Search results for: deep hole

Authors: Feng Yang

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Machine learning (ML), especially deep learning (DL), has been extensively applied to many applications in recently years and gained great success in solving different problems, including scientific problems. However, conventional ML/DL methodologies are purely data-driven which have the limitations, such as need of ample amount of labelled training data, lack of consistency to physical principles, and lack of generalizability to new problems/domains. Recently, there is a growing consensus that ML models need to further take advantage of prior knowledge to deal with these limitations. Physics-informed machine learning, aiming at integration of physics/domain knowledge into ML, has been recognized as an emerging area of research, especially in the recent 2 to 3 years. In this work, physics-informed ML, specifically physics-informed neural network (NN), is employed and implemented to estimate the displacements at x, y, z directions in a solid mechanics problem that is controlled by equilibrium equations with boundary conditions. By incorporating the physics (i.e. the equilibrium equations) into the learning process of NN, it is showed that the NN can be trained very efficiently with a small set of labelled training data. Experiments with different settings of the NN model and the amount of labelled training data were conducted, and the results show that very high accuracy can be achieved in fulfilling the equilibrium equations as well as in predicting the displacements, e.g. in setting the overall displacement of 0.1, a root mean square error (RMSE) of 2.09 × 10−4 was achieved.

Keywords: deep learning, neural network, physics-informed machine learning, solid mechanics

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Authors: Shaira L. Kee, Michael Aaron G. Sy, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

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Skin diseases are considered the fourth most common disease, with melanoma and non-melanoma skin cancer as the most common type of cancer in Caucasians. The alarming increase in Skin Cancer cases shows an urgent need for further research to improve diagnostic methods, as early diagnosis can significantly improve the 5-year survival rate. Machine Learning algorithms for image pattern analysis in diagnosing skin lesions can dramatically increase the accuracy rate of detection and decrease possible human errors. Several studies have shown the diagnostic performance of computer algorithms outperformed dermatologists. However, existing methods still need improvements to reduce diagnostic errors and generate efficient and accurate results. Our paper proposes an ensemble method to classify dermoscopic images into benign and malignant skin lesions. The experiments were conducted using the International Skin Imaging Collaboration (ISIC) image samples. The dataset contains 3,297 dermoscopic images with benign and malignant categories. The results show improvement in performance with an accuracy of 88% and an F1 score of 87%, outperforming other existing models such as support vector machine (SVM), Residual network (ResNet50), EfficientNetB0, EfficientNetB4, and VGG16.

Keywords: deep learning - VGG16 - efficientNet - CNN – ensemble – dermoscopic images - melanoma

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Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: M. Al Huwayz, A. Salhi, S. Alhassan, S. Alotaibi, A. Almalki, M.Almunyif, A. Alhassni, M. Henini

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Recently, there has been much research carried out to investigate quantum dots (QDs) lasers with the aim to increase the gain of quantum well lasers. However, one of the difficulties with these structures is that electrically active defects can lead to serious issues in the performance of these devices. It is therefore essential to fully understand the types of defects introduced during the growth and/or the fabrication process. In this study, the effects of Gamma radiation on the electrically active defects in p-i-n InAs/InGaAsQDs laser structures grown by Molecular Beam Epitaxy (MBE) technique on GaAs substrates were investigated. Deep Level Transient Spectroscopy (DLTS), current-voltage (I-V), and capacitance-voltage (C-V) measurements were performed to explore these effects on the electrical properties of these QDs lasers. I-V measurements showed that as-grown sample had better electrical properties than the irradiated sample. However, DLTS and Laplace DLTS measurements at different reverse biases revealed that the defects in the-region of the p-i-n structures were decreased in the irradiated sample. In both samples, a trap with an activation energy of ~ 0.21 eV was assigned to the well-known defect M1 in GaAs layers

Keywords: quantum dots laser structures, gamma radiation, DLTS, defects, nAs/IngaAs

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Authors: Kirolos Gerges Yakoub Gerges

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Self-sustaining agricultural machines act in stochastic surroundings and therefore, should be capable of perceive the surroundings in real time. This notion can be done using image sensors blended with superior device learning, mainly Deep mastering. Deep convolutional neural networks excel in labeling and perceiving colour pix and since the fee of RGB-cameras is low, the hardware cost of accurate notion relies upon heavily on memory and computation power. This paper investigates the opportunity of designing lightweight convolutional neural networks for semantic segmentation (pixel clever class) with reduced hardware requirements, to allow for embedded usage in self-reliant agricultural machines. The usage of compression techniques, a lightweight convolutional neural community is designed to carry out actual-time semantic segmentation on an embedded platform. The community is skilled on two big datasets, ImageNet and Pascal Context, to apprehend as much as four hundred man or woman instructions. The 400 training are remapped into agricultural superclasses (e.g. human, animal, sky, road, area, shelterbelt and impediment) and the capacity to provide correct actual-time perception of agricultural environment is studied. The network is carried out to the case of self-sufficient grass mowing the usage of the NVIDIA Tegra X1 embedded platform. Feeding case-unique pics to the community consequences in a fully segmented map of the superclasses within the picture. As the network remains being designed and optimized, handiest a qualitative analysis of the technique is entire on the abstract submission deadline. intending this cut-off date, the finalized layout is quantitatively evaluated on 20 annotated grass mowing pictures. Light-weight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show aggressive performance on the subject of accuracy and speed. It’s miles viable to offer value-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: centrifuge pump, hydraulic energy, agricultural applications, irrigationaxial flux machines, axial flux applications, coreless machines, PM machinesautonomous agricultural machines, deep learning, safety, visual perception

