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2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021

作者： Bajenting, Edmund B. Ompoc, Ralph Eimerson B. Palmero, Reynaldo G. Adtoon, Jetron J. Linsangan, Noel B. University of Mindanao Computer Engineering Program College of Engineering Education Davao Philippines Mapua University School of Electrical Electronics and Computer Engineering Manila Philippines

ISBN: (纸本)9781665401678

This study focused on designing a motorcycle speed limiter using GPS with throttle modification. The study was brought about the increase in road accidents involving motorcycle vehicles tagged to be overspeeding and some not wearing helmets. The designed system focused on controlling the throttle of the motorcycle through a microcontroller and a GPS. Also, communication from the helmet to the motorcycle was established to complete the system functionality. The system does not allow the use of the said motorcycle once the rider does not wear the helmet prescribed for the said motorcycle. Speed control of the designed system relies solely upon the GPS, making it different from typical sensory for speed and PID algorithm applied in servo motor to control the throttle acceleration. The selected road stretch was assigned 30 kph,40kph, and 60 kph, respectively. Initial tests showed a responsive rate of all parameters of the helmet and LCD design. © 2021 IEEE.

关键词： Microcontrollers

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An Expanded Latent Space Autoencoder for Land Cover Classification of Remote Sensing Images from EuroSAT

An Expanded Latent Space Autoencoder for Land Cover Classifi...

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2024 Brazilian Symposium on Robotics (SBR), and 2024 Workshop on Robotics in Education (WRE)

作者： Emerson Vilar Oliveira Luiz Marcos Garcia Gonçalves Graduate Program in Electrical and Computer Engineering Universidade Federal do Rio Grande do Norte Natal Brazil

ISBN: (数字)9798350391084

ISBN: (纸本)9798350391091

Image classification has been instrumental in the interpretation and labeling of images in the field of remote sensing, computer vision, and in robotics applications. Machine learning and artificial intelligence algorithms, particularly artificial neural networks, are extensively utilized for this purpose. In this work we propose the Expanded Latent Space Autoencoder (ELSA) with a case use application to classify land cover data. The main idea on the ELSA network structure is to utilize the latent spaces of multiple internal autoencoders in order to create an expanded latent space. This expanded latent space extracts more information from the input data, and serves as input features for a more simpler classifier network. In order to evaluate the proposed network's ability to extract features and classify complex and multispectral images we employed it to the EuroSAT dataset. The results demonstrate a remarkable capacity for feature extraction using the ELSA network, with lower complexity, trained with a reduced number of images. The classifier network achieved a final accuracy of 98.7%, matching or exceeding the performance of more complex state-of-the-art models.

关键词： Machine learning algorithms Instruments Autoencoders Land surface Machine learning Feature extraction Robot sensing systems Labeling Remote sensing Image classification

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Deterministic Quantum Repeater with Single Atoms in Cavities

arXiv

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arXiv 2024年

作者： Lei, Yisheng Department of Electrical and Computer Engineering and Applied Physics Program Northwestern University EvanstonIL60208 United States

Efficient quantum repeaters are needed to combat photon losses in fibers in future quantum networks. Single atom coupled with photonic cavity offer a great platform for photon-atom gate. Here I propose a quantum repeater scheme with deterministic entanglement generation and entanglement swapping based on photon-atom gates. It can be implemented with various types of atomic systems and require much less experimental complexity compared with other repeater protocols. With current available experimental techniques and reasonable improvements, high entanglement distribution rates can be achieved. A multiplexing configuration of 5-10 single atoms in cavities, secret key rates in order of 100s Hz to kHz can be achieved for communication distance of 1000km, and a few Hz to 10s Hz can be achieved for communication distance of 10000km with longer atomic coherence time and lower photon-atom gate error rate. This proposal paves the way to demonstrate efficient entanglement distribution with quantum repeaters in near future. © 2024, CC BY.

关键词： Atoms

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KenCoh: A Ranked-Based Canonical Coherence 1

arXiv

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arXiv 2024年

作者： Talento, Mara Sherlin D.P. Roy, Sarbojit Ombao, Hernando Statistics Program Computer Electrical and Mathematical Science & Engineering King Abdullah University of Science and Technology Saudi Arabia

In this paper, we consider the problem of characterizing a robust global dependence between two brain regions where each region may contain several voxels or channels. This work is driven by experiments to investigate the dependence between two cortical regions and to identify differences in brain networks between brain states, e.g., alert and drowsy states. The most common approach to explore dependence between two groups of variables (or signals) is via canonical correlation analysis (CCA). However, it is limited to only capturing linear associations and is sensitive to outlier observations. These limitations are crucial because brain network connectivity is likely to be more complex than linear and that brain signals may exhibit heavy-tailed properties. To overcome these limitations, we develop a robust method, Kendall canonical coherence (KenCoh), for learning monotonic connectivity structure among neuronal signals filtered at given frequency bands. Furthermore, we propose the KenCoh-based permutation test to investigate the differences in brain network connectivity between two different states. Our simulation study demonstrates that KenCoh is competitive to the traditional variance-covariance estimator and outperforms the later when the underlying distributions are heavy-tailed. We apply our method to EEG recordings from a virtual-reality driving experiment. Our proposed method led to further insights on the differences of frontal-parietal cross-dependence network when the subject is alert and when the subject is drowsy and that left-parietal channel drives this dependence at the beta-band. © 2024, CC BY.

关键词： Statistics

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Multi-scale pulmonary nodule classification with deep feature fusion via residual network

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 14829-14840页

作者： Zhang, Guokai Zhu, Dandan Liu, Xiao Chen, Mingle Itti, Laurent Luo, Ye Lu, Jianwei School of Software Engineering Tongji University Shanghai China High School Affiliated to Fudan University Shanghai China Department of Computer Science and Neuroscience Program University of Southern California Los Angeles United States Institute of Translational Medicine Tongji University Shanghai China

The early stage detection of benign and malignant pulmonary nodules plays an important role in clinical diagnosis. The malignancy risk assessment is usually used to guide the doctor in identifying the cancer stage and making follow-up prognosis plan. However, due to the variance of nodules on size, shape, and location, it has been a big challenge to classify the nodules in computer aided diagnosis system. In this paper, we design a novel model based on convolution neural network to achieve automatic pulmonary nodule malignancy classification. By using our model, the multi-scale features are extracted through the multi-convolution process, and the structure of residual blocks allows the network to capture more high-level and semantic information. Moreover, a strategy is proposed to fuse the features from the last avg-pooling layer and the ones from the last residual block to further enhance the performance of our model. Experimental results on the public Lung Image Database Consortium dataset demonstrate that our model can achieve a lung nodule classification accuracy of 87.5 % which outperforms state-of-the-art methods. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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Expanding the Surgical Robotics Community: An Intuitive Sim-to-Real Control Framework for Raven-II with a Budget-Friendly Gamepad Controller

Expanding the Surgical Robotics Community: An Intuitive Sim-...

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International Symposium on Medical Robotics (ISMR)

作者： Mai Bui Natalie Chalfant Cuiling Sun Sean Fabrega Haonan Peng Kevin Huang Yun-Hsuan Su Department of Computer Science Mount Holyoke College South Hadley MA Department of Electrical and Computer Engineering University of Washington Seattle WA Picker Engineering Program Smith College Northampton MA

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

This paper proposes a low-cost interface and refined digital twin for the Raven-II surgical robot. Previous simulations of the Raven-II, e.g. via the Asynchronous Multibody Framework (AMBF), presented salient drawbacks, including control inputs inconsistent with Raven-II software, and lack of stable, high-fidelity physical contact simulations. This work bridges both of these gaps, both (1) enabling robust, simulated contact mechanics for dynamic physical interactions with the Raven-II, and (2) developing a universal input format for both simulated and physical platforms. The method furthermore proposes a low cost, commodity game-controller interface for controlling both virtual and real realizations of Raven-II, thus greatly reducing the barrier to access for Raven-II research and collaboration. Overall, this work aims to eliminate the inconsistencies between simulated and real representations of the Raven-II. Such a development can expand the reach of surgical robotics research. Namely, providing end-to-end transparency between the simulated AMBF and physical Raven-II platforms enables a software testbed previously unavailable, e.g. for training real surgeons, for creating digital synthetic datasets, or for prototyping novel architectures like shared control strategies. Experiments validate this transparency by comparing joint trajectories between digital twin and physical testbed given identical inputs. This work may be extended and incorporated into recent efforts in developing modular or common software infrastructures for both simulation and control of real robotic devices, such as the Collaborative Robotics Toolkit (CRTK).

关键词： Training Medical robotics Costs Collaboration Surgery computer architecture Software

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Introducing spectral attention for long-range dependency in time series forecasting 24

Introducing spectral attention for long-range dependency in ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Bong Gyun Kang Dongjun Lee HyunGi Kim DoHyun Chung Sungroh Yoon Interdisciplinary Program in Artificial Intelligence Seoul National University Department of Electrical and Computer Engineering Seoul National University Department of Future Automotive Mobility Seoul National University Interdisciplinary Program in Artificial Intelligence Seoul National University and Department of Electrical and Computer Engineering Seoul National University

ISBN: (纸本)9798331314385

Sequence modeling faces challenges in capturing long-range dependencies across diverse tasks. Recent linear and transformer-based forecasters have shown superior performance in time series forecasting. However, they are constrained by their inherent inability to effectively address long-range dependencies in time series data, primarily due to using fixed-size inputs for prediction. Furthermore, they typically sacrifice essential temporal correlation among consecutive training samples by shuffling them into mini-batches. To overcome these limitations, we introduce a fast and effective Spectral Attention mechanism, which preserves temporal correlations among samples and facilitates the handling of long-range information while maintaining the base model structure. Spectral Attention preserves long-period trends through a low-pass filter and facilitates gradient to flow between samples. Spectral Attention can be seamlessly integrated into most sequence models, allowing models with fixed-sized look-back windows to capture longrange dependencies over thousands of steps. Through extensive experiments on 11 real-world time series datasets using 7 recent forecasting models, we consistently demonstrate the efficacy of our Spectral Attention mechanism, achieving state-of-the-art results.

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In situ Observation of Lithium Stripping and Plating Process in an Open-cell All-solid-state Lithium Metal Battery

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1287-1288页

作者： Wu, C. Zhao, L. Ai, Q. Risal, S. Lou, J. Yao, Y. Fan, Zheng Materials Science and Engineering Program and Texas Center for Superconductivity at the University of Houston University of Houston Houston TX United States Department of Electrical and Computer Engineering University of Houston Houston TX United States Department of Materials Science and Nano Engineering Rice University Houston TX United States Department of Engineering Technology University of Houston Houston TX United States

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Gauge Length Optimization of das using Fiber Bragg Grating Strain Measurements

Gauge Length Optimization of das using Fiber Bragg Grating S...

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Bragg Gratings, Photosensitivity and Poling in Glass Waveguides and Materials, BGPP 2022

作者： Weber, Guilherme Heim Jahnert, Frederico Gomes, Danilo Fernandes Martelli, Cicero Dreyer, Uilian José Da Silva, Jean Carlos Cardozo Pipa, Daniel Rodrigues De Camargo, Sérgio Taveira Da Silva, Manoel Feliciano Da Silva, Marco José Graduate Program in Electrical and Computer Engineering Universidade Tecnológica Federal Do Paraná Curitiba80230-901 Brazil Petrobras Research and Development Program Rio de Janeiro21941-970 Brazil

The gauge length parameter selection affects the quality of data measured in Distributed Acoustic Sensing. This paper uses Fiber Bragg Grating's strain measurements in controlled experiments to promote optimizatio... 详细信息

ISBN: (纸本)9781957171166

关键词： Strain measurement

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Transfer Learning-based Lane Line Detection System for Visual Path Following Control

Transfer Learning-based Lane Line Detection System for Visua...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Allan S. Almeida Tiago T. Ribeiro Andre G. S. Conceicao Postgraduate Program in Electrical Engineering Federal University of Bahia Salvador Brazil Department of Electrical and Computer Engineering Federal University of Bahia Salvador Brazil

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

One of the most important tasks in autonomous driving and autonomous vehicle navigation is detecting a path or trajectory that the vehicle should follow. Over the past few years, some learning-based works have stood out more than traditional computer vision techniques in detecting such lanes. In this paper we present an approach to solve the lane line detection problem in the context of visual path following by using a residual factorized convolutional neural network. Experimental results show a promising model that can detect lane lines even under severe lighting conditions and in the presence of occlusions and shadows. The path detection system was tested along with a visual path following formulation based on Nonlinear Model Predictive Control. Still, it can be used for any controller in the context of visual navigation for autonomous vehicles. Nonetheless, the proposed model architecture strikes a remarkable balance between accuracy and efficiency, making the system suitable for real-time applications.

关键词： Measurement Visualization Accuracy Navigation Computational modeling Lighting computer architecture Trajectory Convolutional neural networks Autonomous vehicles

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