Manjunath Kounte | Reva University (original) (raw)
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Papers by Manjunath Kounte
2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), May 1, 2018
International Journal of Wireless and Mobile Computing, 2022
DOAJ (DOAJ: Directory of Open Access Journals), Sep 1, 2022
International journal of recent technology and engineering, Sep 30, 2019
VANETs have developed as one of the largest potential topics in the field of automotive industrie... more VANETs have developed as one of the largest potential topics in the field of automotive industries with promising and challenging futures in various aspects. VANETs permit intelligent vehicles to generate their own organized network without the need of the stable network. In this paper we introduce VANETs and its comparison with MANETs, standard wireless access in VANETs like WAVE model partly based on OSI model. We present a comprehensive study on routing protocol in VANETs like position-based routing, Geo-Cast based routing, etc. and scheduling in VANETs like deadline-based scheduling, hybrid-based scheduling, etc. This paper presents open research issues in VANETs highlighting challenges like security and privacy issues, network congestion control issues, etc, numerous routing and scheduling issues in VANETs.
International Journal of Advanced Computer Science and Applications, 2023
The emergence of the Internet of Things (IoT) has revolutionized the way we interact with the phy... more The emergence of the Internet of Things (IoT) has revolutionized the way we interact with the physical world. The rapid growth of IoT devices has led to a pressing need for robust security measures. Two promising approaches that can enhance IoT security are blockchain and artificial intelligence (AI). Blockchain can offer a decentralized and tamper-proof framework, ensuring the confidentiality and integrity of IoT data. AI can analyze large volumes of real-time data and detect anomalies in response to security threats in the IoT ecosystem. This paper explores the potential of these technologies and how they complement each other to provide a secured IoT system. Our main argument is that combining blockchain with AI can provide a robust solution for securing IoT networks and safeguarding the privacy of IoT users. This survey paper aims to provide a comprehensive understanding of the potential of these technologies for securing IoT networks and discuss the challenges and opportunities associated with their integration. It also provides a discussion on the current state of research on this topic and presents future research directions in this area.
Wireless Personal Communications
International Journal of Power Electronics and Drive Systems, Aug 1, 2023
The video codecs are focusing on a smart transition in this era. A future area of research that h... more The video codecs are focusing on a smart transition in this era. A future area of research that has not yet been fully investigated is the effect of deep learning on video compression. The paper's goal is to reduce the ringing and artifacts that loop filtering causes when high-efficiency video compression is used. Even though there is a lot of research being done to lessen this effect, there are still many improvements that can be made. In This paper we have focused on an intelligent solution for improvising in-loop filtering in high efficiency video coding (HEVC) using a deep convolutional neural network (CNN). The paper proposes the design and implementation of deep CNN-based loop filtering using a series of 15 CNN networks followed by a combine and squeeze network that improves feature extraction. The resultant output is free from double enhancement and the peak signal-to-noise ratio is improved by 0.5 dB compared to existing techniques. The experiments then demonstrate that improving the coding efficiency by pipelining this network to the current network and using it for higher quantization parameters (QP) is more effective than using it separately. Coding efficiency is improved by an average of 8.3% with the switching based deep CNN in-loop filtering.
International Journal of Power Electronics and Drive Systems, Aug 1, 2023
The present research focuses on developing an intelligent traffic management solution for trackin... more The present research focuses on developing an intelligent traffic management solution for tracking the vehicles on roads. Our proposed work focuses on a much better you only look once (YOLOv4) traffic monitoring system that uses the CSPDarknet53 architecture as its foundation. Deep-sort learning methodology for vehicle multi-target detection from traffic video is also part of our research study. We have included features like the Kalman filter, which estimates unknown objects and can track moving targets. Hungarian techniques identify the correct frame for the object. We are using enhanced object detection network design and new data augmentation techniques with YOLOv4, which ultimately aids in traffic monitoring. Until recently, object identification models could either perform quickly or draw conclusions quickly. This was a big improvement, as YOLOv4 has an astoundingly good performance for a very high frames per second (FPS). The current study is focused on developing an intelligent video surveillance-based vehicle tracking system that tracks the vehicles using a neural network, image-based tracking, and YOLOv4. Real video sequences of road traffic are used to test the effectiveness of the method that has been suggested in the research. Through simulations, it is demonstrated that the suggested technique significantly increases graphics processing unit (GPU) speed and FSP as compared to baseline algorithms.
2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)
2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)
The basic processing unit of HEVC is CTU. It can possess various size from 64×64 to 8×8 and it in... more The basic processing unit of HEVC is CTU. It can possess various size from 64×64 to 8×8 and it increasing coding efficiency as size is large. The computational complexity is an issue to be focused as HEVC has many pros to be considered as a best video compression technique. This paper focus on reducing the computational complexity of high-efficiency video coding (HEVC) in intra prediction by using combining depth decision and deep learning techniques. The proposed method provides a neural network for depth analysis of CTU followed by a deep learning network with multiple sizes of kernels for convolution and pervasive parameters that are trainable, from the database provided. A database provided here is constructed considering both the image frame from video and encoding abilities of CU. Database has the image frame data indicating the image value of CU and a vector of 16x1 depending on CU’s encoding details. It has a label to indicate, whether the CU is split or not. Initially image frame that is of huge size is assorted to various scales and split is created. Followed by modelling the partitions into a three level classification problem. To solve classification issue, a deep learning based CNN structure that possess various size kernels and parameters for convolution is developed, that should be analyzed and learned through a database that is established. The results show a dip in the encoding time of intra mode in HEVC for the given database
Transport and Telecommunication Journal
The evolution of vehicles has always been continuous with respect to growth in technology.The con... more The evolution of vehicles has always been continuous with respect to growth in technology.The concept of the Internet of Vehicles (IoV) is the process of allowing vehicles to interact with each other to provide real-time information. This paper introduces the various aspects of IoV and their components. Despite the fact that there are more and more vehicles connected to the IoV, there are still many unknown issues and potentials that needs to be identified to carry out research. In order to identify and classify the current difficulties in implementing and using IoV in urban cities, various research publications on the topic were analysed in this paper. The limitations of the Internet of Vehicular technology are also described. Additionally, a number of current and potential remedies that address the highlighted problems were briefly covered. The background information and reasons for evolving heterogeneous vehicular networks are thoroughly reviewed in this research. Also highlights...
Wireless Personal Communications
Acadlore Transactions on AI and Machine Learning
Video compression gained its relevance with the boon of the internet, mobile phones, variable res... more Video compression gained its relevance with the boon of the internet, mobile phones, variable resolution acquisition device etc. The redundant information is explored in initial stages of compression that’s is prediction. Inter prediction that is prediction within the frame generates high computational complexity when working with traditional signal processing procedures. The paper proposes the design of a deep convolutional neural network model to perform inter prediction by crossing out the flaws in the traditional method. It briefs the modeling of network, mathematics behind each stage and evaluation of the proposed model with sample dataset. The video frame’s coding tree unit (CTU) of 64x64 is the input, the model converts and store it as a 16-element vector with the goodness of CNN network. It gives an overview of deep depth decision algorithm. The evaluation process shows that the model performs better for compression with less computational complexity.
2022 7th International Conference on Communication and Electronics Systems (ICCES)
Advances in parallel computing, Nov 3, 2022
Direct Sequence Code Division Multiple Access (DS-CDMA) is a schemewhere several users transmit t... more Direct Sequence Code Division Multiple Access (DS-CDMA) is a schemewhere several users transmit their data simultaneously over a common wireless communication channel,by spreading each data by distinct codes. At the receiver, the individual data are detected by appropriate decoding. In this paper, a new smart receiver is proposed for detecting DS-CDMA signals based on a multilayer Feed Forward Neural Network (FFNN). The proposed receiver detects the transmitted data when the received signal is distorted due to channel noise, nearfar effect and Rayleigh fading. The channel state information is indirectly captured during the training of the FFNN and hence the conventional channel state estimation using pilot signal or training sequences is eliminated. Experimental results show that the performance of the proposed receiver in terms of detection accuracy is superior to similar competitive demodulators.
Journal of Southwest Jiaotong University
Prediction in HEVC exploits the redundant information in the fame to improve compression efficien... more Prediction in HEVC exploits the redundant information in the fame to improve compression efficiency. The computational complexity of prediction is comparatively high as it recursively calculates the depth by comparing the rate-distortion optimization cost (RDO) exhaustively. The deep learning technology has shown a good mark in this area compared to traditional signal processing because of its content-based analysis and learning ability. This paper proposes a deep depth decision algorithm to predict the depth of the coding tree unit (CTU) and store it as a 16-element vector, and this model is pipelined to the HEVC encoder to compare the time taken and bit rate of encoding. The comparison chart clearly shows the reduction in computational time and enhancement in bitrate while encoding. The dataset used here is generated for the model with 110000 frames of the various resolutions, split into test, training, and validation, and trained on a depth decision model. The trained model inter...