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Authors: Zeinab Jafari A., Ali Sharifnezhad B., Mohammad Razi C., Mohammad Haghpanahi D., Arash Maghsoudi

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Background and Objective: Sports-related rupture of the anterior cruciate ligament (ACL) and following injuries have been associated with various disorders, such as long-lasting changes in muscle activation patterns in athletes, which might last after ACL reconstruction (ACLR). The rupture of the ACL might result in abnormal patterns of movement execution, extending the treatment period and delaying athletes’ return to sports (RTS). As ACL injury is especially prevalent among athletes, the lengthy treatment process and athletes’ absence from sports are of great concern to athletes and coaches. Thus, estimating safe time of RTS is of crucial importance. Therefore, using a deep neural network (DNN) to classify the health levels of ACL in injured athletes, this study aimed to estimate the safe time for athletes to return to competitions. Methods: Ten athletes with ACLR and fourteen healthy controls participated in this study. Three health levels of ACL were defined: healthy, six-month post-ACLR surgery and nine-month post-ACLR surgery. Athletes with ACLR were tested six and nine months after the ACLR surgery. During the course of this study, surface electromyography (sEMG) signals were recorded from five knee muscles, namely Rectus Femoris (RF), Vastus Lateralis (VL), Vastus Medialis (VM), Biceps Femoris (BF), Semitendinosus (ST), during single-leg drop landing (SLDL) and forward hopping (SLFH) tasks. The Pseudo-Wigner-Ville distribution (PWVD) was used to produce three-dimensional (3-D) images of the energy distribution patterns of sEMG signals. Then, these 3-D images were converted to two-dimensional (2-D) images implementing the heat mapping technique, which were then fed to a deep convolutional neural network (DCNN). Results: In this study, we estimated the safe time of RTS by designing a DCNN classifier with an accuracy of 90 %, which could classify ACL into three health levels. Discussion: The findings of this study demonstrate the potential of the DCNN classification technique using sEMG signals in estimating RTS time, which will assist in evaluating the recovery process of ACLR in athletes.

Keywords: anterior cruciate ligament reconstruction, return to sports, surface electromyography, deep convolutional neural network

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Authors: Sushish Baral, Pratibha Joshi, Yaman Maharjan

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Glaucoma is a chronic eye condition that causes vision loss that is irreversible. Early detection and treatment are critical to prevent vision loss because it can be asymptomatic. For the identification of glaucoma, multiple deep learning algorithms are used. Transformer-based architectures, which use the self-attention mechanism to encode long-range dependencies and acquire extremely expressive representations, have recently become popular. Convolutional architectures, on the other hand, lack knowledge of long-range dependencies in the image due to their intrinsic inductive biases. The aforementioned statements inspire this thesis to look at transformer-based solutions and investigate the viability of adopting transformer-based network designs for glaucoma detection. Using retinal fundus images of the optic nerve head to develop a viable algorithm to assess the severity of glaucoma necessitates a large number of well-curated images. Initially, data is generated by augmenting ocular pictures. After that, the ocular images are pre-processed to make them ready for further processing. The system is trained using pre-processed images, and it classifies the input images as normal or glaucoma based on the features retrieved during training. The Vision Transformer (ViT) architecture is well suited to this situation, as it allows the self-attention mechanism to utilise structural modeling. Extensive experiments are run on the common dataset, and the results are thoroughly validated and visualized.

Keywords: glaucoma, vision transformer, convolutional architectures, retinal fundus images, self-attention, deep learning

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Authors: Jingting Luo, Yong Wang, Ying Wang

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Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from the VOC data set and train with the INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2, and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

Keywords: pedestrian detection, feature detection, convolutional neural network, real-time detection, YOLOv3

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Authors: Sudarat Issarapanacheewin, Katcharin Wetchakun, Sukon Phanichphant, Wiyong Kangwansupamonkon, Natda Wetchakun

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The photocatalytic degradation of Methylene blue (MB) and Rhodamine B (RhB) in the presence of ZnWO4-Bi2WO6 composite under visible light irradiation (λ ≥ 400 nm) were studied in this research. The structural and photophysical properties of ZnWO4-Bi2WO6 composite on the photocatalytic degradation process were investigated. The as-prepared ZnWO4-Bi2WO6 composite photocatalyst exhibits wide absorption in the visible-light region and display superior visible-light-driven photocatalytic activities in degradation of MB and RhB. The enhanced photocatalytic activity was attributed to electron-hole separation with the appropriate band potential and the physicochemical properties of ZnWO4 and Bi2WO6. The main active species for the degradation of organic dyes were investigated to explain the enhancement of photocatalytic performance of ZnWO4-Bi2WO6 composite. The possible photocatalytic degradation pathway of aqueous MB and RhB dyes and charge transfer of ZnWO4-Bi2WO6 composite was proposed.

