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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Lou, Bin Xie, Anhuan Zhu, Shiqiang Liu, Yaru Gu, Jason Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

A solar-powered jumping-gliding Martian aircraft is proposed as a new tool for Mars exploration, designed especially to perform long-term wide-range detection. A completely new way of continues detection performed by aircraft is created. A preliminary design of the jumping-gliding Martian aircraft is carried out while its flight performance is analyzed afterwards. The conceptual design of a large-aspect ratio foldable wing with a thin fuselage and a jumping device is put forward. The solar energy model, aerodynamic model and weight mode are all built. The energy balance models are established for the periods of detecting mission that the aircraft lands on the ground for harvesting solar energy and flies in the sky. The gliding performance of this aircraft is particularly discussed here. © 2022 IEEE.

关键词： Aspect ratio

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Low-cost Mars Terrain Classification System Based on Coarse-grained Annotation

Low-cost Mars Terrain Classification System Based on Coarse-...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhang, Jian Chen, Yeheng Zhu, Shiqiang Li, Yuehua Xie, Tian Gu, Jason Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Due to communication latency with remote ground sites, automatic recognition of Mars terrain is essential for the path-planning of rovers. Currently, most vision-based terrain classification require thousands of fine-grained training samples, while the undefined terrains on Mars are difficult to be classified or fine-grained labeled. Actually, most of the terrain categories can only be coarse-grained labeled due to several limitations, such as overlapped sub-regions, blurred borders, etc. To solve this problem, CACMT (Coarse-grained Annotation-based Classification for Mars Terrain) is proposed to generate the global fine-grained classification map from the local coarse-grained data. Specifically, the complete pipeline is decomposed into (i) annotation rules with unique design, (ii) hierarchical feature fusion network for predicting sub-features of terrain (iii) and a generator for outputting dense terrain categories of Mars. Finally, the results of actual data on Mars demonstrate that the terrain sub-features can be successfully recognized and a dense terrain classification map can be generated applying only coarse-grained labeled images. © 2022 IEEE.

关键词： Motion planning

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Acoustic Beamforming via Interference-Plus-Noise Covariance Matrix Construction for Interferences and Noise Attenuation

Acoustic Beamforming via Interference-Plus-Noise Covariance ...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Xiao, Yongxiong Zhu, Shiqiang Song, Wei Wan, Minhong Gu, Jason Li, Te Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

The interference-plus-noise covariance matrix (INCM) is essential in improving an acoustic beamformer's interference and noise attenuation performance. In practical implementation, INCM reconstruction is required to remove the signal of interest (SOI) components from the sample covariance matrix. However, some of the interference and noise components are inevitably removed during the INCM reconstruction process to avoid distortion of the desired speech, which deteriorates the interference and noise attenuation performance of the beamformer. This paper proposes constructing an INCM with as much information on the interferences and noise as possible by adding covariance matrices of the spherically diffuse noise, background noise, and interferences. The final INCM is reconstructed by using the principal eigenvector and definition of INCM. The beamformer's weight coefficients are computed by the linearly constrained minimum variance (LCMV) formulation. The proposed method is validated by experiments using a circular microphone array mounted on a tour robot in an exhibition hall. The results show that the proposed beamformer improves the robustness of automatic speech recognition and the performance of robot audition. © 2022 IEEE.

关键词： Beamforming

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CHAN: Cross-Modal Hybrid Attention Network for Temporal Language Grounding in Videos

CHAN: Cross-Modal Hybrid Attention Network for Temporal Lang...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wen Wang Ling Zhong Guang Gao Minhong Wan Jason Gu Zhejiang Lab Research Center for Intelligent Robotics China Department of Electrical and Computer Engineering Dalhousie University Canada

The goal of temporal language grounding (TLG) task is to temporally localize the most semantically matched video segment with respect to a given sentence query in an untrimmed video. How to effectively incorporate the cross-modal interactions between video and language is the key to improve grounding performance. Previous approaches focus on learning correlations by computing the attention matrix between each frame-word pair, while ignoring the global semantics conditioned on one modality for better associating the complex video contents and sentence query of the target modality. In this paper, we propose a novel Cross-modal Hybrid Attention Network, which integrates two parallel attention fusion modules to exploit the semantics of each modality and interactions in cross modalities. One is Intra-Modal Attention Fusion, which utilizes gated self-attention to capture the frame-by-frame and word-by-word relations conditioned on the other modality. The other is Inter-Modal Attention Fusion, which utilizes query and key features derived from different modalities to calculate the co-attention weights and further promote inter-modal fusion. Experimental results show that our CHAN significantly outperforms several existing state-of-the-arts on three challenging datasets (ActivityNet Captions, Charades-STA and TACOS), demonstrating the effectiveness of our proposed method.

