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๐Ÿ” grenoble inp ๐Ÿ“‚ Engineering
Showing 471 results for "grenoble inp" in Engineering
Engineering Preprint PDF DOI

BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal

Phat Lam ยท 2026

Electroencephalography (EEG) is highly susceptible to artifact contamination, such as electrooculographic (EOG) and electromyographic (EMG) interference, which severely degrades signal quality and hinโ€ฆ

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Graph Theoretical Outlier Rejection for 4D Radar Registration in Feature-Poor Environments

Georg Dorndorf, Daniel Adolfsson, Masrur Doostdar ยท 2026

Automotive 4D imaging radar is well suited for operation in dusty and low-visibility environments, but scan registration remains challenging due to scan sparsity and spurious detections caused by noisโ€ฆ

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Optimal Robust Adaptive Beamforming for a General-Rank Signal Model via Equivalence of Maximin and Minimax SINR Problems

Yongwei Huang, Zhenhui Huang, Sergiy A. Vorobyov, Zhi-Quan Luo ยท 2026

The globally optimal robust adaptive beamforming (RAB) solution is studied for worst-case signal-to-interference-plus-noise ratio (SINR) maximization (the maximin SINR problem) under convex and closedโ€ฆ

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Engineering Preprint PDF DOI

Dynamic Heartbeat Modeling with Recurrent Neural Networks and Inverse Gaussian Point Process

Runwei Lin, Ying Wang ยท 2026

Heart rate variability (HRV) analysis is important for the assessment of autonomic cardiovascular regulation. The inverse Gaussian process (IGP) has been widely used for beat-to-beat HRV modeling, as โ€ฆ

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Engineering Preprint PDF DOI

MonoEM-GS: Monocular Expectation-Maximization Gaussian Splatting SLAM

Evgenii Kruzhkov, Sven Behnke ยท 2026

Feed-forward geometric foundation models can infer dense point clouds and camera motion directly from RGB streams, providing priors for monocular SLAM. However, their predictions are often view-dependโ€ฆ

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Engineering Preprint PDF DOI

Exploring Temporal Representation in Neural Processes for Multimodal Action Prediction

Marco Gabriele Fedozzi, Yukie Nagai, Francesco Rea, Alessandra Sciutti ยท 2026

Inspired by the human ability to understand and predict others, we study the applicability of Conditional Neural Processes (CNP) to the task of self-supervised multimodal action prediction in roboticsโ€ฆ

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DiV-INR: Extreme Low-Bitrate Diffusion Video Compression with INR Conditioning

Eren Cetin, Lucas Relic, Yuanyi Xue, Markus Gross, Christopher Schroers, Roberto Azevedo ยท 2026

We present a perceptually-driven video compression framework integrating implicit neural representations (INRs) and pre-trained video diffusion models to address the extremely low bitrate regime (<0.0โ€ฆ

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State and Trajectory Estimation of Tensegrity Robots via Factor Graphs and Chebyshev Polynomials

Edgar Granados, Patrick Meng, Charles Tang, Shrimed Sangani, William R. Johnson III, Rebecca Kramer-Bottiglio, Kostas Bekris ยท 2026

Tensegrity robots offer compliance and adaptability, but their nonlinear, and underconstrained dynamics make state estimation challenging. Reliable continuous-time estimation of all rigid links is cruโ€ฆ

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Area Optimization of Open-Source Low-Power INA in 130nm CMOS using Hybrid Mixed-Variable PSO

Avishka Herath, Chanula Luckshan, Lochana Katugaha, Udara Mendis, Kithmin Wickremasinghe ยท 2026

As open-source silicon initiatives democratize access to integrated circuit development using multi-project environments, silicon area has become a premium resource. However, minimizing this layout arโ€ฆ

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Modeling isotropic polyconvex hyperelasticity by neural networks -- sufficient and necessary criteria for compressible and incompressible materials

Gian-Luca Geuken, Patrick Kurzeja, David Wiedemann, Martin Zlatic, Marko Cana{dj}ija, Jorn Mosler ยท 2026

