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George Shaker

Publications and source records attributed to George Shaker.

24 records · Page 2Linked to original sources
arXiv2025

CRADMap: Applied Distributed Volumetric Mapping with 5G-Connected Multi-Robots and 4D Radar Perception

Sparse and feature SLAM methods provide robust camera pose estimation. However, they often fail to capture the level of detail required for inspection and scene awareness tasks. Conversely, dense SLAM approaches generate richer scene reconstructions but impose a prohibitive computational load to create 3D maps. We present a novel distributed volumetric mapping framework designated as CRADMap that addresses these issues by extending the state-of-the-art (SOTA) ORBSLAM3 system with the COVINS on the backend for global optimization. Our pipeline for volumetric reconstruction fuses dense keyframes at a centralized server via 5G connectivity, aggregating geometry, and occupancy information from multiple autonomous mobile robots (AMRs) without overtaxing onboard resources. This enables each AMR to independently perform mapping while the backend constructs high-fidelity real-time 3D maps. To operate Beyond the Visible (BtV) and overcome the limitations of standard visual sensors, we automated a standalone 4D mmWave radar module that functions independently without sensor fusion with SLAM. The BtV system enables the detection and mapping of occluded metallic objects in cluttered environments, enhancing situational awareness in inspection scenarios. Experimental validation in Section~\ref{sec:IV} demonstrates the effectiveness of our framework.

cs.RO↗
arXiv2025

Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study

Human Activity Recognition has gained significant attention due to its diverse applications, including ambient assisted living and remote sensing. Wearable sensor-based solutions often suffer from user discomfort and reliability issues, while video-based methods raise privacy concerns and perform poorly in low-light conditions or long ranges. This study introduces a Frequency-Modulated Continuous Wave radar-based framework for human activity recognition, leveraging a 60 GHz radar and multi-dimensional feature maps. Unlike conventional approaches that process feature maps as images, this study feeds multi-dimensional feature maps -- Range-Doppler, Range-Azimuth, and Range-Elevation -- as data vectors directly into the machine learning (SVM, MLP) and deep learning (CNN, LSTM, ConvLSTM) models, preserving the spatial and temporal structures of the data. These features were extracted from a novel dataset with seven activity classes and validated using two different validation approaches. The ConvLSTM model outperformed conventional machine learning and deep learning models, achieving an accuracy of 90.51% and an F1-score of 87.31% on cross-scene validation and an accuracy of 89.56% and an F1-score of 87.15% on leave-one-person-out cross-validation. The results highlight the approach's potential for scalable, non-intrusive, and privacy-preserving activity monitoring in real-world scenarios.

cs.ET↗
arXiv2019

Triple-Poles Complementary Split Ring Resonator for Sensing Diabetics Glucose Levels at cm-Band

Microwave sensors are very promising for sensing the blood glucose levels non-invasively for their non-ionizing nature, miniaturized sizing, and low health risks for diabetics. All these features offer the possibility for realizing a portable non-invasive glucose sensor for monitoring glucose levels in real time. In this article, we propose a triple poles complementary split ring resonator (CSRR) produced on a FR4 substrate in microstrip technology in the cm-wave band (1-6 GHz). The proposed bio-sensor can detect the small variations in the dielectric properties (relative permittivity and dielectric losses) of glucose in the blood mimicking aqueous solutions due their intense interaction with the electromagnetic field at harmonic resonances. The resonator exhibits higher sensitivity performance at the different resonances compared to the single and double-poles counterparts as demonstrated by simulations in a 3D full-wave EM solver.

physics.med-ph↗
arXiv2019

Glucose Levels Sensing using Whispering Gallery Modes at mm-Wave Band

In this study, an integrated low-cost and complexity mm-wave structure of Whispering Gallery Mode (WGM) is proposed for sensing the glucose levels in mimicking aquatic solutions of concentrations similar to type 2 diabetics. The bio-sensor is composed of a curved dielectric waveguide coupled to a dielectric disc resonator that is loaded with the glucose sample under test. The intense WGM field induced towards the boundary of the DDR is exploited to detect the slight variations in the dielectric properties of different glucose levels via tracing the variations in the magnitude of the transmission coefficient S_{21} in the mm-wave frequency band (49-70 GHz). The WGM resonator shows a high sensitivity performance (0.025-0.077 dB/(mg/dL)) at the lower-order modes WGH_{600} and WGH_{700} as demonstrated by simulations in a 3D full-wave EM solver (Ansys HFSS).

physics.app-ph↗
arXiv2019

Low-cost low-power in-vehicle occupant detection with mm-wave FMCW radar

In this paper, we use a low-cost low-power mm-wave frequency modulated continuous wave (FMCW) radar for the in-vehicle occupant detection. We propose an algorithm using Capon filter for the joint range-azimuth estimation. Then, the minimum necessary features are extracted to train machine learning classifiers to have reasonable computational complexity while achieving high accuracy. In addition, experiments were carried out in a minivan to detect occupancy of each row using support vector machine (SVM). Finally, our proposed system achieved 97.8% accuracy on average in finding the defined scenarios. Moreover, the system can correctly identify if the vehicle is occupied or not with 100% accuracy.

eess.SP↗