Keywords: composite, dyes, photocatalytic activity, ZnWO4-Bi2WO6

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Authors: Ti-An Tsai, Chun-Chih Wang, Hung-Wen Wang, I-Ling Chang, Lien-Wen Chen

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In this study, we demonstrate a high-resolution refractive index sensor based on a magnetic photonic crystal (MPC) composed of a triangular lattice array of air holes embedded in Si matrix. A microcavity is created by changing the radius of an air hole in the middle of the photonic crystal. The cavity filled with gyrotropic materials can serve as a refractive index sensor. The shift of the resonant frequency of the sensor is obtained numerically using finite difference time domain method under different ambient conditions having refractive index from n = 1.0 to n = 1.1. The numerical results show that a tiny change in refractive index of Δn = 0.0001 is distinguishable. In addition, the spectral response of the MPC sensor is studied while an external magnetic field is present. The results show that the MPC sensor exhibits a dramatic improvement in resolution.

Keywords: magnetic photonic crystal, refractive index sensor, sensitivity, high-resolution

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Authors: Jean-Francois Rajotte, Martin Sotir, Frank Gouineau

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The installation and management of traffic monitoring devices can be costly from both a financial and resource point of view. It is therefore important to take advantage of in-place infrastructures to extract the most information. Here we show how low-quality urban road traffic images from cameras already available in many cities (such as Montreal, Vancouver, and Toronto) can be used to estimate traffic flow. To this end, we use a pre-trained neural network, developed for object detection, to count vehicles within images. We then compare the results with human annotations gathered through crowdsourcing campaigns. We use this comparison to assess performance and calibrate the neural network annotations. As a use case, we consider six months of continuous monitoring over hundreds of cameras installed in the city of Montreal. We compare the results with city-provided manual traffic counting performed in similar conditions at the same location. The good performance of our system allows us to consider applications which can monitor the traffic conditions in near real-time, making the counting usable for traffic-related services. Furthermore, the resulting annotations pave the way for building a historical vehicle counting dataset to be used for analysing the impact of road traffic on many city-related issues, such as urban planning, security, and pollution.

Keywords: traffic monitoring, deep learning, image annotation, vehicles, roads, artificial intelligence, real-time systems

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Authors: Naser Ojaroudi Parchin, Atta Ullah, Haleh Jahanbakhsh Basherlou, Raed A. Abd-Alhameed, Peter S. Excell

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A design of beam-steerable antenna array for the future cellular communication (5G) is presented. The proposed design contains eight elements of compact end-fire antennas arranged on the top edge of smartphone printed circuit board (PCB). Configuration of the antenna element consists of the conductive patterns on the top and bottom copper foil layers and a substrate layer with a via-hole. The simulated results including input-impedance and also fundamental radiation properties have been presented and discussed. The impedance bandwidth (S₁₁ ≤ -10 dB) of the antenna spans from 17.5 to 21 GHz (more than 3 GHz bandwidth) with a resonance at 19 GHz. The antenna exhibits end-fire (directional) radiation beams with wide-angle scanning property and could be used for the future 5G beam-forming. Furthermore, the characteristics of the array design in the vicinity of user-hand are studied.

Keywords: beam-steering, end-fire radiation mode, mobile-phone antenna, phased array

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Authors: Bashar Tarawneh, Yasser Hakam

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Rapid Impact Compaction (RIC) is a modern dynamic compaction device mainly used to compact sandy soils, where silt and clay contents are low. The device uses the piling hammer technology to increase the bearing capacity of soils through controlled impacts. The RIC device uses "controlled impact compaction" of the ground using a 9-ton hammer dropped from the height between 0.3 m to 1.2 m onto a 1.5 m diameter steel patent foot. The delivered energy is about 26,487 to 105,948 Joules per drop. To evaluate the performance of this technique, three project sites in the United Arab Emirates were improved using RIC. In those sites, a loose to very loose fine to medium sand was encountered at a depth ranging from 1.0m to 4.0m below the ground level. To evaluate the performance of the RIC, Cone Penetration Tests (CPT) were carried out before and after improvement. Also, load tests were carried out post-RIC work to assess the settlements and bearing capacity. The soil was improved to a depth of about 5.0m below the ground level depending on the CPT friction ratio (the ratio between sleeve friction and tip resistance). CPT tip resistance was significantly increased post ground improvement work. Load tests showed enhancement in the soil bearing capacity and reduction in the potential settlements. This study demonstrates the successful application of the RIC for middle-deep improvement and compaction of the ground. Foundation design criteria were achieved in all site post-RIC work.

Keywords: compaction, RIC, ground improvement, CPT

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Authors: Lin Li, Farshad Amini

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The flipped classroom has been introduced to promote collaborative learning and higher-order learning objectives. In contrast to the traditional classroom, the flipped classroom has students watch prerecorded lecture videos before coming to class and then “class becomes the place to work through problems, advance concepts, and engage in collaborative learning”. In this paper, the active flipped learning combines flipped classroom with active learning that is to establish an active flipped learning (AFL) model, aiming to promote active learning, stress deep learning, encourage student engagement and highlight data-driven personalized learning. Because students have watched the lecture prior to class, contact hours can be devoted to problem-solving and gain a deeper understanding of the subject matter. The instructor is able to provide students with a wide range of learner-centered opportunities in class for greater mentoring and collaboration, increasing the possibility to engage students. Currently, little is known about the extent to which AFL improves engineering students’ performance. This paper presents the preliminary study on the core course of sophomore students in Engineering Mechanics. A series of survey and interviews have been conducted to compare students’ learning engagement, empowerment, self-efficacy, and satisfaction with the AFL. It was found that the AFL model taking advantage of advanced technology is a convenient and professional avenue for engineering students to strengthen their academic confidence and self-efficacy in the Engineering Mechanics by actively participating in learning and fostering their deep understanding of engineering statics and dynamics

