Mass Detection Method Research Articles

Study of the thermal characteristics of propellant tanks in microgravity and its application to propellant gauging

The use of the thermal propellant gauging (TPG) method for propellant mass detection in aerospace tanks inevitably involves the continuous heating of the tank, and the understanding of the heat transfer mechanism of the tanks in a microgravity environment plays a guiding role in the implementation of the TPG. In this paper, the thermal characteristics of propellant tanks under microgravity are investigated by simulation and it is found that with the weakening of gravity, the heat transfer slows down and ‘heat concentration’ occurs in the vicinity of the heater. The effect of this property on the implementation of the TPG was then investigated by simulation, and it was found that in the microgravity environment, the accuracy of the TPG detection can be improved by adjusting the locations of the heaters and temperature sensors on the external side of the tank wall.

Characterization of Secondary Metabolites of Leaf Buds from Some Species and Hybrids of Populus by Gas Chromatography Coupled with Mass Detection and Two-Dimensional High-Performance Thin-Layer Chromatography Methods with Assessment of Their Antioxidant Activity.

Poplars provide medicinal raw plant materials used in pharmacy. Leaf buds are one of the herbal medicinal products collected from poplars, having anti-inflammatory and antiseptic properties, but there are no quality standards for their production and there is a need to determine their botanical sources. Therefore, the chemical compositions of the leaf buds from four species and varieties of poplars, Populus balsamifera, P. × berolinensis, P. × canadensis 'Marilandica', and P. wilsonii were investigated and compared using gas chromatography coupled with mass detection (GC-MS) and two-dimensional high-performance thin-layer chromatography (2D-HPTLC) in order to search for taxa characterized by a high content of biologically active compounds and with a diverse chemical composition that determines their therapeutic effects. The presence of 163 compounds belonging to the groups of flavonoids, phenolic acids derivatives, glycerides, and sesquiterpenes was revealed. Moreover, the conditions for the separation and identification of biologically active compounds occurring in analyzed leaf buds using 2D-HPTLC were optimized and used for metabolomic profiling of the studied poplars, enabling their fast and simple botanical identification. The total phenolic (TPC) and flavonoid (TFC) contents of examined extracts were determined and their antioxidant capacities were estimated by spectrophotometric DPPH, ABTS, and FRAP assays. Based on the analysis of phytochemicals and antioxidant activity, P. × berolinensis buds were selected as the raw plant material for medicinal purposes with the highest content of active compounds and the strongest antioxidant activity.

Chip-level mass detection for micro-LED displays based on regression analysis and deep learning.

Though micro-light-emitting diode (micro-LED) displays are regarded as the next-generation emerging display technology, challenges such as defects in LED's light output power and radiation patterns are critical to the commercialization success. Here we propose an electroluminescence mass detection method to examine the light output quality from the on-wafer LED arrays before they are transferred to the display substrate. The mass detection method consists of two stages. In the first stage, the luminescent image is captured by a camera by mounting an ITO (indium-tin oxide) transparent conducting glass on the LED wafer. Due to the resistance of the ITO contact pads and on-wafer n-type electrodes, we develop a calibration method based on the circuit model to predict the current flow on each LED. The light output power of each device is thus calibrated back by multi-variable regression analysis. The analysis results in an average variation as low as 6.89% for devices predicted from luminescent image capturing and actual optical power measurement. We also examine the defective or non-uniform micro-LED radiation profiles by constructing a 2-D convolutional neural network (CNN) model. The optimized model is determined among three different approaches. The CNN model can recognize 99.45% functioning LEDs, and show a precision of 96.29% for correctly predicting good devices.

