This study presents a new fractal antenna design for multiband applications, which is made by applying Giuseppe Peano and Sierpinski carpet to the basic microstrip patch antenna. The Giuseppe Peano is applied to the p...
This study presents a new fractal antenna design for multiband applications, which is made by applying Giuseppe Peano and Sierpinski carpet to the basic microstrip patch antenna. The Giuseppe Peano is applied to the patch edges while the Sierpinski carpet is created inside the patch area. The antenna is deposited on a 1.6-thick FR4 epoxy substrate with a dielectric constant of 4.4. The proposed antenna is created using the High-Frequency Structure Simulator (HFSS). According to the simulation results, the fractal antenna resonates At seven resonant frequencies namely, 2.02 GHz (-24.43), 3.51 GHz (-16.46 dB), 3.88 GHz (-18.64 dB), 6.59 GHz (-21.42 dB), 7.32 GHz (-15.62 dB), 7.71 GHz (-35.63 dB), 8.05 GHz (-17.67 dB) with suitable radiation behavior.
The JARVIS AI Support System represents a remarkable fusion of modern technology, blending a sophisticated GUI design, seamless voice control, and inventive features like the captivating “Air Canvas” facilitated by ...
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ISBN:
(数字)9798350354379
ISBN:
(纸本)9798350354386
The JARVIS AI Support System represents a remarkable fusion of modern technology, blending a sophisticated GUI design, seamless voice control, and inventive features like the captivating “Air Canvas” facilitated by OpenCV. This AI-driven virtual assistant offers users a natural and intuitive experience, allowing them to effortlessly perform tasks such as browsing the web, interacting with a chatbot, and executing dynamic voice- controlled actions. Moreover, the system showcases advanced capabilities including motion detection and facial recognition with an accuracy of 95% in multiple runs. Leveraging the power of computervision, the Air Canvas feature empowers users to express creativity through fluid hand gestures, while voice commands effortlessly manage diverse tasks. This innovative project presents an approachable way to interact with technology in the world of AI.
In order to improve the online monitoring level of user side power supply full path, a user side power supply full path online monitoring method based on machine vision technology was designed. Debugging the online us...
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ISBN:
(数字)9798350366099
ISBN:
(纸本)9798350366105
In order to improve the online monitoring level of user side power supply full path, a user side power supply full path online monitoring method based on machine vision technology was designed. Debugging the online user side power supply line and calculating the standard output voltage value of the circuit; Select three-level indicators for online monitoring of the full path of user side power supply, and apply Analytic Hierarchy Process to divide indicator weights; Based on machine vision technology, obtain online monitoring images of the full path of user side power supply, extract sift features of visual images using BP neural network, and achieve online monitoring of the full path of user side power supply. The experimental results show that the method has a high recall rate, low misjudgment rate, and short monitoring response time for online monitoring of the entire power supply path, which improves the effectiveness of online monitoring of the entire power supply path on the user side.
This technology is capable of producing a project-based method for blind persons to learn Braille. Whatever system is currently in use is still an analogue system, but we are now using a digital method. This system in...
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Brushless DC (BLDC) motor is the first option for lightweight industrial application and electric vehicles because of its high power density and high torque and low maintenance and suitable speed range. The vehicle en...
Brushless DC (BLDC) motor is the first option for lightweight industrial application and electric vehicles because of its high power density and high torque and low maintenance and suitable speed range. The vehicle environment is very dynamic, nonlinear, and noisy. This paper has investigated comparative research between two control strategy of BLDC motors: The speed control strategy applying a model predictive control (MPC) and BLDC motor stator current control method applying hysteresis current controllers. Suggested MPC strategy does not require Hall sensors and applies position sensor for gaining the back-EMF waveforms. The both methods were studied and simulated with the same working conditions. Also, qualitative and quantitative analyzes were performed for both methods and the results of both methods were obtained. The simulations in MATLAB-SIMULINK show that the target control method of the predictive model works better than the hysteresis current control method in terms of torque and following the reference speed.
Images captured in a poor environment is a big problem to deal with. The environment contains haze, fog, smog, rain or even the lighting conditions could be poor. One of these issues are images captured in low light a...
