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9th International Workshop on Brain Lesion workshop, BrainLes 2023 and 3rd Stroke Workshop on Imaging and Treatment CHallenges, SWITCH 2023 Held in Conjunction with 26th Medical Image Computing and computer Assisted Intervention, MICCAI 2023

作者： Kasliwal, Aditya Sagaram, Sankarshanaa Srivastava, Laven Seth, Pratinav Khan, Adil Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Mechanical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031761591

Semantic segmentation of brain tumours is a fundamental task in medical image analysis that can help clinicians in diagnosing the patient and tracking the progression of any malignant entities. Accurate segmentation of brain lesions is essential for medical diagnosis and treatment planning. However, failure to acquire specific MRI imaging modalities can prevent applications from operating in critical situations, raising concerns about their reliability and overall trustworthiness. This paper presents a novel multi-modal approach for brain lesion segmentation that leverages information from four distinct imaging modalities while being robust to real-world scenarios of missing modalities, such as T1, T1c, T2, and FLAIR MRI of brains. Our proposed method can help address the challenges posed by artifacts in medical imagery due to data acquisition errors (such as patient motion) or a reconstruction algorithm’s inability to represent the anatomy while ensuring a trade-off in accuracy. Our proposed regularization module makes it robust to these scenarios and ensures the reliability of lesion segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantic Segmentation

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Research on Image Subject Accessing Model under Foggy Weather 14

Research on Image Subject Accessing Model under Foggy Weathe...

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14th International Conference on Electronics, Communications and Networks, CECNet 2024

作者： Li, Zhiyi Zhang, Songtao Meng, Fanlei Zhang, Lijuan School of Instrument Science and Electrical Engineering Jilin University Jilin China Computer Science and Technology Guohao Academy Tongji University Shanghai200092 China College of Internet of Things Engineering Wuxi University Wuxi214105 China School of Computer Science and Engineering Changchun University of Technology Changchun130012 China

ISBN: (纸本)9781643685632

Imaging results under outdoor conditions are easily affected by foggy weather which may produce blur, low contrast, intensity shift and other visual degradation effects. This paper proposed an accessing model of fog density based on discrete wavelet transform features of multi-channel mean subtracted contrast normalized coefficients. Besides, corner feature intensity is utilized to extract highlevel features of foggy images. The accessing model used support vector regression to predict fog density with the help of a fog density accessing dataset collected from human eyes. The experimental results show that the subjective accessing method proposed in this paper has a high correlation with human eyes accessing results on the testing dataset, and is better than the general referenceless image spatial quality evaluator model. The accessing model can reflect the image quality as human eyes in foggy scenes. © 2024 The Authors.

关键词： Support vector regression

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Solar Observation with the Fourier Transform *** Results of Solar Spectrum near the CO 4.66μm and Mg I 12.32μm

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Research in Astronomy and Astrophysics 2023年第12期23卷 128-137页

作者： Xianyong Bai Zhiwei Feng Zhiyong Zhang Yuanyong Deng Xiaoming Zhu Kaifan Ji Song Feng Xiao Yang Yongliang Song National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 101408China Yunnan Observatories Chinese Academy of SciencesKunming 650011China Faculty of Information Engineering and Automation/Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and TechnologyKunming 650500China

An infrared solar spectrum observed by ground-based telescopes is seriously affected by the background radiation both from the telescope and sky,relative to the visible *** accuracy is also influenced by the spectral resolution of the Fourier transform *** the paper,we developed a CO_(2)gas cell and installed it in the sample compartment to calibrate the spectral resolution of the Bruker IFS-125HR at infrared *** measured spectral resolution is 0.00342±0.00086 cm^(-1)and 0.0059±0.00024 cm^(-1)at the wavenumbers of798 cm^(-1)and 2136 cm^(-1),*** also updated a fully reflective sunlight feeding system to observe the solar spectrum near CO 4.66μm and Mg I 12.32μ*** quickly pointing the sunlight feeding system about1 degree away from the solar disk center,we are able to measure the background radiation from the telescope and the sky at Huairou Solar Observing *** removing the background radiation,our observed solar spectrum at CO 4.66μm is consistent with that from the National Solar *** Mg I 12.32μm working line selected by the Accurate Infrared Magnetic Field Measurements of the Sun(AIMS)project is also *** method is helpful not only for the spectral resolution calibration and background radiation correction of AIMS but also for other infrared astronomical telescopes.

