326 papers
This paper introduces Space-O-RAN, a distributed control architecture that extends Open RAN principles to satellite constellations by deploying lightweight onboard applications and hierarchical coordination via SpaceRIC and terrestrial SMO to enable intelligent, autonomous, and interoperable Non-Terrestrial Networks for 6G.
This paper proposes a self-supervised UAV trajectory planning framework that integrates learning-based depth perception with differentiable optimization and neural time allocation to achieve robust, label-free navigation in 3D environments, significantly outperforming state-of-the-art methods in tracking accuracy and control efficiency.
This paper presents an iterative tangential interpolation algorithm for reducing large-scale MIMO systems that leverages interpolation weight freedom and low-rank data to optimize error proxies, ensuring monotonic error reduction while offering trade-offs between computational complexity and approximation performance comparable to standard methods.
This paper proposes and experimentally validates a robust control strategy for regulating the outlet temperature of a counter-current heat exchanger by modeling it as a structured bilinear system and implementing both an output feedback controller with a state observer and a purely integral control law.
This paper introduces zono-conformal prediction, a data-efficient method that utilizes zonotopes to generate less conservative, statistically valid uncertainty sets for both regression and classification tasks by directly embedding these sets into the base predictor via a single linear program.
This foundational concept paper proposes the Adaptive Quantized Planetary Crater Detection System (AQ-PCDSys), an architecture integrating quantized neural networks and adaptive multi-sensor fusion to enable real-time, high-fidelity crater detection on resource-constrained, radiation-hardened space exploration hardware.
This paper proposes Learning-IPM, a novel framework that utilizes an LSTM network to project the interior-point method's central path from early stable iterations while enforcing operational constraints, thereby significantly accelerating optimal power flow solutions without compromising feasibility or accuracy.
This paper proposes a continuous, time-invariant feedback control strategy that achieves almost global asymptotic stabilization to a desired location on the n-sphere while avoiding star-shaped and conic constraints by dynamically guiding the state either along geodesics or toward the antipode of unsafe regions.
This paper proposes a dispatch-aware deep neural network (DA-DNN) that accelerates optimal transmission switching by embedding a differentiable DC-OPF layer to enforce physical constraints and generate feasible solutions without relying on costly pre-solved labels, thereby achieving scalability, robustness, and generalization to untrained system configurations.
This paper presents a radiation-hardened 130 nm CMOS delta-sigma current digitizer capable of achieving over 200 dB dynamic range and sub-picoampere resolution for beam loss monitoring, while maintaining a 10 µs response time for machine protection and demonstrating robust performance up to 100 Mrad total ionizing dose.
This paper introduces the GenAI Workbench, a conceptual Model-Based Systems Engineering framework that integrates multimodal data and generative AI to bridge the gap between detailed component design and holistic system architecture by automatically extracting requirements and generating system structures from source documents.
This paper presents a multidisciplinary design optimization framework using OpenMDAO and Dymos that simultaneously optimizes low-thrust trajectories and spacecraft power systems for asteroid orbit insertion, explicitly coupling variable-specific impulse Hall thruster performance with time-varying solar power availability to overcome the limitations of traditional simplifying assumptions.
This paper proposes a real-time, loosely coupled GNSS and IMU integration framework using Factor Graph Optimization that enhances service availability in challenging urban environments by trading off some positioning accuracy for improved computational efficiency compared to traditional batch methods.
This paper presents a real-time, tightly coupled GNSS-IMU integration method based on factor graph optimization that utilizes incremental optimization with fixed-lag marginalization to achieve robust, high-accuracy positioning in dense urban environments, as validated by experiments on the UrbanNav dataset.
This paper proposes a two-time-scale design for local safety filters in networked systems that eliminates the need for global coordination by using derivative estimation, while providing explicit bounds on safety degradation relative to the time-scale parameter and estimation errors.
This study analyzes the long-term effectiveness of eco-routing under population growth in six U.S. cities, revealing that while emissions scale superlinearly with population and eco-routing creates carbon bottlenecks, targeted capacity expansion on these critical links significantly reduces both emissions and travel time without compromising routing efficiency.
This paper bridges learning-to-communicate in multi-agent systems with decentralized stochastic control by formalizing the problem under a common-information framework, identifying quasi-classical information structures as a tractable subclass, and developing provable algorithms with quasi-polynomial complexity for such scenarios.
This paper proposes a unified co-optimization framework for utility-scale off-grid renewable power-to-hydrogen systems that coordinates heterogeneous electrolyzers and other resources to maximize hydrogen production while ensuring frequency security through a novel system-level response model and stage-wise transient constraints.
This paper proposes an SEIRV epidemic model with relapse and intervention to analyze malware propagation dynamics, establish stability conditions, identify key influencing parameters, and optimize cost-effective control strategies using a hybrid gradient-based framework calibrated with real-world infection data.
This paper proposes an alternating learning framework for cooperative multi-agent reinforcement learning under communication constraints, where a global agent observes only a subset of local agents, and proves that this approach converges to an approximate Nash equilibrium with improved sample complexity compared to methods operating on the full joint state space.