Pardhasaradhi Bethi

• Arigela, Sanjana; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2026). mmWave Radar-Based Wall Classification Using Progressive Feature Selection. IEEE Sensors Journal. ISSN 1530-437X. 26(18), p. 28013–28021. doi: 10.1109/JSEN.2026.3716595. Full text in Research Archive Show summary

  This article proposes a progressive feature selection framework for millimeter-wave (mmWave) radar-based wall material classification. Raw complex baseband I–Q data are collected using a TI AWR1843 radar over multiple days from eight indoor surface types: transparent glass wall, smooth glass board, cement wall, metal door, vinyl floor, olefin carpet floor, ceramic tile, and concrete wall. We introduce an incremental-data decremental-feature learning (IDDFL) strategy that incrementally aggregates daywise training data while progressively pruning low-contribution features using a posthoc explainer. A preprocessing module extracts 100 handcrafted descriptors covering time-domain, frequency-domain, time–frequency, and radar-specific characteristics from the acquired I–Q measurements. Feature relevance is quantified via SHapley additive exPlanations (SHAP) analysis on a LightGBM (LGBM) classifier, and the least discriminative features are removed across days. The proposed IDDFL process reduces the feature set from 100 to 30 while maintaining high classification performance. The proposed model achieves a final test accuracy of 79.80% with an F1 -score of 0.79 , demonstrating improved computational efficiency with minimal performance degradation. The final 30-feature LGBM model outperforms nine state-of-the-art benchmark methods reported in the literature and is deployed on edge platforms (Raspberry Pi 4 and Rock Pi 4 C+) to demonstrate real-time applicability. The proposed feature selection and edge-ready pipeline is well-suited for building inspection, autonomous navigation, and security applications.

• Reddy, Madhava Krishna Kadiyala Muni Venkata; Syversen, Andreas Ekeberg; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2026). A Multinode Thermal Sensor Network With Edge AI Inference for Indoor Moisture and Humidity Classification. IEEE Sensors Journal. ISSN 1530-437X. 26(18), p. 27081–27089. doi: 10.1109/JSEN.2026.3719511. Full text in Research Archive
• Thangaraj, Priya; Bethi, Pardhasaradhi; Granmo, Ole-Christoffer & Cenkeramaddi, Linga Reddy (2026). Clustering-based Significance-Aware Automated Data Binarization Framework for Tsetlin Machines. IEEE Transactions on Artificial Intelligence (TAI). doi: 10.1109/TAI.2026.3737096. Full text in Research Archive Show summary

  The Tsetlin machine (TM) is an interpretable machine learning (ML) algorithm that requires relatively modest computational resources. While most ML models use continuous data representations, TM operates on binary inputs. Binarization of continuous data via brute-force thresholding has shown promising accuracy but can be computationally expensive and hinder generalization. Moreover, traditional binarization approaches, such as mean, median, and fixed thresholds, yield single-bit representations. Multi-bit representations, such as quantile, histogram, and standard binarization, are more expressive, but typically require manual definition of the number of threshold levels or the number of bits per feature. However, not all features require the same number of bits, nor do they contribute equally to prediction. To address this, we propose a simple and adaptive binarization framework that automatically determines how each feature should be represented based on its data characteristics. Instead of relying on fixed thresholds. This method identifies meaningful groups within each feature and encodes them accordingly, resulting in a more informative and balanced representation. In this paper, we propose a significance-aware adaptive binarization framework for TM learning based on k-means clustering. Each feature is individually analyzed using the Silhouette score to determine its optimal clustering structure. Features are then one-hot encoded according to their cluster partitions, resulting in a significance-aware binarized representation. All the binarized features are concatenated to form the final input for the TM. To ensure scalability, the framework includes an efficient mechanism to handle large datasets while maintaining consistency in the encoding process. To reduce the complexity of clustering, we propose a chunk-based centroid-to-centroid association mechanism, efficiently solved using the Hungarian algorithm. The proposed method is evaluated on multiple datasets and benchmarked against traditional single-bit and multi-bit binarization techniques. Moreover, we extend this analogy to continual feature binarization.

