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检索条件"任意字段=13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

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Integrated Framework for Farmer Centric Agriculture applications Using IoT Architectures 13

Integrated Framework for Farmer Centric Agriculture Applicat...

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13th ieee international conference on Advanced Networks and Telecommunication systems (ieee ANTS)

作者： Sunil, J. S. Charitha, R. Kalpana, P. Prasad, Raja Vara Indian Inst Informat Technol IIIT Sri City Elect & Commun Engn Sri City 517646 AP India

ISBN: (纸本)9781728137155

Role of Internet of things (IoT) in Agriculture is predominant over the past several years. Knowledge bases of new technological trends in agriculture can help farmers in real time to increase the productivity of whole chain of agriculture processes. timely suggestions and automation in certain areas of the agriculture industry is possible by connecting farmers, knowledge bases, experts, sensors, actuators, markets and other factors using evolving technologies like IoT. End-to-end platforms to increase the qualitative and quantitative Agriculture production is essential to transform agriculture into a more profitable sector. In this context, a integrated framework is proposed in this paper with a combination of modern technologies like IoT, Mobile and Android software modules that can address most of the above issues. Proposed framework automates the process of connecting, communicating and conveying different aspects of efficient and economic agriculture practices by empowering even the illiterate farmers with latest mobile GUI. Suggestion module from experts relating diseases, water requirements, Data anaysis and forecasting module and Farmers Ecommerce are some of the value Add-on applications integrated into the proposed framework. Developed framework is tested with some of the test cases and future scope of the improvements for the developed framework are given in this paper.

关键词： Internet of things (IoT) ARIMA Wireless Sensor Networks Integrated Framework

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Contending memory in heterogeneous SoCs: Evolution in NVIDIA Tegra embedded platforms

Contending memory in heterogeneous SoCs: Evolution in NVIDIA...

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

作者： Nicola Capodieci Roberto Cavicchioli Ignacio Sañudo Olmedo Marco Solieri Marko Bertogna University of Modena and Reggio Emilia Modena Italy

ISBN: (数字)9781728144030

ISBN: (纸本)9781728144047

Modern embedded platforms are known to be constrained by size, weight and power (SWaP) requirements. In such contexts, achieving the desired performance-per-watt target calls for increasing the number of processors rather than ramping up their voltage and frequency. Hence, generation after generation, modern heterogeneous System on Chips (SoC) present a higher number of cores within their CPU complexes as well as a wider variety of accelerators that leverages massively parallel compute architectures. Previous literature demonstrated that while increasing parallelism is theoretically optimal for improving on average performance, shared memory hierarchies (i.e. caches and system DRAM) act as a bottleneck by exposing the platform processors to severe contention on memory accesses, hence dramatically impacting performance and timing predictability. In this work we characterize how subsequent generations of embedded platforms from the NVIDIA Tegra family balanced the increasing parallelism of each platform's processors with the consequent higher potential on memory interference. We also present an open-source software for generating test scenarios aimed at measuring memory contention in highly heterogeneous SoCs.

关键词： Central Processing Unit Engines Interference Random access memory Graphics processing units Memory management

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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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Local Spatio-Temporal Propagation Based Adaptive Model Generation and Update for High Frame Rate and Ultra-Low Delay Foreground Detection

Local Spatio-Temporal Propagation Based Adaptive Model Gener...

引用

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

作者： Peikun Cai Songlin Du Takeshi Ikenaga Graduate School of Information Production and Systems Waseda University Kitakyushu Japan School of Automation Southeast University Nanjing China

ISBN: (数字)9781728144030

ISBN: (纸本)9781728144047

High frame rate and ultra-low delay matching system plays an increasingly important role in human-machine interactive applications, which demands better experience and higher accuracy. Foreground detection is an indispensable preprocessing step to make the system suitable for complex scenes. Although many foreground detection algorithms have been proposed, few can achieve high speed in hardware due to their high complexity or high consumption. Based on the foreground detection algorithm ViBe, this paper proposes a local spatio-temporal propagation based adaptive model generation and update strategy for high frame rate and ultra-low delay foreground detection. Our algorithm predicts whether a region is a foreground by setting up detecting points, thereby adaptively adjusting the number of pixels that needs to be modeled. Secondly, the local linear illumination correlation is used to update models, which makes the algorithm more robust to illumination changes. the evaluation results show that the proposed algorithm successfully achieves real-time processing on the field-programmable gate array (FPGA) at a resolution of 640×480 pixels, with a delay of 0.908ms/frame.

关键词： Adaptation models Delays Lighting Prediction algorithms Correlation Detection algorithms Cameras

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Processor Pipelining Method for Efficient Deep Neural Network Inference on embedded Devices

Processor Pipelining Method for Efficient Deep Neural Networ...

