It is believed that using electric vehicles (EVs) for transportation is essential for addressing environmental and sustainable development challenges. Current ecofriendly concerns, such as the fast depletion of fossil...
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The modern rapid development of means and technologies for education is aimed at reducing classroom classes and remote control of knowledge and skills of education seekers. The adaptation of the education system to th...
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ISBN:
(数字)9798350374865
ISBN:
(纸本)9798350374872
The modern rapid development of means and technologies for education is aimed at reducing classroom classes and remote control of knowledge and skills of education seekers. The adaptation of the education system to the remote regime has become especially relevant throughout the world during the period of quarantine restrictions caused by COVID-19. In Ukraine, the urgency of developing and implementing info-communication systems in the education process has been intensified by the martial law since February 24, 2022. The urgent need to rebuild a large number of buildings and structures destroyed and damaged by hostilities makes special demands on graduates of construction educational institutions. However, the fundamental studying of specialists in this field in remote mode requires specialized info-communication systems, creating of which need taking part of specialists in the field. On-line testing is the main means of guaranteeing the quality of the modern educational process. Therefore, the article presents the specialized on-line testing system "Fast Knowledge Test", which was developed at the Kyiv National University of Construction and architecture with the participation of stakeholders. The system operates during 2021-2023 and while the monitoring of students’ knowledge and skills remains with the teacher, but the service provides for its automation.
Socio-spatial segregation is the physical separation of different social, economic, or demographic groups within a geographic space, often resulting in unequal access to resources, services, and opportunities. The lit...
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While memristive devices are highly attractive as memory cells, they are also capable of performing computations, paving the way to futuristic in-memory computing architecture. Several memristive logic families have b...
While memristive devices are highly attractive as memory cells, they are also capable of performing computations, paving the way to futuristic in-memory computing architecture. Several memristive logic families have been proposed, and approaches to map gate-level logic circuits to such memristive implementations have been introduced. In this paper, we focus on the CRS logic family that offers several advantages compared to the more often considered IMPLY and MAGIC families. A central feature of CRS is the ability of one physical memristive device to realize varying logic gates over several clock cycles. Our method computes a schedule, i.e., an assignment which logic gate of a given circuit is executed on which memristive device during which clock cycles. Using an optimal MaxSAT model, it can minimize the resulting schedule’s duration (depth), the cost of the used memristors, or the cost of additional cache register cells, while satisfying all dependencies needed for correct computation. In addition to results of the scheduling procedure itself, we report a physical experiment that demonstrates one of the schedules and discuss the energy benefits of the CRS family.
In this paper, a low-cost method of 3D printed all-metal waveguide effective conductivity improvement is proposed and studied. The approach is a combination of internal surface polishing to reduce the roughness follow...
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ISBN:
(数字)9782874870774
ISBN:
(纸本)9798350385892
In this paper, a low-cost method of 3D printed all-metal waveguide effective conductivity improvement is proposed and studied. The approach is a combination of internal surface polishing to reduce the roughness followed by coating a high-conductivity layer through electroplating. Both methods allow to reduce total power losses within the waveguide which are impacted by the conductivity of the metal. A set of mm-wave test vehicles was developed in WR-28 geometry (26.5 GHz to 40 GHz) being a straight and twisted transmission line section along with a narrowband filter to experimentally validate the approach. The models were 3D printed using Powder Bed Fusion out of stainless-steel powder, dry polished using glass microbeads, and then coated with copper. Up to 40% power loss reduction was obtained with respect to raw prints proving the performance of the approach.
The integration of artificial intelligence (AI) and 6G networks aims to enhance agricultural applications by enabling adaptive and efficient communication systems. Fusing the potential and strengths of machine learnin...
