Rock Valley College

Abstract Emerging sub-7 nm systems-on-chip (SoCs) dissipate power densities exceeding 1 W mm⁻², leaving only a narrow thermal guard-band before devices cross their safe-operating limits. ARM’s System Monitoring and Control Framework (SMCF) unifies access to on-chip monitors through a single register map, yet production-grade verification of the sensors themselves remains largely manual and occurs late in the development life-cycle. This paper presents a self-checking, SMCF-driven automated test engine that validates a ring-oscillator temperature sensor entirely within an ARM Fast Models virtual platform prior to first silicon. To enhance analytical rigor, we incorporate formal modeling of thermal noise and sensor drift using a stochastic simulation framework, providing insight into system stability under statistical variance. The architecture further generalizes to support other sensor types—such as voltage droop or current monitors—through a sensor-agnostic LISA+ specification and test engine interface. A novel adaptive test pattern generation strategy is introduced, using functional coverage metrics and statistical predictions to dynamically prioritize stimulus and maximize validation efficiency. Additionally, an AI-guided coverage estimator is embedded for predictive validation feedback. The C + + testbench consumes LISA-generated register definitions, drives enable and reset clocks over PV Bus, and exhaustively sweeps the frequency-to-temperature (F↔T) golden table with ± 1% accuracy. A Robot Framework layer publishes every check as a keyword, enabling readable scripts, interactive HTML dashboards, and seamless Jenkins continuous-integration hooks. Initial experiments achieve 100% functional coverage in less than 90 s of wall-clock run-time, while capturing edge-case thermal violations and maximizing pre-silicon validation throughput.

Abstract As the convergence of Information Technology (IT) infrastructures with Industrial Control Systems (ICS)progresses, ensuring the security of Operational Technology (OT) has grown vastly important, with vulnerabilitiesarising from insufficient encryption and authentication in protocols like Modbus/Transmission Control Protocol(TCP). This paper introduces a flexible real-time simulation framework designed for Operational Technology (OT)threat detection, integrating protocol-specific traffic parsing with anomaly detection driven by machine learning.Through the use of algebraic modelling, extraction of statistical features, and examination of temporal behaviourin a multistage detection pipeline, and by employing both supervised and unsupervised models like Random Forestand Autoencoders, the system is able to efficiently detect threats. By using a preprocessing layer custom-tailoredto normalize Modbus/TCP inputs, we are able to improve detection precision and scalability. By using toolslike Docker, Flask and PyShark, the framework allows for flexible traffic replay and surveillance. Using publicICS datasets like the Secure Water Treatment Dataset (SWaT) and the Battle of Attack Detection AlgorithmsDataset (BATADAL), the framework simulates both realistic attack scenarios and normal operations, boostingdetection accuracy by up to 18% compared to rule based methods and the tree-based models demon- strate superiorperformance with 99-100% F1 scores and sub-millisecond inference times, confirming their exceptional suitabilityfor real-time OT anomaly de- tection. A real-time Flask-SocketIO dashboard facilitates live system monitoring andvisualization, demonstrating the system’s robustness in dynamic ICS contexts and underscoring its readiness forOperational Technology (OT) security solution prototyping

Abstract This paper presents the design and partial implementation of a sustainable electric vehicle (EV) charging system utilizing an inductive charging lane powered by solar panels. The proposed model enables EVs to charge while in motion using embedded coils and wireless energy transfer, thereby aiming to eliminate range anxiety and enhance green mobility. A prototype is under development, focusing on solar integration, proximity sensing, and intelligent energy management. System testing and performance evaluation are yet to be conducted. The work lays a foundation for future implementation of scalable and eco-friendly transportation infrastructure.

This study investigates the modeling of magnetostriction in ferrite materials within magnetoelectric composites, focusing on the influence of domain wall pinning on local stress distributions to enhance performance in aerospace systems such as smart sensors and actuators for vibration damping and structural integrity monitoring. By mimicking magnetostriction through thermoelasticity, the research employs finite element method (FEM) software to simulate stress fields arising from domain wall evolution. Two primary cases are examined: a 90° domain wall configuration (Case 1) and a realistic inclusion scenario (Case 2), both incorporating pinning to replicate experimental observations from piezoresponse force microscopy (PFM), which can inform the design of lightweight, multifunctional composites for aircraft morphing structures. In Case 1, the geometry features a square matrix with a 90° domain wall and a central pinning point, represented as a small circle with a radius of 0.2 mm. Material properties include linear isotropic models for the matrix (Young's modulus 150 GPa, Poisson's ratio 0.3) and anisotropic tetragonal crystal properties for domains, with thermal expansion coefficients applied to induce strain. Boundary conditions constrain all edges, simulating clamped conditions relevant to embedded aerospace components. Domain wall motion is modeled in three stages: initial, second, and last. In the initial stage, the pinning causes contraction and expansion, resulting in a maximum von Mises stress of 87 MPa at the domain boundary corner—nearly half that of unpinned scenarios. As the wall progresses, stress increases to 102 MPa in the second stage and 109 MPa in the last, with minimum stresses at matrix boundaries. The pinning disrupts smooth wall growth, blocking motion in one direction and altering induced stress fields, which could affect reliability in high-vibration aerospace environments like engine mounts. Case 2 extends this to a realistic circular inclusion within the matrix, incorporating pinning to induce misalignment in domain walls. Using identical material properties and boundary conditions, domain evolution is tracked across four stages. The pinning point at coordinates (5.15, 2.17) leads to non-smooth motion, with areas of domains changing progressively. Stress distributions show escalation: initial stage maximum of 78 MPa at boundaries, rising to 107 MPa, 151 MPa, and 192 MPa in subsequent stages. The pinning exhibits contraction in the x-direction and expansion in y, ultimately reducing the structure to two dominant domains, offering insights for optimizing energy harvesting in aerospace sensors under variable loads. Additionally, domain wall motion in the opposite direction is analyzed for both cases. In Case 1, this yields stresses of 78 MPa initially, increasing to 92 MPa, 139 MPa, and 195 MPa. For Case 2, opposite motion avoids blocking by the pin, with domain 1 and 3 expanding while 2 and 4 contract. Comparisons reveal asymmetry introduced by pinning, highlighted in stress contours, contrasting with symmetric unpinned cases. Results demonstrate that pinning significantly alters strain and stress, reducing smoothness in domain evolution and inducing directional biases, aligning with PFM observations of local ferroelectric switching near interfaces. This modeling provides insights into microstructural control of magnetoelectric coupling, with von Mises stresses quantifying pinning's impact on composite behavior for applications in aerospace magnetic field sensors and power converters. Future extensions could incorporate Voronoi tessellation for grain boundary effects, enhancing realism in multiferroic material simulations to support advanced aerospace technologies like autonomous structural health monitoring systems.

In this paper, a multi-frequency shark-fin antenna has been developed using ANSYS HFSS, exhibiting operational capabilities at three discrete frequencies: 1.575GHz (GPS), 3.55GHz (n78), and 4GHz (n77). To enhance antenna performance, the incorporation of a superstrate has been executed effectively, enhancing gain. Employing a Microstrip patch architecture with inset feed mechanisms, the antenna configuration comprises two vertically positioned 4GHz antennas facing against each other, accompanied by two horizontally arranged antennas. The design is used to accommodate in a shark-fin casing for aerodynamic purposes.

Abstract The storage and analysis of CCTV footage pose a significant challenge in modern surveillance systems. This paper presents an innovative and modern solution for efficient storage optimization and analysis through motion-based frame removal techniques. By leveraging the OpenCV library redundant frames are removed, resulting in substantial storage reduction without losing important motion events. The methodology uses Python’s OpenCV, Django, SQLite, and Bootstrap, ensuring reliability, scalability, usability, and effective data visualisation. Experimental results have demonstrated the efficacy across different camera setups and their ability to preserve critical information. The approach offers a practical solution for decreasing cost, improving analysis efficiency, and enhancing usability. This research contributes to the field of CCTV surveillance by providing a practical and industry-oriented solution. The findings inspire further improvements in storage optimization and pave the way for more intelligent surveillance systems.

Language Models today provide a high accuracy across a large number of\ndownstream tasks. However, they remain susceptible to adversarial attacks,\nparticularly against those where the adversarial examples maintain considerable\nsimilarity to the original text. Given the multilingual nature of text, the\neffectiveness of adversarial examples across translations and how machine\ntranslations can improve the robustness of adversarial examples remain largely\nunexplored. In this paper, we present a comprehensive study on the robustness\nof current text adversarial attacks to round-trip translation. We demonstrate\nthat 6 state-of-the-art text-based adversarial attacks do not maintain their\nefficacy after round-trip translation. Furthermore, we introduce an\nintervention-based solution to this problem, by integrating Machine Translation\ninto the process of adversarial example generation and demonstrating increased\nrobustness to round-trip translation. Our results indicate that finding\nadversarial examples robust to translation can help identify the insufficiency\nof language models that is common across languages, and motivate further\nresearch into multilingual adversarial attacks.\n