nodewatcher - A modular open networks growing platform.

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nodewatcher is one of the projects of wlan slovenija open wireless network. Its main goal is the development of an open source network planning, deployment, monitoring and maintanance platform with emphasis on community. This is the development branch with future (3.0) version of nodewatcher which is still being developed and does not have all the functionality found in the 2.0 version. For stable 2.0 version, switch to the master branch.



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Project Byzantium, a working group of HacDC ( is proud to announce the release of v0.5 beta of Byzantium Linux, a live distribution of Linux which makes it fast and easy to construct an ad-hoc wireless mesh network which can augment or replace the existing telecommunications infrastructure in the event that it is knocked offline (for example, due to a natural disaster) or rendered untrustworthy (through widespread surveillance or disconnection by hostile entities). This release was developed in the days following Hurricane Sandy, and was perfected while the core development team was assisting with disaster relief efforts in the Red Hook neighborhood of New York City in November of 2012. Byzantium Linux is designed to run on any x86 computer with at least one 802.11 a/b/g/n wireless interface. Byzantium can be burned to a CD- or DVD-ROM (the .iso image is a bit over 370 megabytes in size), booted from an external hard drive, or can even be installed in parallel with an existing operating system without risk to the user's data and software. Byzantium Linux will act as a node of the mesh and will automatically connect to other mesh nodes and act as an access point for wifi-enabled mobile devices. This release of Byzantium Linux also incorporates seamless interoperability with mesh networks constructed using the Commotion Wireless ( firmware.

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B.A.T.M.A.N. (better approach to mobile ad-hoc networking) is a routing protocol for multi-hop ad-hoc mesh networks.

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t81_558_deep_learning - Washington University (in St

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Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.