Linga Reddy Cenkeramaddi

• Dhuli, Sateeshkrishna; Atik, Fouzul; Chhabra, Anamika; Singh, Prem & Cenkeramaddi, Linga Reddy (2024). Performance Analysis of Gossip Algorithms for Large Scale Wireless Sensor Networks. IEEE Open Journal of the Computer Society (OJ-CS). 5, p. 290–302. doi: 10.1109/OJCS.2024.3397345.
• Chakravarthi Mahipathi, Ashoka; Bethi, Pardhasaradhi; Lingadevaru, Purushottama; Srihari, Pathipati; D'souza, John & Cenkeramaddi, Linga Reddy (2024). A Survey on Waveform Design for Radar-Communication Convergence. IEEE Access. ISSN 2169-3536. 12, p. 75442–75461. doi: 10.1109/ACCESS.2024.3404628.
• Tiwari, Vaibhav; Pandey, Chandrasen; Yadav, Satyendra Singh; Roy, Diptendu Sinha; Pandey, Om Jee & Cenkeramaddi, Linga Reddy (2024). Maximizing Coverage and Energy Conservation in B5G Networks Using Hexagonal Tiling to Deploy FT-S2ES. IEEE Open Journal of the Communications Society. ISSN 2644-125X. 5, p. 2541–2554. doi: 10.1109/OJCOMS.2024.3389591.
• Jain, Prateek; Joshi, Amit Mahesh; Mohanty, Saraju P. & Cenkeramaddi, Linga Reddy (2024). Non-Invasive Glucose Measurement Technologies: Recent Advancements and Future Challenges. IEEE Access. ISSN 2169-3536. 12, p. 61907–61936. doi: 10.1109/ACCESS.2024.3389819.
• Mondikathi, Chiranjeevi; Dhuli, V Sateeshkrishna; Enduri, Murali Krishna & Cenkeramaddi, Linga Reddy (2024). Identifying and Ranking of Best Influential Spreaders with Extended Clustering Coefficient Local Global Centrality Method. IEEE Access. ISSN 2169-3536. 12, p. 52539–52554. doi: 10.1109/ACCESS.2024.3387745.
• Gupta, Khushi Anil; Yeduri, Sreenivasa Reddy & Cenkeramaddi, Linga Reddy (2024). Breathing Rate Classification using Piezoresistive Sensor utilizing Continuous Wavelet Transform and Lightweight CNN. IEEE Access. ISSN 2169-3536. 12, p. 50362–50375. doi: 10.1109/ACCESS.2024.3384983.
• Mohammad, Buran Basha; Dhuli, V Sateeshkrishna; Enduri, Murali Krishna & Cenkeramaddi, Linga Reddy (2024). Convex Isolating Clustering Centrality to Discover the Influential Nodes in Large Scale Networks. IEEE Access. ISSN 2169-3536. 12, p. 48404–48419. doi: 10.1109/ACCESS.2024.3384242.
• A., Soumya; C., Krishna Mohan & Cenkeramaddi, Linga Reddy (2024). High Precision Single Shot Object Detection in Automotive Scenarios, Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - (Volume 2). SciTePress. ISSN 978-989-758-679-8. p. 604–611. doi: 10.5220/0012383100003660.
• Chilamkurthy, Naga Srinivasarao; Hakeem, Shaik Abdul; Tiwari, Satish K.; Ghosh, Anirban; Cenkeramaddi, Linga Reddy & Pandey, Om Jee (2024). Optimal Routing Protocol in LPWAN Using SWC: A Novel Reinforcement Learning Framework. IEEE Sensors Journal. ISSN 1530-437X. 24(9), p. 15607–15619. doi: 10.1109/JSEN.2024.3378463.
• Sonny, Amala; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2024). Dynamic Targets Occupancy Status Detection utilizing mmWave Radar Sensor and Ensemble Machine Learning. IEEE Open Journal of the Industrial Electronics Society (OJ-IES). ISSN 2644-1284. 5, p. 251–263. doi: 10.1109/OJIES.2024.3377012.
• Amendola Netto Andrade, José; Cenkeramaddi, Linga Reddy & Jha, Ajit (2024). Image Translation and Reconstruction Using a Single Dual Mode Lightweight Encoder. IEEE Access. ISSN 2169-3536. 12, p. 26787–26799. doi: 10.1109/ACCESS.2024.3365831.
• Birkeland, Simen; Fjeldvik, Lin Julie; Noori, Nadia Saad; Yeduri, Sreenivasa Reddy & Cenkeramaddi, Linga Reddy (2024). Video based hand gesture recognition dataset using thermal camera. Data in Brief. ISSN 2352-3409. doi: 10.1016/j.dib.2024.110299.
• Joshi, Anushka; Raman, Balasubramanian; C., Krishna Mohan & Cenkeramaddi, Linga Reddy (2024). A new machine learning approach for estimating shear wave velocity profile using borelog data. Soil Dynamics and Earthquake Engineering. ISSN 0267-7261. 177. doi: 10.1016/j.soildyn.2023.108424.
• Pretto, Riccardo; Dayal, Aveen; Oghbasilasie Haile, Yonas; Amendola Netto Andrade, José; Cenkeramaddi, Linga Reddy & Robbersmyr, Kjell Gunnar [Show all 7 contributors for this article] (2023). Motor Bearing Fault Classification using Laser Sensor and Light Weight CNN, 2023 IEEE International Symposium on Smart Electronic Systems (iSES). IEEE conference proceedings. ISSN 979-8-3503-8324-9. p. 27–31. doi: 10.1109/iSES58672.2023.00017.
• Amendola Netto Andrade, José; Cenkeramaddi, Linga Reddy & Jha, Ajit (2023). Drone Landing on Moving UGV Platform With Reinforcement Learning Based Offsets, 2023 IEEE International Symposium on Smart Electronic Systems (iSES). IEEE conference proceedings. ISSN 979-8-3503-8324-9. p. 16–21. doi: 10.1109/iSES58672.2023.00015.
• Sahu, Prajwal Kumar; Boppu, Srinivas; Shafik, Rishad Ahmed; Tunheim, Svein Anders; Granmo, Ole-Christoffer & Cenkeramaddi, Linga Reddy (2023). Enhancing Inference Performance through Include only Literal Incorporation in Tsetlin Machine, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE conference proceedings. ISSN 979-8-3503-4477-6. doi: 10.1109/ISTM58889.2023.10455016.
• Akella, Sriram Mahateja; Mubasier, Kamar; Michallik, Maximilian; Yeduri, Sreenivasa Reddy & Cenkeramaddi, Linga Reddy (2023). Distributed Q-Learning-Based UAV-Assisted Small World Wireless Network for Energy-Efficient and Delay-Critical Data Transmission, 2023 2nd International Conference on 6G Networking (6GNet). IEEE conference proceedings. ISSN 979-8-3503-0673-6. p. 1–7. doi: 10.1109/6GNet58894.2023.10317651.
• P N, Sivaranjini; Manikandan, M. Sabarimalai & Cenkeramaddi, Linga Reddy (2023). Compressed Sensing Based SpO2 and PR Estimation for Improving Resource Utilization Efficiency of Wearables in IoMT Applications, 2023 IEEE 9th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA). IEEE conference proceedings. ISSN 979-8-3503-4338-0. p. 30–35. doi: 10.1109/ICSIMA59853.2023.10373442.
• Varghese V., Jomole; Manikandan, M. Sabarimalai & Cenkeramaddi, Linga Reddy (2023). Lightweight Time-Domain HRV Index Extraction from Compressive ECG Measurements for Resource Constrained Edge Health Computing Devices, 2023 IEEE 9th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA). IEEE conference proceedings. ISSN 979-8-3503-4338-0. p. 12–17. doi: 10.1109/ICSIMA59853.2023.10373467.
• Dondup, Thacchan; Manikandan, M. Sabarimalai & Cenkeramaddi, Linga Reddy (2023). EEG Based Emotion Recognition using Variational Mode Decomposition and Convolutional Neural Network for Affective Computing Interfaces. In Zhou, Jing (Eds.), 2023 11th International Conference on Control, Mechatronics and Automation (ICCMA). IEEE conference proceedings. ISSN 979-8-3503-1568-4. p. 37–42. doi: 10.1109/ICCMA59762.2023.10374647.
• P N, Sivaranjini; Manikandan, M. Sabarimalai & Cenkeramaddi, Linga Reddy (2023). Lightweight Photoplethysmogram (PPG) Waveform Change Detection for Resource-Constrained IoT Enabled Remote Health Monitoring Devices. In Zhou, Jing (Eds.), 2023 11th International Conference on Control, Mechatronics and Automation (ICCMA). IEEE conference proceedings. ISSN 979-8-3503-1568-4. p. 330–335. doi: 10.1109/ICCMA59762.2023.10374839.
• A., Soumya; Cenkeramaddi, Linga Reddy; Chalavadi, Vishnu; Lanjewar, Yash Vinod & C., Krishna Mohan (2023). Multi-Target Classification using Deep Learning Models for Automotive Applications. In Zhou, Jing (Eds.), 2023 11th International Conference on Control, Mechatronics and Automation (ICCMA). IEEE conference proceedings. ISSN 979-8-3503-1568-4. p. 31–36. doi: 10.1109/ICCMA59762.2023.10375062.
• Nelson, Wilson Ayyanthole; Jha, Ajit; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2023). Estimation of UAV Count Using Thermal Imaging and Lightweight CNN, 2023 The 11th International Conference on Control, Mechatronics and Automation (ICCMA 2023). IEEE conference proceedings. ISSN 978-1-6654-9048-1. p. 92–96. doi: 10.1109/ICCMA59762.2023.10374791.
• Amendola Netto Andrade, José; Cenkeramaddi, Linga Reddy & Jha, Ajit (2023). Single Reinforcement Learning Policy for Landing a Drone Under Different UGV Velocities and Trajectories. In Zhou, Jing (Eds.), 2023 11th International Conference on Control, Mechatronics and Automation (ICCMA). IEEE conference proceedings. ISSN 979-8-3503-1568-4. p. 115–120. doi: 10.1109/ICCMA59762.2023.10374818.
• Nelson, Wilson Ayyanthole; Yeduri, Sreenivasa Reddy; Jha, Ajit; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2023). RL-Based Energy-Efficient Data Transmission Over Hybrid BLE/LTE/Wi-Fi/LoRa UAV-Assisted Wireless Network. IEEE/ACM Transactions on Networking. ISSN 1063-6692. doi: 10.1109/TNET.2023.3332296.
• Yakkati, Rakesh Reddy; Yeduri, Sreenivasa Reddy; Tripathy, Rajesh Kumar & Cenkeramaddi, Linga Reddy (2023). Multi-Channel Time-Frequency Domain Deep CNN Approach for Machinery Fault Recognition Using Multi-Sensor Time-Series. IEEE Access. ISSN 2169-3536. 11, p. 116570–116580. doi: 10.1109/ACCESS.2023.3324961. Full text in Research Archive
• Kusum, Lata & Cenkeramaddi, Linga Reddy (2023). FPGA-Based PUF Designs: A Comprehensive Review and Comparative Analysis. Cryptography. ISSN 2410-387X. 7(4). doi: 10.3390/cryptography7040055. Full text in Research Archive
• Mondikathi, Chiranjeevi; Dhuli, Sateeshkrishna; Enduri, Murali Krishna & Cenkeramaddi, Linga Reddy (2023). ICDC: Ranking Influential Nodes in Complex Networks Based on Isolating and Clustering Coefficient Centrality Measures. IEEE Access. ISSN 2169-3536. 11, p. 126195–126208. doi: 10.1109/ACCESS.2023.3328345. Full text in Research Archive
• A., Soumya; C., Krishna Mohan & Cenkeramaddi, Linga Reddy (2023). Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review. Sensors. ISSN 1424-8220. 23(21). doi: 10.3390/s23218901. Full text in Research Archive
• Sinha, Nidhi; M. A., Ganesh Kumar; Joshi, Amit Mahesh & Cenkeramaddi, Linga Reddy (2023). DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series Features. IEEE Access. ISSN 2169-3536. 11, p. 117643–117655. doi: 10.1109/ACCESS.2023.3325705. Full text in Research Archive
• Joshi, Anushka; Raman, Balasubramanian; C., Krishna Mohan & Cenkeramaddi, Linga Reddy (2023). Application of a new machine learning model to improve earthquake ground motion predictions. Natural Hazards. ISSN 0921-030X. doi: 10.1007/s11069-023-06230-4.
• K, Naveen Kumar; C., Krishna Mohan & Cenkeramaddi, Linga Reddy (2023). The Impact of Adversarial Attacks on Federated Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. ISSN 0162-8828. 46(5), p. 2672–2691. doi: 10.1109/TPAMI.2023.3322785. Show summary

