Single Mask Research Articles

An exploratory framework to identify dust on photovoltaic panels in offshore floating solar power stations

Offshore floating solar power stations represent a new frontier in energy development. These stations maximize solar energy use, reduce land consumption and promote algae growth. However, moist marine air makes airborne particles like sand adhere more easily to PV panels, reducing photoelectric conversion efficiency and causing overheating and potential damage. Additionally, drones and fixed cameras face challenges due to complex maritime factors and complicating image analysis. To address these issues, this paper proposes an exploratory framework for identifying dust regions on photovoltaic panels specifically for offshore floating solar power stations. The framework aims to address how to accurately and reliably identify dust accumulation on PV panels, even in images with complex background interference. The framework is designed to be compatible with existing image recognition models and incorporates specialized enhancement modules to further improve recognition accuracy and efficiency. In this study, we utilized a dust feature enhancement module based on the HLS color space, combined with Mask R-CNN, to demonstrate the framework's feasibility and effectiveness. Experimental results show that, compared to a single Mask R-CNN, the framework significantly reduces misidentification and omission of dust areas, boosting the confidence level and effectively meeting the operational needs of offshore floating solar power stations.

Physiochemical Coupled Dynamic Nanosphere Lithography Enabling Multiple Metastructures from Single Mask.

Metastructures are widely used in photonic devices, energy conversion, and biomedical applications. However, to fabricate multiple patterns continuously in single etching protocol with highly tunable photonic properties is challenging. Here, a simple and robust dynamic nanosphere lithography is proposed by inserting a spacer between the nanosphere assembly and the wafer. The nanosphere diameter decrease and uneven penetration of the spacer during etching lead to a dynamic masking process. Coupled anisotropic physical ion sputtering and ricocheting with isotropic chemical radical etching achieve highly tunable structures with various 3D patterns continuously forming through a single etching process. Specifically, the nanosphere diameters define the periodicity, the etched spacer forms the upper parts, and the wafer forms the lower parts. Each part of the structure is highly tunable through changing nanosphere diameter, spacer thickness, and etch conditions. Using this protocol, numerous structures of varying sizes including nanomushrooms, nanocones, nanopencils, and nanoneedles with diverse shapes are realized as proof of concepts. The broadband antireflection ability of the nanostructures and their use in surface-enhanced Raman spectroscopy are also demonstrated for practical application. This method substantially simplifies the fabrication procedure of various metastructures, paving the way for its application in multiple disciplines especially in photonic devices.

Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms

Monitoring cow body weight is crucial to support farm management decisions due to its direct relationship with the growth, nutritional status, and health of dairy cows. Cow body weight is a repeated trait, however, the majority of previous body weight prediction research only used data collected at a single point in time. Furthermore, the utility of deep learning-based segmentation for body weight prediction using videos remains unanswered. Therefore, the objectives of this study were to predict cow body weight from repeatedly measured video data, to compare the performance of the thresholding and Mask R-CNN deep learning approaches, to evaluate the predictive ability of body weight regression models, and to promote open science in the animal science community by releasing the source code for video-based body weight prediction. An Intel RealSense D435 camera was installed on Virginia Tech dairy complex to collect top-view videos of 10 lactating Holstein cows twice a day for 28 days after milking sessions and 2 non-lactating, pregnant Jersey cows once a week upon calving. At the same time, the ground truth body weight records were collected using a walk-over weighing system. A total of 40,405 depth images and depth map files were obtained. Three approaches were investigated to segment the cow's body from the background, including single thresholding, adaptive thresholding, and Mask R-CNN. Four image-derived biometric features, such as dorsal length, abdominal width, height, and volume, were estimated from the segmented images and fitted using ordinary least squares, ridge regression, least absolute shrinkage and selection operator, and linear mixed models. Two cross-validation designs, forecasting and leave-three-cows-out, were used to evaluate prediction performance. The Pearson correlation between image-derived biometric features and scale-based body weight ranged from 0.74 to 0.95. On average, the Mask-RCNN approach combined with a linear mixed model resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.98 and 2.03%, respectively, in the forecasting cross-validation. The Mask-RCNN approach was also the best in the leave-three-cows-out cross-validation, followed by adaptive and single thresholding. The prediction coefficients of determination and mean absolute percentage error of the Mask-RCNN coupled with the linear mixed model were 0.90 and 4.70%, respectively. Our results suggest that deep learning-based segmentation improves the prediction performance of cow body weight from longitudinal depth video data.