Keywords: active learning, engineering mechanics, flipped classroom, performance

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Authors: Zubair Ahmed, Andrea Barbieri

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The need to reduce energy consumption has prompted a considerable research effort for developing alternative energy-efficient lighting systems to replace conventional light sources (i.e., incandescent and fluorescent lamps). Organic light emitting device (OLED) technology offers the distinctive possibility to fabricate large area flat devices by vacuum or solution processing. Lanthanide β-diketonates complexes, due to unique photophysical properties of Ln(III) ions, have been explored as emitting layers in OLED displays and in solid-state lighting (SSL) in order to achieve high efficiency and color purity. For such applications, the excellent photoluminescence quantum yield (PLQY) and stability are the two key points that can be achieved simply by selecting the proper organic ligands around the Ln ion in a coordination sphere. Regarding the strategies to enhance the PLQY, the most common is the suppression of the radiationless deactivation pathways due to the presence of high-frequency oscillators (e.g., OH, –CH groups) around the Ln centre. Recently, a different approach to maximize the PLQY of Ln(β-DKs) has been proposed (named 'Escalate Coordination Anisotropy', ECA). It is based on the assumption that coordinating the Ln ion with different ligands will break the centrosymmetry of the molecule leading to less forbidden transitions (loosening the constraints of the Laporte rule). The OLEDs based on such complexes are available, but with low efficiency and stability. In order to get efficient devices, there is a need to develop some new Ln complexes with enhanced PLQYs and stabilities. For this purpose, the Ln complexes, both visible and (NIR) emitting, of variant coordination structures based on the various fluorinated/non-fluorinated β-diketones and O/N-donor neutral ligands were synthesized using a one step in situ method. In this method, the β-diketones, base, LnCl₃.nH₂O and neutral ligands were mixed in a 3:3:1:1 M ratio in ethanol that gave air and moisture stable complexes. Further, they were characterized by means of elemental analysis, NMR spectroscopy and single crystal X-ray diffraction. Thereafter, their photophysical properties were studied to select the best complexes for the fabrication of stable and efficient OLEDs. Finally, the OLEDs were fabricated and investigated using these complexes as emitting layers along with other organic layers like NPB,N,N′-Di(1-naphthyl)-N,N′-diphenyl-(1,1′-biphenyl)-4,4′-diamine (hole-transporting layer), BCP, 2,9-Dimethyl-4,7-diphenyl-1,10-phenanthroline (hole-blocker) and Alq3 (electron-transporting layer). The layers were sequentially deposited under high vacuum environment by thermal evaporation onto ITO glass substrates. Moreover, co-deposition techniques were used to improve charge transport in the devices and to avoid quenching phenomena. The devices show strong electroluminescence at 612, 998, 1064 and 1534 nm corresponding to ⁵D₀ →⁷F₂(Eu), ²F₅/₂ → ²F₇/₂ (Yb), ⁴F₃/₂→ ⁴I₉/₂ (Nd) and ⁴I1₃/₂→ ⁴I1₅/₂ (Er). All the devices fabricated show good efficiency as well as stability.

Keywords: electroluminescence, lanthanides, paramagnetic NMR, photoluminescence

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Authors: Ewelina Grabowska, Martyna Marchelek

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Among the various semiconductors, nanosized TiO2 has been widely studied due to its high photosensitivity, low cost, low toxicity, and good chemical and thermal stability. However, there are two main drawbacks to the practical application of pure TiO2 films. One is that TiO2 can be induced only by ultraviolet (UV) light due to its intrinsic wide bandgap (3.2 eV for anatase and 3.0 eV for rutile), which limits its practical efficiency for solar energy utilization since UV light makes up only 4-5% of the solar spectrum. The other is that a high electron-hole recombination rate will reduce the photoelectric conversion efficiency of TiO2. In order to overcome the above drawbacks and modify the electronic structure of TiO2, some semiconductors (eg. CdS, ZnO, PbS, Cu2O, Bi2S3, and CdSe) have been used to prepare coupled TiO2 composites, for improving their charge separation efficiency and extending the photoresponse into the visible region. It has been proved that the fabrication of p-n heterostructures by combining n-type TiO2 with p-type semiconductors is an effective way to improve the photoelectric conversion efficiency of TiO2. SrTiO3 is a good candidate for coupling TiO2 and improving the photocatalytic performance of the photocatalyst because its conduction band edge is more negative than TiO2. Due to the potential differences between the band edges of these two semiconductors, the photogenerated electrons transfer from the conduction band of SrTiO3 to that of TiO2. Conversely, the photogenerated electrons transfer from the conduction band of SrTiO3 to that of TiO2. Then the photogenerated charge carriers can be efficiently separated by these processes, resulting in the enhancement of the photocatalytic property in the photocatalyst. Additionally, one of the methods for improving photocatalyst performance is addition of nanoparticles containing one or two noble metals (Pt, Au, Ag and Pd) deposited on semiconductor surface. The mechanisms were proposed as (1) the surface plasmon resonance of noble metal particles is excited by visible light, facilitating the excitation of the surface electron and interfacial electron transfer (2) some energy levels can be produced in the band gap of TiO2 by the dispersion of noble metal nanoparticles in the TiO2 matrix; (3) noble metal nanoparticles deposited on TiO2 act as electron traps, enhancing the electron–hole separation. In view of this, we recently obtained series of TiO2@SrTiO3 and SrTiO3 photocatalysts loaded with noble metal NPs. using photodeposition method. The M- TiO2@SrTiO3 and M-SrTiO3 photocatalysts (M= Rh, Rt, Pt) were studied for photodegradation of phenol in aqueous phase under UV-Vis and visible irradiation. Moreover, in the second part of our research hydroxyl radical formations were investigated. Fluorescence of irradiated coumarin solution was used as a method of ˙OH radical detection. Coumarin readily reacts with generated hydroxyl radicals forming hydroxycoumarins. Although the major hydroxylation product is 5-hydroxycoumarin, only 7-hydroxyproduct of coumarin hydroxylation emits fluorescent light. Thus, this method was used only for hydroxyl radical detection, but not for determining concentration of hydroxyl radicals.