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Design and Development of a Fixed-Wing Tail-Sitter Tethered VTOL Drone with Custom Autopilot and Robust Control Techniques 16th

Design and Development of a Fixed-Wing Tail-Sitter Tether...

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16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Safaee, Ahmad Aguiar, A. Pedro Santos, Filipe Neves Moreira, Antonio Paulo SYSTEC-ARISE Research Center for Systems and Technologies Faculty of Engineering University of Porto Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering Technology and Science CRIIS-Centre for Robotics in Industry and Intelligent Systems Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering and DEEC/FEUP - Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto4200-465 Portugal

ISBN: (纸本)9783031817236

The need for aerial platforms capable of sustained operation is critical in fields such as surveillance, agricultural monitoring, disaster response, and temporary telecommunication systems. Traditional unmanned aerial vehicles (UAVs) are hampered by limited battery capacity and inefficient energy utilization. This paper introduces an autonomous vertical flight drone tethered to a ground-based platform, which overcomes these limitations. The drone is designed to optimize its position and energy usage by harnessing wind currents, functioning similarly to a kite when conditions permit. This is enabled by a custom-designed autopilot system comprising both hardware and software tailored for this application. We detail the model identification process for three vertical take-off and landing (VTOL) drone systems, employing authentic data that measures lateral positional errors from the tether. Robust mixed sensitivity controllers are developed and implemented. The effectiveness of these controllers is validated through simulations showing their capability to handle uncertainties. Furthermore, the practical application and robust performance of the system are confirmed by a real-world case study, highlighting its potential in real operational environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： VTOL/STOL aircraft

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A Target-based Calibration Method for LiDAR-Visual-Thermal Multi-Sensor System

A Target-based Calibration Method for LiDAR-Visual-Thermal M...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Yuan, Songyu Xie, Tian Zhu, Shiqiang Chen, Yeheng Li, Yuehua Zheng, Tao Gu, Jason Research Center for Intelligent Robotics Research Institute of Interdis-ciplinary Innovation Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

As the basis of multi-sensor fusion, accurate extrinsic calibration among multi-sensors is vital for hetero-geneous information fusion. However, most existing methods only focus on the calibration between two specific heteroge-neous sensors, such as camera-LiDAR, LiDAR-thermal, and etc. which may cause inevitable accumulation when applied to a multi-sensor system. To address this problem, a novel calibration target and a high-accuracy method are proposed to simultaneously calibrate the intrinsic and extrinsic of a 3D LiDAR, a thermal camera and a visible camera without any user intervention. The proposed method only relies on one calibration board, to calibrate all the intrinsic and extrinsic parameters in one-step, without any strict pose requirement or long-time optimization. Furthermore, 2D-3D corresponding features can be extracted with higher precision by considering sensor model, comparing to the common feature extraction methods. Experiments with Velodyne VLP-16 LiDAR, ZED camera and D843NT thermal camera demonstrate that the proposed method can complete calibration among three of them automatically. Finally, this paper also presents a target-based method of uniformly expressing the calibration errors among multi-sensor system, with a competitive performance against most state-of-the-art methods. © 2022 IEEE.

关键词： Calibration

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Leveraging Wavelets and Deep CNN for Sleep Pattern Recognition in Road Safety: An EEG Study 6th

Leveraging Wavelets and Deep CNN for Sleep Pattern Recogni...

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6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023

作者： Arif, Saad Munawar, Saba Marie, Rashiq Rafiq Shah, Syed Aziz Department of Mechanical Engineering HITEC University Taxila47080 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Wah Campus Wah Cantt47040 Pakistan College of Computer Science and Engineering Taibah University Medina41477 Saudi Arabia Research Centre for Intelligent Healthcare Coventry University CoventryCV1 5FB United Kingdom

ISBN: (纸本)9783031530814

A major cause of fatal traffic accidents and injuries worldwide is drowsy driving. The fatal aftereffects can be considerably reduced, and road safety can rise, with earlier and more efficient diagnosis. In this study, electroencephalography (EEG) biosignals-based brain-computer interface (BCI) is established for early identification of human inattentiveness during driving activities. EEG signals are recorded from six EEG electrode positions viz Fp1, Fp2, F7, F8, O1, and O2. Spectral information of the alpha and beta frequency bands is extracted in terms of continuous wavelet transform (CWT) with a temporal correlation for complete experiments. CWT components are processed as scalograms which are color images used for visual inspection of brain states. The scalogram images of 10 s epochs are stored as the image dataset. The SqueezeNet deep convolutional neural network (CNN) is fine-tuned with transfer learning for effective drowsiness detection from CWT scalograms. Transfer-learned CNN classification model achieved the best results with 89.8% accuracy, 88.0% precision, 96.1% recall, 89.9% F1 -score, 87.7% specificity, and 79.7% Matthew’s correlation coefficient. The statistical significance of the achieved results is validated with a p-value of less than 0.05. Using wavelet information of EEG biosignals, the suggested BCI methodology offers a viable method for quicker and more accurate driving drowsiness detection. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electroencephalography

