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International Winter Workshop on Brain-computer Interface (BCI)

作者： Vladislav Mun Berdakh Abibullaev Computer Science Department School of Engineering and Digital Sciences Nazarbayev University Kazakhstan Robotics and Mechatronics Department School of Engineering and Digital Sciences Nazarbayev University Kazakhstan

With an ever-growing demand for BCI-based systems, numerous algorithms and machine learning systems have been proposed over the past few decades. Although state-of-theart approaches have reached practically appropriate levels of accuracy, most are often regarded as black-box models, which need more explainability. However, for cases where a Neural Network is used as the base for a classifier, a Layerwise Relevance Propagation (LRP) approach can be utilized to analyze the decision boundaries considered by the network. By calculating the importance of the neuron in each layer, the LRP can also be used as an effective model complexity reduction technique through the inactivation (pruning) of the neural pathways. The following work investigates the usability of the LRP framework in the field of BCI. This study provides an example of the practical application of the LRP with respect to the EEG (ERP) dataset, along with visual heatmap and scalp map examples of the LRP. Furthermore, the work analyzes the impact of network pruning on heatmap visualization and the model’s accuracy while also practically determining the maximum cutoff range for pruning BCI models.

关键词： Heating systems Deep learning Analytical models Visualization Brain modeling Transformers Electroencephalography

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Modeling Misinformation with Q-Learning, Nim, and Multi-Agents

Modeling Misinformation with Q-Learning, Nim, and Multi-Agen...

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2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022

作者： Huang, Elijah Mui, Phil University of California Irvine Donald Bren School of Information and Computer Sciences Fremont United States Aspiring Scholars Directed Research Program Department of Computer Science and Engineering Fremont United States

ISBN: (纸本)9781665473453

Misinformation or so called 'fake news' has become a pressing issue around the world. This research proposes modeling the spread of misinformation through Q-learning, the game of Nim, and multi-Agent simulations. Through analyzing theoretical properties of the model and studying factors in how misinformation affects it, we show how the model reflects the real-life impact of fake news. The scalability of the framework and its emulation of real-human behavior make it versatile and believable. Through further study of this model, our hope is to shed light on effective tactics for combating fake news in the real world. © 2022 IEEE.

关键词： Game theory

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RoboRover – A Jetson Nano Powered Robot Car for Object Following, Obstacle Avoidance, and Wall Following

RoboRover – A Jetson Nano Powered Robot Car for Object Foll...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Trinh Huynh James Walter Federica Aveta Robotics Department University of Michigan Ann Arbor Michigan USA Electrical and Computer Engineering Program Wentworth Insitute of Technology Boston Massachusetts USA

An intelligent and self-sufficient robot is essential across a wide range of fields, including transportation, industry, space exploration, and defense. Mobile robots possess the capability to undertake diverse tasks such as handling materials, aiding in disaster scenarios, conducting patrols, and executing rescue operations. As a result, the development of an autonomous robot that can navigate through both unchanging and ever-changing surroundings has become important. The primary objective of mobile robot navigation revolves around ensuring the seamless and secure traversal of the robot through complex environments, starting from an initial position, and reaching a designated goal position. This paper presents the design and implementation of a Jetson Nano powered robot car which uses local sensors to interact with an unknown environment. Object following, obstacle avoidance, and wall following features are built for the car to navigate to reach its desired destinations.

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Photonic Crystals with Split Ring Unit Cells for Subwavelength Light Confinement

Photonic Crystals with Split Ring Unit Cells for Subwaveleng...

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CLEO: science and Innovations, S and I 2022

作者： Arnold, Kellen P. Halimi, Sami I. Allen, Joshua A. Hu, Shuren Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37235 United States Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37235 United States

We report a split ring photonic crystal that demonstrates an order of magnitude larger peak energy density compared to traditional photonic crystals. The split ring offers highly focused optical energy in an accessibl...

ISBN: (纸本)9781557528209

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A Web Application for Training Quantum Classifiers via Evolutionary Algorithms

A Web Application for Training Quantum Classifiers via Evolu...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Giovanni Acampora Carlos Cano Gutiérrez Angela Chiatto José Manuel Soto-Hidalgo Autilia Vitiello Department of Physics “Ettore Pancini” University of Naples Federico II Naples Italy Department of Computer Science and Artificial Intelligence University of Granada Granada Spain Department of Computer Engineering Automatics and Robotics University of Granada Granada Spain

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

Variational Quantum Classifiers (VQCs) stand out as one of the most popular classification approaches within the Quantum Machine Learning (QML) community. They serve as an accessible entry point for newcomers to QML world, particularly those with a background in classical machine learning, due to their workflow similarity to conventional neural networks. In essence, VQCs boast a layered structure conducive to training via a classical optimizer. Recently, Evolutionary Algorithms (EAs) have emerged as a suitable methodology for this training goal. However, current quantum tools provide implementations for various gradient-based optimizers and just one EA tailored for VQC training. Moreover, it is important to note that access to this toolset remains largely confined to expert users. This highlights the necessity for platforms that facilitate the utilization of EAs for VQC training by non-experts in quantum or evolutionary computing. This work introduces a web application designed to implement quantum classifiers to be trained on user-supplied datasets by means of a set of EAs, which can be customized by users. The capabilities of the proposed web application are shown in the experimental session, where a VQC is trained to classify a well-known dataset using a variety of EAs.

关键词： Training computers Quantum entanglement Neural networks Evolutionary computation Machine learning Registers

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Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 IEEE.

关键词： Tumors

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Realistic Data Generation for 6D Pose Estimation of Surgical Instruments

arXiv

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arXiv 2024年

作者： Barragan, Juan Antonio Zhang, Jintan Zhou, Haoying Munawar, Adnan Kazanzides, Peter Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01608 United States

Automation in surgical robotics has the potential to improve patient safety and surgical efficiency, but it is difficult to achieve due to the need for robust perception algorithms. In particular, 6D pose estimation of surgical instruments is critical to enable the automatic execution of surgical maneuvers based on visual feedback. In recent years, supervised deep learning algorithms have shown increasingly better performance at 6D pose estimation tasks;yet, their success depends on the availability of large amounts of annotated data. In household and industrial settings, synthetic data, generated with 3D computer graphics software, has been shown as an alternative to minimize annotation costs of 6D pose datasets. However, this strategy does not translate well to surgical domains as commercial graphics software have limited tools to generate images depicting realistic instrument-tissue interactions. To address these limitations, we propose an improved simulation environment for surgical robotics that enables the automatic generation of large and diverse datasets for 6D pose estimation of surgical instruments. Among the improvements, we developed an automated data generation pipeline and an improved surgical scene. To show the applicability of our system, we generated a dataset of 7.5k images with pose annotations of a surgical needle that was used to evaluate a state-of-the-art pose estimation network. The trained model obtained a mean translational error of 2.59 mm on a challenging dataset that presented varying levels of occlusion. These results highlight our pipeline’s success in training and evaluating novel vision algorithms for surgical robotics applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Rearranging the Environment to Maximize Energy with a Robotic Circuit Drawing

Rearranging the Environment to Maximize Energy with a Roboti...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Tan, Xianglong Liu, Zhikang Yu, Chen Schwertfeger, Sören Rosendo, Andre Hamlyn Centre Imperial College London United Kingdom University of York Department of Computer Science United Kingdom School of Information Science and Technology ShanghaiTech University China Worcester Polytechnic Institute Robotics Engineering United States

ISBN: (纸本)9781665481090

The ability of acquiring energy form environments is greatly beneficial for robot to function in uncertain environments. In this work, we present a robot capable of drawing circuits with conductive ink while also rearranging the visual world to receive maximum energy from a power source. A range of circuit drawing tasks is designed to simulate real-world scenarios, including avoiding physical obstacles and regions that would discontinue drawn circuits. We adopt the state-of-the-art Transporter networks for pick-and-place manipulation from visual observation. We conduct experiments in both simulation and real-world settings, and our results show that, with a small number of demonstrations, the robot learns to rearrange the placement of objects (removing obstacles and bridging areas unsuitable for drawing) and to connect a power source with a minimum amount of conductive ink. As autonomous robots become more and more involved in our daily lives, our proposed approach brings a novel way for machines to keep themselves functional by rearranging their surroundings to create their own electric circuits. © 2022 IEEE.

关键词： Timing circuits

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A computer Vision Approach to Plant Growth Monitoring in an Embedded CubeSat Module

A Computer Vision Approach to Plant Growth Monitoring in an ...

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IEEE Conference on Aerospace

作者： Alexis Lopez Olaoluwayimika Olugbenle Michael C.F. Bazzocchi Department of Mechanical and Aerospace Engineering Astronautics and Robotics Laboratory (ASTRO Lab) Clarkson University Potsdam NY USA Department of Electrical and Computer Engineering Astronautics and Robotics Laboratory (ASTRO Lab) Clarkson University Potsdam NY USA Department of Mechanical and Aerospace Engineering Astronautics and Robotics Laboratory (ASTRO Lab) Clarkson University Potsdam NY Department of Earth and Space Science and Engineering York University Toronto Canada

The investigation and development of space-based food production systems are essential to improve the reliability and availability of fresh sustenance for astronauts. With their compact size and low-cost production, CubeSats can provide a unique platform for plant-based in-space experiments. Additionally, the combination of CubeSats and computer vision can allow for monitoring the health of plants during their growth cycles. This paper investigates the electronics and data handling of a crop growth module, otherwise referred to as an environmental monitoring and control subsystem (EMCS), as well as the integration of computer vision techniques for plant growth and development. Using the Otsu thresholding and holistically-nested edge detection algorithms, image segmentation and edge detection were performed, respectively. A support vector machine (SVM) was also employed to classify foliage and provide feedback on the plant’s health. The results from the system show that the computer vision approach can accurately predict the health of the plants based on color and texture. This study builds a foundation for future plant health monitoring research in deep space environments.

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Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine

Sentiment Analysis in Indonesian Trading using Lexicon-based...

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computer science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Raymond Sunardi Oetama Yanfi Yanfi Masagus M. Ikhsan Assiddiq U.P Sani Muhamad Isa Information System DepartmentFaculty of Engineering and Informatics Universitas Multimedia Nusantara Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%.

关键词： Support vector machines Training Sentiment analysis Vocabulary Dictionaries Social networking (online) Blogs

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