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Flower automata pattern-based discrimination of fibromyalgia from control subjects using fusion of sleep EEG and ECG signals

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IEEE Access 2025年 13卷 99032-99047页

作者： Barua, Prabal Datta Kobayashi, Makiko Dogan, Sengul Baygin, Mehmet Tuncer, Turker Paul, Jose Kunnel Iype, Thomas Acharya, U.R. Australia Kumamoto University Graduate School of Science and Technology Kumamoto860-8555 Japan Kumamoto860-8555 Japan Firat University Technology Faculty Department of Digital Forensics Engineering Elazig Turkey Erzurum Technical University Faculty of Engineering and Architecture Department of Computer Engineering Erzurum Turkey Government Medical College Department of Neurology Kerala Thiruvananthapuram India University of Southern Queensland School of Mathematics Physics and Computing Springfield Australia

Electroencephalogram (EEG) and electrocardiogram(ECG) signals provide vital insights into brain and heart activity and are widely used in automated medical diagnostics. This study introduces a novel, multimodal fibromyalgia detection system developed by the fusion of EEG and ECG signals recorded during sleep stages 2 and 3. The novelty of the model is the use of dynamic and interpretable feature engineering framework comprising of two innovations: (i) Flower Automata Pattern (FAP) for self-organized pattern-based feature extraction, and (ii) Attention-Driven Wavelet Transform and Absolute Maximum Pooling (ADWTAMP) method for signal decomposition and compression. Three feature selection strategies—Neighborhood Component Analysis (NCA), Chi2, and the intersection of NCA and Chi2 (NCA˄Chi2) —are employed to generate robust feature vectors, which are classified using k-nearest neighbors (kNN) and support vector machine (SVM) under the leave-one-record-out cross-validation (LORO CV) scheme. The final decision is derived through an iterative voting and greedy fusion approach. The proposed model achieved classification accuracies of 99.36% and 98.37% for sleep stages 2 and 3, respectively. Key advantages of the model include its high accuracy, low computational requirements (CPU-only execution), and explainable architecture. To the best of our knowledge, this is the first multimodal automata-based classification framework designed for fibromyalgia detection. © 2013 IEEE.

关键词： Electrocardiography

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Analysis and Design of Mobile Model for DTN

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Chinese Journal of Electronics 2023年第4期18卷 635-638页

作者： Zhiting Lin Yugui Qu Yusheng Ji Baohua Zhao Department of Computer Science University of Science and Technology of China Hefei China Province Key Laboratory of Software in Computing and Communication Hefei China Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

Delay tolerant networks may become unexpectedly partitioned due to node mobility or variation in signal strength. However, most widely used models in some relative works are generally very simplistic. In order to exploit intelligent forwarding algorithms, a novel nodal mobile model of delay tolerant networks is presented to map reality with more accuracy. And several approaches are introduced to analyze the network structure, such as n-cliques, n-clans, degree, closeness and betweenness. Our research results showed that the centralizations became smaller when the wireless connections were concerned. It meant that quite a number of nodes became potential relays because of the new structure of networks.

关键词： Wireless communication Analytical models Wireless sensor networks Protocols Social networking (online) Sensitivity analysis Social network theory

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Comparison of Fusion Methodologies Using CNV and RNA-Seq for Cancer Classification: A Case Study on Non-Small-Cell Lung Cancer 1st

Comparison of Fusion Methodologies Using CNV and RNA-Seq for...

