Centre for Air Borne System

India boasts a rich heritage of monuments that are of historical and cultural significance. These monuments are continuously subjected to environmental degradation, human activity, and structural aging. Traditional structural health monitoring (SHM) techniques, such as visual inspection and contact sensors, are often invasive, localized, and ineffective for Indian Heritage sites, which are architecturally complex. Ground-Based Synthetic Aperture Radar (GBSAR) is a remote sensing technique that offers a non-contact, weather-independent, and highresolution microwave imaging alternative for SHM. This paper proposes a multistatic GBSAR framework designed specifically for the SHM of Indian monuments. By leveraging multiple spatially distributed transmit and receive nodes, the proposed system enhances spatial resolution and enables full-field deformation monitoring, even in occluded or inaccessible areas. Simulation results presented here indicate that the multistatic configuration significantly improves imaging quality compared to monostatic GBSAR. This framework provides improved deformation maps, making it a viable early warning tool for non-invasive SHM.

The traditional document-based System Engineering approach has inherent shortcomings such as ambiguity of information and communication, incompleteness, inflexibility, and lack of effective traceability. MBSE addresses these problems by defining system models which are maintained digitally and can be evolved throughout the life cycle. The MBSE models help to analyze, design, and verify systems from a variety of perspectives depending on the type of system and the organisation goal. The generation of models by the System Engineering team with feedback from different stakeholders and their dissemination to Design team, Subsystem teams, Integration team and Verification/ Testing teams can go a long way in filling the gaps that commonly exist between Requirements and Design; between Design and Realization; and between Realization and Verification/Testing and final Acceptance of the system. MBSE is especially useful for complex systems involving multiple stakeholders, work centers, users and long running lifecycles with changing project teams.This paper brings out the implementation of MBSE from the unique perspective of an Airborne Surveillance System considering a representative set of subsystems and also discusses the problems and challenges of MBSE implementation.

This paper presents the design and simulation of a compact spiral antenna for electronic support measures (ESM) applications. The field of ESM necessitates an advanced system for the effective detection, localization, and identification of electromagnetic signals. Antennas at the front end of receivers in ESM systems, play a crucial role in effectively capturing these signals. The inherent wide bandwidth of spiral antennas makes them suitable for ESM applications as they operate across diverse frequency bands without the need for frequency tuning. In addition, the unidirectional and high gain characteristics of spiral antennas substantially improve the system’s capacity to detect and analyze signals with precision, while their compact design ensures ease of integration and versatility. The planar spiral antenna proposed in this paper operates over a wide frequency range of 1GHz to 18GHz, maintains a return loss of less than 10dB throughout the entire frequency range, achieves a maximum gain of 6.236dBi, has a compact balun height of 13.5mm, and has an excellent axial ratio of less than 1.7 at all frequencies.

The paper discusses the methodology of transforming an Airborne Active Electronically Scanned Antenna Array(AESA) with RF manifolds into a digitized AESA. The modified design of Beamforming Unit (BFU) provides systematic grouping of the RF outputs from the bays directly to 8 digital receive channels along with 2 Guard channels which allows adoption of state-of-the-art algorithms such as Space Time Adaptive Processing for radar. This provides enhanced capability for radar detections in cluttered environment. BFU has been modified keeping the same form factor as earlier to meet the structural requirement of the Antenna array.

Synthetic Aperture Radar is an active microwave remote sensing imagining technology. It produces images having unique characteristics like High resolution, Multimode, Multifrequency, Multi polarisation, large dynamic range, wide frequency content, and high textures. Earlier, due to constraints in memory and processing, large raw data (I and Q samples) along with the meta-data from the airborne platform were down-linked to ground stations to form higher-level images. Nowadays, due to advancements in signal processing hardware, sophisticated images are synthesized on board for instant Airborne Intelligence Extraction utilization and onward submission to Airborne Sensor Management System for generating Integrated Sensor Picture. However, to overcome the downlink bandwidth constraints and limited airborne storage, onboard compression of imageries is indispensable. Similarly, on the ground, to exploit intelligence and for imminent mission references, compression is required. This paper proposes a novel method to use various wavelets for Discrete Wavelet Transform combined with Set Partitioning in Hierarchical Trees encoder for compressing four image polarization planes (HH, HV, VH, and VV). This research study reveals that using different wavelets for each polarisation plane has advantages in terms of Compression ratio and Mean Square Error over applying the same wavelets for all the polarization planes.

