Gandhiraj R

EDUCATION

B.E (ECE; M.Tech (Digital Systems and Communication); PhD (Computational Engineering and Networking)

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Engineering

Scopus Publications

Performance Appraisal Using Leach Protocol in Wireless Energy Harvesting System
Karthikesh Cheni, Manyam Greeshma, Sana Udith Kumar, Somu Thanusha, and R. Gandhiraj
Springer Nature Singapore



Enhancing BER Performance in Phase Noise using KNN-based Symbol Detection
Sharanya Krishnamurthy, Manoj Panda, Ramesh Chinthala, and Gandhiraj R
IEEE
The performance of a wireless system is directly dependent on the accuracy of retrieving the transmitted message from the noisy signal received at the demodulation end. Over the years many methods have been developed to reduce the Bit Error Rate(BER) at the receiver. Recently, machine learning algorithms have been explored as an alternative to traditional symbol detection methods to enhance overall performance. M-ary QAM is a popular digital modulation scheme used in Wireless communication owing to its high data rate. Nevertheless, for a given signal-to-noise ratio, the BER for QAM is higher than the BPSK or QPSK systems, which provide a lower data rate. The BER can be reduced using ML classifiers such as the K-Nearest Neighbours Algorithm (KNN) to leverage the high data rate of QAM and have more efficient symbol detection. This paper describes the implementation of KNN to QAM symbol detection and the resultant decrease in BER is observed to be around 4-6 dB.


Empowering Object Detection in Dynamic Environments with AI-Driven MIMO Radar Technology
N S S V Praneeth, T Sathya Narayana, B Rupphak Sai Kiran, Mirza Rizwan Baig, Gandhiraj R, and Anandaselvakarthik T
IEEE
Millimeter-wave radars are increasingly used for surveillance and automotive applications, as optical-based systems have a hard time in complex environments such as night, fog, etc. These applications use advanced object detection and classification algorithms that require huge amounts of datasets for training and testing, which is hard to obtain considering that they utilize the same radar configuration and contain required scenarios that enhance your model accuracy. Moreover, in signal processing perspective, angular resolution is constrained by the number of receivers present in the radar. So, to account for these problems, we propose a virtual radar that can generate datasets through code, which can be used to train and test a model, and an AI model that won't be constrained by angular resolution.


Network Provisioning in SDN: Optimizing Network Resources for Enhanced Performance
G D Nithin, D Yathis Kumar, S. Nithin, K S Sree Hari Nathan, and R Gandhiraj
IEEE
Software defined Networking (SDN) provides a comprehensive view of the network and is good at handling changes in a traditional and simple network without needing a lot of updates. The purpose of this study was to determine the performance of SDN in various scenarios. However, the usual SDN architecture faced problems in handling bottlenecks. In our early research, we referred a lot of research papers for creating a model for a regular wired network with less nodes and traffic using standard SDN architecture. We developed the model using NetSim and observed the various network parameters such as overall throughput, delay and jitter. The recorded throughput exhibited a decrease, accompanied by increased delay and jitter, when assessing this basic network configuration. Later, we propose a new model with improved SDN performance, which resembles a normal wireless IoT network in real-time. The proposed model contains a novel priority and congestion control mechanism, called as ‘Updated SDN’, which has worked well in reducing network congestion especially in practical situations. It also maintains network's quality of service. The model provided results which had a significant improvement of 2% from the previous model. In Overall, our model looks at network delays and congestion at the SDN controller and delivers the packet properly with minimized loss. The results were analyzed by comparing the results of updated SDN and the standard SDN.


Malicious Node Detection in VANETs via Enhanced DSR and ML
Palaniappan AN, Pranav J, Shivamanikkavasakam S, Santosh Kumar, and Gandhiraj R
IEEE
Being vulnerable to various security threats like other wireless networks, Vehicular Ad-hoc Networks (VANETs) need an effective intrusion detection system to ensure security in VANETs. This paper proposes an intrusion detection system for VANETs using machine learning techniques. The system is implemented in NetSim where a VANET is created with a malicious node causing blackhole/sinkhole attack. The malicious node is detected using enhanced Dynamic Source Routing (DSR) protocol and watchdog timer. Once detected, the route through the malicious node is avoided in future transmissions. The packet trace from NetSim simulation is used as input dataset for Support Vector Machine (SVM) and Random Forest models for classifying nodes as malicious or not. The models provide over 99% accuracy, precision, recall and F1-score in detecting malicious nodes, outperforming previous approaches. The system provides an effective intrusion detection framework for securing VANETs against attacks using simulations and machine learning.