This work investigates different sufficient and necessary criteria for hyperelastic, isotropic polyconvex material models, focusing on neural network implementations for compressible and incompressiblโ€ฆ

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Cyber-Physical System Design Space Exploration for Affordable Precision Agriculture

Pawan Kumar, Hokeun Kim ยท 2026

Precision agriculture promises higher yields and sustainability, but adoption is slowed by the high cost of cyber-physical systems (CPS) and the lack of systematic design methods. We present a cost-awโ€ฆ

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Geometrically Plausible Object Pose Refinement using Differentiable Simulation

Anil Zeybek, Rhys Newbury, Snehal Dikhale, Nawid Jamali, Soshi Iba, Akansel Cosgun ยท 2026

State-of-the-art object pose estimation methods are prone to generating geometrically infeasible pose hypotheses. This problem is prevalent in dexterous manipulation, where estimated poses often interโ€ฆ

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Real-Time Regulation of Direct Ink Writing Using Model Reference Adaptive Control

Mandana Mohammadi Looey, Amrita Basak, Satadru Dey ยท 2026

Direct Ink Writing (DIW) has gained attention for its potential to reduce printing time and material waste. However, maintaining precise geometry and consistent print quality remains challenging underโ€ฆ

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Multi-material Direct Ink Writing and Embroidery for Stretchable Wearable Sensors

Lukas Cha, Ryman Hashem, Ria Prakash, Tanguy Declety, Wenze Zhang, Liang He ยท 2026

The development of wearable sensing systems for sports performance tracking, rehabilitation, and injury prevention has driven growing demand for smart garments that combine comfort, durability, and acโ€ฆ

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Consensus in Plug-and-Play Heterogeneous Dynamical Networks: A Passivity Compensation Approach

Yongkang Su, Sei Zhen Khong, Lanlan Su ยท 2026

This paper investigates output consensus in heterogeneous dynamical networks within a plug-and-play framework. The networks are interconnected through nonlinear diffusive couplings and operate in the โ€ฆ

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Regularizing INR with diffusion prior self-supervised 3D reconstruction of neutron computed tomography data

Maliha Hossain, Haley Duba-Sullivan, Amirkoushyar Ziabari ยท 2026

Recently, generative diffusion priors have made huge strides as inverse problem solvers, including the ability to be adapted for inference on out-of-distribution data. Concurrently, implicit neural reโ€ฆ

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High-Fidelity Digital Twin Dataset Generation for Inverter-Based Microgrids Under Multi-Scenario Disturbances

Osasumwen Cedric Ogiesoba-Eguakun, Kaveh Ashenayi, Suman Rath ยท 2026

Public power-system datasets often lack electromagnetic transient (EMT) waveforms, inverter control dynamics, and diverse disturbance coverage, which limits their usefulness for training surrogate modโ€ฆ

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Degeneracy-Resilient Teach and Repeat for Geometrically Challenging Environments Using FMCW Lidar

Katya M. Papais, Wenda Zhao, Timothy D. Barfoot ยท 2026

Teach and Repeat (T&R) topometric navigation enables robots to autonomously repeat previously traversed paths without relying on GPS, making it well suited for operations in GPS-denied environments suโ€ฆ

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KISS-IMU: Self-supervised Inertial Odometry with Motion-balanced Learning and Uncertainty-aware Inference

Jiwon Choi, Hogyun Kim, Geonmo Yang, Juhui Lee, Younggun Cho ยท 2026

Inertial measurement units (IMUs), which provide high-frequency linear acceleration and angular velocity measurements, serve as fundamental sensing modalities in robotic systems. Recent advances in deโ€ฆ

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A Fully Open-source Implementation of an Analog 8-PAM Demapper for High-speed Communications

Mohamed Aiham Hemza, Alex Alvarado, Krzysztof Herman, Piyush Kaul ยท 2026

Spectrally-efficient communication systems rely on the use of multi-level modulation formats. At the receiver side, a demodulator is often used to extract soft information about the transmitted bits. โ€ฆ

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