arXiv subjects

Christopher Paolini

Publications and source records attributed to Christopher Paolini.

7 recordsLinked to original sources

arXiv2026

Comparative Performance of Graphene-Enabled Transmitarray Antenna and Reflectors for Wide-Angle Terahertz Beam Steering

This work compares a hemispherical graphene-based transmitarray antenna with its planar reflector counterpart for wide-angle beam steering in the THz regime. The theoretical framework of the planar reflector is formulated and numerically evaluated, yielding an elevation beam-steering range of \pm60{\deg}. In contrast, the transmitarray extends the elevation steering range to \pm78{\deg} while maintaining full 360{\deg} azimuthal coverage. The planar reflector exhibits a larger HPBW variation of 26.48{\deg}, compared with 13.1{\deg} for the transmitarray, resulting in a broader reflected-beam distribution and reduced directional power density, directivity, and gain. Meanwhile, the transmitarray maintains a more stable and controllable beamwidth response with greater directional power concentration over a wide steering range. The performance advantages of the transmitarray are demonstrated through comparisons with experimental results reported in the literature for planar reflectors and antennas. We further provide a comprehensive assessment of the performance advantages of the transmitarray over planar configurations across the remaining metrics.

cs.NI↗

arXiv2026

Active Hemispherical Metasurface Transmitarray Antenna for Wide-Angle 3D Beam Steering and Target Tracking

A stacked multilayer hemispherical graphene-based transmitarray antenna operating at 250 GHz is developed to achieve wide-angle 3D beam steering. This work presents the first fully automated CST Studio Suite macro for constructing the multilayer hemispherical geometry and configuring 121 independently tunable graphene sectors. Numerical results confirm that the integration of voltage-controlled graphene sectors with FCC gold patches enables wide-angle beam steering, achieving an elevation scanning range of -78{\deg} to 78{\deg} in {\theta} and full 360{\deg} azimuthal coverage while maintaining stable radiation characteristics and broadband impedance matching (|S11| < -10 dB) with a relative bandwidth of 40%. The antenna achieves a directivity range of 11.38 - 14.72 dBi and an SLL range of -6.8 to -9.8 dB across all investigated steering directions. It also maintains high antenna efficiencies from 71% to 80% with a 5% degradation due to imperfections across the entire scanning range, making it a promising candidate in practical moving-target-tracking applications.

cs.NI↗

arXiv2026

Graphene-based Hemispherical Transmitarray Antenna for Wide-Angle Beam Steering and Ultrafast Moving Target Tracking

This work expands the application of dynamically tunable graphene to implement a hemispherical transmitarray antenna tailored for wide-angle electronic beam steering and ultrafast moving-target tracking at 250 GHz. The proposed hemispherical transmitarray antenna features a dynamically reconfigurable graphene-based multilayer configuration consisting of gold radiating patches, biased graphene sectors, hBN dielectric layers, and a centrally positioned hornfeed. The analytical framework of graphene-based reconfigurable metasurfaces including graphene surface-conductivity modeling, voltage-controlled surface impedance, transmission-coefficient, and conformal array-factor analysis, has been established for hemispherical transmitarray antenna for the first time. Moreover, the analytical and practical mapping between desired beam directions and the corresponding graphene bias voltages have been developed for the transmitarray antenna for the first time. Our transmitarray antenna possesses exceptional 3D beamsteering, providing wide-angle elevation scanning from -87 degrees to 87 degrees and full 360-degree azimuthal coverage. It also exhibits an antenna efficiency ranging from 68% to 80% with a 10% degradation.

cs.NI↗

arXiv2026

Stereo Vision-Based Fall Prediction and Detection using Human Pose Estimation on the AMD Kria K26 SOM

Background and Objective: Falls among elderly people can cause serious injury and reduce quality of life. Timely prediction and detection are essential to prevent harm and support well-being. We propose a portable, low-power, battery-operated, vision-based fall prediction and detection system using HPE on an AMD Kria K26 System-on-Module (SOM). The objective is a non-intrusive, privacy-preserving system for real-time fall detection. Methods: The system uses an Intel RealSense D455 range-sensing camera connected to the K26 SOM by USB. It captures synchronized RGB and depth frames, 640 x 480 x 3 and 640 x 480 pixels, at 60 FPS. The SOM runs a three-stage pipeline with quantized YOLOX, Anchor-to-Joint (A2J), and fall-detection models. YOLOX identifies human bounding boxes from RGB frames, then discards the RGB frames to preserve privacy. A2J uses depth frames to estimate 15 joint keypoints per person. A CNN uses selected joint coordinates (x, y, z) to classify fall activity. YOLOX was trained on CrowdHuman; A2J on ITOP, MP-3DHP, UR Fall Detection, and a custom SDSU PSG dataset; and the CNN on UR Fall Detection and SDSU PSG. The design used a single-core DPU with a serial pipeline and a dual-core DPU running YOLOX and A2J with multiple threads. Results: Quantized accuracy was evaluated using IoU >= 50% for YOLOX, mAP with a 10-cm rule for A2J, and classification accuracy, (TP + TN)/(TP + TN + FP + FN), for the CNN. Accuracies were 74%, 84.13%, and 75.85%. Throughput improved from 2.5 FPS for the single-threaded pipeline to 4.5 FPS for the multi-threaded version. Conclusion: Results demonstrate the feasibility of privacy-preserving fall detection on an AMD Kria K26 edge device. On-device HPE and fall classification runs without cloud dependency, supporting elderly monitoring and assistive healthcare. Future work will improve model accuracy and speed.