Minimal Energy Cluster Head Selection in LEACH for WSNs : A Sea Lion Inspired Algorithm
Pooja K, P Dharshini Muthu Bala, Rishi Kumar, Saaral S, and Gandhiraj R
IEEE
Due to the increasing significance and applications of wireless sensor networks (WSNs), research has focused on addressing the crucial issue of depletion. Minimizing energy consumption is essential for extending the network lifetime. LEACH, a popular routing algorithm for WSNs, helps achieve this goal. However, LEACH has certain limitations. This work introduces PDU-SLnO LEACH, which proposes solutions to these limitations. The key idea involves dividing the communication area into clusters and allocating cluster heads for each cluster using the PDU-SLnO cluster head selection algorithm, thereby reducing transmission distances and overall energy consumption. The results demonstrate a reduction in total energy consumption and the throughput is also increased by 20%. There is an extension of network lifetime and increased stability compared to the original LEACH.


A Monte Carlo simulation study of L-band emission upon gamma radiolysis of water
K.A Pradeep Kumar, G.A Shanmugha Sundaram, S. Venkatesh, R. Gandhiraj, and R. Thiruvengadathan
Elsevier BV



Performance analysis of novel Joint GPS Anti-jamming and Anti-spoofing detection
Balaji B, Gandhiraj R, and Shanmugha Sundaram G A
IEEE
Global Positioning System (GPS), a technology integrated into an extensive range of contemporary devices and applications, is increasingly vulnerable to harmful interference, including spoofing and jamming. These hostile actions pose significant risks, causing disturbances that range from minor to critically operational. This study introduces a novel, hybrid countermeasure for GPS systems, combating such threats. Our method incorporates innovative signal processing, Direction of Arrival (DOA) estimation, and an adaptive filter using the Least Mean Squares (LMS) algorithm and Kalman filter. This unique blend effectively suppresses both jamming and spoofing impacts, substantially boosting the resistance and security of GPS systems against such intrusions. A thorough examination of simulated GPS signals, tested under diverse attack conditions, underlines the strength and efficiency of our proposed approach.


Comparative Analysis of Skin Lesions Classification Using Machine Learning Classifiers and Lesnet-22 Architecture
Balathaarani N, Gandhiraj R, and Manoj Kumar Panda
IEEE
One of the prevalent and lethal cancers in the world is skin cancer. Skin cancer cases have been rising in recent years, and this is expected to increase exponentially. It is curative if the skin lesions are detected early. Diagnosis is important because of the similarities in its types like melanoma (MEL), seborrheic keratosis (SK), actinic keratosis (AKEIC), basal cell carcinoma (BCC), benign keratosis (BKL), dermatofibroma (DF), melanocytic nevus (NEV), squamous cell carcinoma (SCC), and vascular lesion (VASC) . Therefore, there is a growing demand for computer-assisted recognition approaches for dermoscopic images of skin lesions. Many automated methods for diagnosing skin lesions have been proposed, but they have not yet proven to be very accurate. This study proposes a computer-assisted recognition approach to classify skin lesions. Initially, the preprocessing technique eliminates the digital artifacts from the dermoscopic images, and features are extracted based on the feature fusion approach. Moreover, fully automated detection and segmentation approaches are employed to effectively localize the skin lesions. Two methods are proposed for classification: the firstly, the analysis and comparison of different machine learning approaches is performed. The second method employs a deep learning framework-based approach. To validate the proposed methodologies, dermoscopic images from International Skin Imaging Collaboration (ISIC-2017) and ISIC-2019 datasets have been used. The skin lesions classification performance of the proposed novel LesNet-22 architecture has achieved 94% and 91% of accuracy for ISIC-2017 and ISIC-2019 respectively, which outperforms the existing classifiers.


