Advance Research in Analog and Digital Communications

Vector Calculus-Based Electromagnetic Field Analysis: Finite-Element Modelling and Validation of a Rectangular Waveguide

Shweta Zambare, Shivani Shendage, Bhakti Gavali, Asmita Chavan, Vaibhav Godase — Mon, 19 Oct 2026 00:00:00 +0000

Vector calculus provides the mathematical language through which electromagnetic fields are described, interpreted, and computed. Electric field intensity, magnetic flux density, charge density, and current density are spatially distributed quantities whose relationships are naturally expressed through gradient, divergence, and curl operators. This study presents an application-oriented framework for using vector calculus in electromagnetic field analysis, linking differential operators with Gauss’s law, Faraday’s law, Ampère–Maxwell law, and the continuity equation. The study further explains how coordinate systems, boundary conditions, vector identities, and numerical discretization support practical field computation. A representative computational workflow is developed for a rectangular-waveguide problem, where the continuous vector formulation is converted into a finite-element model, and the resulting electric-field distribution is compared with the analytical TE10 behavior. The discussion emphasizes how vector calculus enables field visualization, flux and circulation analysis, energy-flow evaluation, and numerical electromagnetic simulation. The resulting framework is relevant to antennas, microwave circuits, waveguides, sensors, wireless systems, and other electromagnetic engineering applications.

Offline Forensic Correlation of Volatile Memory and Network Traffic for Process-Level Network Activity Attribution

Banumathi A., Udhaya S., Poorni M., Tamizharasan N. — Sat, 31 Oct 2026 00:00:00 +0000

Many digital forensics investigations will need to analyze both volatile memory and captured network traffic to be able to reconstruct process-based network behavior. Unfortunately, such evidence is analyzed separately, and thus, it becomes challenging to tie any particular network communication to the process behind it. This study introduces NAAS (Network Activity Attribution System), an offline forensics tool that correlates volatile memory artifacts and PCAPs for process-based network activity attribution. Volatility 3 and TShark are utilized in NAAS to analyze process and network connection artifacts in memory dumps and network flow information from PCAPs, respectively. The extracted evidence is normalized into a shared representation and matched based on endpoint addresses, ports, transport protocols, and timestamps. The approach generates process-network correlations, a confidence score for the correlation, a timeline of the consolidated forensics, and a list of network events that are not sufficiently supported by the provided memory evidence. Four controlled scenarios were used in the initial study with F1-scores from 87.7% to 94.5%. Nevertheless, the lack of sample sizes, confusion matrices, metadata about the datasets, and repeatable experimental runs makes replication of these results impossible. For this reason, the new evaluation methodology defines the required characteristics of the datasets, the approach to generating ground-truth data, the correlation rules, and computational measurements needed for validation of the approach. The framework is expected to be useful for reconstructing an incident offline and triaging evidence. In particular, its output should be understood as evidence-backed process-network associations and not proof of process causality.

Stable Period as a Primary Performance Indicator for Energy-Balanced WSN Clustering Protocols: A Case Study with LEACH, LEACH-C, RE-LEACH, and DART-WSN

Amrita Soni, Kapil Malviya, Shiv Ram Mansore — Mon, 05 Oct 2026 00:00:00 +0000

Background: Network lifetime in Wireless Sensor Networks (WSNs) is conventionally reported using First Node Death (FND), Half Node Death (HND), and Last Node Death (LND). These metrics do not fully capture quality of service, since a high LND can result from a few isolated surviving nodes rather than genuinely useful network behaviour.

Objectives: This study adopts the Stable Period (SP) - the number of rounds from start-up until the first node dies, numerically equal to First Node Death (FND) - as the primary performance indicator for WSN clustering protocols, being the only phase in which every deployed node is alive (full node-level coverage). Three established protocols - LEACH, LEACH-C, and RE-LEACH - are compared against DART-WSN, the authors' own previously proposed protocol, using SP as the central metric, supported by the Instability Period (IP), the SP-to-LND Ratio, and a composite Stable Period Quality Index (SPQI).

Methodology: A 100-node WSN was deployed over a 100 m × 100 m field with a fixed base station and simulated in MATLAB using the first-order radio energy model over 2000 rounds. Cluster-head election, residual energy, and node-death timing were recorded and used to compute all metrics.

Results: LEACH-C achieved the longest Stable Period (870 rounds, 92.9%) and the highest SPQI (0.939) at the default weighting alpha = 0.6; a sensitivity analysis shows that LEACH ranks first for alpha below about 0.52. DART-WSN gave the shortest SP (548 rounds, 53.4%), which is attributed to its large early cluster-head counts (15-18 per round). LEACH recorded the highest LND (1105 rounds), consistent with late-surviving nodes rather than useful network operation.

Conclusions: Protocols optimised for LND sacrifice Stable Period, and the SPQI ranking depends on the chosen weighting. Reporting SP, IP, and SP Ratio alongside FND, HND, and LND is recommended for WSN protocol evaluation.