Dual view deep learning for enhanced breast cancer screening using mammography

Breast cancer has the highest incidence rate among women in Ethiopia compared to other types of cancer. Unfortunately, many cases are detected at a stage where a cure is delayed or not possible. To address this issue, mammography-based screening is widely accepted as an effective technique for early detection. However, the interpretation of mammography images requires experienced radiologists in breast imaging, a resource that is limited in Ethiopia. In this research, we have developed a model to assist radiologists in mass screening for breast abnormalities and prioritizing patients. Our approach combines an ensemble of EfficientNet-based classifiers with YOLOv5, a suspicious mass detection method, to identify abnormalities. The inclusion of YOLOv5 detection is crucial in providing explanations for classifier predictions and improving sensitivity, particularly when the classifier fails to detect abnormalities. To further enhance the screening process, we have also incorporated an abnormality detection model. The classifier model achieves an F1-score of 0.87 and a sensitivity of 0.82. With the addition of suspicious mass detection, sensitivity increases to 0.89, albeit at the expense of a slightly lower F1-score of 0.79.

Statistical discrimination using different machine learning models reveals dissimilar key compounds of soybean leaves in targeted polyphenol-metric metabolomics in terms of traits and cultivation

Soybean (SB) leaves (SLs) contain diverse flavonoids with health-promoting properties. To investigate the chemical constituents of SB and their correlations across phenotypes, growing periods, and environmental factors, a validated separation method for mass detection was used with targeted metabolomics. Thirty-six polyphenols (1 coumestrol, 5 flavones, 18 flavonols, and 12 isoflavones) were identified in SLs, 31 of which were quantified. Machine learning (ML) modelling was used to differentiate between the variety, bean color, growing period, and cultivation area and identify the key compounds responsible for these differences. The isoflavone and flavonol profiles were influenced by the growing period and cultivation area based on bootstrap forest modelling. The neural model showed the best predictive capacity for SL differences among the various ML models. Discriminant polyphenols can differ depending on the ML method applied; therefore, a cautious approach should be ensured when using statistical ML outputs, including orthogonal partial least squares discriminant analysis.

Development of an Ultra-High Performance Liquid Chromatography method for the simultaneous mass detection of tobacco biomarkers in urine

The quantification of tobacco exposure biomarkers is relevant to follow the patients’ tobacco use. They allow to discriminate between tobacco users, non-users, passive smokers, and nicotine products users, such as in nicotine replacement therapy. The aim of this study was to develop and validate a quantification method of tobacco biomarkers of choice – nicotine, cotinine, trans-3′-hydroxycotinine, anatabine and anabasine – in urine. The challenge was to develop an easy and rapid liquid chromatography method requiring only one extraction step and allowing simultaneous detections. Some methods are described in the literature but need specific investment in terms of instrumentation and users training. Here, the developed method had to be carried out with instrumentation easily accessible for medical laboratories. The extraction of the analytes was performed by Supported Liquid Extraction (SLE), which consists in liquid–liquid extraction but supported by a sorbent. It allows to insure efficient neutrals extraction with less organic solvent and without any emulsion formation. 200 µl of basified urine – analytes of interest are neutral in this condition – were loaded on Novum SLE 96-Well Plates (Phenomenex) and analytes were eluted with 1 % formic acid in dichloromethane/propan-2-ol (95/5). After solvent evaporation, samples were reconstituted with 100 µl of water for injection. A mass detector (QDa, Waters) was used to detect analytes, this pre-optimised quadrupole mass analyser being less expensive and requiring less adjustments than traditional mass spectrometers while benefiting of the reliability of mass spectral data. This detector was integrated after an Ultra-high performance liquid chromatography (UHPLC) separation on a BEH C18 column (Waters) at a flow rate of 0.5 ml/min. A gradient elution of H2O (pH 10 with NH4OH) and CH3CN was used. Finally, the developed method was validated. This new method is conclusive to assess the patients’ tobacco exposure and is easy to implement in medical laboratories.

Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study

Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical environments. In this study, we explored the domain generalization of deep learning methods for mass detection in digital mammography and analyzed in-depth the sources of domain shift in a large-scale multi-center setting. To this end, we compared the performance of eight state-of-the-art detection methods, including Transformer based models, trained in a single domain and tested in five unseen domains. Moreover, a single-source mass detection training pipeline was designed to improve the domain generalization without requiring images from the new domain. The results show that our workflow generalized better than state-of-the-art transfer learning based approaches in four out of five domains while reducing the domain shift caused by the different acquisition protocols and scanner manufacturers. Subsequently, an extensive analysis was performed to identify the covariate shifts with the greatest effects on detection performance, such as those due to differences in patient age, breast density, mass size, and mass malignancy. Ultimately, this comprehensive study provides key insights and best practices for future research on domain generalization in deep learning based breast cancer detection.