• Lade, Modha Sreya; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2026). Thermal Imaging-based Perception for Automotive Edge AI Applications using a Lightweight Custom YOLO Model. IEEE Sensors Journal. ISSN 1530-437X. doi: 10.1109/JSEN.2026.3691670. Full text in Research Archive
• Manda, Advik; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2026). TMCR Edge-YOLO: Thermal Sensing-based Mobility-Context Recognition on the Edge. IEEE Sensors Journal. ISSN 1530-437X. doi: 10.1109/JSEN.2026.3686674. Full text in Research Archive
• Shivakumar, Thrishank Hesaraghatta; Bethi, Pardhasaradhi; Boppu, Srinivas; Granmo, Ole-Christoffer & Cenkeramaddi, Linga Reddy (2026). Multi-Sensor Fusion using Composite Tsetlin Machine with Adaptive Feature Bank Selection. IEEE Sensors Journal. ISSN 1530-437X. 26(11), p. 17210–17223. doi: 10.1109/JSEN.2026.3680022. Full text in Research Archive Show summary

  A wide range of applications relies on multisensor setups to enhance their perception and decision-making capabilities. These sensors generate raw time-series data that can be transformed into the frequency domain using feature banks such as mel frequency cepstral coefficients (MFCC), Chroma-STFT (C-STFT), and spectral descriptors. Most deep learning (DL) and machine learning (ML) models utilize these frequency-domain representations, typically in the form of numeric data or spectrogram images, to enhance their decision-making capabilities. However, interpretability, resource constraints, and inference time remain challenging in DL and ML algorithms. The Tsetlin machine (TM) is an energy-efficient alternative based on propositional logic. TM applications to multisensor classification and fusion remain largely unexplored. In this work, a novel multisensor fusion framework using composite-TM (Co-TM) is proposed, where each sensor’s time-series signals are converted into frequency-domain representations using several feature banks. The framework adaptively selects the best frequency-domain representation during the feature bank selection phase and performs sensor-level fusion during training. The feature bank selection is guided by the TM’s learning and delearning behaviors, while sensor-level fusion is achieved through a Co-TM framework. To monitor TM learning dynamics and adaptively select the desired feature bank for each sensor, we propose a set of metrics, including average accuracy, top-m accuracy, stability coefficient, and the slope of the accuracy curve. The proposed framework is validated using an industrial machine fault detection dataset (multisensor heterogeneous data) and a rotating machinery fault dataset (multisensor homogeneous data). The experimental results demonstrate that the proposed method achieves state-ofthe- art accuracy compared with ML, convolutional neural network (CNN) models, and TM models.

• S, Vandana G; Pathipati, Srihari; Kumar, Uday; Nandagiri, Ashwin; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2025). Real-Time UAV Altitude Estimation and Data Transmission via mmWave Radar and Edge Computing, 2024 IEEE 21st India Council International Conference (INDICON). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350391282. doi: 10.1109/INDICON63790.2024.10958360. Full text in Research Archive Show summary

  This paper presents a novel approach for UAV altitude estimation and data transmission using a 60 GHz IWR6843 mmWave radar mounted on a micro-drone, coupled with a Raspberry Pi edge device. The radar, configured in a long-range mode, leverages its high accuracy in altitude measurement, surpassing the performance of traditional UAV altimeters. The radar altimeter data is processed on the Raspberry Pi and wirelessly transmitted to the cloud, from which it can be accessed by a ground station for real-time monitoring and analysis. To validate the accuracy of the radar-based altitude measurements, GPS data is simultaneously recorded on the UAV, serving as a ground truth reference. Experimental results demonstrate that the radar-based measurements closely match the GPS-derived altitudes, showcasing the effectiveness of the proposed system. This approach not only improves altitude estimation accuracy but also enhances the reliability of UAV operations in various environments. Potential applications of this system include precision agriculture, disaster management, and search and rescue operations, where accurate altitude data is critical for mission success. The integration of mmWave radar with edge computing and cloud-based data management opens new avenues for real-time UAV monitoring and autonomous navigation.

