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Efficient ECG Analysis with High F1 Score and Low Computation Complexity

Efficient ECG Analysis with High F1 Score and Low Computatio...

引用

IEEE international Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and applications

作者： Andrii Yavorskyi Bohdan Tyshchenko Taras Panchenko Faculty of Computer Science and Cybernetics Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (纸本)9781665426060

Electrocardiograms (ECG) are important signals of the human body to analyze its state, monitor health, and even save a life when early deviations appear. Official statistics reveal that one of the major death causes is heart disease and its consequences. In this paper, the new approach to the detection of Atrial Fibrillation (AF) Arrhythmia in real-time is developed and presented. this type of cardiac arrhythmia affects many people. If it remains undetected, it could develop into chronic disability or even could result in early mortality. Also, it is hard to diagnose this disease because of its non-regular nature, especially during the early stages. Early detection is an important issue because it can help to save and extend human life. real-time non-invasive and effective approaches to detect AF are being developed, but their properties still could be improved. the performance of such models and methods depends on the data quality and feature engineering largely. ECG as a heart electrical activity record is used to monitor heart activity and to detect a broad range of heart disabilities. Our research uses the dataset of PhysioNet computing in Cardiology Challenge 2017. It consists of 8528 single-lead ECG records of heart rhythms. the proposed method and appropriate model outperform the best known State-of-the-Art results in terms of $F$ 1 score, which is the main metric of the Challenge results mentioned above. Also, the presented approach is more computationally effective and less energy-consuming. the latter allows an embedded implementation of this model.

关键词： Heart Measurement Analytical models Computational modeling Electrocardiography real-time systems Biomedical monitoring

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EDGE ARTIFICIAL INTELLIGENCE: A MULTI-CAMERA VIDEO SURVEILLANCE APPLICATION 17

EDGE ARTIFICIAL INTELLIGENCE: A MULTI-CAMERA VIDEO SURVEILLA...

引用

ASME international Design Engineering Technical conferences / 41st Annual Computers and Information in Engineering conference (IDETC-CIE) / 17th IEEE/ASME international conference on Mechatronic and embedded systems and applications (MESA)

作者： Berardini, Daniele Mancini, Adriano Zingaretti, Primo Moccia, Sara Univ Politecn Marche Dept Informat Engn Ancona Italy Scuola Super Sant Anna BioRobot Inst Pisa Italy Scuola Super Sant Anna Dept Excellence Robot & AI Pisa Italy

ISBN: (纸本)9780791885437

Nowadays, video surveillance has a crucial role. Analyzing surveillance videos is, however, a time consuming and tiresome procedure. In the last years, artificial intelligence paved the way for automatic and accurate surveillance-video analysis. In parallel to the development of artificial-intelligence methodologies, edge computing is becoming an active field of research with the final goal to provide cost-effective and real time deployment of the developed methodologies. In this work, we present an edge artificial intelligence application to video surveillance. Our approach relies on a set of four IP cameras, which acquire video frames that are processed on the edge using the NVIDIA (R) Jetson Nano. A state-of-the-art deep-learning model, called Single Shot multibox Detector (SSD) MobileNetV2 network, is used to perform object and people detection in realtime. the proposed infrastructure obtained an inference speed of similar to 10.0 Frames per Second (FPS) for each parallel video stream. these results prompt the possibility of translating our work into a real word scenario. the integration of the presented application into a wider monitoring system with a central unit could bring benefits to the overall infrastructure. Indeed our application could send only video-related high-level information to the central unit, allowing it to combine information with data coming from other sensing devices without unuseful data overload. this would ensure a fast response in case of emergency or detected anomalies. We hope this work will contribute to stimulate the research in the field of edge artificial intelligence for video surveillance.

关键词： Artificial intelligence Surveillance Sensors Emergencies Monitoring systems

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Event-Driven Data Pipeline for Network Management systems 11

Event-Driven Data Pipeline for Network Management Systems

引用

11th international conference on computing, Communication and Networking Technologies, ICCCNT 2020

作者： Ponnuswami, Ganesan Kailasam, Sriram DInesh, DIleep Aroor School of Computing and Electrical Engineering Indian Institute of Technology Mandi Himachal Pradesh India

ISBN: (纸本)9781728168517

Developing an event-driven applications for network management systems(NMS) requires data-wrangling operations to be performed in a data pipeline with low latency and high throughput. Data stream processing engines(SPEs) enables the pipeline to feature extract complex data in near real time. We present a low latency and high throughput data pipeline that performs operations such as data cleansing, filtering, aggregations and join of streams on pipeline stages for event-driven machine learning applications. When the data stream has imbalance in arrival of records, a standalone system handle it ineffectively that reduces the performance of data pipeline. In this work, the system powered by SPEs handle the imbalance by scaling out resources and degree of parallelism such that performance is not degraded. © 2020 IEEE.