2022 2nd Asian Conference on Innovation in Technology (ASIANCON)
Transactions on Emerging Telecommunications Technologies
2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), May 1, 2018
International Journal of Wireless and Mobile Computing, 2022
DOAJ (DOAJ: Directory of Open Access Journals), Sep 1, 2022
International journal of recent technology and engineering, Sep 30, 2019
VANETs have developed as one of the largest potential topics in the field of automotive industrie... more VANETs have developed as one of the largest potential topics in the field of automotive industries with promising and challenging futures in various aspects. VANETs permit intelligent vehicles to generate their own organized network without the need of the stable network. In this paper we introduce VANETs and its comparison with MANETs, standard wireless access in VANETs like WAVE model partly based on OSI model. We present a comprehensive study on routing protocol in VANETs like position-based routing, Geo-Cast based routing, etc. and scheduling in VANETs like deadline-based scheduling, hybrid-based scheduling, etc. This paper presents open research issues in VANETs highlighting challenges like security and privacy issues, network congestion control issues, etc, numerous routing and scheduling issues in VANETs.
International Journal of Advanced Computer Science and Applications, 2023
The emergence of the Internet of Things (IoT) has revolutionized the way we interact with the phy... more The emergence of the Internet of Things (IoT) has revolutionized the way we interact with the physical world. The rapid growth of IoT devices has led to a pressing need for robust security measures. Two promising approaches that can enhance IoT security are blockchain and artificial intelligence (AI). Blockchain can offer a decentralized and tamper-proof framework, ensuring the confidentiality and integrity of IoT data. AI can analyze large volumes of real-time data and detect anomalies in response to security threats in the IoT ecosystem. This paper explores the potential of these technologies and how they complement each other to provide a secured IoT system. Our main argument is that combining blockchain with AI can provide a robust solution for securing IoT networks and safeguarding the privacy of IoT users. This survey paper aims to provide a comprehensive understanding of the potential of these technologies for securing IoT networks and discuss the challenges and opportunities associated with their integration. It also provides a discussion on the current state of research on this topic and presents future research directions in this area.
Wireless Personal Communications
International Journal of Power Electronics and Drive Systems, Aug 1, 2023
The video codecs are focusing on a smart transition in this era. A future area of research that h... more The video codecs are focusing on a smart transition in this era. A future area of research that has not yet been fully investigated is the effect of deep learning on video compression. The paper's goal is to reduce the ringing and artifacts that loop filtering causes when high-efficiency video compression is used. Even though there is a lot of research being done to lessen this effect, there are still many improvements that can be made. In This paper we have focused on an intelligent solution for improvising in-loop filtering in high efficiency video coding (HEVC) using a deep convolutional neural network (CNN). The paper proposes the design and implementation of deep CNN-based loop filtering using a series of 15 CNN networks followed by a combine and squeeze network that improves feature extraction. The resultant output is free from double enhancement and the peak signal-to-noise ratio is improved by 0.5 dB compared to existing techniques. The experiments then demonstrate that improving the coding efficiency by pipelining this network to the current network and using it for higher quantization parameters (QP) is more effective than using it separately. Coding efficiency is improved by an average of 8.3% with the switching based deep CNN in-loop filtering.
International Journal of Power Electronics and Drive Systems, Aug 1, 2023
The present research focuses on developing an intelligent traffic management solution for trackin... more The present research focuses on developing an intelligent traffic management solution for tracking the vehicles on roads. Our proposed work focuses on a much better you only look once (YOLOv4) traffic monitoring system that uses the CSPDarknet53 architecture as its foundation. Deep-sort learning methodology for vehicle multi-target detection from traffic video is also part of our research study. We have included features like the Kalman filter, which estimates unknown objects and can track moving targets. Hungarian techniques identify the correct frame for the object. We are using enhanced object detection network design and new data augmentation techniques with YOLOv4, which ultimately aids in traffic monitoring. Until recently, object identification models could either perform quickly or draw conclusions quickly. This was a big improvement, as YOLOv4 has an astoundingly good performance for a very high frames per second (FPS). The current study is focused on developing an intelligent video surveillance-based vehicle tracking system that tracks the vehicles using a neural network, image-based tracking, and YOLOv4. Real video sequences of road traffic are used to test the effectiveness of the method that has been suggested in the research. Through simulations, it is demonstrated that the suggested technique significantly increases graphics processing unit (GPU) speed and FSP as compared to baseline algorithms.