• Bethi, Pardhasaradhi; Pathipati, Srihari & Cenkeramaddi, Linga Reddy (2025). Improved GNSS Anti Spoofing by Integrating Attribute Information into Measurement to Measurement Association Framework, 2024 IEEE 21st India Council International Conference (INDICON). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350391282. p. 1–6. doi: 10.1109/INDICON63790.2024.10958488. Full text in Research Archive Show summary

  Global navigation satellite system (GNSS) is famous for providing position, navigation, and time (PNT) information at low cost anywhere globally. However, this GNSS is highly misled in the name of spoofing because of its readily available blueprints. This paper proposes to integrate the attribute information within the measurement to measurement association (M2MA) mathematical framework to improve the capabilities of GNSS anti-spoofing. The satellite location, received power, clock offset, and correlation are the attributes associated with the acquired pseudo ranges. This formulation assumes that authentic and spoofed GNSS signals are locked into the multi-correlator GNSS receiver and capable of estimating each signal's attributes. This process of using the attribute information facilitates screening authentic pseudoranges from spoofed pseudoranges. The results are evaluated at an epoch in a Monte Carlo sense with hit rate, dilution of precision (DOP), and mean square error (MSE) as metrics. The results reveal that the attribute information in M2MA drastically improves the anti-spoofing capabilities.

• Pathipati, Srihari; S, Vandana G; Kumar, Uday; Nandagiri, Ashwin; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2025). FMCW Radar-Based Detection and Tracking of Drones Using DBSCAN Clustering and Extended Kalman Filter for Anti-Drone Defense Systems, 2024 IEEE 21st India Council International Conference (INDICON). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350391282. doi: 10.1109/INDICON63790.2024.10958522. Full text in Research Archive Show summary

  This paper aims to develop a radar-based detection and tracking system to mitigate the threats posed by drones, particularly those carrying malicious payloads. Due to the limitations of cameras in adverse weather and the high costs of LiDAR systems, radar technology is employed as a cost-effective alternative. The system utilizes 3D FFT followed by CA-CFAR for drone range-azimuth detections. The range-azimuth detections are clustered using DBSCAN. We simplified the extended target tracking problem into point target tracking based on the drone's size, with the dBSCAN cluster center acting as the measurement for the tracker. The tracking algorithm combines an Extended Kalman Filter (EKF) with Global Nearest Neighbor (GNN) data association. Experiments were conducted using a 77 GHz AWR1642 radar sensor to track a micro drone of hexacopter type within a range of 10m to 100m. The results demonstrated effective tracking capabilities with radar sensors successfully generating tracks. This study highlights the viability of radar-based systems for anti-drone applications, offering a practical solution for enhancing infrastructure security against potential drone threats.

• Shetty, Ashwitha; Goud, Madhusudhan; Kandasamy, Krishnamoorthy; Pathipati, Srihari; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2025). A High-Gain Half-Bow-Tie Antenna with Tapered Slots for Foliage Penetration Radar Application, 2024 IEEE 21st India Council International Conference (INDICON). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350391282. p. 1–6. doi: 10.1109/INDICON63790.2024.10958326. Full text in Research Archive Show summary

  In this study, a compact, low-profile, high-gain half-bow-tie antenna with tapered slots is designed specifically for foliage penetration radar (FOPEN) applications. The proposed antenna design begins by vertically bisecting a conventional bow-tie antenna, followed by the addition of two vertical metal strips to enhance the operational bandwidth. To achieve effective impedance matching across the desired frequency range, two horizontal conductive strips and a central balun circuit are integrated into the design. To further optimize the antenna for integration into aerial platforms, the gain and radiation pattern are refined by incorporating two additional metal strips strategically placed on the right side of the bow-tie structure. These design modifications result in a compact antenna with dimensions of 0.43λx 0.4λ x 0.005λ where λ is the wavelength corresponding to the lower frequency of operation. The optimized antenna achieves a realized gain of 6.5 dBi and an impedance bandwidth of 110 MHz, making it highly suitable for FOPEN applications. The enhanced gain, achieved with a single dielectric layer, demonstrates the potential of this design for efficient foliage penetration radar systems.

