Abstract:In very long-range underwater acoustic communications, enhancing communication performance often involves augmenting the received signal-to-noise ratio (SNR) through the integration of multiple receiver sensors. However, in cases where sensors are linearly combined, those with inferior performance can significantly impact overall communication efficacy. Consequently, when employing linear combinations, it becomes essential to assign lower weighting factors to sensors exhibiting higher error rates, thereby enhancing overall system performance. This paper introduces a novel approach: a weighted multiple-sensor combination algorithm that leverages the preamble error rate of individual sensors. Through the utilization of turbo coding with a rate of 1/3 and 4/8-ary frequency-shift keying method, extensive experiments spanning very long distances of over 160 km in the East Sea were conducted. The results of these experiments demonstrate that the proposed weighted multiple-sensor combination algorithm markedly improves BER performance compared to conventional linear combination algorithms.
Abstract:Alzheimer’s Disease (AD) is the most prevalent cause of dementia, a neurodegenerative\ndisorder primarily affecting the brain. Mild cases of dementia gradually worsen, resulting in a\ndecline in cognitive abilities, including memory, thinking, reasoning, and behavior. Currently,\nthere is no cure for AD; however, early diagnosis of mild cases can help slow the disease\'s\nprogression, improve its management, and enhance overall quality of life. Existing models for\nearly AD diagnosis encounter challenges, such as low accuracy and precision, poor\ngeneralization and overfitting due to data imbalances in datasets. To address these limitations, we\npropose a novel Deep Learning (DL) based model for early AD diagnosis. Our model comprises\nthree main components: image processing and data augmentation, feature extraction based on\nVGG19 (Visual Geometry Group 19), and a Multi-Layer Perceptron (MLP) based classifier\ncategorizing input Magnetic Resonance Imaging (MRI) scans into four classes representing AD\'s\ndevelopmental stages: Very-Mild-Demented, Mild-Demented, Moderate-Demented, and NonDemented. We optimized our model using the ADAM Algorithm and applied it to images selected\nfrom the Open Access Series of Imaging Studies (OASIS) dataset. Our model yielded impressive\nresults, achieving an accuracy of 92.58%, an AUC of 99%, and precision, recall, and F-measure\nvalues of 0.907, 0.894, and 0.890, respectively. Furthermore, we conducted a comparative\nanalysis with several state-of-the-art models, consistently demonstrating that our proposed model\noutperforms them.
Abstract:The study aims to derive the full multipliers of the economic variables that affect income, which are government expenditure, tax, money supply, the general level of prices, the exchange rate, and the lag of income. The reduced form of the income model was used by deriving the total expenditure equation from which the various income multipliers were derived. These multipliers are in line with macroeconomic theory in determining the effectiveness of economic policy, whether fiscal, monetary, price, income, or exchange, via the exchange rate. The study concluded that the higher the elasticities of economic variables, the higher the effectiveness of economic policy using one of the variables and vice versa. The economic state of the specific economy also affects the effectiveness of economic policy. In addition, the multipliers derived in this study facilitate the analysis of economic policy and show the extent of its effectiveness in different cases.
Abstract:Over the past few years, the smart city concept has become a prominent solution to address urban challenges, including economic, social, and environmental issues. These cities strive to offer superior services in healthcare, energy management, transportation, and education by leveraging IoT devices. These devices are pivotal in data collection, analysis, and facilitating citizen interactions. Incorporating IoT applications into our surroundings augments automation, efficiency, and user comfort. To achieve this vision, it is imperative to ensure robust security, privacy, effective deployment, authentication, and resilience against cyber-attacks. Several recent studies have shown promising results regarding the use of blockchain for addressing IoT security concerns, especially in access control and authentication domains. The smart city concept, while beneficial, still faces numerous challenges regarding information security and privacy. With its unique characteristics like auditability, immutability, transparency, and decentralization, blockchain can significantly boost the growth of smart cities and rectify communication issues among IoT devices. This research explores blockchain\'s potential to resolve access control and authentication challenges in IoT when merged with it. It will also offer an in-depth analysis and evaluation of recently proposed solutions in the literature focusing on integrating IoT with blockchain.
Abstract:This paper investigates the response and failure of circumferential sharp-notched C2700 brass tubes with four different notch depths of 0.2, 0.4, 0.6, 0.8, and 1.0 mm subjected to cyclic bending. The wall thickness is 1.5 mm for all tested tubes, and cyclic bending loads are applied until buckling failure occurs. The experimental moment-curvature relationships exhibit a stable loop from the first bending cycles. An increase in the notch depth results in the decrease of the peak bending moment. The experimental relationships between ovalization and curvature demonstrate symmetry, serrations, and a growth pattern as cycles progress, regardless of the notch depth. Regarding the curvature-number of cycles required to initiate buckling relationships, it can be observed that five notch depths correspond to five parallel straight lines when plotted on double logarithmic coordinates. Finally, this study employs the theoretical formulation proposed by Lee in 2010 to describe the aforementioned relationships. The theoretical analysis is compared with experimental data, revealing a close alignment between the two approaches.
