• Investigative Magnetic Resonance Imaging
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• 제25권2호
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• pp.81-92
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• 2021

The role of neuroimaging in patients with acute ischemic stroke has been gradually increasing. The ultimate goal of stroke imaging is to make a streamlined imaging workflow for safe and efficient treatment based on optimized patient selection. In the era of multimodal comprehensive imaging in strokes, imaging based on computed tomography (CT) has been preferred for use in acute ischemic stroke, because, despite the unique strengths of magnetic resonance imaging (MRI), MRI has a longer scan duration than does CT-based imaging. However, recent improvements, such as multicoil technology and novel MRI acceleration techniques, including parallel imaging, simultaneous multi-section imaging, and compressed sensing, highlight the potential of comprehensive MR-based imaging for strokes. In this review, we discuss the role of stroke imaging in acute ischemic stroke management, as well as the strengths and limitations of MR-based imaging. Given these concepts, we review the current MR acceleration techniques that could be applied to stroke imaging and provide an overview of the previous research on each essential sequence: diffusion-weighted imaging, gradient-echo, fluid-attenuated inversion recovery, contrast-enhanced MR angiography, and MR perfusion imaging.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.257-268
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• 2022

When there is a missing value in the raw data, if ignore the missing values and proceed with the analysis, the accuracy decrease due to the decrease in the number of sample. The method of imputation and analyzing patterns and significant values can compensate for the problem of lower analysis quality and analysis accuracy as a result of bias rather than simply removing missing values. In this study, we proposed to study irregular data patterns and missing processing methods of data using machine learning techniques for the study of correction of missing values. we would like to propose a plan to replace the missing with data from a similar past point in time by finding the situation at the time when the missing data occurred. Unlike previous studies, data correction techniques present new algorithms using DNN and KNN-MLE techniques. As a result of the performance evaluation, the ANAE measurement value compared to the existing missing section correction algorithm confirmed a performance improvement of about 0.041 to 0.321.

• Korean Journal of Radiology
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• 제25권1호
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• pp.86-102
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• 2024

Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and surgical guidance. However, available imaging modalities (computed tomography [CT], conventional magnetic resonance imaging [MRI], and 18fluorodeoxyglucose positron emission tomography [PET]/CT) have limitations. The advent of new imaging techniques and novel molecular imaging agents have revealed molecular processes in the tumor microenvironment as an application for the early diagnosis and assessment of PM as well as real-time guided surgical resection, which has changed clinical management. In contrast to clinical imaging, which is purely qualitative and subjective for interpreting macroscopic structures, radiomics and artificial intelligence (AI) capitalize on high-dimensional numerical data from images that may reflect tumor pathophysiology. A predictive model can be used to predict the occurrence, recurrence, and prognosis of PM, thereby avoiding unnecessary exploratory surgeries. This review summarizes the role and status of different imaging techniques, especially new imaging strategies such as spectral photon-counting CT, fibroblast activation protein inhibitor (FAPI) PET/CT, near-infrared fluorescence imaging, and PET/MRI, for early diagnosis, assessment of surgical indications, and recurrence monitoring in patients with PM. The clinical applications, limitations, and solutions for fluorescence imaging, radiomics, and AI are also discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권5호
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• pp.1369-1389
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• 2024

In the era of 6G, the rapid increase in communication data volume poses higher demands on traditional channel estimation techniques and those based on deep learning, especially when processing large-scale data as their computational load and real-time performance often fail to meet practical requirements. To overcome this bottleneck, this paper introduces quantum computing techniques, exploring for the first time the application of Quantum Generative Adversarial Networks (QGAN) to broadband channel estimation challenges. Although generative adversarial technology has been applied to channel estimation, obtaining instantaneous channel information remains a significant challenge. To address the issue of instantaneous channel estimation, this paper proposes an innovative QGAN with a dual-module design in the generator. The adversarial loss function and the Mean Squared Error (MSE) loss function are separately applied for the parameter updates of these two modules, facilitating the learning of statistical channel information and the generation of instantaneous channel details. Experimental results demonstrate the efficiency and accuracy of the proposed dual-module QGAN technique in channel estimation on the Pennylane quantum computing simulation platform. This research opens a new direction for physical layer techniques in wireless communication and offers expanded possibilities for the future development of wireless communication technologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1540-1561
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• 2024

This research proposes a novel approach for Tamil Handwritten Character Recognition (THCR) that combines feature selection and ensemble learning techniques. The Tamil script is complex and highly variable, requiring a robust and accurate recognition system. Feature selection is used to reduce dimensionality while preserving discriminative features, improving classification performance and reducing computational complexity. Several feature selection methods are compared, and individual classifiers (support vector machines, neural networks, and decision trees) are evaluated through extensive experiments. Ensemble learning techniques such as bagging, and boosting are employed to leverage the strengths of multiple classifiers and enhance recognition accuracy. The proposed approach is evaluated on the HP Labs Dataset, achieving an impressive 95.56% accuracy using an ensemble learning framework based on support vector machines. The dataset consists of 82,928 samples with 247 distinct classes, contributed by 500 participants from Tamil Nadu. It includes 40,000 characters with 500 user variations. The results surpass or rival existing methods, demonstrating the effectiveness of the approach. The research also offers insights for developing advanced recognition systems for other complex scripts. Future investigations could explore the integration of deep learning techniques and the extension of the proposed approach to other Indic scripts and languages, advancing the field of handwritten character recognition.

