检索结果-内蒙古大学图书馆

Classification of Infant Sleep/Wake States: Cross-Attention among Large Scale Pretrained Transformer Networks using Audio, ECG, and IMU Data

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Chang, Kai Chieh Hasegawa-Johnson, Mark McElwain, Nancy L. Islam, Bashima Department of Electrical and Computer Engineering University of Illinois Urbana Champaign United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign United States Department of Human Development and Family Studies University of Illinois Urbana-Champaign United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute United States

Infant sleep is critical to brain and behavioral development. Prior studies on infant sleep/wake classification have been largely limited to reliance on expensive and burdensome polysomnography (PSG) tests in the laboratory or wearable devices that collect single-modality data. To facilitate data collection and accuracy of detection, we aimed to advance this field of study by using a multi-modal wearable device, LittleBeats (LB), to collect audio, electrocardiogram (ECG), and inertial measurement unit (IMU) data among a cohort of 28 infants. We employed a 3-branch (audio/ECG/IMU) large scale transformer-based neural network (NN) to demonstrate the potential of such multi-modal data. We pretrained each branch independently with its respective modality, then finetuned the model by fusing the pretrained transformer layers with cross-attention. We show that multi-modal data significantly improves sleep/wake classification (accuracy = 0.880), compared with use of a single modality (accuracy = 0.732). Our approach to multi-modal mid-level fusion may be adaptable to a diverse range of architectures and tasks, expanding future directions of infant behavioral research. © 2023, CC BY.

关键词： Electrocardiograms

Grasp synthesis for novel objects using heuristic-based and data-driven active vision methods

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Natarajan, Sabhari Brown, Galen Calli, Berk Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States Computer Science Department Worcester Polytechnic Institute WorcesterMA United States

In this work, we present several heuristic-based and data-driven active vision strategies for viewpoint optimization of an arm-mounted depth camera for the purpose of aiding robotic grasping. These strategies aim to efficiently collect data to boost the performance of an underlying grasp synthesis algorithm. We created an open-source benchmarking platform in simulation (https://***/galenbr/2021ActiveVision), and provide an extensive study for assessing the performance of the proposed methods as well as comparing them against various baseline strategies. We also provide an experimental study with a real-world setup by utilizing an existing grasping planning benchmark in the literature. With these analyses, we were able to quantitatively demonstrate the versatility of heuristic methods that prioritize certain types of exploration, and qualitatively show their robustness to both novel objects and the transition from simulation to the real world. We identified scenarios in which our methods did not perform well and scenarios which are objectively difficult, and present a discussion on which avenues for future research show promise. Copyright © 2021, The Authors. All rights reserved.

关键词： Heuristic methods

Gravity Compensation of the dVRK-Si Patient Side Manipulator based on Dynamic Model Identification

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Zhou, Haoying Yang, Hao Deguet, Anton Fichera, Loris Wu, Jie Ying Kazanzides, Peter Department of Robotics Engineering Worcester Polytechnic Institute MA United States Laboratory for Computational Sensing and Robotics JHU MD United States Department of Computer Science Vanderbilt University TN United States Department of Computer Science Johns Hopkins University MD United States

The da Vinci Research Kit (dVRK, also known as dVRK Classic) is anopen-source teleoperated surgical robotic system whose hardware is obtainedfrom the first generation da Vinci Surgical System (Intuitive, Sunnyvale, CA,USA). The dVRK has greatly facilitated research in robot-assisted surgery overthe past decade and helped researchers address multiple major challenges inthis domain. Recently, the dVRK-Si system, a new version of the dVRK which usesmechanical components from the da Vinci Si Surgical System, became available tothe community. The major difference between the first generation da Vinci andthe da Vinci Si is in the structural upgrade of the Patient Side Manipulator(PSM). Because of this upgrade, the gravity of the dVRK-Si PSM can no longer beignored as in the dVRK Classic. The high gravity offset may lead to relativelylow control accuracy and longer response time. In addition, althoughsubstantial progress has been made in addressing the dynamic modelidentification problem for the dVRK Classic, further research is required onmodel-based control for the dVRK-Si, due to differences in mechanicalcomponents and the demand for enhanced control performance. To address theseproblems, in this work, we present (1) a novel full kinematic model of thedVRK-Si PSM, and (2) a gravity compensation approach based on the dynamic modelidentification. © 2025, CC BY.

