arXiv subjects

Anju Rani

Publications and source records attributed to Anju Rani.

At least 19 recordsLinked to original sources

arXiv2026

Quantum Secure Time Transfer for Satellites

We experimentally demonstrate an entanglement-based Quantum-Secure Time Transfer (QSTT) system in an emulated low Earth orbit satellite-to-ground channel using a type-0 Sagnac-based entangled-photon source. Our new QSTT system delivers a finite Quantum Key Distribution (QKD) key rate of approximately $3~$bits$~\text{s}^{-1}$ with a $10^{-10}$ security parameter, providing a $30$-fold increase in the QKD key rate compared to the state-of-the-art entanglement-based QKD system delivered by the Micius satellite. Beyond this high key rate outcome, novel to our QSTT system is a GPS-free clock synchronization, optimized use of QKD, embedded post-quantum security, and obfuscation of the system configuration via use of a pre-shared key. Collectively, these enhancements deliver the most efficient and secure deployment of QSTT to date, and point the way forward to high-accuracy ultra-secure time transfer in space.

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arXiv2026

DART: A Vision-Language Foundation Model for Comprehensive Rope Condition Monitoring

The condition monitoring (CM) of synthetic fibre ropes (SFRs) used in offshore, maritime, and industrial settings demands more than a classifier: inspectors need continuous severity estimates, maintenance recommendations, anomaly flags, deterioration timelines, and automated reports, all from a single inspection image. We present DART (Damage Assessment via Rope Transformer), a vision-language foundation model that addresses the full rope inspection workflow through a unified multi-task architecture. DART extends the Joint-Embedding Predictive Architecture (JEPA) to the cross-modal domain by coupling a Vision Transformer (ViT-H/14) with Llama-3.2-3B-Instruct via a Severity-Conditioned Cross-Modal Fusion (SC-CMF) module. Three architectural innovations drive the model's versatility: (1) HD-MASK, a saliency-guided masking strategy that focuses self-supervised reconstruction on damage-dense patches; (2) per-class learnable severity gates that adaptively weight language grounding by damage category; and (3) a Contrastive Damage Disentanglement (CDD) loss that shapes the embedding space to simultaneously encode damage type, severity ordering, and cross-modal semantics. Trained once on 4,270 images spanning 14 fine-grained rope damage classes, the frozen DART backbone supports downstream tasks without any task-specific fine-tuning: damage classification (93.22 % accuracy, 91.04 % macro-F1, +38.5 pp over a vision-only baseline), continuous severity regression (Spearman rho = 0.94, within-1-ordinal accuracy 99.6 %), few-shot recognition (89.2 % macro-F1 at 20 shots). These results demonstrate that DART functions as a general-purpose CM backbone that goes well beyond classification, providing actionable inspection intelligence from a single shared representation.

cs.CV↗

arXiv2026

Imagery Dataset for Remaining Useful Life Estimation of Synthetic Fibre Ropes

Remaining useful life (RUL) estimation of synthetic fibre ropes (SFRs) is critical for safe operation in offshore-crane, wind turbine installation, and heavy-load handling applications, where rope failure can result in catastrophic safety incidents and costly downtime. Despite growing research interest in data-driven condition monitoring, there is no publicly available image dataset that captures the complete degradation lifecycle of SFRs under controlled cyclic fatigue loading. To address this gap, we present a novel image dataset comprising approximately 34,700 high-resolution images of eleven Dyneema SK75/78 high-modulus polyethylene (HMPE) rope samples subjected to cyclic fatigue on a sheave-bend test stand at seven distinct axial load levels ranging from 60 kN to 280 kN. Ropes were loaded until mechanical failure, with fatigue lifetimes ranging from 695 cycles to 8,340 cycles. After every fixed number of sheave cycles (an inspection burst), ten images were captured at different cross-sectional positions along the rope, providing spatially representative sampling of surface degradation throughout the rope's entire service life. The images obtained from each load are annotated with the corresponding elapsed cycle count, enabling a direct computation of RUL for any rope in the sequence. This dataset aims to support a broad range of machine learning (ML) tasks including RUL regression, damage progression modelling, anomaly detection, and load-conditioned prognostics. The dataset is intended to serve as a benchmark resource for the development and comparison of vision-based condition monitoring (CM) and prognostics algorithms for SFRs.

cs.CV↗

arXiv2025

Combined Quantum and Post-Quantum Security Performance Under Finite Keys

Recent advances in quantum-secure communication have highlighted the value of hybrid schemes that combine Quantum Key Distribution (QKD) with Post-Quantum Cryptography (PQC). Yet most existing hybrid designs omit realistic finite-key effects on QKD key rates and do not specify how to maintain security when both QKD and PQC primitives leak information through side-channels. These gaps limit the applicability of hybrid systems in practical, deployed networks. In this work, we advance a recently proposed hybrid QKD-PQC system by integrating tight finite-key security to the QKD primitive and improving the design for better scalability. This hybrid system employs an information-theoretically secure instruction sequence that determines the configurations of different primitives and thus ensures message confidentiality even when both the QKD and the PQC primitives are compromised. The novelty in our work lies in the implementation of the tightest finite-key security to date for the BBM92 protocol and the design improvements in the primitives of the hybrid system that ensure the processing time scales linearly with the size of secret instructions.

