Chaitanya Duggineni - Academia.edu (original) (raw)

Papers by Chaitanya Duggineni

Ad Hoc Sens. Wirel. Networks, 2022

Young Scientist, 2019

Національний університет харчових технологій ДОсліДження КОрмОВих сумішей нА ОснОВі ОлійнОї і зер... more Національний університет харчових технологій ДОсліДження КОрмОВих сумішей нА ОснОВі ОлійнОї і зернОВОї сирОВини і ГіДрОКсиДіВ нАтріЮ тА КАліЮ Анотація. Дана стаття містить дослідження фізико-хімічних показників створених кормових сумішей на основі зернової сировини і водних розчинів гідроксиду натрію та гідроксиду калію. Створенні кормові суміші мають підвищену харчову цінність в порівнянні з окремо взятими компонентами олійної і зернової сировини (льон, соняшник, кукурудза). Отримані результати досліджень щодо хімічного складу олійної та зернової сировини дозволили провести розрахунок її енергетичної цінності на загальну масу. Суміш складається так, щоб недоліки (низький вміст білка, нестача вітамінів тощо) одних компонентів компенсувати перевагами інших компонентів. Проведені дослідження по екструдуванню та гранулюванню кормових сумішей дозволили отримати відповідні кормові продукти та дозволили визначити їх хімічний склад. За рахунок використання водорозчинних лугів розширюється асортимент існуючої кормової бази. Ключові слова: олійна сировина, хімічні показники, кормова суміш, гідроксид натрію, гідроксид калію, комбікорм.

Young Scientist, 2019

Ad Hoc Sens. Wirel. Networks, 2022

Wireless Personal Communications, 2017

Arising issue of spectrum scarcity is resolved by emerging cognitive radio technology. In which, ... more Arising issue of spectrum scarcity is resolved by emerging cognitive radio technology. In which, unoccupied portion of licensed spectrum would be granted for unlicensed user in order to upgrade the spectrum utilization. Spectrum sensing is an enabling task for labeling the spectrum holes and away from spectrum congestion in cognitive radio networks. Impact of secondary user's cooperation developed cooperative spectrum sensing for effective spectrum sensing, many secondary users are to be associated in sensing the idle channels. Adversary secondary users (SU) mimic as primary user in cooperative sensing scheme, called primary user emulation attack (PUEA). If any SU forges the sensing data, that is known as spectrum sensing data falsification attack (SSDF). Rescuing the network against these two attacks is the major issue of cognitive radio networks. To address these challenges, we propose cognitive radio network to ensure security in cooperative spectrum sensing for video streaming application. Sensing based clustering algorithm assembles the cognitive radios into clusters to mitigate cooperative overhead and consumed energy. In order to avert PUEA attacks, we introduce authentication signature ID algorithm which provides authority for licensed user to use spectrum. To diminish SSDF attacks, we exploit Hamming algorithm to abolish forgery node from spectrum sensing decision. Furthermore PRN channel allocation algorithm is employed to precisely deliver the video with high quality and lower delay. For video transmission in cognitive radio network, we need to compress the video frames by block truncation codingpattern fitting in order to reduce bandwidth consumption. This compression technique offers good video transmission with high compression ratio in cognitive radio network, due to trade-off among quality, bit rate and decoding time. Our proposed framework demonstrates the simulation with improved detection rate of idle channel and reduced the delay for high quality video transmission.

Advances in Intelligent Systems and Computing, 2017

Wideband spectrum sensing (WSS) has been recommended as an efficient approach to enhance the spec... more Wideband spectrum sensing (WSS) has been recommended as an efficient approach to enhance the spectrum utilization of cognitive radio users. As WSS involves high sampling rate and long sensing duration, complex multi-channel analog-to-digital converters (ADC) are required for processing. In this context, Compressive Sensing (CS) facilitates in decreasing the sampling rate thereby reducing the processing time and complexity. Compressive Sensing (CS) is a technique for reconstructing a signal from sparse number of samples when compared to Nyquist sampling. Energy minimization being the key feature of CS has been applied to spectrum sensing in cognitive radio networks (CRN) in this work. The detection of primary user (PU) signal is carried out using the sparse representation of received signals. The received PU signal is compressed in the time domain to extract the minimum energy coefficients and recovered using l1-minimization and Differential Evolution (DE) algorithms. Simulation results for various compression rates are analyzed.