  Federated learning (FL) has emerged as a powerful machine learning technique that enables the development of models from decentralized data sources. However, the decentralized nature of FL makes it vulnerable to adversarial attacks. In this survey, we provide a comprehensive overview of the impact of malicious attacks on FL by covering various aspects such as attack budget, visibility, and generalizability, among others. Previous surveys have primarily focused on the multiple types of attacks and defenses but failed to consider the impact of these attacks in terms of their budget, visibility, and generalizability. This survey aims to fill this gap by providing a comprehensive understanding of the attacks' effect by identifying FL attacks with low budgets, low visibility, and high impact. Additionally, we address the recent advancements in the field of adversarial defenses in FL and highlight the challenges in securing FL. The contribution of this survey is threefold: first, it provides a comprehensive and up-to-date overview of the current state of FL attacks and defenses. Second, it highlights the critical importance of considering the impact, budget, and visibility of FL attacks. Finally, we provide ten case studies and potential future directions towards improving the security and privacy of FL systems

• Andrade, José Amendola Netto; Cenkeramaddi, Linga Reddy & Jha, Ajit (2023). Embedding Clustering using Lightweight Contrastive Learning For Cross-Modal Classification, 2023 8th International Conference on Computer and Communication Systems (ICCCS). IEEE conference proceedings. ISSN 978-1-6654-5612-8. p. 732–737. doi: 10.1109/ICCCS57501.2023.10150770. Show summary

  Deep learning models excel at computer vision tasks, but they require a lot of memory and computation power. Thermal image classification has gained popularity due to the robustness of this modality in the face of adverse weather and lighting conditions. Images from both visible and/or thermal modalities, on the other hand, must frequently be processed in memory constrained environments, such as embedded edge applications. We propose a novel cross-modal constrastive learning model with a novel objective function that learns and averages mappings from both visible and thermal modalities into a shared embedding space for cross-modal data clustering and classification. A lightweight architecture is used to generate embedding from images in both modalities, and the proposed algorithm yields cross-modal centroids that can be efficiently used for clustering and classification in either modality. We demonstrate the model's accuracy by running experiments on a dataset that contains both thermal and visible counterparts of the same objects.