Simulation based Closed Loop Scenario Fuzzing for Safety Critical ADAS Applications
Arpit S Agarkar, Gandhiraj R, Manoj Kumar Panda, and Saksham Srivastava
IEEE
Autonomous Vehicles (AVs), including self-driving cars, should not be approved by regulatory agencies unless there is a considerably higher level of confidence in their dependability and safety. Simulation-based testing ensures a greater level of rigor for AV controllers as it provides the freedom to create a wide variety of scenarios. However, current simulation-based testing techniques primarily focus on simple scenarios, rather than scaling up to complex driving situations that require sophisticated awareness of the surroundings. On the other hand, testing AVs on streets and highways may miss numerous infrequent events. Despite the significant growth in the AV market, the means for thorough testing are still inadequate. Real-world testing is time-consuming, expensive, and, more importantly, risky. Additionally, there is a lack of a system to automatically generate crucial scenarios. In this paper, a framework for generating various mutated scenarios to test AV software is proposed, with a focus on the Advanced Driver Assistance Systems (ADAS) features. The framework offers reliable development of multiple traffic scenarios generated via a feedback-based fuzzing algorithm to rigorously test the ADAS features. These scenarios are semantically valid, and no two scenarios are similar, thus eliminating redundancy. Furthermore, many of the generated scenarios adhere to the specifications outlined by New Car Assessment Program (NCAP) standards. The framework utilizes the OpenScenario standards to describe the static and dynamic elements of the urban traffic scenarios simulated by the simulator. Overall, there has been as increase in percentage of finding safety critical scenarios from the base scenario.


Driver Drowsiness Detection and Warning using Facial Features and Hand Gestures
Arpit S Agarkar, R Gandhiraj, and Manoj Kumar Panda
IEEE
According to National Highway Traffic Safety Administration (NHTSA), drowsy driving is one of the primary causes of accidents. Numerous valuable lives can be saved, accidents can be reduced or avoided, and the cost of injury and damage to infrastructure may be reduced with a timely alert or warning to the negligent driver. Advanced Driver Assistance Systems (ADAS) consists of the active safety system which includes the detection of the driver's face to determine their level of drowsiness. This paper provides a camera-based technique which relies on fiducial components, such as lips, eye movement, and hand gestures of the driver which are often natural responses of a human to yawning. A front camera installed on the windscreen is used to continually monitor the driver and Raspberry Pi is utilized for processing the images. The proposed warning system gives an audio warning when the driver is yawning or going into the state of drowsiness. Results illustrate that the proposed technique is effective at detecting signs of driver's drowsiness and yawning. It can differentiate between when the driver's hand is placed over the mouth to infer it as yawning and when it is touching other parts of the face to infer it as not yawning.


Improving Pneumonia Detection Using Segmentation and Image Enhancement
Ethiraj Thipakaran, R. Gandhiraj, and Manoj Kumar Panda
Springer Nature Singapore



Arrhythmia detection—An Enhanced Method Using Gramian Angular Matrix for Deep Learning
Keerthana Krishnan, R. Gandhiraj, and Manoj Kumar Panda
Springer Nature Singapore



Enhancing GPS Position Estimation Using Multi-Sensor Fusion and Error-State Extended Kalman Filter
A Aravind, V Ashwin, S Chandeep, P S S S Yasaswi, R Gandhiraj, and Ga Shanmugha Sundaram
IEEE
An optimally designed GPS receiver can typically achieve 3-5 meters accuracy. This accuracy is insufficient for autonomous navigation where the margin of error must be low. Some existing sensor fusion approaches (IMU and GPS fusion using EKF) can achieve 2-3 meters (average error of 2.66 meters) accuracy, but the existing systems cannot function if the GPS receiver is not functional. Other modern techniques like Differential GPS are expensive to implement. Hence an accurate, cost-effective, and available alternative is required. As an alternative to Conventional GPS which typically has an accuracy of 3-5m which is not sufficient for autonomous navigation, a system that consists of data from LiDAR, IMU, and GPS has been implemented using sensor fusion with the help of error state extended Kalman filter. The implemented system was put to test using data synthesized from Carla Simulator. The proposed system was shown to have improved accuracy by reducing the position error by nearly 95%.