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ISBN:
(数字)9798350308259
ISBN:
(纸本)9798350308266
The integration of artificial intelligence (AI) and 6G networks aims to enhance agricultural applications by enabling adaptive and efficient communication systems. Fusing the potential and strengths of machine learning (ML) technology and the high-speed, low-latency communication capabilities of 6G networks, we can create intelligent farming systems that can enhance productivity, reduce resource wastage, and ensure sustainable agricultural practices. To tackle resource wastage and power efficiency of 6G-enabled devices, we present an AI-based solution that utilizes pre-trained Deep Learning (DL) models to classify Orthogonal Frequency Division Multiplexing (OFDM) modulated Quadrature Amplitude Modulation (QAM) signals. The proposed approach involves recognizing signals based on the classification results at the receiving nodes, prompting them to switch their radio modulation schemes to options such as 4-QAM, 16-QAM, and 64-QAM. Taking advantage of DL models for QAM classification and dynamically adjusting modulation schemes, the proposed solution can optimize resource utilization, improve data transmission quality, and enhance overall network performance in agricultural applications. We used pre-trained ResNet50, MobileNetV2, and a scratch CNN model. Our results show that pretrained models performed better than scratch CNN models and demonstrate the feasibility of using pretrained DL models for modulation scheme classification, offering promising opportunities for the development of adaptive and intelligent agricultural systems.
Agriculture is a fundamental component of human civilization. It contributes to the economy while also providing sustenance. Plant foliage or crops are susceptible to many illnesses during agricultural agriculture. Th...
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ISBN:
(数字)9798331543624
ISBN:
(纸本)9798331543631
Agriculture is a fundamental component of human civilization. It contributes to the economy while also providing sustenance. Plant foliage or crops are susceptible to many illnesses during agricultural agriculture. The illnesses impede the development of their respective species. Timely and accurate identification and categorization of illnesses may mitigate the risk of further harm to the plants. The identification and categorization of these disorders have emerged as significant challenges. The conventional methods used by farmers to forecast and identify plant leaf diseases may be tedious and inaccurate. Challenges may occur while endeavoring to manually forecast illness kinds. The failure to promptly identify and categorize plant diseases may lead to the devastation of crops, causing a substantial reduction in yield. Farmers using automated image processing techniques in their fields may mitigate losses and enhance productivity. This study proposes using a deep learning framework utilizing the latest convolutional neural network model EfficientNet to identify tomato illnesses based on 18,161 plain and segmented photos of tomato leaves. The efficacy of two segmentation theories, namely U-net and Modified U-net, for leaf segmentation is shown. The proposed methodology for plant disease identification is straightforward and computationally efficient, necessitating less time for predictions compared to previous deep learning methodologies. This document presents the computed accuracies for several plant and leaf diseases.
We present a system that transforms speech into physical objects by combining 3D generative Artificial Intelligence (AI) with robotic assembly. The system leverages natural language input to make design and manufactur...
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Currently, real-world complex engineering problems often have more than three objectives that need to be optimized simultaneously. Existing algorithms for solving them mostly rely on the shape of the Pareto front to m...
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As climate change intensifies, resulting in more severe rainfall events, coastal cities globally are witnessing significant life and property losses. A growingly crucial component for flood prevention and relief are u...
As climate change intensifies, resulting in more severe rainfall events, coastal cities globally are witnessing significant life and property losses. A growingly crucial component for flood prevention and relief are urban storm flood simulations, which aid in informed decision-making for emergency management. The vastness of data and the intricacies of 3D computations can make visualizing the urban flood effects on infrastructure daunting. This study offers a 3D visualization of the repercussions of hurricane storm surge flooding on Galveston, TX residences, illustrating the impact on each structure and road across varied storm conditions. We employ target detection to pinpoint house door locations, using door inundation as a metric to gauge potential flood damage. Within a GIS-based framework, we model the damage scope for residences exposed to varying storm intensities. Our research achieves three core goals: 1) Estimating the storm inundation levels on homes across different storm conditions; 2) Assessing first-floor elevations to categorize housing damages into three distinct groups; and 3) Through visualization, showcasing the efficacy of a proposed dike designed to shield Galveston Island from future storm surge and flood events.
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