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International Electronics Symposium (IES)

作者： Abdullah Iskandar Takafumi Nakanishi Achmad Basuki Ryotaro Okada Takashi Kitagawa Department of Creative Multimedia Technology Electronics Engineering Polytechnic Institute of Surabaya Surabaya Indonesia Department of Data Science Musashino University Tokyo Japan Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Japan

ISBN: (数字)9781728195308

ISBN: (纸本)9781728195643

In this paper, we present gaze-music transformation realization method by realizing gaze emotion detection system and interconnecting the detection system and an automatic music media creation system. This method automatically creates a music media contents by a detected emotion from human gaze. When it is possible to realize a system that acquires human gaze data from a camera device, detects emotions from the data, and creates media content in response to those emotions, it will contribute to creative activities corresponding to human emotion. Our method connects a gaze emotion detection system and an automatic music creation system by the similarity of words that represent emotions and impressions. It is possible to compare emotions by comparing the emotions expressed by each different vocabulary using the similarity of words.

关键词： Media Eyelids Pupils Feature extraction Histograms Cameras Emotion recognition

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Unconventional methods in voynich manuscript analysis 25

Unconventional methods in voynich manuscript analysis

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25th International Conference on Soft Computing, MENDEL 2019

作者： Zelinka, Ivan Zmeskal, Oldrich Windsor, Leah Cai, Zhiqiang Department of Computer Science Faculty of Electrical Engineering and Computer Science VŠB-TU Czech Republic Institute of Physical and Applied Chemistry Faculty of Chemistry Brno University of Technology Czech Republic Institute for Intelligent Systems University of Memphis United States

This paper discusses the possible use of unconventional algorithms on analysis and categorization of the unknown text, including documents written in unknown languages. Scholars have identified about ten famous manuscripts, mostly encrypted or written in the unknown language. The most famous is the Voynich manuscript, an illustrated codex hand-written in an unknown language or writing system. Using carbon-dating methods, the researchers determined its age as the early 15th century (between 1404-1438). Many professional and amateur cryptographers have studied the Voynich manuscript, and none has deciphered its meaning as yet, including American and British code-breakers and cryptologists. While there exist many hypotheses about the meaning and structure of the document, they have yet to be confirmed empirically. In this paper, we discuss two different kinds of unconventional approaches for how to handle manuscripts with unidentified writing systems and determine whether its properties are characterized by a natural language, or is only historical fake text. © 2019, Brno University of Technology. All rights reserved.

关键词： Complex networks

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ROS Based Real-Time Motion Control for Robotic Visual Arts Exhibit Using Decawave Local Positioning System

ROS Based Real-Time Motion Control for Robotic Visual Arts E...

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American Control Conference

作者： Mahmoud A.K. Gomaa Oscar De Silva George K.I. Mann Raymond G. Gosine Robert Hengeveld Intelligent Systems Lab Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’s Canada Department of Fine Arts Memorial University of Newfoundland Grenfell Canada

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

In this work, we address the problem of real-time control and localization of an autonomous art exhibit designed using Mecanum wheeled omnidirectional mobile robots. Currently, the exhibit is animated using open-loop control as there are no practical means of implementing a localization system in art galleries. This paper proposes a nonlinear model predictive controller (NMPC) supported by a Decawave localization system for trajectory tracking of the robots. A Robot Operating System (ROS) based NMPC is implemented to control the motion of the robot for a smooth and drift-free trajectory tracking. The Automatic Control and Dynamic Optimization (ACADO) toolkit is used to find the optimal control action while considering all constraints on system states and inputs. A Local Positioning System (LPS) is implemented using Decawave 1001 modules to provide position feedback with an Optitrack motion capture system providing ground truth information for validation. The proposed control approach is first evaluated using the Virtual Robot Experimentation Platform (V-REP), which additionally provides means of trajectory design and simulation of the robot in the desired exhibition space. Thereafter, laboratory experiments are conducted to evaluate the performance of the proposed control and localization systems. The results show a smooth and drift-free performance of the system with less than 10% error of the robot size, which can be deployed at gallery spaces with minimal setup requirements.

