34,738 research outputs found
Enhancing Workflow with a Semantic Description of Scientific Intent
Peer reviewedPreprin
Metodologia Per la Caratterizzazione di amplificatori a basso rumore per UMTS
In questo lavoro si presenta una metodologia di
progettazione elettronica a livello di sistema,
affrontando il problema della caratterizzazione dello spazio di progetto dell' amplificatore a basso rumore costituente il primo stadio di un front end a conversione diretta per UMTS realizzato in tecnologia CMOS con lunghezza di canale .18u. La metodologia è sviluppata al fine di valutare in modo quantititativo le specifiche ottime di sistema per il front-end stesso e si basa sul concetto di Piattaforma Analogica, che prevede la costruzione di un modello di prestazioni per il blocco analogico basato su
campionamento statistico di indici di prestazioni del blocco stesso, misurati tramite simulazione di dimensionamenti dei componenti attivi e passivi soddisfacenti un set di equazioni specifico della topologia circuitale. Gli indici di prestazioni vengono successivamente ulizzati per parametrizzare modelli comportamentali utilizzati nelle fasi di ottimizzazione a livello di sistema. Modelli comportamentali atti a rappresentare i sistemi RF sono stati pertanto studiati per ottimizzare la scelta delle metriche di prestazioni. L'ottimizzazione dei set di
equazioni atti a selezionare le configurazione di
interesse per il campionamento ha al tempo stesso richiesto l'approfondimento dei modelli di dispositivi attivi validi in tutte le regioni di funzionamento, e lo studio dettagliato della progettazione degli amplificatori a basso rumore basati su degenerazione induttiva. Inoltre,
il problema della modellizzazione a livello di sistema degli effetti della comunicazione tra LNA e Mixer è stato affrontato proponendo e analizzando diverse soluzioni. Il lavoro ha permesso di condurre un'ottimizzazione del front-end UMTS, giungendo a specifiche ottime a livello di sistema per l'amplificatore stesso
Generic Traces and Constraints, GenTra4CP revisited
The generic trace format GenTra4CP has been defined in 2004 with the goal of
becoming a standard trace format for the observation of constraint solvers over
finite domains. It has not been used since. This paper defines the concept of
generic trace formally, based on simple transformations of traces. It then
analyzes, and occasionally corrects, shortcomings of the proposed initial
format and shows the interest that a generic tracer may bring to develop
portable applications or to standardization efforts, in particular in the field
of constraints
A Systematic Approach to Constructing Incremental Topology Control Algorithms Using Graph Transformation
Communication networks form the backbone of our society. Topology control
algorithms optimize the topology of such communication networks. Due to the
importance of communication networks, a topology control algorithm should
guarantee certain required consistency properties (e.g., connectivity of the
topology), while achieving desired optimization properties (e.g., a bounded
number of neighbors). Real-world topologies are dynamic (e.g., because nodes
join, leave, or move within the network), which requires topology control
algorithms to operate in an incremental way, i.e., based on the recently
introduced modifications of a topology. Visual programming and specification
languages are a proven means for specifying the structure as well as
consistency and optimization properties of topologies. In this paper, we
present a novel methodology, based on a visual graph transformation and graph
constraint language, for developing incremental topology control algorithms
that are guaranteed to fulfill a set of specified consistency and optimization
constraints. More specifically, we model the possible modifications of a
topology control algorithm and the environment using graph transformation
rules, and we describe consistency and optimization properties using graph
constraints. On this basis, we apply and extend a well-known constructive
approach to derive refined graph transformation rules that preserve these graph
constraints. We apply our methodology to re-engineer an established topology
control algorithm, kTC, and evaluate it in a network simulation study to show
the practical applicability of our approachComment: This document corresponds to the accepted manuscript of the
referenced journal articl
DataHub: Collaborative Data Science & Dataset Version Management at Scale
Relational databases have limited support for data collaboration, where teams
collaboratively curate and analyze large datasets. Inspired by software version
control systems like git, we propose (a) a dataset version control system,
giving users the ability to create, branch, merge, difference and search large,
divergent collections of datasets, and (b) a platform, DataHub, that gives
users the ability to perform collaborative data analysis building on this
version control system. We outline the challenges in providing dataset version
control at scale.Comment: 7 page
A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
The benefits of automating design cycles for Bayesian inference-based
algorithms are becoming increasingly recognized by the machine learning
community. As a result, interest in probabilistic programming frameworks has
much increased over the past few years. This paper explores a specific
probabilistic programming paradigm, namely message passing in Forney-style
factor graphs (FFGs), in the context of automated design of efficient Bayesian
signal processing algorithms. To this end, we developed "ForneyLab"
(https://github.com/biaslab/ForneyLab.jl) as a Julia toolbox for message
passing-based inference in FFGs. We show by example how ForneyLab enables
automatic derivation of Bayesian signal processing algorithms, including
algorithms for parameter estimation and model comparison. Crucially, due to the
modular makeup of the FFG framework, both the model specification and inference
methods are readily extensible in ForneyLab. In order to test this framework,
we compared variational message passing as implemented by ForneyLab with
automatic differentiation variational inference (ADVI) and Monte Carlo methods
as implemented by state-of-the-art tools "Edward" and "Stan". In terms of
performance, extensibility and stability issues, ForneyLab appears to enjoy an
edge relative to its competitors for automated inference in state-space models.Comment: Accepted for publication in the International Journal of Approximate
Reasonin
Use of induced acceleration to quantify the (de)stabilization effect of external and internal forces on postural responses
Due to the mechanical coupling between the body segments, it is impossible to see with the naked eye the causes of body movements and understand the interaction between movements of different body parts. The goal of this paper is to investigate the use of induced acceleration analysis to reveal the causes of body movements. We derive the analytical equations to calculate induced accelerations and evaluate its potential to study human postural responses to support-surface translations. We measured the kinematic and kinetic responses of a subject to sudden forward and backward translations of a moving platform. The kinematic and kinetics served as input to the induced acceleration analyses. The induced accelerations showed explicitly that the platform acceleration and deceleration contributed to the destabilization and restabilization of standing balance, respectively. Furthermore, the joint torques, coriolis and centrifugal forces caused by swinging of the arms, contributed positively to stabilization of the center of mass. It is concluded that induced acceleration analyses is a valuable tool in understanding balance responses to different kinds of perturbations and may help to identify the causes of movement in different pathologies
Tracking the Tracker from its Passive Sonar ML-PDA Estimates
Target motion analysis with wideband passive sonar has received much
attention. Maximum likelihood probabilistic data-association (ML-PDA)
represents an asymptotically efficient estimator for deterministic target
motion, and is especially well-suited for low-observable targets; the results
presented here apply to situations with higher signal to noise ratio as well,
including of course the situation of a deterministic target observed via clean
measurements without false alarms or missed detections. Here we study the
inverse problem, namely, how to identify the observing platform (following a
two-leg motion model) from the results of the target estimation process, i.e.
the estimated target state and the Fisher information matrix, quantities we
assume an eavesdropper might intercept. We tackle the problem and we present
observability properties, with supporting simulation results.Comment: To appear in IEEE Transactions on Aerospace and Electronic System
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