151 Depth Video Data-Enabled Predictions of Longitudinal Dairy Cow Body Weight Using Thresholding and Mask R-Cnn Algorithms

Abstract Cow body weight monitoring is critical in farm management because it is associated with the growth, nutritional status, and health of dairy cows. Regularly collecting body weight records often requires human labor or expensive equipment. Recently, a low-cost 3D depth sensor camera-enabled computer vision analysis has been proposed to address the body weight prediction challenges. Cow body weight is a repeated trait; however, past body weight prediction research only used data collected at a single time point. Furthermore, the utility of deep learning on body weight predictions remains unanswered. Therefore, the objectives of this study were to predict cow body weight from repeatedly measured video data and compare the performance of thresholding and Mask R-CNN deep learning approaches. We set up an Intel Realsense D435 camera at the Virginia Tech Kentland farm to collect top-view videos for 10 Holstein and 2 Jersey cows for 28 days, twice per day. Real body weight records were also collected by a walk-over-weighing system simultaneously. A total of 40,405 depth images and depth map files were obtained. We explored three approaches to segment the cow body from the background, including single thresholding, adaptive thresholding, and Mask R-CNN. Four image-derived biometric measurements, length, width, height, and volume, were estimated from segmented images and fitted in ordinary least squares. Two cross-validation designs, forecasting and leave-several-cow-out, were used to evaluate prediction performance. The correlation between image-derived biometric measurements and body weight ranged from 0.84 to 0.95. On average, the Mask-RCNN approach resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.96 and 3.54% in the forecasting cross-validation, respectively. The predictive performance of the single and adaptive thresholding approaches was similar. The Mask-RCNN approach was also the best in the leave-several-cow-out cross-validation, followed by adaptive and single thresholding. The prediction coefficients of determination of Mask-RCNN for leave-one-cow-out, leave-two-cow-out, leave-three-cow-out, leave-four-cow-out were 0.05, 0.69, 0.85, and 0.90, respectively, while mean absolute percentage errors were 4.51%, 4.51%, and 4.59%, 4.70% respectively. Our results suggest predicting cow body weight from depth video data using deep learning is feasible.

Observation on the bias current variation of a single mask triple GEM chamber

Gas Electron Multiplier (GEM) detector, one of the advanced members of the Micro Pattern Gas Detector (MPGD) group, is widely used in High Energy Physics (HEP) experiments. The high rate handling capability and spatial resolution make it a desired tracking detector for high rate HEP experiments. Investigation of the long-term stability is an essential criterion for any tracking device used in HEP experiments. To investigate the long-term stability of a Single Mask (SM) triple GEM detector prototype, it is irradiated continuously using a 55Fe X-ray source of energy 5.9 keV. The chamber is operated with Ar/CO2 gas mixture in continuous flow mode. The gain and energy resolution of the chamber are calculated from the 5.9 keV X-ray peak and studied as a function of time. The applied voltage, divider current and also the environmental parameters (ambient temperature, pressure and relative humidity) are recorded continuously. It is observed that at a fixed applied voltage, the divider current of the detector is changing with time and as a result, the gain of the detector also changes. A systematic investigation is carried out to understand the probable reasons behind the observed variation in divider current and also to find its possible remedies. The details of the experimental setup, methodology and results are discussed in this article.

Charging-up effect and uniformity study of a single mask triple GEM detector

The Gas Electron Multiplier (GEM) detector is one of the advanced members of the Micro Pattern Gas Detector (MPGD) family, used in High Energy Physics (HEP) experiments as a tracking device due to its high rate handling capability and good spatial resolution. The uniformity in the performance of the detector is an essential criterion for any tracking device.The presence of the dielectric medium (Kapton) inside the active volume of the GEM chamber changes its behaviour when exposed to external irradiation. This phenomenon is known as the charging-up effect. In this article, the charging-up effect over the entire surface of a Single Mask (SM) triple GEM chamber of dimension 10 cm × 10 cm is reported. The study of uniformity in terms of gain, energy resolution and count rate for the same chamber is also presented before and after the charging-up of the GEM foils.

