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Research on Indoor Passive Location Based on LoRa Fingerprint 1

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6th EAI international conference on machine learning and Intelligent Communications, MLICOM 2021

作者： Wang, Heng Chen, Yuzhen Zhang, Qingheng Zhang, Shifan Ye, Haibo Li, Xuan-Song School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

ISBN: (数字)9783031044090

ISBN: (纸本)9783031044083

Indoor positioning based on signal fingerprint has always been a hot research topic. But most research requires the object or person to be positioned to carry a positioning device, which is not applicable in some special scenarios. this paper selects LoRa (Long Range) as the research target and proposes an indoor passive positioning system based on LoRa fingerprint. We design and implement the signal sent from the LoRa node devices to the LoRa gateway device and get the RSSI of the nodes, also send it to the proxy server for receiving and processing. In the data processing stage, the difference-limiting filtering algorithm is used to eliminate abnormal data, and the GaussianNB (Gaussian-Naive Bayes) algorithm is used to learn and train the model. through experiments, the accuracy rates of the two-class and multi-class prediction in the range of 3m are 97.1% and 95.5%, respectively, which verifies the feasibility of applying LoRa signal to indoor passive positioning. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Indoor positioning systems

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Research on Tibetan-Chinese Neural machine Translation Integrating Statistical Method 23

Research on Tibetan-Chinese Neural Machine Translation Integ...

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Proceedings of the 2023 6th international conference on machine learning and Natural Language processing

作者： Maoxian Zhou Qinghai Normal University China

ISBN: (纸本)9798400709241

In recent years, with the emergence of deep learning methods, Neural machine Translation has become a new research direction of machine translation. Due to the scarcity of digital resources in Tibetan, there is only a small-scale Tibetan-Chinese bilingual parallel corpus to train translation models. In the case of insufficient resources, the performance of Neural machine Translation decreases significantly. In this regard, considering that the word alignment results in Tibetan-Chinese Statistical machine Translation are better, and the alignment information in Tibetan-Chinese Neural machine Translation model is significantly different from that in Tibetan-Chinese Statistical machine Translation, this paper proposes a Tibetan-Chinese Neural machine Translation method that integrates statistical methods. We using Statistical machine Translation method to generate word alignment information of Tibetan-Chinese parallel corpus to supervise the training process of Tibetan-Chinese Neural machine Translation model to make the model achieve more accurate translation and alignment effect. Experimental results show that this method can effectively improve translation quality in low resource environment.

关键词： Low Resource Neural machine Translation Statistical machine Translation Tibetan-Chinese machine Translation Word Alignment

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Unsupervised machine learning for Anomaly Detection in Wi-Fi Based IoT Networks

Unsupervised Machine Learning for Anomaly Detection in Wi-Fi...

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

作者： Fatima tu Zahra Yavuz S. Bostanci Mujdat Soyturk VeNIT Lab. Marmara University Istanbul Turkey BigTRI Bilişim A.Ş. Istanbul Turkey Dept. of Computer Engineering Marmara University Istanbul Turkey

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

With the advent of 5G, there has been a significant surge in the deployment of Internet of things (IoT) devices. this proliferation, while promising, has simultaneously highlighted crucial security vulnerabilities of IoT devices due to their inherent lack of robust security features. this research focuses on enhancing the security of the IoT ecosystem through continuous monitoring to identify potential network problems and cyber threats, which often present themselves as anomalous network patterns. this paper evaluates seven unsupervised machine learning (ML) models for anomaly detection in Wi-Fi networks, utilizing data from an IoT test bed under both normal and anomalous conditions. the models are trained on real, unlabeled IoT network data, aiming to detect irregularities in link quality and connectivity. the findings are based on the assessment of these models using a separate, labeled dataset for validation. the models are subsequently deployed and assessed within the IoT system through real-time evaluation and their performance is compared. this method improves both the security and operational reliability of IoT networks by proactively identifying and resolving issues, thereby reducing downtime and ensuring the continuous operation of IoT systems.