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arXiv2025

A New Quantum Secure Time Transfer System

High-precision clock synchronization is essential for a wide range of network-distributed applications. In the quantum space, these applications include communication, sensing, and positioning. However, current synchronization techniques are vulnerable to attacks, such as intercept-resend attacks, spoofing, and delay attacks. Here, we propose and experimentally demonstrate a new quantum secure time transfer (QSTT) system, subsequently used for clock synchronization, that largely negates such attacks. Novel to our system is the optimal use of self-generated quantum keys within the QSTT to information-theoretically secure the maximum amount of timing data; as well as the introduction, within a hybrid quantum/post-quantum architecture, of an information-theoretic secure obfuscated encryption sequence of the remaining timing data. With these enhancements, we argue that our new system represents the most robust implementation of QSTT to date.

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arXiv2025

Obfuscated Quantum and Post-Quantum Cryptography

In this work, we present an experimental deployment of a new design for combined quantum key distribution (QKD) and post-quantum cryptography (PQC). Novel to our system is the dynamic obfuscation of the QKD-PQC sequence of operations, the number of operations, and parameters related to the operations; coupled to the integration of a GPS-free quantum synchronization protocol within the QKD process. We compare the performance and overhead of our QKD-PQC system relative to a standard QKD system with one-time pad encryption, demonstrating that our design can operate in real time with little additional overhead caused by the new security features. Since our system can offer additional defensive strategies against a wide spectrum of practical attacks that undermine deployed QKD, PQC, and certain combinations of these two primitives, we suggest that our design represents one of the most secure communication systems currently available. Given the dynamic nature of its obfuscation attributes, our new system can also be adapted in the field to defeat yet-to-be-discovered practical attacks.

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arXiv2025

CLIPTime: Time-Aware Multimodal Representation Learning from Images and Text

Understanding the temporal dynamics of biological growth is critical across diverse fields such as microbiology, agriculture, and biodegradation research. Although vision-language models like Contrastive Language Image Pretraining (CLIP) have shown strong capabilities in joint visual-textual reasoning, their effectiveness in capturing temporal progression remains limited. To address this, we propose CLIPTime, a multimodal, multitask framework designed to predict both the developmental stage and the corresponding timestamp of fungal growth from image and text inputs. Built upon the CLIP architecture, our model learns joint visual-textual embeddings and enables time-aware inference without requiring explicit temporal input during testing. To facilitate training and evaluation, we introduce a synthetic fungal growth dataset annotated with aligned timestamps and categorical stage labels. CLIPTime jointly performs classification and regression, predicting discrete growth stages alongside continuous timestamps. We also propose custom evaluation metrics, including temporal accuracy and regression error, to assess the precision of time-aware predictions. Experimental results demonstrate that CLIPTime effectively models biological progression and produces interpretable, temporally grounded outputs, highlighting the potential of vision-language models in real-world biological monitoring applications.

cs.CV↗

arXiv2025

FungalZSL: Zero-Shot Fungal Classification with Image Captioning Using a Synthetic Data Approach

The effectiveness of zero-shot classification in large vision-language models (VLMs), such as Contrastive Language-Image Pre-training (CLIP), depends on access to extensive, well-aligned text-image datasets. In this work, we introduce two complementary data sources, one generated by large language models (LLMs) to describe the stages of fungal growth and another comprising a diverse set of synthetic fungi images. These datasets are designed to enhance CLIPs zero-shot classification capabilities for fungi-related tasks. To ensure effective alignment between text and image data, we project them into CLIPs shared representation space, focusing on different fungal growth stages. We generate text using LLaMA3.2 to bridge modality gaps and synthetically create fungi images. Furthermore, we investigate knowledge transfer by comparing text outputs from different LLM techniques to refine classification across growth stages.

cs.CV↗

arXiv2025

Combined Quantum and Post-Quantum Security for Earth-Satellite Channels

Experimental deployment of quantum communication over Earth-satellite channels opens the way to a secure global quantum Internet. In this work, we present results from a real-time prototype quantum key distribution (QKD) system, which entails the development of optical systems including the encoding of entangled photon pairs, the development of transmitters for quantum signaling through an emulated Earth-satellite channel, and the development of quantum-decoding receivers. A unique aspect of our system is the integration of QKD with existing cryptographic methods to ensure quantum-resistant security, even at low-key rates. In addition, we report the use of specially designed error-reconciliation codes that optimize the security versus key-rate trade-off. Our work demonstrates, for the first time, a deployment of the BBM92 protocol that offers both post-quantum security via the advanced encryption standard (AES) and quantum security via an entanglement-based QKD protocol. If either the AES or the QKD is compromised through some adversary attack, our system still delivers state-of-the-art communications secure against future quantum computers.

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arXiv2024

Passive polarization-encoded BB84 protocol using a heralded single-photon source

The BB84 quantum key distribution protocol set the foundation for achieving secure quantum communication. Since its inception, significant advancements have aimed to overcome experimental challenges and enhance security. In this paper, we report the implementation of a passive polarization-encoded BB84 protocol using a heralded single-photon source. By passively and randomly encoding polarization states with beam splitters and half-wave plates, the setup avoids active modulation, simplifying design and enhancing security against side-channel attacks. The heralded single-photon source ensures a low probability of multi-photon emissions, eliminating the need for decoy states and mitigating photon number splitting vulnerabilities. The quality of the single-photon source is certified by measuring the second-order correlation function at zero delay, $g^{2}(0)=0.0408\pm0.0008$, confirming a very low probability of multi-photon events. Compared to conventional BB84 or BBM92 protocols, our protocol provides optimized resource trade-offs, with fewer detectors (compared to BBM92) and no reliance on external quantum random number generators (compared to typical BB84) to drive Alice's encoding scheme. Our implementation achieved a quantum bit error rate of 7% and a secure key rate of 5 kbps. These results underscore the practical, secure, and resource-efficient framework our protocol offers for scalable quantum communication technologies.

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arXiv2024

Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Survey

In recent years, 3D point clouds (PCs) have gained significant attention due to their diverse applications across various fields, such as computer vision (CV), condition monitoring (CM), virtual reality, robotics, autonomous driving, etc. Deep learning (DL) has proven effective in leveraging 3D PCs to address various challenges encountered in 2D vision. However, applying deep neural networks (DNNs) to process 3D PCs presents unique challenges. This paper provides an in-depth review of recent advancements in DL-based industrial CM using 3D PCs, with a specific focus on defect shape classification and segmentation within industrial applications. Recognizing the crucial role of these aspects in industrial maintenance, the paper offers insightful observations on the strengths and limitations of the reviewed DL-based PC processing methods. This knowledge synthesis aims to contribute to understanding and enhancing CM processes, particularly within the framework of remaining useful life (RUL), in industrial systems.

cs.CV↗

arXiv2024

Time Series Dataset for Modeling and Forecasting of $N_2O$ in Wastewater Treatment

In this paper, we present two years of high-resolution nitrous oxide ($N_2O$) measurements for time series modeling and forecasting in wastewater treatment plants (WWTP). The dataset comprises frequent, real-time measurements from a full-scale WWTP, with a sample interval of 2 minutes, making it ideal for developing models for real-time operation and control. This comprehensive bio-chemical dataset includes detailed influent and effluent parameters, operational conditions, and environmental factors. Unlike existing datasets, it addresses the unique challenges of modeling $N_2O$, a potent greenhouse gas, providing a valuable resource for researchers to enhance predictive accuracy and control strategies in wastewater treatment processes. Additionally, this dataset significantly contributes to the fields of machine learning and deep learning time series forecasting by serving as a benchmark that mirrors the complexities of real-world processes, thus facilitating advancements in these domains. We provide a detailed description of the dataset along with a statistical analysis to highlight its characteristics, such as nonstationarity, nonnormality, seasonality, heteroscedasticity, structural breaks, asymmetric distributions, and intermittency, which are common in many real-world time series datasets and pose challenges for forecasting models.

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arXiv2024

Wastewater Treatment Plant Data for Nutrient Removal System

This paper introduces the Agtrup (BlueKolding) dataset, collected from Denmark's Agtrup wastewater treatment plant, specifically designed to enhance phosphorus removal via chemical and biological methods. This rich dataset is assembled through a high-frequency Supervisory Control and Data Acquisition (SCADA) system data collection process, which captures a wide range of variables related to the operational dynamics of nutrient removal. It comprises time-series data featuring measurements sampled to a frequency of two minutes across various control, process, and environmental variables. The comprehensive dataset aims to foster significant advancements in wastewater management by supporting the development of sophisticated predictive models and optimizing operational strategies. By providing detailed insights into the interactions and efficiencies of chemical and biological phosphorus removal processes, the dataset serves as a vital resource for environmental researchers and engineers focused on improving the sustainability and effectiveness of wastewater treatment operations. The ultimate goal of this dataset is to facilitate the creation of digital twins and the application of machine learning techniques, such as deep reinforcement learning, to predict and enhance system performance under varying operational conditions.

