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Brazilian Symposium on Computing System Engineering (SBESC)

作者： Jeferson Menegazzo Aldo von Wangenheim Graduate Program in Computer Science (PPGCC) Image Processing and Computer Graphics Lab (LAPIX) Brazilian National Institute for Digital Convergence (INCoD) Federal University of Santa Catarina (UFSC) Florianópolis Brazil

ISBN: (数字)9781728182865

ISBN: (纸本)9781728182872

The demand for a variety of situational data from the traffic environment and its participants has intensified with the development of applications in Intelligent Transport Systems (ITS). Among these data, the road surface type classification is one of the most important and can be used in the entire ITS domain. For its widespread application, it is necessary to employ a robust technology for the generation of raw data and to develop of a reliable and stable model to process these data in order to produce the classification. The developed model must operate correctly in different vehicles, under different driving styles and in different environments in which a vehicle can travel. In this work we employ inertial sensors, represented by accelerometers and gyroscopes, which are a safe, non-polluting, and low-cost alternative, ideal for large-scale use. We collect nine datasets with contextual variations, including three different vehicles, with three different drivers, in three different environments, in which there are three different road surface types, in addition to variations in the conservation state and presence of anomalies and obstacles such as potholes and speed bumps. After data collection, these data were used in experiments to evaluate various aspects, such as the influence of the vehicle data collection point, the analysis domain, the model input features, and the data window. Afterwards we evaluated the learning and generalization capacity of the models for unknown contexts. In a third step, the data were used in three Deep Neural Network (DNN) models: LSTM-based, GRU-based, and CNN-based. Through a multi-aspect and multi-contextual analysis, we considered the CNN-based model as the best one, which obtained an average accuracy between the data collection placements of 94.27% for learning and 92.70% for validation, classifying the road surface between asphalt, cobblestone or dirt road segments.

关键词： Roads Sensors Data models Vehicles Sensor systems Deep learning Data collection

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Microcantilever-integrated photonic circuits for broadband laser beam scanning

arXiv

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arXiv 2022年

作者： Azadeh, Saeed Sharif Mak, Jason C.C. Chen, Hong Luo, Xianshu Chen, Fu-Der Chua, Hongyao Weiss, Frank Alexiev, Christopher Stalmashonak, Andrei Jung, Youngho Straguzzi, John N. Lo, Guo-Qiang Sacher, Wesley D. Poon, Joyce K.S. Max Planck Institute of Microstructure Physics Weinberg 2 Halle06120 Germany University of Toronto Department of Electrical and Computer Engineering 10 King's College Road Ontario TorontoM5S 3G4 Canada Advanced Micro Foundry Pte. Ltd. 11 Science Park Road Singapore Science Park II 117685 Singapore

Laser beam scanning is central to many applications, including displays, microscopy, three-dimensional mapping, and quantum information. Reducing the scanners to microchip form factors has spurred the development of very-large-scale photonic integrated circuits of optical phased arrays and focal plane switched arrays. An outstanding challenge remains to simultaneously achieve a compact footprint, broad wavelength operation, and low power consumption. Here, we introduce a laser beam scanner that meets these requirements. Using microcantilevers embedded with silicon nitride nanophotonic circuitry, we demonstrate broadband, one- and two-dimensional steering of light with wavelengths from 410 nm to 700 nm. The microcantilevers have ultracompact ~0.1 mm2 areas, consume ~31 to 46 mW of power, are simple to control, and emit a single light beam. The microcantilevers are monolithically integrated in an active photonic platform on 200-mm silicon wafers. The microcantilever-integrated photonic circuits miniaturize and simplify light projectors to enable versatile, power-efficient, and broadband laser scanner microchips. © 2022, CC BY-NC-ND.