关键词： Biological system modeling Computational modeling Ecosystems Real-time systems Data models Internet of things Reliability Anomaly detection Wireless fidelity Monitoring

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3D AttU-NET for Brain Tumor Segmentation with a Novel Loss Function

3D AttU-NET for Brain Tumor Segmentation with a Novel Loss F...

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international conference on Information Systems and Computer Networks (ISCON)

作者： Rahul Roy B. Annappa Shubham Dodia Dept. of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangalore India

In the United States of America (USA), every year 150,000 patients are registered with a secondary brain tumor that is not generated in the brain. this necessitates the need for early brain tumor detection, which in turn will help patients to live longer. For clinical evaluation and treatment, precise segmentation of brain tumors in MRI images is required. this process can be aided by machine learning and efficient image processing, but manual imaging can be time-consuming. In this study, we aim to develop an 3D automated segmentation algorithm with a novel loss function. A 3D attention UNET CNN model was trained using the novel loss function, which was calculated by taking the weighted average of dice loss and focal loss to overcome the class imbalance. Results show the enhancement in the segmentation performance of attention UNET model with an average increase of 5% in the Dice coefficient for all three classes. However, the model’s performance was not as strong for enhanced and core tumors. Further research may be needed to optimize performance in these areas.

关键词： Solid modeling Image segmentation three-dimensional displays machine learning algorithms Magnetic resonance imaging Manuals Brain modeling

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Fiber-Optic signal Based Deep learning for Sleep Medicine Research

Fiber-Optic Signal Based Deep Learning for Sleep Medicine Re...

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Pattern Recognition and Artificial Intelligence (PRAI), international conference on

作者： Hua Wang Chen-Jie Li Xiu-Tao Cui Long Li School of Mechatronic Engineering and Automation Shanghai University Shanghai P. R. China Shanghai Key Laboratory of Power Station Automation Technology Shanghai P. R. China DewertOKIN Technology Group Co. Ltd JiaXing P. R. China Bewatec Medical Devices Co. Ltd JiaXing P.R. China

As living standards continue to improve, people are becoming increasingly concerned about their own health and the quality of their sleep. However, currently available sleep monitoring devices often have issues with uncomfortable wear and low data accuracy, which limit users’ experience and the effectiveness of data analysis. At the same time, there is increasing attention to real-time monitoring technology based on human physiological indicators in response to the demand for personalized healthcare management. In this context, fiber optic signal technology has emerged. By utilizing fiber optic sensing technology, it is possible to non-invasively acquire subtle human signals, such as respiratory rate and heart rate. this paper proposes an algorithm combining microprocessor and machine learning technology, which can effectively identify the user’s sleep state and Physical change, and improve the accuracy of prediction.

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Classification of Electromyography signal from Residual Limb of Hand Amputees 6th

Classification of Electromyography Signal from Residual Limb...

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6th international conference on Electrical, Control and Computer Engineering, InECCE 2021

作者： Norali, Ahmad Nasrul Noor, Anas Mohd Zakaria, Zulkarnay Al-Mahdi, Yasser Said Mohammed Fook, Chong Yen Bakar, Asyraf Hakimi Abu Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Arau02600 Malaysia Universiti Malaysia Perlis Perlis Arau02600 Malaysia