Keywords: composites TiO2, SrTiO3, photocatalysis, phenol degradation

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Authors: Kishore Kumar R., Kaustav Sengupta, Shalini Sood Sehgal, Poornima Santhanam

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Fashion is a human expression that is constantly changing. One of the prime factors that consistently influences fashion is the change in colour preferences. The role of colour in our everyday lives is very significant. It subconsciously explains a lot about one’s mindset and mood. Analyzing the colours by extracting them from the outfit images is a critical study to examine the individual’s/consumer behaviour. Several research works have been carried out on extracting colours from images, but to the best of our knowledge, there were no studies that extract colours to specific apparel and identify colour patterns geographically. This paper proposes a framework for accurately extracting colours from T-shirt images and predicting dominant colours geographically. The proposed method consists of two stages: first, a U-Net deep learning model is adopted to segment the T-shirts from the images. Second, the colours are extracted only from the T-shirt segments. The proposed method employs the iMaterialist (Fashion) 2019 dataset for the semantic segmentation task. The proposed framework also includes a mechanism for gathering data and analyzing India’s general colour preferences. From this research, it was observed that black and grey are the dominant colour in different regions of India. The proposed method can be adapted to study fashion’s evolving colour preferences.

Keywords: colour analysis in t-shirts, convolutional neural network, encoder-decoder, k-means clustering, semantic segmentation, U-Net model

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Authors: Malek Mahmoudi, Jonas Keruckas, Dmytro Volyniuk, Jurate Simokaitiene, Juozas V. Grazulevicius

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Host materials have been discovered as one of the most appealing methods for harvesting triplet states in organic materials for application in organic light-emitting diodes (OLEDs). The ideal host-guest system for emission in thermally delayed fluorescence OLEDs with 20% guest concentration for efficient energy transfer has been demonstrated in the present investigation. In this work, 3,3'-bis[9-(4-fluorophenyl) carbazole] (bFPC) has been used as the host, which induces balanced charge carrier transport for high-efficiency OLEDs.For providing a complete characterization of the synthesized compound, photophysical, photoelectrical, charge-transporting, and electrochemical properties of the compound have been examined. Excited-state lifetimes and singlet-triplet energy gaps were measured for characterization of photophysical properties, while thermogravimetric analysis, as well as differential scanning calorimetry measurements, were performed for probing of electrochemical and thermal properties of the compound. The electrochemical properties of this compound were investigated by cyclic voltammetry (CV) method, and ionization potential (IPCV) value of 5.68 eV was observed. UV–Vis absorption and photoluminescence spectrum of a solution of the compound in toluene (10-5 M) showed maxima at 302 and 405 nm, respectively. Photoelectron emission spectrometry was used for the characterization of charge-injection properties of the studied compound in solid. The ionization potential of this material was found to be 5.78 eV, and time-of-flight measurement was used for testing charge-transporting properties and hole mobility estimated using this technique in a vacuum-deposited layer reached 4×10-4 cm2 V-1s-1. Since the compound with high charge mobilities was tested as a host in an organic light-emitting diode. The device was fabricated by successive deposition onto a pre-cleaned indium tin oxide (ITO) coated glass substrate under a vacuum of 10-6 Torr and consisting of an indium-tin-oxide anode, hole injection and transporting layer(MoO3, NPB), emitting layer with bFPC as a host and 4CzIPN (2,4,5,6-tetra(9-carbazolyl)isophthalonitrile) which is a new highly efficient green thermally activated delayed fluorescence (TADF) material as an emitter, an electron transporting layer(TPBi) and lithium fluoride layer topped with aluminum layer as a cathode exhibited the highest maximum current efficiency and power efficiency of 33.9 cd/A and 23.5 lm/W, respectively and the electroluminescence spectrum showed only a peak at 512nm. Furthermore, the new bicarbazole-based compound was tested as a host in thermally activated delayed fluorescence organic light-emitting diodes are reaching luminance of 25300 cd m-2 and external quantum efficiency of 10.1%. Interestingly, the turn-on voltage was low enough (3.8 V), and such a device can be used for highly efficient light sources.

