• Transactions on Electrical and Electronic Materials
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• v.14 no.2
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• pp.71-77
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• 2013

Advanced semiconductor manufacturing technology requires methods to maximize tool efficiency and improve product quality by reducing process variability. Real-time plasma process monitoring and diagnosis have become crucial for fault detection and classification (FDC) and advanced process control (APC). Additional sensors may increase the accuracy of detection of process anomalies, and optical monitoring methods are non-invasive. In this paper, we propose the use of a chromatic data acquisition system for real-time in-situ plasma process monitoring called the Plasma Eyes Chromatic System (PECS). The proposed system was initially tested in a six-inch research tool, and it was then further evaluated for its potential to detect process anomalies in an eight-inch production tool for etching blanket oxide films. Chromatic representation of the PECS output shows a clear correlation with small changes in process parameters, such as RF power, pressure, and gas flow. We also present how the PECS may be adapted as an in-situ plasma arc detector. The proposed system can provide useful indications of a faulty process in a timely and non-invasive manner for successful run-to-run (R2R) control and FDC.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• Genomics & Informatics
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• v.5 no.2
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• pp.46-55
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• 2007

The convergence of molecular and genetic disciplines with non-invasive imaging technologies has provided an opportunity for earlier detection of disease processes which begin with molecular and cellular abnormalities. This emerging field, known as molecular imaging, is a relatively new discipline that has been rapidly developed over the past decade. It endeavors to construct a visual representation, characterization, and quantification of biological processes at the molecular and cellular level within living organisms. One of the goals of molecular imaging is to translate our expanding knowledge of molecular biology and genomic sciences into good patient care. The practice of molecular imaging is still largely experimental, and only limited clinical success has been achieved. However, it is anticipated that molecular imaging will move increasingly out of the research laboratory and into the clinic over the next decade. Non-invasive in vivo molecular imaging makes use of nuclear, magnetic resonance, and in vivo optical imaging systems. Recently, an interest in Positron Emission Tomography (PET) has been revived, and along with optical imaging systems PET is assuming new, important roles in molecular genetic imaging studies. Current PET molecular imaging strategies mostly rely on the detection of probe accumulation directly related to the physiology or the level of reporter gene expression. PET imaging of both endogenous and exogenous gene expression can be achieved in animals using reporter constructs and radio-labeled probes. As increasing numbers of genetic markers become available for imaging targets, it is anticipated that a better understanding of genomics will contribute to the advancement of the molecular genetic imaging field. In this report, the principles of non-invasive molecular genetic imaging, its applications and future directions are discussed.

• Investigative Magnetic Resonance Imaging
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• v.23 no.1
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• pp.46-54
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• 2019

Purpose: The aim of this study was to evaluate the diagnostic performance of a computer-aided detection (CAD) system used with automated breast ultrasonography (ABUS) for suspicious lesions detected on breast MRI, and CAD-false lesions. Materials and Methods: We included a total of 40 patients diagnosed with breast cancer who underwent ABUS (ACUSON S2000) to evaluate multiple suspicious lesions found on MRI. We used CAD ($QVCAD^{TM}$) in all the ABUS examinations. We evaluated the diagnostic accuracy of CAD and analyzed the characteristics of CAD-detected lesions and the factors underlying false-positive and false-negative cases. We also analyzed false-positive lesions with CAD on ABUS. Results: Of a total of 122 suspicious lesions detected on MRI in 40 patients, we excluded 51 daughter nodules near the main breast cancer within the same quadrant and included 71 lesions. We also analyzed 23 false-positive lesions using CAD with ABUS. The sensitivity, specificity, positive predictive value, and negative predictive value of CAD (for 94 lesions) with ABUS were 75.5%, 44.4%, 59.7%, and 62.5%, respectively. CAD facilitated the detection of 81.4% (35/43) of the invasive ductal cancer and 84.9% (28/33) of the invasive ductal cancer that showed a mass (excluding non-mass). CAD also revealed 90.3% (28/31) of the invasive ductal cancers measuring larger than 1 cm (excluding non-mass and those less than 1 cm). The mean sizes of the true-positive versus false-negative mass lesions were $2.08{\pm}0.85cm$ versus $1.6{\pm}1.28cm$ (P < 0.05). False-positive lesions included sclerosing adenosis and usual ductal hyperplasia. In a total of 23 false cases of CAD, the most common (18/23) cause was marginal or subareolar shadowing, followed by three simple cysts, a hematoma, and a skin wart. Conclusion: CAD with ABUS showed promising sensitivity for the detection of invasive ductal cancer showing masses larger than 1 cm on MRI.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5595-5597
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• 2015

Electrical impedance tomography (EIT) is a new non-invasive, mobile screening method which does not use ionizing radiation to the human breast. It is based on the theory that cancer cells display altered local dielectric properties, thus demonstrating measurably higher conductivity values. This article reviews the utilisation of EIT in breast cancer detection. It could be used as an adjunct to mammography and ultrasonography for breast cancer screening.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.545-559
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• 2010

This paper presents the results of a pilot study and verification of a concept of a novel methodology for damage detection and assessment of water distribution system. The unique feature of the proposed noninvasive methodology is the use of accelerometers installed on the pipe surface, instead of pressure sensors that are traditionally installed invasively. Experimental observations show that a sharp change in pressure is always accompanied by a sharp change of pipe surface acceleration at the corresponding locations along the pipe length. Therefore, water pressure-monitoring can be transformed into acceleration-monitoring of the pipe surface. The latter is a significantly more economical alternative due to the use of less expensive sensors such as MEMS (Micro-Electro-Mechanical Systems) or other acceleration sensors. In this scenario, monitoring is made for Maximum Pipe Acceleration Gradient (MPAG) rather than Maximum Water Head Gradient (MWHG). This paper presents the results of a small-scale laboratory experiment that serves as the proof of concept of the proposed technology. The ultimate goal of this study is to improve upon the existing SCADA (Supervisory Control And Data Acquisition) by integrating the proposed non-invasive monitoring techniques to ultimately develop the next generation SCADA system for water distribution systems.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.99-103
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• 2000