Microwave Tomography Data Deconstruct of Spatially Diverse C-Band Scatter Components Using Clustering Algorithms
G. A. Shanmugha Sundaram, R. Gandhiraj, B. N. Binoy, S. I. Harun, and S. N. Surya
Institute of Electrical and Electronics Engineers (IEEE)
Communication signals that propagate through free space are subject to multi-path interference due to scattering by various objects in the propagation channel. The effect is especially severe in complex situations in dense urban environments. To investigate the problem, a typical multi-static detection scenario is reconstructed under controlled laboratory conditions, from which suitable data sets are created. Data-driven models are then employed in EDGE computing platforms to profile the scatter centers based on the subjective manner in which they affect the signals. These have been interpreted primarily based on clustering algorithm (CA) operations– using a select suite of pre-processing models that effectively tame the variations in the C-band spatial-temporal data. A subset of the data of interest could then be subjected to an optional, compute-intensive machine learning (ML) approach. The relative advantages of the proposed method vis-a-vis an array of conventional schemes are highlighted, while also considering its carbon friendly attribute. Given the more significant association of the data to antenna radiation patterns, estimation of the latter can now be performed free of any anechoic chamber set up in a time and cost agnostic manner. The benefit of this work would lie in the realm of mid-band 5G-NR (and the future 6G) cellular communication systems deployment, where optimizing the distributed antenna location attributes on time and cost-constrained scales becomes imperative before any large-scale deployment.


BEP Analysis of Filter Bank Multicarrier Under IQ Imbalance
R. Suraj, M. Venkatesh, C. Charumathi, Alekhya Kapavarapu, K. Pradeep Raj, R. Gandhiraj, and G. A. Shanmugha Sundaram
Springer Singapore



Steering Angle Prediction for Autonomous Driving using Federated Learning: The Impact of Vehicle-To-Everything Communication
Aparna M P, Gandhiraj R, and Manoj Panda
IEEE
When it comes to the application of new technology, the automotive industry is one of the most rapidly expanding industries in the world. The recent trend in this field is autonomous driving using machine learning (ML) techniques. The training of ML models that can provide human-like driving decisions requires a large amount of heterogeneous data to be collected from multiple vehicles for training, testing and validation of the autonomous driving system. This large volume of heterogeneous data can be obtained using connected vehicles, where each vehicle can share the collected data with a central server using vehicle-to-everything (V2X) communication. The objective of this work is to analyze and compare the performances of the ‘centralized’ and ‘federated’ approaches to training the ML models using V2X communication under various channel conditions. The specific application being considered for this work is the ‘prediction of the steering angle using a vision-based dataset’. The results obtained in our study indicate that: (i) even though the conventional ML approach may work reasonably well up to a certain bit error rate (BER) where the ML model is trained using noisy images, its performance degrades at higher BER values due to noise-overfitting, and (ii) the federated learning (FL) approach can indeed provide a better alternative to the centralized ML approach for the considered application, consuming less bandwidth.


V2X Based Emergency Corridor for Safe and Fast Passage of Emergency Vehicle
Amit Ashish, R Gandhiraj, and Manoj Panda
IEEE
In today's world, traffic jams are major concerns for emergency vehicles like Ambulances, Police cars and Fire Brigade trucks which get stuck in traffic and unable to reach their destinations in time, resulting in a possible loss of human lives. A better control over the transportation system can be achieved through the V2X based smart transportation. To deal with such emergency situations, this dissertation proposes a framework for automatic “Emergency Corridor” creation for speedy clearance of emergency vehicles. The traffic signal controller dynamically suspends the routine movement of traffic flow to provide the green phase to the approach containing the emergency vehicle using V2I communication while V2V communication makes the vehicles in the front to make way for the emergency vehicle. The work is simulated in the VEINS framework i.e. combination of SUMO and OMNeT++. We have compared the end to end travel time of Emergency vehicle with and without the proposed algorithm in two different traffic demand situations.


Big Data based system for Biomedical Image Classification
Priyanka R, Shrinithi S, and Gandhiraj R
IEEE
Medical field deals with large volume of data. Among them, the medical images are extremely significant. The existing traditional methods are not efficient enough to manage this huge amount of data. For the efficient management and storage, big data technology is used. Hence, in this work, the processing and analysis of such huge amount of biomedical image data has been done by conglomerating technologies like Big Data and Machine Learning. The designed system takes as input, the features extracted from various Diabetic Retinopathy (DR) images. These features are used for further classification. The classification has been performed using K-Nearest Neighbor machine learning classifier (KNN) implemented in Hadoop MapReduce framework, in order to detect the absence or presence of DR. The performance of the Hadoop MapReduce framework has been analyzed using the execution time. The analysis has been done by comparing the execution time for the classification performed by Hadoop MapReduce framework and the execution time for the classification performed by the Python framework for datasets of different sizes. At the end of the analysis, it has been found that the Hadoop MapReduce framework can handle bigger datasets more efficiently for classification than the Python framework.


