Hima Valiveti - Academia.edu (original) (raw)
Papers by Hima Valiveti
2023 8th International Conference on Communication and Electronics Systems (ICCES)
2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2021
The combination of the array along with the elements of antenna and the algorithm that processes ... more The combination of the array along with the elements of antenna and the algorithm that processes the dedicated signal is known as Smart Antenna. By using this technology, the base station transmits the signal of each user and receives only that in the individual user direction. The technology of Smart Antenna which attempts to inform the problem by using the advanced technology of signal processing is called as beam-forming. Through the beam-forming which is adapti ve, the narrower beam is formed by the base station and nulls towards the users and interfering users. The diverse types of the adaptive algorithms are used to inform the smart antenna weights. Various algorithms of adaptive beamforming used in the current study are Recursive Least squares (RLS), Lease Mean square (LMS) and sample Matrix Inversion (SMI) algorithms.
2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN)
2023 International Conference on Inventive Computation Technologies (ICICT)
2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)
Sustainability, Dec 21, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Software Defined Networks, Jul 1, 2022
Journal of Physics: Conference Series
There has been an alarming increase in the number of accidents that occur due to drowsiness while... more There has been an alarming increase in the number of accidents that occur due to drowsiness while driving. In order to reduce roadside accidents, the detection of driver fatigue or drowsiness is crucial. Detecting fatigue during driving is crucial for reducing accidents, as well as improving the safety of both the driver and the passengers. Various methods can be used to detect drowsiness among drivers, but fuzzy logic-based detection stands out for its ability to avoid false alarms. As part of the proposed system, we are using eye-tracking in combination with methods such as Haar cascade to identify the level of drowsiness of the driver. This system has been tested in real-time.
Traitement du Signal, 2021
Manual tumor diagnosis from magnetic resonance images (MRIs) is a time-consuming procedure that m... more Manual tumor diagnosis from magnetic resonance images (MRIs) is a time-consuming procedure that may lead to human errors and may lead to false detection and classification of the tumor type. Therefore, to automatize the complex medical processes, a deep learning framework is proposed for brain tumor classification to ease the task of doctors for medical diagnosis. Publicly available datasets such as Kaggle and Brats are used for the analysis of brain images. The proposed model is implemented on three pre-trained Deep Convolution Neural Network architectures (DCNN) such as AlexNet, VGG16, and ResNet50. These architectures are the transfer learning methods used to extract the features from the pre-trained DCNN architecture, and the extracted features are classified by using the Support Vector Machine (SVM) classifier. Data augmentation methods are applied on Magnetic Resonance images (MRI) to avoid the network from overfitting. The proposed methodology achieves an overall accuracy of ...
International Journal of Communication Systems, 2019
Spectrum sensing in cognitive radio networks is vital and is used for identifying the user presen... more Spectrum sensing in cognitive radio networks is vital and is used for identifying the user presence or absence in the available spectrum. Energy detection and matched filter detection are the few methods to identify the user presence in the spectrum. There are various authors that proposed their research on spectrum sensing using matched filter detection with fixed threshold and predefined dynamic threshold. In this paper, authors proposed the novel matched filter detection method with dynamic threshold by using generalized likelihood ratio test (GLRT) and Neyman Pearson (NP) observer detection criteria. Due to which the probability of detection (P D) is increased, probability of false alarm (P fa) and probability of missed detection (P md) has been reduced when compare with the existing methods. The results are simulated using MATLab Software and also plotted the receiver operating characteristic (ROC) curve for estimation of the receiver sensitivity.
2022 IEEE 7th International conference for Convergence in Technology (I2CT)
Multimedia Tools and Applications
Inventive Computation Technologies
In a Cooperative Spectrum Sensing environment, malicious secondary users (SU) degrade the overall... more In a Cooperative Spectrum Sensing environment, malicious secondary users (SU) degrade the overall performance of the radio network. The existing techniques need to assume an upper bound on the number of such users in a network, to identify them. In the present work, we use the robust distance based on minimum covariance determinant (MCD) to identify the malicious users in the network without assuming such an upper bound. Further, we validate the performance of the proposed RD method in random, always high and always low selfish attacks scenarios.