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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Carrillo-Perez, Francisco Morales, Juan Carlos Castillo-Secilla, Daniel Guillen, Alberto Rojas, Ignacio Herrera, Luis Javier Department of Computer Architecture and Technology University of Granada. C.I.T.I.C. Periodista Rafael Gómez Montero 2 Granada18014 Spain

ISBN: (纸本)9783030881627

Lung cancer is one of the most frequent cancer types, and one among those causing more deceases worldwide. Nowadays, in order to improve the diagnosis of cancer more screenings are performed to the same patient and various biological sources are being gathered. Fusing the information provided by these sources can lead to a more robust diagnosis, which can improve the prognosis of the patient. In this work, a comparison of fusion methodologies (early and intermediate) using RNA-Seq and Copy Number Variation data for Non-Small-Cell Lung Cancer classification is performed. We found that great results can be attained using both fusion methodologies, with an AUC of 0.984 for the early fusion and 0.989 for the intermediate fusion, improving those obtained by each source of information independently (0.978 RNA-Seq and 0.910 Copy Number Variation). This work shows that fusion methodologies can enhance the classification of non-small-cell lung cancer, and that these methodologies can be promising for the diagnosis of other cancer types. © 2021, Springer Nature Switzerland AG.

关键词： RNA

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COVID-19 Detection Method from Chest CT Scans via the Fusion of Slice Information and Lung Segmentation 1st

COVID-19 Detection Method from Chest CT Scans via the Fusion...

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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Toledano Pavón, Jesús Morales Vega, Juan Carlos Carrillo-Perez, Francisco Herrera, Luis Javier Rojas, Ignacio Department of Computer Architecture and Technology University of Granada. C.I.T.I.C. Periodista Rafael Gómez Montero 2 Granada18014 Spain

ISBN: (纸本)9783030881627

Due to the high spread of the COVID-19 virus, several diagnosis support systems are being developed in order to detect the disease in a faster and accurate way. In this paper, a stacking method for Computed Tomography (CT) scans has been implemented for the pre-processing step. The method combines both slice normalization and lung segmentation in a single output image using RGB color channels, providing more information from the input slices to the CNN models. The binary classification step starts with a slice-level prediction, which applies fine-tuning to the whole model and dense layers are changed by a custom scheme to improve the performance. Then, a patient-level prediction is performed by fixing a threshold percentage of COVID positive slices that allows to make the final prediction, classifying patients as COVID or NORMAL. The accuracy and metrics obtained show the robustness of the presented method in comparison to using the normalised slices or the masks independently. Given the results obtained, the proposed method can accurately detect the COVID-19 disease and the fusion of information improves the results obtained. © 2021, Springer Nature Switzerland AG.

关键词： COVID-19

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A simulation of a memory subsystem using a highly energy-efficient but erroneous MRAM 9

A simulation of a memory subsystem using a highly energy-eff...

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9th International Symposium on Computing and Networking Workshops, CANDARW 2021

作者： Saito, Daiki Hirofuchi, Takahiro Arai, Hiroko Sato, Yukinori Toyohashi University of Technology Department of Computer Science And Engineering Aichi Japan Digital Architecture Research Center Tokyo Japan Research Center For Emerging Computing Technologies Tsukuba Japan

ISBN: (纸本)9781665428354

In order to reduce power consumption of the main memory of computers, the use of MRAM (magnetoresistive random access memory) instead of DRAM is attractive, since MRAM does not require periodic refresh energy while achieving sufficiently small read/write latencies. Among various types of MRAM devices, VC-MRAM (voltage-controlled MRAM) has a great potential to realize massively energy-efficient memory subsystems. Its write energy is 2 times orders of magnitudes smaller than that of STT-MRAM. The problem with VC-MRAM, however, is that its write error rate (i.e., 10-4 with today's typical technologies) is much higher than the acceptable error rate that has been supposed to be required for the main memory (i.e., 10-9). In this paper, we investigate whether such errors derived from an erroneous memory device cause a problem in actual program execution in the memory subsystem with hierarchical cache memory. We develop a memory subsystem simulator that mimics the structure of the multi-level caches and the main memory based on a VC-MRAM model, and observe cell-level operations in the main memory throughout the execution of benchmark programs. From results, we find the number of bit corruptions depends greatly on the cache size and the error model of the device. This is because the number of memory accesses itself can be significantly reduced by passing through the cache. Furthermore, we observe that the occurrences of bit corruptions more than 3 bits that cannot be handled properly by the hamming ECC codes seldom happen. This observation leads us to the insight that VC-MRAM with Hamming ECC can be used as main memory in some applications if these are executed on CPUs with a large cache memory. © 2021 IEEE.

