Search results for " Network"
showing 10 items of 6428 documents
GaN and SiC Device Characterization by a Dedicated Embedded Measurement System
2023
This work proposes a comparison among GaN and SiC device main parameters measured with a dedicated and low-cost embedded system, employing an STM32 microcontroller designed to the purpose. The system has the advantage to avoid the use of expensive laboratory measurement equipment to test the devices, allowing to obtain their behavior in operating conditions. The following KPIs (Key Performance Indicators) are measured and critically compared: threshold voltage, on-resistance and input capacitance. All the measurements are carried out in a short time interval and on a wide range of switching frequencies, ranging from 10 kHz to 1 MHz. This investigation is focused on the deviation of the figu…
Sicilian Network for Inflammatory Bowel Disease (SN-IBD). A propensity score-matched comparison of infliximab and adalimumab in naïve and non-naïve p…
2018
Background: In the absence of head-to-head trials, there is an unmeet need to better understand the relative effectiveness of different biologics in inflammatory bowel disease (IBD). The Sicilian Network for Inflammatory Bowel Disease (SN-IBD) is a group composed by all Sicilian centres which continuously enter in a web-based software all clinical data of IBD patients treated with biologics. Methods: Data of all incident Crohn’s disease (CD) patients treated with infliximab (IFX) and adalimumab (ADA) from January 2013 to April 2017 were extracted from the cohort of SN-IBD. Patients were divided in biologic-naïve and non-naïve, and the two groups were analysed singularly. We used a one-to-tw…
Dynamic Regret Analysis for Online Tracking of Time-varying Structural Equation Model Topologies
2020
Identifying dependencies among variables in a complex system is an important problem in network science. Structural equation models (SEM) have been used widely in many fields for topology inference, because they are tractable and incorporate exogenous influences in the model. Topology identification based on static SEM is useful in stationary environments; however, in many applications a time-varying underlying topology is sought. This paper presents an online algorithm to track sparse time-varying topologies in dynamic environments and most importantly, performs a detailed analysis on the performance guarantees. The tracking capability is characterized in terms of a bound on the dynamic re…
Location-Free Spectrum Cartography
2019
Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals, as occurs in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps rel…
Non-cooperative Aerial Base Station Placement via Stochastic Optimization
2019
Autonomous unmanned aerial vehicles (UAVs) with on-board base station equipment can potentially provide connectivity in areas where the terrestrial infrastructure is overloaded, damaged, or absent. Use cases comprise emergency response, wildfire suppression, surveillance, and cellular communications in crowded events to name a few. A central problem to enable this technology is to place such aerial base stations (AirBSs) in locations that approximately optimize the relevant communication metrics. To alleviate the limitations of existing algorithms, which require intensive and reliable communications among AirBSs or between the AirBSs and a central controller, this paper leverages stochastic…
Dynamic network identification from non-stationary vector autoregressive time series
2018
Learning the dynamics of complex systems features a large number of applications in data science. Graph-based modeling and inference underpins the most prominent family of approaches to learn complex dynamics due to their ability to capture the intrinsic sparsity of direct interactions in such systems. They also provide the user with interpretable graphs that unveil behavioral patterns and changes. To cope with the time-varying nature of interactions, this paper develops an estimation criterion and a solver to learn the parameters of a time-varying vector autoregressive model supported on a network of time series. The notion of local breakpoint is proposed to accommodate changes at individu…
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
A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks
2019
International audience; In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme, that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink)…
Nonlinear Distribution Regression for Remote Sensing Applications
2020
In many remote sensing applications, one wants to estimate variables or parameters of interest from observations. When the target variable is available at a resolution that matches the remote sensing observations, standard algorithms, such as neural networks, random forests, or the Gaussian processes, are readily available to relate the two. However, we often encounter situations where the target variable is only available at the group level, i.e., collectively associated with a number of remotely sensed observations. This problem setting is known in statistics and machine learning as multiple instance learning (MIL) or distribution regression (DR). This article introduces a nonlinear (kern…
Inference of Spatio-Temporal Functions over Graphs via Multi-Kernel Kriged Kalman Filtering
2018
Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes, given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel-based kriged Kalman filter that accounts for the spatio-temporal variations, and offers efficient online reconstruction, even for dynamically evolving network topologies. The kernel-based learning framework bypasses the need for statistical information by capitalizing on the smoothness that graph signals exhibit with respect to the underlying graph. To address the challenge o…