An aircraft consists of various cavities including air intake ducts, cockpit, radome, inlet and exhaust of heat exchangers, passage for engine bay/other bay cooling etc. These cavities are prime radar cross-section (RCS) contributors of aircraft. The major such cavity is air intake duct, and it contributes significantly to frontal sector RCS of an aircraft. The RCS reductions of air intake duct is very important to achieve a low RCS (or stealthy) aircraft configuration. In general, radar absorbing materials (RAM) are getting utilized for RCS reduction of air intake duct. It can also be noticed that a large number of fasteners are used for integration of air intake duct with the aircraft structures. The installation of fasteners on RAS may lead to degradation of RCS performance of air intake. However, no such studies are reported in the literature on the impact of rivets on the RCS performance of RAS air intake duct. In this paper, radar absorbing material of thickness 6.25 mm is designed which givens more than -10 dB reflection loss from 4 to 18GHz of frequencies. Next, the effect of rivet installation on these RAS is carried out using three different rivet configurations. The RCS performance of RAS is evaluated for duct of different lengths from 1 to 18GHz of frequencies. In order to see the RCS performance, five different air intake cases are considered The RCS performance with increase in percentage surface area of rivet heads to RAS is reported in detail. At the last, an open-source aircraft CAD model is considered and the RCS performance of RAS air intake with and without rivets is evaluated.

In this paper, deep learning-based approach for the design of radar absorbing structure using resistive frequency selective surface is proposed. In the present design, reflection coefficient is used as input of deep learning model and the Jerusalem cross based unit cell dimensions is predicted as outcome. Sequential neural network based deep learning model with adaptive moment estimation optimizer is used for designing multi frequency band absorbers. The model is used for designing radar absorber from L to Ka band depending on unit cell parameters and thickness. The outcome of deep learning model is further compared with full-wave simulation software and an excellent match is obtained. The proposed model can be used for the low-cost design of various radar absorbing structures using a single unit cell and thickness across the band of frequencies.

In this paper, a regression-based machine learning model is used for the design of cavity backed slotted antenna. This type of antenna is commonly used in military and aviation communication systems. Initial reflection coefficient data of cavity backed slotted antenna is generated using electromagnetic solver. These reflection coefficient data is then used as input for training regression-based machine learning model. The model is trained to predict the dimensions of cavity backed slotted antenna based on the input reflection coefficient for a wide frequency band varying from 1 GHz to 8 GHz. This approach allows for rapid prediction of optimal antenna configurations, reducing the need for repeated physical testing and manual adjustments, may lead to significant amount of design and development cost saving. The proposed model also demonstrates its versatility in predicting multi frequency resonance across 1 GHz to 8 GHz. Also, the proposed approach demonstrates the potential for leveraging machine learning in advanced antenna design, enhancing efficiency and accuracy in practical applications such as radar, military identification systems and secure communication networks.

The paper presents an innovative methodology for designing frequency selective surface (FSS) based radar absorbing materials using machine learning (ML) technique. In conventional electromagnetic design, unit cell dimensions of FSS are used as input and absorption coefficient is then predicted for a given design. In this paper, absorption coefficient is considered as input to ML model and image of FSS unit cell is predicted. Later, this image is used for generating the FSS unit cell parameters. Eleven different ML models are studied over a wide frequency band of 1GHz to 30GHz. Out of which six ML models (i.e. (a) Random Forest classification, (b) K- Neighbors Classification, (c) Grid search regression, (d) Random Forest regression, (e) Decision tree classification, and (f) Decision tree regression) show training accuracy more than 90%. The absorption coefficients with varying frequencies of these predicted images are subsequently evaluated using commercial electromagnetic solver. The performance of these ML models is encouraging, and it can be used for accelerating design and optimization of high performance FSS based radar absorbing material for advanced electromagnetic applications in future.