Keywords: thermally-activated delayed fluorescence, host material, ionization energy, charge mobility, electroluminescence

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Authors: Oday A. Al-Juhaishi, Sa`ad M. Ismail, Hung-Hsun Yen, Christina M. Murray, Helen M. S. Davies

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The functional anatomy of the fascia in the greyhound is still poorly understood, and incompletely described. The basic knowledge of fascia stems mainly from anatomical, histological and ultrastructural analyses. In this study, twelve specimens of hindlimbs from six fresh greyhound cadavers (3 male, 3 female) were used to examine the topographical relationships of the superficial fascia with the skin and underlying tissue. The first incision was made along the dorsal midline from the level of the thoracolumbar junction caudally to the level of the mid sacrum. The second incision was begun at the level of the first incision and extended along the midline of the lateral aspect of the hindlimb distally, to just proximal to the tarsus, and, the skin margins carefully separated to observe connective tissue links between the skin and superficial fascia, attachment points of the fascia and the relationships of the fascia with blood vessels that supply the skin. A digital camera was used to record the anatomical features as they were revealed. The dissections identified fibrous septa connecting the skin with the superficial fascia and deep fascia in specific areas. The presence of the adipose tissue was found to be very rare within the superficial fascia in these specimens. On the extensor aspects of some joints, a fusion between the superficial fascia and deep fascia was observed. This fusion created a subcutaneous bursa in the following areas: a prepatellar bursa of the stifle, a tarsal bursa caudal to the calcaneus bone, and an ischiatic bursa caudal to the ischiatic tuberosity. The evaluation of blood vessels showed that the perforating vessels passed through fibrous septa in a perpendicular direction to supply the skin, with the largest branch noted in the gluteal area. The attachment points between the superficial fascia and skin were mainly found in the region of the flexor aspect of the joints, such as caudal to the stifle joint. The numerous fibrous septa between the superficial fascia and skin that have been identified in some areas, may create support for the blood vessels that penetrate fascia and into the skin, while allowing for movement between the tissue planes. The subcutaneous bursae between the skin and the superficial fascia where it is fused with the deep fascia may be useful to decrease friction between moving areas. The adhesion points may be related to the integrity and loading of the skin. The attachment points fix the skin and appear to divide the hindlimb into anatomical compartments.

Keywords: attachment points, fibrous septa, greyhound, subcutaneous bursa, superficial fascia

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Authors: Hansraj Riteesh Bookun, Cassandra Monique Hidajat

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This pilot study investigated the efficacy of a newly designed shoe which will act as an intermittent pneumatic compression device to augment venous flow in the lower limb. The aim was to assess the degree with which a wearable intermittent compression device can increase the venous flow in the popliteal vein. Background: Deep venous thrombosis and chronic venous insufficiency are relatively common problems with significant morbidity and mortality. While mechanical and chemical thromboprophylaxis measures are in place in hospital environments (in the form of TED stockings, intermittent pneumatic compression devices, analgesia, antiplatelet and anticoagulant agents), there are limited options in a community setting. Additionally, many individuals are poorly tolerant of graduated compression stockings due to the difficulty in putting them on, their constant tightness and increased associated discomfort in warm weather. These factors may hinder the management of their chronic venous insufficiency. Method: The device is lightweight, easy to wear and comfortable, with a self-contained power source. It features a Bluetooth transmitter and can be controlled with a smartphone. It is externally almost indistinguishable from a normal shoe. During activation, two bladders are inflated -one overlying the metatarsal heads and the second at the pedal arch. The resulting cyclical increase in pressure squeezes blood into the deep venous system. This will decrease periods of stasis and potentially reduce the risk of deep venous thrombosis. The shoe was fitted to 2 healthy participants and the peak systolic velocity of flow in the popliteal vein was measured during and prior to intermittent compression phases. Assessments of total flow volume were also performed. All haemodynamic assessments were performed with ultrasound by a licensed sonographer. Results: Mean peak systolic velocity of 3.5 cm/s with standard deviation of 1.3 cm/s were obtained. There was a three fold increase in mean peak systolic velocity and five fold increase in total flow volume. Conclusion: The device augments venous flow in the leg significantly. This may contribute to lowered thromboembolic risk during periods of prolonged travel or immobility. This device may also serve as an adjunct in the treatment of chronic venous insufficiency. The study will be replicated on a larger scale in a multi—centre trial.

Keywords: venous, intermittent compression, shoe, wearable device

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Authors: Tom O'Mahony

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This article considers the design of a student-centered learning environment in engineering education. The learning environment integrates a number of components, including project-based learning, collaborative learning, two-stage assignments, active learning lectures, and a flipped-classroom. Together these elements place the individual learner and their learning at the center of the environment by focusing on understanding, enhancing relevance, applying learning, obtaining rich feedback, making choices, and taking responsibility. The evolution of this environment from 2014 to the present day is outlined. The impact of this environment on learners and their learning is evaluated via student questionnaires that consist of both open and closed-ended questions. The closed questions indicate that students found the learning environment to be really interesting and enjoyable (rated as 4.7 on a 5 point scale) and encouraged students to adopt a deep approach towards studying the course materials (rated as 4.0 on a 5 point scale). A content analysis of the open-ended questions provides evidence that the project, active learning lectures, and flipped classroom all contribute to the success of this environment. Furthermore, this analysis indicates that the two-stage assessment process, in which feedback is provided between a draft and final assignment, is the key component and the dominant theme. A limitation of the study is the small class size (less than 20 learners per year), but, to some degree, this is compensated for by the longitudinal nature of the study.