• 농업과학연구
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• 제47권1호
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• pp.119-130
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• 2020

With the current rapid growth and increase in the world's population, the demand for nutritious food and fibers and fuel will increase. Therefore, there is a serious need for the use of breeding programs with the full potential to produce high-yielding crops. However, existing breeding techniques are unable to meet the demand criteria even though genotyping techniques have significantly progressed with the discovery of molecular markers and next-generation sequencing tools, and conventional phenotyping techniques lag behind. Well-organized high-throughput plant phenotyping platforms have been established recently and developed in different parts of the world to address this problem. These platforms use several imaging techniques and technologies to acquire data for quantitative studies related to plant growth, yield, and adaptation to various types of abiotic or biotic stresses (drought, nutrient, disease, salinity, etc.). Phenotyping has become an impediment in genomics studies of plant breeding. In recent years, phenomics, an emerging domain that entails characterizing the full set of phenotypes in a given species, has appeared as a novel approach to enhance genomics data in breeding programs. Imaging techniques are of substantial importance in phenomics. In this study, the importance of current imaging technologies and their applications in plant phenotyping are reviewed, and their advantages and limitations in phenomics are highlighted.

• 디지털콘텐츠학회 논문지
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• 제11권2호
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• pp.185-194
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• 2010

디지털 콘텐츠 산업은 전반적으로 이미 원 소스 멀티 유즈 시대에 접어들고 있다. 원작 소설 성공으로 영화로 재탄생되는가하면, 영화의 성공을 통하여 원작인 소설이 다시금 주목받는 경우가 생겨나며, 소설과 영화가 동시에 공개되는 경우도 있다. 본 논문은 게르만신화 서사구조를 갖고 있는 판타지 영화를 조셉 캠밸(J. Campbell)의 영웅서사구조 12단계 분류를 적용하여 소설의 서사구조를 시각화한 영화장면을 비교 분석하였다. 소설의 서사구조를 각색, 시각화 하는 경우 원작의 분위기, 스케일, 내용을 관객이 이해할 수 있는 충분한 시각적인 설명이 필요하며 적합한 이펙트를 사용했을 때 관객의 감정이입을 유도할 수 있다. 원작을 각색하여 시각화 하는 경우 원작 스케일과 작가의 메시지를 시각화하는 과정에 있어서 기초자료로 활용될 수 있을 것이며 사전에 관객 호응도를 단계별로 검토하여 시각적 기법(특수효과, 장면전환)에 적용할 수 있을 것이다.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.1129-1136
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• 2009

디지털 콘텐츠 산업은 전반적으로 이미 원 소스 멀티 유즈의 시대에 접어들고 있다. 원작 소설의 성공으로 영화로 재탄생되는가하면, 영화의 성공을 통하여 원작인 소설이 다시금 주목받는 경우가 생겨나며, 소설과 영화가 동시에 공개되는 경우도 있다. 본 논문은 게르만신화의 서사구조를 갖고 있는 판타지 영화를 조셉켐밸(J.Campbell)의 영웅서사 구조 12단계 분류를 적용하여 소설의 서사구조를 시각화한 영화장면을 비교분석하였다. 소설의 서사구조를 각색, 시각화 하는 경우 원작의 분위기, 스케일, 내용을 관객이 이해할 수 있는 충분한 시각적인 설명이 필요하며 적합한 이펙트를 사용했을 때 관객의 감정이입을 유도할 수 있다. 원작을 각색하여 시각화 하는 경우 원작의 스케일과 작가의 메시지를 시각화하는 과정에서 있어 기초자료로 활용될 수 있을 것이며 사전에 관객의 호응도를 단계별로 검토하여 적절한 시각적 기법(특수효과, 장면전화 등)을 적용할 수 있을 것이다.

• 한국IT서비스학회지
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• 제11권3호
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• pp.161-175
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• 2012

A novel concept of ICT based service, which is called N-device service, is proposed and implemented in the form of N-device smart education services. The N-device services are different from the existing N-screen services in terms of the number of devices a user is using for a specific service at the same time. A N-device service consists of several smart devices which are different in size, software and hardware platform, mobility, manufacturer and the like by fully utilizing the characteristic of each device. The major and unique features of N-device services are analyzed and depicted in a strict way. Those are shared role for each device, service by configurable device, one sensor multiple use, real-time data sharing, role shifting and new market creation. It is turned out that the N-device service is more human centric compared with existing ICT based services and can give richer digital experience to users because non-existent services become possible with the new concept. However, there exist several barriers to be implemented and commercialized in the real world. Those include high development cost and time, small market and different application platforms of devices. These barriers can be overcome by technical advances and cooperation between relevant parties. The smart education is very prominent area, in which the novel concept can be applied with high priority because Korean government announced its plan to boost up new educational services with big budget and these are acceptable by enthusiastic Korean parents and students. That's why the N-device service is implemented in the education area, which is called as "livessam(live teacher)," with four smart devices, IPTV, tablet PC, PC and smart phone. By providing both detailed service scenario and techniques, we shows that the novel education service is technically feasible and acceptable by teachers and students.