In this paper, an improved clustering technique for large textual datasets by leveraging fine-tuned word embeddings is presented. WEClustering technique is used as the base model. WEClustering model is fur-ther improvements incorporating fine-tuning contextual embeddings, advanced dimensionality reduction methods, and optimization of clustering algorithms. Experimental results on benchmark datasets demon-strate significant improvements in clustering metrics such as silhouette score, purity, and adjusted rand index (ARI). An increase of 45% and 67% of median silhouette score is reported for the proposed WE-Clustering_K++ (based on K-means) and WEClustering_A++ (based on Agglomerative models), respec-tively. The proposed technique will help to bridge the gap between semantic understanding and statistical robustness for large-scale text-mining tasks.

Traditional Graph Neural Network (GNN) approaches for fake news detection (FND) often depend on auxiliary, non-textual data such as user interaction histories or content dissemination patterns. However, these data sources are not always accessible, limiting the effectiveness and applicability of such methods. Additionally, existing models frequently struggle to capture the detailed and intricate relationships within textual information, reducing their overall accuracy. In order to address these challenges Advanced Text Analysis Graph Neural Network (ATA-GNN) is proposed in this paper. The proposed model is designed to operate solely on textual data. ATA-GNN employs innovative topic modelling (clustering) techniques to identify typical words for each topic, leveraging multiple clustering dimensions to achieve a comprehensive semantic understanding of the text. This multi-layered design enables the model to uncover intricate textual patterns while contextualizing them within a broader semantic framework, significantly enhancing its interpretative capabilities. Extensive evaluations on widely used benchmark datasets demonstrate that ATA-GNN surpasses the performance of current GNN-based FND methods. These findings validate the potential of integrating advanced text clustering within GNN architectures to achieve more reliable and text-focused detection solutions.

A critical stage in the evolving landscape of VLSI design is the design phase that is transformed into register-transfer level (RTL), which specifies system functionality through hardware description languages like Verilog. Generally, evaluating the quality of an RTL design demands full synthesis via electronic design automation (EDA) tool is time-consuming process that is not well-suited to rapid design iteration and optimization. Although recent breakthroughs in machine Learning (ML) have brought early prediction models, these methods usually do not provide robust and generalizable solutions with respect to a wide range of RTL designs. This paper proposes a pre-synthesis framework that makes early estimation of power, performance and area (PPA) metrics directly from the hardware description language (HDL) code making direct use of library files instead of toggle files. The proposed framework introduces a bit-level representation referred to as the simple operator graph (SOG), which uses single-bit operators to generate a generalized and flexible structure that closely mirrors the characteristics of post synthesis design. The proposed model bridges the RTL and post-synthesis design, which will help in precisely predicting key metrics. The proposed tree-based ML framework shows superior predictive performance PPA estimation. Validation is carried out on 147 distinct RTL designs. The proposed model with 147 different designs shows accuracy of 98%, 98%, and 90% for WNS, TNS and power, respectively, indicates significant accuracy improvements relative to state-of-the-art methods.

In the evolving landscape of integrated circuit design, detecting Hardware Trojans (HTs) within a multi entity based design cycle presents significant challenges. This research proposes an innovative machine learning-based methodology for identifying malicious logic gates in gate-level netlists. By focusing on path retrace algorithms. The methodology is validated across three distinct cases, each employing different machine learning models to classify HTs. Case I utilizes a decision tree algorithm for node-to-node comparisons, significantly improving detection accuracy through the integration of Principal Component Analysis (PCA). Case II introduces a graph-to-graph classification using a Graph Neural Network (GNN) model, enabling the differentiation between normal and Trojan-infected circuit designs. Case III applies GNN-based node classification to identify individual compromised nodes and its location. Additionally, nearest neighbor (NN) method has been combined with GNN graph-to-graph in Case II and GNN node-to-node in Case III. Despite the potential of GNN model graph-to-graph classification, NN approach demonstrated superior performance, with the first nearest neighbor (1st NN) achieving 73.2% accuracy and the second nearest neighbor (2nd NN) method reaching 97.7%. In comparison, the GNN model achieved an accuracy of 62.8%. Similarly, GNN model node-to-node classification, NN approach demonstrated superior performance, with the 1st NN achieving 93% accuracy and the 2nd NN method reaching 97.7%. In comparison, the GNN model achieved an accuracy of 79.8%. However, higher and higher NN will lead to large code coverage for the identification of HTs.