• Yakkati, Rakesh Reddy; Yeduri, Sreenivasa Reddy; Tripathy, Rajesh Kumar & Cenkeramaddi, Linga Reddy (2023). Time frequency domain deep CNN for automatic background classification in speech signals. International Journal of Speech Technology. ISSN 1381-2416. doi: 10.1007/s10772-023-10042-z.
• Sonny, Amala; Yeduri, Sreenivasa Reddy & Cenkeramaddi, Linga Reddy (2023). Q-learning-based unmanned aerial vehicle path planning with dynamic obstacle avoidance. Applied Soft Computing. ISSN 1568-4946. 147. doi: 10.1016/j.asoc.2023.110773. Show summary

  Recently, unmanned aerial vehicles (UAVs) have shown promising results for autonomous sensing. UAVs have been deployed for multiple applications that include surveillance, mapping, tracking, and search operations. Finding an efficient path between a source and a goal is a critical issue that has been the focus of recent exploration. Many path-planning algorithms are utilized to find an efficient path for a UAV to navigate from a source to a goal with obstacle avoidance. Despite the extensive literature and numerous research proposals for path planning, dynamic obstacle avoidance has not been addressed with machine learning. When the obstacles are dynamic, i.e., they can change their position over time, and the constraints of the path planning algorithm become more challenging. This in turn adds a layer of complexity to the path planning algorithm. To address this challenge, a Q-learning algorithm is proposed in this work to facilitate efficient path planning for UAVs with both static and dynamic obstacle avoidance. We introduced the Shortest Distance Prioritization policy in the learning process which marginally reduces the distance that the UAV has to travel to reach the goal. Further, the proposed Q-learning algorithm adopts a grid-graph-based method to solve the path-planning problem. It learns to maximize the reward based on the agent’s behavior in the environment. Through results, the performance comparison between the proposed approach and state-of-the-art path planning approaches such as A-star, Dijkstra, and Sarsa algorithms are evaluated in terms of learning time and path length. We show through results that the proposed approach results in improved performance when compared to state-of-the-art approaches. Further, the effect of an increased number of obstacles are evaluated on the performance of the proposed approach.

• Bhardwaj, Jyotirmoy; Harman, Christopher Peter; Gardiyawasam Pussewalage, Harsha & Cenkeramaddi, Linga Reddy (2023). Real-Time Missing Data Estimation in Water Networks, 2023 IEEE Applied Sensing Conference (APSCON). IEEE conference proceedings. ISSN 978-1-6654-6163-4. doi: 10.1109/APSCON56343.2023.10101123. Show summary

  Estimating missing data (e.g., pressure) in geographically distributed nodes is one of the fundamental challenges in a water distribution network (WDN). The Model-Based Kalman Filter is a simple solution that can estimate missing data in systems with a linear Gaussian state model. However, in large WDN, the state model is non-linear, and accurate system dynamics are unknown. Thus, we investigate the use of Data-Driven Kalman Filter (DDKF), in which the Recurrent Neural Network (RNN) updates the Kalman Gain. This data-driven approach has the potential to reduce approximation errors caused by the state model’s nonlinearity. As a result, this work demonstrates the application of a DDKF in a WDN. In addition, we demonstrate numerically that the DDKF can overcome approximation errors caused by flow equations and can estimate missing data in a WDN.

• Jomole, Varghese V; Manikandan, M. Sabarimalai & Cenkeramaddi, Linga Reddy (2023). Compressed ECG Sensing Based Fast/Slow HR and Regular/Irregular Rhythm Recognition for Resource-Constrained Health Monitoring Devices, 2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE conference proceedings. ISSN 979-8-3503-2138-8. doi: 10.1109/ECAI58194.2023.10193903. Show summary

  By considering the resource-constrained affordable wearable or portable health monitoring devices, in this paper, we present a lightweight digital compressed ECG sensing with recognition of fast/slow and regular/irregular heartbeat patterns by using beat-to-beat intervals (BBIs) directly computed from compressed sensing (CS) measurements without ECG reconstruction process. For extracting BBIs, we presented a fast straightforward R-peak detection method in the CS domain without using sets of search-back detection rules unlike other methods. On the standard MIT-BIH arrhythmia database, the CS-ECG based R-peak detection method had an average accuracy of 99.47% with false positives of 381 beats and false negatives of 195 beats for a total of 109021 beats. The CS-ECG based HR classification method, with three classes of bradycardia, tachycardia and normal, had an accuracy of 96.75%, 91.18% and 99.02% based on the feature of number of BBIs and an accuracy of 95.64%, 82.35% and 98.81% based on the feature of average BBIs. The CS-ECG based regular/irregular rhythm (RIR) recognition method had a sensitivity (SE) of 93.17%, specificity (SP) of 96.87% and accuracy (ACC) of 94.83% based on the variation between the BBIs that are determined by comparing the difference between the successive BBIs with a predefined BBI threshold. The proposed method can reduce computational resources and energy reduction of 75% with the CS-based data reduction factor of 4 that has a great potential in energy-efficiency of battery operated wearable devices in long-term continuous health monitoring environments.

• P N, Sivaranjini; Manikandan, M. Sabarimalai & Cenkeramaddi, Linga Reddy (2023). Fast Quality-Aware AMDF Based Pulse Rate Estimation from Compressed PPG Measurements for Wearable Vital Signs Monitor, 2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE conference proceedings. ISSN 979-8-3503-2138-8. doi: 10.1109/ECAI58194.2023.10194196. Show summary

  Modern wearable or portable health monitoring devices are capable of photoplethysmogram (PPG) sensing, processing, analyzing, storing and transferring signal and parameters wirelessly but are generally energy constrained and have more false alarms under noisy PPG recordings. In this paper, we present computationally-efficient reliable pulse rate (PR) estimation in compressed sensing (CS) domain. The proposed CS-PPG based PR estimation method consists of measurement generation, high-pass filtering, average magnitude difference function (AMDF) features based signal quality assessment (SQA), and AMDF based quality-aware PR estimation. The proposed unified framework is evaluated using a wide variety of normal and pathological PPG signals taken from five standard databases. The proposed framework had an average sensitivity (SE) of 98.75% and specificity (SP) of 63.35%. Results show that the CS-PPG based quality-aware PR estimation method had a mean absolute error (MAE) of 3.1 ± 4.6%, Bland-Altman ratio (BAR) of 9.2% and root-mean-square error (RMSE) of 5.6 which are not only comparable with the results of the PR estimation method with the original PPG signals but also the proposed framework reduces 80% of the overall computational load. The proposed unified framework has great benefits in reducing processing time and energy consumption and thus can maximize battery lifetime of battery-operated health monitors.

• Chilamkurthy, Naga Srinivasarao; Karna, Niteesh; Vuddagiri, Vamsidhar; Tiwari, Satish K.; Ghosh, Anirban & Cenkeramaddi, Linga Reddy [Show all 7 contributors for this article] (2023). Energy-Efficient and QoS-Aware Data Transfer in Q-Learning-Based Small-World LPWANs. IEEE Internet of Things Journal. ISSN 2327-4662. 10(24), p. 22636–22649. doi: 10.1109/JIOT.2023.3304337.
• Nemalidinne, Siva Mouni; Yeduri, Sreenivasa Reddy; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2023). Short Packet Communications in UAV-NOMA System With Imperfect SIC. IEEE Communications Letters. ISSN 1089-7798. 27(10), p. 2852–2856. doi: 10.1109/LCOMM.2023.3304063.
• Sonny, Amala; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2023). Carry Object Detection Utilizing mmWave Radar Sensors and Ensemble-Based Extra Tree Classifiers on the Edge Computing Systems. IEEE Sensors Journal. ISSN 1530-437X. 23(17), p. 20137–20149. doi: 10.1109/JSEN.2023.3295574. Show summary

  Indoor human-carried object detection refers to the use of technologies and methods to detect objects that may be carried by individuals in indoor environments. This can include weapons, explosives, drugs, or other contraband that may endanger the safety and security of individuals or facilities. Detecting potential threats carried by individuals inside buildings is thus a critical and ongoing requirement in a variety of settings, including airports, schools, railway stations, and other public places. It is extremely challenging to detect these objects accurately using noncontact methods. Here, we present a noncontact carry object detection method based on mmWave radar and machine learning. We adopted a tree-based feature selection to reduce the complexity and increase the reliability of the detection process. The performance of the proposed approach has been compared to that of various state-of-the-art approaches. Finally, we deployed the models on various edge computing platforms, including Raspberry Pi, Nvidia Jetson Nano, and AGX Xavier.

