Search results for: deep layer

Authors: Deniz Guven, Wil Ward, Jinming Duan, Li Bai

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Over the past two decades or so, Optical Coherence Tomography (OCT) has been used to diagnose retina and optic nerve diseases. The retinal nerve fibre layer, for example, is a powerful diagnostic marker for detecting and staging glaucoma. With the advances in optical imaging hardware, the adoption of OCT is now commonplace in clinics. More and more OCT images are being generated, and for these OCT images to have clinical applicability, accurate automated OCT image segmentation software is needed. Oct image segmentation is still an active research area, as OCT images are inherently noisy, with the multiplicative speckling noise. Simple edge detection algorithms are unsuitable for detecting retinal layer boundaries in OCT images. Intensity fluctuation, motion artefact, and the presence of blood vessels also decrease further OCT image quality. In this paper, we introduce a new method for segmenting three-dimensional (3D) OCT images. This involves the use of a Kalman filter, which is commonly used in computer vision for object tracking. The Kalman filter is applied to the 3D OCT image volume to track the retinal layer boundaries through the slices within the volume and thus segmenting the 3D image. Specifically, after some pre-processing of the OCT images, points on the retinal layer boundaries in the first image are identified, and curve fitting is applied to them such that the layer boundaries can be represented by the coefficients of the curve equations. These coefficients then form the state space for the Kalman Filter. The filter then produces an optimal estimate of the current state of the system by updating its previous state using the measurements available in the form of a feedback control loop. The results show that the algorithm can be used to segment the retinal layers in OCT images. One of the limitations of the current algorithm is that the curve representation of the retinal layer boundary does not work well when the layer boundary is split into two, e.g., at the optic nerve, the layer boundary split into two. This maybe resolved by using a different approach to representing the boundaries, such as b-splines or level sets. The use of a Kalman filter shows promise to developing accurate and effective 3D OCT segmentation methods.

Keywords: optical coherence tomography, image segmentation, Kalman filter, object tracking

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Authors: A. Kavya Sri, K. Sai Vineela, R. Vanitha, S. Rohith

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Large pieces of ice that break from the glaciers are known as icebergs. The threat that icebergs pose to navigation, production of offshore oil and gas services, and underwater pipelines makes their detection crucial. In this project, an automated iceberg tracking method using deep learning techniques and satellite images of icebergs is to be developed. With a temporal resolution of 12 days and a spatial resolution of 20 m, Sentinel-1 (SAR) images can be used to track iceberg drift over the Southern Ocean. In contrast to multispectral images, SAR images are used for analysis in meteorological conditions. This project develops a web-based graphical user interface to detect and track icebergs using sentinel-1 images. To track the movement of the icebergs by using temporal images based on their latitude and longitude values and by comparing the center and area of all detected icebergs. Testing the accuracy is done by precision and recall measures.

Keywords: synthetic aperture radar (SAR), icebergs, deep learning, spatial resolution, temporal resolution

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Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

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Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

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Authors: Ali Berkan Ural

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This paper presents the analysis, evaluation, and pre-diagnosis of early stage breast based diagnostic problems (breast cancer, nodulesorlumps) by Computer Aided Diagnosing (CAD) system from mammogram radiological images. According to the statistics, the time factor is crucial to discover the disease in the patient (especially in women) as possible as early and fast. In the study, a new algorithm is developed using advanced image processing and deep learning method to detect and classify the problem at earlystagewithmoreaccuracy. This system first works with image processing methods (Image acquisition, Noiseremoval, Region Growing Segmentation, Morphological Operations, Breast BorderExtraction, Advanced Segmentation, ObtainingRegion Of Interests (ROIs), etc.) and segments the area of interest of the breast and then analyzes these partly obtained area for cancer detection/lumps in order to diagnosis the disease. After segmentation, with using the Spectrogramimages, 5 different deep learning based methods (specified Convolutional Neural Network (CNN) basedAlexNet, ResNet50, VGG16, DenseNet, Xception) are applied to classify the breast based problems.

Keywords: computer aided diagnosis, breast cancer, region growing, segmentation, deep learning

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Authors: Chad Brown

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This paper establishes general conditions for the convergence rates of nonparametric sieve estimators with dependent data. We present two key results: one for nonstationary data and another for stationary mixing data. Previous theoretical results often lack practical applicability to deep neural networks (DNNs). Using these conditions, we derive convergence rates for DNN sieve estimators in nonparametric regression settings with both nonstationary and stationary mixing data. The DNN architectures considered adhere to current industry standards, featuring fully connected feedforward networks with rectified linear unit activation functions, unbounded weights, and a width and depth that grows with sample size.

