Performance Analysis and Resource Allocation in MIMO-OFDM Systems

Angriawan, Prof. Muhamad

doi:10.17762/ijnpme.v5i02.44

Cited by 5 publications

(4 citation statements)

References 4 publications

(4 reference statements)

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“…The applied stress, reinforcing strength, and TiC particle size are the causes of the discrepancy in reading. Porosity gap is decreased as a result of grain refining, [32,33] (94.73Hv), whereas the STC procedure yields a hardness value of about 83.56Hv. Fig.…”

Section: Tensile Strengthmentioning

confidence: 99%

Mechanical Property Evaluation of Stir-Squeeze Cast Al-Based Nano SiC Composites

Habelalmateen,

Srinivasan,

Pant

et al. 2024

E3S Web Conf.

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This study focuses on the production of stir-squeeze cast hybrid composites made of aluminium with reinforcements. Base metal AA6105 is combined with SiC/TiC. TiC is changed from 3 to 9 wt% while SiC is kept constant (3wt%).Nine specimens are created using the deviation of the stir casting parameters of Stirrer speed (SP) 350-550rpm, Stir-time (TI) 15-25 min, and Reinforcement (RE) 3-19 w.t% in accordance with L9 Taguchi's design. By applying pressure of 60 MPa during squeeze casting, reinforced composites are created. Both stir casting and squeeze cast specimens are examined for their mechanical characteristics, such as tensile strength (TS) and hardness (BHN). Based on elongation, tension, and strain, TS was assessed for both processes. The outcomes showed that SQC sample L6 obtained the maximum TS of 303.19 MPa while STC specimen only managed to reach 269.88 MPa. The STC method produced a hardness value of roughly 83.56 Hv, whereas the greatest hardness value of 94.73 Hv was obtained with SQC sample L9 (SP-550 rpm, TI-25 min & RE-6%).

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Section: Tensile Strengthmentioning

confidence: 99%

Mechanical Property Evaluation of Stir-Squeeze Cast Al-Based Nano SiC Composites

Habelalmateen,

Srinivasan,

Pant

et al. 2024

E3S Web Conf.

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show abstract

“…Results from the experiments indicate that good performance was attained. By utilizing an SVM classifier, Padol et al [17] aimed to abet in the illness identification and classification of grape leaves. They first identified the damaged area to do segmentation by using KNN and later extracted features like color and texture data.…”

Section: Support Vector Machine'smentioning

confidence: 99%

Efficient Disease Identification Method for Crop Leaf using Deep Learning Techniques

Kumar,

Champa

2023

IJRITCC

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Many prime grain-producing nations have implemented steps to limit export of grains as COVID-19 has expanded over the globe; food security has sparked significant worry from a number of stakeholders. One of the most crucial concerns facing all nations is how to increase grain output. However, the diseases occur in crops remain a challenge for countless farmers, therefore it is critical to understand their severity promptly and precisely to guide the them in taking additional measures to lessen the chances of plants being affected furthermore. This paper describes a deep learning model for the identification of crop diseases that can achieve high accuracy with low processing power. The model, called the inception v3 network, has been tested on a tomato leaf dataset and has obtained a average identification accuracy of 98.00% and further the ensemble of two inception v3 models with slight diversity achieved an accuracy of 98.11%. The results suggest that this model could be useful in improving food security by helping farmers quickly and accurately identify crop diseases and take appropriate measures to prevent further spread.

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“…In [18] proposed WatercolorGAN, a deep learning-based approach for watercolor style transfer. They employ a generative adversarial network (GAN) architecture to generate watercolor-style images from input photographs or digital images, capturing the unique characteristics and textures associated with watercolor paintings.…”

Section: Literature Surveymentioning

confidence: 99%

A New Automatic Watercolour Painting Algorithm Based on Dual Stream Image Segmentation Model with Colour Space Estimation

Zhou,

Sirivesmas,

Joneurairatana

et al. 2023

IJRITCC

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Image processing plays a crucial role in automatic watercolor painting by manipulating the digital image to achieve the desired watercolor effect. segmentation in automatic watercolor painting algorithms is essential for region-based processing, color mixing and blending, capturing brushwork and texture, and providing artistic control over the final result. It allows for more realistic and expressive watercolor-like paintings by processing different image regions individually and applying appropriate effects to each segment. Hence, this paper proposed an effective Dual Stream Exception Maximization (DSEM) for automatic image segmentation. DSEM combines both color and texture information to segment an image into meaningful regions. This approach begins by converting the image from the RGB color space to a perceptually-based color space, such as CIELAB, to account for variations in lighting conditions and human perception of color. With the color space conversion, DSEM extracts relevant features from the image. Color features are computed based on the values of the color channels in the chosen color space, capturing the nuances of color distribution within the image. Simultaneously, texture features are derived by computing statistical measures such as local variance or co-occurrence matrices, capturing the textural characteristics of the image. Finally, the model is applied over the deep learning model for the classification of the color space in the painting. Simulation analysis is performed compared with conventional segmentation techniques such a CNN and RNN. The comparative analysis states that the proposed DSEM exhibits superior performance compared to conventional techniques in terms of color space estimation, texture analysis and region merging. The performance of classification with DSEM is ~12% higher than the conventional techniques.

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Performance Analysis and Resource Allocation in MIMO-OFDM Systems

Cited by 5 publications

References 4 publications

Mechanical Property Evaluation of Stir-Squeeze Cast Al-Based Nano SiC Composites

Mechanical Property Evaluation of Stir-Squeeze Cast Al-Based Nano SiC Composites

Efficient Disease Identification Method for Crop Leaf using Deep Learning Techniques

A New Automatic Watercolour Painting Algorithm Based on Dual Stream Image Segmentation Model with Colour Space Estimation

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