Comparative Study of the Performance of Two Different Luciferases for the Analysis of Fumonisin B1 in Wheat Samples

AbstractThe development of two different immunoassays for the determination of fumonisin B1 in wheat samples is reported. A previously described mimopeptide for fumonisin B1 (FB1) was used to produce fusion proteins in combination with two different luciferases: Gaussia luciferase (GLuc) and NanoLuc luciferase (NLuc). The production, expression and the development of two immunoassays based on these fusion proteins (A2‐GLuc and A2‐NLuc) is detailed. The assay showing the best performance, A2‐NLuc, with a limit of detection of 0.61 ng mL−1 and a dynamic range from 1.9 to 95 ng mL−1, was employed for the analysis of spiked wheat samples, a reference matrix material, as well as naturally contaminated wheat samples. The recoveries obtained in the spiked samples were acceptable, between 81.5 and 109 %, with relative standard deviations lower than 14 %. The analysis of naturally contaminated wheat was validated by a liquid chromatography coupled to tandem mass detection method.

Mass sensing by symmetry breaking and localization of motion in an array of electrostatically coupled nonlinear MEMS resonators

This paper investigates the global dynamics of an array of electrostatically coupled nonlinear MEMS resonators before and after a symmetry breaking event by means of nonlinear normal modes, nonlinear forced responses curves and bifurcation analysis. The possible coexistence of isolated solutions is explained by means of energy balance and limit point tracking. The results show that the energy balance method and limit point tracking can be used equivalently to detect the birth of isolated solutions, but only the limit point tracking can predict accurately their merging. Moreover, the localization of motion and the merging of isolated solutions resulting from a symmetry breaking of the underlying nonlinear normal mode can be exploited to provide alternative mass detection methods. The amount of mass that can be detected by this method is directly related to the initial level of asymmetry.

Anchor-free YOLOv3 for mass detection in mammogram

Automatic detection of mass in mammograms is a big challenge and plays a crucial role in assisting radiologists for accurate diagnosis. Applying excellent deep learning based detection networks to medical image is a direction. In this paper, an anchor-free YOLOv3 network is proposed for mass detection in mammogram. The main improvements are as follows. Anchor-based (AB) detection scheme may involve too many negative boxes, which may aggravate the unbalance between positive boxes and negative boxes. Aiming at this problem, a novel anchor-free (AF) detection scheme is developed for box regression. To alleviate the problem caused by using mean square error (MSE) as box regression loss, generalized intersection over union (GIoU) loss is adopted to train bounding box regression. Focal loss is adopted as the objectness prediction loss, which can prevent the network degradation caused by the vast number of easy negatives. Moreover, a new feature aggregation manner by summing is designed and employed in the up to down pathway. Comparative experiments are performed on two databases (publicly available dataset INbreast and private dataset TXMD). For INbreast dataset, the proposed method achieved 0.95 true positive rate (TPR) with 1.7 false positives per image (FPs/I). For TXMD dataset, the proposed method achieved 0.94 TPR with 5.75 FPs/I. Experimental results demonstrate that the proposed method outperforms YOLOv3 network and gets a promising result compared with existing mass detection methods.