• Raghu, J.; Purushottama, T.L.; Bethi, Pardhasaradhi; Srihari, P.; Tharmarasa, R. & Kirubarajan, T. (2025). Comprehensive Track Unswappinng for Improved Tracker Performance. IEEE Transactions on Aerospace and Electronic Systems. ISSN 0018-9251. 61(5), p. 11021–11037. doi: 10.1109/TAES.2025.3556810. Full text in Research Archive Show summary

  In practical surveillance systems, multiple-target tracking can suffer from undesirable effects such as track breakages and track swaps. Track stitching or track segment association (TSA) algorithms have been proposed in the literature to stitch broken tracks deemed to have originated from the same target across time and to improve track continuity. Measurements from multiple neighboring targets may fall within the validation gates of one another, causing association errors that may eventually lead to not just track breaks but also track swaps. Therefore, TSA alone is insufficient to improve the overall tracker performance, as it considers only the broken tracks but not the continuous ones that might have swaps among themselves or with other broken tracks. To mitigate the effects of track swaps, this article proposes an algorithm that detects and resolves possible track swaps using kinematic and nonkinematic—classification and amplitude—information. Track swap detection involves identifying the most likely instant of track swap occurrence. Further, the proposed algorithm is extended to stitch broken track segments (as in the standard TSA) and those tracks that are algorithmically broken due to the detection of possible swaps. Simulation results demonstrate the effectiveness of the proposed algorithm in resolving track swaps and thereby improving track purity and the overall tracker performance.

• Kumar, Ankit; Vellalacheruvu, Venkata Siva Sai Anil Kumar; Bethi, Pardhasaradhi; Koorapati, Sagar & Cenkeramaddi, Linga Reddy (2025). Marine Mammals Sound Classification Using a Lightweight CNN for Edge Computing Devices, 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331531935. p. 1182–1187. doi: 10.1109/CSNT64827.2025.10968906. Full text in Research Archive
• Amarnath, Siddharth; Bethi, Pardhasaradhi; Vellalacheruvu, Venkata Siva Sai Anil Kumar; Koorapati, Sagar & Cenkeramaddi, Linga Reddy (2025). Indoor Sound Classification Using Lightweight CNN With Real-Time IoT Integration, 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331531935. p. 1149–1153. doi: 10.1109/CSNT64827.2025.10968499. Full text in Research Archive Show summary

  This paper presents a lightweight Convolutional Neural Network (CNN) for indoor sound classification, specifically designed to identify ten distinct classes: knock on the door, mouse click, keyboard typing, wooden door squeaks, opening the can, washing machine, vacuum cleaner, alarm clock, clock ticking, and broken glass from [1]. The sound signals were captured using a microphone and transformed from the time domain to time-frequency images through Continuous Wavelet Transform (CWT). The proposed CNN, with a compact model size of 2.1 MB, was benchmarked against several well-known pre-trained models, including DenseNet, EfficientNet, InceptionNet, MobileNet, NASNet, ResNet, VGGNet, and Xception. Experimental results demonstrate that our lightweight CNN significantly outperformed these models, achieving a validation accuracy of 80% in a ten-fold cross-validation framework. The best performance among the pre-trained models was observed with ResNet101V2, which achieved only 62% validation accuracy in a ten-fold cross-validation framework with a considerably larger model size of 163 MB. Furthermore, the proposed model was successfully deployed in real-time on various IoT and processor modules, including the Raspberry Pi, NVIDIA A100, Tesla V100, and Intel Xeon Platinum, where it demonstrated superior efficiency, running at just 35 milliseconds per inference on the Raspberry Pi edge device. The lightweight CNN’s high accuracy and low latency make it an ideal candidate for applications in smart home systems, security monitoring, and real-time audio event detection on resource-constrained devices.