ISBN: (纸本)9789811686894

Several researchers had worked on collecting electromyography (EMG) signal from amputees and come out with dataset that could be utilized for study in EMG signal processing and classification for decoding of amputee movement intention. this paper presents the work on classification of EMG signal based on the residual limb of amputees with intuitive hand movement based on interactive exercises. Dataset is obtained from NINAPRO public database website where 11 amputee subjects performed intuitive exercise of 17 hand gestures and EMG signal is acquired from the residual arm. Eight feature extraction methods are performed to obtain the EMG feature which are Mean, Minimum, Median, Skewness, Kurtosis, Approximate Entropy, Fuzzy Entropy and Kolmogorov Complexity. Two classifiers are used for EMG classification which are k-Nearest Neighbour and Ensemble classifier. Results shows average accuracy of 87.65% with Ensemble classifier for classification of movement exercise with all features of EMG is used as input to classifier. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Classification of Vulnerable Road Users based on Range-Doppler Maps of 77 GHz MIMO Radar using Different machine learning Approaches 22

Classification of Vulnerable Road Users based on Range-Doppl...

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6th international conference on Graphics and signal processing, ICGSP 2022

作者： Bayram, Fatih S. Pütz, Florian Weiß, Julian Radtke, Roman Jesser, Alexander Stache, Nicolaj C. Institute for Intelligent Cyber-Physical Systems Heilbronn University of Applied Sciences Germany Center for Machine Learning Heilbronn University of Applied Sciences Germany

ISBN: (纸本)9781450396370

this paper involves the development of an intelligent delineator for road traffic detecting potential conflict situations between motor vehicles and vulnerable road users at an early stage. By emitting warning signals, collisions between the road users concerned can then be prevented. the prototype used here includes, among other sensors, a high-resolution FMCW radar capable of detecting, and imaging objects. the goal of this work is to develop a machine learning (ML) model for object classification of vulnerable road users in radar frames. A 77 GHz chirp-sequence radar is used to record Range-Doppler maps from object classes of car, bicyclist, pedestrian and empty street at different locations. Objective of this is to cover different levels of background noise in the data caused by the different environments due to trees or bushes. For the data acquisition, simple traffic scenarios have been simulated at Heilbronn University. In selecting a suitable ML algorithm for the classifier, the main challenge was that modern machine learning methods are data-based models which require a lot of data and are generally lacking in explainability, such as neural networks. However, the great advantage is that the correlations in the data are learned automatically. With knowledge-based methods, on the other hand, the big advantage is that they are explainable and require much less data, but assume an extensive domain knowledge. Hybrid learning, also called Informed ML, represents a combination of the methods previously mentioned and their advantages. In this paper, one approach from each of these methods is selected as well as trained, and its results are compared to each other. the respective approaches investigated are a deep neural network (DNN), a Support Vector machine (SVM), and a hybrid model of a SVM and a specific neural network for feature extraction called Autoencoder (AE). In this comparison the SVM performs with prediction accuracies around 80%. the hybrid model perfo

关键词： Support vector machines

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A Review on Workload Characteristics for Multi Core Embedded Architectures using machine learning and Deep learning Techniques 6

A Review on Workload Characteristics for Multi Core Embedded...

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6th international conference on Wireless Communications, signal processing and Networking (IEEE WiSPNET)

作者： Jayanthi, E. Vallikannu, A. L. Hindustan Inst Technol Chennai Tamil Nadu India Hindustan Inst Technol ECE Chennai Tamil Nadu India

ISBN: (纸本)9781665440868

Optimization of energy consumption in system on-chip (SoC) become a challenging task in real-time. Detection of best core for each workload is an additional critical task. To overcome these challenges, we analyzed the different machine learning and deep learning algorithms for mapping each workload on the Quad-core platforms. In this paper, we adopted support vector machines (SVM), naive baize, random forest and KNN for prediction of the best core for each workload. Initially, we observe the workload characterization in terms of memory, instruction cycles, branch data's and developed as a database. In second phase, we deployed the ML and DL algorithms with trained database to predict accurately the best core for each workload. In the third phase, prediction accuracy, energy consumption metrics are observed and compared with the traditional algorithms. the proposed model is executed on Rasp -pi Quad-Core hardware platform and ML algorithms are simulated on the python IDE. Simulation results illustrates the prediction accuracy is achieved up to 99% on LSTM prediction for IomT benchmark and 8.6 % on the energy consumption metric when compared to other ML techniques.