• Andrade, José Amendola Netto; Dayal, Aveen; Cenkeramaddi, Linga Reddy & Jha, Ajit (2023). Edge-Distributed Fusion of Camera-LiDAR for Robust Moving Object Localization. IEEE Access. ISSN 2169-3536. 11, p. 73583–73598. doi: 10.1109/ACCESS.2023.3295212. Full text in Research Archive Show summary

  Object localization plays a crucial role in computational perception, enabling applications ranging from surveillance to autonomous navigation. This can be leveraged by fusing data from cameras and LiDARs (Light Detection and Ranging). However, there are challenges in employing current fusion methods in edge devices, while keeping the process flexible and modular. This paper presents a method for multiple object localization that fuses LiDAR and camera data with low-latency, flexibility and scalability. Data is obtained from 360° surround view four cameras and a scanning LiDAR distributed over embedded devices. The proposed technique: 1) discriminates dynamic multiple objects in the scene from raw point clouds, clusters their respective points to obtain a compact representation in 3D space; and 2) asynchronously fuse the centroids with data from object detection neural networks for each camera for detection, localization, and tracking. The proposed method meets above functionalities with low-latency fusion and increased field of view for safer navigation, even with intermittent flow of labels and bounding boxes from models. That makes our system distributed, modular, scalable and agnostic to the object detection model, distinguishing it from the current state-of-art. Finally, the proposed method is implemented and validated in both indoor environment and publicly available outdoor KITTI 360 data set. The fusion occurs much faster and accurate when compared with traditional non-data driven fusion technique and the latency is competitive with other non-embedded deep learning fusion methods. The mean error is estimated to be ≈ 5 cm and precision of 2 cm for indoor navigation of 15 m (error percentage of 0.3 %). Similarly, mean error of 30 cm and precision of 3 cm for outdoor navigation of 35 m on KITTI 360 data set (error percentage of 0.8 %).

• Sonny, Amala; Yeduri, Sreenivasa Reddy & Cenkeramaddi, Linga Reddy (2023). Autonomous UAV Path Planning Using Modified PSO for UAV-Assisted Wireless Networks. IEEE Access. ISSN 2169-3536. 11, p. 70353–70367. doi: 10.1109/ACCESS.2023.3293203. Full text in Research Archive Show summary

  Recently, unmanned aerial vehicles (UAVs) have attained considerable attention for providing reliable and cost-effective communication due to the flexibility of deployment and line of sight (LoS) propagation. Efficient UAV path planning is one of the key aspects that need to be addressed to minimize energy consumption and satisfy the rate requirements of the user. Thus, in this work, we propose a novel framework that utilizes the modified Particle Swarm Optimization (PSO) algorithm for UAV path planning to support the rate requirements of the user. In the proposed framework, the problem of joint path planning and energy consumption is formulated to improve the instantaneous sum rate of the user. In order to solve the formulation, the proposed framework involves two steps. Initially, the line of sight probability is used to obtain an optimal destination location at which the UAV is in LoS with the user and offers the required downlink rate. Following that, the modified PSO is used to find the most energy-efficient path from the source to the destination. Through experiments, we show that the proposed framework provides a three-dimensional (3D) path in a complex environment, and has the ability to avoid obstacles in the path. In addition, it minimizes energy consumption and travel time and improves the user rate as compared to the state-of-the-art methods. Finally, the performance of the proposed framework is tested in three different scenarios and shows that the proposed method performs better than the state-of-the-art methods in all scenarios.

• Salata, Gianluca; Cenkeramaddi, Linga Reddy; Huynh, Khang; Robbersmyr, Kjell Gunnar & Jha, Ajit (2023). Time-Frequency Analysis and Fault Prediction of Motor Bearings Using Millimeter-Wave Radar. IEEE Sensors Journal. ISSN 1530-437X. 23(16), p. 18718–18728. doi: 10.1109/JSEN.2023.3292899. Show summary

  Industrial motors are ubiquitous and used extensively in various industrial applications. The aim of increasing productivity, efficiency, lowering down, and mean time between failures while maintaining safety necessitates the continuous condition monitoring of abnormalities, faults in motors, and even predict them as early as possible to maintain their smooth functioning. In this article, we propose and experimentally demonstrate the use of millimeter-wave (mmW) radar for noncontact, nondestructive detection, characterization, and finally prediction of the probability of failure by interrogating its vibrating shaft at a given set of bearing conditions. Precisely, we obtain experimental raw 1-D time series data from mmW radar and transform them into time–frequency representations using wavelet transformation. Then, the features are extracted from the time–frequency representations, specifically the instantaneous velocity trajectory that distinguishes the vibrating shaft due to faulty bearing from healthy bearing. Furthermore, using maximum likelihood estimation (MLE), the velocity profile of the vibrating shaft follows Gaussian distribution from collected data at different motor shaft rotations per second (rps) (5, 15, and 25 Hz) with healthy and faulty bearings. While the mean of distribution for all the cases remains constant, the variance of latter increases with the increase in rps. Finally, the probabilistic approach and the model parameters are exploited to quantify the probability of motor failure in terms of complementary cumulative distribution function (CCDF). It is observed that the CCDF for the faulty case is greater than that of healthy case for all velocities and rps.

• Chalavadi, Vishnu; Khandelwal, Jayesh; C., Krishna Mohan & Cenkeramaddi, Linga Reddy (2023). EVAA - Exchange Vanishing Adversarial Attack on LiDAR Point Clouds in Autonomous Vehicles. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892. 61. doi: 10.1109/TGRS.2023.3292372. Show summary

  In addition to red-green-blue (RGB) camera sensors, light detection and ranging (LiDAR) plays an important role in autonomous vehicles (AVs) to perceive their surroundings. Deep neural networks (DNNs) are able to achieve cutting-edge 3-D object detection and segmentation performance using LiDAR point clouds. LiDAR-enabled AVs provide human perception by segmenting LiDAR point clouds into meaningful regions and providing semantic context to the AV user. However, the generation of point clouds to provide semantic segmentation in AVs is not reliable and secure, which may result in traffic accidents. We propose a novel adversarial attack against LiDAR point clouds in AVs in this article. We devised an exchange vanishing adversarial attack (EVAA) to deceive LiDAR point clouds by introducing targeted noise on specific objects (e.g., vehicles and driveways). In two autonomous driving datasets with 3-D object annotations, NuScenes and PandaSet, we evaluate the performance of our proposed attack framework. We achieve an attack success rate (ASR) of ≈63 % and ASR of ≈29 % on both NuScenes and PandaSet datasets, respectively.

• Kusum, Lata & Cenkeramaddi, Linga Reddy (2023). Deep Learning for Medical Image Cryptography: A Comprehensive Review. Applied Sciences. ISSN 2076-3417. 13(14). doi: 10.3390/app13148295. Full text in Research Archive Show summary

  Electronic health records (EHRs) security is a critical challenge in the implementation and administration of Internet of Medical Things (IoMT) systems within the healthcare sector’s heterogeneous environment. As digital transformation continues to advance, ensuring privacy, integrity, and availability of EHRs become increasingly complex. Various imaging modalities, including PET, MRI, ultrasonography, CT, and X-ray imaging, play vital roles in medical diagnosis, allowing healthcare professionals to visualize and assess the internal structures, functions, and abnormalities within the human body. These diagnostic images are typically stored, shared, and processed for various purposes, including segmentation, feature selection, and image denoising. Cryptography techniques offer a promising solution for protecting sensitive medical image data during storage and transmission. Deep learning has the potential to revolutionize cryptography techniques for securing medical images. This paper explores the application of deep learning techniques in medical image cryptography, aiming to enhance the privacy and security of healthcare data. It investigates the use of deep learning models for image encryption, image resolution enhancement, detection and classification, encrypted compression, key generation, and end-to-end encryption. Finally, we provide insights into the current research challenges and promising directions for future research in the field of deep learning applications in medical image cryptography.