International Journal of Biomedical Engineering and Technology, 2018

Wireless communications have a rapid growth that leads to huge demand on the deployment of new wi... more Wireless communications have a rapid growth that leads to huge demand on the deployment of new wireless services in both licensed and unlicensed frequency spectrum. The performance of Cooperative Spectrum Sensing (CSS) in cognitive radio is based on two factors: cooperative gain and cooperative overhead. There are many other dominating factors that affect the performance of CSS like the presence of attacks, energy efficiency, sensing time and delay, spectrum efficiency, etc. The major threat to CSS is Spectrum Sensing Data Falsification (SSDF) attack. Existing methods to mitigate the SSDF attack doesn't focus on energy efficiency which is performed here. To deal with these challenges, we propose an energy efficient mechanism against SSDF attack for healthcare application in Cognitive Radio Networks (CRNs). We propose a Malicious Node Identification Algorithm (MIA) to mitigate the problem of SSDF attack in the network and to eliminate the redundant data at the fusion centre, Balanced Cluster-based Redundancy Check Aggregation (BCRCA) mechanism is proposed. Finally, the decision taken by the fusion centre is based on Weighted Selection Combining (WSC) technique that improves spectrum decision accuracy.

2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

2021 2nd International Conference on Smart Electronics and Communication (ICOSEC)

2021 6th International Conference on Inventive Computation Technologies (ICICT)

In recent years, volume of data traffic has increased due to many multimedia applications that ar... more In recent years, volume of data traffic has increased due to many multimedia applications that are supported by devices such as laptops, smartphone, tablets etc. Multiple users accessed by devices when connected to the internet simultaneously lead to overflow of the data traffic. Many solutions have been introduced to overcome these data traffic problems but the existing radio resources and cellular operators are unable to completely mitigate these problems. Device to Device (D2D) communication in cellular networks is designed to maximize the interference management and resource allocation by off-loading the high volume of data traffic. The current paper throws light on the latest works and developments of D2D communication and its evolution alongside Software Defined Networking (SDN) and emphasizes on the energy efficiency and signaling cost of the SDN based D2D networks.

2021 6th International Conference on Inventive Computation Technologies (ICICT)

The Cooperative Spectrum Sensing (CSS) offers efficient performance ability since the sensing dat... more The Cooperative Spectrum Sensing (CSS) offers efficient performance ability since the sensing data is gathered from numerous Secondary Users (SU) for the final decision making regarding the existence of Primary User (PU) in the spectrum bands. CSS network is vulnerable to security attacks like spectrum sensing data falsification attack (SSDFA) and primary user emulation attack (PEA). This article evaluates the performance of the network in the existence of PEA and SSDFA with the help of hard decision fusion rules AND, OR, and K-out-of-N (KoN) rules. The probability of error and packet delivery ratio is the parameters used to estimate the performance of the network.

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...

2021 6th International Conference on Inventive Computation Technologies (ICICT), 2021

Stuck-at faults occur in secure non-volatile memories due to endurance issues. In non-volatile me... more Stuck-at faults occur in secure non-volatile memories due to endurance issues. In non-volatile memories data is available to access for many years even after the power shut down, which enables unauthorized access to an attacker. Here, we use Advanced Encryption Standard (AES) algorithm which is powerful encryption algorithm in recent years to provide security for confidential data in cloud computing systems. We are also using random characteristics of the AES algorithm along with rotational shift operation that helps in fixing the faults present in main memories. The implementation of Randshift technique in FPGA design flow by using simulation analysis shows less energy consumption and area-efficient parameters compared to recently proposed fault-tolerant techniques.

Cognitive Informatics and Soft Computing

E3S Web of Conferences

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 ...

Traitement du Signal

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 ...

2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC)

Ad Hoc Sens. Wirel. Networks, 2022

Young Scientist, 2019

Ad Hoc Sens. Wirel. Networks, 2022

Wireless Personal Communications, 2017

Advances in Intelligent Systems and Computing, 2017

International Journal of Biomedical Engineering and Technology, 2018

2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

2021 2nd International Conference on Smart Electronics and Communication (ICOSEC)

2021 6th International Conference on Inventive Computation Technologies (ICICT)

International Journal of Pervasive Computing and Communications

2021 6th International Conference on Inventive Computation Technologies (ICICT), 2021

Cognitive Informatics and Soft Computing

E3S Web of Conferences

Traitement du Signal

2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC)