Ajit Jha

• Båserud, Tilla Egerhei; Johansen, Joakim; Jha, Ajit & Tyapin, Ilya (2026). Dual-Arm Robotic Textile Unfolding with Depth-Corrected Perception and Fold Resolution. Robotics. 15(4). doi: 10.3390/robotics15040078. Full text in Research Archive Show summary

  Reliable textile recycling requires automated unfolding to expose hidden hard components such as zippers, buttons, and metal fasteners, which otherwise risk damaging machinery and compromising downstream processes. This paper presents the design and implementation of an automated textile unfolding system based on a dual-arm robotic manipulation framework. The system uses two Interbotix WidowX 250s 6-DoF robotic arms and an Intel RealSense L515 LiDAR camera for visual perception. The unfolding process consists of three stages: initial dual-arm stretching to reduce major folds, refinement through a second stretch targeting the lower region, and a machine-learning stage that employs a YOLOv11 framework trained on depth-encoded textile images, followed by a depth-gradient-based estimator for fold direction. The system applies an extremity-based grasping strategy that selects leftmost and rightmost textile points from a custom error-corrected depth map, enabling robust grasp point selection, and a fold direction estimation method based on depth gradients around the detected fold. The most confident fold region is selected, an unfolding direction is determined using depth ranking, and the textile is manipulated until a flat state is confirmed through depth uniformity. Experiments show that depth correction significantly reduces spatial error in the robot frame, while segmentation and extremity detection achieve high accuracy across varied fold configurations, and the YOLOv11n-based model reaches 98.8% classification accuracy, while fold direction is estimated correctly in 87% of test cases. By enabling robust, largely autonomous textile unfolding, the system demonstrates a practical approach that could support safer and more efficient automated textile recycling workflows.

• Halvorsen, Torbjørn Seim; Tyapin, Ilya & Jha, Ajit (2025). Autonomous Textile Sorting Facility and Digital Twin Utilizing an AI-Reinforced Collaborative Robot. Electronics (Basel). 14(13). doi: 10.3390/electronics14132706. Full text in Research Archive Show summary

  This paper presents the design and implementation of an autonomous robotic facility for textile sorting and recycling, leveraging advanced computer vision and machine learning technologies. The system enables real-time textile classification, localization, and sorting on a dynamically moving conveyor belt. A custom-designed pneumatic gripper is developed for versatile textile handling, optimizing autonomous picking and placing operations. Additionally, digital simulation techniques are utilized to refine robotic motion and enhance overall system reliability before real-world deployment. The multi-threaded architecture facilitates the concurrent and efficient execution of textile classification, robotic manipulation, and conveyor belt operations. Key contributions include (a) dynamic and real-time textile detection and localization, (b) the development and integration of a specialized robotic gripper, (c) real-time autonomous robotic picking from a moving conveyor, and (d) scalability in sorting operations for recycling automation across various industry scales. The system progressively incorporates enhancements, such as queuing management for continuous operation and multi-thread optimization. Advanced material detection techniques are also integrated to ensure compliance with the stringent performance requirements of industrial recycling applications.

• Narayanan, Sivaranjini Perikamana; Jha, Ajit; Cenkeramaddi, Linga Reddy & Manikandan, M. Sabarimalai (2025). Deep Learning-Based Road Activity Image Classification for Intelligent Traffic Management, 2025 17th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331533526. doi: 10.1109/ECAI65401.2025.11095446. Full text in Research Archive Show summary

  Automatic vehicle classification is a crucial aspect of intelligent transportation systems for monitoring traffic flow, security enforcement, automated parking, driver assistance, and autonomous vehicle control. These systems face challenges such as limited computational resources, limited sensor range, and communication channels, which demand fast and lightweight algorithms for accurate and reliable performance. This paper presents an accurate, fast, and lightweight convolutional neural network (CNN)-based vehicle image classification model for intelligent traffic management applications using the urban scene database. The dataset includes multiple scenes captured during morning, evening, and night, presenting diverse lighting and environmental conditions. The performance of the proposed model is evaluated in terms of class-wise accuracy, overall accuracy, and computational resources. The proposed CNN model achieves an overall accuracy of 99.33% with an inference speed of 188 fps and a model size of 16.6 MB, and outperforms most of the benchmark pretrained models. The classification accuracy highlights the model's reliability and adaptability to real-world challenges such as variable lighting conditions.

• Andrade, José Amendola Netto; Cenkeramaddi, Linga Reddy & Jha, Ajit (2024). Drone Landing and Reinforcement Learning: State-of-Art, Challenges and Opportunities. IEEE Open Journal of Intelligent Transportation Systems. 5, p. 520–539. doi: 10.1109/OJITS.2024.3444487. Full text in Research Archive Show summary

  Unmanned aerial vehicles, and special multirotor drones, have shown great relevance in a plethora of missions that require high affordance, field of view, and precision. Their limited payload capacity and autonomy make its landing a crucial task. Despite many attempts in the literature to address drone landing, challenges and open gaps still exist. Reinforcement Learning has gained notoriety in a variety of control problems, with recent proposals for drone landing applications. This work aims to present a systematic literature review on works employing Deep Reinforcement Learning for multirotor drone landing in both static and dynamic platforms. It also revisits Reinforcement Learning Algorithms, the main frameworks and simulators adopted for specific landing operations. The comprehensive analysis performed on reviewed works revealed that there are important untackled challenges when it comes to wind disturbances, unpredictability of moving landing targets, sensor latency, and sim-to-real gap. Finally, we present our critical analysis of how recent state-of-the-art deep learning concepts can be combined with reinforcement learning to leverage the latter in addressing the open gaps in future works.

• Nathala, Sai Sree; Yakkati, Rakesh Reddy; Breland, Daniel Skomedal; Yeduri, Sreenivasa Reddy; Manikandan, M. Sabarimalai & Jha, Ajit [Show all 8 contributors for this article] (2024). A Deep CNN-based Hand Gestures Recognition using High-Resolution Thermal Imaging, 2024 IEEE 19th Conference on Industrial Electronics and Applications - ICIEA. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350360868. doi: 10.1109/ICIEA61579.2024.10665263. Full text in Research Archive
• Andrade, José Amendola Netto; Cenkeramaddi, Linga Reddy & Jha, Ajit (2024). Image Translation and Reconstruction Using a Single Dual Mode Lightweight Encoder. IEEE Access. 12, p. 26787–26799. doi: 10.1109/ACCESS.2024.3365831. Full text in Research Archive Show summary

  The richness of textures and semantic information from RGB images can be supplemented in computer vision by the robustness of thermal images to light variations and weather artifacts. While many models rely on inputs from one sensor modality, image translation among modalities can be a solution. The existing works use large models that only work in one translation direction. This cause problems in limited computation applications, as well as a lack of flexibility to work interchangeably for different modalities. Three channel cameras extract visually rich features, but processing them on embedded platforms becomes a bottleneck. Furthermore, edge computing systems impose the burden of compressing data to be sent elsewhere. To address these issues, we propose a novel architecture with a single lightweight encoder capable of working in dual mode, encoding inputs from both grayscale an thermal images into very compact latent vectors. The encoding is then used for cross-modal image translation, grayscale image colorization and thermal image reconstruction, thus allowing 1) different downstream tasks on different modalities, 2) visually rich features from grayscale images and 3) data compression. Four different generators are employed and the training occurs in adversarial fashion with two discriminators. The loss function proposed contains not only adversarial terms but also reconstruction error terms. They induce consistency and contrast preservation across translation and reconstruction. The results backed by evaluation over multiple metrics demonstrate that the model performs the tasks with competitive quality of translation/reconstruction of images with different lighting conditions. Finally, we perform ablation studies to demonstrate the effectiveness of loss terms combined.

• 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. Full text in Research Archive 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.

• Pretto, Riccardo; Dayal, Aveen; Haile, Yonas Oghbasilasie; Andrade, José Amendola Netto; 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 (Institute of Electrical and Electronics Engineers). ISSN 9798350383249. p. 27–31. doi: 10.1109/iSES58672.2023.00017. Full text in Research Archive
• Andrade, José Amendola Netto; 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 (Institute of Electrical and Electronics Engineers). ISSN 9798350383249. p. 16–21. doi: 10.1109/iSES58672.2023.00015. Full text in Research Archive
• 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 (Institute of Electrical and Electronics Engineers). ISSN 9781665456128. p. 732–737. doi: 10.1109/ICCCS57501.2023.10150770. Full text in Research Archive 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.

• 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. 32(3), p. 1951–1966. doi: 10.1109/TNET.2023.3332296. Full text in Research Archive
• 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. 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 %).

• 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. Full text in Research Archive 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.

• 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. Full text in Research Archive 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.

• Jha, Ajit; Cenkeramaddi, Linga Reddy & Royo, Santiago (2023). Generalized multi-cavity laser self-mixing interferometry based on scattering theory. Optics Express. 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

• 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. Full text in Research Archive 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.

• Nelson, Wilson Ayyanthole; Jha, Ajit; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2023). Estimation of UAV Count Using Thermal Imaging and Lightweight CNN. In Zhou, Jing (Eds.), 2023 The 11th International Conference on Control, Mechatronics and Automation (ICCMA 2023). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665490481. p. 92–96. doi: 10.1109/ICCMA59762.2023.10374791. Full text in Research Archive
• Andrade, José Amendola Netto; 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 (Institute of Electrical and Electronics Engineers). ISSN 9798350315684. p. 115–120. doi: 10.1109/ICCMA59762.2023.10374818. Full text in Research Archive
• 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, p. 1807–1826. doi: 10.1109/JSEN.2021.3139124. Full text in Research Archive Show summary

  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.

• 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 (Institute of Electrical and Electronics Engineers). ISSN 9798350399226. p. 136–141. doi: 10.1109/iSES54909.2022.00037. Full text in Research Archive 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.

• 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. In -, IEEE (Eds.), IEEE 2022 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665470957. doi: 10.1109/ICECCME55909.2022.9988693. Full text in Research Archive
• 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 (Institute of Electrical and Electronics Engineers). ISSN 9781665448932. p. 78–83. doi: 10.1109/ANTS52808.2021.9936962. Full text in Research Archive Show summary

  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. Full text in Research Archive
• Pandya, Aarav; Jha, Ajit & Cenkeramaddi, Linga Reddy (2021). A velocity estimation technique for a monocular camera using mmwave FMCW radars. Electronics (Basel). 10(19). doi: 10.3390/electronics10192397. Full text in Research Archive
• Cenkeramaddi, Linga Reddy; Rai, Prabhat Kumar; Dayal, Aveen; Bhatia, Jyoti; Pandya, Aarav & J, Soumya [Show all 8 contributors for this article] (2021). A Novel Angle Estimation for mmWave FMCW radars using Machine Learning. IEEE Sensors Journal. ISSN 1530-437X. 21(8), p. 9833–9843. doi: 10.1109/JSEN.2021.3058268. Full text in Research Archive Show summary

  In this article, we present a novel machine learning based angle estimation and field of view (FoV) enhancement techniques for mmWave FMCW radars operating in the frequency range of 77 - 81 GHz. Field of view is enhanced in both azimuth and elevation. The Elevation FoV enhancement is achieved by keeping the orientation of antenna elements in elevation. In this orientation, radar focuses the beam in vertical direction there by enhancing the elevation FoV. An Azimuth FoV enhancement is achieved by mechanically rotating the radar horizontally, which has antenna elements in the elevation. With the proposed angle estimation technique for such rotating radars, root mean square error (RMSE) of 2.56 degrees is achieved. These proposed techniques will be highly useful for several applications in cost-effective and reliable autonomous systems such as ground station traffic monitoring and control systems for both on ground and aerial vehicles.