关键词： Silicon nitride

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EEG and HRV-Based Assessment of Neurosurgeons Training for Anxiety Regulation and Stress Monitoring

EEG and HRV-Based Assessment of Neurosurgeons Training for A...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Pasquale Arpaia Giovanni Carone Nicolò Castelli Giovanni D'Errico Ludovica Gargiulo Luigi Maffei Giovanna Mastrati Nicola Moccaldi Marco Nalin Alessandro Perin Mauro Picciafuoco Cosimo Puttilli Pedro M. Ramos Rachele Robbio Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy Interdepartmental Center for Research on Management and Innovation in Healthcare (CIRMIS) University of Naples Federico II Naples Italy I.R.C.C.S. Carlo Besta Neurological Institute Milan Italy Department of Applied Science and Technology Polytechnic University of Turin Turin Italy Department of Electrical and Computer Engineering Instituto Superior Técnico University of Lisbon Lisbon Portugal Department of Engineering for Innovation University of Salento Lecce Italy ab medica spa Milan Italy

A neurofeedback (NF) -supported training is proposed to enable neurosurgeons to learn how to regulate their emotions. Electroencephalographic (EEG) signal and heart rate (HR) of 5 neurosurgeons were acquired in 4 sessions while performing NF-based emotion regulation (ER). Subjects tried to counteract anxious and stressful states through NF, that is by decreasing the power in the beta band measured in the midline areas of the scalp. The assessment of the neurofeedback effectiveness was based on the use of Heart Rate Variability (HRV) in combination with EEG. Therefore, EEG signal was exploited in real-time to guide the feedback and also in post processing, together with the HRV, to assess the effectiveness of the whole training. As the EEG signal is concerned, the results showed that the power in the beta band decreased within each session when the ER ability was strengthened, as reported in the literature. On the contrary, the HRV did not exhibit the expected increasing trend within each trial. When considering each the session on the whole, thus computing the HRV on a greater time interval, the HRV during the NF training is typically higher than the HRV computed during the rest and negative baseline phases.

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Quantum State Tomography using Quantum Machine Learning

arXiv

引用

arXiv 2023年

作者： Innan, Nouhaila Siddiqui, Owais Ishtiaq Arora, Shivang Ghosh, Tamojit Koçak, Yasemin Poyraz Paragas, Dominic Galib, Abdullah Al Omar Khan, Muhammad Al-Zafar Bennai, Mohamed Quantum Physics and Magnetism Team LPMC Faculty of Sciences Ben M’sick Hassan II University of Casablanca Morocco Quantum Formalism Fellow Zaiku Group Ltd Liverpool United Kingdom Department of Physics COMSATS University Islamabad45550 Pakistan Technical University of Munich Munich Germany Department of Physics Indian Institute of Technology Madras Tamil Nadu Chennai600036 India Istanbul University-Cerrahpaşa Computer Technology Department Istanbul Turkey University of California BerkeleyCA United States School of Computer Science and Applied Mathematics University of the Witwatersrand 1 Jan Smuts Ave Braamfontein Johannesburg Gauteng2000 South Africa

Quantum State Tomography (QST) is a fundamental technique in Quantum Information Processing (QIP) for reconstructing unknown quantum states. However, the conventional QST methods are limited by the number of measurements required, which makes them impractical for large-scale quantum systems. To overcome this challenge, we propose the integration of Quantum Machine Learning (QML) techniques to enhance the efficiency of QST. In this paper, we conduct a comprehensive investigation into various approaches for QST, encompassing both classical and quantum methodologies;We also implement different QML approaches for QST and demonstrate their effectiveness on various simulated and experimental quantum systems, including multi-qubit networks. Our results show that our QML-based QST approach can achieve high fidelity (98%) with significantly fewer measurements than conventional methods, making it a promising tool for practical QIP applications. © 2023, CC BY.

关键词： Machine learning

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Monolithically integrated, broadband, high-efficiency silicon nitride-on-silicon waveguide photodetectors in a visible-light integrated photonics platform

arXiv

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arXiv 2022年

作者： Lin, Yiding Yong, Zheng Luo, Xianshu Azadeh, Saeed Sharif Mikkelsen, Jared Sharma, Ankita Chen, Hong Mak, Jason C.C. Lo, Patrick Guo Qiang Sacher, Wesley D. Poon, Joyce K.S. Max Planck Institute of Microstructure Physics Weinberg 2 Halle06120 Germany Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S 3G4 Canada Advanced Micro Foundry Pte Ltd 11 Science Park Road Singapore Science Park II 117685 Singapore 138634 Singapore