Enhanced Performance of Novel Patch Antenna Sub-array Design for Use in L-Band Ground Station Receiver Terminals Linked to Aerospace Platforms
T. A. Ajithlal, R. Gandhiraj, G. A. Shanmugha Sundaram, and K. A. Pradeep Kumar
Springer Nature Singapore



Evidence of Scatter in C-band Spatio-temporal Signals using Machine Learning Models
I Harun Surej, S Karthic, G Vigneshwara, T Jeyashri, R Thiruvengadathan, R Gandhiraj, K.A Pradeep Kumar, B.N Binoy, G.A Shanmugha Sundaram, and D.S Harish Ram
IEEE
Signal propagating through free space in wireless communication is subject to additive noise by line-of-sight and non-line-of-sight objects in the propagation medium. This leads to a lot of interference and scattering due to multipath effects. This research work aims to identify such contributors in the propagation channel and characterize them based on their signal scattering property. A data-driven modelling approach is used in place of the traditional math-based modelling. K-means clustering along with other data interpretation methods were used to identify the scatterers. The scatterers are either characterized as absorbing or reflecting type based on the way the signal is affected. Five independent datasets using the C-band frequency were collected under laboratory conditions and used for the study. The ideal dataset from the manufacturer was used as the benchmark. The results identified the scatterers from the experimental dataset and enabled the estimation of their dimensions and material composition in laboratory conditions.


Detection of Interference in C-Band Signals using K-Means Clustering
S Surya Natarajan, R Ateesh Varun, G Shivasubramanian, D Thamayandran, M Dharani, R Gandhiraj, G A Shanmugha Sundaram, A K Pradeep Kumar, N B Binoy, R Thiruvengadathan,et al.
IEEE
Interference is a main disruptive phenomenon which degrades the performance of communication systems and in general the quality of signal acquisition. Real-time communication through a channel is never free from signal disrupting phenomena like interference, distortion and noise. Hence it is essential to study their effects and methods of identifying them. Conventional methods to identify, estimate and mitigate interference are model driven. A data driven approach is far more efficient and adaptable than model driven methods. In this paper, we exemplify the use of a data driven approach to identify signatures of interference based on analysis of the acquired RF data.


Simulation of Dual Polarization Radar for Rainfall Parameter and Drop Size Distribution Estimation
C. Pratibha, K. Manish Reddy, L. Bharathi, M. Manasa, and R. Gandhiraj
Springer International Publishing



Phase-modulated stepped frequency waveform design for low probability of detection radar signals
R. Vignesh, G. A. Shanmugha Sundaram, and R. Gandhiraj
Springer Singapore



Capacity Analysis of Correlated MIMO in GEOSAT Downlink Land Mobile System
S Jaiyant Gopal, J Ramnarayan, S Kirthiga, M Jayakumar, M Nirmala Devi, R Gandhiraj, and Subhash Chandra Bera
IEEE
The spatial correlation in satellite downlink MIMO systems influence the maximum achievable capacity. As higher Rician factor leads to reduced spatial correlation effects, it is essential to analyze the influence of Rician factor over system performance. In this work, Loo channel model is considered. The probability density function of this model exhibits a non-zero mean emphasizing the presence of LOS component in addition to scattering and multipath fading effects and hence leading to high Rician factor. Critical parameters such as channel capacity, dependence of antenna spacing on correlation coefficient and outage capacity for 1% to 10% outage probability are analyzed for LMS MIMO system considering spatial correlation effects.

