Ajit Jha

• Amendola, José; Cenkeramaddi, Linga Reddy & Jha, Ajit (2024). Image Translation and Reconstruction Using a Single Dual Mode Lightweight Encoder. IEEE Access. ISSN 2169-3536. 12, s. 26787–26799. doi: 10.1109/ACCESS.2024.3365831.
• Pretto, Riccardo; Dayal, Aveen; Oghbasilasie Haile, Yonas; Amendola Netto Andrade, José; Cenkeramaddi, Linga Reddy & Robbersmyr, Kjell Gunnar [Vis alle 7 forfattere av denne artikkelen] (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. s. 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. s. 16–21. doi: 10.1109/iSES58672.2023.00015.
• 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. s. 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. I Zhou, Jing (Red.), 2023 11th International Conference on Control, Mechatronics and Automation (ICCMA). IEEE conference proceedings. ISSN 979-8-3503-1568-4. s. 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.
• 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. s. 732–737. doi: 10.1109/ICCCS57501.2023.10150770. Vis sammendrag

  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.

• 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, s. 73583–73598. doi: 10.1109/ACCESS.2023.3295212. Fulltekst i vitenarkiv Vis sammendrag

  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 %).

• 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), s. 18718–18728. doi: 10.1109/JSEN.2023.3292899. Vis sammendrag

  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.

• 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), s. 533–546. doi: 10.1121/10.0020292. Vis sammendrag

  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.

• 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), s. 16508–16522. doi: 10.1364/OE.484086. Fulltekst i vitenarkiv Vis sammendrag

  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

• 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), s. 3395–3407. doi: 10.1109/JSEN.2023.3234335. Vis sammendrag

  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), s. 3173–3182. doi: 10.1109/JSEN.2022.3231790. Vis sammendrag

  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.

• 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. s. 136–141. doi: 10.1109/iSES54909.2022.00037. Vis sammendrag

  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.

• Sanfilippo, Filippo; Thorstensen, Rein Terje; Jha, Ajit; Jiang, Zhiyu & Robbersmyr, Kjell Gunnar (2022). A Perspective Review on Digital Twins for Roads, Bridges, and Civil Infrastructures. I -, IEEE (Red.), IEEE 2022 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-7095-7. doi: 10.1109/ICECCME55909.2022.9988693.
• Nelson, Wilson Ayyanthole; Yeduri, Sreenivasa Reddy; Jha, Ajit; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2022). Hybrid BLE/LTE/Wi-Fi/LoRa Switching Scheme for UAV-Assisted Wireless Networks, 2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). IEEE conference proceedings. ISSN 978-1-6654-4893-2. s. 78–83. doi: 10.1109/ANTS52808.2021.9936962. Vis sammendrag

  The unmanned aerial vehicles are deployed in multiple layers to monitor an area and report the information to the ground control station. When we use a single communication protocol such as Bluetooth Low Energy (BLE)/Wi-Fi with low range, the data has to pass through multiple hops for data transfer. This, in turn, increases the delay for data transmission. Even though LoRa protocol supports longer distances, the delay is more due to the limited bandwidth. Thus, in this work, we propose a hybrid BLE/LTE/Wi-Fi/LoRa switching scheme that consumes lower energy in addition to reducing the average delay in the network. The proposed scheme switches between the communication technologies based on the lower energy consumption. The performance of the proposed hybrid switching scheme is compared with the individual communication protocols in terms of both energy consumption and average delay. Through extensive numerical results, we show that the proposed hybrid switching scheme performs better in comparison to the individual communication technologies.

• Dayal, Aveen; Cenkeramaddi, Linga Reddy & Jha, Ajit (2022). Reward criteria impact on the performance of reinforcement learning agent for autonomous navigation. Applied Soft Computing. ISSN 1568-4946. 126. doi: 10.1016/j.asoc.2022.109241.
• Nelson, Wilson Ayyanthole; Kumar, Abhinav; Jha, Ajit & Cenkeramaddi, Linga Reddy (2022). Embedded Sensors, Communication Technologies, Computing Platforms and Machine Learning for UAVs: A Review. IEEE Sensors Journal. ISSN 1530-437X. 22, s. 1807–1826. doi: 10.1109/JSEN.2021.3139124. Vis sammendrag