2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)
2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)
The basic processing unit of HEVC is CTU. It can possess various size from 64×64 to 8×8 and it in... more The basic processing unit of HEVC is CTU. It can possess various size from 64×64 to 8×8 and it increasing coding efficiency as size is large. The computational complexity is an issue to be focused as HEVC has many pros to be considered as a best video compression technique. This paper focus on reducing the computational complexity of high-efficiency video coding (HEVC) in intra prediction by using combining depth decision and deep learning techniques. The proposed method provides a neural network for depth analysis of CTU followed by a deep learning network with multiple sizes of kernels for convolution and pervasive parameters that are trainable, from the database provided. A database provided here is constructed considering both the image frame from video and encoding abilities of CU. Database has the image frame data indicating the image value of CU and a vector of 16x1 depending on CU’s encoding details. It has a label to indicate, whether the CU is split or not. Initially image frame that is of huge size is assorted to various scales and split is created. Followed by modelling the partitions into a three level classification problem. To solve classification issue, a deep learning based CNN structure that possess various size kernels and parameters for convolution is developed, that should be analyzed and learned through a database that is established. The results show a dip in the encoding time of intra mode in HEVC for the given database
Transport and Telecommunication Journal
The evolution of vehicles has always been continuous with respect to growth in technology.The con... more The evolution of vehicles has always been continuous with respect to growth in technology.The concept of the Internet of Vehicles (IoV) is the process of allowing vehicles to interact with each other to provide real-time information. This paper introduces the various aspects of IoV and their components. Despite the fact that there are more and more vehicles connected to the IoV, there are still many unknown issues and potentials that needs to be identified to carry out research. In order to identify and classify the current difficulties in implementing and using IoV in urban cities, various research publications on the topic were analysed in this paper. The limitations of the Internet of Vehicular technology are also described. Additionally, a number of current and potential remedies that address the highlighted problems were briefly covered. The background information and reasons for evolving heterogeneous vehicular networks are thoroughly reviewed in this research. Also highlights...
Wireless Personal Communications
Acadlore Transactions on AI and Machine Learning
Video compression gained its relevance with the boon of the internet, mobile phones, variable res... more Video compression gained its relevance with the boon of the internet, mobile phones, variable resolution acquisition device etc. The redundant information is explored in initial stages of compression that’s is prediction. Inter prediction that is prediction within the frame generates high computational complexity when working with traditional signal processing procedures. The paper proposes the design of a deep convolutional neural network model to perform inter prediction by crossing out the flaws in the traditional method. It briefs the modeling of network, mathematics behind each stage and evaluation of the proposed model with sample dataset. The video frame’s coding tree unit (CTU) of 64x64 is the input, the model converts and store it as a 16-element vector with the goodness of CNN network. It gives an overview of deep depth decision algorithm. The evaluation process shows that the model performs better for compression with less computational complexity.
2022 7th International Conference on Communication and Electronics Systems (ICCES)
Advances in parallel computing, Nov 3, 2022
Direct Sequence Code Division Multiple Access (DS-CDMA) is a schemewhere several users transmit t... more Direct Sequence Code Division Multiple Access (DS-CDMA) is a schemewhere several users transmit their data simultaneously over a common wireless communication channel,by spreading each data by distinct codes. At the receiver, the individual data are detected by appropriate decoding. In this paper, a new smart receiver is proposed for detecting DS-CDMA signals based on a multilayer Feed Forward Neural Network (FFNN). The proposed receiver detects the transmitted data when the received signal is distorted due to channel noise, nearfar effect and Rayleigh fading. The channel state information is indirectly captured during the training of the FFNN and hence the conventional channel state estimation using pilot signal or training sequences is eliminated. Experimental results show that the performance of the proposed receiver in terms of detection accuracy is superior to similar competitive demodulators.
Journal of Southwest Jiaotong University
Prediction in HEVC exploits the redundant information in the fame to improve compression efficien... more Prediction in HEVC exploits the redundant information in the fame to improve compression efficiency. The computational complexity of prediction is comparatively high as it recursively calculates the depth by comparing the rate-distortion optimization cost (RDO) exhaustively. The deep learning technology has shown a good mark in this area compared to traditional signal processing because of its content-based analysis and learning ability. This paper proposes a deep depth decision algorithm to predict the depth of the coding tree unit (CTU) and store it as a 16-element vector, and this model is pipelined to the HEVC encoder to compare the time taken and bit rate of encoding. The comparison chart clearly shows the reduction in computational time and enhancement in bitrate while encoding. The dataset used here is generated for the model with 110000 frames of the various resolutions, split into test, training, and validation, and trained on a depth decision model. The trained model inter...
2022 2nd Asian Conference on Innovation in Technology (ASIANCON)
Transactions on Emerging Telecommunications Technologies