关键词： Core prediction Embedded workloads Rasp-pi 3 machine learning Energy Consumption

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Research on Optimizing Real-Time Data processing in High-Frequency Trading Algorithms using machine learning

Research on Optimizing Real-Time Data Processing in High-Fre...

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

作者： Yuxin Fan Zhuohuan Hu Lei Fu Yu Cheng Liyang Wang Yuxiang Wang School of Engineering and Applied Science University of Pennsylvania Toronto Canada Independent Researcher San Francisco USA School of Computer Science and Engineering Changshu Institute of Technology Suzhou China Independent Researcher Mclean VA USA Olin Business School Washington University in St. Louis St. Louis MO USA School of Business Stevens Institute of Technology New York NY USA

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

High-frequency trading (HFT) represents a pivotal and intensely competitive domain within the financial markets. the velocity and accuracy of data processing exert a direct influence on profitability, underscoring the significance of this field. the objective of this work is to optimise the real-time processing of data in high-frequency trading algorithms. the dynamic feature selection mechanism is responsible for monitoring and analysing market data in real time through clustering and feature weight analysis, with the objective of automatically selecting the most relevant features. this process employs an adaptive feature extraction method, which enables the system to respond and adjust its feature set in a timely manner when the data input changes, thus ensuring the efficient utilisation of data. the lightweight neural networks are designed in a modular fashion, comprising fast convolutional layers and pruning techniques that facilitate the expeditious completion of data processing and output prediction. In contrast to conventional deep learning models, the neural network architecture has been specifically designed to minimise the number of parameters and computational complexity, thereby markedly reducing the inference time. the experimental results demonstrate that the model is capable of maintaining consistent performance in the context of varying market conditions, thereby illustrating its advantages in terms of processing speed and revenue enhancement.

关键词： Accuracy Profitability Computational modeling Scalability signal processing algorithms Process control Reinforcement learning Feature extraction Data processing Real-time systems

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A Comparative Analysis of machine learning and Deep learning Approaches for Circular RNA Classification

A Comparative Analysis of Machine Learning and Deep Learning...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Vivek Rajendran Harishchander Anandaram Sachin Kumar S K. P. Soman S Dhivya Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India School of Computing Kunsan National University Gunsan Republic of South Korea

Circular RNAs (circRNAs) have emerged as crucial regulators of gene expression and potential biomarkers in various biological processes and diseases. However, accurately classifying and analyzing circRNAs necessitates effective representation of their sequences, a challenge that has led to significant research gaps. To address this, the present research project aims to explore and compare the performance of different methods and models, encompassing both machine learning algorithms and deep learning architectures, for sequence classification tasks. the project employs the k-mer extraction technique and the DNA2Vec algorithm to achieve vector representation or sequence embedding. these techniques enable the conversion of DNA sequences into numerical representations suitable for processing by machine learning and deep learning algorithms. A publicly available dataset is utilized for training the models, and their performance is assessed using metrics such as accuracy, precision, recall, and F1 score. Several machine learning algorithms, including K Nearest Neighbour, Random Forest, Logistic Regression, Naive Bayes, SVM (Linear), and SVM (Polynomial), are tested for classifying circRNA and IncRNA sequences. these algorithms exhibit varying levels of performance in accurately categorizing the sequences. Notably, the SVM (Polynomial) algorithm achieves the highest accuracy of 89.4%, demonstrating its proficiency in classifying the sequences with precision. In contrast, the deep learning algorithm ACNN-BLSTM, which combines a convolutional neural network with a recurrent neural network, surpasses the baseline models with an accuracy of 94.0%. this highlights the importance of incorporating both local and sequential information for accurate circRNA prediction. the findings shed light on the strengths and limitations of each model, providing valuable insights into their suitability for circRNA classification and functional analysis. the study emphasizes the significance of

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