关键词： Sun infrared-methods observational-instrumentation interferometers

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Developing a Convolutional Neural Model for Segmentation of Medical Images 1

Developing a Convolutional Neural Model for Segmentation of ...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Arunachalam, M. Kumar, P. Mohan Selvam, L. Kumar, S. Senthil Arunkumar, B. Sri Krishna College of Engineering and Technology Department of Information Technology Coimbatore India Hindusthan Institute of Technology & Sciences Department of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Sethu Institute of Technology Department of Computer Science and Engineering Kariapatti India

ISBN: (纸本)9798350359756

Medical image segmentation is a new biomedical image processing method that has made a significant contribution to sustainable health care. It has now become a major study area in the realm of computer vision research. Medical image processing based on deep convolutional neural networks has become a research hotspot due to the fast development of deep learning techniques. An additional cascade component is included to the architecture in this article to better replicate spatial closure tags' direct dependences. It is also utilised for post-segmentation processing, resolving the conflict between classification performance and network intensity and number of mixing times in a typical convolutional net. Numerous state-of-the-art deep recognition models, and a multi-atlas segmentation methodology, are outperformed by our methodology. © 2023 IEEE.

关键词： Image segmentation

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LPCC Based Music Genrefication Using Hybrid Computational Model 7

LPCC Based Music Genrefication Using Hybrid Computational Mo...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Sul, Astha Sarda, Harshit Billa, Aishwarya Mishra, Tusar Kanti School of Computer Science and Engineering Vellore Institute of Technology Vellore India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Dept. of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350304268

This work deals with spontaneous music genrefication through computational models which in the recent times has been gaining importance rapidly. Through these hybrid computational models implemented users get an enhanced level of satisfaction when their choice of music files genre with least latency is gained. The paper includes the use of LPCC attributes to obtain the features of the music files along with robust classification models such as neural networks. For this purpose, one thousand audio files are taken as the sample set. The input audio file is pre-processed and suitable LPCC features are computed and stored into final vector. The pool of vectors of numerous audio samples are finally utilized to train a neural model. The proposed work trains the model for ten very distinct categories of music such as hip-hop, jazz, metal, pop, blues, classical, country, disco, reggae, and rock. Further, a comparison is also made between all the classifiers such as SVM, ANN and random forest (RF). Comparatively the best accuracy rate of 85.53\% has been achieved for the proposed work that validates its effectiveness. © 2023 IEEE.

关键词： ANN gtzan dataset LPCC Music Genre

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An Effective Counterfeit Medicine Authentication System Using Blockchain and IoT 4

An Effective Counterfeit Medicine Authentication System Usin...

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4th International Conference for Emerging technology, INCET 2023

作者： Shalini, S. Sheela, S. Abhishek, S. Bhavyashree, P. Gunashree, C. Rohan, K.S. VTU University Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350335750

The current pharmaceutical sector is dealing with several issues. The pharmaceutical sector nowadays is dominated by counterfeit medications. Health research funding groups estimate that between 10 and 30 percent of the pharmaceuticals in the market are counterfeited. Globally, this fatal issue is creating several serious health risks. WHO estimates that 30% of all medications worldwide are fake. In practically every region of the world, this issue is becoming worse by the second. As drugs travel across several scattered networks, it is challenging to identify fakes. By integrating Blockchain technology into the network, the pharmaceutical supply chain may be made safer. Every area of the network keeps track of the medications through a record, making it possible to identify any introduction of fake medications. System transparency and dependability are improved with Blockchain security. By employing Blockchain technology, the initiative seeks to guarantee medicine quality, transaction security, and data protection. By eliminating information about expired and fake medications from the system, this effort increases system dependability and helps to make the Blockchain a stable and widely used technology. To determine whether or not counterfeit medications have been distributed, IOT technology is also employed. © 2023 IEEE.

