Four circularly polarized single feed microstrip patch antennas are introduced in this paper. Circular polarization is achieved by a single crescent-cut, a single crescent extension, a double crescent-cut and a double...
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ISBN:
(数字)9798350388282
ISBN:
(纸本)9798350388299
Four circularly polarized single feed microstrip patch antennas are introduced in this paper. Circular polarization is achieved by a single crescent-cut, a single crescent extension, a double crescent-cut and a double crescent extension on a circular patch antenna. All four antennas are designed to operate in the Industrial, Scientific and Medical (ISM) band. The antennas are designed and simulated to obtain parameters by an electromagnetic simulation tool named ADS (Advanced Design system). The return loss of the single crescent-cut antenna is -20.58 dB, the single crescent extension is 16.95 dB, the double crescent-cut is -34.98 dB and the double crescent extension is -31.47 dB between 2.35 GHz to 2.45 GHz that show good input impedance matching. The axial ratios are found less than 3 dB in their resonance frequencies. Smith charts of these antennas have a dip at 2.4, 2.38, 2.42 and 2.42 GHz respectively demonstrating the ability to achieve circular polarization within the frequency range of the ISM band signifies the capability of this system. Performance analyses based on different parameters of the four antennas have been done at the end.
The present paper focused on the Meta-material-inspired Koch fractal antenna. The whole structure has been designed with a fractal antenna and metamaterial. The fractal antenna has two specific qualities like area cov...
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This paper presents an integrated framework for the comprehensive analysis of diseases affecting pomegranate fruit. The suggested system, which makes use of deep learning techniques, includes semantic segmentation for...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
This paper presents an integrated framework for the comprehensive analysis of diseases affecting pomegranate fruit. The suggested system, which makes use of deep learning techniques, includes semantic segmentation for diseased region localization, multiclass illness classification, and image processing-based severity assessment. Initially, four common diseases—Alternaria, Anthracnose, Bacterial Blight, and Cer-cospora—are successfully identified through the use of a Convolutional Neural Network (CNN) for multiclass classification. Then, annotated photos are used as training data for Semantic Segmentation, which uses the UNet architecture to identify the unhealthy spots within the fruit. The area and percentage of the segmented region are determined, and thresholds for high, medium, and low severity levels are then defined. The segmentation results are then used as inputs for severity estimate. Early identification and intervention tactics are made easier by the severity level prediction, which is based on the proportion of the segmented region. In order to improve pomegranate fruit management methods, the suggested framework provides a thorough method of disease analysis by combining categorization, semantic segmentation, and severity prediction.
Increasing demand for wearable sensors for multipurpose applications have made it an essential part of our everyday life. In such a scenario;reusability, flexibility, cost effectiveness, and integrability of such sens...
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The main objective is to select a piezoelectric material which is pure and eco-friendly to fabricate the pressure sensor and also adopt an appropriate method for the synthesis of the piezoelectric material. The synthe...
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Lead-free perovskites have garnered significant attention in recent years owing to their notable chemical stability and the absence of toxicity. The substitution of the lead element in the perovskite structure adverse...
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ISBN:
(数字)9798350385779
ISBN:
(纸本)9798350385786
Lead-free perovskites have garnered significant attention in recent years owing to their notable chemical stability and the absence of toxicity. The substitution of the lead element in the perovskite structure adversely affects its overall performance. The double perovskite material does not make any compromises in this regard. Different properties of lead-free double perovskite CS2InAgBr6 were studied experimentally as well as theoretically in previous studies. Here, crystal orientation-dependent structural and electronic properties of CS2InAg(Ch-xBrx)6 are simulated using DFT under halide mixing conditions. The crystal structure and electronic band dispersion profiles are influenced by Br-content (x), leading to a change in the structural property and crystal orientation-dependent band structure which in turn changes electron concentrations and the electron's and hole's effective mass as well. The transformation from a face-centered cubic structure undergoes orthorhombic structures for the Br-contents in the range of x=0.167,0.5, and 0.833, and monoclinic structures for x=0.33 and 0.67. A gradual decrease in the energy bandgap is noted at critical points F, X, K, and L with increasing Br-content which demonstrates that mixed halide perovskites are suitable for tunable wavelength light sources. The crystal orientation-dependent electron- and hole-effective masses facilitate the fabrication of electronic devices with tunable transport properties under halide mixing conditions.
The Internet of Things (IoT) is a global network of 'smart gadgets' that can sense their environment, connect to them, and communicate with people and other systems. This articles presents an IoT control cente...
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Epilepsy is a common chronic brain disease characterized by recurrent seizures. The most effective way to detection the arrival of an epileptic seizure is EEG analysis. Various methods have been proposed in the detect...
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The electric power sector is making significant changes to the power grid in order to make the power supply more stable, meet rising demand, and optimize the use of distributed generators. The Internet of Things (IoT)...
The electric power sector is making significant changes to the power grid in order to make the power supply more stable, meet rising demand, and optimize the use of distributed generators. The Internet of Things (IoT) and digital technologies are being used simultaneously in smart microgrids, a key strategy for future power grids. IoT -based smart energy monitoring and management systems are a type of technology that uses devices and software connected to the Internet to monitor, control, and manage the energy use of the micro grid. In this paper, IoT-based technology is used to create a smart energy monitoring, management, and protection system for a smart microgrid. The whole system can provide real-time monitoring, control, protection, and efficient management of the microgrid's energy resources, as well as ways to detect electric theft. Using wireless communication technology, the IoT platform can send and receive measured data from the control panel room. Once access permissions to the Blynk IoT cloud software for the ESP32 and ESP8266 modules for the system are set up, micro grid operators can simply monitor all electrical parameters and control relay modules of the control panel room, electric distribution poles, and energy meters using smartphones or personal computers at any-time from anywhere. So, the IoT-based smart energy monitoring, management, and control strategy presented in this research is set up to improve energy use efficiency, reduce energy costs, and improve grid stability through real-time monitoring, control, and protection systems.
The development of autonomous vehicles has seen rapid progress in the last few years due to its enormous potential. Autonomous driving technologies depend on accurate discrimination of traffic signs. To improve the sa...
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ISBN:
(数字)9798350355499
ISBN:
(纸本)9798350355505
The development of autonomous vehicles has seen rapid progress in the last few years due to its enormous potential. Autonomous driving technologies depend on accurate discrimination of traffic signs. To improve the safety and efficiency of autonomous vehicles, this paper aims to create a reliable model that can recognize traffic signs from pictures taken by onboard cameras. For this task, the use of the generative adversarial network (GAN), more specifically, an autoencoder/generator, and a discriminator/classifier are investigated in this work. The suggested approach uses a discriminator network for classification after a convolutional autoencoder that artificially creates suitable training images from real photos. By harnessing the adversarial training process, the autoencoder can produce more diverse features, thereby enhancing overall classification accuracy. The effectiveness of our approach in accurately classifying traffic signs with high-performance metrics is reported through extensive experimentation and evaluation. The proposed GAN model demonstrates competitive performance and achieves classification accuracy of 97.06% where the accuracy of the second-best model is 96.39%.
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