• 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. 9, p. 49537–49546. doi: 10.1109/ACCESS.2021.3069387. Full text in Research Archive Show summary

  The back-reflection of emitted laser beam (optical feedback, also know as selfmixing) from various external interfaces are sufficient to cause instability, and prohibiting its use in various fields such as communication, spectroscopy, imaging to name a few. So it is desirable to study the laser dynamics and the conditions causing it to be stable in spite of strong optical feedback. With the aid of mathematical formulation, simulation and backed by experimental evidences, it is demonstrated that the frequency deviation of the laser emission due to current (intensity) modulation alters the dynamic state and boundary conditions of the system such that even under large optical feedback strength, the laser may attain stability and retain single modal state. The frequency deviation resulting from former is shown to modify the phase of the system in opposite direction to that induced by the later, showing that there exists an optimal modulation current which compensates the effect of optical feedback and may be used to retain the laser in single modal stationary state. The method thus provides a methodology to avoid optical feedback-induced instability in semiconductor lasers by using the proper amplitude of current (intensity) modulation.

• 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), p. 10445–10453. doi: 10.1109/JSEN.2021.3061608. Full text in Research Archive Show summary

  The sign language digits based on hand gestures have been utilized in various applications such as human-computer interaction, robotics, health and medical systems, health assistive technologies, automotive user interfaces, crisis management and disaster relief, entertainment, and contactless communication in smart devices. The color and depth cameras are commonly deployed for hand gesture recognition, but the robust classification of hand gestures under varying illumination is still a challenging task. This work presents the design and deployment of a complete end-to-end edge computing system that can accurately provide the classification of hand gestures captured from thermal images. A thermal dataset of 3200 images was created with each sign language digit having 320 thermal images. The solution presented here utilizes live images taken from a low-resolution thermal camera of 32×32 pixels, feeding into a novel light weight deep learning model based on bottleneck motivated from deep residual learning for classification of hand gestures. The edge computing system presented here utilizes Raspberry pi with a thermal camera making it highly portable. The designed system achieves an accuracy of 99.52% on the test data set with an added advantage of accuracy being invariable to background lighting conditions as it is based on thermal imaging.

• Bhatia, Jyoti; Dayal, Aveen; Jha, Ajit; Vishvakarma, Santosh Kumar; J, Soumya & B., Srinivas M. [Show all 11 contributors for this article] (2021). Classification of Targets Using Statistical Features from Range FFT of mmWave FMCW Radars. Electronics (Basel). 10(16). doi: 10.3390/electronics10161965. Full text in Research Archive
• 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. 60. doi: 10.1109/TGRS.2021.3097134. Full text in Research Archive Show summary

  This article presents a computer-aided object detection and localization method from lidar 3-D point cloud data. This topic of interest is in the framework of autonomous mooring, where the ship is tied to the rigid structure on-shore (bollard) for autonomous maritime navigation. Using shape and features priors, unlike matching the whole object template to the experimental 3-D point cloud representation of the scene, two customized algorithms: 1) 3-D feature matching (3-DFM) and 2) mixed feature-correspondence matching (MFCM) are presented. The proposed algorithms discriminate and extract the 3-D points corresponding to the noncooperative bollard’s surface from the background, thus capable of classification, localization, and representing it using a unique coordinate in the 3-D world. The proposed algorithms are tested and validated by implementing upon an experimental dataset of 105 scenes where the bollard is at different positions and orientations with respect to lidar mounted on the robotic arm. Statistical and probabilistic-based approaches are taken into account to determine the performance of proposed algorithms. Model parameters’ estimation implies that errors resulting from the 3-DFM algorithm follow homoscedastic bimodal Gaussian distribution with individual Gaussian components having mean 0.03 and 0.09 m, and both have an equal standard deviation of 0.01 m. Furthermore, the posterior component assignment probability is used to identify and cluster the scenes that contribute to relatively larger errors. Finally, an improved algorithm, MFCM, is proposed, whose errors follow unimodal Gaussian distribution with a mean and a standard deviation of 0.03 and 0.01 m, respectively, thus mitigating the shortcomings of the former.

• Bhatia, Jyoti; Dayal, Aveen; Jha, Ajit; Vishvakarma, Santosh Kumar; J, Soumya & B., Srinivas M. [Show all 11 contributors for this article] (2021). Object Classification Technique for mmWave FMCW Radars using Range-FFT Features, 2021 International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728191270. p. 111–115. doi: 10.1109/COMSNETS51098.2021.9352894. Full text in Research Archive
• 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. 21(23). doi: 10.1109/JSEN.2021.3119977. Full text in Research Archive Show summary

  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.

• 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 9781728151687. p. 1182–1187. doi: 10.1109/ICIEA48937.2020.9248282. Full text in Research Archive
• Jha, Ajit; Subedi, Dipendra; Løvsland, Per-Ove; Tyapin, Ilya; Cenkeramaddi, Linga Reddy & Beferull-Lozano, Baltasar [Show all 7 contributors for this article] (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 9781728151687. p. 1171–1175. doi: 10.1109/ICIEA48937.2020.9248169. Full text in Research Archive Show summary

  Bollard is a vital component of mooring system. It is the anchor point for mooring ropes to be fixed in order to secure the vessel or ship. An algorithm that translates the segmented mask of bollard output from masked R-CNN along with bounding box and associated class probability to its corresponding edge coordinate and finally to the single reference point for efficient detection and classification of bollard towards autonomous mooring is presented. At first stage, Mask R-CNN framework is trained with custom built bollard. The model obtained from the training is inferred with real data resulting in instance segment of bollard. The segmented mask obtained contains relatively large amount of the data points representing the whole area of bollard, which typically is not desirable. In order to precisely localize the bollard with one reference co-ordinate, the proposed algorithm is applied to segmented mask. Firstly, it translates the segmented mask to only four co-ordinate points, where each point correspond to the edge of bollard. Further, from the edges, the reference point is estimated. This causes significant reduction in point of interest (POI) and has potential to reduce the error encountered during pose estimation of the bollard in 3D thus making the autonomous mooring more precise and accurate.

• 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. In Guerrero, Juan (Eds.), 2020 6th IEEE International Symposium on Smart Electronic Systems. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9780738142647. p. 258–261. Full text in Research Archive
• Jha, Ajit & Royo, Santiago (2020). Optical method based detection and wavelets based processing of acoustic waves. IEEE Conference on Industrial Electronics and Applications. p. 927–932. doi: 10.1109/ICIEA48937.2020.9248418. Full text in Research Archive
• Subedi, Dipendra; Jha, Ajit; Tyapin, Ilya & Hovland, Geir (2020). Camera-LiDAR Data Fusion for Autonomous Mooring Operation. IEEE Conference on Industrial Electronics and Applications. p. 1176–1181. doi: 10.1109/ICIEA48937.2020.9248089. Full text in Research Archive Show summary

  The use of camera and LiDAR sensors to sense the environment has gained increasing popularity in robotics. Individual sensors, such as cameras and LiDARs, fail to meet the growing challenges in complex autonomous systems. One such scenario is autonomous mooring, where the ship has to be tied to a fixed rigid structure (bollard) to keep it stationary safely. The detection and pose estimation of the bollard based on data fusion from the camera and LiDAR are presented here. Firstly, a single shot extrinsic calibration of LiDAR with the camera is presented. Secondly, the camera-LiDAR data fusion method using camera intrinsic parameters and camera to LiDAR extrinsic parameters is proposed. Finally, the use of an image-based segmentation method to segment the corresponding point cloud from the fused camera-LiDAR data is developed and tailored for its application in autonomous mooring operation.