Keywords: sieve extremum estimates, nonparametric estimation, deep learning, neural networks, rectified linear unit, nonstationary processes

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Authors: Akm Badrul Alam, Yoshiaki Fujii, Nahid Hasan Dipu, Shakil Ahmed Razo

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Shale rockmasses often include silt layers, impacting slope stability in construction and mining. Analyzing their interaction is crucial for long-term stability. A study used an elastoplastic model, incorporating the stress transfer method and Coulomb's criterion, to assess a shale rock mass with silt layers. It computed stress distribution, assessed failure potential, and identified vulnerable regions where nodal forces were calculated for a comprehensive analysis. A shale rock mass ranging from 14.75 to 16.75 meters thick, with silt layers varying from 0.36 to 0.5 meters, was considered in the model. It examined four silt layer conditions: horizontal (SiHL), vertical (SiVL), inclined against slope (SiIincAGS), and along slope (SilincALO). Mechanical parameters like uniaxial compressive strength (UCS), tensile strength (TS), Young’s modulus (E), Poisson’s ratio, and density were adjusted for varied scenarios: UCS (0.5 to 5 MPa), TS (0.1 to 1 MPa), and E (6 to 60 MPa). In elastic analysis of shale rock masses, stress distributions vary based on layer properties. When shale and silt layers have the same elasticity modulus (E), stress concentrates at corners. If the silt layer has a lower E than shale, marginal changes in maximum stress (σmax) occur for SilHL. A decrease in σmax is evident at SilVL. Slight variations in σmax are observed for SilincAGS and SilincALO. In the elastoplastic analysis, the overall decrease of 20%, 40%, 60%, 80%, and 90% was considered. For SilHL:(i) Same E, UCS, and TS for silt layer and shale, UCS/TS ratio 5: strength decrease led to shear (S), tension then shear (T then S) failure; noticeable failure at 60% decrease, significant at 80%, collapse at 90%. (ii) Lower E for silt layer, same strength as shale: No significant differences. (iii) Lower E and UCS, silt layer strength 1/10: No significant differences. For SilVL: (i) Same E, UCS, and TS for silt layer and shale, UCS/TS ratio 5: Similar effects as SilHL. (ii) Lower E for silt layer, same strength as shale: Slip occurred. (iii) Lower E and UCS, silt layer strength 1/10: Bitension failure also observed with larger slip. For SilincAGS: (i) Same E, UCS, and TS for silt layer and shale, UCS/TS ratio 5: Effects similar to SilHL. (ii) Lower E for silt layer, same strength as shale: Slip occurred. (iii) Lower E and UCS, silt layer strength 1/10: Tension failure also observed with larger slip. For SilincALO: (i) Same E, UCS, and TS for silt layer and shale, UCS/TS ratio 5: Similar to SilHL with tension failure. (ii) Lower E for silt layer, same strength as shale: No significant differences; failure diverged. (iii) Lower E and UCS, silt layer strength 1/10: Bitension failure also observed with larger slip; failure diverged. Toppling failure was observed for lower E cases of SilVL and SilincAGS. The presence of silt interlayers in shale greatly impacts slope stability. Designing slopes requires careful consideration of both the silt and shale's mechanical properties. The temporal degradation of strength in these layers is a major concern. Thus, slope design must comprehensively analyze the immediate and long-term mechanical behavior of interlayer silt and shale to effectively mitigate instability.

Keywords: shale rock masses, silt layers, slope stability, elasto-plastic model, temporal degradation

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Authors: Un-Hwan Park, Jun-Hyeok Heo, In-Sung Lee, Tae-Hyeon Oh, Dae-Kyu Park

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Sound absorbing material is used as automotive interior material. Sound absorption coefficient should be predicted to design it. But it is difficult to predict sound absorbing coefficient because it is comprised of several material layers. So, its targets are achieved through many experimental tunings. It causes a lot of cost and time. In this paper, we propose the process to estimate the sound absorption coefficient with multi-layer structure. In order to estimate the coefficient, physical properties of each material are used. These properties also use predicted values by Foam-X software using the sound absorption coefficient data measured by impedance tube. Since there are many physical properties and the measurement equipment is expensive, the values predicted by software are used. Through the measurement of the sound absorption coefficient of each material, its physical properties are calculated inversely. The properties of each material are used to calculate the sound absorption coefficient of the multi-layer material. Since the absorption coefficient of multi-layer can be calculated, optimization design is possible through simulation. Then, we will compare and analyze the calculated sound absorption coefficient with the data measured by scaled reverberation chamber and impedance tubes for a prototype. If this method is used when developing automotive interior materials with multi-layer structure, the development effort can be reduced because it can be optimized by simulation. So, cost and time can be saved.

Keywords: sound absorption material, sound impedance tube, sound absorption coefficient, optimization design

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Authors: M. Meenasaranya, S. Saravanan

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Nonlinear stability analysis is carried out to determine the effect of surface temperature modulation in an infinite horizontal porous layer heated from below. The layer is saturated by an electrically conducting, viscous, incompressible and Newtonian fluid. The Brinkman model is used for momentum equation, and the Boussinesq approximation is invoked. The system is assumed to be bounded by rigid boundaries. The energy theory is implemented to find the global exponential stability region of the considered system. The results are analysed for arbitrary values of modulation frequency and amplitude. The existence of subcritical instability region is confirmed by comparing the obtained result with the known linear result. The vertical magnetic field is found to stabilize the system.

Keywords: Brinkman model, energy method, magnetic field, surface temperature modulation

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Authors: Tsega Asresa Mengistu, Getahun Tigistu

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Computer vision is an artificial intelligence subfield that allows computers and systems to retrieve meaning from digital images. It is applied in various fields of study self-driving cars, video surveillance, agriculture, Quality control, Health care, construction, military, and everyday life. Aromatic and medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, and other natural health products for therapeutic and Aromatic culinary purposes. Herbal industries depend on these special plants. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs, and going to export not only industrial raw materials but also valuable foreign exchange. There is a lack of technologies for the classification and identification of Aromatic and medicinal plants in Ethiopia. The manual identification system of plants is a tedious, time-consuming, labor, and lengthy process. For farmers, industry personnel, academics, and pharmacists, it is still difficult to identify parts and usage of plants before ingredient extraction. In order to solve this problem, the researcher uses a deep learning approach for the efficient identification of aromatic and medicinal plants by using a convolutional neural network. The objective of the proposed study is to identify the aromatic and medicinal plant Parts and usages using computer vision technology. Therefore, this research initiated a model for the automatic classification of aromatic and medicinal plants by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides the root, flower and fruit, latex, and barks. The study was conducted on aromatic and medicinal plants available in the Ethiopian Institute of Agricultural Research center. An experimental research design is proposed for this study. This is conducted in Convolutional neural networks and Transfer learning. The Researcher employs sigmoid Activation as the last layer and Rectifier liner unit in the hidden layers. Finally, the researcher got a classification accuracy of 66.4 in convolutional neural networks and 67.3 in mobile networks, and 64 in the Visual Geometry Group.

Keywords: aromatic and medicinal plants, computer vision, deep convolutional neural network

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Authors: Supriya Manikrao Ovhal

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In India, the summer monsoon rainfall occurs owing to large scale convection with reference to continental ITCZ. It was found that convection over tropical ocean increases with SST from 26 to 28 degree C, and when SST is above 29 degree C, it sharply decreases for warm pool areas of Indian and for monsoon areas of West Pacific Ocean. The reduction in convection can be influenced by large scale subsidence forced by nearby or remotely generated deep convection, thus it was observed that under the influence of strong large scale rising motion, convection does not decreases but increases monotonically with SST even if SST value is higher than 29.5 degree C. Since convection is related to SST gradient, that helps to generate low level moisture convergence and upward vertical motion in the atmosphere. Strong wind fields like cross equatorial low level jet stream on equator ward side of the warm pool are produced due to convection initiated by SST gradient. Areas having maximum SST have low SST gradient, and that result in feeble convection. Hence it is imperative to mention that the oceanic role (other than SST) could be prominent in influencing large Scale Atmospheric convection. Since warm oceanic surface somewhere or the other contributes to penetrate the heat radiation to the subsurface of the ocean, and as there is no studies seen related to oceanic subsurface role in large Scale Atmospheric convection, in the present study, we are concentrating on the oceanic subsurface contribution in large Scale Atmospheric convection by considering the SST gradient, mixed layer depth (MLD), thermocline, barrier layer. The present study examines the probable role of subsurface ocean parameters in influencing convection.

Keywords: sst, d20, olr, wind

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Authors: Walid El Kamash

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Sand columns (or granular piles) can be employed as soil strengthening for flexible constructions such as road embankments, oil storage tanks in addition to multistory structures. The challenge of embedding the sand columns in soft soil is that the surrounding soft soil cannot avail the enough confinement stress in order to keep the form of the sand column. Therefore, the sand columns which were installed in such soil will lose their ability to perform needed load-bearing capacity. The encasement, besides increasing the strength and stiffness of the sand column, prevents the lateral squeezing of sands when the column is installed even in extremely soft soils, thus enabling quicker and more economical installation. This paper investigates the improvement in load capacity of the sand column by encasement through a comprehensive parametric study using the 3-D finite difference analysis for the soft clay of soil in Egypt. Moreover, the study was extended to include a comparison study between encased sand column and Deep Mixed columns (DM). The study showed that confining the sand by geosynthetic resulted in an increment of shear strength. That result paid the attention to use encased sand stone rather than deep mixed columns due to relative high permeability of the first material.

Keywords: encased sand column, Deep mixed column, numerical analysis, improving soft soil

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Authors: Mohammad Jamalabadi, Noemi Zabari, Lukasz Bratasz

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Panel paintings -complex multi-layer structures consisting of wood support and a paint layer composed of a preparatory layer of gesso, paints, and varnishes- are among the category of cultural objects most vulnerable to relative humidity fluctuations and frequently found in museum collections. The current environmental specifications in museums have been derived using the criterion of crack initiation in an undamaged, usually new gesso layer laid on wood. In reality, historical paintings exhibit complex crack patterns called craquelures. The present paper analyses the structural response of a paint layer with a virtual network of rectangular cracks under environmental loadings using a three-dimensional model of a panel painting. Two modes of loading are considered -one induced by one-dimensional moisture response of wood support, termed the tangential loading, and the other isotropic induced by drying shrinkage of the gesso layer. The superposition of the two modes is also analysed. The modelling showed that minimum distances between cracks parallel to the wood grain depended on the gesso stiffness under the tangential loading. In spite of a non-zero Poisson’s ratio, gesso cracks perpendicular to the wood grain could not be generated by the moisture response of wood support. The isotropic drying shrinkage of gesso produced cracks that were almost evenly spaced in both directions. The modelling results were cross-checked with crack patterns obtained on a mock-up of a panel painting exposed to a number of extreme environmental variations in an environmental chamber.

Keywords: fracture saturation, surface cracking, paintings on wood, wood panels

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Authors: Jinil Patel, Sarthak Patel, Sarthak Thakkar, Deepti Saraswat

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Recently, the COVID-19 outbreak has spread across the world, leading the World Health Organization to classify it as a global pandemic. To save the patient’s life, the COVID-19 symptoms have to be identified. But using an AI (Artificial Intelligence) model to identify COVID-19 symptoms within the allotted time was challenging. The RT-PCR test was found to be inadequate in determining the COVID status of a patient. To determine if the patient has COVID-19 or not, a Computed Tomography Scan (CT scan) of patient is a better alternative. It will be challenging to compile and store all the data from various hospitals on the server, though. Federated learning, therefore, aids in resolving this problem. Certain deep learning models help to classify Covid-19. This paper will have detailed work of certain deep learning models like VGG19, ResNet50, MobileNEtv2, and Deep Learning Aggregation (DLA) along with maintaining privacy with encryption.

Keywords: federated learning, COVID-19, CT-scan, homomorphic encryption, ResNet50, VGG-19, MobileNetv2, DLA

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Authors: A. Nazeri, R. Ziaie Moayed, H. Ghiasinejad

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The present research has been performed to investigate the effect of base course application on load-settlement characteristics of sandy subgrade using plate load test. The main parameter investigated in this study was the subgrade reaction coefficient. The model tests were conducted in a 1.35 m long, 1 m wide, and 1 m deep steel test box of Imam Khomeini International University (IKIU Calibration Chamber). The base courses used in this research were in three different thicknesses of 15 cm, 20 cm, and 30 cm. The test results indicated that in the case of using base course over loose sandy subgrade, the values of subgrade reaction coefficient can be increased from 7  to 132 , 224 , and 396  in presence of 15 cm, 20 cm, and 30 cm base course, respectively.

Keywords: modulus of subgrade reaction, plate load test, base course, sandy subgrade

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Authors: Joy Cao, Min Zhou

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Purpose: Acute Type A aortic dissection is a well-known cause of extremely high mortality rate. A highly accurate and cost-effective non-invasive predictor is critically needed so that the patient can be treated at earlier stage. Although various CFD approaches have been tried to establish some prediction frameworks, they are sensitive to uncertainty in both image segmentation and boundary conditions. Tedious pre-processing and demanding calibration procedures requirement further compound the issue, thus hampering their clinical applicability. Using the latest physics informed deep learning methods to establish an accurate and cost-effective predictor framework are amongst the main goals for a better Type A aortic dissection treatment. Methods: Via training a novel physics-informed deep residual network, with non-invasive 4D MRI displacement vectors as inputs, the trained model can cost-effectively calculate all these biomarkers: aortic blood pressure, WSS, and OSI, which are used to predict potential type A aortic dissection to avoid the high mortality events down the road. Results: The proposed deep learning method has been successfully trained and tested with both synthetic 3D aneurysm dataset and a clinical dataset in the aortic dissection context using Google colab environment. In both cases, the model has generated aortic blood pressure, WSS, and OSI results matching the expected patient’s health status. Conclusion: The proposed novel physics-informed deep residual network shows great potential to create a cost-effective, non-invasive predictor framework. Additional physics-based de-noising algorithm will be added to make the model more robust to clinical data noises. Further studies will be conducted in collaboration with big institutions such as Cleveland Clinic with more clinical samples to further improve the model’s clinical applicability.

Keywords: type-a aortic dissection, deep residual networks, blood flow modeling, data-driven modeling, non-invasive diagnostics, deep learning, artificial intelligence.

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Authors: Renata L. C. Leão, João E. Nascimento, Natalia C. E. S. Nascimento, Elaine S. Vasconcelos, Mércia L. Oliveira

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The quality control procedures of a radiopharmaceutical include the assessment of its chemical purity. The method suggested by international pharmacopeias consists of a thin layer chromatographic run. In this paper, the method proposed by the United States Pharmacopeia (USP) is compared to a direct method to determine the final concentration of aminopolyether in Fludeoxyglucose (¹⁸F-FDG) preparations. The approach (no chromatographic run) was achieved by placing the thin-layer chromatography (TLC) plate directly on an iodine vapor chamber. Both methods were validated and they showed adequate results to determine the concentration of aminopolyether in ¹⁸F-FDG preparations. However, the direct method is more sensitive, faster and simpler when compared to the reference method (with chromatographic run), and it may be chosen for use in routine quality control of ¹⁸F-FDG.

Keywords: chemical purity, Kryptofix 222, thin layer chromatography, validation

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Authors: A. Pourahmad, Sh. Gharipour

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The present work describes the preparation and characterization of nanosized SiO₂@PbS core-shell particles by using a simple wet chemical route. This method utilizes silica spheres formation followed by successive ionic layer adsorption and reaction method assisted lead sulphide shell layer formation. The final product was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), UV–vis spectroscopic, infrared spectroscopy (IR) and transmission electron microscopy (TEM) experiments. The morphological studies revealed the uniformity in size distribution with core size of 250 nm and shell thickness of 18 nm. The electron microscopic images also indicate the irregular morphology of lead sulphide shell layer. The structural studies indicate the face-centered cubic system of PbS shell with no other trace for impurities in the crystal structure.

Keywords: core-shell, nanostructure, semiconductor, optical property, XRD

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Authors: Vinayaka Gude Divya Sampath

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The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management.

Keywords: deep learning, disaster management, flood prediction, urban flooding

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Authors: Jie Zhang, Qianyu Guo, Tieyi Zhang, Zhiyong Feng, Xiaohong Li

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With the widespread popularity of mobile devices and the development of artificial intelligence (AI), deep learning (DL) has been extensively applied in Android apps. Compared with traditional Android apps (traditional apps), deep learning based Android apps (DL-based apps) need to use more third-party application programming interfaces (APIs) to complete complex DL inference tasks. However, existing methods (e.g., FlowDroid) for detecting sensitive information leakage in Android apps cannot be directly used to detect DL-based apps as they are difficult to detect third-party APIs. To solve this problem, we design DLtrace; a new static information flow analysis tool that can effectively recognize third-party APIs. With our proposed trace and detection algorithms, DLtrace can also efficiently detect privacy leaks caused by sensitive APIs in DL-based apps. Moreover, using DLtrace, we summarize the non-sequential characteristics of DL inference tasks in DL-based apps and the specific functionalities provided by DL models for such apps. We propose two formal definitions to deal with the common polymorphism and anonymous inner-class problems in the Android static analyzer. We conducted an empirical assessment with DLtrace on 208 popular DL-based apps in the wild and found that 26.0% of the apps suffered from sensitive information leakage. Furthermore, DLtrace has a more robust performance than FlowDroid in detecting and identifying third-party APIs. The experimental results demonstrate that DLtrace expands FlowDroid in understanding DL-based apps and detecting security issues therein.

Keywords: mobile computing, deep learning apps, sensitive information, static analysis

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Authors: Chahrazed Bendenia, Souhila Bendenia, Samia Moulebhar, Hanaa Merad-Dib, Sarra Merabet, Sid Ahmed Khantar, Baghdad Hadri

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In recent years, organic solar cells (OSCs) have become the fundamental concern of researchers thanks to their advantages in terms of flexibility, manufacturing processes and low cost. The performance of these devices is influenced by various factors, such as the layers introduced in the stacking of the solar cell realized. In our work, the modeling of a reverse OSC under AM1.5G illumination will be determined. The photo-active polymer/fullerene layer will be analyzed from the polymer variation of this layer using the SCAPS simulator to extract the J-V characteristics: open circuit voltage (Voc), short circuit current (Jsc), filling factor (FF) and power conversion efficiency (η). The results obtained indicated that the materials used have a significant impact on improving the photovoltaic parameters of the devices studied.

Keywords: solar, polymer, simulator, characteristics

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Authors: Sajjad Esmaeili, Robert Krause, Lukas Gerlich, Alireza Mohammadian Kia, Benjamin Uhlig

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This investigation aims to develop the feasible and qualitative process parameters for the thin films fabrication into ultra-large through-silicon-vias (TSVs) as vertical interconnections. The focus of the study is on TSV metallization and its challenges employing new materials for the purpose of rapid signal propagation in the microsystems technology. Cobalt metal-organic chemical vapor deposition (Co-MOCVD) process enables manufacturing an adhesive and excellent conformal ultra-thin film all the way through TSVs in comparison with the conventional non-conformal physical vapor deposition (PVD) process of copper (Cu) seed layer. Therefore, this process provides a Cu seed-free layer which is capable of direct Cu electrochemical deposition (Cu-ECD) on top of it. The main challenge of this metallization module is to achieve the proper alternative seed layer with less roughness, sheet resistance and granular organic contamination (e.g. carbon) which intensify the Co corrosion under the influence of Cu electrolyte.

Keywords: Cobalt MOCVD, direct Cu electrochemical deposition (ECD), metallization technology, through-silicon-via (TSV)

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Authors: Lihong Wu, Raymond Young

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The ability to learn is one of the most critical skills in the information age. However, many students do not have a clear understanding of what learning is, what they are learning, and why they are learning. Many students study simply to pass rather than to learn something useful for their career and their life. They have a misconception about learning and a wrong attitude towards learning. This research explores student attitudes to study in management education and explores how to intercede to lead students from shallow to deeper modes of learning.

Keywords: knowledge, surface learning, deep learning, education

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Authors: Yong Zhang

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Light has proven to be useful in a wide range of biomedical applications such as fluorescence imaging, photoacoustic imaging, optogenetics, photodynamic therapy, photothermal therapy, and light controlled drug/gene delivery. Taking photodynamic therapy (PDT) as an example, PDT has been proven clinically effective in early lung cancer, bladder cancer, head, and neck cancer and is the primary treatment for skin cancer as well. However, clinical use of PDT is severely constrained by the low penetration depth of visible light through thick tissue, limiting its use to target regions only a few millimeters deep. One way to enhance the range is to use invisible near-infrared (NIR) light within the optical window (700–1100nm) for biological tissues, extending the depth up to 1cm with no observable damage to the intervening tissue. We have demonstrated use of NIR-to-visible upconversion fluorescent nanoparticles (UCNPs), emitting visible fluorescence when excited by a NIR light at 980nm, as a nanotransducer for PDT to convert deep tissue-penetrating NIR light to visible light suitable for activating photosensitizers. The unique optical properties of UCNPs enable the upconversion wavelength to be tuned and matched to the activation absorption wavelength of the photosensitizer. At depths beyond 1cm, however, tissue remains inaccessible to light even within the NIR window, and this critical depth limitation renders existing phototherapy ineffective against most deep-seated cancers. We have demonstrated some new treatment modalities for deep-seated cancers based on UCNP hydrogel implants and miniaturized, wirelessly powered optoelectronic devices for light delivery to deep tissues.

Keywords: upconversion, fluorescent, nanoparticle, bioimaging, photodynamic therapy

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Authors: R. Karmouch

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A thin gold metal layer was deposited on the top of silicon oxide films containing embedded Si nanocrystals (Si-nc). The sample was annealed in gas containing nitrogen, and subsequently characterized by photoluminescence. We obtained 3-fold enhancement of photon emission from the Si-nc embedded in silicon dioxide covered with a Gold layer as compared with an uncovered sample. We attribute this enhancement to the increase of the spontaneous emission rate caused by the coupling of the Si-nc emitters with the surface plasmons (SP). The evolution of PL emission with laser irradiated time was also collected from covered samples, and compared to that from uncovered samples. In an uncovered sample, the PL intensity decreases with time, approximately with two decay constants. Although the decrease of the initial PL intensity associated with the increase of sample temperature under CW pumping is still observed in samples covered with a gold layer, this film significantly contributes to reduce the permanent deterioration of the PL intensity. The resistance to degradation of light-emitting silicon nanocrystals can be increased by SP coupling to suppress the permanent deterioration. Controlling the permanent photodeterioration can allow to perform a reliable optical gain measurement.

Keywords: photodeterioration, silicon nanocrystals, ion implantation, photoluminescence, surface plasmons

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Authors: Golam Saklayen, Sheikh Rashel al Ahmed, Ferdous Rahman, Ismail Abu Bakar

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Investigation on Lanthanum Fluoride LaF3 layer embedding Silicon Nanocrystals (Si-NCs) fabricated using a novel one-step chemical method has been reported in this presentation. Application of this material has been tested for low-voltage operating non-volatile memory and Schottkey-junction solar cell. Colloidal solution of Si-NCs in hydrofluoric acid (HF) was prepared from meso-porous silicon by ultrasonic vibration (sonication). This solution prevents the Si-NCs to be oxidized. On a silicon (Si) substrate, LaCl3 solution in HCl is allowed to react with the colloidal solution of prepared Si-NCs. Since this solution contains HF, LaCl3 reacts with HF and produces LaF3 crystals that deposits on the silicon substrate as a layer embedding Si-NCs. This a novel single step chemical way of depositing LaF3 insulating layer embedding Si-NCs. The X-Ray diffraction of the deposited layer shows a polycrystalline LaF3 deposition on silicon. A non-stoichiometric LaF3 layer embedding Si-NCs was found by EDX analysis. The presence of Si-NCs was confirmed by SEM. FTIR spectroscopy of the deposited LaF3 powder also confirmed the presence of Si-NCs. The size of Si-NCs was found to be inversely proportional to the ultrasonic power. After depositing proper contacts on the back of Si and LaF3, the devices have been tested as a non-volatile memory and solar cell. A memory window of 525 mV was obtained at a programming and erasing bias of 2V. The LaF3 films with Si NCs showed strong absorption and was also found to decrease optical transmittance than pure LaF3 film of same thickness. The I-V characteristics of the films showed a dependency on the incident light intensity where current changed under various light illumination. Experimental results show a lot of promise for Si-NCs-embedded LaF3 layer to be used as an insulating layer in MIS devices as well as an photoactive material in Schottkey junction solar cells.