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Preoperative imaging in primary hyperparathyroidism patients using 4DCT subtraction maps, a report of three cases

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Radiology Case Reports 2023年第8期18卷 2814-2822页

作者： Krol, Jorian P. Veerbeek, Tessa Deden, Laura N. Joosten, Frank B.M. Slump, Cornelis H. Oyen, Wim J.G. Department of Radiology & Nuclear Medicine Rijnstate Hospital Arnhem Netherlands Department of Robotics and Mechatronics Faculty of Electrical Engineering Mathematics and Computer Sciences University of Twente Enschede Netherlands Department of Biomedical Sciences and Humanitas Clinical and Research Centre Department of Nuclear Medicine Humanitas University Milan Italy Department of Radiology & Nuclear Medicine Radboud University Medical Centre Nijmegen Netherlands

Four-dimensional computed tomography (4DCT) is one of the preoperative imaging modalities that can be used to localize a parathyroid adenoma in primary hyperparathyroidism patients however, sensitivity differs in literature and could be improved especially for multiglandular hyperplasia or double adenomas. The most robust feature on the 4DCT for the differentiation between parathyroid adenoma and thyroid gland tissue is arterial enhancement. To make this better visible, we have developed a subtraction map that shows arterial enhancement as a color scale to increase sensitivity for 4DCT. In this report of 3 cases, we present the usefulness of this subtraction map in a 54-year-old male, a 57-year-old female and a 51-year-old male. Subtraction maps may increase sensitivity for 4DCT, especially for multiglandular hyperplasia or double adenomas. © 2023

关键词： Four dimensional computed tomography Parathyroid adenoma Parathyroid hyperplasia Primary hyperparathyroidism Subtraction map

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Modeling and Parameter Identification for Human-robot Coupled systems in Powered Lower Limb Prostheses.

Modeling and Parameter Identification for Human-robot Couple...

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WRC Symposium on Advanced robotics and Automation (WRC SARA)

作者： Yongshan Huang Xin Wang Dingkun Liang Jiaming Xiong Anhuan Xie Jason Gu Research Center for Intelligent Robotics Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

Good trajectory tracking results are the basis for the implementation of other upper-level algorithms in powered lower limb prostheses, which rely on dynamics modeling and good parameter identification. However, powered lower limb prosthesis are human-robot coupled systems with human-inthe-loop, and it has been a challenge to model them as well as to perform parameter identification. This paper proposes a modeling method that can realize the decoupling of human-machine, and by constructing the linearized models of human-machine coupled system and prosthetic subsystem about the physical parameter sets respectively, and finally obtaining the relationship of their corresponding physical parameter sets, thus the method of indirectly identifying the parameters of human-machine coupled system through the prosthetic subsystem can be realized. The experimental results verify the feasibility and effectiveness of the method proposed in this paper. And the experimental results also shows that prosthetic controller should take advantage of dynamic of hip motion in order to make the wearer more comfortable and fluid when swinging the leg, and will also make the prosthesis more energy efficient.

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Coupled Transformed Induced Tensor Nuclear Norm for Robust Tensor Completion

Coupled Transformed Induced Tensor Nuclear Norm for Robust T...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Mengjie Qin Zheyuan Lin Minhong Wan Chunlong Zhang Jason Gu Te Li Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax

This study focuses on exploring the problem of low-rank tensor completion (LRTC), where the main challenge lies in accurately representing the inherent structures of the underlying data. Typical approaches exhibit either suboptimal performance due to the lack of suitable priors or combine many constraints, resulting in increased model complexity. In this paper, we propose a robust transformed t-SVD by utilizing the discrete cosine transform in conjunction with a unitary transformation matrix. Nonetheless, the t-SVD-based framework exhibits a deficiency in its capacity to flexibly explore diverse correlations across tensor modes. To address this issue, we employ the representation of the tensor as a series of multi-dimensional unfolding tensors, which fully capture the embedded structure in the original data. Moreover, on the basis of this tensor representation, we construct a transformed t-SVD method within the LRTC model and solve it by the alternating-direction method of multiplier (ADMM) approach. Extensive experiments demonstrate that the proposed model outperforms existing state-of-the-art methods.

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