• Nelson, Wilson Ayyanthole; Jha, Ajit; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2023). Estimation of number of unmanned aerial vehicles in a scene utilizing acoustic signatures and machine learning. Journal of the Acoustical Society of America. ISSN 0001-4966. 154(1), p. 533–546. doi: 10.1121/10.0020292. Show summary

  With the exponential growth in unmanned aerial vehicle (UAV)-based applications, there is a need to ensure safe and secure operations. From a security perspective, detecting and localizing intruder UAVs is still a challenge. It is even more challenging to accurately estimate the number of intruder UAVs on the scene. In this work, we propose a simple acoustic-based technique to detect and estimate the number of UAVs. Our method utilizes acoustic signals generated from the motion of UAV motors and propellers. Acoustic signals are captured by flying an arbitrary number of ten UAVs in different combinations in an indoor setting. The recorded acoustic signals are trimmed, processed, and arranged to create an UAV audio dataset. The UAV audio dataset is subjected to time-frequency transformations to generate audio spectrogram images. The generated spectrogram images are then fed to a custom lightweight convolutional neural network (CNN) architecture to estimate the number of UAVs in the scene. Following training, the proposed model achieves an average test accuracy of 93.33% as compared to state-of-the-art benchmark models. Furthermore, the deployment feasibility of the proposed model is validated by running inference time calculations on edge computing devices, such as the Raspberry Pi 4, NVIDIA Jetson Nano, and NVIDIA Jetson AGX Xavier.

• Garg, Shreeya; Shukla, Urvashi Prakash & Cenkeramaddi, Linga Reddy (2023). Detection of Depression Using Weighted Spectral Graph Clustering With EEG Biomarkers. IEEE Access. ISSN 2169-3536. 11, p. 57880–57894. doi: 10.1109/ACCESS.2023.3281453. Full text in Research Archive
• Sharma, Geetanjali; Joshi, Amit Mahesh; Gupta, Richa & Cenkeramaddi, Linga Reddy (2023). DepCap: A Smart Healthcare Framework for EEG Based Depression Detection Using Time-Frequency Response and Deep Neural Network. IEEE Access. ISSN 2169-3536. 11, p. 52327–52338. doi: 10.1109/ACCESS.2023.3275024. Full text in Research Archive
• Gangireddy, Narendra Kumar Reddy; Manikandan, M. Sabarimalai; N. V. L., Narasimha Murty & Cenkeramaddi, Linga Reddy (2023). Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices. IEEE Access. ISSN 2169-3536. 11, p. 41708–41740. doi: 10.1109/ACCESS.2023.3269584. Full text in Research Archive
• Jha, Ajit; Cenkeramaddi, Linga Reddy & Royo, Santiago (2023). Generalized multi-cavity laser self-mixing interferometry based on scattering theory. Optics Express. ISSN 1094-4087. 31(10), p. 16508–16522. doi: 10.1364/OE.484086. Full text in Research Archive Show summary

  We present a generalized mathematical model and algorithm for the multi-cavity self-mixing phenomenon based on scattering theory. Scattering theory, which is extensively used for travelling wave is exploited to demonstrate that the self-mixing interference from multiple external cavities can be modelled in terms of individual cavity parameters recursively. The detailed investigation shows that the equivalent reflection coefficient of coupled multiple cavities is a function of both attenuation coefficient and the phase constant, hence propagation constant. The added benefit with recursively model is that it is computationally very efficient to model large number of parameters. Finally, with the aid of simulation and mathematical modelling, we demonstrate how the individual cavity parameters such as cavity length, attenuation coefficient, and refractive index of individual cavities can be tuned to get a self-mixing signal with optimal visibility. The proposed model intends to leverage system description for biomedical applications when probing multiple diffusive media with distinct characteristics, but could be equally extended to any setup in general

• Sharma, Jaya; Peketi, Divya; C., Vishnu; Cenkeramaddi, Linga Reddy; B.H., Shekar & C., Krishna Mohan (2023). Deformable and Structural Representative Network for Remote Sensing Image Captioning, VISAPP 2023 : 18th International Conference on Computer Vision Theory and Applications. SciTePress. ISSN 978-989-758-634-7. p. 56–64. doi: 10.5220/0011625900003417. Show summary

  Remote sensing image captioning has greater significance in image understanding that generates textual description of aerial images automatically. Majority of the existing architectures work within the framework of encoder-decoder structure. However, it is noted that the existing encoder-decoder based methods for remote sensing image captioning avoid fine-grained structural representation of objects and lack deep encoding representation of an image. In this paper, we propose a novel structural representative network for capturing fine-grained structures of remote sensing imagery to produce fine grained captions. Initially, a deformable network has been incorporated on intermediate layers of convolutional neural network to take out spatially invariant features from an image. Secondly, a contextual network is incorporated in the last layers of the proposed network for producing multi-level contextual features. In order to extract dense contextual features, an attention mechanism is accomplished in contextual networks. Thus, the holistic representations of aerial images are obtained through a structural representative network by combining spatial and contextual features. Further, features from the structural representative network are provided to multi-level decoders for generating spatially semantic meaningful captions. The textual descriptions obtained due to our proposed approach is demonstrated on two standard datasets, namely, the Sydney-Captions dataset and the UCM-Captions dataset. The comparative analysis is made with recently proposed approaches to exhibit the performance of the proposed approach and hence argue that the proposed approach is more suitable for remote sensing image captioning tasks.

• Jain, Sushil Kumar; Joshi, Amit Mahesh & Cenkeramaddi, Linga Reddy (2023). Dielectric Modulated Bilayer Electrode Top Contact OTFT for Label Free Biosensing. IEEE Access. ISSN 2169-3536. 11, p. 23714–23725. doi: 10.1109/ACCESS.2023.3253563. Full text in Research Archive Show summary

  In this paper, dielectric modulated bilayer electrodes top contact organic field effect transistor (DMBETC-OTFT) is investigated as a biosensing device for label-free detection of biomolecules. The nanocavity used for biomolecule detection is created by etching the oxide in a conventional OTFT device. Neutral and charged biomolecules can be detected by the proposed device using their respective dielectric constants and charge densities. Subthreshold swing (SS), on-current ( ION ), and on-off current ratio ( ION/IOFF ) are the main biosensing performance characteristics computed and compared for different gate work function ( ϕm ) and cavity thickness ( Tgap ) for the proposed biosensor device. The change in drain current ( ID ), as well as the ION/IOFF ratio, have both been calculated to investigate the sensitivity of the proposed biosensor. The influence of the gate work function is also investigated to improve the sensitivity of the proposed device. According to the finding of this study, using a gate work function with a lower value results in a significant increase in sensitivity. For charged biomolecules ( Qf = +1×1012cm−2 ) with dielectric constant of biomoecules (K = 12), the highest drain current sensitivity is 4.5×103 . The drain current sensitivity achieved is four times greater, when comparing the proposed device to the latest published work of metal controlled dielectric modulated OTFT-based sensor. The proposed device also has a high ION/IOFF sensitivity of 4.60×102 when VGS = −3.0 V and VDS = −1.5 V. In light of its high sensitivity, low cost, and bio-compatibility, the DMBETC-OTFT biosensor holds great promise for the advancement of new demanding flexible biosensing applications.

• Gunnery, Srinath; Bethi, Pardhasaradhi; Mahipathi, Ashoka Chakravarthi; H, Prashantha Kumar; Pathipati, Srihari & Cenkeramaddi, Linga Reddy (2023). Performance Analysis of Spectrum Sharing Radar in Multipath Environment. IEEE Open Journal of the Communications Society. ISSN 2644-125X. 4, p. 922–935. doi: 10.1109/OJCOMS.2023.3240116. Full text in Research Archive Show summary

  Radar based sensing and communication systems sharing a common spectrum have become a potential research problem in recent years due to spectrum scarcity. The spectrum sharing radar (SSR) is a new technology that uses the total available bandwidth (BW) for both radar based sensing and communication. Unlike traditional radar, the SSR divides the total available BW into radar-only and mixed-use bands. In a radar-only band, only radar sensor signals can be transmitted and received. In contrast, radar and communication signals can both be transmitted and received in the mixed-use band. Taking such BW sharing into account, this paper investigates the performance of SSR in an information-theoretic sense. To evaluate performance, mutual information (MI), spectral efficiency (SE) and capacity (C) metrics are used. Initially, this paper considered a clean environment (no multipath) in order to evaluate performance metrics in the mixed-use band with and without successive interference cancellation. Following that, this paper addresses the performance of BW allocation by allocating low to high BW in mixed-band. Furthermore, the performance metrics are extended to account for the multipath environment, and the same analogy as in a clean environment is used. In addition, the MI and SE of traditional radar system is taken into account when comparing the performance of SSR with and without the use of the SIC. Finally, MI and capacity results show that using the SIC scheme in a mixed-use band yields performance comparable to traditional radar and communication system. In terms of SE, the SSR with SIC scheme outperforms traditional radar and communication system.

• Dayal, Aveen; M., Aishwarya; S., Abhilash; C., Krishna Mohan; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2023). Adversarial Unsupervised Domain Adaptation for Hand Gesture Recognition Using Thermal Images. IEEE Sensors Journal. ISSN 1530-437X. 23(4), p. 3493–3504. doi: 10.1109/JSEN.2023.3235379. Show summary

  Hand gesture recognition has a wide range of applications, including in the automotive and industrial sectors, health assistive systems, authentication, and so on. Thermal images are more resistant to environmental changes than red–green–blue (RGB) images for hand gesture recognition. However, one disadvantage of using thermal images for the aforementioned task is the scarcity of labeled thermal datasets. To tackle this problem, we propose a method that combines unsupervised domain adaptation (UDA) techniques with deep-learning (DL) technology to remove the need for labeled data in the learning process. There are several types and methods for implementing UDA, with adversarial UDA being one of the most common. In this article, the first time in this field, we propose a novel adversarial UDA model that uses channel attention and bottleneck layers to learn domain-invariant features across RGB and thermal domains. Thus, the proposed model leverages the information from the labeled RGB data to solve the hand gesture recognition task using thermal images. We evaluate the proposed model on two hand gesture datasets, namely, Sign Digit Classification and Alphabet Gesture Classification, and compare it to other benchmark models in terms of accuracy, model size, and model parameters. Our model outperforms the other state-of-the-art methods on the Sign Digit Classification and Alphabet Gesture Classification datasets and achieves 91.32% and 80.91% target test accuracy, respectively.

• Nelson, Wilson Ayyanthole; Gupta, Khushi Anil; Koduru, Balu Harshavardan; Kumar, Abhinav; Jha, Ajit & Cenkeramaddi, Linga Reddy (2023). Recent Advances in Thermal Imaging and its Applications Using Machine Learning: A Review. IEEE Sensors Journal. ISSN 1530-437X. 23(4), p. 3395–3407. doi: 10.1109/JSEN.2023.3234335. Show summary

  Recent advancements in thermal imaging sensor technology have resulted in the use of thermal cameras in a variety of applications, including automotive, industrial, medical, defense and space, agriculture, and other related fields. Thermal imaging, unlike RGB imaging, does not rely on background light, and the technique is nonintrusive while also protecting privacy. This review article focuses on the most recent advancements in thermal imaging technology, key performance parameters, an overview of its applications, and machine-learning techniques applied to thermal images for various tasks. This article begins with the most recent advancements in thermal imaging, followed by a classification of thermal cameras and their key specifications, and finally a review of machine-learning techniques used on thermal images for various applications. This detailed review article is highly useful for designing thermal imaging-based applications using various machine-learning techniques.

• Nelson, Wilson Ayyanthole; Kumar, Abhinav; Jha, Ajit & Cenkeramaddi, Linga Reddy (2023). Multitarget Angle of Arrival Estimation Using Rotating mmWave FMCW Radar and Yolov3. IEEE Sensors Journal. ISSN 1530-437X. 23(3), p. 3173–3182. doi: 10.1109/JSEN.2022.3231790. Show summary

  It is still challenging to accurately localize unmanned aerial vehicles (UAVs) from a ground control station (GCS) using various sensors. The mmWave frequency-modulated continuous wave (FMCW) radars offer excellent performance for target detection and localization in harsh environments and low lighting conditions. However, the estimated angle of arrival (AoA) of targets in the captured scene is quite poor. This article focuses on improving AoA estimation by combining the cutting-edge machine learning (ML) algorithms with a mechanical radar rotor setup. An mmWave FMCW radar system is mounted on a programmable rotor to capture range–angle maps of targets at various locations. The range–angle images are then labeled and trained further with the Yolov3 algorithm. Subsequent testing reveals that for detected target objects, the centroid of the bounding boxes from the detected objects provides accurate AoA estimation with very low root mean square error (RMSE). The results show that the proposed approach outperforms traditional methods in terms of performance and estimation accuracy.

• B. H., Shekar; Mannan, Shazia; Habtu, Hailu; C., Krishna Mohan & Cenkeramaddi, Linga Reddy (2022). Performance Analysis of Deep Neural Networks for Covid-19 Detection from Chest Radiographs, Fifteenth International Conference on Machine Vision (ICMV 2022). SPIE - The International Society for Optics and Photonics. ISSN 9781510666191. doi: https:/doi.org/10.1117/12.2679620.
• Pardhasaradhi, Bethi; Lingdevaru, Purushottama; BN, Balarami Reddy; Pathipati, Srihari & Cenkeramaddi, Linga Reddy (2022). Robust Positioning and Grubbs Outlier Test for Navigation in GPS Spoofing Environment, 2022 IEEE 19th India Council International Conference (INDICON). IEEE conference proceedings. ISSN 978-1-6654-7350-7. p. 1–6. doi: 10.1109/INDICON56171.2022.10039906. Show summary

  Global positioning system (GPS) is favored to provide the position, velocity, and time (PVT) details across the globe. This paper proposes an epoch-by-epoch robust positioning algorithm followed by the Grubbs outlier test to address the GPS spoofing problem. We propose to accept both authentic and spoofed GPS signals to compute the robust positions. The robust positioning considers all possible combinations of measurements and generates several position estimates, which contain actual position, spoof position, and biased positions. In this case, the positions evolved due to spoof pseudorange measurements must be removed. Hence, we model eliminating spoof locations as an outlier problem and is addressed using Grubbs outlier test. The median of the processed data after the Grubbs test is the positional information at that epoch. Moreover, this problem is also extended to the Kalman filter’s (KF) framework to address the time-varying kinematics of the target. Simulations are carried out for various numbers of actual and spoofed pseudorange measurements. In order to illustrate the robustness of the proposed technique, position root mean square error (PRMSE) is taken as a metric.

• Pardhasaradhi, Bethi; Gunnery, Srinath; Mahipathi, Ashoka Chakravarthi; Pathipati, Srihari & Cenkeramaddi, Linga Reddy (2022). A GNSS Position Spoofing Mitigation Algorithm using Sparse Estimation, 2022 IEEE 19th India Council International Conference (INDICON). IEEE conference proceedings. ISSN 978-1-6654-7350-7. p. 1–6. doi: 10.1109/INDICON56171.2022.10039936. Show summary

  The Global Navigation Satellite Systems (GNSS) are widespread for providing Position, Velocity, and Time (PVT) information across the globe. The GNSS usually employs the Extended Kalman Filter (EKF) framework to estimate the PVT information of the receiver. The GNSS receivers PVT information is falsified by using a mimic GNSS signals is called a spoofing attack. This paper focuses mainly to combat the spoofing attack using sparse estimation theory. A generalized mathematical model is proposed for authentic and spoofed pseudoranges at the GNSS receiver. After that, a generalized pseudorange measurement model is presented by combining the authentic and spoofed pseudorange measurements. It is assumed that, only a part of satellite signals are spoofed. Further, the GNSS receiver’s state is estimated by mitigating the spoofed pseudoranges and it is formulated as a Least Absolute Shrinkage and Selection Operator (LASSO) optimization problem. The simulated results, compares the proposed LASSO based EKF algorithm with traditional EKF framework. It is observed that, the proposed algorithm suppresses the spoofing effect. Moreover, the Position Root Mean Square Error (PRMSE) of the proposed algorithm decreases by increasing the number of spoofed measurements.