There are two approaches in the study of emotion in the physiological psychology. The first is to clarify the brain mechanism of emotion, and the second is to evaluate objectively emotions using physiological responses along with our feeling experience. The method presented here belongs to the second one. Our method is based on the "level-crossing point detection" method. which involves the analysis of frequency fluctuations of EEG and is characterized by estimation of emotionality using coefficients of slopes in the log-power spectra of frequency fluctuation in alpha waves on both the left and right frontal lobe. In this paper we introduce a new theory of estimation on an individual's emotional state by using our non-invasive and easy measurement apparatus.

• Journal of Genetic Medicine
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• v.17 no.2
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• pp.68-72
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• 2020

Purpose: Trisomy 21, the cause of Down syndrome (DS) with various medical problems, is the most common aneuploidy during the fetal period. For diagnosis, a non-invasive screening test using maternal blood, which cannot be confirmed and invasive confirmation test with a risk of miscarriage, may be performed. The trophoblast retrieval and isolation of the cervix (TRIC) have been proposed by some researchers as an alternative to overcome the limitations of current tests. We experimented using TRIC to identify the possibility of trisomy 21 for the first time in Asia. Materials and Methods: Three cases of DS were analyzed confirmed by invasive tests (chorionic villus sampling, amniocentesis). All samples of trophoblasts immediately were immersed in phosphate-buffered saline and processed with formalin for fixation. The trophoblasts were isolated using an anti-human leukocyte antigen-G antibody coupled to magnetic nanoparticles. β-human chorionic gonadotropin (hCG)-expressing cells were considered as trophoblast cells, and the detection rate calculated. DS was confirmed by fluorescence in situ hybridization (FISH). Results: The mean trophoblast detection rate using β-hCG was 78.1%, and the detection rate using FISH was 22.2%. In all cases, the trisomy of chromosome 21 was identified. Conclusion: Trophoblast can be obtained from the five weeks of gestation and has a high detection rate, so it is noted that it can replace the current prenatal genetic test. To realize the clinical application as a prenatal genetic test, we will need additional efforts to identify trisomy 21 as well as other chromosomal abnormalities in future large-scale studies.

• Journal of Sensor Science and Technology
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• v.33 no.2
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• pp.112-116
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• 2024

Gas-sensor technology for volatile organic compounds (VOC) biomarker detection offers significant advantages for noninvasive diagnostics, including rapid response time and low operational costs, exhibiting promising potential for disease diagnosis. Colorimetric gas sensors, which enable intuitive analysis of gas concentrations through changes in color, present additional benefits for the development of personal diagnostic kits. However, the traditional method of visually monitoring these sensors can limit quantitative analysis and consistency in detection threshold evaluation, potentially affecting diagnostic accuracy. To address this, we developed a machine vision platform based on metal-organic framework (MOF) for colorimetric gas sensor arrays, designed to accurately detect disease-related VOC biomarkers. This platform integrates a CMOS camera module, gas chamber, and colorimetric MOF sensor jig to quantitatively assess color changes. A specialized machine vision algorithm accurately identifies the color-change Region of Interest (ROI) from the captured images and monitors the color trends. Performance evaluation was conducted through experiments using a platform with four types of low-concentration standard gases. A limit-of-detection (LoD) at 100 ppb level was observed. This approach significantly enhances the potential for non-invasive and accurate disease diagnosis by detecting low-concentration VOC biomarkers and offers a novel diagnostic tool.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.5
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• pp.1929-1934
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• 2015

Purpose: To determine the diagnostic yield of primary circulating tumor cells in women with suspicion of breast cancer, detected as a result of an abnormal mammography. Materials and Methods: Consecutive women presenting for breast biopsy as a result of a mammogram BiRADs of 3 or more, had an 8ml blood sample taken for primary circulating tumor cell (CTC) detection. Mononuclear cells were obtained using differential gel centrifugation and CTCs identified using standard immunocytochemistry using anti-mammoglobin. A test was determined to be positive if 1 CTC was detected. Results: A total of 144 women with a mean age of $54.7{\pm}15.6$ years participated, 78/144 (53.0%) had breast cancer on biopsy, 65/140 (46.3%) benign pathologies and 1(0.7%) non-Hogkins lymphoma. Increasing BiRADs scores were associated with increased cancer detection (p=0.004, RR 1.00, 4.24, 8.50). CTC mammoglobin positive had a sensitivity of 81.1% and specificity of 90.9%, with positive and negative predictive values of 90.9% and 81.1% respectively. Mammoglobin positive CTCs detected 87% of invasive cancers, while poorly differentiated cancers were negative for mammoglobin. Only 50% of in situ cancers and none of the intraductal cancers had CTCs detected. Menopausal status did not affect the diagnostic yield of the CTC test, which was higher in women with BiRADS 4 mammograms. There was a significant trend (p<0.0001 Chi squared for trends) in CTC detection frequency from intraductal, in situ and invasive (OR 1.00, 8.00, 472.00). Conclusions: The use of primary CTC detection in women suspected of breast cancer has potential uses, especially with invasive cancer, but it failed to detect intra-ductal cancer and 50% of in situ cancer. There was no difference in the diagnostic yield between pre and post menopausal women. To confirm its use in reducing biopsies in women with BIRADs 4a mammagrams and in the detection of interval invasive breast cancer, larger studies are needed.