关键词： MRAM devices

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A Train with Automatic Functioning based on IoT with Solar Energy Source

A Train with Automatic Functioning based on IoT with Solar E...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Padmakala, S. Alkawak, Omar A. Reddy, G. Vidya Sagar Narendranathan, S.K. Muthuraman, M.S. Sendilvelan, S. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamil Nadu Chennai India Al-Mustaqbal University College Department of Air Conditioning and Refrigeration Technical Engineering Babil Hilla Iraq School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Agni College of Technology Department of Mechanical Engineering Tamil Nadu Chennai India Psna College of Engineering and Technology Department of Mathematics Tamil Nadu Dindigul India Dr. M.G.R Educational and Research Institute Department of Mechanical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665474511

The railway industry is one of the most sustainable modes of transport. To reduce the carbon footprints, renewable energy sources are initiated. The proposal of attaching solar panels to railway sleepers, smart garbage bin, lighting, automatic closing and opening of doors that are accompanied with real time monitoring system with multiple technologies. Among the multiple technology the Internet of Things (IoT) plays a significant role by sending information and monitoring the overall functions. The solar energy obtained is used to generate electricity and further they are stored in battery for future usage. © 2023 IEEE.

关键词： Carbon footprint

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PROV-IO+: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems

arXiv

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arXiv 2023年

作者： Han, Runzhou Zheng, Mai Byna, Suren Tang, Houjun Dong, Bin Dai, Dong Chen, Yong Kim, Dongkyun Hassoun, Joseph Thorsley, David Wolf, Matthew Department of Electrical and Computer Engineering Iowa State University United States Department of Computer Science and Engineering The Ohio State University United States Scientific Data Management Group Lawrence Berkeley National Laboratory United States Computer Science Department University of North Carolina Charlotte United States Systems Architecture Lab Samsung United States Samsung Advanced Institute of Technology Samsung Korea Republic of

Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing provenance solutions cannot address the challenges due to their incompatible provenance models and/or system implementations. In this paper, we analyze four representative scientific workflows in collaboration with the domain scientists to identify concrete provenance needs. Based on the first-hand analysis, we propose a provenance framework called PROV-IO+, which includes an I/O-centric provenance model for describing scientific data and the associated I/O operations and environments precisely. Moreover, we build a prototype of PROV-IO+ to enable end-to-end provenance support on real HPC systems with little manual effort. The PROV-IO+ framework can support both containerized and non-containerized workflows on different HPC platforms with flexibility in selecting various classes of provenance. Our experiments with realistic workflows show that PROV-IO+ can address the provenance needs of the domain scientists effectively with reasonable performance (e.g., less than 3.5% tracking overhead for most experiments). Moreover, PROV-IO+ outperforms a state-of-the-art system (i.e., ProvLake) in our experiments. © 2023, CC BY-NC-SA.

关键词： Life cycle

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ANALYSIS OF CONCRETE CRACKS AND FATIGUE IN SMART CITIES USING YOLOV3

ANALYSIS OF CONCRETE CRACKS AND FATIGUE IN SMART CITIES USIN...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Archana, U. Sharma, Sarvdaman Singh, Sitesh Kumar Sureshkumar, R. Senthilkumar, P. Harish Babu, T. Department of Computer Science and Design R.M.K. Engineering College Chennai India Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Oromia Nekemte Ethiopia Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai India KL School of Architecture Koneru Lakshmaiah Education Foundation Greenfield Vaddeswaram Andhra Pradesh Guntur India Department of Electrical and Electronics Engineering Sethu Institute of Technology Tamilnadu India

