection networ...

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IEEE international conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Danxuan Wu Qijun Chen Tongji University Shanghai China

Anomaly detection, aiming to detect items, events, or observations that do not match expected patterns or other items in a data set, is a crucial problem in many fields such as network intrusion detection, fraud detection and so on. Due to the rarity of labeled anomalous datasets, the research aims at detecting anomalies without anomalous samples and only using normal ones which called unsupervised learning. While traditional machine learning devoted to find a hard marginal between abnormal and normal ones, some unavoidable disturbance along with the signal can cause deviation. Moreover, the deep learning research of this subject focus on construct an autoencoder network only using normal data to memory the patterns of the normality. However, considering the generalization ability of neural network, it may occur that abnormal ones can reconstruct well thus leading the mistake of predicting. In this passage, to overcome the above two problem, we design a noise generator to simulate some reasonable noises, utilize and combine the neural network and memory bank to strengthen the ability of the network to memorize the patterns of the normal instances. We experiment and compare our algorithm on the MIMII sound anomaly dataset and the corresponding results shows the effectiveness and efficiency of our approach. It reaches 91.37 % in Accuracy and 93.76 % in F1 score. To our knowledge, it is the state of the art work up to now.

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Scanning Electron Microscope Image Data Acquisition and Transmission System

Scanning Electron Microscope Image Data Acquisition and Tran...

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Yang Zhang Zuye Wang Yong Zhao School of Instrument Science and Opto-Electronics Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Scanning electron microscopy (SEM) has emerged as a pivotal tool in scientific research and engineering, offering unparalleled insights into the micro- and nano-scale world. However, traditional SEM systems often entail bulky setups and complex data acquisition processes, limiting their accessibility and efficiency in various applications. this paper designs a desktop SEM image data acquisition and transmission system based on FPGA. the system is divided into two parts: signal modulation and AD sampling circuit and image data processing and transmission circuit. By designing a circuit to process SE/BSE signals, signal modulation and analog-to-digital conversion are achieved. the sampling data is processed using the internal resources of the FPGA and transmitted to the PC through the USB3.0 high-speed interface, and finally displayed as a grayscale image. the system realizes real-time collection, processing and transmission of SEM signals, providing convenience and efficiency for scientific research and engineering applications. Experimental verification shows that the system performs well and has certain application potential.

关键词： Scanning electron microscopy machine learning algorithms Microscopy Data acquisition Modulation Gray-scale Data processing Universal Serial Bus Real-time systems Field programmable gate arrays

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Research on Surface Defect Detection Method of Metal Workpiece Based on machine learning 6

Research on Surface Defect Detection Method of Metal Workpie...

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6th IEEE international conference on Intelligent Computing and signal processing, ICSP 2021

作者： He, Hongyang Yuan, Mingang Liu, Xiushan Guangdong Polytechnic Normal University The School of Electronics and Information Guangzhou China

ISBN: (纸本)9780738143705

Some uncontrollable defects will occur on the surface of metal workpieces during processing. the existence of surface defects not only affects the appearance of the finished product, but also affects the quality to a certain extent. Surface defect detection of metal workpieces can effectively improve product quality and production efficiency, and is an important link in the process of product quality control. Although there are many different types of surface defect detection methods, in the actual production process, due to the characteristics of multiple types and irregular distribution of the surface defects of metal workpieces, in most cases, manual inspection or simple machine inspection is still used to detect the surface of metal workpieces. Defect inspections often lead to missed inspections and false inspections. the defect detection efficiency, accuracy and precision of metal workpieces still need to be further improved. this paper studies the method of detecting the surface defects of metal workpieces based on deep learning, provides the surface defect recognition accuracy and defect detection rate of metal workpieces, and provides references for the staff and scientific researchers engaged in metal workpiece defect detection. © 2021 IEEE.