Keywords: deep approaches, formative assessment, project-based learning, student-centered learning

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Authors: Omar Elharrouss, Youssef Hmamouche, Assia Kamal Idrissi, Btissam El Khamlichi, Amal El Fallah-Seghrouchni

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Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with the traditional methods like Sobel and Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results while the image output contains many erroneous edges. To overcome this, n this paper, by using the mechanism of residual learning, a refined edge detection network is proposed (RED-Net). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, we make the pooling outputs at each stage connected with the output of the previous layer. Also, after each layer, we use an affined batch normalization layer as an erosion operation for the homogeneous region in the image. The proposed methods are evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.

Keywords: edge detection, convolutional neural networks, deep learning, scale-representation, backbone

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Authors: Jingpeng Gao, Xinrui Mao, Zhibin Deng

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Aiming at the low recognition rate on the composite signal modulation in low signal to noise ratio (SNR), this paper proposes a modulation recognition algorithm based on ICanny-CNN. Firstly, the radar signal is transformed into the time-frequency image by Choi-Williams Distribution (CWD). Secondly, we propose an image processing algorithm using the Guided Filter and the threshold selection method, which is combined with the hole filling and the mask operation. Finally, the shallow convolutional neural network (CNN) is combined with the idea of the depth-wise convolution (Dw Conv) and the point-wise convolution (Pw Conv). The proposed CNN is designed to complete image classification and realize modulation recognition of radar signal. The simulation results show that the proposed algorithm can reach 90.83% at 0dB and 71.52% at -8dB. Therefore, the proposed algorithm has a good classification and anti-noise performance in radar signal modulation recognition and other fields.

Keywords: modulation recognition, image processing, composite signal, improved Canny algorithm

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Authors: Neritan Shkodrani, Klearta Rrushi, Anxhela Shaha

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Generally, the design of axial load capacity of deep foundations is based on the data provided from field tests, such as SPT (Standard Penetration Test) and CPT (Cone Penetration Test) tests. This paper reports the results of axial load capacity analysis of drilled shafts at a construction site at Semani, in Fier county, Fier prefecture in Albania. In this case, the axial load capacity analyses are based on the data of 416 SPT tests and 12 CPTU tests, which are carried out in this site construction using 12 boreholes (10 borings of a depth 30.0 m and 2 borings of a depth of 80.0m). The considered foundation widths range from 0.5m to 2.5 m and foundation embedment lengths is fixed at a value of 25m. SPT – based analytical methods from the Japanese practice of design (Building Standard Law of Japan) and CPT – based analytical Eslami and Fellenius methods are used for obtaining axial ultimate load capacity of drilled shafts. The considered drilled shaft (25m long and 0.5m - 2.5m in diameter) is analyzed for the soil conditions of each borehole. The values obtained from sets of calculations are shown in different charts. Then the reported axial load capacity values acquired from SPT and CPTU data are compared and some conclusions are found related to the mentioned methods of calculations.

Keywords: deep foundations, drilled shafts, axial load capacity, ultimate load capacity, allowable load capacity, SPT test, CPTU test

Procedia PDF Downloads 100

Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: A. Karimzad Ghavidel, M. Zadshakouyan

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Laser cutting is a very common production method for cutting 2D polymeric parts. Developing of polymer composites with nano-fibers makes important their other properties like laser workability. The aim of this research is investigation of the influence different laser cutting conditions on the dimensional accuracy of parts and holes from poly methyl methacrylate (PMMA)/carbon nanotubes (CNTs) material. Experiments were carried out by considering of CNTs (in four level 0,0.5, 1 and 1.5% wt.%), laser power (60, 80, and 100 watt) and cutting speed 20, 30, and 40 mm/s as input variable factors. The results reveal that CNTs adding improves the laser workability of PMMA and the increasing of power has a significant effect on the part and hole size. The findings also show cutting speed is effective parameter on the size accuracy. Eventually, the statistical analysis of results was done, and calculated mathematical equations by the regression are presented for determining relation between input and output factor.

Keywords: dimensional accuracy, PMMA, CNTs, laser cutting

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Authors: Rajesh Kumar Gautam, Debabrata Seth