2022 International Conference on Computer Communication and Informatics (ICCCI), 2022
E3S Web of Conferences, 2021
Depth estimation is a computer vision technique that is critical for autonomous schemes for sensi... more Depth estimation is a computer vision technique that is critical for autonomous schemes for sensing their surroundings and predict their own condition. Traditional estimating approaches, such as structure from motion besides stereo vision similarity, rely on feature communications from several views to provide depth information. In the meantime, the depth maps anticipated are scarce. Gathering depth information via monocular depth estimation is an ill-posed issue, according to a substantial corpus of deep learning approaches recently suggested. Estimation of Monocular depth with deep learning has gotten a lot of interest in current years, thanks to the fast expansion of deep neural networks, and numerous strategies have been developed to solve this issue. In this study, we want to give a comprehensive assessment of the methodologies often used in the estimation of monocular depth. The purpose of this study is to look at recent advances in deep learning-based estimation of monocular ...
E3S Web of Conferences, 2021
In several applications, such as scene interpretation and reconstruction, precise depth measureme... more In several applications, such as scene interpretation and reconstruction, precise depth measurement from images is a significant challenge. Current depth estimate techniques frequently provide fuzzy, low-resolution estimates. With the use of transfer learning, this research executes a convolutional neural network for generating a high-resolution depth map from a single RGB image. With a typical encoder-decoder architecture, when initializing the encoder, we use features extracted from high-performing pre-trained networks, as well as augmentation and training procedures that lead to more accurate outcomes. We demonstrate how, even with a very basic decoder, our approach can provide complete high-resolution depth maps. A wide number of deep learning approaches have recently been presented, and they have showed significant promise in dealing with the classical ill-posed issue. The studies are carried out using KITTI and NYU Depth v2, two widely utilized public datasets. We also examine...
International Journal of Pervasive Computing and Communications
Purpose Road accidents, an inadvertent mishap can be detected automatically and alerts sent insta... more Purpose Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence. Design/methodology/approach The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes...
2023 8th International Conference on Communication and Electronics Systems (ICCES)
2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2021
The combination of the array along with the elements of antenna and the algorithm that processes ... more The combination of the array along with the elements of antenna and the algorithm that processes the dedicated signal is known as Smart Antenna. By using this technology, the base station transmits the signal of each user and receives only that in the individual user direction. The technology of Smart Antenna which attempts to inform the problem by using the advanced technology of signal processing is called as beam-forming. Through the beam-forming which is adapti ve, the narrower beam is formed by the base station and nulls towards the users and interfering users. The diverse types of the adaptive algorithms are used to inform the smart antenna weights. Various algorithms of adaptive beamforming used in the current study are Recursive Least squares (RLS), Lease Mean square (LMS) and sample Matrix Inversion (SMI) algorithms.
2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN)
2023 International Conference on Inventive Computation Technologies (ICICT)
2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)
Sustainability, Dec 21, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Software Defined Networks, Jul 1, 2022
Journal of Physics: Conference Series
There has been an alarming increase in the number of accidents that occur due to drowsiness while... more There has been an alarming increase in the number of accidents that occur due to drowsiness while driving. In order to reduce roadside accidents, the detection of driver fatigue or drowsiness is crucial. Detecting fatigue during driving is crucial for reducing accidents, as well as improving the safety of both the driver and the passengers. Various methods can be used to detect drowsiness among drivers, but fuzzy logic-based detection stands out for its ability to avoid false alarms. As part of the proposed system, we are using eye-tracking in combination with methods such as Haar cascade to identify the level of drowsiness of the driver. This system has been tested in real-time.