关键词： Mobile robots Robot kinematics Wheels Trajectory Art Kinematics

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The Sensorium competition on predicting large-scale mouse primary visual cortex activity

arXiv

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arXiv 2022年

作者： Willeke, Konstantin F. Fahey, Paul G. Bashiri, Mohammad Pede, Laura Burg, Max F. Blessing, Christoph Cadena, Santiago A. Ding, Zhiwei Lurz, Konstantin-Klemens Ponder, Kayla Muhammad, Taliah Patel, Saumil S. Ecker, Alexander S. Tolias, Andreas S. Sinz, Fabian H. International Max Planck Research School for Intelligent Systems University of Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Institute of Computer Science Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Institute for Theoretical Physics University of Tübingen Germany Max Planck Institute for Dynamics and Self-Organization Göttingen Germany Electrical and Computer Engineering Rice University Houston United States

The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. Artificial neural networks (ANNs) can serve a variety of goals for improving our understanding of this complex system, not only serving as predictive digital twins of sensory cortex for novel hypothesis generation in silico, but also incorporating bio-inspired architectural motifs to progressively bridge the gap between biological and machine vision. The mouse has recently emerged as a popular model system to study visual information processing, but no standardized large-scale benchmark to identify state-of-the-art models of the mouse visual system has been established. To fill this gap, we propose the SENSORIUM benchmark competition. We collected a large-scale dataset from mouse primary visual cortex containing the responses of more than 28,000 neurons across seven mice stimulated with thousands of natural images, together with simultaneous behavioral measurements that include running speed, pupil dilation, and eye movements. The benchmark challenge will rank models based on predictive performance for neuronal responses on a held-out test set, and includes two tracks for model input limited to either stimulus only (SENSORIUM) or stimulus plus behavior (SENSORIUM+). We provide a starting kit to lower the barrier for entry, including tutorials, pre-trained baseline models, and APIs with one line commands for data loading and submission. We would like to see this as a starting point for regular challenges and data releases, and as a standard tool for measuring progress in large-scale neural system identification models of the mouse visual system and beyond. © 2022, CC BY-NC-ND.

关键词： Neural networks

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Joint Energy Beamforming and Optimization for intelligent Reflecting Surface Enhanced Communications

Joint Energy Beamforming and Optimization for Intelligent Re...

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IEEE Wireless Communications and Networking Conference Workshops (WCNCW)

作者： Yuze Zou Shimin Gong Jing Xu Wenqing Cheng Dinh Thai Hoang Dusit Niyato School of Electronic Information and Communications Huazhong University of Science and Technology China School of Intelligent Systems Engineering Sun Yat-sen University China School of Electrical and Data Engineering University of Technology Sydney Australia School of Computer Science and Engeering Nanyang Technological University Singapore

ISBN: (数字)9781728151786

ISBN: (纸本)9781728151793

To face the scarcity of wireless spectrum resources and explosive energy consumption due to rapid growth of mobile devices and Internet of Things terminals, intelligent reflecting surface (IRS) has recently gained a lot of attention and become as one of the promising solutions. In this paper, we consider an IRS-enhanced multiple-input single- output (MISO) system, in which the IRS is wireless powered by the access point (AP) in power splitting scheme. We aim to maximize the signal-to-noise ratio (SNR) of the end user by jointly optimizing the AP's beamforming as well as the phase-shift and the power splitting ratio of the IRS elements. To tackle the non-convexity of the formulated problem due to the coupling of optimization variables, we devise a two-stage approximation algorithm by analyzing and then decomposing the structure of the problem. Specifically, the algorithm first tunes the phase-shift of IRS elements to align the equivalent channel of IRS reflected path to that of the direct link. After that, we adopt a successive convex approximation based method to achieve a near optimal solution for the reformulated problem iteratively. The simulation results show that our proposed two-stage approximation algorithm can solve the jointly SNR maximization problem efficiently.

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Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform

arXiv

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arXiv 2021年

作者： Dharejo, Fayaz Ali Zawish, Muhammad Deeba, Farah Zhou, Yuanchun Dev, Kapal Khowaja, Sunder Ali Qureshi, Nawab Muhammad Faseeh The Computer Net-Work Information Center Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China Walton Institute for Information and Communication Systems Science Waterford Institute of Technology Ireland Department of institute of intelligent systems University of Johannesburg South Africa Faculty of Engineering and Technology University of Sindh Jamshoro Pakistan The Department of Computer Education Sungkyunkwan University Seoul Korea Republic of

Multimodal medical images are widely used by clinicians and physicians to analyze and retrieve complementary information from high-resolution images in a non-invasive manner. Loss of corresponding image resolution adversely affects the overall performance of medical image interpretation. Deep learning based single image super resolution (SISR) algorithms has revolutionized the overall diagnosis framework by continually improving the architectural components and training strategies associated with convolutional neural networks (CNN) on low-resolution images. However, existing work lacks in two ways: i) the SR output produced exhibits poor texture details, and often produce blurred edges, ii) most of the models have been developed for a single modality, hence, require modification to adapt to a new one. This work addresses (i) by proposing generative adversarial network (GAN) with deep multi-attention modules to learn high-frequency information from low-frequency data. Existing approaches based on the GAN have yielded good SR results;however, the texture details of their SR output have been experimentally confirmed to be deficient for medical images particularly. The integration of wavelet transform (WT) and GANs in our proposed SR model addresses the aforementioned limitation concerning textons. While the WT divides the LR image into multiple frequency bands, the transferred GAN uses multi-attention and upsample blocks to predict high-frequency components. Additionally, we present a learning method for training domain-specific classifiers as perceptual loss functions. Using a combination of multi-attention GAN loss and a perceptual loss function results in an efficient and reliable performance. Applying the same model for medical images from diverse modalities is challenging, our work addresses (ii) by training and performing on several modalities via transfer learning. Using two medical datasets, we validate our proposed SR network against existing state-of-the-art

关键词： Wavelet transforms

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Optimizing Multi-Hop Quantum Communication using Bidirectional Quantum Teleportation Protocol

arXiv

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arXiv 2025年

作者： Ikken, N. Kumar, P. Slaoui, A. Kar, B. Laamara, R. Ahl Almousa, M. El-Latif, A.A. Abd LPHE-Modeling and Simulation Faculty of Sciences Mohammed V University Rabat Morocco Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Centre of Physics and Mathematics CPM Faculty of Sciences Mohammed V University in Rabat Rabat Morocco EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

In this paper, we introduce a new method for Bidirectional Quantum Teleportation called Bidirectional Quantum Teleportation using the Modified Dijkstra Algorithm and Quantum Walk (BQT-MDQW). This method uses different types of entangled states, such as the GHZ-Bell state, W-Bell state, and Cluster-Bell state, to improve quantum communication in multi-hop quantum wireless networks. We focus on the W-Bell state and compare the quantum Dijkstra algorithm with the classical Dijkstra method to see which one works better. We apply both versions to quantum and classical simulators, measuring their performance through fidelity, memory utilization, and throughput calculations. Our results show that the shortest path problem may be solved with significantly reduced computer complexity using the quantum Dijkstra algorithm based on quantum walks. The introduction of a quantum walk, which permits dynamic transitions between quantum channels and the effective exploration of quantum network states, is an important part of the protocol. Using the capacity of the quantum walk to adjust to changing quantum states, we also introduce a method for successfully identifying unitary matrices under varying quantum channels. The bidirectional teleportation structure of the protocol is designed to solve the multi-hop teleportation problem in quantum wireless networks. In addition, we present quantum Dijkstra’s algorithm, which uses quantum gates to significantly decrease computational complexity and solve the networking problem by building on the quantum walk framework. This method shows how quantum computing may be used to solve arbitrary optimization issues such as the shortest path problem. Finally, we present a novel multi-hop quantum teleportation system encompassing both unidirectional and bidirectional communication, as introduced in the quantum Dijkstra algorithm system. This system significantly enhances communication in quantum networks by enabling efficient and reliable information

关键词： Quantum channel

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Gru-Inc: An Inception-Attention Based Approach Using Gru for Human Activity Recognition

SSRN

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SSRN 2022年

作者： Mim, Taima Rahman Amatullah, Maliha Afreen, Sadia Yousuf, Mohammad Abu Uddin, Shahadat Alyami, Salem A. Hasan, Khondokar Fida Moni, Mohammad Ali Information and Communication Technology Bangladesh University Of Professionals Mirpur Cantonment Dhaka Bangladesh Institute of Information Technology Jahangirnagar University Savar Dhaka Bangladesh Complex Systems Research Group Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia Riyadh13318 Saudi Arabia Centre for Cyber Security Research & Innovation RMIT University 124 La Trobe Street MelbourneVIC3000 Australia Artificial Intelligence & Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Human Activity Recognition is one of the most popular research fields nowadays, and many machine learning algorithms have been successfully implemented to achieve high-performance results. Although handcrafted feature extraction techniques were used in the past, ANN is now more popular. In this work, the GRU-INC model has been proposed, which is an Inception-Attention based approach using GRU that effectively makes use of the temporal and spatial information of the time-series data. The proposed model achieved an F1-score of 96.27%, 90.05%, 90.30%, 99.12% and 95.99% on the publicly available datasets such as UCI-HAR, OPPORTUNITY, PAMAP2, WISDM and Daphnet respectively. GRU along with Attention Mechanism was utilized for the temporal part and Inception module along with CBAM was exploited for the spatial part of the model. The proposed architecture was evaluated against the state-of-the-art as well as few existing works and it is proved that the GRU-INC model has a higher recognition rate as well as lower computational cost. © 2022, The Authors. All rights reserved.

关键词： Machine learning

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EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble data Association

EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble...

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2020 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Yanmin Wu Yunzhou Zhang Delong Zhu Yonghui Feng Sonya Coleman Dermot Kerr Faculty of Robot Science and Engineering Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China The Chinese University of Hong Kong China School of Computing and Intelligent Systems Ulster University N. Ireland UK

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this work, we propose an ensemble data associate strategy for integrating the parametric and nonparametric statistic tests. By exploiting the nature of different statistics, our method can effectively aggregate the information of different measurements, and thus significantly improve the robustness and accuracy of data association. We then present an accurate object pose estimation framework, in which an outliers-robust centroid and scale estimation algorithm and an object pose initialization algorithm are developed to help improve the optimality of pose estimation results. Furthermore, we build a SLAM system that can generate semi-dense or lightweight object-oriented maps with a monocular camera. Extensive experiments are conducted on three publicly available datasets and a real scenario. The results show that our approach significantly outperforms state-of-the-art techniques in accuracy and robustness. The source code is available on https://***/yanmin-wu/EAO-SLAM.

关键词： Simultaneous localization and mapping Navigation Pose estimation Semantics Robustness Nonparametric statistics intelligent robots

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Integrating ARIMA and Bidirectional LSTM to Predict ETA in Multi-Airport systems

Integrating ARIMA and Bidirectional LSTM to Predict ETA in M...

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Integrated Communications, Navigation and Surveillance Conference, ICNS

作者： Lechen Wang Xuechun Li Jianfeng Mao School of Science and Engineering The Chinese University of Hong Kong Shenzhen Guangdong China School of Science and Engineering Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Shenzhen Guangdong China

ISBN: (数字)9781728172705

ISBN: (纸本)9781728172712

Traffic states prediction in air transportation systems is a challenging problem and has not been fully explored because it is subject to many more highly correlated factors and a more complicated traffic management scheme compared to urban transportation systems. It becomes a more formidable task when facing a multi-airport system (MAS), in which several major airports are closely located and tightly coupled with each other through limited terminal airspace. In this work, we propose a novel method using a time series model and recurrent neural network to make the estimated time of arrival (ETA) for a flight to an MAS, which can be potentially utilized for flight delay prediction and congestion analysis. The experiment utilizes two months of 4D trajectories data from Beijing Capital International Airport (PEK) to Shenzhen Bao'an International airport (ZGSZ). The entire prediction work is decomposed into two sub-problems, en-route travel time prediction which is from flight origin to the entering gate of MAS, defined as the location is 300km from the airport in MAS, and terminal maneuvering area (TMA) travel time prediction which is from the entrance to flight's destination. The auto-regressive integrated moving average (ARIMA), a time series prediction model, is used to predict travel time in en-route under given the flight departure time. Bidirectional long short term memory (LSTM), a recurrent neural network, is developed to forecast travel time in the arrival approach by utilizing spatio-temporal features. To design the input features, we use density-based spatial clustering (DBSCAN) with the help of the Voronoi diagram to extract spatial information from every historical flight trajectory of aircraft operated in an MAS, then select the observation time window to capture the temporal information for each flight. The Multivariate Stacked Fully connected-Bidirectional LSTM (MSFCB-LSTM) model is constructed to make shortterm forecasting using spatio-temporal feature

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