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arXiv2024

Defect Detection in Synthetic Fibre Ropes using Detectron2 Framework

Fibre ropes with the latest technology have emerged as an appealing alternative to steel ropes for offshore industries due to their lightweight and high tensile strength. At the same time, frequent inspection of these ropes is essential to ensure the proper functioning and safety of the entire system. The development of deep learning (DL) models in condition monitoring (CM) applications offers a simpler and more effective approach for defect detection in synthetic fibre ropes (SFRs). The present paper investigates the performance of Detectron2, a state-of-the-art library for defect detection and instance segmentation. Detectron2 with Mask R-CNN architecture is used for segmenting defects in SFRs. Mask R-CNN with various backbone configurations has been trained and tested on an experimentally obtained dataset comprising 1,803 high-dimensional images containing seven damage classes (placking high, placking medium, placking low, compression, core out, chafing, and normal respectively) for SFRs. By leveraging the capabilities of Detectron2, this study aims to develop an automated and efficient method for detecting defects in SFRs, enhancing the inspection process, and ensuring the safety of the fibre ropes.

cs.CV↗

arXiv2023

Imagery Dataset for Condition Monitoring of Synthetic Fibre Ropes

Automatic visual inspection of synthetic fibre ropes (SFRs) is a challenging task in the field of offshore, wind turbine industries, etc. The presence of any defect in SFRs can compromise their structural integrity and pose significant safety risks. Due to the large size and weight of these ropes, it is often impractical to detach and inspect them frequently. Therefore, there is a critical need to develop efficient defect detection methods to assess their remaining useful life (RUL). To address this challenge, a comprehensive dataset has been generated, comprising a total of 6,942 raw images representing both normal and defective SFRs. The dataset encompasses a wide array of defect scenarios which may occur throughout their operational lifespan, including but not limited to placking defects, cut strands, chafings, compressions, core outs and normal. This dataset serves as a resource to support computer vision applications, including object detection, classification, and segmentation, aimed at detecting and analyzing defects in SFRs. The availability of this dataset will facilitate the development and evaluation of robust defect detection algorithms. The aim of generating this dataset is to assist in the development of automated defect detection systems that outperform traditional visual inspection methods, thereby paving the way for safer and more efficient utilization of SFRs across a wide range of applications.

cs.CV↗

arXiv2023

Free Space Continuous Variable Quantum Key Distribution with Discrete Phases

Quantum Key Distribution (QKD) offers unconditional security in principle. Many QKD protocols have been proposed and demonstrated to ensure secure communication between two authenticated users. Continuous variable (CV) QKD offers many advantages over discrete variable (DV) QKD since it is cost-effective, compatible with current classical communication technologies, efficient even in daylight, and gives a higher secure key rate. Keeping this in view, we demonstrate a discrete modulated CVQKD protocol in the free space which is robust against polarization drift. We also present the simulation results with a noise model to account for the channel noise and the effects of various parameter changes on the secure key rate. These simulation results help us to verify the experimental values obtained for the implemented CVQKD.

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arXiv2022

BBM92 quantum key distribution over a free space dusty channel of 200 meters

Free space quantum communication assumes importance as it is a precursor for satellite-based quantum communication needed for secure key distribution over longer distances. Prepare and measure protocols like BB84 consider the satellite as a trusted device, which is fraught with security threat looking at the current trend for satellite-based optical communication. Therefore, entanglement-based protocols must be preferred, so that one can consider the satellite as an untrusted device too. The current work reports the implementation of BBM92 protocol, an entanglement-based QKD protocol over 200 m distance using an indigenous facility developed at Physical Research Laboratory (PRL), Ahmedabad, India. Our results show the effect of atmospheric aerosols on sift key rate, and eventually, secure key rate. Such experiments are important to validate the models to account for the atmospheric effects on the key rates achieved through satellite-based QKD.

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arXiv2021

Polarization-orbital angular momentum duality assisted entanglement observation for indistinguishable photons

Duality in the entanglement of identical particles manifests that entanglement in only one variable can be revealed at a time. We demonstrate this using polarization and orbital angular momentum (OAM) variables of indistinguishable photons generated from parametric down conversion. We show polarization entanglement by sorting photons in even and odd OAM basis, while sorting them in two orthogonal polarization modes reveals the OAM entanglement. The duality assisted observation of entanglement can be used as a verification for the preservation of quantum indistinguishability over communication channels. Indistinguishable photons entangled in complementary variables could also evoke interest in distributed quantum sensing protocols and remote entanglement generation.

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