Search results for "Software"
showing 10 items of 7396 documents
Implementing Immersive Clustering with VR Juggler
2005
Continuous, rapid improvements in commodity hardware have allowed users of immersive visualization to employ high-quality graphics hardware, high-speed processors, and significant amounts of memory for much lower costs than would be possible with high-end, shared memory computers traditionally used for such purposes. Mimicking the features of a single shared memory computer requires that the commodity computers act in concert—namely, as a tightly synchronized cluster. In this paper, we describe the clustering infrastructure of VR Juggler that enables the use of distributed and clustered computers for the display of immersive virtual environments. We discuss each of the potential ways to syn…
A Lightweight Software Architecture for Robot Navigation and Visual Logging through Environmental Landmarks Recognition
2006
A robot architecture with real-time performance in navigation tasks is presented. The system architecture is multi-threaded with shared memory and fast message passing through static signalling. In this paper, we focused on the reactive layer components and its straightforward implementation. The proposed architecture is described with reference to an experimental setup, in which the robot task is visual logging of environmental landmarks detected on the basis of sensor readings. Our experimental results show how the robot is able to identify, make snapshots and log a set of landmarks by matching 2D geometric patterns.
Iconic framework for cooperative coding
2018
The description of an innovative framework built on top of Web-based visual programming environment is the primary aim of this contribution. In the last decade, many frameworks oriented to visual languages have been introduced in literature to improve the skill on programming languages, but at the best of our knowledge, no framework has been specially designed to support collaborative work on heterogeneous distributed environments. Therefore, SIRENE introduces a new framework in which beginners and experts can cooperate to develop algorithms by using a visual and iconic paradigm. Students, in the classroom or connected from everywhere, can be involved into the definition of the algorithm, c…
Local Leadership and Its Limits in the Deployment of Sustainable Mobility Policies
2020
The indisputable need for new urban mobility policies, which has already been recognised in numerous international, European and national legal instruments, undoubtedly requires a local role in the redefinition of these policies. This action must be carried out in a regulatory context that does not always provide local authorities with all the legal instruments to do so. In this contribution, the primary measures being used to achieve greater sustainability of urban mobility are outlined, as well as a description of the effective room for manoeuvre local authorities normally have, analysing the current limitations they face for a more ambitious deployment and possible models of alternative …
The effects of using participatory working time scheduling software on working hour characteristics and wellbeing : a quasi-experimental study of irr…
2020
Background: Studies in the health care sector indicate that good work time control is associated with better perceived wellbeing but also with non-ergonomic work schedules, such as compressed work schedules. Participatory working time scheduling is a collaborative approach to scheduling shift work. Currently, there is a lack of information on whether working hour characteristics and employees' wellbeing in irregular shift work change after implementing participatory working time scheduling. Objective: To investigate the effects of using digital participatory working time scheduling software on working hour characteristics and well-being among Finnish hospital employees. Participants and met…
A New Model for Sigma-Delta Modulator Oriented to Digitally Controlled DC/DC Converter
2007
Recent research activities have shown the feasibility and advantages of using digital controller ICs specifically developed for high-frequency switching converters, highlighting a challenging future trend in Switched-mode power supplies (SMPS) applications. Up to a few years ago, the application of digital control for SMPS was impractical due to the high cost and low performance of DSP and microcontroller systems, even if the advantages that digital controllers offer were well known, such as immunity to analog component variations and ability to implement sophisticated control schemes and system diagnostics. Digital controller ICs potentially offer other advantages from the integrated desig…
Deep Completion Autoencoders for Radio Map Estimation
2022
Radio maps provide metrics such as power spectral density for every location in a geographic area and find numerous applications such as UAV communications, interference control, spectrum management, resource allocation, and network planning to name a few. Radio maps are constructed from measurements collected by spectrum sensors distributed across space. Since radio maps are complicated functions of the spatial coordinates due to the nature of electromagnetic wave propagation, model-free approaches are strongly motivated. Nevertheless, all existing schemes for radio occupancy map estimation rely on interpolation algorithms unable to learn from experience. In contrast, this paper proposes a…
Physics-aware Gaussian processes in remote sensing
2018
Abstract Earth observation from satellite sensory data poses challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression has excelled in biophysical parameter estimation tasks from airborne and satellite observations. GP regression is based on solid Bayesian statistics, and generally yields efficient and accurate parameter estimates. However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only. Very often a forward model encoding the well-understood physical relations between the state vector and the radiance observations is available though and could be useful to improve pre…
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval
2016
Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes. However, they face difficulties in coping with large training data sets. With the increasing amount of optical remote sensing data made available for analysis and the possibility of using a large amount of simulated data from radiative transfer models (RTMs) to train kernel MLRAs, efficient data reduction techniques will need to be implemented. Active learning (AL) methods enable to select the most informative samples in a data set. This letter introduces six AL methods for achieving optimized biophysical variable estimat…
Rapid parameter estimation of discrete decaying signals using autoencoder networks
2021
Machine learning: science and technology 2(4), 045024 (2021). doi:10.1088/2632-2153/ac1eea