关键词： Metals

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KAS-IDS: A machine learning based Intrusion Detection System 6

KAS-IDS: A Machine Learning based Intrusion Detection System

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6th IEEE international conference on signal processing, Computing and Control, ISPCC 2021

作者： Chahal, Jasmeen Kaur Gandhi, Vidhyotma Kaushal, Payal Ramkumar, K.R. Kaur, Amanpreet Mittal, Sudesh Institute Of Engineering Technology Chitkara University Punjab India

ISBN: (纸本)9781665425520

the Internet has become an integral part of our life as we perform day-to-day work like e-Banking, e-Education and e-Commerce. With this, the threat of attackers and hackers has also been increasing. A crucial part has been played by an Intrusion Detection System (IDS) detect such malicious acts. Unfortunately, most of the commercial IDSs are based on misuse based that are developed to capture the already known attacks only. these require frequent updation of signatures and have minimum capacity to capture new attacks. therefore, anomaly-based IDS is an effective alternative for this problem. Many of the researchers adopt various techniques to enhance the efficiency of the IDS. However, the false alarm rate and detection rate are challenging *** paper proposes a technique called KAS-IDS, i.e. K-Means and Adaptive SVM based Intrusion Detection System. In the first step, the clusters of data have been made using K-Means and second, the classification has been performed using adaptive SVM. A well-known dataset has been used to perform the experiments. the outcomes represents that our approach has better performance as compared to the individual algorithm when it comes to detection accuracy and false alarm rate. © 2021 IEEE.

关键词： Personal computing

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NR-V2X Quality of Service Prediction through machine learning with Nested Cross-Validation Scheme

NR-V2X Quality of Service Prediction Through Machine Learnin...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： İbrahim Yazici Emre Gures Türk Telekom R&D Department Acıbadem Istanbul Turkey Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

the proliferation of connected vehicles and the advent of New Radio (NR) technologies have ushered in a new era of intelligent transportation systems. Ensuring reliable and low-latency communication between vehicles and their surrounding environment is of utmost importance for the success of these systems. this paper presents a novel approach to predict Quality of Service (QoS) in Vehicle-to-Everything (V2X) communications through nested cross-validation. Our methodology employs several machine learning (ML) methods to predict some QoS metrics, such as packet delivery ratio (PDR), and through-put, in NR-based V2X scenarios. In ML employment, nested cross-validation approach, unlike conventional cross-validation approach, prevents information leakage from parameter selection into hyperparameter selection, and this results in getting more robust results in terms of overfitting. the study utilizes real-world NR-V2X datasets to train and validate the proposed ML methods. through extensive experiments, we demonstrate the efficacy of our approach in accurately predicting QoS parameters, even in dynamic and challenging vehicular environments. In summary, our research contributes to the advancement of NR-based V2X communication systems by introducing employment of ML methods with a novel approach for QoS prediction. the combination of accurate predictions through nested cross-validation not only enhances the reliability of communication in connected vehicles' landscape but also has a supportive role for stakeholders to make informed decisions for the optimization and management of vehicular networks.

关键词： Connected vehicles Employment Quality of service machine learning signal processing Reliability Stakeholders Vehicle dynamics Vehicle-to-everything Optimization

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machine learning based Epileptic Seizure Detection from EEG signal: Bangladesh Perspective

Machine Learning based Epileptic Seizure Detection from EEG ...

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international conference on Electrical Information and Communication Technology (EICT)

作者： Shupta Das Suraiya Akter Mumu Abdus Salam M. A. H. Akhand Dept. of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh Shaheed Sheikh Abu Naser Specialized Hospital & Prince Hospital Khulna Bangladesh

Epileptic seizures (ES) pose significant challenges to individuals' well-being and quality of life, with a global impact affecting millions of people. Detecting and classifying ES is essential for proper medical intervention and management. this study addresses ES detection using EEG analysis, focusing specifically on the context of Bangladesh. this study presents a EEG-based method for detecting epileptic seizures differentiating between seizure and non-seizure signals. Utilizing empirical mode decomposition, essential subcomponents are extracted from EEG signals, from which three distinct features were derived. Neural Network (NN), the well-known machine learning model, is employed for ES classification from the extracted features. the significance of this research lies in the utilization of real-life EEG data from a hospital in Khulna, Bangladesh, rather than relying solely on publicly available benchmark datasets. processing real-life EEG data presents unique challenges, including data preprocessing and variability in EEG signals due to different devices and channel configurations. the proposed model achieved remarkable average accuracies, with 91.05% using 50 hidden neurons in the NN. To validate the effectiveness of the proposed method, outcomes were also compared with publicly available benchmark dataset. the proposed method offers a reliable and efficient approach for ES detection, particularly relevant for real-life healthcare settings in Bangladesh, contributing to improved epilepsy management.