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In LTE-Advanced, device-to-device (D2D) communication is a technology enables to increase the coverage and capacity coverage in metropolitan areas. Improvements in energy efficiency, throughput, latency, spectrum effi...
In LTE-Advanced, device-to-device (D2D) communication is a technology enables to increase the coverage and capacity coverage in metropolitan areas. Improvements in energy efficiency, throughput, latency, spectrum efficiency, and interference reduction have all been proposed. It is used in many domains, including applications, network traffic offloading, and public safety, and is recognized as one of the viable ways for the 5G wireless communications system. LTE advanced might perform worse in D2D communications because of multipath impact and delay dispersion. It is an essential and important element for the design of receivers in mobile communication systems. In this study, the spectrum efficiency, throughput, data rate, signal-to-interference-pulse-noise ratio, and latency of LTE-based D2D communications will be examined (SINR).
Over the past few years, infrared (IR) motion detection has become more important in domains such as security, wildlife monitoring, and nighttime surveillance, when standardvision-based systems frequently fail. The a...
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The main purpose of conducting this research is to design and implement a single precision floating-point arithmetic logic unit (ALU) that considered as a part of the math coprocessor. The main advantage of floating-p...
The main purpose of conducting this research is to design and implement a single precision floating-point arithmetic logic unit (ALU) that considered as a part of the math coprocessor. The main advantage of floating-point representation is that it can support more values than fixed-point and integer representations. Summation, Subtraction, multiplication and division are arithmetic functions in these calculations. In this floating-point unit, input must be provided in IEEE-754 format, which is 32 single precision floating point values. The application of This arithmetic unit is located in the math coprocessor. Commonly referred to as reduced instruction set computation (RISC) processor. In this processor, for a signal processing, a value with high accuracy is required and as it is an iterative process, the calculation should be as fast as possible. A fixed-point and integer central processing unit (CPU) can't meet the requirements. The floating-point representation can calculate very large or very small process quickly and accurately. The system designed, verified and implemented with Verilog hardware description language using Intel Altera software tools.
The proceedings contain 65 papers. The special focus in this conference is on Applied Intelligence. The topics include: A Domain Adaptation Deep Learning Network for EEG-Based Motor Imagery Classification;T-GraphDTA: ...
ISBN:
(纸本)9789819709021
The proceedings contain 65 papers. The special focus in this conference is on Applied Intelligence. The topics include: A Domain Adaptation Deep Learning Network for EEG-Based Motor Imagery Classification;T-GraphDTA: A Drug-Target Binding Affinity Prediction Framework Based on Protein Pre-training Model and Hybrid Graph Neural Network;imputation of Compound Property Assay Data Using a Gene Expression Programming-Based Method;identification of Parkinson’s Disease Associated Genes Through Explicable Deep Learning and Bioinformatic;enzyme Turnover Number Prediction Based on Protein 3D Structures;challenges in Realizing Artificial Intelligence Assisted Sign Language Recognition;Efficient and Accurate Document Parsing and Verification Based on OCR Engine;intelligent Comparison of Bidding Documents Based on Algorithmic Analysis and Visualization;image Denoising Method with Improved Threshold Function;EEG Channels Selection Based on BiLSTM and NSGAII;fasterPlateNet: A Faster Deep Neural Network for License Plate Detection and Recognition;an Improved Seq-Deepfake Detection Method;multimodal Depression Recognition Using Audio and Visual;functional Semantics Analysis in Deep Neural Networks;FDA-PointNet++: A Point Cloud Classification Model Based on Fused Downsampling Strategy and Attention Module;collision Detection Method Based on Improved Whale Optimization Algorithm;AF-FCOS: An Improved Anchor-Free Object Detection Method;semi-supervised Clustering Algorithm Based on L1 Regularization and Extended Pairwise Constraints;automated Text Recognition and Review System for Enhanced Bidding Document Analysis;machine vision-Based Defect Classification Algorithm for Rolled Packages;self-Guided Local Prototype Network for Few-Shot Medical Image Segmentation;The Rise of AI-Powered Writing: How ChatGPT is Revolutionizing Scientific Communication for Better or for Worse;IGWO-FNN Based Position control for Stepper Motor.
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