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Designing networks for IoT sensors can be a learning process

#artificialintelligence

In Boulder, Colo., network-connected stoplights can be adjusted to allow emergency vehicles to have free access when needed. The city uses "smart signs" connected to servers that count the cars that go in and out of parking garages and post messages when spots are filling up. And in the public library, staff members can walk up and down aisles with a wand that will tell them if a book is missing or placed in the wrong spot. These are just a few examples of the various Internet of Things (IoT) sensors and other connected devices in Boulder, where electrical, solar and HVAC systems are also tied into IP networks. Designing a wireless network to support these applications was a learning process for the city's IT department, says Benjamin Edelen, a senior system administrator there.


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Machine Learning with Signal Processing Techniques

#artificialintelligence

Stochastic Signal Analysis is a field of science concerned with the processing, modification and analysis of (stochastic) signals. Anyone with a background in Physics or Engineering knows to some degree about signal analysis techniques, what these technique are and how they can be used to analyze, model and classify signals. Data Scientists coming from a different fields, like Computer Science or Statistics, might not be aware of the analytical power these techniques bring with them. In this blog post, we will have a look at how we can use Stochastic Signal Analysis techniques, in combination with traditional Machine Learning Classifiers for accurate classification and modelling of time-series and signals. At the end of the blog-post you should be able understand the various signal-processing techniques which can be used to retrieve features from signals and be able to classify ECG signals (and even identify a personby their ECG signal), predict seizures from EEG signals, classify and identify targets in radar signals, identify patients with neuropathy or myopathyetc from EMG signals by using the FFT, etc etc.


Machine Learning with Signal Processing Techniques

@machinelearnbot

Stochastic Signal Analysis is a field of science concerned with the processing, modification and analysis of (stochastic) signals. Anyone with a background in Physics or Engineering knows to some degree about signal analysis techniques, what these technique are and how they can be used to analyze, model and classify signals. Data Scientists coming from a different fields, like Computer Science or Statistics, might not be aware of the analytical power these techniques bring with them. In this blog post, we will have a look at how we can use Stochastic Signal Analysis techniques, in combination with traditional Machine Learning Classifiers for accurate classification and modelling of time-series and signals. At the end of the blog-post you should be able understand the various signal-processing techniques which can be used to retrieve features from signals and be able to classify ECG signals (and even identify a person by their ECG signal), predict seizures from EEG signals, classify and identify targets in radar signals, identify patients with neuropathy or myopathyetc from EMG signals by using the FFT, etc etc.