Development and Validation of Three Potential Genotoxic Impurities by Liquid Chromatography Single Quad Mass Detector in Iomeprol

A liquid chromatography with single quadrupole mass detection method was developed for the determination of potential genotoxic impurities (PGIs) in the Iomeprol active pharmaceutical ingredient. Chromatographic separation was achieved on an Agilent Eclipse plus C8 column (100 mm x 2.1 mm x 1.8 μm) with 0.1% formic acid in water as mobile phase A and acetonitrile as mobile phase B in gradient elution mode at a 0.1 mL/min. Executed validation summary demonstrated that the mass detection method had highly sensitive and selective. A linear calibration curve (correlation coefficient, r> 0.999) was attained at the concentration range of 0.1-125 ppm for three PGI’s. The Limit of Detection of Imp-A, Imp-B and Imp-C in drug substance of Iomeprol is 0.05 ppm. The accuracy was confirmed by calculated recoveries (98.4-101.5%). The precision was tested at three levels: injection repeatability, analysis repeatability and intermediate precision. The calculated relative standard deviations were within the specification. The developed method was able to quantitate all three PGI’s at a concentration level of 1 µg/mL.

Molecular Imaging of Brown Adipose Tissue Mass.

Brown adipose tissue (BAT), a uniquely thermogenic tissue that plays an important role in metabolism and energy expenditure, has recently become a revived target in the fight against metabolic diseases, such as obesity, diabetes, and non-alcoholic fatty liver disease (NAFLD). Different from white adipose tissue (WAT), the brown adipocytes have distinctive features including multilocular lipid droplets, a large number of mitochondria, and a high expression of uncoupling protein-1 (UCP-1), as well as abundant capillarity. These histologic characteristics provide an opportunity to differentiate BAT from WAT using imaging modalities, such as PET/CT, SPECT/CT, MRI, NIRF and Ultrasound. However, most of the reported imaging methods were BAT activation dependent, and the imaging signals could be affected by many factors, including environmental temperatures and the states of the sympathetic nervous system. Accurate BAT mass detection methods that are independent of temperature and hormone levels have the capacity to track the development and changes of BAT throughout the lifetime of mammals, and such methods could be very useful for the investigation of potential BAT-related therapies. In this review, we focus on molecular imaging modalities that can detect and quantify BAT mass. In addition, their detection mechanism and limitations will be discussed as well.

Breast Mass Detection in Mammography Based on Image Template Matching and CNN.

In recent years, computer vision technology has been widely used in the field of medical image processing. However, there is still a big gap between the existing breast mass detection methods and the real-world application due to the limited detection accuracy. It is known that humans locate the regions of interest quickly and further identify whether these regions are the targets we found. In breast cancer diagnosis, we locate all the potential regions of breast mass by glancing at the mammographic image from top to bottom and from left to right, then further identify whether these regions are a breast mass. Inspired by the process of human detection of breast mass, we proposed a novel breast mass detection method to detect breast mass on a mammographic image by stimulating the process of human detection. The proposed method preprocesses the mammographic image via the mathematical morphology method and locates the suspected regions of breast mass by the image template matching method. Then, it obtains the regions of breast mass by classifying these suspected regions into breast mass and background categories using a convolutional neural network (CNN). The bounding box of breast mass obtained by the mathematical morphology method and image template matching method are roughly due to the mathematical morphology method, which transforms all of the brighter regions into approximate circular areas. For regression of a breast mass bounding box, the optimal solution should be searched in the feasible region and the Particle Swarm Optimization (PSO) is suitable for solving the problem of searching the optimal solution within a certain range. Therefore, we refine the bounding box of breast mass by the PSO algorithm. The proposed breast mass detection method and the compared detection methods were evaluated on the open database Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed method is superior to all of the compared detection methods in detection performance.

A Novel Frequency Stabilization Approach for Mass Detection in Nonlinear Mechanically Coupled Resonant Sensors.

Frequency stabilization can overcome the dependence of resonance frequency on amplitude in nonlinear microelectromechanical systems, which is potentially useful in nonlinear mass sensor. In this paper, the physical conditions for frequency stabilization are presented theoretically, and the influence of system parameters on frequency stabilization is analyzed. Firstly, a nonlinear mechanically coupled resonant structure is designed with a nonlinear force composed of a pair of bias voltages and an alternating current (AC) harmonic load. We study coupled-mode vibration and derive the expression of resonance frequency in the nonlinear regime by utilizing perturbation and bifurcation analysis. It is found that improving the quality factor of the system is crucial to realize the frequency stabilization. Typically, stochastic dynamic equation is introduced to prove that the coupled resonant structure can overcome the influence of voltage fluctuation on resonance frequency and improve the robustness of the sensor. In addition, a novel parameter identification method is proposed by using frequency stabilization and bifurcation jumping, which effectively avoids resonance frequency shifts caused by driving voltage. Finally, numerical studies are introduced to verify the mass detection method. The results in this paper can be used to guide the design of a nonlinear sensor.

Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images

In this study, mass detection from breast ultrasonography images was realized using deep neural networks. Dataset is a collection of publicly available ultrasonography images which were classified by their biopsy results. A total of 153 breast ultrasonography images that contain 89 malign and 64 benign tumours were used. Image augmentation and deep neural network software was developed using Python 3,5 environment on Visual Studio Community 2017 IDE. A hybrid method including Keras ImageDataGenerator Class and image preprocessing techniques was introduced. Twenty images from both classes were randomly split from the dataset for testing after the network was designed. The network had a success rate of 100% at an epoch value of 70. The result of this study was compared with the result of another study that implemented type-2 fuzzy inference system with a success rate of 99,34%. 
 As a conclusion, it can be expressed that the deep neural networks are more successful than fuzzy inference systems in tumour detection from breast ultrasonography images. Therefore, it can be more convenient to use deep neural network technology in computer aided detection systems for mass detection from breast ultrasonography images.

Mass detection in mammograms by bilateral analysis using convolution neural network

Background and objectiveAutomatic detection of the masses in mammograms is a big challenge and plays a crucial role to assist radiologists for accurate diagnosis. In this paper, a bilateral image analysis method based on Convolution Neural Network (CNN) is developed for mass detection in mammograms. MethodsThe proposed bilateral mass detection method consists of two networks: a registration network for registering bilateral mammograms and a Siamese-Faster-RCNN network for mass detection using a pair of registered mammograms. In the first step, self-supervised learning network is built to learn the spatial transformation between bilateral mammograms. This network can directly estimate spatial transformation by maximizing an image-wise similarity metric and corresponding points labeling is not needed. In the second step, an end-to-end network combining the Region Proposal Network (RPN) and a Siamese Fully Connected (Siamese-FC) network is designed. Different from existing methods, the designed network integrates mass detection on single image with registered bilateral images comparison. ResultsThe proposed method is evaluated on three datasets (publicly available dataset INbreast and private dataset BCPKUPH and TXMD). For INbreast dataset, the proposed method achieves 0.88 true positive rate (TPR) with 1.12 false positives per image (FPs/I). For BCPKUPH dataset, the proposed method achieves 0.85 TPR with 1.86 FPs/I. For TXMD dataset, the proposed method achieves 0.85 TPR with 2.70 FPs/I. ConclusionsRegistration experimental result shows that the proposed method is suitable for bilateral mass detection. Mass detection experimental results show that the proposed method performs better than unilateral mass detection method, different bilateral connection schemes and image level fusion bilateral schemes.

Abstract P3-08-26: Clinico-pathological characteristics of metaplastic breast cancer as compared to normal TNBC: A single center analysis

