Scenes in your Smart Home The smart home aims to simplify and improve the experience of those who occupy it by means of a system of technologies and components. The scenario, understood as a set of pre-set actions and commands to be performed for a purpose, is its maximum expression. The most famous are those linked to the entry/exit of the house and to the alternation of the occupants' moments of wakefulness and rest.

Openhab, unlike HomeAssistant, doesn’t offer a lot of security features or deployment prebuilt options for hardening out of the box. Through these steps you can deploy in minutes an Openhab instance in your home lab in a safe way, with automatic HTTPS and authentication for users outside the LAN. 1 Target After few steps you will have a full working Openhab instance in your home lab, secured with a rock-stable reverse proxy, a simple way to obtain and maintain secure HTTPS connections and username-password authentication for users outside the LAN.

You can deploy any kind of tool in your smart home lab, but if you work on it during holidays, nights, post work hours, for sure, you will forget every time the way you choose to access to your services (ip, port, name…).

Is there an easy way to handle, collect and secure these services in just few minutes?

Implementation of the operation of PREFIX SUM inclusive in NvidiaCuda environment. Objective A C-Cuda program is supplied in which to insert the kernel to process the vector PREFIX SUM inclusive of a vector supplied in input. The developed kernel will have to use Shuffle operations. The program will therefore be compatible with Cuda devices with Compute Capabilities not lower than 3.0. The PREFIX SUM problem has long been considered as an example of how an apparently serial problem can be parallelized.

Target The goal is to optimize and parallelize a program provided in C for handwriting recognition by means of a back-propagation neural network. It is required that a parallelized version be produced on a processor that takes full advantage of the OpenMP compiler library and one that takes advantage of Nvidia Cuda technology. It is also required that particular attention be paid to memory access in the Cuda version. Environment The parallel version of the OpenMP program was achieved by developing and testing on a computer equipped with an Intel i7700k Kaby Lake and an ATI R9 290 video card (which is why the CUDA version was developed on another system).

Requirements Create a client-server application in order to monitor network nodes and centrally collect reports and logs. Server Requirements Functional The system must allow the monitoring of events and configurations of nodes (events to be detected) through a single centre. The server must be the controller of the entire network. The information received from the agents must be periodically updated (polling) Optional: The communication must be real time, continuous.

Abstract From the beginning of Computer Science the cache was the simplest and fastest way to speedup a system that use permanent data. Today, most of services are going to web and cloud architecture, and cache needs to be updated to new way of use resources. The most famous tool to do it is Memcached [2], written in C, it’s a simple Client-Server application that stores Key-Value elements in RAM and exposes a TCP interface to get and set it.