RECENT SCHOLAR PUBLICATIONS

Empowering Object Detection in Dynamic Environments with AI-Driven MIMO Radar Technology
N Praneeth, TS Narayana, BRS Kiran, MR Baig, R Gandhiraj, ...
2024 IEEE Region 10 Symposium (TENSYMP), 1-6 2024



Enhancing BER Performance in Phase Noise using KNN-based Symbol Detection
S Krishnamurthy, M Panda, R Chinthala
2024 15th International Conference on Computing Communication and Networking 2024



Performance Appraisal Using Leach Protocol in Wireless Energy Harvesting System
K Cheni, M Greeshma, SU Kumar, S Thanusha, R Gandhiraj
International Conference on Computing and Machine Learning, 433-442 2024



Minimal Energy Cluster Head Selection in LEACH for WSNs: A Sea Lion Inspired Algorithm
K Pooja, PDM Bala, R Kumar, S Saaral, R Gandhiraj
2024 International Conference on Wireless Communications Signal Processing 2024



Performance analysis of novel Joint GPS Anti-jamming and Anti-spoofing detection
B Balaji, R Gandhiraj, GA Shanmugha Sundaram
2023 IEEE 20th India Council International Conference (INDICON), 723-728 2023



Simulation based Closed Loop Scenario Fuzzing for Safety Critical ADAS Applications
AS Agarkar, R Gandhiraj, MK Panda, S Srivastava
2023 IEEE 20th India Council International Conference (INDICON), 759-764 2023



Comparative Analysis of Skin Lesions Classification Using Machine Learning Classifiers and Lesnet-22 Architecture
N Balathaarani, R Gandhiraj
2023 IEEE 20th India Council International Conference (INDICON), 355-361 2023



Spatial Diversity Deconstruct of C-band Scatter Components in Multistatic RaDaR Datasets using Machine Learning Techniques
SS GA, H Surej, S Karthic, R Gandhiraj, BN Binoy, PK KA, ...
Authorea Preprints 2023



A Monte Carlo simulation study of L-band emission upon gamma radiolysis of water
KAP Kumar, GAS Sundaram, S Venkatesh, R Gandhiraj, ...
Radiation Physics and Chemistry 207, 110883 2023



Driver drowsiness detection and warning using facial features and hand gestures
AS Agarkar, R Gandhiraj, MK Panda
2023 2nd International Conference on Vision Towards Emerging Trends in 2023



Enhancing GPS Position Estimation Using Multi-Sensor Fusion and Error-State Extended Kalman Filter
A Aravind, V Ashwin, S Chandeep, P Yasaswi, R Gandhiraj, ...
2022 International Conference on Distributed Computing, VLSI, Electrical 2022



Microwave tomography data deconstruct of spatially diverse c-band scatter components using clustering algorithms
GAS Sundaram, R Gandhiraj, BN Binoy, SI Harun, SN Surya
IEEE Access 10, 98013-98033 2022



Improving Pneumonia Detection Using Segmentation and Image Enhancement
E Thipakaran, R Gandhiraj, MK Panda
Congress on Intelligent Systems, 801-819 2022



Arrhythmia detection—An Enhanced Method Using Gramian Angular Matrix for Deep Learning
K Krishnan, R Gandhiraj, MK Panda
Congress on Intelligent Systems, 785-798 2022



BEP Analysis of Filter Bank Multicarrier Under IQ Imbalance
R Suraj, M Venkatesh, C Charumathi, A Kapavarapu, K Pradeep Raj, ...
Evolution in Signal Processing and Telecommunication Networks: Proceedings 2022



Software Defined Radio-Based GPS Spoofing Attack Model on Road Navigation System
J Jetto, R Gandhiraj, GA Shanmugha Sundaram, KP Soman
Soft Computing and Signal Processing: Proceedings of 3rd ICSCSP 2020, Volume 2022



Big data based system for biomedical image classification
R Priyanka, S Shrinithi, R Gandhiraj
2021 Fourth international conference on electrical, computer and 2021



Spoofing Attack Model on Road Navigation System
J Jetto, R Gandhiraj, GAS Sundaram, KP Soman
Soft Computing and Signal Processing: Proceedings of 3rd ICSCSP 2020, Volume 2021



V2X based emergency corridor for safe and fast passage of emergency vehicle
A Ashish, R Gandhiraj, M Panda
2021 12th International Conference on Computing Communication and Networking 2021



Steering angle prediction for autonomous driving using federated learning: The impact of vehicle-to-everything communication
MP Aparna, R Gandhiraj
2021 12th International Conference on Computing Communication and Networking 2021