ISBN: (纸本)9798350305708

This study focuses on the application of the YOLOv3 (You Only Look Once version 3) object detection algorithm for the analysis of concrete cracks and fatigue within Smart Cities. Concrete infrastructure plays a crucial role in urban environments, and its structural integrity is paramount for ensuring public safety and sustainable development. The proposed approach utilizes YOLOv3 to identify and localize instances of concrete cracks and signs of fatigue in images and videos captured from various urban settings. By leveraging deep learning techniques, this research aims to enhance the efficiency and accuracy of monitoring and maintaining concrete structures, contributing to the overall resilience and longevity of Smart Cities. The findings of this study present valuable insights into the potential of utilizing advanced computer vision methods for proactive infrastructure management in the urban landscape. © 2023 IEEE.

关键词： Concrete cracks Fatigue analysis Infrastructure monitoring Smart Cities YOLOv3

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Tighter Bounds of Speedup Factor of Partitioned EDF for Constrained-Deadline Sporadic Tasks 42

Tighter Bounds of Speedup Factor of Partitioned EDF for Cons...

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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： Liu, Xingwu Chen, Zizhao Han, Xin Sun, Zhenyu Guo, Zhishan School of Mathematical Sciences Dalian University of Technology China SK-Lab of Media Convergence Production Technology and Systems Chinaso Inc School of Software Technology Dalian University of Technology China SK-Lab of Computer Architecture ICT CAS University of CAS Beijing China Department of Electrical and Computer Engineering University of Central Florida United States

ISBN: (纸本)9781665428026

Even though earliest-deadline-first (EDF) is optimal in terms of uniprocessor schedulability, it is co-NP-hard to precisely verify uniprocessor schedulability for constrained-deadline task sets. The most efficient way to solve this problem in polynomial time is via a partially linear approximation of the demand bound function. Such approximation leads to a simple uniprocessor schedulability testing with speedup factor ρ. Such a result further leads to Deadline-Monotonic Partitioned-EDF on multi-processors with speedup factor of 1 + ρ − 1/m (where m is the number of processors). The current state of the art results indicate that ρ is within the range [1.5, 14/9]. Especially, it has been a conjecture that ρ = 1.5. This paper improves the range of ρ to (1.5026, 1.5380). The improved lower bound disproves the conjecture of lower bound 1.5. A novel technique is to construct an auxiliary function that is larger than the approximate demand bound function but keeps the supremum ρ unchanged. It solves the dilemma that beating the lower bound 1.5 requires extremely large task sets, while the large size makes it difficult to check the schedulability. This technique not only enables us to disprove 1.5 by a task set of only eight tasks, but also sheds light on future work in transferring/downsizing task sets and deriving utilization bound based tests for various workload abstraction models, such as DAG tasks. ©2021 IEEE

关键词： Polynomial approximation

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Event Graphs: Syntax, Semantics, and Implementation 23

Event Graphs: Syntax, Semantics, and Implementation

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Proceedings of the Winter Simulation Conference

作者： Murat M. Gunal Yahya Ismail Osais Gerd Wagner Faculty of Engineering and Architecture Fenerbahce University Ataşehir Istanbul TURKEY Computer Engineering Department Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran SAUDI ARABIA Brandenburg University of Technology Cottbus GERMANY

ISBN: (纸本)9798350369663

This tutorial aims to introduce Event Graphs (EGs), invented 40 years ago by Lee Schruben to allow event-based modeling of discrete dynamic systems. Their simplicity and naturalness in causality modelling and simulation modelling made EGs popular in research and practice. In a simulation, an event causes state changes in a system as well as other events to happen in the future. EGs provide a parsimonious diagram representation for the Event Scheduling paradigm of Discrete Event Simulation. We first introduce their visual syntax and informal semantics, and then present a recent extension by adding objects to EGs. Our tutorial also includes an introduction to the formal semantics of EGs and a Python implementation for executing EGs.

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