关键词： Surgical equipment

An Analytical Framework for Control Synthesis of Cyber-Physical Systems with Safety Guarantee

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Niu, Luyao Al Maruf, Abdullah Clark, Andrew Mertoguno, J. Sukarno Poovendran, Radha The Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States The Network Security Lab Department of Electrical and Computer Engineering University of Washington SeattleWA98195-2500 United States Information and Cyber Sciences Georgia Tech Research Institute AtlantaGA30332 United States

Cyber-physical systems (CPS) are required to operate safely under fault and malicious attacks. The simplex architecture and the recently proposed cyber resilient architectures, e.g., Byzantine fault tolerant++ (BFT++), provide safety for CPS under faults and malicious cyber attacks, respectively. However, these existing architectures make use of different timing parameters and implementations to provide safety, and are seemingly unrelated. In this paper, we propose an analytical framework to represent the simplex, BFT++ and other practical cyber resilient architectures (CRAs). We construct a hybrid system that models CPS adopting any of these architectures. We derive sufficient conditions via our proposed framework under which a control policy is guaranteed to be safe. We present an algorithm to synthesize the control policy. We validate the proposed framework using a case study on lateral control of a Boeing 747, and demonstrate that our proposed approach ensures safety of the system. © 2022, CC BY.

关键词： Embedded systems

A divide-and-merge point cloud clustering algorithm for LiDAR panoptic segmentation

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhao, Yiming Zhang, Xiao Huang, Xinming Department of Electrical and Computer Engineering Worcester Polytechnic Institute Massachusetts01609 United States

Clustering objects from the LiDAR point cloud is an important research problem with many applications such as autonomous driving. To meet the real-time requirement, existing research proposed to apply the connected-componentlabeling (CCL) technique on LiDAR spherical range image with a heuristic condition to check if two neighbor points are connected. However, LiDAR range image is different from a binary image which has a deterministic condition to tell if two pixels belong to the same component. The heuristic condition used on the LiDAR range image only works empirically, which suggests the LiDAR clustering algorithm should be robust to potential failures of the empirical heuristic condition. To overcome this challenge, this paper proposes a divide-and-merge LiDAR clustering algorithm. This algorithm firstly conducts clustering in each evenly divided local region, then merges the local clustered small components by voting on edge point pairs. Assuming there are N LiDAR points of objects in total with m divided local regions, the time complexity of the proposed algorithm is O(N) + O(m2). A smaller m means the voting will involve more neighbor points, but the time complexity will become larger. So the m controls the trade-off between the time complexity and the clustering accuracy. A proper m helps the proposed algorithm work in real-time as well as maintain good performance. We evaluate the divide-andmerge clustering algorithm on the SemanticKITTI panoptic segmentation benchmark by cascading it with a state-of-the-art semantic segmentation model. The final performance evaluated through the leaderboard achieves the best among all published methods. The proposed algorithm is implemented with C++ and wrapped as a python function. It can be easily used with the modern deep learning framework in python. We will release the code under the following link. Copyright © 2021, The Authors. All rights reserved.

关键词： Economic and social effects

A Compositional Approach to Safety-Critical Resilient Control for Systems with Coupled Dynamics

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Al Maruf, Abdullah Niu, Luyao Clark, Andrew Mertoguno, J. Sukarno Poovendran, Radha The Network Security Lab Department of Electrical and Computer Engineering University of Washington SeattleWA98195-2500 United States The Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Information and Cyber Sciences Georgia Tech Research Institute AtlantaGA30332 United States

Complex, interconnected Cyber-physical Systems (CPS) are increasingly common in applications including smart grids and transportation. Ensuring safety of interconnected systems whose dynamics are coupled is challenging because the effects of faults and attacks in one sub-system can propagate to other sub-systems and lead to safety violations. In this paper, we study the problem of safety-critical control for CPS with coupled dynamics when some sub-systems are subject to failure or attack. We first propose resilient-safety indices (RSIs) for the faulty or compromised sub-systems that bound the worstcase impacts of faulty or compromised sub-systems on a set of specified safety constraints. By incorporating the RSIs, we provide a sufficient condition for the synthesis of control policies in each failure- and attack- free sub-systems. The synthesized control policies compensate for the impacts of the faulty or compromised sub-systems to guarantee safety. We formulate sum-of-square optimization programs to compute the RSIs and the safety-ensuring control policies. We present a case study that applies our proposed approach on the temperature regulation of three coupled rooms. The case study demonstrates that control policies obtained using our algorithm guarantee system's safety constraints. © 2022, CC BY.