Present day airborne radars have to perform multiple tasks like detection, tracking, and identification of targets in the presence of advanced EW countermeasures like RF jamming, and must be able to separate true targets from noise and clutter conditions caused by complex physical conditions, such as mountainous terrain, and environmental conditions. Although conventional signal processing methods for target detection have achieved a great level of maturity, they still continue to perform poorly under low SNR conditions and model imperfections due to statistical variations. Meanwhile, performing target detection using neural network-based methods has also been gathering increased attention. We show that various Convolutional Neural Network(CNN) and Recurrent Neural Network(RNN) based techniques improve target detection at low SNR conditions. The CNN-based architecture proposed in this paper performs single shot radar target detection on RD map, and localization of the target. Various enhancements to the technique is also proposed. The proposed method shows improvement over conventional and state-of-the-art deep learning based methods. Furthermore, we evaluate the proposed method on real airborne data and prove its effectiveness.

Automatic Target Recognition (ATR) is a prominent step in Maritime Domain Awareness (MDA). These missions are aided by data from multiple sensors like Electro Optic-Infrared (EO/IR) cameras, Synthetic Aperture Radar (SAR), etc. Recent studies of classifying SAR images have been accomplished using supervised deep learning techniques to enhance the performance of classification. In this paper, we use transfer learning on VGG-16 architecture pre-trained on ImageNet dataset. The VGG-16 architecture is extended using a 2-step modular approach for the data classes in MASATI-V2 binary and MSTAR-SAR multiclass datasets. The model is then trained by suitable tuning of hyperparameters, and we report accuracies of 100% for binary classification and 99.18% for SAR multiclass classification. This approach reduces the overfitting in case of smaller dataset volume that is typical in SAR imagery in maritime domain, while reducing the time taken for training and deployment of the model.

Background: Rosemary (Rosmarinus officinalis) is renown medicinal herb indigenous to Mediterranean region. Rosemary extracts provide a major source of secondary metabolites usually used in food and to make medicine. Objectives: The main objective of the present research was to investigate the anticancer property of standardized rosemary extract in human malignant breast cancer cell by assessing its effect on apoptosis. Materials and Methods: Standardized rosemary extract was evaluated for cytotoxicity using MTT assay, while Fluorescence microscopy evaluated the apoptotic effects of standardized extracts in breast cancer cells using acridine orange/propidium iodide dyes. Subsequently, flow cytometry and western blotting were carried out to assess the apoptotic effect of standardized rosemary extract. Results: The standardized rosemary extracts demonstrated significant cytotoxic effect against MCF-7 with IC50 values of 50.75µg/mL, Fluorescence microscopy and Annexin V-propidium iodide double-staining showed that standardized rosemary extract potentiates apoptosis. On the other hand, western blotting results showed upregulation of proapoptotic proteins, activation of PARP-1, Caspase 9 and concentration dependent decrease in the expression of anti-apoptotic protein Bcl-2 with a related increase in the expression level of proapoptotic protein BAX. Conclusion:  In conclusion standardized rosemary extract mediated through the suppression of cell growth and the induction of apoptosis in breast cancer cells. Thus, it has the capability to be developed as an anticancer agent to subside the progression of cancer; however, substantial studies are required.

Abstract The COVID-19 pandemic has strained testing capabilities worldwide. There is an urgent need to find economical and scalable ways to test more people. We presentTapestry, a novel quantitative nonadaptive pooling scheme to test many samples using only a few tests. The underlying molecular diagnostic test is any real-time RT-PCR diagnostic panel approved for the detection of the SARS-CoV-2 virus. In cases where most samples are negative for the virus, Tapestry accurately identifies the status of each individual sample with a single round of testing in fewer tests than simple two-round pooling. We also present a companion Android applicationBYOM Smart Testingwhich guides users through the pipetting steps required to perform the combinatorial pooling. The results of the pooled tests can be fed into the application to recover the status and estimated viral load for each individual sample.NOTE: This protocol has been validated with in vitro experiments that used synthetic RNA and DNA fragments and additionally, its expected behavior has been confirmed using computer simulations. Validation with clinical samples is ongoing. We are looking for clinical collaborators with access to patient samples. Please contact the corresponding author if you wish to validate this protocol on clinical samples.

This paper discusses the application of Lagrange interpolation and cubic spline interpolation to predict acceleration/velocity and to adapt better window lengths for any range of target acceleration. The estimated velocity/acceleration is then smoothed using Kalman and adaptive Kalman filters. Simulation results show that in a ‘high-level noise’ scenario, the interpolated adaptive filter gives a more accurate estimation than the existing method of using a rectangular window function. Track initialization error minimized with spline interpolation was comparable to that minimized with Lagrange interpolation.