• Yakkati, Rakesh Reddy; Pardhasaradhi, Bethi; Zhou, Jing & Cenkeramaddi, Linga Reddy (2022). A Machine Learning Based GNSS Signal Classification, 2022 IEEE International Symposium on Smart Electronic Systems. IEEE conference proceedings. ISSN 979-8-3503-9922-6. p. 532–535. doi: 10.1109/iSES54909.2022.00116. Show summary

  The global navigation satellite system (GNSS) provides accurate position, velocity, and time data all over the world. However, GNSS are susceptible to multipath effects in suburban, urban, and indoor environments. Furthermore, it is vulnerable to intentional interference such as jamming and spoofing, which can result in either no or false position estimates. This study focuses on classifying received GNSS signals in a multi-correlation GNSS receiver as interference-free, multipath, jamming, or spoofing. To classify the GNSS signal, the average power and distortion correlation features are extracted from the multi-correlation output. Various machine learning algorithms such as neural networks, support vector machines, nearest neighbors, kernel approxi-mation, decision trees, discriminant analysis, naive Bayes, and ensemble classifiers are investigated and quantified using test accuracy and confusion matrix.

• Yakkati, Rakesh Reddy; Pardhasaradhi, Bethi; Yeduri, Sreenivasa Reddy; Pandey, Om Jee & Cenkeramaddi, Linga Reddy (2022). Power Transmission Line Classification From Images Using Pre-Trained Deep Learning Models, 2022 IEEE International Symposium on Smart Electronic Systems. IEEE conference proceedings. ISSN 979-8-3503-9922-6. p. 394–397. doi: 10.1109/iSES54909.2022.00086. Show summary

  Identifying the type of a power transmission line from images is a fascinating field. It can lead to applications like tower detection, line inspection, location detection, multi-fitting detection, fault detection, foreign object detection, etc. The data collection involves 348 real-time images captured from the UAV and 696 synthetic images. Based on the geometry of the line, the whole data is classified into three classes: simple, real, and Duplo lines. This paper feeds real and synthetic images to the pretrained deep neural networks (DNN) for the line inspection application. Comprehensively top-32 pretrained models were tested on the dataset and evaluated the classification performance. In addition, the DNN algorithms are also tested on the Raspberry Pi hardware platform to know the run-time feasibility of this real-time application.

• Jha, Ajit; Cenkeramaddi, Linga Reddy & Royo, Santiago (2022). Frequency Domain Analysis and Filter Design of Continuous Wave Frequency Modulated Optical Feedback Signal for Photonic Sensing, 2022 IEEE International Symposium on Smart Electronic Systems. IEEE conference proceedings. ISSN 979-8-3503-9922-6. p. 136–141. doi: 10.1109/iSES54909.2022.00037. Show summary

  Optical feedback (OF) refers to the beating of the frequency shifted back reflected laser emission from the remote (vibrating) object with that of incident radiation in the laser cavity cause rapid fluctuation of the laser emission. This fluctuations (also known as fringes or optical feedback signal (OFS)) contain the relevant information regarding the vibrating object. Optical feedback, and its associated variants e.g. continuous wave frequency modulated OF (CWFM-OF) has been extensively used for non-contact, non-destructive and self-aligned photonic sensing applications such as displacement, vibrations, ranging and imaging to name few. Despite its advantages, the optical feedback signal (OFS) require processing them in time and/or frequency domain to extract the relevant parameters. In this article, we present detailed frequency domain analysis of OFS. Among the many parameters we thoroughly investigate the effect of intensity modulation index (m) and external distance (L ext ) on the bandwidth of OFS and then determine the (active) filter parameters required to process them. Finally we discuss the trade-off between different parameters for optimal performance and signal processing of OFS.

• Chakkapall, Suryateja Manikanta; Boppu, Srinivas; Cenkeramaddi, Linga Reddy & Barathram, Ramkumar (2022). Hand Gesture Recognition System in the Complex Background for Edge Computing Devices, 2022 IEEE International Symposium on Smart Electronic Systems. IEEE conference proceedings. ISSN 979-8-3503-9922-6. p. 13–18. doi: 10.1109/iSES54909.2022.00016. Show summary

  Hand gesture recognition offers a wide range of contactless applications. Some applications include designing a sign language recognition system for communicating with people having disabilities, health-care, automobiles, security, etc. For deaf and hard-of-hearing people, sign language recognition is a game-changer and has been studied for years. Unfortunately, each study has its limitations and cannot be used commercially. Some inves-tigations have shown that detecting sign language is possible, but commercialization is prohibitively expensive. This paper investi-gates a robust system to implement hand gesture recognition in a complex background. To test the hand gesture recognition system design, we developed an American sign language recognition for the letters A-J in a complex environment. The MediaPipe Hands framework, used in the developed system, helps successfully detect the hand landmark positions. The machine learning techniques are built on top of the obtained hand landmark positions. The developed system achieves 98.1% accuracy in gesture recognition with an inference time of around 50 ms. Subsequently, the system is successfully ported to Raspberry Pi 4 and NVIDIA's Jetson AGX Xavier and tested.

• Yakkati, Rakesh Reddy; Gade, Anurag; Koduru, Balu Harshavardan; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2022). Classification of UAVs Using Time-Frequency Analysis of Remote Control Signals and CNN, 2022 IEEE International Symposium on Smart Electronic Systems. IEEE conference proceedings. ISSN 979-8-3503-9922-6. p. 1–6. doi: 10.1109/iSES54909.2022.00014. Show summary

  Unmanned aerial vehicles (UAVs) have re-cently gained a significant interest in the research com-munity owing to their unrivaled commercial chances in wireless communications, search and rescue, surveillance, logistics, delivery, and intelligent agriculture. In safety-critical applications such as intrusions, identifying the type of drone enhances the countermeasures. This paper proposes classifying UAVs from radio frequency (RF) fingerprints using time-frequency transformation and con-volutional neural networks (CNN). The proposed method-ology involves RF fingerprints' wavelet synchrosqueezed transform (WSST) followed by a proposed lightweight CNN model. The methodology is verified on a data set containing fifteen different classes of drone's RF fingerprint. The proposed CNN model size, Raspberry Pi deployment feasibility, and accuracy are compared with the existing pre-trained state-of-art deep learning models. The proposed model achieves a testing accuracy of 99.09% at 387 kilobytes (KB) size and can run on Raspberry Pi in 25.54 milliseconds.

