Wednesday, November 25, 2015

Markov Chains

A great explanation: Understanding Markov Chains

See here more publications about Markov Chains

Monday, November 23, 2015

Robotic War

A new paper by Manfred Sneps-Sneppe - Telecommunications and sensors in robotic war

Sunday, November 22, 2015

Find online courses

Friday, November 20, 2015

Mining Groups of Mobile Users

A new paper: Mining Groups of Mobile Users in Int. J. of Wireless and Mobile Computing, 2015 Vol.9, No.3, pp.211 - 217

Monday, November 02, 2015

EmTech 2015

We continue to share links for monitoring the interesting events in Twitter. Now it is EmTech symposium, MIT, Cambridge 2015

Twitter Live Feed: Emtech 2015

/via Bluetooth Data Points

Thursday, October 29, 2015

Data mining first aid

Here is a list of statistical and machine learning concepts that are in every data scientist’s toolbox.

Wednesday, October 21, 2015

On hyper-local web pages

Network proximity and web. How to access to wireless tags from web?

Monday, October 19, 2015

The Physical Web in Smart Cities

Our new paper for RTUWO conference: Dmitry Namiot, Manfred Sneps-Sneppe "The Physical Web in Smart Cities"

In this paper, we discuss the physical web projects based on network proximity for Smart Cities. In general, the Physical Web is an approach for connecting any physical object to the web. With this approach, we can navigate and control physical objects in the world surrounding mobile devices. Alternatively, we can execute services on mobile devices, depending on the surrounding physical objects. Technically, there are different ways to enumerate physical objects. In this paper, we will target the models based on the wireless proximity.

Monday, October 12, 2015

Sunday, September 27, 2015

Network Proximity for Content Discovery

Our new paper: I. D. Yousef and D. Namiot, “Network proximity for content discovery,” International Journal of Interactive Mobile Technologies (iJIM), vol. 9, no. 4, pp. 42–48, 2015.

The paper describes our approach for using wireless sensors on mobile phones for delivering new data to mobile subscribers. We propose a new practical approach for social context-aware data retrieval based on mobile phones as a sensor concept. This approach uses Wi-Fi and Bluetooth modules located on mobile phones as sensors for getting proximity information that can open (discover) access to any user-generated content or content published in the social networks. A special mobile service (context-aware browser client for Android) can present that information to mobile subscribers. The potential use-cases for the proposed approach include all projects associated with hyper-local news data. For example, news services in Smart City projects, proximity marketing, indoor data delivery, etc.

Saturday, September 26, 2015

Meta-Data in SDN API

Our new paper: M. Sneps-Sneppe and D. Namiot, “Metadata in sdn api for wsn,” in New Technologies, Mobility and Security (NTMS), 2015 7th International Conference on, pp. 1–5, IEEE Paris, France, 2015.

This paper discusses the system aspects of the development of applied programming interfaces in Software-Defined Networking (SDN). SDN is a prospect software enablement for Wireless Sensor Networks (WSN). So, application layer SDN API will be the main application API for WSN. Almost all existing SDN interfaces use so-called Representational State Transfer (REST) services as a basic model. This model is simple and straightforward for developers, but often does not support the information (metadata) necessary for programming automation. In this article, we cover the issues of representation of metadata in the SDN API.

Friday, September 25, 2015

On events recognition in optical sensing systems

Aleksey Fedorov, Maxim Anufriev, Andrey Zhirnov, Konstantin Stepanov, Evgeniy Nesterov, Dmitry Namiot, Valery Karasik, Alexey Pnev "Gaussian mixture model for events recognition in optical time-domain reflectometry based sensing systems"

The novel approach for recognition of particular classes of non-conventional events in signals from phase-sensitive optical time-domain reflectometry is proposed. The proposed algorithmic solution is based on the adaptive filtering for de-nosing of signals and Gaussian Mixture Model with the feature space formed by the cepstral coefficients for their clustering. We use experimentally measured signals from phase-sensitive optical time-domain reflectometry based sensing systems for evidence of the suggested algorithm. Our results show that two classes of events can be detected and distinguished between two classes with the probability being close to 0.9. Proposed algorithmic solution can be used in real-time distributed fiber optic sensing systems for control of protected areas.