关键词： Smart contract

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A Novel Pressure Sensor with Composite Sensitive Film for High Precision Measurement at Broad Range 38

A Novel Pressure Sensor with Composite Sensitive Film for Hi...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Chen, Xiaopeng Ling, Sijia Tong, Hanyang Xiao, Yujing Zhang, Jin Yu, Zhengyin Jin, Qinghui Ningbo University The Faculty of Electrical Engineering and Computer Science China Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology China

ISBN: (纸本)9798331508890

Microelectromechanical system (MEMS) based pressure sensors have been utilized for decades;however, new trends in pressure sensors have recently emerged, such as increased sensitivity, a broader range and reduced chip size. This paper presents a tiny 0.6mm×0.8mm piezoresistive pressure sensor that offers exceptional sensitivity (0.27mV/kPa/3.3V) at a wide range of pressures (0-1MPa). The innovative composite pressure-sensitive film of the sensor is composed of a 10μm "*"-shaped single-crystalline silicon structure and a remarkably thin thickness of only 2.5μm poly-silicon film. It is developed utilizing the thin-film under bulk-silicon technique. And simultaneous improvement of detection sensitivity and linearity is achieved by etching out the nonlinear deformation region of the film. © 2025 IEEE.

关键词： Pressure sensors

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Comparative Analysis of Customised Neural Network Model and ML Classifiers for Brain Stroke Prediction

Comparative Analysis of Customised Neural Network Model and ...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Tejashree, S. Varshitha, S. Kodipalli, Ashwini Rao, Trupthi Kumarswamy, S. Global Academy of Technology Department of Cse Karnataka Bangalore India Global Academy of Technology Artificial Intelligence and Data Science and Engineering Karnataka Bangalore India Global Academy of Technology Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

An abrupt interruption of the brain's blood supply leads to stroke, which causes severe damage to the brain. Due to this condition, there are people disabled and thus leading to death. In this work, the dataset contains ten features where environmental tests are done for the stroke which is utilized to evaluate the efficiency of Machine Learning classifiers and Deep Learning Artificial Neural Network. The purpose of this paper is to provide a forecast of the likelihood of a brain stroke occurring at an early stage using Machine Learning and Deep Learning techniques. The accuracy and efficiency of various types of Machine Learning models and customized Deep Learning models have been compared based on this impression. The custom Deep Learning model here used is an Artificial Neural Network with dropouts having the highest accuracy of 97.06% using RMSprop optimizer and a Decision Tree Machine Learning Classifier, achieved the highest accuracy of 95.98%. © 2023 IEEE.

关键词： Statistical tests

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Distributed Truss Computation in Dynamic Graphs

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Tsinghua science and technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

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RISC-V Multicore for Miniature DNA Sequencers

RISC-V Multicore for Miniature DNA Sequencers

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Beyene, Abel Wu, ZhongPan Hammad, Karim Ghafar-Zadeh, Ebrahim Magierowski, Sebastian York University Electrical Engineering and Computer Science Toronto Canada Technology and Maritime Transport Arab Academy for Science Cairo Egypt

ISBN: (纸本)9798350300260

Hand-sized DNA sequencers have demonstrated excellent scientific utility. They also present exciting opportunities for the expansion of DNA analysis to a diverse range of uses and users. But this potential is currently blunted by mini-sequencers' lack of on-board computing. In this paper we explore the potential of embedded processors to address this gap. We outline the general hardware and software organization of existing systems and identify a deep learning bioinformatics task as an exemplary workload for processors embedded in miniature sequencers. We then describe a candidate multicore RISC-V arrangement and experimentally study the relative performance, power, and cost trade-offs for alternate configurations of this arrangement. The experiments show no saturation in performance scaling up to at least 6 cores with maximum throughput and efficiency achieved with 512KB and 128KB L2 cache sizes respectively. © 2023 IEEE.

关键词： DNA sequencing embedded computing machine learning multicore RISC-V

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