Visible and near-infrared spectrum photonic integrated circuits are quickly becoming a key technology to address the scaling challenges in quantum information and biosensing. Thus far, integrated photonic platforms in this spectral range have lacked integrated photodetectors. Here, we report the first silicon nitride-on-silicon waveguide photodetectors that are monolithically integrated in a visible light photonic platform on silicon. Owing to a leaky-wave silicon nitride-on-silicon design, the devices achieved a high external quantum efficiency of > 60% across a record wavelength span from λ ∼ 400 nm to ∼ 640 nm, an opto-electronic bandwidth up to 9 GHz, and an avalanche gain-bandwidth product up to 173 ± 30 GHz. As an example, a photodetector was integrated with a wavelength-tunable microring in a single chip for on-chip power monitoring. © 2022, CC BY-NC-ND.

关键词： Photons

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Object Detection for Smart Factory Processes by Machine Learning

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Procedia computer science 2021年 184卷 581-588页

作者： Lukas Malburg Manfred-Peter Rieder Ronny Seiger Patrick Klein Ralph Bergmann Business Information Systems II University of Trier 54296 Trier Germany Institute of Computer Science University of St.Gallen 9000 St.Gallen Switzerland German Research Center for Artificial Intelligence (DFKI) Branch University of Trier Behringstraße 21 54296 Trier Germany

The production industry is in a transformation towards more autonomous and intelligent manufacturing. In addition to more flexible production processes to dynamically respond to changes in the environment, it is also essential that production processes are continuously monitored and completed in time. Video-based methods such as object detection systems are still in their infancy and rarely used as basis for process monitoring. In this paper, we present a framework for video-based monitoring of manufacturing processes with the help of a physical smart factory simulation model. We evaluate three state-of-the-art object detection systems regarding their suitability to detect workpieces and to recognize failure situations that require adaptations. In our experiments, we are able to show that detection accuracies above 90% can be achieved with current object detection methods.

关键词： Process Monitoring Object Detection computer Vision Machine Learning Industry 4.0 Cyber-Physical Production Systems

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An Interval Difference Method of Second Order for Solving an Elliptical BVP 13th

An Interval Difference Method of Second Order for Solving an...

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13th International Conference on Parallel Processing and Applied Mathematics, PPAM 2019

作者： Marciniak, Andrzej Jankowska, Malgorzata A. Hoffmann, Tomasz Institute of Computing Science Poznan University of Technology Piotrowo 2 Poznan60-965 Poland Department of Computer Science State University of Applied Sciences in Kalisz Poznanska 201-205 Kalisz62-800 Poland Institute of Applied Mechanics Poznan University of Technology Jana Pawla II 24 Poznan60-965 Poland Poznan Supercomputing and Networking Center Jana Pawla II 10 Poznan61-139 Poland

ISBN: (纸本)9783030432218

In the article we present an interval difference scheme for solving a general elliptic boundary value problem with Dirichlet’ boundary conditions. The obtained interval enclosure of the solution contains all possible numerical errors. A numerical example we present confirms that the exact solution belongs to the resulting interval enclosure. © 2020, Springer Nature Switzerland AG.

关键词： Enclosures

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Identifying Traffic Congestion Pattern using K-means Clustering Technique 4

Identifying Traffic Congestion Pattern using K-means Cluster...

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4th International Conference on Internet of Things: Smart Innovation and Usages, IoT-SIU 2019

作者： Mondal, Md. Ashifuddin Rehena, Zeenat Dept. of Computer Science and Engineering Narula Institute of Technology Agarpara Kolkata700109 India Dept. of Computer Science and Engineering Aliah University II A/27 Newtown Kolkata700160 India

ISBN: (纸本)9781728112534

With increase in urbanization and socio-economical growth, the number of vehicles in major metropolitan cities is increasing day by day. Therefore, traffic congestion is becoming a major concern of metropolitan cities all over the world. This results in tremendous air pollution, loss of valuable time and money of citizens. Hence, traffic congestion monitoring of different road segments is very essential for analyzing the problem associated with smooth mobility. Identifying the problematic road segments within the city is one of the important job for the transport authority to assess the road condition. That will assist the government agencies or policy makers to optimize traffic rules and regulations. This work identifies traffic congestion pattern which can classify the different road segments based on traffic density and average speed of vehicles. The traffic parameters are captured by in-road stationary sensors deployed in road segments. The proposed system uses k-means clustering algorithm to categorize the different road segments. © 2019 IEEE.