关键词： Pipelines

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Improved Deep Residual Convolution Neural Network for Detection of Arrythmia from Electrocardiographic Data

Improved Deep Residual Convolution Neural Network for Detect...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Ankita Shukla Izharuddin Department of Computer Engineerinmg ZHCET AMU Aligarh Uttar Pradesh India

ISBN: (纸本)9781665487351

Deep Learning (DL) has enormous applications in different areas, including medical field. It is very critical task to detect irregularities in heart signals. For better monitoring of heart, easy-to-use and non-invasive tool is the electrocardiogram (ECG). To find cardiac arrhythmias, doctors frequently utilize ECG. In order to automate the categorization of arrhythmias, this research suggests an enhanced Deep Residual Convolution Neural Network (CNN) Model. Preprocessing of ECG Data is the first step in the suggested method. Focal loss will be used to solve imbalance classification difficulty among classes. is in this study. Performance evaluation is done by measuring Accuracy, Recall, Precision and F1-score for MIT-BIH arrhythmia database. Additionally, with accuracy of 99.55 % , our upgraded ResNet50 model surpassed other established models in the literature.

关键词： Training Heart Convolution Arrhythmia Neural networks Electrocardiography real-time systems

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Online learning on tiny micro-controllers for anomaly detection in water distribution systems 11

Online learning on tiny micro-controllers for anomaly detect...

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11th IEEE international conference on Consumer Electronics, ICCE-Berlin 2021

作者： Pau, Danilo Khiari, Abderrahim Denaro, Davide System Research and Applications STMicroelectronics Agrate Brianza Italy Universita Degli Studi di Padova STMicroelectronics Padova Italy

ISBN: (纸本)9781665428316

Researchers have developed novel approaches and algorithms to aid in the planning, design, and administration of water distribution systems (WDS) since the 1960s. While early research focused on simulating the hydraulic of such systems, the 1980s saw a shift in focus to water quality subjects. Recent study has delved deeper into system reliability and resilience. this paper introduces a resource constrained anomaly detector for WDS to monitor its reliability. the proposed solution uses Reservoir computing (RC), in particular Deep Echo State Network (DeepESN), to achieve high accuracy with low complexity. Furthermore, thanks to the special properties of DeepESN, online learning capabilities of this model is implemented in the micro-controller (MCU) to ensure the continuous adaptability over time of the model to adapt to the time varying data distribution. In most cases, researchers have relied on either hypothetical water distribution systems or a handful of actual systems for use as benchmark test systems. Skoltech Anomaly Benchmark (SKAB) dataset was used to measure the performance of those models. It consists of several anomalies in a sensorized WDS. the online learning feature is proposed in two different approaches: single iteration and batch decomposition Which give options for different implementations depending on the application needs and the MCU memory constraints. the accuracy, complexity and robustness of the proposed models are comprehensively validated through comparative analysis of many sets of hyper-parameters. © 2021 IEEE.

关键词： Benchmarking

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Teaching Daily Life Skills in Autism Spectrum Disorder (ASD) Interventions Using the Social Robot Pepper 1

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11th international conference on Robotics in Education (RiE)

作者： Efstratiou, Rafaela Karatsioras, Charalambos Papadopoulou, Maria Papadopoulou, Cristina Lytridis, Chris Bazinas, Christos Papakostas, George A. Kaburlasos, Vassilis G. Novel Therapeut & Consulting Unit Praxis El Venizelou 42 Kavala 65302 Greece Aristotle Univ Thessaloniki Papageorgiou Gen Hosp Div Child Neurol & Metab Disorders Dept Pediat 4 Thessaloniki 56403 Greece Int Hellen Univ Dept Comp Sci HUMAIN Lab Kavala 65404 Greece

ISBN: (数字)9783030674113

ISBN: (纸本)9783030674106;9783030674113

Advances in robot capabilities over the last few years have resulted in the introduction of robots in many disciplines. Amongst them, the field of special education attracts scientific attention, towards studying the effect of social robots in the treatment of children with Autism Spectrum Disorder (ASD). this paper presents a pilot study in a real therapeutic setting, using the social robot Pepper to interact with three children with ASD in specially designed educational scenarios regarding monetary transactions. the goal of these scenarios is to enhance short-term and long-term memory, as well as communication and social skills, through exercises involving coins and banknotes. Ultimately, these exercises will be part of a broader effort to help children with autism acquire daily life skills and self-reliance. the pilot study was evaluated using observation sheets completed by the therapist, which were then used to qualitatively assess the effectiveness of the proposed approach. the preliminary results show that the intervention results in increasing engagement and the motivation in communicating.

关键词： Social robots Special education applications Autism spectrum disorder (ASD) intervention Child-robot interaction

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Proceedings of the 11th Workshop on Parallel Programming and Run-time Management Techniques for Many-Core Architectures / 9th Workshop on Design Tools and Architectures for Multicore embedded computing Platforms, PARMA-DITAM 2020

Proceedings of the 11th Workshop on Parallel Programming and...