Abstract:A smart city is a sustainable and efficient urban center that provides a high quality of life to its inhabitants through optimal management of its resources. Energy management is one of the most demanding issues within such urban centers owing to the complexity of the energy systems and their vital role. Therefore, significant attention and effort need to be dedicated to this problem. Modelling and simulation are the major tools commonly used to assess the technological and policy impacts of smart solutions, as well as to plan the best ways of shifting from current cities to smarter ones. This paper presents the design and implementation of energy management system (EMS) based on hybrid sources system containing wind turbine, Photovoltaic (PV) and battery and load, all connected to the utility grid which is used as a backup source. The objectives of this system are, primarily, to satisfy the house load power demand and, secondly, to manage the power between its different components. The performances of the proposed supervisor control is tested using MATLAB/Simulink and the implementation of system is do on a Zybo-Z7 card based on zynq FPGA device.
Abstract:Applying ceramic materials like TiO2 through cold spraying is acknowledged as a complex procedure. The intricacy stems from the need for feedstock particles to undergo plastic deformation to bond with the substrate during cold spraying. However, inducing such deformation in ceramics, known for their hardness and brittleness, is not straightforward. Additionally, the underlying bonding mechanism is not fully clear. This study sought to understand the effects of different substrates and gas pressures on the TiO2 application method. We experimented with TiO2 particles and metals such as copper and aluminum, exposing them to varied gas pressures to delve deeper into the dynamics of cold spraying ceramics onto metallic surfaces. We utilized sophisticated instruments like the focused ion beam and the transmission electron microscope to scrutinize the interfacial structures of TiO2 particles with pure copper (C1020) as well as pure aluminum (AA1050). The data revealed that as we adjusted the gas pressure from 0.7 to 3.0 MPa, there was a corresponding increase in the coating\'s bond strength, registering between 1.52 to 3.46 MPa for C1020 and 0.45 to 4.15 MPa for AA1050. An ultra-thin amorphous oxide layer, under 5 nm in thickness, was detected at the juncture of TiO2 with both metals. The shift in process gas pressure emerged as a pivotal element affecting the bond strength of the TiO2 layer.
Abstract:To detect spoofing in e-Learning is of great significance in enhancing the reliability of e-learning systems and protecting the copyright of learning content. We propose a new method to detect spoofing in e-learning systems without causing inconvenience to the student\'s learning, based on the implicit face contrast in randomly selected time period during online learning. First, we show the web service model for the face recognition with message signature that can be used for the purpose of identifying learners in e-learning systems. Next, we propose a method to detect spoofing by implicit face recognition in a random time period during online learning. The application of the proposed method to the e-learning system using SOAP has shown that it can effectively detect the impostor without causing significant inconvenience to learners.
Abstract:A sudden increase in pore water pressure causes the effective stress to decrease significantly, which in turn causes a loss of shear strength and the subsequent outcome of allowing the soil to behave as a liquid, resulting in soil liquefaction. Agartala, the capital of Tripura, is located in northeastern India, and the entire region is considered seismically active according to the seismic zoning map of India as per IS 1893:2002 (Part 1); the entire region is classified as Zone-V. Significant earthquakes have struck this region, including the 1897 Shillong and 1950 Assam earthquakes. The city is vulnerable to liquefaction due to alluvial soil and a low groundwater table. Several parameters are involved in assessing liquefaction potential in the deterministic method and have different input parameter uncertainties, resulting in inconsistent outcomes. As a result, evaluating liquefaction susceptibility requires a thorough probability approach that considers parameter uncertainty. In this work, the soil liquefaction potential of Agartala City is assessed using fuzzy logic and compared to the conventional Reliability technique using the first-order second-moment method. Soil liquefaction potential was properly calculated using both approaches for the 71 boreholes in and around Agartala city. The outcomes are shown as contour maps representing spatial variations of the probability of liquefaction at different depths. In this study, Fuzzy logic is used to model the relationships between different soil parameters, which can help better understand the soil\'s behaviour during liquefaction. This developed method can assess the vulnerability of liquefaction potential and select appropriate materials and construction processes.
Abstract:Developing high quality learning contents (LCs) and improving their interactions in online learning with e-learning contents is important for greater cognitive abilities of a learner. The aim of this research is to design LCs with new self-feedback structure by considering the personalized characteristics of a learner in order to ensure his greater cognitive abilities through improved interactions. A new form of LCs is developed, which learning nodes consists of knowledge units of 5-10 minutes with each unit linked to assessment and control objects. The communication interface of the learning management system (LMS) is extended for a learner to support not only learning and navigation but self-assessment and repeated learning when interacting with LCs. Experiments were performed for three years to analyse the collected achievements of 376 online learners. The results show that the self-feedback is a significant factor that gives positive influence on the learner’s cognition during the COVID-19 pandemic. The average score of participants were increased by 0.2 than before 2020.