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• Narayanan, Sivaranjini Perikamana; Jha, Ajit; Cenkeramaddi, Linga Reddy & Manikandan, M. Sabarimalai (2025). Deep Learning-Based Road Activity Image Classification for Intelligent Traffic Management. Full text in Research Archive
• Sirisilla, Sacharitha; Bethi, Pardhasaradhi; Zhou, Jing; Jha, Ajit & Cenkeramaddi, Linga Reddy (2025). Lightweight 2D CNN Fusion for Human Activity Recognition Using Multi-Transceiver CSI Data on Edge Devices. Full text in Research Archive
• Nathala, Sai Sree; Yakkati, Rakesh Reddy; Breland, Daniel Skomedal; Yeduri, Sreenivasa Reddy; Manikandan, M. Sabarimalai & Jha, Ajit [Show all 8 contributors for this article] (2024). A Deep CNN-based Hand Gestures Recognition using High-Resolution Thermal Imaging. Full text in Research Archive
• Andrade, José Amendola Netto; Cenkeramaddi, Linga Reddy & Jha, Ajit (2023). Embedding Clustering using Lightweight Contrastive Learning For Cross-Modal Classification. Full text in Research Archive
• Andrade, José Amendola Netto; Cenkeramaddi, Linga Reddy & Jha, Ajit (2023). Drone Landing on Moving UGV Platform With Reinforcement Learning Based Offsets. Full text in Research Archive
• Pretto, Riccardo; Dayal, Aveen; Haile, Yonas Oghbasilasie; Andrade, José Amendola Netto; 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. Full text in Research Archive
• Robsrud, Didrik Nicolai; Ovsthus, Orjan; Muggerud, Lars; Andrade, José Amendola Netto; 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 (Institute of Electrical and Electronics Engineers). ISSN 9798350315684. doi: 10.1109/ICCMA59762.2023.10375017. Full text in Research Archive
• Øvsthus, Ørjan; Robsrud, Didrik Nicolai; Muggerud, Lars; Andrade, José Amendola Netto; Cenkeramaddi, Linga Reddy & Tyapin, Ilya [Show all 7 contributors for this article] (2023). Mobile Robotic Manipulator Based Autonomous Warehouse Operations. In Zhou, Jing (Eds.), 2023 The 11th International Conference on Control, Mechatronics and Automation (ICCMA 2023). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665490481. doi: 10.1109/ICCMA59762.2023.10374743. Full text in Research Archive
• 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. Full text in Research Archive
• Nelson, Wilson Ayyanthole; Jha, Ajit; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2023). Estimation of UAV Count Using Thermal Imaging and Lightweight CNN. Full text in Research Archive
• Amendola, José; Cenkeramaddi, Linga Reddy & Jha, Ajit (2023). Single Reinforcement Learning Policy for Landing a Drone under Different UGV Velocities and Trajectories. Full text in Research Archive
• 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. Full text in Research Archive
• 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. Full text in Research Archive
• Sanfilippo, Filippo; Thorstensen, Rein Terje; Jha, Ajit; Jiang, Zhiyu & Robbersmyr, Kjell Gunnar (2021). A Perspective of Road Infrastructure and Bridges Digital Twins. Full text in Research Archive
• Bhatia, Jyoti; Dayal, Aveen; Jha, Ajit; Vishvakarma, Santosh Kumar; J, Soumya & B., Srinivas M. [Show all 11 contributors for this article] (2021). Object Classification Technique for mmWave FMCW Radars using Range-FFT Features. Full text in Research Archive
• 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. Full text in Research Archive Show summary

  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.

• Cenkeramaddi, Linga Reddy; Bhatia, Jyoti; Jha, Ajit; Vishvakarma, Santosh Kumar & J, Soumya (2020). A Survey on Sensors for Autonomous Systems. Full text in Research Archive
• Jha, Ajit; Subedi, Dipendra; Løvsland, Per-Ove; Tyapin, Ilya; Cenkeramaddi, Linga Reddy & Beferull-Lozano, Baltasar [Show all 7 contributors for this article] (2020). Autonomous mooring towards autonomous maritime navigation and offshore operations. Full text in Research Archive
• 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. Full text in Research Archive
• Cenkeramaddi, Linga Reddy; Jha, Ajit & Ellore, Ram Chandra Kamavaram (2025). Photonic laser sensor based multipoint motor bearing fault classification. Universitetet i Agder. Full text in Research Archive
• Nelson, Wilson Ayyanthole; Cenkeramaddi, Linga Reddy; Jha, Ajit & Kumar, Abhinav (2024). Smart Sensing and Communications for UAVs/Drones. Universitetet i Agder. ISSN 9788284272023. Full text in Research Archive Show summary