Effect of relative humidity on the long-term operation of a single mask triple GEM chamber

The characteristic studies of a Single Mask (SM) triple Gas Electron Multiplier (GEM) detector are carried out using Ar/CO2 gas mixture in 70/30 volume ratio in continuous flow mode. A 55Fe X-ray source is used for this work. The gain and energy resolution are studied from the 5.9 keV X-ray energy spectra. After correcting the effects of ambient temperature and pressure variation, the effect of ambient relative humidity on the gain and energy resolution of the chamber is investigated. The details of the experimental setup, measurement methods and results are presented in this article.

Fruit maturity grading framework for small dataset using single image multi-object sampling and Mask R-CNN

Fruit maturity grading is the key factor for fruit export where maturity consistence and standard are required. This paper proposes a non-destructive approach for pineapple maturity grading and pineapple localization based on object segmentation framework which has enhanced for training a robust model with small dataset. We introduced a multi-object sampling technique in augmentation process to generate images from a small dataset taken under controlled conditions to generalize the model for a more practical dataset. We identify the robustness of object segmentation models, Mask R-CNN over other models, e.g., Faster R-CNN, RetinaNet and CenterMask through mean average precision (mAP), detection ratio to explore the false positives detected, precision-recall curve and computational time. The optimal threshold selection which is crucial especially for sensitive small dataset to achieve high detection performance is proposed in this work using mAP and detection ratio. Our proposed framework enhances the model generalization and achieves mAP of 86.7%, AP50 and AP75 at 97.98% and APunripe, APpartially_ripe and APfully_ripe of 99.20%, 96.58% and 98.63%, respectively. Additional insights of the models developed with our small dataset are explained along with the experimental results which are suggested for future.

Physiological Effects of Single and Double Face Mask Use with Moderate and Vigorous Exercise

ABSTRACT Introduction/Purpose Exercise is associated with increased exhalation of infectious particles in respiratory disease, and face mask use has become routine during the coronavirus disease 2019 (COVID-19) pandemic to mitigate particle spread. As of February 2022, the U.S. Centers for Disease Control and Prevention (CDC) continues to recommend the use of double masking (i.e., cloth masks worn over a surgical mask) to minimize particle leakage seen with single-mask use. Studies to date have examined the effects of single masks on exercise safety and performance, but the potential added burden during exercise of an additional mask layer has not been evaluated. The purpose of our study was to compare the effects of single and double face mask use during moderate to vigorous exercise. Methods Fifteen healthy, physically active young adults (mean age = 21.1 ± 0.8 yr; 8 males and 7 females; V˙O2peak = 46.3 ± 11.5 mL·kg−1⋅min−1) completed the study. This included an initial assessment of V˙O2peak, with subjects returning for three exercise test sessions performed while wearing either a surgical face mask, a cloth face mask, or double masks (i.e., a cloth mask worn over a surgical mask) in a random order. Test sessions consisted of a progressive cycling protocol with 10-min stages at 40%, 60%, and 75% oxygen consumption reserve (VO2R). Assessments were performed after 5 and 10 min at each exercise intensity and included heart rate, respiratory rate, oxygen saturation, and ratings of perceived exertion and dyspnea. Results At exercise intensities from 40% to 75% VO2R, there were no significant differences between cloth masks, surgical masks, and double masking in the measured subjective and physiologic parameters. Conclusions When compared with surgical and cloth face masks in common use, for the outcomes measured in this study, double masking in accordance with CDC recommendations does not result in significant impairments during moderate to vigorous exercise up to 75% VO2R in healthy young adults.

Brand Analysis in Social Networks Using Deep Learning Techniques

In recent years, the importance of social media data has increased with the developments in information and communication technologies, and data volume, velocity, variety, veracity, and value have been affected by these developments. Because of the popularity of social networks, the analysis of social media data has also become an important issue for large companies whose brand identity is very crucial. User comments, shares, and explanations in social networks can be used to obtain information about the brand and product. Besides, deep learning techniques, which have become popular recently and provide high accuracy, can be employed for big data analysis in social networks. The number of studies examining the brand image in social networks is quite limited. In this context, we developed a model that performs brand analysis using deep learning techniques in social networks by considering the Starbucks Coffee Company, one of the world's largest coffeehouse chains. We trained our model with Faster Region-based Convolutional Neural Network (Faster R-CNN), Single Shot Multibox Detector (SSD), Mask R-CNN, and You Only Look Once (YOLO) algorithms. We then tested the model on data from Instagram and compared the results. In the light of our results, we have shown that analyzes using deep learning techniques in social networks can significantly affect the image of companies and their brands.