  Unmanned aerial vehicles (UAVs) are increasingly becoming popular due to their use in many commercial and military applications, and their affordability. The UAVs are equipped with various sensors, hardware platforms and software technologies which enable them to support the diverse application portfolio. Sensors include vision-based sensors such as RGB-D cameras, thermal cameras, light detection and ranging (LiDAR), mmWave radars, ultrasonic sensors, and an inertial measurement unit (IMU) which enable UAVs for autonomous navigation, obstacle detection, collision avoidance, object tracking and aerial inspection. To enable smooth operation, UAVs utilize a number of communication technologies such as wireless fidelity (Wi-Fi), long range (LoRa), long-term evolution for machine-type communication (LTE-M), etc., along with various machine learning algorithms. However, each of these different technologies come with their own set of advantages and challenges. Hence, it is essential to have an overview of the different type of sensors, computing and communication modules and algorithms used for UAVs. This paper provides a comprehensive review on the state-of-the-art embedded sensors, communication technologies, computing platforms and machine learning techniques used in autonomous UAVs. The key performance metrics along with operating principles and a detailed comparative study of the various technologies are also studied and presented. The information gathered in this paper aims to serve as a practical reference guide for designing smart sensing applications, low-latency and energy efficient communication strategies, power efficient computing modules and machine learning algorithms for autonomous UAVs. Finally, some of the open issues and challenges for future research and development are also discussed.

• Breland, Daniel Skomedal; Dayal, Aveen; Jha, Ajit; Yalavarthy, Phaneendra K.; Pandey, Om Jee & Cenkeramaddi, Linga Reddy (2021). Robust Hand Gestures Recognition using a Deep CNN and Thermal Images. IEEE Sensors Journal. ISSN 1530-437X. doi: 10.1109/JSEN.2021.3119977. Vis sammendrag

  Medical systems and assistive technologies, human-computer interaction, human-robot interaction, industrial automation, virtual environment control, sign language translation, crisis and disaster management, entertainment and computer games, and so on all use RGB cameras for hand gesture recognition. However, their performance is limited especially in low-light conditions. In this paper, we propose a robust hand gesture recognition system based on high-resolution thermal imaging that is light-independent. A dataset of 14,400 thermal hand gestures is constructed, separated into two color tones. We also propose using a deep CNN to classify high-resolution hand gestures accurately. The proposed models were also tested on Raspberry Pi 4 and Nvidia AGX edge computing devices, and the results were compared to the benchmark models. The model also achieves an accuracy of 98.81% and an inference time of 75.138 ms on Nvidia Jetson AGX. In contrast to hand gesture recognition systems based on RGB cameras, which have limited performance in the dark-light conditions, the proposed system based on reliable high resolution thermal images is well-suited to a wide range of applications.