cs.CV↗

arXiv2025

Enhancing Road Safety Through Multi-Camera Image Segmentation with Post-Encroachment Time Analysis

Traffic safety analysis at signalized intersections is essential for reducing vehicle and pedestrian collisions, yet traditional crash-based studies are limited by data sparsity and reporting latency. This paper presents a multi-camera computer vision framework for real-time safety assessment through Post-Encroachment Time (PET) computation, demonstrated at the intersection of H Street and Broadway in Chula Vista, California. Four synchronized cameras provide continuous visual coverage, with frames processed on NVIDIA Jetson AGX Xavier edge devices using YOLOv11 segmentation for vehicle detection. Detected vehicle polygons are transformed into a unified bird's-eye map via homography, enabling alignment across overlapping camera views. A pixel-level PET algorithm tracks temporal occupancy at each spatial location by measuring the time between successive vehicle passages, enabling fine-grained hazard visualization through dynamic heatmaps with 3.3 sq-cm spatial resolution. Timestamped vehicle trajectories and PET data are stored in an SQL database for longitudinal analysis. Results across multiple time intervals demonstrate the framework's ability to identify high-risk regions with sub-second temporal sensitivity and real-time edge throughput, generating 800 x 800 logarithmic heatmaps at an average of 2.68 FPS. This paper validates decentralized vision-based PET analysis for intelligent transportation systems and presents a scalable methodology for high-resolution, real-time intersection safety evaluation.

cs.CV↗

arXiv2022

Implementation of the Digital QS-SVM-based Beamformer on an FPGA Platform

To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, the digital beamformer is performed by the hybrid antenna array at the frequency of operation of 10 GHz. The proposed digital beamformer, as a spatial filter, is capable of performing Direction of Arrival (DOA) estimation and beamforming. The most well-established machine learning technique of support vector machine (SVM) for the DoA estimation is limited to problems with linearly-separable datasets. To overcome the aforementioned constraint, in the proposed beamformer, the QS-SVM classifier with a small regularizer has been used for the DoA estimation in addition to the two beamforming techniques of LCMV and MVDR. The QS-SVM-based beamformer has been deployed in an FPGA board, as demonstrated in detail in this work. The implementation results have verified the strong performance of the QS-SVM-based beamformer in suppressing undesired signals, deep nulls with powers less than -10 dB in undesired signals, and transferring desired signals. Furthermore, we have demonstrated that the performance of the QS-SVM-based beamformer consists of other advantages of average latency time in the order of milliseconds, performance efficiency of more than 90\%, and throughput of about 100\%.

cs.NI↗

arXiv2022

Implementation of a Three-class Classification LS-SVM Model for the Hybrid Antenna Array with Bowtie Elements in the Adaptive Beamforming Application

To address three significant challenges of massive wireless communications including propagation loss, long-distance transmission, and channel fading, we aim at establishing the hybrid antenna array with bowtie elements in a compact size for beamforming applications. In this work we rigorously demonstrate that bowtie elements allow for a significant improvement in the beamforming performance of the hybrid antenna array compared to not only other available antenna arrays, but also its geometrical counterpart with dipole elements. We have achieved a greater than 15 dB increase in SINR values, a greater than 20% improvement in the antenna efficiency, a significant enhancement in the DoA estimation, and 20 increments in the directivity for the hybrid antenna array with bowtie elements, compared to its geometrical counterpart, by performing a three-class classification LS-SVM (LeastSquares Support Vector Machine) optimization method. The proposed hybrid antenna array has shown a 3D uniform directivity, which is accompanied by its superior performance in the 3D uniform beam-scanning capability. The directivities remain almost constant at 40.83 dBi with the variation of angle {\theta}, and 41.21 dBi with the variation of angle {\phi}. The unrivaled functionality and performance of the hybrid antenna array with bowtie elements makes it a potential candidate for beamforming applications in massive wireless communications.

cs.LG↗