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Essayez les liens ci-dessous ou une recherche Rechercher : Internet of Things IoT describes an emerging trend in which a large number of integrated devices objects are connected to the Internet.
These connected devices communicate with people and other elements and often provide sensor data to Cloud Storage and Cloud Computing resources where data is processed and analyzed to obtain important information.
Many development boards available on the market, including microcontrollers and single board computers SBCwhich are built around SoCs System-on-a-Chip.
Designed for IoT applications, the ESP32-Devkit is a highly integrated, low-power SoC development board with dual Wi-Fi connectivity and Bluetooth BLE!
This workshop will provide the basic elements for implementing an IoT application with the use of the ESP32-Devkit programmable card and the ThingSpeak IoT 生徒と遊ぶゲーム platform and its Thingview mobile application for channel visualization.
CLOSE The emergence of big data and the development of Internet of Things IoT technologies have played a more prominent role in the expansion of smart city projects and initiatives.
Big data provides the important potential for cities to extract valuable insights and patterns ウォールストリートウルフオンライン無料を見る a large amount of data collected through various sources.
As an important element in the Smart Cities concept, the IoT allows the integration of sensors, radio-frequency identification, and Blue-tooth in the real-world environment using highly networked services.
Smart Traffic Management is a most known IOT application where a large amount of stream data traffic signals collected from sensors should be processed to regulate the flow of traffic through the city in response to demand.
The data generated by sensors devices is large in volume and random in nature and needs to be analyzed using a big data analytics engine in order to extract the critical information or to understand the user behavioral patterns.
For phrase ベースプロショップ無料ゲーム right!, using ウォールストリートウルフオンライン無料を見る traffic signals collected for different sensors we can analyze and monitor the traffic on a particular route.
In this workshop, we will design and implement a Big Data Streaming Architecture for Real-time Smart City Traffic Monitoring.
We will demonstrate how Container management technologies like Kubernetes make it possible to implement modern big data pipelines like Real-time Smart city Traffic Monitoring.
These containers serve as the nodes for apache Spark clusters; other Big Data frameworks like Kafka and Cassandra; as well as for analytics, business intelligence, ETL, Dashboard, Visualizationand data science tools.
We will use Apache Kafka to collect data generated by sensors devices and Apache Spark to analyze and process traffic data and visualize the processed data in a real time traffic-monitoring dashboard.
Because the IoT can be viewed as an embedded system with specific functions and IPs Intellectual Proprietiesthe implementation of IoT on FPGA will depend on the complexity of the IoT and this web page type of the chosen FPGA.
Some platforms like ZedBord offer the possibility to choose which set of tasks to be implemented in SW on ARM CPU for example and which tasks to be implemented in HW as an IP.
This workshop session will give the possibility of attendees to have 最高のボートゲームxbox 360 real practice on FPGA with a step by step approach to master IoT implementation on FPGA CLOSE An IoT can be viewed as an embedded system with specific functions and IPs Intellectual Proprieties.
An IoT is often connected to the internet.
But due to its mobility and lack of energy, it has an unpredictable behavior.
Each IoT component is part of a global infrastructure composed of smart components.
These components should be interoperable and secure.
The Networking infrastructures and the related services offer to the IoT all what a mobile embedded system need to ensure correctly its specific function.
Virtualization is a key issue in future IoT architectures.
The future of the Internet of Things applications will depend on the development of sophisticated and secured IoT platforms and architectures specifically designed for smart objects and which can contains embedded intelligence, and can easily make connection to smart networks.
This presentation will focus on the use of codesign to master IoT mobility and complexity.
Abstract: ウォールストリートウルフオンライン無料を見る the digitization of most of our everyday life routines, we became surrounded with « smart » gadgets and apps that derive their « intelligence » from sophisticated machine learning algorithms.
One of the key tasks in machine learning is supervised classification.
Examples of applications of supervised classification range from the classification of faces, natural language and handwriting to the detection of credit card frauds, sentiment analysis and even now personalized medicine.
In this presentation, we will learn theoretical and practical concepts of one of the most powerful machine learning classification techniques namely ensemble methods.
The main reason behind the power of these techniques is that they rely on the « wisdom » of multiple weak learners to provide more accurate predictions.
We will start learning step by step the basic concepts of classification as well as state-of-the-art evaluation metrics and techniques.
We also will learn one of the simplest yet powerful classification models namely decision tree and we will then learn how to make stronger models by leveraging multiple week decision tree estimators into an ensemble classifier.
Practical examples will also be shown on decision trees, gradient boosted trees and random forest in single process mode as well as in parallel and distribution computation for efficiently handling big data scenarios.
Abstract: Classification is a fundamental task in machine learning and data mining.
Existing classification methods are designed to classify unknown instances within a set of previously known training classes.
Such a classification takes the form of a prediction within a closed-set of classes.
However, due to the growth of data collection in many real-world applications and especially in big data scenarios, the training data could only represent a partial view of the domain and thus it may not contain training examples for all possible classes.
A more realistic scenario that fits real-world applications is to consider the possibility of encountering instances that do not belong to any of the training classes, i.
In such situation, existing closed-set classifiers will assign a training label to these instances resulting in a misclassification.
In this talk, I will introduce Galaxy-X, a novel multi-class classification approach for open-set recognition problems.
For each class of the training set, Galaxy-X creates a ウォールストリートウルフオンライン無料を見る bounding hyper-sphere that encompasses the distribution of the class by enclosing all of its instances.
In such manner, this method is able to distinguish instances resembling previously seen classes from those that are of unknown ones.
To adequately evaluate open-set classification, I will also introduce a novel evaluation procedure.
The presentation will also include an implementation tutorial of Galaxy-X as well as a challenging application example on face classification.
This work has obtained the best paper and best presentation award at the international conference on Machine Learning and Data Mining that was held in New York, NY, 2016.
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