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Thioflavin-T (ThT) play a key role of an important biologically active fluorescent sensor for amyloid fibrils. ThT molecule has been developed a method to detect the analysis of different type of diseases such as neurodegenerative disorders, Alzheimer’s, Parkinson’s, and type II diabetes. ThT was used as a fluorescent marker to detect the formation of amyloid fibril. In the presence of amyloid fibril, ThT becomes highly fluorescent. ThT undergoes twisting motion around C-C bonds of the two adjacent benzothiazole and dimethylaniline aromatic rings, which is predominantly affected by the micro-viscosity of the local environment. The present study articulates photophysics and torsional dynamics of biologically active molecule ThT in the presence of deep-eutectic solvents (DESs). DESs are environment-friendly, low cost and biodegradable alternatives to the ionic liquids. DES resembles ionic liquids, but the constituents of a DES include a hydrogen bond donor and acceptor species, in addition to ions. Due to the presence of the H-bonding network within a DES, it exhibits structural heterogeneity. Herein, we have prepared two different DESs by mixing urea with choline chloride and N, N-diethyl ethanol ammonium chloride at ~ 340 K. It was reported that deep eutectic mixture of choline chloride with urea gave a liquid with a freezing point of 12°C. We have experimented by taking two different concentrations of ThT. It was observed that at higher concentration of ThT (50 µM) it forms aggregates in DES. The photophysics of ThT as a function of temperature have been explored by using steady-state, and picoseconds time-resolved fluorescence emission spectroscopic techniques. From the spectroscopic analysis, we have observed that with rising temperature the fluorescence quantum yields and lifetime values of ThT molecule gradually decreases; this is the cumulative effect of thermal quenching and increase in the rate of the torsional rate constant. The fluorescence quantum yield and fluorescence lifetime decay values were always higher for DES-II (urea & N, N-diethyl ethanol ammonium chloride) than those for DES-I (urea & choline chloride). This was mainly due to the presence of structural heterogeneity of the medium. This was further confirmed by comparison with the activation energy of viscous flow with the activation energy of non-radiative decay. ThT molecule in less viscous media undergoes a very fast twisting process and leads to deactivation from the photoexcited state. In this system, the torsional motion increases with increasing temperature. We have concluded that beside bulk viscosity of the media, structural heterogeneity of the medium play crucial role to guide the photophysics of ThT in DESs. The analysis of the experimental data was carried out in the temperature range 288 ≤ T = 333K. The present articulate is to obtain an insight into the DESs as media for studying various photophysical processes of amyloid fibrils sensing molecule of ThT.

Keywords: deep eutectic solvent, photophysics, Thioflavin T, the torsional rate constant

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Authors: Pitigalage Chamath Chandira Peiris

Abstract:

A widely researched domain in the field of image processing in recent times has been single image super-resolution, which tries to restore a high-resolution image from a single low-resolution image. Many more single image super-resolution efforts have been completed utilizing equally traditional and deep learning methodologies, as well as a variety of other methodologies. Deep learning-based super-resolution methods, in particular, have received significant interest. As of now, the most advanced image restoration approaches are based on convolutional neural networks; nevertheless, only a few efforts have been performed using Transformers, which have demonstrated excellent performance on high-level vision tasks. The effectiveness of CNN-based algorithms in image super-resolution has been impressive. However, these methods cannot completely capture the non-local features of the data. Enhancer is a simple yet powerful Transformer-based approach for enhancing the resolution of images. A method for single image super-resolution was developed in this study, which utilized an efficient and effective transformer design. This proposed architecture makes use of a locally enhanced window transformer block to alleviate the enormous computational load associated with non-overlapping window-based self-attention. Additionally, it incorporates depth-wise convolution in the feed-forward network to enhance its ability to capture local context. This study is assessed by comparing the results obtained for popular datasets to those obtained by other techniques in the domain.

Keywords: single image super resolution, computer vision, vision transformers, image restoration

Procedia PDF Downloads 100

Authors: M. P. Rajeeshkumar, K. V. Aneesh Kumar, J. L. Otero-Ferrer, A. Lombarte, M. Hashim, N. Saravanane, V. N.Sanjeevan, V. M. Tuset

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The anglerfishes are widely distributed from shallow to deep-water habitats and are highly diverse in morphology, behaviour, and niche occupancy patterns. To understand this interspecific variability and degree of niche overlap, we performed a functional analysis of five species inhabiting Indian waters where diversity of deep-sea anglerfishes is very high. The sensory capacities (otolith shape and eye size) were also studied to improve the understanding of coexistence of species. The analyses of fish body and otolith shape clustered species in two morphotypes related to phylogenetic lineages: i) Malthopsis lutea, Lophiodes lugubri and Halieutea coccinea were characterized by a dorso-ventrally flattened body with high swimming ability and relative small otoliths, and ii) Chaunax spp. were distinguished by their higher body depth, lower swimming efficiency, and relative big otoliths. The sensory organs did not show a pattern linked to depth distribution of species. However, the larger eye size in M. lutea suggested a nocturnal feeding activity, whereas Chaunax spp. had a large mouth and deeper body in response to different ecological niches. Therefore, the present study supports the hypothesis of spatial and temporal segregation of anglerfishes in the Indian waters, which can be explained from a functional approach and understanding from sensory capabilities.

Keywords: functional traits, otoliths, niche overlap, fishes, Indian waters

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Authors: Sara Kanzi, Izzet Sakalli

Abstract:

The effect of Lorentz symmetry breaking (LSB) on the Hawking radiation of Schwarzschild-like black hole found in the bumblebee gravity model (SBHBGM) is studied in the framework of quantum gravity. To this end, we consider Hawking radiation spin-0 (bosons) and spin-12particles (fermions), which go in and out through the event horizon of the SBHBGM. We use the modified Klein-Gordon and Dirac equations, which are obtained from the generalized uncertainty principle (GUP) to show how Hawking radiation is affected by the GUP and LSB. In particular, we reveal that independent of the spin of the emitted particles, GUP causes a change in the Hawking temperature of the SBHBGM. Furthermore, we compute the semi-analytic greybody factors (for both bosons and fermions) of the SBHBGM. Thus, we reveal that LSB is effective on the greybody factor of the SBHBGM such that its redundancy decreases the value of the greybody factor. Our findings are graphically depicted.

Keywords: bumblebee gravity model, Hawking radiation, generalized uncertainty principle, Lorentz symmetry breaking

Procedia PDF Downloads 132