Traitement du Signal, 2021
Manual tumor diagnosis from magnetic resonance images (MRIs) is a time-consuming procedure that m... more Manual tumor diagnosis from magnetic resonance images (MRIs) is a time-consuming procedure that may lead to human errors and may lead to false detection and classification of the tumor type. Therefore, to automatize the complex medical processes, a deep learning framework is proposed for brain tumor classification to ease the task of doctors for medical diagnosis. Publicly available datasets such as Kaggle and Brats are used for the analysis of brain images. The proposed model is implemented on three pre-trained Deep Convolution Neural Network architectures (DCNN) such as AlexNet, VGG16, and ResNet50. These architectures are the transfer learning methods used to extract the features from the pre-trained DCNN architecture, and the extracted features are classified by using the Support Vector Machine (SVM) classifier. Data augmentation methods are applied on Magnetic Resonance images (MRI) to avoid the network from overfitting. The proposed methodology achieves an overall accuracy of ...
International Journal of Communication Systems, 2019
Spectrum sensing in cognitive radio networks is vital and is used for identifying the user presen... more Spectrum sensing in cognitive radio networks is vital and is used for identifying the user presence or absence in the available spectrum. Energy detection and matched filter detection are the few methods to identify the user presence in the spectrum. There are various authors that proposed their research on spectrum sensing using matched filter detection with fixed threshold and predefined dynamic threshold. In this paper, authors proposed the novel matched filter detection method with dynamic threshold by using generalized likelihood ratio test (GLRT) and Neyman Pearson (NP) observer detection criteria. Due to which the probability of detection (P D) is increased, probability of false alarm (P fa) and probability of missed detection (P md) has been reduced when compare with the existing methods. The results are simulated using MATLab Software and also plotted the receiver operating characteristic (ROC) curve for estimation of the receiver sensitivity.
2022 IEEE 7th International conference for Convergence in Technology (I2CT)
Multimedia Tools and Applications
Inventive Computation Technologies
In a Cooperative Spectrum Sensing environment, malicious secondary users (SU) degrade the overall... more In a Cooperative Spectrum Sensing environment, malicious secondary users (SU) degrade the overall performance of the radio network. The existing techniques need to assume an upper bound on the number of such users in a network, to identify them. In the present work, we use the robust distance based on minimum covariance determinant (MCD) to identify the malicious users in the network without assuming such an upper bound. Further, we validate the performance of the proposed RD method in random, always high and always low selfish attacks scenarios.
2022 International Conference on Computer Communication and Informatics (ICCCI), 2022
E3S Web of Conferences, 2021
Depth estimation is a computer vision technique that is critical for autonomous schemes for sensi... more Depth estimation is a computer vision technique that is critical for autonomous schemes for sensing their surroundings and predict their own condition. Traditional estimating approaches, such as structure from motion besides stereo vision similarity, rely on feature communications from several views to provide depth information. In the meantime, the depth maps anticipated are scarce. Gathering depth information via monocular depth estimation is an ill-posed issue, according to a substantial corpus of deep learning approaches recently suggested. Estimation of Monocular depth with deep learning has gotten a lot of interest in current years, thanks to the fast expansion of deep neural networks, and numerous strategies have been developed to solve this issue. In this study, we want to give a comprehensive assessment of the methodologies often used in the estimation of monocular depth. The purpose of this study is to look at recent advances in deep learning-based estimation of monocular ...
E3S Web of Conferences, 2021
In several applications, such as scene interpretation and reconstruction, precise depth measureme... more In several applications, such as scene interpretation and reconstruction, precise depth measurement from images is a significant challenge. Current depth estimate techniques frequently provide fuzzy, low-resolution estimates. With the use of transfer learning, this research executes a convolutional neural network for generating a high-resolution depth map from a single RGB image. With a typical encoder-decoder architecture, when initializing the encoder, we use features extracted from high-performing pre-trained networks, as well as augmentation and training procedures that lead to more accurate outcomes. We demonstrate how, even with a very basic decoder, our approach can provide complete high-resolution depth maps. A wide number of deep learning approaches have recently been presented, and they have showed significant promise in dealing with the classical ill-posed issue. The studies are carried out using KITTI and NYU Depth v2, two widely utilized public datasets. We also examine...
International Journal of Pervasive Computing and Communications
Purpose Road accidents, an inadvertent mishap can be detected automatically and alerts sent insta... more Purpose Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence. Design/methodology/approach The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes...