Network protocol design and evaluation requires either full implementation of the considered protocol and evaluation in a real network, or a simulation based on a model. There is also a middle approach in which both simulation and emulation are used to evaluate a protocol. In this article the Partov engine, which provides both simulation and emulation capabilities simultaneously, is presented. Partov benefits from a layered and platform-independent architecture. As a pure simulator, it provides an extensible plugin-based platform that can be configured to perform both real-time and non-real-time discrete-event simulations. It also acts as an emulator, making interaction with real networks possible in real time. Additionally, a declarative XML-based language is used, acting as a glue between simulation and emulation modules and plugins. It supports dynamic network modelling and simulation based on continuous time Markov chains. Partov is compared with other well-known tools such as NS-3 and real processes such as Hping3. It is shown that Partov requires less overhead and is much more scalable than NS-3.

In this paper we address a seemingly simple question: Is there a universal packet scheduling algorithm? More precisely, we analyze (both theoretically and empirically) whether there is a single packet scheduling algorithm that, at a network-wide level, can match the results of any given scheduling algorithm. We find that in general the answer is “no”. However, we show theoretically that the classical Least Slack Time First (LSTF) scheduling algorithm comes closest to being universal and demonstrate empirically that LSTF can closely, though not perfectly, replay a wide range of scheduling algorithms in realistic network settings. We then evaluate whether LSTF can be used in practice to meet various network-wide objectives by looking at three popular performance metrics (mean FCT, tail packet delays, and fairness); we find that LSTF performs comparable to the state-of-the-art for each of them.

ION conference on inertial measurement systems, navigation and localization.

Demand Response (DR) in residential sector is considered to play a key role in the smart grid framework because of its disproportionate amount of peak energy use and massive integration of distributed local renewable energy generation in conjunction with battery storage devices. In this paper, first a quick overview about residential demand response and its optimization model at single home and multi-home level is presented. Then a description of state-of-the-art optimization methods addressing different aspects of residential DR algorithms such as optimization of schedules for local RE based generation dispatch, battery storage utilization and appliances consumption by considering both cost and comfort, parameters uncertainty modeling, physical based dynamic consumption modeling of various appliances power consumption at single home and aggregated homes/community level are presented. The key issues along with their challenges and opportunities for residential demand response implementation and further research directions are highlighted.

TeraRanger One is the most advanced distance sensor for robotics.

Its performance and lightweight design enable robotics applications that were previously impossible with slower sonar sensors or big heavy lasers. The sensor is fully eye-safe, pre-calibrated and ROS compatible. Simply attach it to your robot and you’re ready to go!

The sensor comes in two case designs but can also be customized in shape and colour for special applications. Please contact us for personalized solutions and configurations.

This paper presents an experimental evaluation of different line extraction algorithms on 2D laser scans for indoor environment. Six popular algorithms in mobile robotics and computer vision are selected and tested. Experiments are performed on 100 real data scans collected in an office environment with a map size of 80m × 50m. Several comparison criteria are proposed and discussed to highlight the advantages and drawbacks of each algorithm, including speed, complexity, correctness and precision. The results of the algorithms are compared with the ground truth using standard statistical methods.

by Truong X. Nghiem, Rahul Mangharam

Peak power consumption is a universal problem across energy control systems in electrical grids, buildings, and industrial automation where the uncoordinated operation of multiple controllers result in temporally correlated electricity demand surges (or peaks). While there exist several different approaches to balance power consumption by load shifting and load shedding, they operate on coarse grained time scales and do not help in de-correlating energy sinks. The Energy System Scheduling Problem is particularly hard due to its binary control variables. Its complexity grows exponentially with the scale of the system, making it impossible to handle systems with more than a few variables.

We developed a scalable approach for fine-grained scheduling of energy control systems that novelly combines techniques from control theory and computer science. The original system with binary control variables are approximated by an averaged system whose inputs are the utilization values of the binary inputs within a given period. The error between the two systems can be bounded, which allows us to derive a safety constraint for the averaged system so that the original system's safety is guaranteed. To further reduce the complexity of the scheduling problem, we abstract the averaged system by a simple single-state single-input dynamical system whose control input is the upper-bound of the total demand of the system. This model abstraction is achieved by extending the concept of simulation relations between transition systems to allow for input constraints between the systems. We developed conditions to test for simulation relations as well as algorithms to compute such a model abstraction. As a consequence, we only need to solve a small linear program to compute an optimal bound of the total demand. The total demand is then broken down, by solving a linear program much smaller than the original program, to individual utilization values of the subsystems, whose actual schedule is then obtained by a low-level scheduling algorithm. Numerical simulations in Matlab show the effectiveness and scalability of our approach.

At Sensors Expo today, Libelium announced the addition of ion selective sensor probes to the Waspmote Smart Water sensor platform, for increased sensitivity and accuracy in water quality monitoring.

The Waspmote Smart Water platform is an ultra low-power sensor node designed for use in rugged environments and deployment in Smart Cities in hard-to-access locations to detect changes and potential risk to public health in real time. Waspmote Smart Water is suitable for potable water monitoring, chemical leakage detection in rivers, remote measurement of swimming pools and spas, corrosion and limescale deposit, fish tank monitoring and seawater pollution levels.

Box Plus/Minus (BPM) is a box score-based metric for evaluating basketball players' quality and contribution to the team. It is the latest version of a stat previously called Advanced Statistical Plus/Minus; it is NOT a version of Adjusted Plus/Minus, which is a play-by-play regression metric.

Glossary

• GP: Games Played
• MPG: Minutes Per Game
• ORPM: Player's estimated on-court impact on team offensive performance, measured in points scored per 100 offensive possessions
• DRPM: Player's estimated on-court impact on team defensive performance, measured in points allowed per 100 defensive possessions
• RPM: Player's estimated on-court impact on team performance, measured in net point differential per 100 offensive and defensive possessions. RPM takes into account teammates, opponents and additional factors
• WAR: The estimated number of team wins attributable to each player, based on RPM

A problem I have faced with xfce on an old HP. Solved in the proposed hacky way.

The need for fast response demand side participation (DSP) has never been greater due to increased wind power penetration. White domestic goods suppliers are currently developing a ‘smart’ chip for a range of domestic appliances (e.g. refrigeration units, tumble dryers and storage heaters) to support the home as a DSP unit in future power systems. This paper presents an aggregated population-based model of a single compressor fridge-freezer. Two scenarios (i.e. energy efficiency class and size) for valley filling and peak shaving are examined to quantify and value DSP savings in 2020. The analysis shows potential peak reductions of 40 MW to 55 MW are achievable in the Single wholesale Electricity Market of Ireland (i.e. the test system), and valley demand increases of up to 30 MW. The study also shows the importance of the control strategy start time and the staggering of the devices to obtain the desired filling or shaving effect.

Plug-loads are often neglected in commercial demand response (DR) despite being a major contributor to building energy consumption. Improvements in technology like smart power strips are prompting the incorporation of plug-loads as a DR resource alongside building HVAC and lighting. Office scale battery storage (OSBS) systems are also candidates as a DR resource due to their ability to run on battery power. In this work, we present a model predictive control (MPC) framework for optimal load-shedding of plug-loads and OSBS.We begin with discussion of the context of this work, and present two models of OSBS systems. A model predictive controller for OSBS and plug-load load-shed scheduling is presented. We discuss casting the MPC as a dynamic program, and an algorithm to solve the dynamic program. Simulation results show the efficacy and utility of dynamic programming, and quantify the performance of OSBS systems.

The performance, reliability, cost, size and energy usage of computing systems can be improved by one or more orders of magnitude by the systematic use of modern control and optimization methods. Computing systems rely on the use of feedback algorithms to schedule tasks, data and resources, but the models that are used to design these algorithms are validated using open-loop metrics. By using closed-loop metrics instead, such as the gap metric developed in the control community, it should be possible to develop improved scheduling algorithms and computing systems that have not been over-engineered. Furthermore, scheduling problems are most naturally formulated as constraint satisfaction or mathematical optimization problems, but these are seldom implemented using state of the art numerical methods, nor do they explicitly take into account the fact that the scheduling problem itself takes time to solve. This paper makes the case that recent results in real-time model predictive control, where optimization problems are solved in order to control a process that evolves in time, are likely to form the basis of scheduling algorithms of the future. We therefore outline some of the research problems and opportunities that could arise by explicitly considering feedback and time when designing optimal scheduling algorithms for computing systems.

Use the measrdroid app

• to collect interesting usage statistics.
• to access raw data from your mobile device.

Measured data will be used for a research project in computer science. They claim that all uploaded data samples are strictly kept private according to their privacy agreement. They do not collect social data.

A report from the first Coderdojo of season 2015-16.

Nice robot for the everyone from Sharp.
Several interesting applications for the everyday life depicted in this video.

zssh (Zmodem SSH) is a program for interactively transferring files to a remote machine while using the secure shell (ssh). It is intended to be a convenient alternative to scp , allowing to transfer files without having to open another session and re-authenticate oneself.

zssh is an interactive wrapper for ssh used to switch the ssh connection between the remote shell and file transfers. This is achieved by using another tty/pty pair between the user and the local ssh process to plug either the user's tty (remote shell mode) or another process (file transfer mode) on the ssh connection.

HandBrake is a tool for converting video from nearly any format to a selection of modern, widely supported codecs. Converts video from nearly any format. With an easy-to-use GUI.

To process a transaction, you need first to make sure the sender owns the asset he wants to transfer, and make sure he will not trade it twice.

In the blockchain, information is stored in blocks that record all transactions ever done through the network. Hence, it allows validating both the existence of assets to be traded and ownership.

To avoid double spending, the technology requests several nodes to agree on a transaction to process it. A validation is also artificially difficult to achieve: miners leverage computer power to solve complex cryptographic problems (the proof-of-work). Every time a problem is cracked, a block is added to the chain, and all the transactions it includes are thus validated. The updated chain, including the new block, is shared with other nodes and becomes the new reference; this process leverages cryptography to prevent duplicate transactions.