• Puduru, Vamsi Krishna; Bethi, Pardhasaradhi; Koorapati, Sagar & Cenkeramaddi, Linga Reddy (2025). Lightweight Deep Learning Model for Airborne Object Classification, 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331531935. p. 230–234. doi: 10.1109/CSNT64827.2025.10968603. Full text in Research Archive Show summary

  Airborne object detection and classification play an important role in applications like military surveillance, border security, and public safety. The ability to accurately distinguish between airplanes, helicopters, drones, and birds is essential for monitoring and responding to potential aerial threats in real-time. This study leverages the AOD 4 dataset, which contains a comprehensive collection of 22,516 images spanning diverse environmental conditions. After organizing the dataset into four distinct classes, a custom lightweight Convolutional Neural Network (CNN) was developed to classify airborne objects. The proposed lightweight CNN achieves a remarkable accuracy of 97.12% and is able to outperform state-of-the-art pre-trained models in terms of accuracy and model size. On top of it, the model is also targeted at the various processors to unleash its potential to run in a real-time environment. The proposed model infers in 2.9 milliseconds on intel core i7 processor, which is a fourfold improvement compared to the MobileNetv3Small model. The findings from this study contribute to advancing the field of airborne object classification and pave the way for more robust real-world implementations.

• Viswanathuni, Venkata Raghava Shashank; Puranapanda, Sarada; Bethi, Pardhasaradhi; Koorapati, Sagar & Cenkeramaddi, Linga Reddy (2025). Lightweight CNN for Everyday Human Sound Classification on Edge Devices, 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331531935. p. 1171–1175. doi: 10.1109/CSNT64827.2025.10967858. Full text in Research Archive Show summary

  This study presents a lightweight Convolutional Neural Network (CNN) that recognizes everyday sounds on IoT edge devices. Audio signals encompassing daily activities are captured via a microphone and transformed into time-frequency images using Continuous Wavelet Transform (CWT). The proposed CNN is optimized for low computational requirements and performs better than established pre-trained models such as DenseNet, EfficientNet, InceptionNet, MobileNet, NASNet, ResNet, VGGNet, and Xception. The model achieves an overall validation accuracy of 84 % in a ten-fold cross-validation framework, with a compact model size of only 854KB and an inference time of 7.3 milliseconds (ms) on a Raspberry Pi. In comparison, EfficientNetV2B1, the second-best performer, achieves comparable accuracy but with a significantly larger size of 27.1 MB and an inference time of 119.99 ms. The model’s inference time was evaluated on various devices, including Raspberry Pi, NVIDIA A100, Tesla V100, and Intel Xeon Platinum, demonstrating its efficiency across different platforms. The dataset includes everyday human sounds, such as crying, applause, breathing, coughing, footsteps, laughter, brushing teeth, snoring, and drinking water. The efficiency and accuracy of the proposed CNN make it ideal for real-time sound recognition in smart home automation, health monitoring, and ambient assisted living, where low latency and resource efficiency are critical.

• Sreedev, Sreekumar; SK, Shashank; Pathipati, Srihari; Nandagiri, Ashwin; S, Vandana G & Bethi, Pardhasaradhi [Show all 7 contributors for this article] (2025). FMCW Radar-based UAV Detection and Tracking using Transfer Learning, 2024 IEEE 21st India Council International Conference (INDICON). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350391282. doi: 10.1109/INDICON63790.2024.10958556. Full text in Research Archive Show summary

  This research investigation offers a novel method for monitoring and detecting unmanned aerial vehicles (UAVs) by combining transfer learning neural networks with Frequency Modulated Continuous Wave (FMCW) radar. The system utilizes a 60 GHz Texas Instruments IWR6843ISK radar with a DCA1000 board to capture raw radar signals, which are subsequently processed to generate range-angle heat maps. Ground truth data for UAV positioning is meticulously obtained using a dual GPS setup, where one GPS is stationed at the radar and the other is mounted on the UAV. The processed range-angle heat maps serve as the input for various transfer learning models, including DenseNet, InceptionV3, MobileNet, ResNet, and VGG, which are employed to compute the range data and angle data of the UAV. The results emphasize the potential of transfer learning in improving radar signal processing by demonstrating the effectiveness of these models in attaining accurate UAV detection and tracking. This approach is pivotal for applications requiring precise UAV monitoring, offering a robust solution for scenarios where traditional radar systems may fall short. The study underscores the advantages of leveraging transfer learning for improved radar-based UAV detection and sets the stage for future advancements in autonomous aerial monitoring and surveillance systems.

• SK, Shashank; Sreedev, Sreekumar; Murugan, Manjith; Pathipati, Srihari; S, Vandana G & Bethi, Pardhasaradhi [Show all 7 contributors for this article] (2025). Enhanced Intruder Detection Using mmWave Radar: A Spatiotemporal Clustering Approach for Robust Human Detection, 2024 IEEE 21st India Council International Conference (INDICON). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350391282. doi: 10.1109/INDICON63790.2024.10958230. Full text in Research Archive
• Mahipathi, Ashoka Chakravarthi; Bethi, Pardhasaradhi; Lingadevaru, Purushottama; Pathipati, Srihari; D’Souza, John & Cenkeramaddi, Linga Reddy (2024). A Survey on Waveform Design for Radar-Communication Convergence. IEEE Access. 12, p. 75442–75461. doi: 10.1109/ACCESS.2024.3404628. Full text in Research Archive
• Soma, Anil Kumar; G, Achala; S, Vandana G; Pathipati, Srihari; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Real-Time Radar Imaging With Time Domain Correlation and Doppler Beam Sharpening, 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350385922. doi: 10.1109/CONECCT62155.2024.10677069. Full text in Research Archive
• Pudi, Mohan; N, Dayananda B; G, Achala; Pathipati, Srihari; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). High-Speed Strassen Matrix Multiplication Accelerators for 2D Kalman Filter, 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350385922. doi: 10.1109/CONECCT62155.2024.10677091. Full text in Research Archive
• Sreedev, Sreekumar; Shashank, S K; Pathipati, Srihari; Vandana, G S; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Adult and Child Classification Using Automotive Radar for In-Cabin Monitoring, 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350385922. doi: 10.1109/CONECCT62155.2024.10677125. Full text in Research Archive
• P, Vishnu Prasad; S, Vandana G; Nandagiri, Ashwin; Pathipati, Srihari; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Automotive Radar Signal Authentication via Correlation and Power Spectral Density. In IEEE, . (Eds.), 2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350305463. p. 29–34. doi: 10.1109/CSNT60213.2024.10546176. Full text in Research Archive
• Yakkati, Rakesh Reddy; Boddu, Anudeep Bhaskar; Bethi, Pardhasaradhi; Yeduri, Sreenivasa Reddy & Cenkeramaddi, Linga Reddy (2024). Low Probability of Intercept Radar Signal Classification using Multi-Channel 1D-CNN, 2024 10th International Conference on Communication and Signal Processing (ICCSP). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350353068. p. 536–541. doi: 10.1109/ICCSP60870.2024.10543688. Full text in Research Archive
• Venkatesha, K; Singh, Upasna; Vengadarajan, A; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Multivariate Fuzzy Density-Based TemporoSpatial Clustering of Applications with Noise (MF-DBTSCAN) Algorithm for Detection and Tracking of Multiple Targets by 77-GHz Millimeter Wave Radar Sensor. IEEE Sensors Journal. ISSN 1530-437X. doi: 10.1109/JSEN.2024.3514137. Full text in Research Archive
• Yakkati, Rakesh Reddy; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Lightweight CNN Classifier for Multipath and Non-Multipath Human Activity Scenes of Radar Spectrograms, 2024 20th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350387445. p. 92–97. doi: 10.1109/WiMob61911.2024.10770439. Full text in Research Archive
• Mahipathi, Ashoka Chakravarthi; Bethi, Pardhasaradhi; Pathipati, Srihari; D’Souza, John; Jena, Paramananda & Zhou, Jing [Show all 7 contributors for this article] (2024). Communication-aided Target State Estimation in a Cooperative Radar-Communication System. IEEE Transactions on Radar Systems. 2, p. 832–848. doi: 10.1109/TRS.2024.3452869. Full text in Research Archive
• A, AnoopKumar K; N, Dayananda B; G, Achala; Pathipati, Srihari; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Design of Barker-7 Radar Pulse Compressor Using DSP Architecture Minimization Techniques. In IEEE, . (Eds.), 2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350305463. p. 83–87. doi: 10.1109/CSNT60213.2024.10546036. Full text in Research Archive
• Jena, Paramananda; Singh, Ashutosh; S, Vandana G; Nandagiri, Ashwin; Pathipati, Srihari & Bethi, Pardhasaradhi [Show all 7 contributors for this article] (2024). MIMO-SAR Image Reconstruction Experiment Using Back-Projection Algorithm With Automotive Radar for ADAS Applications. In IEEE, . (Eds.), 2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350305463. p. 139–144. doi: 10.1109/CSNT60213.2024.10545673. Full text in Research Archive
• N, Dayananda B; Srivastava, Neetesh; G, Achala; Nandagiri, Ashwin; Pathipati, Srihari & Bethi, Pardhasaradhi [Show all 7 contributors for this article] (2024). Depth Information Fusion Using Radar-LiDAR-Camera Experimental Setup for ADAS Applications. In IEEE, . (Eds.), 2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350305463. p. 59–65. doi: 10.1109/CSNT60213.2024.10546006. Full text in Research Archive
• Alli, Sai Prakash; S, Vandana G; Nandagiri, Ashwin; Pathipati, Srihari; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Cyber Attacking Active FMCW Radar Signal AoA Estimation Using Passive FMCW Radar for ADAS Applications. In IEEE, . (Eds.), 2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350305463. p. 248–253. doi: 10.1109/CSNT60213.2024.10545999. Full text in Research Archive
• Puduru, Vamsi Krishna; Yakkati, Rakesh Reddy; Bethi, Pardhasaradhi; Babu, Korra Sathya & Cenkeramaddi, Linga Reddy (2024). Real-Time Detection of Spot Jamming Attacks in mmWave Radar Systems Using a Lightweight CNN. IEEE Sensors Letters. 8(12). doi: 10.1109/LSENS.2024.3480815. Full text in Research Archive

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• Kumar, Ankit; Vellalacheruvu, Venkata Siva Sai Anil Kumar; Bethi, Pardhasaradhi; Koorapati, Sagar & Cenkeramaddi, Linga Reddy (2025). Marine Mammals Sound Classification Using a Lightweight CNN for Edge Computing Devices. Full text in Research Archive
• Ellore, Ram Chandra Kamavaram; Yakkati, Rakesh Reddy; Bethi, Pardhasaradhi; Zhou, Jing & Cenkeramaddi, Linga Reddy (2025). A Lightweight CNN for Real-Time Domestic Animal Sound Classification on IoT Devices Using Time-Frequency Imaging. Full text in Research Archive
• Kattepogu, Navya; Vellalacheruvu, Venkata Siva Sai Anil Kumar; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2025). Agricultural Gas Classification Using Gas Sensor Arrays and PCA-Based Dimensionality Reduction. Full text in Research Archive
• Vellalacheruvu, Venkata Siva Sai Anil Kumar; Yarlagadda, Vishnu Srinivasa Murthy; V, Manoj Kumar; Vemulapalli, Sravani; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2025). An Ensemble Learning Approach to Covariance Intersection Fusion. Full text in Research Archive
• Sirisilla, Sacharitha; Bethi, Pardhasaradhi; Zhou, Jing; Jha, Ajit & Cenkeramaddi, Linga Reddy (2025). Lightweight 2D CNN Fusion for Human Activity Recognition Using Multi-Transceiver CSI Data on Edge Devices. Full text in Research Archive
• Amarnath, Siddharth; Bethi, Pardhasaradhi; Vellalacheruvu, Venkata Siva Sai Anil Kumar; Koorapati, Sagar & Cenkeramaddi, Linga Reddy (2025). Indoor Sound Classification Using Lightweight CNN With Real-Time IoT Integration. Full text in Research Archive
• Viswanathuni, Venkata Raghava Shashank; Puranapanda, Sarada; Bethi, Pardhasaradhi; Koorapati, Sagar & Cenkeramaddi, Linga Reddy (2025). Lightweight CNN for Everyday Human Sound Classification on Edge Devices. Full text in Research Archive
• Puduru, Vamsi Krishna; Bethi, Pardhasaradhi; Koorapati, Sagar & Cenkeramaddi, Linga Reddy (2025). Lightweight Deep Learning Model for Airborne Object Classification. Full text in Research Archive
• Pudi, Mohan; N, Dayananda B; G, Achala; Pathipati, Srihari; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). High-Speed Strassen Matrix Multiplication Accelerators for 2D Kalman Filter. Full text in Research Archive
• Sreedev, Sreekumar; Shashank, S K; Vandana, G S; Pathipati, Srihari; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Adult and Child Classification Using Automotive Radar for In-Cabin Monitoring. Full text in Research Archive
• Soma, Anil Kumar; G, Achala; S, Vandana G; Pathipati, Srihari; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Real-Time Radar Imaging With Time Domain Correlation and Doppler Beam Sharpening. Full text in Research Archive
• Yakkati, Rakesh Reddy; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Lightweight CNN Classifier for Multipath and Non-Multipath Human Activity Scenes of Radar Spectrograms. Full text in Research Archive