关键词： Traffic congestion

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A novel binary modified beluga whale optimization algorithm using ring crossover and probabilistic state mutation for enhanced bladder cancer diagnosis

引用

Informatics in Medicine Unlocked 2024年 50卷

作者： Gharaibeh, Hasan Alawad, Noor Aldeen Nasayreh, Ahmad Al Mamlook, Rabia Emhamed Makhadmeh, Sharif Naser Bashkami, Ayah Abualigah, Laith Ezugwu, Absalom E. Computer Science Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid 21163 Jordan Department of Business Administration Trine University IN United States Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libya Department of Information Technology King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan Department Medical Laboratory Sciences – Faculty of Science Al Balqa Applied University Salt 19117 Jordan Department of Cyber Security and Cloud Computing Faculty of Information Technology Applied Science Private University Amman 11937 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq 25113 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajman P.O. Box 346 United Arab Emirates

Bladder cancer (BC) remains a significant global health challenge, requiring the development of accurate predictive models for diagnosis. In this study, a new Binary Modified White Whale Optimization (B-MBWO) algorithm is proposed to address the BC problem. The proposed method utilizes circular transitivity optimization and the Probabilistic State Mutation Algorithm (PSMA) to enhance its optimization performance. The new method is called the BBWORCPS algorithm. High-dimensional and complex medical datasets pose challenges to the original optimization algorithms in addressing the BC problem, motivating the proposed modifications to the original Beluga Whale Optimization algorithm. These enhancements, including quantum-inspired mutation and circular crossing, aim to improve solution space exploration and enhance the algorithm's effectiveness in handling intricate feature spaces. Through comprehensive experiments on BC datasets, the superiority of the BBWORCPS algorithm in terms of feature selection accuracy and computational efficiency is demonstrated compared to existing optimization methods. The obtained findings suggest that BBWORCPS offers a promising approach for developing more precise and reliable predictive models for bladder cancer analysis. © 2024 The Author(s)

关键词： Binary modified beluga whale optimization Bladder cancer Feature selection Machine learning Predictive modeling

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Financial Fraud Detection using Quantum Graph Neural Networks

arXiv

引用

arXiv 2023年

作者： Innan, Nouhaila Sawaika, Abhishek Dhor, Ashim Dutta, Siddhant Thota, Sairupa Gokal, Husayn Patel, Nandan Khan, Muhammad Al-Zafar Theodonis, Ioannis Bennai, Mohamed Quantum Physics and Magnetism Team LPMC Faculty of Sciences Ben M’sick Hassan II University of Casablanca Morocco Quantum Formalism Fellow Zaiku Group Ltd Liverpool United Kingdom Indian Institute of Technology Bombay India Department of Data Science and Engineering Indian Institute of Science Education and Research Bhopal India Dwarkadas J. Sanghvi College of Engineering India Guru Nanak Institutions Technical Campus Hyderabad India ASTI Academy Dubai United Arab Emirates Vellore Institute of Technology Chennai India School of Computer Science and Applied Mathematics University of the Witwatersrand 1 Jan Smuts Ave Braamfontein Johannesburg Gauteng2000 South Africa Department of Physics School of Applied Mathematical and Physical Sciences National Technical University of Athens Greece

Financial fraud detection is essential for preventing significant financial losses and maintaining the reputation of financial institutions. However, conventional methods of detecting financial fraud have limited effectiveness, necessitating the need for new approaches to improve detection rates. In this paper, we propose a novel approach for detecting financial fraud using Quantum Graph Neural Networks (QGNNs). QGNNs are a type of neural network that can process graph-structured data and leverage the power of Quantum Computing (QC) to perform computations more efficiently than classical neural networks. Our approach uses Variational Quantum Circuits (VQC) to enhance the performance of the QGNN. In order to evaluate the efficiency of our proposed method, we compared the performance of QGNNs to Classical Graph Neural Networks using a real-world financial fraud detection dataset. The results of our experiments showed that QGNNs achieved an AUC of 0.85, which outperformed classical GNNs. Our research highlights the potential of QGNNs and suggests that QGNNs are a promising new approach for improving financial fraud detection. © 2023, CC BY.

关键词： Timing circuits

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