Institution profile

Polytechnique Montreal

Academic institutionnorthamerica · ca
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Research library408linked papers
Opportunities0open roles
Selected work

Representative Papers

Performance Optimization of Energy-Harvesting Underlay Cognitive Radio Networks Using Reinforcement Learning

Jun 19, 2023International Conference on Wireless Communications and Mobile Computing

In energy-constrained cognitive radio networks, secondary users (SUs) must coexist with primary users (PUs) while optimizing performance under stringent energy limitations. Method: This paper proposes a dynamic joint energy harvesting and data transmission decision framework. It innovatively treats PU communication signals—not as interference but as exploitable radio-frequency (RF) energy—and designs a dual-source adaptive energy harvesting mechanism. Integrating time-switching protocols with a deep Q-network (DQN), the framework jointly optimizes spectrum sensing, transceiver mode switching, and transmit power allocation. Contribution/Results: The proposed method significantly improves the SU’s average data rate, exhibits stable convergence, and consistently outperforms conventional benchmark strategies across diverse channel conditions and energy constraints. It establishes a novel paradigm for green, self-sustaining cognitive access by enabling SUs to autonomously harvest ambient RF energy from PU transmissions.

10 citationsRead paper

Constrained Multi-Objective Bayesian Optimization with Application to Aircraft Design

Jun 20, 2022AIAA AVIATION 2022 Forum

Bayesian optimization (BO) methods for computationally expensive, nonlinearly constrained multi-objective optimization problems—such as aircraft conceptual design—often suffer from ill-conditioning in multi-objective acquisition functions, leading to unstable surrogate updates and poor convergence. Method: This paper extends the SEGOMOE framework by introducing a novel regularization mechanism directly into the multi-objective acquisition function, synergistically integrating Kriging surrogates, a Mixture-of-Experts (MoE) architecture, and an enhanced SEGO algorithm. Contribution/Results: The proposed approach systematically alleviates the trade-off between ill-conditioning and convergence in constrained multi-objective BO. Empirical evaluation on aircraft design tasks demonstrates that it achieves high-quality Pareto fronts using only 5% of the function evaluations required by NSGA-II, significantly improving the efficiency of identifying high-fidelity, low-cost compromise solutions.

8 citationsRead paper

An Addendum to NeBula: Toward Extending Team CoSTAR’s Solution to Larger Scale Environments

Apr 18, 2025IEEE Transactions on Field Robotics

Autonomous collaborative exploration in ultra-large-scale, unstructured underground environments remains challenging due to severe communication constraints, navigation uncertainty, and lack of prior maps. Method: This work extends TEAM CoSTAR’s NeBula autonomy system with a full-stack enhancement framework integrating semantic-geometric joint mapping, distributed POMDP-based global planning under communication constraints, adaptive filtering for localization, Gaussian process–based probabilistic traversability modeling, edge-cloud cooperative communication protocols, and aerial-ground heterogeneous multi-agent task allocation. Contribution/Results: The framework achieves, for the first time, robust long-range mapping (>5 km²), sub-meter localization accuracy (<0.3 m), and decentralized collaborative decision-making in kilometer-scale underground spaces (e.g., limestone mines). Validated in the DARPA Subterranean Challenge and real-world mine deployments, it improves mission completion rate by 37%, significantly advancing scalability, robustness, and coordination in autonomous underground exploration.

6 citationsRead paper

From Mesh Completion to AI Designed Crown

Jan 09, 2025International Conference on Medical Image Computing and Computer-Assisted Intervention

To address the time-consuming, manual-intensive nature of dental crown design—where balancing accuracy and manufacturability remains challenging—this paper proposes a geometry-prior-guided generative 3D completion framework. Methodologically, it integrates conditional mesh reconstruction using implicit neural representations (INRs), dental anatomical constraint embedding, multi-scale geometric adversarial training, and CAD-compatible topology optimization, enabling an end-to-end AI-driven pipeline from incomplete crown scans to clinically deployable designs. Its key innovation lies in the first unified modeling of anatomical semantic consistency, biomechanical adaptability, and CAD/CAM production readiness. Evaluated on real-world clinical data, the method achieves a mean surface error of only 0.087 mm and attains a 92% pass rate on automated Dental CAD validation; average design time per case is reduced to under 15 minutes.

2 citationsRead paper
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