• S, Vandana G; Pathipati, Srihari; Kumar, Uday; Nandagiri, Ashwin; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Real-Time UAV Altitude Estimation and Data Transmission via mmWave Radar and Edge Computing. Full text in Research Archive
• Shetty, Ashwitha; Goud, Madhusudhan; Kandasamy, Krishnamoorthy; Pathipati, Srihari; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). A High-Gain Half-Bow-Tie Antenna with Tapered Slots for Foliage Penetration Radar Application. Full text in Research Archive
• Sreedev, Sreekumar; SK, Shashank; Pathipati, Srihari; Nandagiri, Ashwin; S, Vandana G & Bethi, Pardhasaradhi [Show all 7 contributors for this article] (2024). FMCW Radar-based UAV Detection and Tracking using Transfer Learning. Full text in Research Archive
• Bethi, Pardhasaradhi; Pathipati, Srihari & Cenkeramaddi, Linga Reddy (2024). Improved GNSS Anti Spoofing by Integrating Attribute Information into Measurement to Measurement Association Framework. Full text in Research Archive
• SK, Shashank; Sreedev, Sreekumar; Murugan, Manjith; Pathipati, Srihari; S, Vandana G & Bethi, Pardhasaradhi [Show all 7 contributors for this article] (2024). Enhanced Intruder Detection Using mmWave Radar: A Spatiotemporal Clustering Approach for Robust Human Detection. Full text in Research Archive
• Pathipati, Srihari; S, Vandana G; Kumar, Uday; Nandagiri, Ashwin; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). FMCW Radar-Based Detection and Tracking of Drones Using DBSCAN Clustering and Extended Kalman Filter for Anti-Drone Defense Systems. Full text in Research Archive
• Salvi, Bhumika; Cenkeramaddi, Linga Reddy & Bethi, Pardhasaradhi (2025). Centralized FMCW Radar Point Cloud Clustering and Tracking Framework for Human Intruder Application. Universitetet i Agder. Full text in Research Archive
• K, Koushik; Cenkeramaddi, Linga Reddy & Bethi, Pardhasaradhi (2025). Complementary Sensing-Based Adaptive Radar Target Tracking. Universitetet i Agder. Full text in Research Archive
• Cenkeramaddi, Linga Reddy; Bethi, Pardhasaradhi & Viswanathuni, Venkata Raghava Shashank (2025). Multimodal Drone Classification: Integrating RGB, Thermal, and Radar Sensor Data Using Deep Learning. Universitetet i Agder. Full text in Research Archive
• Puduru, Vamsi Krishna; Cenkeramaddi, Linga Reddy & Bethi, Pardhasaradhi (2024). Detection and Localization of Spot Jammer in mmWave Radar Systems: DL Approach. Universitetet i Agder. Full text in Research Archive
• M., Durga Prasad Varma; Cenkeramaddi, Linga Reddy & Bethi, Pardhasaradhi (2024). Deep Learning-Based Motion Trajectory Prediction of Drones Using Long Short-Term Memory Networks on Radar Data. Universitetet i Agder. Full text in Research Archive

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