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international conference on High Performance computing

作者： Akshay Parashar Arun Abraham Deepak Chaudhary Vikram Nelvoy Rajendiran On-Device AI Research &Development Samsung R&D Institute Bangalore Bengaluru India

Myriad applications of Deep Neural Networks (DNN) and the race for better accuracy have paved the way for the development of more computationally intensive network architectures. Execution of these heavy networks on embedded devices needs highly efficient real-time DNN inference frameworks. But the sequential architecture of popular DNNs makes it difficult to parallelize its operations among different processors. We propose a novel pipelining method pluggable on top of conventional inference frameworks and capable of parallelizing DNN inference on heterogeneous processors without impacting the accuracy. We partition the network into subnets, by estimating the optimal split points, and pipeline these subnets across multiple processors. the results shows that the proposed method achieves up to 68% improvement in the frames per second (FPS) rate of popular network architectures like VGG19, DenseNet-121 and ResNet-152. Moreover, we show that our method can be used to extract even more performance out of high performance chipsets, by better utilizing the capabilities of its AI processor ecosystem. We also showcase that our method can be easily extended to other low performance chipsets, where this additional performance gain is crucial to deploy real-time AI applications. Our results show performance improvement of up to 47% in the FPS rate on these chipsets without the need of specialized AI hardware.

关键词： Performance evaluation Program processors Ecosystems Neural networks Computer architecture Network architecture real-time systems

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Multi-hop Routing Implementation in Spatially Distributed Wireless Sensor Networks for Large-Scale Environmental Monitoring applications 10

Multi-hop Routing Implementation in Spatially Distributed Wi...

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

作者： Ahmad, Afzal Pasha, Muhammad Adeel Masud, Shahid Schappacher, Manuel Belhouichet, Assila Sikora, Axel Lahore Univ Management Sci LUMS Dept Elect Engn Lahore Pakistan Hsch Offenburg Inst Reliable Embedded Syst & Commun Elect Offenburg Germany

ISBN: (纸本)9781728140698

With the surge in global data consumption with proliferation of Internet of things (IoT), remote monitoring and control is increasingly becoming popular with a wide range of applications from emergency response in remote regions to monitoring of environmental parameters. Mesh networks are being employed to alleviate a number of issues associated with single-hop communication such as low area coverage, reliability, range and high energy consumption. Low-power Wireless Personal Area Networks (LoWPANs) are being used to help realize and permeate the applicability of IoT. In this paper, we present the design and test of ieee 802.15.4-compliant smart IoT nodes with multi-hop routing. We first discuss the features of the software stack and design choices in hardware that resulted in high RF output power and then present field test results of different baseline network topologies in both rural and urban settings to demonstrate the deployability and scalability of our solution.

关键词： Wireless Sensor Networks Routing Protocols Environmental Monitoring

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Formal Modeling and Verification of EDCA Based on Probabilistic Model Checking

Formal Modeling and Verification of EDCA Based on Probabilis...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Yixuan Zan Xiaojuan Li Yong Guan Rui Wang Jie Zhang College of Information Engineering Capital Normal University Beijing China Beijing Engineering Research Center of High Reliability Embedded System Capital Normal University Beijing China Beijing Advanced Innovation Center for Imaging Theory and Technology Capital Normal University Beijing China Beijing Key Laboratory of Light Industrial Robots and Safety Verification Capital Normal University Beijing China College of Information Science and Technology Beijing University of Chemical Technology Beijing China

ISBN: (纸本)9781728176505

Enhanced Distributed Channel Access (EDCA) is a distributed control protocol to guarantee the network quality of service (QoS). It extends the access mode based on the traditional ieee 802.11, and becomes an effective means to provide highly reliable transmission of real-time applications. the four access categories of it show the ability to guarantee various priority queues when the network load varies frequently, and multimedia data transmission has the characteristics of high real-time and concurrency. therefore, it is necessary to analyse and verify the high reliability of the protocol according to requirements. In this paper, we formalize the EDCA protocol and design three transmission modes respectively. then we verify the probability accessibility and feasibility of EDCA, and find that the higher priority queue with lower parameter value will affect the sending of low priority. More importantly, we compare the performance of Distributed Coordination Function (DCF) in collision probability and transmission probability. Finally, we demonstrate the reliability and practicability of EDCA via detailed properties studies in different network conditions, and propose innovative ideas for existing problems, which provide guidance reference for implementation and improvement.

关键词： Protocols Computational modeling High performance computing Decentralized control Quality of service Model checking Probabilistic logic

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LambDP: Data Processing Framework for Terminal applications in IoTs Services

LambDP: Data Processing Framework for Terminal Applications ...

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ieee international conference on Cloud computing, CLOUD

作者： Liqing Zhao Bo Cheng Junliang Chen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Data processing is the basis and kernel for implementing terminal applications in Internet of things(IoTs) services. As a large amount of sensing devices need to be connected to the IoTs service, it will bring an enormous number of heterogeneous data, and the data is difficult to be directly used by terminal applications in upper layer. So we need a data processing framework to achieve data integration and processing in IoTs services. In this paper, we introduce a data processing framework, LambDP, which includes a protocol stack system, a publish/subscribe system and a data processing platform. Protocol stack system offers dynamical protocol adaptation. Publish/subscribe center is a message queue, which can provide transfer and distribution for data. Data processing platform is based on lambda theory to implement parallel computing streaming mode and batch mode of data. In addition, we use the workflow pattern to encapsulate the business logic in LambDP into components, which can provide data processing services to upper layers applications. As for the performance of the data processing framework based on Lambda, we will deploy the data processing platform to give the verification.

关键词： Cloud computing Protocols Data integration Data processing real-time systems Sensors Internet of things

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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...

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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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