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Emotion Decoding: An Extensive Examination of Electroencephalogram signals Using Explainable machine learning

Emotion Decoding: An Extensive Examination of Electroencepha...

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international conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Anindya Nag Hirak Mondal Sheikh Ridwan Raihan Kabir Md. Rabiul Islam Md. Estiak Ahmed S M Hasan Jamil Computer Science and Engineering Discipline Khulna University Khulna Bangladesh Department of Computer Science and Engineering North Western University Khulna Bangladesh Department of Biomedical Engineering Islamic University Kushtia Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Emotion plays a substantial impact on an individual's mental processes and social interactions. It functions as a connection between an individual's emotions and their behaviors, or it may be stated that it occasionally impacts one's life choices. Given the individual variations in emotional patterns and their manifestations, the investigation of these phenomena should rely on methodologies that are applicable across diverse populations. In order to improve accuracy and pick specific features, the process of emotion detection utilizing brain waves or EEG data necessitates the utilization of effective signal processing techniques. Researchers have been working on several methods of human-machine interface technologies for a considerable period. In recent years, they have achieved significant progress in the automated comprehension of emotions through brain signals. Our research involved the classification and testing of three emotional states using EEG signals collected from the widely accessible EEG Brainwave Dataset: Feeling Emotions from kaggle, utilizing seven machine learning techniques. this study presented a methodology that employed machine learning to identify emotions using the EEG Brainwave dataset. the study also conducted a thorough evaluation of several machine learning algorithms to assess their overall effectiveness. the study employed the Local Interpretable Model-Agnostic Explanations (LIME) to generate interpretable predictions and gain insights into the elements that influence the model's predictions. Despite the interpretability of the ML models, the XGBoost classifier had the greatest accuracy of 99% in recognition.

关键词： Support vector machines Emotion recognition Sociology Predictive models signal processing Brain modeling Feature extraction

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Intelligent Reflecting Surfaces Aided Millimetre Wave Blockage Prediction For Vehicular Communication 6

Intelligent Reflecting Surfaces Aided Millimetre Wave Blocka...

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6th IEEE international Symposium on Telecommunication Technologies, ISTT 2022

作者： Woon, Fu Seong Leow, Chee Yen Universiti Teknologi Malaysia Wireless Communication Centre Faculty of Electrical Engineering Johor Bahru81310 Malaysia

ISBN: (数字)9781665489423

ISBN: (纸本)9781665489423

the ability of millimeter-wave (mmWave) to deliver gigabit throughput has led to its widespread adoption in Fifth Generation (5G) networks. However, mmWave links between Base Station (BS) and users can be easily obstructed by obstacles. In vehicular networks with dynamic environments and mobile users, the mmWave link blockage issue is even more pronounced. In order to preserve the mmWave link in the vehicular network, it is necessary to predict blockages. For blockage prediction, sensor information from Lidar, Radar, and cameras has been considered. Nonetheless, these non-radio frequency methods necessitate the use of additional equipment and signal processing, which raises the implementation cost and complexity. the existing literature also considers the use of BS and user's Radio Frequency (RF) signatures to predict blockage. However, users' mobility has not been taken into account. An Intelligent Reflecting Surface (IRS), on the other hand, has been viewed as a promising method for providing an alternate path by reflecting the mmWave signal between the BS and user in order to improve the reliability of vehicular networks. therefore, this research investigates the IRS-assisted blockage prediction in order to determine the future link status in the vehicular environment with respect to user mobility. the proposed solution employs a number of active elements that are randomly distributed on the IRS to obtain the RF signatures. Furthermore, it utilises machine learning (ML) techniques to learn the pre-blockage wireless signatures, which can predict future blockages. the results indicate that the proposed method can predict blockages between a single IRS and a moving user with a greater than 98 percent accuracy up to one second before they occur. © 2022 IEEE.

关键词： machine learning

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