  Reliable sensing and energy-efficient communication are an integral part of unmanned aerial vehicle (UAV)-based applications. However, it is challenging to ensure reliable sensing under all weather conditions. Furthermore, enabling robust and energy-efficient communication of this sensed data for seamless autonomous operations is equally demanding. Low-complexity and lightweight algorithms play a vital role in this regard. As a result, the goal of this Ph.D. research project is to design, develop, and implement reliable sensing and energy-efficient communication techniques to provide seamless autonomous operations for UAVs and ground control stations. To achieve the intended goal, one of the objectives is directed toward developing novel lightweight low-complexity smart sensing algorithms that attain improved detection performance in adverse weather and lighting conditions too. In this regard, this research focuses on utilizing thermal, acoustic, and mmWave frequency-modulated continuous wave (FMCW) radar sensors with lightweight machine learning algorithms to obtain improved detection and localization performance. The sensed information along with telemetry data is required to be transmitted to other UAVs or ground control stations to facilitate efficient and secure coordination of autonomous UAVs. Hence, the secondary objective of this research addresses the challenge of achieving energy-efficient data transmission for a UAV-assisted wireless network. The research for attaining energy-efficient data transmission focuses on the design of a dynamic and adaptively optimized switching algorithm that leverages the distinct features of the various onboard communication modules. This thesis is a compendium of seven publications that are organized into five chapters. Overall the primary objective is to develop reliable smart sensing algorithms along with energy-efficient communication schemes to enable seamless autonomous UAV operations. The first chapter briefly introduces the various challenges dealt in the thesis and provides an overview of the thesis structure. Chapter 2 provides the necessary literary material to navigate the subsequent chapters. Chapter 3 discusses three sensing approaches using thermal, acoustic, and mmWave FMCW radar sensors to achieve improved detection and localization performance. In chapter 4, the notion of an adaptively optimized switching algorithm for energyefficient communication is conceived. Two approaches to form the hybrid communication switching network are discussed. Finally, chapter 5 concludes the Ph.D. dissertation by summarizing the proposed approaches and providing avenues for future research.

• Cenkeramaddi, Linga Reddy; Jha, Ajit & Dayal, Aveen (2020). INDO-NORWEGIAN COLLABORATION THESIS/REPORT: SURROUND VIEW SYSTEM FOR ADVANCED DRIVER ASSISTANCE SYSTEM. Universitetet i Agder. Full text in Research Archive
• Cenkeramaddi, Linga Reddy; Jha, Ajit & Bhusal, Hari (2020). Design and Implementation of Self-Powered Long Range IoT Device Based on LoRaWAN. Universitetet i Agder. Full text in Research Archive Show summary

  This project proposes a self-powered long-range wireless sensor node based on Long Range Wide Area Network (LoRaWAN) with various sensing capabilities. The nodes have been designed in such a way that these are self-sustainable throughout the components’ lifespan. These are completely powered by ambient solar energy harvesting. Also, these nodes can be deployed on a large scale and are maintenance-free. In addition, these nodes can be deployed in remote places where the accessibility is limited, and maintenance is difficult. The wireless sensor nodes can be deployed both in indoor and outdoor environments with sufficient light levels for the solar panel, such as indoor lights in the indoor environment and ambient sunlight in the outdoor environment. The fully functional wireless sensor node is designed and tested. The developed long-range node has a power consumption that is lower in comparison to the amount of power harvested in the ambient environment. The designed node senses the air quality parameters: the level of carbon dioxide, amount of humidity, pressure levels, temperature, and the total organic volatile compounds and gases. It is possible to add several other sensors without modifying the base hardware.

• 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. Full text in Research Archive
• 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. Full text in Research Archive
• Cenkeramaddi, Linga Reddy; Jha, Ajit & Bhatia, Agam (2020). INDO-NORWEGIAN COLLABORATION THESIS/REPORT: LOAD BALANCING USING mmWAVE RADARS AND CAMERA. Universitetet i Agder. Full text in Research Archive

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