Keywords: silicon nanocrystals (Si NCs), LaF3, colloidal solution, Schottky junction solar cell

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Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

Abstract:

The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

Procedia PDF Downloads 201

Authors: Ng Mei Han, Nu'man Abdul Hadi

Abstract:

Extraction of ferulic acid from palm pressed fiber using deep eutectic solvent (DES) of choline chloride-acetic acid (ChCl-AA) and choline chloride-citric acid (ChCl-CA) are reported. Influence of water content in DES on the extraction efficiency was investigated. ChCl-AA and ChCl-CA experienced a drop in viscosity from 9.678 to 1.429 and 22.658 ± 1.655 mm2/s, respectively as the water content in the DES increased from 0 to 50 wt% which contributed to higher extraction efficiency for the ferulic acid. Between 41,155 ± 940 mg/kg ferulic acid was obtained after 6 h reflux when ChCl-AA with 30 wt% water was used for the extraction compared to 30,940 ± 621 mg/kg when neat ChCl-AA was used. Although viscosity of the DES could be improved with the addition of water, there is a threshold where the DES could tolerate the presence of water without changing its solvent behavior. The optimum condition for extraction of ferulic acid from palm pressed fiber was heating for 6 h with DES containing 30 wt% water.

Keywords: deep eutectic solvent, extraction, ferulic acid, palm fibre

Procedia PDF Downloads 58

Authors: Brody R. Clark, Chaminda Gallage, John Yeaman

Abstract:

Multigrade bitumen asphalt is a quality asphalt product that is not utilised in many places globally. Multigrade bitumen is believed to be less sensitive to temperature, which gives it an advantage over conventional binders. Previous testing has shown that asphalt temperature changes greatly with depth, but currently the industry standard is to nominate a single temperature for design. For detailed design of asphalt roads, perhaps asphalt layers should be divided into nominal layer depths and different modulus and fatigue equations/values should be used to reflect the temperatures of each respective layer. A collaboration of previous laboratory testing conducted on multigrade bitumen asphalt beams under a range of temperatures and loading conditions was analysed. The samples tested included 0% or 15% recycled asphalt pavement (RAP) to determine what impact the recycled material has on the fatigue life and stiffness of the pavement. This paper investigated the temperature susceptibility of multigrade bitumen asphalt pavements compared to conventional binders by combining previous testing that included conducting a sweep of fatigue tests, developing complex modulus master curves for each mix and a study on how pavement temperature changes through pavement depth. This investigation found that the final design of the pavement is greatly affected by the nominated pavement temperature and respective material properties. This paper has outlined a potential revision to the current design approach for asphalt pavements and proposes that further investigation is needed into pavement temperature and its incorporation into design.

Keywords: asphalt, complex modulus, fatigue life, flexural stiffness, four point bending, multigrade bitumen, recycled asphalt pavement

Procedia PDF Downloads 337

Authors: Zabeehullah, Fahim Arif, Yawar Abbas

Abstract:

Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.

Keywords: SDN, IoT, DL, ML, DRS

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Authors: V. Marzal, J. C. Torres, B. Garcia-Camara, Manuel Cano-Garcia, Xabier Quintana, I. Perez Garcilopez, J. M. Sanchez-Pena

Abstract:

At the present time, the use of nanostructures in complex media, like liquid crystals, is widely extended to manipulate their properties, either electrical or optical. In addition, these media can also be used to control the optical properties of the nanoparticles, for instance when they are resonant. In this work, the change on electrical properties of a liquid crystal cell by adding TiO₂ nanoparticles on one of the alignment layers has been analyzed. These nanoparticles, with a diameter of 100 nm and spherical shape, were deposited in one of the substrates (ITO + polyimide) by spin-coating in order to produce a homogeneous layer. These substrates were checked using an optical microscope (objective x100) to avoid potential agglomerates. The liquid crystal cell is then fabricated, using one of these substrates and another without nanoparticles, and filled with E7. The study of the electrical response was done through impedance measurements in a long range of frequencies (3 Hz- 6 MHz) and at ambient temperature. Different nanoparticle concentrations were considered, as well as pure E7 and an empty cell for comparison purposes. Results about the effective dielectric permittivity and conductivity are presented along with models of equivalent electric circuits and its physical interpretation. As a summary, it has been observed the clear influence of the presence of the nanoparticles, strongly modifying the electric response of the device. In particular, a variation of both the effective permittivity and the conductivity of the device have been observed. This result requires a deep analysis of the effect of these nanoparticles on the trapping of free ions in the device, allowing a controlled manipulation and frequency tuning of the electrical response of these devices.

Keywords: alignment layer, electrical behavior, liquid crystal, TiO₂ nanoparticles

Procedia PDF Downloads 182