Design and Fabrication of Reliable Power Efficient Bistable MEMS Switch Using Single Mask Process

A bistable DC switch based on buckled beams and thermal actuation has been designed, fabricated and characterised. The buckled beams, which require very low actuation force and provide a sufficiently large contact force, are designed based on a parametric study. U-shaped thermal actuators are designed to provide the actuation force with minimum electrical power. The compact switch is fabricated using a simple single mask process on a SOI wafer. The average switching power is measured to be 60 $mW$ , while the average switching delay is 350 ${\mu }s$ . The power-delay product of about $20~{\mu }J$ is the lowest reported so far for bistable MEMS switches based on thermal actuation. The switch retains functionality even after 5 million switching cycles. [2020-0026]

Dense feature pyramid network for cartoon dog parsing

While traditional cartoon character drawings are simple for humans to create, it remains a highly challenging task for machines to interpret. Parsing is a way to alleviate the issue with fine-grained semantic segmentation of images. Although well studied on naturalistic images, research toward cartoon parsing is very sparse. Due to the lack of available dataset and the diversity of artwork styles, the difficulty of the cartoon character parsing task is greater than the well-known human parsing task. In this paper, we study one type of cartoon instance: cartoon dogs. We introduce a novel dataset toward cartoon dog parsing and create a new deep convolutional neural network (DCNN) to tackle the problem. Our dataset contains 965 precisely annotated cartoon dog images with seven semantic part labels. Our new model, called dense feature pyramid network (DFPnet), makes use of recent popular techniques on semantic segmentation to efficiently handle cartoon dog parsing. We achieve a mIoU of 68.39%, a Mean Accuracy of 79.4% and a Pixel Accuracy of 93.5% on our cartoon dog validation set. Our method outperforms state-of-the-art models of similar tasks trained on our dataset: CE2P for single human parsing and Mask R-CNN for instance segmentation. We hope this work can be used as a starting point for future research toward digital artwork understanding with DCNN. Our DFPnet and dataset will be publicly available.

Implementation of a Single Emulsion Mask for Three-Dimensional (3D) Microstructure Fabrication of Micromixers Using the Grayscale Photolithography Technique.

Three-dimensional (3D) microstructures have been exploited in various applications of microfluidic devices. Multilevel structures in micromixers are among the essential structures in microfluidic devices that exploit 3D microstructures for different tasks. The efficiency of the micromixing process is thus crucial, as it affects the overall performance of a microfluidic device. Microstructures are currently fabricated by less effective techniques due to a slow point-to-point and layer-by-layer pattern exposure by using sophisticated and expensive equipment. In this work, a grayscale photolithography technique is proposed with the capability of simultaneous control on lateral and vertical dimensions of microstructures in a single mask implementation. Negative photoresist SU8 is used for mould realisation with structural height ranging from 163.8 to 1108.7 µm at grayscale concentration between 60% to 98%, depending on the UV exposure time. This technique is exploited in passive micromixers fabrication with multilevel structures to study the mixing performance. Based on optical absorbance analysis, it is observed that 3D serpentine structure gives the best mixing performance among other types of micromixers.

External Alignment Marks Technique for Front-to-Back Side Alignment Using Single-Side Mask Aligner

Some sensors based on Micro-Electro-Mechanical System (MEMS) require lithographic process on both sides of the wafer. In these cases, it is important to provide patterns alignment carefully on both sides of a substrate. To achieve such purpose, a Double-Sided mask (DSM) aligner system is needed which is not found in research centers with limited equipment. In this work, a simple technique for Front-to-Back Side (FBS) alignment is presented using standard Single Side Mask (SSM) aligner system, which is accessible even in laboratories with limited facilities. The essential component of the proposed technique is to utilize external alignment marks (EAM) on the transparent substrate. EAM technique is an uncomplicated and low cost approach that requires no specialized technical knowledge. The transparent substance is attached to a specimen and expands it to get EAM features. A particular process is planned that leads to experimental results. The results indicate that the alignment offset in the center of the specimen is less than the sides. After applying statistical analysis on obtained experimental data and ensuring of normal data distribution, the following values are calculated respectively, along x and y axes: (1) accuracy (10.96 μm and 7.96 μm); (2) precision (2.68 μm and 2.77 μm); (3) standard deviation of repeatability (2.78 μm and 2.69 μm); and (4) reproducibility along (3.09 μm and 4.61 μm). The results demonstrate that EAM is practically reliable to minimize the alignment offset. It has an acceptable precision for MEMS applications as well.