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11th Workshop on Parallel Programming and Run-time Management Techniques for Many-Core Architectures / 9th Workshop on Design Tools and Architectures for Multicore embedded computing Platforms, PARMA-DITAM 2020

the proceedings contain 5 papers. the topics discussed include: sparse matrix-dense matrix multiplication on heterogeneous CPU+FPGA embedded system;run-time power modelling in embedded GPUs with dynamic voltage and frequency scaling;fault-tolerant online scheduling algorithms for CubeSats;an OpenMP parallel genetic algorithm for design space exploration of heterogeneous multi-processor embedded systems;and automated precision tuning in activity classification systems: a case study.

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Exploiting VPN Bonding for time Critical Video Transmission on Board Drone

Exploiting VPN Bonding for Time Critical Video Transmission ...

引用

IEEE international Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and applications

作者： Roberta Avanzato Francesco Beritelli Corrado Rametta Michele Russo DIEEI - University of Catania Catania Italy Sapienza University of Rome Roma Italy Vicosystem S.R.L Catania Italy

ISBN: (纸本)9781665426060

the use of drones or UAV (Unmanned Aerial Vehicle) for civilian applications and the related new paradigm of drone communications are arousing great interest in the subject over the last years. the application contexts are various and most of them need to transmit time critical data between the drone and the Internet and, particularly, towards a command and control station. In such a case it is essential to equip the drone with a communication system capable of solving typical concerns such as connection reliability, coverage area, bandwidth limitations, high packet loss rate and similar. this research presents a prototype of a mobile gateway exploiting the VPN (Virtual Private Network) bonding technique and the use of two 4G modems to improve the transmission of real time video to the remote control station. In order to evaluate the effectiveness of the proposed technique a preliminary test has been carried out equipping the drone with a mobile gateway during a flight campaign in rural areas: VMF (Video Multimethod Assessment Fusion) tool and the related index has been employed to evaluate the improvement in terms of video quality perceived by the operator.

关键词： Streaming media Logic gates Autonomous aerial vehicles real-time systems Virtual private networks Reliability Bonding

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the rclc Executor: Domain-specific deterministic scheduling mechanisms for ROS applications on microcontrollers: work-in-progress 14

The rclc Executor: Domain-specific deterministic scheduling ...

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international conference on embedded Software (EMSOFT) / 16th embedded systems Week (ESWEEK)

作者： Staschulat, Jan Luetkebohle, Ingo Lange, Ralph Robert Bosch GmbH Corp Res Renningen Germany

ISBN: (纸本)9781728191959

Robots are networks of a variety of computing devices, such as powerful computing platforms but also tiny microcontrollers. the Robot Operating System (ROS) is the dominant framework for powerful computing devices. While ROS version 2 adds important features like quality of service and security, it cannot be directly applied to microcontrollers because of its large memory footprint. the micro-ROS project has ported the ROS 2 API to microcontrollers. However, the standard ROS 2 concepts are not enough for real-time performance: In the ROS 2 release "Foxy", the standard ROS 2 Executor, which is the central component responsible for handling timers and incoming message data, is neither real-time capable nor deterministic. Domain-specific requirements of mobile robots, like sense-plan-act control loops, cannot be addressed with the standard ROS 2 Executor. In this paper, we present an advanced Executor for the ROS 2 C API which provides deterministic scheduling and supports domain-specific requirements. A proof-of-concept is demonstrated on a 32-bit microcontroller.

关键词： real-time operating systems scheduling microcontrollers robotics

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Multidimensional Neuro-fuzzy System and Fuzzy Coding for a Constant Length Facial Landmark Set Formation

Multidimensional Neuro-fuzzy System and Fuzzy Coding for a C...

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IEEE international Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and applications

作者： Yevgeniy Bodyanskiy Nonna Kulishova Control Systems Research Laboratory Kharkiv National University of Radioelectronics Kharkiv Ukraine Kharkiv National University of Radioelectronics Kharkiv Ukraine

ISBN: (纸本)9781665426060

Facial expression recognition is performed using deep and machine learning. Deep networks guarantee recognition accuracy when large, pre-labeled image sets are available for learning. this condition is not always met. Machine learning approaches will be more useful, especially in real-time tasks, under changing shooting conditions. the main problem in this case is formation of a facial landmarks coordinates vector of constant dimension. Input vector fixed size is one of machine learning algorithms main requirements. However, standard computer vision detectors detect a different number of feature points in each video frame. this paper considers problem of constructing a facial landmarks coordinates vector, which will have a constant length. For this, a fuzzy encoder based on flexible two-dimensional membership functions is proposed, as well as a multidimensional neuro-fuzzy system for encoded data classification.

关键词： Machine learning algorithms Face recognition Neurons Machine learning Streaming media Feature extraction real-time systems

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