• Pandya, Aarav; Jha, Ajit & Cenkeramaddi, Linga Reddy (2021). A velocity estimation technique for a monocular camera using mmwave FMCW radars. Electronics. ISSN 2079-9292. 10(19). doi: 10.3390/electronics10192397. Fulltekst i vitenarkiv
• Jindal, Mehak; Jha, Ajit & Cenkeramaddi, Linga Reddy (2021). Bollard Segmentation and Position Estimation From Lidar Point Cloud for Autonomous Mooring. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892. doi: 10.1109/TGRS.2021.3097134. Fulltekst i vitenarkiv
• Bhatia, Jyoti; Dayal, Aveen; Jha, Ajit; Vishvakarma, Santosh Kumar; J, Soumya & M. B., Srinivas [Vis alle 11 forfattere av denne artikkelen] (2021). Classification of Targets Using Statistical Features from Range FFT of mmWave FMCW Radars. Electronics. ISSN 2079-9292. 10(16). doi: 10.3390/electronics10161965. Fulltekst i vitenarkiv
• Jha, Ajit; Shah, Manoj K.; Jha, Sachin; Cenkeramaddi, Linga Reddy & Royo, Santiago (2021). Current Modulation Induced Stability in Laser Diode Under High Optical Feedback Strength. IEEE Access. ISSN 2169-3536. 9, s. 49537–49546. doi: 10.1109/ACCESS.2021.3069387. Fulltekst i vitenarkiv
• Breland, Daniel Skomedal; Skriubakken, Håvard Birkeland; Dayal, Aveen; Jha, Ajit; Yalavarthy, Phaneendra K. & Cenkeramaddi, Linga Reddy (2021). Deep Learning-Based Sign Language Digits Recognition From Thermal Images With Edge Computing System. IEEE Sensors Journal. ISSN 1530-437X. 21(9), s. 10445–10453. doi: 10.1109/JSEN.2021.3061608.
• Cenkeramaddi, Linga Reddy; Rai, Prabhat Kumar; Dayal, Aveen; Bhatia, Jyoti; Pandya, Aarav & J, Soumya [Vis alle 8 forfattere av denne artikkelen] (2021). A Novel Angle Estimation for mmWave FMCW radars using Machine Learning. IEEE Sensors Journal. ISSN 1530-437X. 21(8), s. 9833–9843. doi: 10.1109/JSEN.2021.3058268.
• Bhatia, Jyoti; Dayal, Aveen; Jha, Ajit; Vishvakarma, Santosh Kumar; J, Soumya & M. B., Srinivas [Vis alle 11 forfattere av denne artikkelen] (2021). Object Classification Technique for mmWave FMCW Radars using Range-FFT Features, 2021 International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE conference proceedings. ISSN 978-1-7281-9127-0. s. 111–115. doi: 10.1109/COMSNETS51098.2021.9352894.
• Jha, Ajit; Subedi, Dipendra; Løvsland, Per-Ove; Tyapin, Ilya; Cenkeramaddi, Linga Reddy & Beferull-Lozano, Baltasar [Vis alle 7 forfattere av denne artikkelen] (2020). Autonomous mooring towards autonomous maritime navigation and offshore operations, 15th IEEE Conference on Industrial Electronics and Applications. Institute of Electrical and Electronics Engineers (IEEE). ISSN 978-1-7281-5168-7. s. 1171–1175. doi: 10.1109/ICIEA48937.2020.9248169.
• Cenkeramaddi, Linga Reddy; Bhatia, Jyoti; Jha, Ajit; Vishvakarma, Santosh Kumar & J, Soumya (2020). A Survey on Sensors for Autonomous Systems, 15th IEEE Conference on Industrial Electronics and Applications. Institute of Electrical and Electronics Engineers (IEEE). ISSN 978-1-7281-5168-7. s. 1182–1187. doi: 10.1109/ICIEA48937.2020.9248282.
• Bhusal, Hari; Khatiwada, Pankaj; Jha, Ajit; J, Soumya; Koorapati, Sagar & Cenkeramaddi, Linga Reddy (2020). A Self-Powered Long-Range Wireless IoT Device Based on LoRaWAN. I Guerrero, Juan (Red.), 2020 6th IEEE International Symposium on Smart Electronic Systems. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-0-7381-4264-7. s. 258–261.
• Subedi, Dipendra; Jha, Ajit; Tyapin, Ilya & Hovland, Geir (2020). Camera-LiDAR Data Fusion for Autonomous Mooring Operation. IEEE Conference on Industrial Electronics and Applications. ISSN 2158-2297. s. 1176–1181. doi: 10.1109/ICIEA48937.2020.9248089. Fulltekst i vitenarkiv
• Jha, Ajit & Royo, Santiago (2020). Optical method based detection and wavelets based processing of acoustic waves. IEEE Conference on Industrial Electronics and Applications. ISSN 2158-2297. s. 927–932. doi: 10.1109/ICIEA48937.2020.9248418.

