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检索条件"任意字段=11th International Conference on Embedded and Real-Time Computing Systems and Applications"

共 3210 条记录，以下是741-750 订阅

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real-time Adaptive Noise and Echo Cancellation on a Compact Microcontroller

Real-Time Adaptive Noise and Echo Cancellation on a Compact ...

引用

international conference on Information, Intelligence, systems and applications (IISA)

作者： Joshua Vandermeer Frank Giuliani Erica Low Katherine thomasson Andreas Spanias SenSIP Center School of ECEE Arizona State University

ISBN: (数字)9781665422284

ISBN: (纸本)9781665422291

this paper provides an analysis of current advancements made in acoustic noise and echo cancellation (ANEC) and describes a real-time acoustic noise and feedback cancellation implementation that is low-cost and effective. Challenges in developing this compact mobile ANEC system included integrating a high order normalized least mean square (NLMS) adaptive algorithm with an analog synchronized twochannel interface. the paper also presents electrical and mechanical details of this small footprint ANEC prototype along with test results. the system was evaluated in terms of performance and computational complexity using objective and subjective evaluations. Metrics used to assess real-time performance included the Echo Return Loss Enhancement (ERLE), the Added Stable Gain (ASG) as well as algorithm convergence curves.

关键词： Measurement Echo cancellers Microcontrollers Prototypes real-time systems Synchronization Convergence

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Conversion of an Unsupervised Anomaly Detection System to Spiking Neural Network for Car Hacking Identification

Conversion of an Unsupervised Anomaly Detection System to Sp...

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international Green computing conference

作者： Yassine Jaoudi Chris Yakopcic Tarek Taha University of Dayton Dayton OH USA

ISBN: (数字)9781665415521

ISBN: (纸本)9781665415538

Across industry, there is an increasing availability of streaming, time-varying data, where it is important to detect anomalous behavior. these data are found in an enormous number of sensor-based applications, in cybersecurity (where anomalous behavior could indicate an attack), and in finance. Spiking Neural Networks (SNNs) have come under the spotlight for machine learning applications due to the extreme energy efficiency of their implementation on neuromorphic processors like the Intel Loihi research chip. In this paper we explore the applicability of spiking neural networks for in vehicle cyberattack detection. We show exemplary results by converting an autoencoder model to spiking form. We present a learning model comparison that shows the proposed SNN autoencoder outperforms a One Class Support Vector Machine and an Isolation Forest. Furthermore, only a slight reduction in accuracy is observed when compared to a traditional autoencoder.

关键词： Anomaly detection Protocols Training Computational modeling Automobiles real-time systems Data models

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Automated time Delay Estimation for Distributed Sensor systems of Electric Vehicles 10

Automated Time Delay Estimation for Distributed Sensor Syste...

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10th IEEE international conference on Intelligent Data Acquisition and Advanced computing systems - Technology and applications (IDAACS)

作者： Pfeiffer, Jakob Wu, Xuyi BMW Grp Petuelring 130 D-80788 Munich Germany Tech Univ Munich Chair Data Proc Arcisstr 21 D-80333 Munich Germany

ISBN: (纸本)9781728140681

Deviations between electric current measurements and reality can cause severe problems in the power train of electric vehicles (EVs). Among others, these are unnecessary power limitations and inaccurate performance coordination during driving or charging. One reason for these deviations are time delays. In this paper, we present three different approaches for time delay estimation (TDE) and evaluate these with real data from power trains of EVs. Besides the accuracy of the TDE, the focus of our evaluation lies on the computational efficiency to enable an execution on automotive electronic control units (ECUs). the Linear Regression approach suffers even from small noise and offsets in the measurement data and is unsuited for our purpose. A better alternative is the Variance Minimization approach. It is not only more noise-resistant but also very efficient after the first execution. Another interesting approach are Adaptive Filters presented by Emadzadeh et al. Unfortunately, Adaptive Filters do not reach the accuracy and efficiency of Variance Minimization in our experiments. thus, we recommend Variance Minimization for TDE of current signals in the power train of EVs.

关键词： automotive current electric power train electric vehicle embedded systems delay detection distributed systems measurements power train sensor signals time delay estimation

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A Low-Power embedded System for real-time sEMG based Event-Driven Gesture Recognition 26

A Low-Power Embedded System for Real-Time sEMG based Event-D...