Abstract Purpose: Metaplastic Breast Cancer (mBC) is uncommon and often behaves aggressive with a high incidence of recurrence. Prognostic factors are poorly understood. We analyzed prognostic factors in a single center series of triple negative mBC and compared those to a patients with ductal TNBC of no special type (dTNBC-NST), treated during the same time period; Materials and methods: We retrospectively analyzed prognostic factors (demographic and clinical pathological features) and distant recurrence of all ER-negative mBC’s (primary operated and neoadjuvant chemotherapy (NAC-)) treated in the University Hospitals Leuven (UHL) between 1/1/2000 and 31/12/2016. TNBC was defined as IHC-ER<1%, IHC-PR<1% and IHC-HER2 0-1+ and FISH negative if HER2 IHC 2/3+. For comparison, only ductal TNBC of no special type were included (dTNBC-NST). Demographic features included age, Body Mass Index (BMI, kg/m²) and detection method. Clinical pathological features included grading, size, lymphovascular invasion (LVI) and nodal involvement. Outcome included distant recurrence rate. The Cox proportional hazards model was used to analyze prognostic factors on distant recurrence rate. A multivariate model was used to correct for possible confounders (grade, nodal stage, tumor size). The clinical pathological variables were compared using Fisher exact test or Mann-Withney U test. All tests are two-sided and a 5% significance level is assumed for all tests. Results on distant recurrent rates are presented as hazard ratios (HR) with 95% confidence intervals (CI). Results: 49 mBC patients were identified; 11 treated with NAC and 38 primary operated. The dTNBC-NST group contained 662 patients; 194 treated with NAC and 468 primary operated. Median follow up was 8.35yrs [5.33 - 12.16] in the mBC and 9.48yrs [5.23-13.44] in the dTNBC-NST group. Median age was 52yr in the mBC and 53yr in the dTNBC-NST (p=0.482). There was a tendency for a higher BMI in the mBC group: median BMI was 26.4kg/m² in mBC and 24.7kg/m² in the dTNBC-NST group (p=0.09, univariate analysis). Tumors were significantly larger in the mBC group with the median size (mm) of 29.5 in mBC and 25.0 in dTNBC-NST (p=0.008). Nodal involvement was 17/49 (34.7%) in the mBC group and 205/662 (31.0%) in dTNBC-NST (p=0.345). LVI was significantly less present in mBC (7.9%) than in dTNBC-NST (24.4%) (p=0.026). A similar proportion in either group received adjuvant chemotherapy (76.3% in mBC and 80.8% in dTNBC-NST) but mBC received more often adjuvant radiotherapy (85.7% in mBC and 60.1% in dTNBC-NST, p<0.01) despite having a similar mastectomy rate (51.0% in mBC and 47.6% in dTNBC-NST, p=0.659). pCR in NAC treated mBC was achieved in 4/11 (36.4%) and in 86/194 (43.9%) of dTNBC-NST (p=0.785). There is a tendency to a higher distant relapse rate in mBC with 28.9% (95% CI: 16.5;42.5) having distant relapse within 5 years compared to 16.6% (95% CI: 13.8;19.7) in dTNBC-NST) [HR 1.722, 95% CI (0.970 - 3.057), p=0.064)]. However, when corrected in a multivariate model (corrected for grade, nodal stage and size) this trend becomes irrelevant [HR 1.189, 95% CI (0.659 - 2.144)]. Conclusion: mBC is larger at diagnosis and less often LVI-pos compared to dTNBC-NST. Adjuvant radiotherapy was more often given to mBC patients. The trend we observed of more distant relapse in mBC disappeared when corrected for tumor characteristics. Citation Format: Jan Ardui, Kevin Punie, Giuseppe Floris, Hava Izci, Hans Wildiers, Ignace Vergote, Patrick Berteloot, Toon Van Gorp, Annouschka Laenen, Ann Smeets, Caroline Weltens, Patrick Neven. Clinico-pathological characteristics of metaplastic breast cancer as compared to normal TNBC: A single center analysis [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-08-26.

Anti-Symmetric Mode Vibration of Electrostatically Actuated Clamped-Clamped Microbeams for Mass Sensing.

This paper details study of the anti-symmetric response to the symmetrical electrostatic excitation of a Micro-electro-mechanical-systems (MEMS) resonant mass sensor. Under higher order mode excitation, two nonlinear coupled flexural modes to describe MEMS mass sensors are obtained by using Hamilton’s principle and Galerkin method. Static analysis is introduced to investigate the effect of added mass on the natural frequency of the resonant sensor. Then, the perturbation method is applied to determine the response and stability of the system for small amplitude vibration. Through bifurcation analysis, the physical conditions of the anti-symmetric mode vibration are obtained. The corresponding stability analysis is carried out. Results show that the added mass can change the bifurcation behaviors of the anti-symmetric mode and affect the voltage and frequency of the bifurcation jump point. Typically, we propose a mass parameter identification method based on the dynamic jump motion of the anti-symmetric mode. Numerical studies are introduced to verify the validity of mass detection method. Finally, the influence of physical parameters on the sensitivity of mass sensor is analyzed. It is found that the DC voltage and mass adsorption position are critical to the sensitivity of the sensor. The results of this paper can be potentially useful in nonlinear mass sensors.