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• 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.
• Havås, Robin; Noori, Nadia Saad; Auby, Christian & Cenkeramaddi, Linga Reddy (2024). Optimized Unmanned Aerial Vehicle (UAV) Localization and Autonomous Navigation Stack for Tightly Closed Industrial Spaces.
• K A, AnoopKumar; B N, Dayananda; G, Achala; Pathipati, Srihari; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Design of Barker-7 Radar Pulse Compressor Using DSP Architecture Minimization Techniques.
• Jena, Paramananda; Singh, Ashutosh; G S, Vandana; 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.
• Alli, Sai Prakash; G S, Vandana; 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 .
• P, Vishnu Prasad; G S, Vandana; Nandagiri, Ashwin; Pathipati, Srihari; Bethi, Pardhasaradhi & Cenkeramaddi, Linga Reddy (2024). Automotive Radar Signal Authentication via Correlation and Power Spectral Density.
• B N, Dayananda; 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.
• A., Soumya; C., Krishna Mohan & Cenkeramaddi, Linga Reddy (2024). High Precision Single Shot Object Detection in Automotive Scenarios.
• Dayal, Aveen; K B, Vimal; Cenkeramaddi, Linga Reddy; C., Krishna Mohan; Kumar, Abhinav & Balasubramanian, Vineeth N (2023). MADG: Margin-based Adversarial Learning for Domain Generalization.
• Pretto, Riccardo; Dayal, Aveen; Oghbasilasie Haile, Yonas; Amendola Netto Andrade, José; Cenkeramaddi, Linga Reddy & Robbersmyr, Kjell Gunnar [Show all 7 contributors for this article] (2023). Motor Bearing Fault Classification using Laser Sensor and Light Weight CNN.
• Amendola Netto Andrade, José; Cenkeramaddi, Linga Reddy & Jha, Ajit (2023). Drone Landing on Moving UGV Platform With Reinforcement Learning Based Offsets.
• Robsrud, Didrik Nicolai; Ovsthus, Orjan; Muggerud, Lars; Amendola Netto Andrade, José; Cenkeramaddi, Linga Reddy & Tyapin, Ilya [Show all 7 contributors for this article] (2023). Lidar-mmW Radar Fusion for Safer UGV Autonomous Navigation with Collision Avoidance. In Zhou, Jing (Eds.), 2023 11th International Conference on Control, Mechatronics and Automation (ICCMA). IEEE conference proceedings. ISSN 979-8-3503-1568-4. doi: 10.1109/ICCMA59762.2023.10375017.
• Øvsthus, Ørjan; Robsrud, Didrik Nicolai; Muggerud, Lars; Amendola Netto Andrade, José; Cenkeramaddi, Linga Reddy & Tyapin, Ilya [Show all 7 contributors for this article] (2023). Mobile Robotic Manipulator Based Autonomous Warehouse Operations, 2023 The 11th International Conference on Control, Mechatronics and Automation (ICCMA 2023). IEEE conference proceedings. ISSN 978-1-6654-9048-1. doi: 10.1109/ICCMA59762.2023.10374743.
• A., Soumya; Cenkeramaddi, Linga Reddy; Vishnu, Chalavadi & C., Krishna Mohan (2023). Multi-Class Object Classification using Deep Learning Models in Automotive Object Detection Scenarios.
• P N, Sivaranjini; Manikandan, M. Sabarimalai & Cenkeramaddi, Linga Reddy (2023). Compressed Sensing Based SpO2 and PR Estimation for Improving Resource Utilization Efficiency of Wearables in IoMT Applications.
• Varghese V., Jomole; Manikandan, M. Sabarimalai & Cenkeramaddi, Linga Reddy (2023). Lightweight Time-Domain HRV Index Extraction from Compressive ECG Measurements for Resource Constrained Edge Health Computing Devices.
• Akella, Sriram Mahateja; Mubasier, Kamar; Michallik, Maximilian; Yeduri, Sreenivasa Reddy & Cenkeramaddi, Linga Reddy (2023). Distributed Q-Learning-Based UAV-Assisted Small World Wireless Network for Energy-Efficient and Delay-Critical Data Transmission.
• Dondup, Thacchan; Manikandan, M. Sabarimalai & Cenkeramaddi, Linga Reddy (2023). EEG Based Emotion Recognition using Variational Mode Decomposition and Convolutional Neural Network for Affective Computing Interfaces.
• P N, Sivaranjini; Manikandan, M. Sabarimalai & Cenkeramaddi, Linga Reddy (2023). Lightweight Photoplethysmogram (PPG) Waveform Change Detection for Resource-Constrained IoT Enabled Remote Health Monitoring Devices.
• A., Soumya; Cenkeramaddi, Linga Reddy; Chalavadi, Vishnu; Lanjewar, Yash Vinod & C., Krishna Mohan (2023). Multi-Target Classification using Deep Learning Models for Automotive Applications.
• Nelson, Wilson Ayyanthole; Jha, Ajit; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2023). Estimation of UAV Count Using Thermal Imaging and Lightweight CNN.
• Ovsthus, Orjan; Robsrud, Didrik Nicolai; Muggerud, Lars; Amendola, José; Cenkeramaddi, Linga Reddy & Tyapin, Ilya [Show all 7 contributors for this article] (2023). Mobile Robotic Manipulator based Autonomous Warehouse Operations.
• Robsrud, Didrik Nicolai; Ovsthus, Orjan; Muggerud, Lars; Amendola, José; Cenkeramaddi, Linga Reddy & Tyapin, Ilya [Show all 7 contributors for this article] (2023). Lidar-mmW Radar Fusion for Safer UGV Autonomous Navigation with Collision Avoidance.
• Amendola, José; Cenkeramaddi, Linga Reddy & Jha, Ajit (2023). Single Reinforcement Learning Policy for Landing a Drone under Different UGV Velocities and Trajectories.
• Bhardwaj, Jyotirmoy; Harman, Christopher Peter; Gardiyawasam Pussewalage, Harsha & Cenkeramaddi, Linga Reddy (2023). Real-Time Missing Data Estimation in Water Networks.
• Sahu, Prajwal Kumar; Boppu, Srinivas; Shafik, Rishad Ahmed; Tunheim, Svein Anders; Granmo, Ole-Christoffer & Cenkeramaddi, Linga Reddy (2023). Enhancing Inference Performance through Include only Literal Incorporation in Tsetlin Machine.
• Yeduri, Sreenivasa Reddy; Jeeru, Sindhusha & Cenkeramaddi, Linga Reddy (2023). Green machine learning protocols for machine-to-machine communication. In Saim, Ghafoor & Rehmani, Mubashir Husain (Ed.), Green Machine Learning Protocols for Future Communication Networks. CRC Press. ISSN 9781003230427. doi: https:/doi.org/10.1201/9781003230427.
• P N, Sivaranjini; Manikandan, M. Sabarimalai & Cenkeramaddi, Linga Reddy (2023). Fast Quality-Aware AMDF Based Pulse Rate Estimation from Compressed PPG Measurements for Wearable Vital Signs Monitor.
• Jomole, Varghese V; Manikandan, M. Sabarimalai & Cenkeramaddi, Linga Reddy (2023). Compressed ECG Sensing Based Fast/Slow HR and Regular/Irregular Rhythm Recognition for Resource-Constrained Health Monitoring Devices.
• Andrade, José Amendola Netto; Cenkeramaddi, Linga Reddy & Jha, Ajit (2023). Embedding Clustering using Lightweight Contrastive Learning For Cross-Modal Classification.
• Chebrolu, Sravanth; Boppu, Srinivas & Cenkeramaddi, Linga Reddy (2023). Hardware Acceleration of a CNN-based Automatic Modulation Classifier.
• Sharma, Jaya; Peketi, Divya; C., Vishnu; Cenkeramaddi, Linga Reddy; B.H., Shekar & C., Krishna Mohan (2023). Deformable and Structural Representative Network for Remote Sensing Image Captioning.
• Yakkati, Rakesh Reddy; Bethi, Pardhasaradhi; Zhou, Jing & Cenkeramaddi, Linga Reddy (2022). A Machine Learning Based GNSS Signal Classification.
• Yakkati, Rakesh Reddy; Bethi, Pardhasaradhi; Yeduri, Sreenivasa Reddy; Pandey, Om Jee & Cenkeramaddi, Linga Reddy (2022). Power Transmission Line Classification From Images Using Pre-Trained Deep Learning Models.
• Jha, Ajit; Cenkeramaddi, Linga Reddy & Royo, Santiago (2022). Frequency Domain Analysis and Filter Design of Continuous Wave Frequency Modulated Optical Feedback Signal for Photonic Sensing.

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