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26th IEEE international conference on Electronics, Circuits and systems (ICECS)

作者： Mongardi, Andrea Ros, Paolo Motto Rossi, Fabio Roch, Massimo Ruo Martina, Maurizio Demarchi, Danilo Politecn Torino Dipartimento Elettron & Telecomunicaz Turin Italy Ist Italiano Tecnol Elect Design Lab Genoa Italy

ISBN: (纸本)9781728109961

Latency and power consumption management are present-day hot topics for wearable devices and IoT applications. this paper presents the implementation of a low power system for hand movement recognition, based on surface ElectroMyoGraphic (sEMG) signals, suitable for human-machine interface. Every time the sEMG signal crosses a predefined threshold an event is generated, implementing the Average threshold Crossing (ATC) technique directly on-boards on the subject arm. Resulting quasi-digital signals, averaged over a fixed time window, are sent to an ARM Cortex-M4F processor which implements a fully-connected Neural Network (NN) able to recognize six different gestures from only three input channels. Dataset creation has involved 25 healthy people, each one performing five movements within five repeated sessions. the NN has been trained using the per-subject holdout validation method, obtaining an accuracy of 96.34%. With a maximum latency of 8.5 ms and an average power consumption of only 0.8mW at full recognition rate, the proposed NN implementation shows promising results.

关键词： energy efficiency embedded machine learning edge computing event-driven surface electromyography

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Resource-Efficient Wearable computing for real-time Reconfigurable Machine Learning: A Cascading Binary Classification 16

Resource-Efficient Wearable Computing for Real-Time Reconfig...

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IEEE 16th international conference on Wearable and Implantable Body Sensor Networks (BSN)

作者： Pedram, Mahdi Rokni, Seyed Ali Nourollahi, Marjan Homayoun, Houman Ghasemzadeh, Hassan Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA George Mason Univ Dept Elect & Comp Engn Fairfax VA 22030 USA

ISBN: (纸本)9781538674772

Advances in embedded systems have enabled integration of many lightweight sensory devices within our daily life. In particular, this trend has given rise to continuous expansion of wearable sensors in a broad range of applications from health and fitness monitoring to social networking and military surveillance. Wearables leverage machine learning techniques to profile behavioral routine of their end-users through activity recognition algorithms. Current research assumes that such machine learning algorithms are trained offline. In reality, however, wearables demand continuous reconfiguration of their computational algorithms due to their highly dynamic operation. Developing a personalized and adaptive machine learning model requires real-time reconfiguration of the model. Due to stringent computation and memory constraints of these embedded sensors, the training/re-training of the computational algorithms need to be memory- and computation-efficient. In this paper, we propose a framework, based on the notion of online learning, for real-time and on-device machine learning training. We propose to transform the activity recognition problem from a multi-class classification problem to a hierarchical model of binary decisions using cascading online binary classifiers. Our results, based on Pegasos online learning, demonstrate that the proposed approach achieves 97% accuracy in detecting activities of varying intensities using a limited memory while power usages of the system is reduced by more than 40%.

关键词： Memory management Feature extraction Computational modeling Signal processing algorithms real-time systems Activity recognition"

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Virtual Farmer: real time Crop Prediction and Automatic Irrigation System

Virtual Farmer: Real Time Crop Prediction and Automatic Irri...

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international conference on computing and Networking Technology (ICCNT)

作者： Dev V. Savla Amogh N. Parab Kaustubh Y. Kekre Jay P. Gala S. Ramchandra Pankaj A. Sonawane DJSCOE Mumbai India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

there are a plethora of problems associated with agriculture and farming which require considerable improvements but remain untouched by technology. Farming is considered as one of the strong pillars of any economy. Considering this, very few technologies exist to aid the farmers in selecting the right crops depending on the environmental factors. Moreover most irrigation systems all around the world require at least some form of human intervention. Considering this, the proposed smart farming solution aims to aid the farmers by means of technology so as to increase their yield by suggesting to them the crops that will be most profitable for them as well as automating the irrigation system for them. this in turn will be a major help to the agriculture community as a whole and also set free the farmers from certain rudimentary tasks.

关键词： Agriculture Sensors Temperature sensors Irrigation Soil moisture Sensor systems Mobile applications

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Improving the Responsiveness of Geospatial Web applications through Client-Side Processing 11th

Improving the Responsiveness of Geospatial Web Applications ...

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11th international conference on Multimedia and Network Information systems (MISSI)

作者： Sroda, Kamila Labuz, Marek Ernst, Sebastian AGH Univ Sci & Technol Dept Appl Comp Sci Krakow Poland

ISBN: (纸本)9783319986784;9783319986777

Web applications which enable interactive editing of geospatial data are becoming increasingly popular, partly due to the growing interest in development of smart city concepts. However, performing calculations on geospatial objects, often expressed using a spheroidal (non-Cartesian) coordinate system, is not trivial. this paper tries to compare two approaches to developing such applications - one based on GIS backend queries, and one which performs the calculations locally on the client's machine. Prototypes implementing both approaches have been prepared, and tests based on real-life scenarios have been carried out. the results are presented along with conclusions, which are used to develop recommendations for the most efficient architectures in various situations.

关键词： Spatial data Web GIS Web mapping Geometry transformation Smart cities

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Intelligent Data Analytics from the Energy Economics of the Greek Debt Crisis

Intelligent Data Analytics from the Energy Economics of the ...

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international conference on Information, Intelligence, systems and applications (IISA)

作者： Maria D. Karasimou Vasileios Antonenas Vasileios Laitsos Vasileios Polychronides Lefteris H. Tsoukalas School of Nuclear Engineering Purdue University West Lafayette IN USA-Volos Greece School of Nuclear Engineering University of Thessally West Lafayette IN USA-Volos Greece

ISBN: (数字)9781665422284

ISBN: (纸本)9781665422291

Intelligent tools are brought to bear on the analysis of energy data from Greece in the period 2010-20. During this period a paradoxical phenomenon is observed namely, an increase in energy productivity in the midst of unprecedented economic decline as measured by Gross Domestic Product (GDP). Preliminary results identify improvements in the overall efficiency of capital due to the collapse in demand following draconian austerity measures. Background, directions and trends are analyzed, policy alternatives examined, and, a new measure of energy utilization with possible real-time applications is proposed.

关键词： Productivity Data analysis Economic indicators Energy measurement Tools Market research real-time systems

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Identification and Validation of Markov Models with Continuous Emission Distributions for Execution times

Identification and Validation of Markov Models with Continuo...

引用

IEEE international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Anna Friebe Alessandro V. Papadopoulos thomas Nolte Mälardalen University Västerås Sweden

ISBN: (数字)9781728144030

ISBN: (纸本)9781728144047

It has been shown that in some robotic applications, where the execution times cannot be assumed to be independent and identically distributed, a Markov Chain with discrete emission distributions can be an appropriate model. In this paper we investigate whether execution times can be modeled as a Markov Chain with continuous Gaussian emission distributions. the main advantage of this approach is that the concept of distance is naturally incorporated. We propose a framework based on Hidden Markov Model (HMM) methods that 1) identifies the number of states in the Markov Model from observations and fits the Markov Model to observations, and 2) validates the proposed model with respect to observations. Specifically, we apply a tree-based cross-validation approach to automatically find a suitable number of states in the Markov model. the estimated models are validated against observations, using a data consistency approach based on log likelihood distributions under the proposed model. the framework is evaluated using two test cases executed on a Raspberry Pi Model 3B+ single-board computer running Arch Linux ARM patched with PREEMPT_RT. the first is a simple test program where execution times intentionally vary according to a Markov model, and the second is a video decompression using the ffmpeg program. the results show that in these cases the framework identifies Markov Chains with Gaussian emission distributions that are valid models with respect to the observations.

关键词： Hidden Markov models Markov processes Timing Probabilistic logic Task analysis Computational modeling real-time systems

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A model driven-based approach for managing unanticipated runtime adaptation of RTE systems 20

A model driven-based approach for managing unanticipated run...

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20th international conference on Parallel and Distributed computing, applications and Technologies, PDCAT 2019

作者： Fredj, Nissaf Hadj Kacem, Yessine Abid, Mohamed ISITCOM University of Sousse CES Laboratory Tunisia CES Laboratory Enis University of Sfax Tunisia

ISBN: (纸本)9781728126166

Nowadays, real-time embedded systems (RTES) and particularly adaptive ones are gaining more use in several domains. they must be always consistent and available to preserve their usefulness and feasibility, as a response to environmental conditions, resources limitations and user requirements. For this reason, they are subject to anticipated and unanticipated runtime reconfigurations. the design of such systems requires an abstract representation of the execution context independently from low-level details, with lower complexity. In this context, designers adopt runtime models which need to be checked and validated at early stages of development. Besides, these models must be updated according to unpredictable execution changes. In the present paper, we propose an MDE-based approach that allows managing runtime adaptive RTES. Our proposal starts by specifying the behavior and structure of runtime adaptive systems. then Model-to-Text (M2T) transformations allows us to generate configuration files to simulate adaptive systems behavior and evaluate their real-time constraints. An additional feature offered by our approach consists to monitor the execution context and update runtime models according to unanticipated reconfigurations. © 2019 IEEE.

关键词： Adaptive systems

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