A fast and automated segmentation method for detection of masses using folded kernel based fuzzy c-means clustering algorithm

Present study proposes a fast, accurate and automated segmentation approach of mammographic images using kernel based fuzzy c-means (FCM) clustering technique. This approach exploits the significant regional features of mammograms which address the properties of different breast densities. The proposed segmentation approach captures those regional features using appropriate kernel and hence apply fuzzy clustering technique for segmenting the masses. This study also introduces kernel based FCM (KFCM) approach in a folded way to process a combination of significant features simultaneously. Suitable choice of kernel size also assists to collect all possible variations of regional features with minimum blocking effect in the output results. Performances of the proposed methodology are analyzed qualitatively and quantitatively in compare to other clustering-based segmentation techniques. Since the proposed approach is able to resolve uncertain and imprecise characteristics of mammograms, it performs superior to other techniques. Convergence time of the proposed method is also assessed and compared with other conventional clustering techniques. Kernel based approach of the proposed segmentation technique reduces the number of data points for clustering and hence convergence speed improves over the conventional algorithms. This study also shows a variation of convergence speed of the proposed segmentation method with different image sizes.

(Invited) Graphene Nano-Electro-Mechanical (NEM) Devices and Extension to Sensor Applications

Graphene, as a monolayer of graphite, has an ultra-high Young’s modulus of ~1 TPa, which makes it a promising candidate for Nano-Electromechanical (NEM) devices. Thanks to highly-stable SP2 bonds in a 2D honeycomb lattice, graphene also features an extremely suppressed electrical noise, making it possible to realize extremly-sensitive sensor. This paper presents an overview on our recent study of novel graphene NEM devices for abrupt switching and ultra-sensitive chemical gas sensing applications. The graphene NEM switches are expected to show minimized electrical leakage, sharp switching response, low actuation voltage, and a high on/off ratio. Three-terminal graphene NEM switches with heterogeneously stacked graphene / h-BN layers are developed, which achieve low-voltage and sub-thermal switching (S ～ 10 mV/dec) at room temperature [1]. We then present nanoscale graphene gas sensors, which detect either resistance or mass changes due to a small number of gas molecules physisorbed onto suspended graphene at room temperature. With the resistance detection method, we show quantized increments in the temporal resistance of approximately 60 Ohm, signifying single CO2 molecule adsorption onto a suspended graphene channel [2][3]. As for the mass detection method, we demonstrate the resonance frequency shift of a doubly-clamped graphene resonator with the mass resolution of hundreds zeotpgram (10-21 g) order [4][5]. We also introduce recent experimental work of applying these graphene nanosensors to detect gas emanated from human skin such as NH3 and acetone of very low concentration of down to ppb level. This research was supported through the Grant-in-Aid for Scientific Research KAKENHI (18H03861, 18K04260, 25220904) from JSPS, COI program of the Japan Science Technology Agency and collaboration grant by TAIYO YUDEN Co. Ltd. [1] N. H. Van, M. Muruganathan, J. Kulothungan and H. Mizuta, Nanoscale 10, 12349-12355 (2018) [2] J. Sun, M. Muruganathan, and H. Mizuta, Sci. Adv., 2, e1501518–e1501518 (2016) [3] M. Muruganathan, J. Sun, T. Imamura, and H. Mizuta. Nano letters 15, 8176 (2015) [4] M. Muruganathan, F. Seto, and H. Mizuta, IJAT 12(1) 24-28 (2018) [5] M. Muruganathan, H. Miyashita, J. Kulothungan, M. Schmidt, H. Mizuta, IEEE Sensors 2018, New Delhi, India, October (2018)