Discrete interferences optimum beamformer in correlated signal and interfering noise

Singh, Satyanand; Singh, Sajai Vir; Yadav, Dinesh; Suman, Sanjay Kumar; Bhagyalakshmi, L.; Singh, Ghanshyam

doi:10.11591/ijece.v12i2.pp1732-1743

Cited by 14 publications

(3 citation statements)

References 40 publications

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“…For training and evaluation, Keras [34] and TensorFlow [35] were utilized on a system equipped with a Core i7 CPU running at 2.6GHz, a 1TB hard disk drive, and 8GB of RAM. The experiments were conducted on the KTH human activity dataset [41].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Hybrid HAR-CNN Model: A Hybrid Convolutional Neural Network Model for Predicting and Recognizing the Human Activity Recognition

Vishwakarma,

Kundu

et al. 2024

JMC

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Human activity recognition (HAR) is an active research area in computer vision from past several years and research is still continuing in this field due to the unavailability of perfect recognition system. The human activity recognition system it covers e-health, patient monitoring, assistive daily living activities, video surveillance, security and behaviour analysis, and sports analysis. Many researchers have suggested techniques that use visual perception to detect human activities. Researchers will need to address problems including light variations in human activity detection, interclass similarity between scenes, the surroundings and recording setting, and temporal variation in order to construct an efficient vision-based human activity recognition system. However, a significant drawback of many deep learning models is their inability to achieve satisfactory results in real-world scenarios due to the conflicts mentioned above. To address this challenge, we developed a hybrid HAR-CNN classifier aimed at enhancing the learning outcomes of Deep CNNs by combining two models: Improved CNN and VGG-19. Using the KTH dataset, we collected 6,000 images for training, validation, and testing of our proposed technique. Our research findings indicate that the Hybrid HAR-CNN model, which combines Improved CNN with VGG-19 Net, outperforms individual deep learning models such as Improved CNN and VGG-19 Net.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Hybrid HAR-CNN Model: A Hybrid Convolutional Neural Network Model for Predicting and Recognizing the Human Activity Recognition

Vishwakarma,

Kundu

et al. 2024

JMC

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“…The waves can be readily broken. There will be a need for 5G antennas and devices to transmit these signals inside of buildings [60].…”

Section: Next-generation Lte 5g Spectrum Allocationsmentioning

confidence: 99%

5G Opportunities in the South Pacific: Leveraging Low-Band Spectrum for Socio-Economic Development

Singh,

Rosak-Szyrocka

et al. 2024

HighTech. Innov. J.

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This paper explores the potential for deployment of 5G communication in the South Pacific, with a particular focus on leveraging the low-band spectrum for socio-economic development. The purpose of this study is to assess the feasibility of deploying 5G infrastructure in the South Pacific region, analyze the socio-economic benefits it may bring, and propose strategies to maximize these benefits. The research methodology includes a comprehensive review of existing literature on 5G deployment strategies, the socio-economic impacts of telecommunications infrastructure, and case studies of similar initiatives in other regions. The findings show that the deployment of 5G technology using low-band spectrum has the potential to significantly improve connectivity, healthcare, education, and economic opportunities in the South Pacific. Additionally, the paper proposes innovative approaches to address challenges such as infrastructure development in remote areas and affordability for marginalized communities. This study contributes to existing literature by providing tailored recommendations for leveraging 5G technology to address socio-economic inequalities in the South Pacific, thereby contributing to the development of telecommunications infrastructure in the region. Provides a new perspective on the possibilities of structure. Doi: 10.28991/HIJ-2024-05-02-020 Full Text: PDF

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“…− 𝜌 (𝑟): it is the material's mass density that is being measured. The density is expressed in kilograms per cubic meter (kg/m 3 ) or grams per cubic centimetre (g/cm 3 ). The electric field as a function of location is denoted by E (r), where r is the position vector.…”

Section: Specific Absorption Ratementioning

confidence: 99%

Measurement and assessment of mobile network electromagnetic radiation pollution in Ramadi, Iraq

et al. 2022

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The electromagnetic radiation produced by cellular towers in Ramadi city, west of Iraq, is measured and analyzed in this research. This contains basic nodes, hub repeaters, and feeders for transmitting the carrier throughout the entire Ramadi city as well as the surrounding areas. As a result, this research focused on this area to measure and assess energy to determine if it is safe for people or has over the danger threshold. The study region was split into fifty-five mobile phone tower locations. In general, electric field intensity levels surpass 1.7 V/m, which is dangerous and should be reduced. However, apart from a few sites (2, 3, 35, 36, 45, 51, and 53), the magnetic field intensity and radiation density are acceptable based on the findings. The energy severity criteria are based on LATNEX corporation's guidelines.

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Discrete interferences optimum beamformer in correlated signal and interfering noise

Cited by 14 publications

References 40 publications

Hybrid HAR-CNN Model: A Hybrid Convolutional Neural Network Model for Predicting and Recognizing the Human Activity Recognition

Hybrid HAR-CNN Model: A Hybrid Convolutional Neural Network Model for Predicting and Recognizing the Human Activity Recognition

5G Opportunities in the South Pacific: Leveraging Low-Band Spectrum for Socio-Economic Development

Measurement and assessment of mobile network electromagnetic radiation pollution in Ramadi, Iraq

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