MOST CITED SCHOLAR PUBLICATIONS

Low cost digital transceiver design for Software Defined Radio using RTL-SDR
SKP Sruthi M B,Abirami M, Akhil Manikkoth, Gandhiraj R
IEEE International Multi Conference on Automation, Computing, Control 2013
Citations: 82



Modern analog and digital communication systems development using GNU Radio with USRP
R Gandhiraj, KP Soman
Telecommunication Systems 56, 367-381 2014
Citations: 53



Real time implementation of FMCW radar for target detection using GNU radio and USRP
S Sundaresan, C Anjana, R Gandhiraj
2015 International Conference on Communications and Signal Processing (ICCSP 2015
Citations: 48



Analog and digital modulation toolkit for software defined radio
R Gandhiraj, R Ram, KP Soman
Procedia Engineering 30, 1155-1162 2012
Citations: 46



Exploiting GNU radio and USRP: An economical test bed for real time communication systems
M Abirami, V Hariharan, MB Sruthi, R Gandhiraj, KP Soman
2013 fourth international conference on computing, communications and 2013
Citations: 43



Auditory-based wavelet packet filterbank for speech recognition using neural network
R Gandhiraj, PS Sathidevi
15th International Conference on Advanced Computing and Communications 2007
Citations: 37



Real-time communication system design using RTL-SDR and Raspberry Pi
R Danymol, T Ajitha, R Gandhiraj
2013 International Conference on Advanced Computing and Communication 2013
Citations: 29



Steering angle prediction for autonomous driving using federated learning: The impact of vehicle-to-everything communication
MP Aparna, R Gandhiraj
2021 12th International Conference on Computing Communication and Networking 2021
Citations: 27



An experimental study on channel estimation and synchronization to reduce error rate in OFDM using GNU radio
C Anjana, S Sundaresan, T Zacharia, R Gandhiraj, KP Soman
Procedia Computer Science 46, 1056-1063 2015
Citations: 24



Plug-ins for gnu radio companion
S Sriram, G Srivasta, R Gandhiraj, KP Soman
International Journal of Computer Applications 52 (16) 2012
Citations: 21



Real-time video streaming using GStreamer in GNU Radio platform
S Nimmi, V Saranya, R Gandhiraj
2014 International Conference on Green Computing Communication and 2014
Citations: 18



Performance analysis of real time OFDM based communication system using GNU radio and USRP
M Abirami, R Gandhiraj, KP Soman
International Journal of Advanced Research in Computer Science and Software 2013
Citations: 16



OpenBTS based microtelecom model: A socio-economic boon to rural communities
N Prasannan, G Xavier, A Manikkoth, R Gandhiraj, R Peter, KP Soman
2013 International Mutli-Conference on Automation, Computing, Communication 2013
Citations: 16



Spectrum Sensing using Compressed Sensing Techniques for Sparse Multiband Signals
SKP Avinash.P, Gandhiraj.R
International Journal of Scientific & Engineering Research 3 (5), 5 2012
Citations: 16



Huffman coding and decoding using Android
J Radhakrishnan, S Sarayu, KG Kurian, D Alluri, R Gandhiraj
2016 International Conference on Communication and Signal Processing (ICCSP 2016
Citations: 13



Applicability of MIMO and OFDM technology to SATCOM
M Suganya, R Gandhiraj
2016 International Conference on Communication and Signal Processing (ICCSP 2016
Citations: 13



Radiation pattern measurement of log-periodic antenna on GNU Radio platform
T Ajitha, E Joy, A AnishJoyce, R Gandhiraj
2014 International Conference on Green Computing Communication and 2014
Citations: 12



Application and analysis of smart meter data along with RTL SDR and GNU radio
M Aswathi, R Gandhiraj, KP Soman
Procedia Technology 21, 317-325 2015
Citations: 11



Driver drowsiness detection and warning using facial features and hand gestures
AS Agarkar, R Gandhiraj, MK Panda
2023 2nd International Conference on Vision Towards Emerging Trends in 2023
Citations: 10



Adaptive noise cancellation using NLMS algorithm in GNU radio
J Adarsh, P Vishak, R Gandhiraj
2017 4th International Conference on Advanced Computing and Communication 2017
Citations: 10