关键词： Safety engineering

FIDNet: LiDAR point cloud semantic segmentation with fully interpolation decoding

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhao, Yiming Bai, Lin Huang, Xinming Department of Electrical and Computer Engineering Worcester Polytechnic Institute MA01609 United States

Projecting the point cloud on the 2D spherical range image transforms the LiDAR semantic segmentation to a 2D segmentation task on the range image. However, the LiDAR range image is still naturally different from the regular 2D RGB image;for example, each position on the range image encodes the unique geometry information. In this paper, we propose a new projection-based LiDAR semantic segmentation pipeline that consists of a novel network structure and an efficient post-processing step. In our network structure, we design a FID (fully interpolation decoding) module that directly upsamples the multi-resolution feature maps using bilinear interpolation. Inspired by the 3D distance interpolation used in PointNet++, we argue this FID module is a 2D version distance interpolation on (θ, φ) space. As a parameter-free decoding module, the FID largely reduces the model complexity by maintaining good performance. Besides the network structure, we empirically find that our model predictions have clear boundaries between different semantic classes. This makes us rethink whether the widely used K-nearest-neighbor post-processing is still necessary for our pipeline. Then, we realize the many-to-one mapping causes the blurring effect that some points are mapped into the same pixel and share the same label. Therefore, we propose to process those occluded points by assigning the nearest predicted label to them. This NLA (nearest label assignment) post-processing step shows a better performance than KNN with faster inference speed in the ablation study. On SemanticKITTI dataset, our pipeline achieves the best performance among all projection-based methods with 64 × 2048 resolution and all point-wise solutions. With a ResNet-34 as the backbone, both the training and testing of our model can be finished on a single RTX 2080 Ti with 11G memory. The code is released here. Copyright © 2021, The Authors. All rights reserved.

关键词： Pipelines

A surface geometry model for LiDAR depth completion

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhao, Yiming Bai, Lin Zhang, Ziming Huang, Xinming Department of Electrical and Computer Engineering Worcester Polytechnic Institute MA01609 United States

— LiDAR depth completion is a task that predicts depth values for every pixel on the corresponding camera frame although only sparse LiDAR points are available. Most of the existing state-of-the-art solutions are based on deep neural networks, which need a large amount of data and heavy computations for training the models. In this letter, a novel non-learning depth completion method is proposed by exploiting the local surface geometry that is enhanced by an outlier removal algorithm. The proposed surface geometry model is inspired by the observation that most pixels with unknown depth have a nearby LiDAR point. Therefore, it is assumed those pixels share the same surface with the nearest LiDAR point, and their respective depth can be estimated as the nearest LiDAR depth value plus a residual error. The residual error is calculated by using a derived equation with several physical parameters as input, including the known camera intrinsic parameters, estimated normal vector, and offset distance on the image plane. The proposed method is further enhanced by an outlier removal algorithm that is designed to remove incorrectly mapped LiDAR points from occluded regions. On KITTI dataset, the proposed solution achieves the best error performance among all existing non-learning methods and is comparable to the best self-supervised learning method and some supervised learning methods. Moreover, since removing outlier points coming from occluded regions is a commonly existing problem in LiDAR-Camera systems, the proposed outlier removal algorithm is a general preprocessing step that is applicable to many robotic systems with both camera and LiDAR sensors. 1 Copyright © 2021, The Authors. All rights reserved.

关键词： Pixels

TTCDist: Fast Distance Estimation From an Active Monocular Camera Using Time-to-Contact

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Burner, Levi Sanket, Nitin J. Fermüller, Cornelia Aloimonos, Yiannis Perception and Robotics Group Electrical and Computer Engineering University of Maryland College Park United States Robotics Engineering Worcester Polytechnic Institute United States

Distance estimation from vision is fundamental for a myriad of robotic applications such as navigation, manipulation, and planning. Inspired by the mammal's visual system, which gazes at specific objects, we develop two novel constraints relating time-to-contact, acceleration, and distance that we call the τ-constraint and Φ-constraint. They allow an active (moving) camera to estimate depth efficiently and accurately while using only a small portion of the image. The constraints are applicable to range sensing, sensor fusion, and visual servoing. We successfully validate the proposed constraints with two experiments. The first applies both constraints in a trajectory estimation task with a monocular camera and an Inertial Measurement Unit (IMU). Our methods achieve 30-70% less average trajectory error while running 25× and 6.2× faster than the popular Visual-Inertial Odometry methods VINS-Mono and ROVIO respectively. The second experiment demonstrates that when the constraints are used for feedback with efference copies the resulting closed loop system's eigenvalues are invariant to scaling of the applied control signal. We believe these results indicate the τ and Φ constraint's potential as the basis of robust and efficient algorithms for a multitude of robotic applications. © 2022, CC BY.

关键词： Mammals