In-Situ Heat Cooling using Thick Graphene and Temperature Monitoring with Single Mask Process

In this paper, in-situ heat cooling with temperature monitoring is reported to solve thermal issues in electric vehicle (EV) batteries. The device consists of a thick graphene cooler on top of the substrate and a platinum-based resistive temperature sensor with an embedded heater above the graphene. The graphene layer is synthesized by using chemical vapor deposition directly on the Ni layer above the Si substrate. The proposed thick graphene heat cooler does not use transfer technology, which involves many process steps and does not provide a high yield. This method also reduces the mechanical damage of the graphene and uses only one photomask. Using this structure, temperature detection and cooling are conducted simultaneously using one device. The temperature coefficient of resistance (TCR) of a 1×1mm(2) temperature sensor on 1-im-thick graphene is 1.573×103 ppm/°C. The heat source cools down 7.3°C from 54.4°C to 47.1°C.

Inscription of Multiple Bragg Gratings in a Single-Mode Polymer Optical Fiber Using a Single Phase Mask and Its Analysis

Multiple Bragg gratings are inscribed in a photosensitive single-mode polymer fiber using phase mask method and also by applying strain to the fiber. Due to the large strain tuning range of the polymer fiber, multiple Bragg gratings are inscribed with a large peak wavelength separation between adjacent peaks. The effect of hysteresis of the polymer fiber is also considered and its influence on the fabrication of the grating is studied. From this paper, it is found that a fiber elongation up to 0.9% of the total fiber length can be used to inscribe multiple Bragg gratings and give a quality grating array. Fiber elongation ${>}{0.9\%}$ of the total length affects the grating reflectivity and bandwidth due to the creep and hysteresis developed in the polymer fiber from the large strain. Within the 0.9% elongation limit, multiple gratings can be inscribed with a maximum peak wavelength separation of 12 nm between adjacent fiber gratings. The fabricated single-mode polymer fiber Bragg grating array can be used in applications, where temperature and humidity compensations are required, while measuring the strain or as multipoint sensors.

Single Mask Selective Release Process for Complex SOI MEMS Device

We present a single mask selective release process for complex SOI MEMS device. Comparing to the one-step dry release process, there are two improvements, the first one is to ensure that the bottom of the suspension beams will not be notching, and have sufficient strength and rigidity, the second one is to ensure that the released structures will not be damaged during wafer dicing. According to the proposed design rules, in the dry release step, most of the device area is released, except the boundaries of the proof mass and the suspension beams. Then, in the wet release step, all the structures will be released, and also increased the gap below the structure. So the suspension beams is protect enabled that the device has sufficient rigidity and not easy to break. To verify this method, a micromachined gyroscope is fabricated and test.

TanDEM-X Water Indication Mask: Generation and First Evaluation Results

The German SAR interferometry mission TanDEM-X performed on two TerraSAR-X satellites flying in close formation will provide a global Digital Elevation Model (DEM). A by-product is so-called the Water Indication Mask (WAM). The purpose of this supplementary information layer is to support the DEM editing process. Water surfaces usually show lower coherence in an interferometric data set due to temporal de-correlation and low backscattering. Consequently the corresponding elevation values derived from the interferogram are random and produce a virtual relief. This paper introduces the operational water body detection workflow that synergistically evaluates amplitude and coherence information. The presented results of two test sites reveal that the methodology is globally applicable, classifications are highly accurate and the algorithm is appropriate for operational image processing. The water body detection consists of two steps: the Water Body Detection (WBD) derived of one single DEM scene and the mosaicking of multiple WBD to a single Water Indication Mask (WAM). The fusion strategy for the final TanDEM-X WAM considers all WBD acquired at different times in two global coverages and bases on a fusion by union containing the results of the amplitude and the coherence.

1802 Randomised Trial of Single Nasal Prong or Face Mask for Respiratory Support for Preterm Infants in Delivery Room (ISRCTN59061709)

BackgroundILCOR recommends that newborns with inadequate breathing or HR < 100 bpm be given respiratory support via a face mask in the delivery room (DR); however, it may be more...

Acute Airway Obstruction Caused by the New Single Use Laryngeal Mask Airway Supreme™

Acute Airway Obstruction Caused by the New Single Use Laryngeal Mask Airway Supreme™