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• Pretto, Riccardo; Dayal, Aveen; Oghbasilasie Haile, Yonas; Amendola Netto Andrade, José; Cenkeramaddi, Linga Reddy & Robbersmyr, Kjell Gunnar [Vis alle 7 forfattere av denne artikkelen] (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 [Vis alle 7 forfattere av denne artikkelen] (2023). Lidar-mmW Radar Fusion for Safer UGV Autonomous Navigation with Collision Avoidance. I Zhou, Jing (Red.), 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 [Vis alle 7 forfattere av denne artikkelen] (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.
• 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 [Vis alle 7 forfattere av denne artikkelen] (2023). Mobile Robotic Manipulator based Autonomous Warehouse Operations.
• Robsrud, Didrik Nicolai; Ovsthus, Orjan; Muggerud, Lars; Amendola, José; Cenkeramaddi, Linga Reddy & Tyapin, Ilya [Vis alle 7 forfattere av denne artikkelen] (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.
• Andrade, José Amendola Netto; Cenkeramaddi, Linga Reddy & Jha, Ajit (2023). Embedding Clustering using Lightweight Contrastive Learning For Cross-Modal Classification.
• 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.
• Sanfilippo, Filippo; Thorstensen, Rein Terje; Jha, Ajit; Jiang, Zhiyu; Knausgård, Kristian Muri & Robbersmyr, Kjell Gunnar (2021). A Perspective of Road Infrastructure and Bridges Digital Twins.
• Nelson, Wilson Ayyanthole; Yeduri, Sreenivasa Reddy; Jha, Ajit; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2021). Hybrid BLE/LTE/Wi-Fi/LoRa Switching Scheme for UAV-Assisted Wireless Networks. Vis sammendrag

  The unmanned aerial vehicles are deployed in multiple layers to monitor an area and report the information to the ground control station. When we use a single communication protocol such as Bluetooth Low Energy (BLE)/Wi-Fi with low range, the data has to pass through multiple hops for data transfer. This, in turn, increases the delay for data transmission. Even though LoRa protocol supports longer distances, the delay is more due to the limited bandwidth. Thus, in this work, we propose a hybrid BLE/LTE/Wi-Fi/LoRa switching scheme that consumes lower energy in addition to reducing the average delay in the network. The proposed scheme switches between the communication technologies based on the lower energy consumption. The performance of the proposed hybrid switching scheme is compared with the individual communication protocols in terms of both energy consumption and average delay. Through extensive numerical results, we show that the proposed hybrid switching scheme performs better in comparison to the individual communication technologies.

• Bhatia, Jyoti; Dayal, Aveen; Jha, Ajit; Vishvakarma, Santosh Kumar; J, Soumya & M. B., Srinivas [Vis alle 11 forfattere av denne artikkelen] (2021). Object Classification Technique for mmWave FMCW Radars using Range-FFT Features.
• Bhusal, Hari; Khatiwada, Pankaj; Jha, Ajit; J, Soumya; Koorapati, Sagar & Cenkeramaddi, Linga Reddy (2020). A Self-Powered Long-range Wireless IoT Device based on LoRaWAN.
• Cenkeramaddi, Linga Reddy; Bhatia, Jyoti; Jha, Ajit; Vishvakarma, Santosh Kumar & J, Soumya (2020). A Survey on Sensors for Autonomous Systems.
• Jha, Ajit; Subedi, Dipendra; Løvsland, Per-Ove; Tyapin, Ilya; Cenkeramaddi, Linga Reddy & Beferull-Lozano, Baltasar [Vis alle 7 forfattere av denne artikkelen] (2020). Autonomous mooring towards autonomous maritime navigation and offshore operations.
• Cenkeramaddi, Linga Reddy; Jha, Ajit & Pandya, Aarav (2020). INDO-NORWEGIAN COLLABORATION THESIS/REPORT: SPEED ESTIMATION FROM MONOCULAR CAMERA BY FUSING RADAR AND OPTICAL FLOW. Universitetet i Agder.
• Cenkeramaddi, Linga Reddy; Jha, Ajit & Bhatia, Agam (2020). INDO-NORWEGIAN COLLABORATION THESIS/REPORT: LOAD BALANCING USING mmWAVE RADARS AND CAMERA. Universitetet i Agder.
• Cenkeramaddi, Linga Reddy; Jha, Ajit & Jindal, Mehak (2020). INDO-NORWEGIAN COLLABORATION THESIS/REPORT: BOLLARD SEGMENTATION FROM 3D POINT CLOUD FOR AUTONOMOUS MOORING. Universitetet i Agder.
• Cenkeramaddi, Linga Reddy; Jha, Ajit & Dayal, Aveen (2020). INDO-NORWEGIAN COLLABORATION THESIS/REPORT: SURROUND VIEW SYSTEM FOR ADVANCED DRIVER ASSISTANCE SYSTEM. Universitetet i Agder.
• Cenkeramaddi, Linga Reddy; Jha, Ajit & Bhusal, Hari (2020). Design and Implementation of Self-Powered Long Range IoT Device Based on LoRaWAN. Universitetet i Agder.

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