Search results for "digital signal"

Measurement uncertainty estimation of a virtual instrument

2002

The law of uncertainty propagation is applied to a PC-based virtual instrument. According to the "guide to the expression of uncertainty in measurement", the measuring model and the various sources of uncertainty have been taken into account. The expressions of the combined output uncertainty in various basic digital signal processing blocks, are obtained starting from each input sample absolute accuracy, without taking into account the parameters which regard the overall behavior of an acquisition board, such as the effective number of bits. In order to verify this procedure, the results obtained by means of the theoretical analysis, are compared with the ones obtained from numerical simul…

Discrete Time Signal Processing Framework with Support Vector Machines

2007

Digital signal processing (DSP) of time series using SVM has been addressed in the literature with a straightforward application of the SVM kernel regression, but the assumption of independently distributed samples in regression models is not fulfilled by a time-series problem. Therefore, a new branch of SVM algorithms has to be developed for the advantageous application of SVM concepts when we process data with underlying time-series structure. In this chapter, we summarize our past, present, and future proposal for the SVM-DSP frame-work, which consists of several principles for creating linear and nonlinear SVM algorithms devoted to DSP problems. First, the statement of linear signal mod…

A new heterogeneous and reconfigurable architecture for image analysis

1993

In the paper a new architecture for image analysis: HERMIA (Heterogeneous and Reconfigurable Machine for Image Analysis) is presented. It has bt:en developed at the University of Palermo, inside the Progetto Finalizzato of the ltalian Council of Researches (CNR): Sistemi informatici e Calcolo Parallelo. The architecture of the HERMIA-machine is reconfigurable, moreover the integration of heterogeneous module, oriented to the solution of specific problems, allows to salve complex problems by search of optimal strategies. Signa! processing units allows the user to handle and integrate multi-sensors signals (from video, scanner, music recorder). Here the generai architecture, the hardware impl…

Self-Organizing Architectures for Digital Signal Processing

2013

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…

Installation and commissioning of the TileCal Read-Out Drivers

2007

TileCal is the hadronic tile calorimeter of the ATLAS experiment at LHC/CERN. The main component of the TileCal back-end electronics is the Read-Out Driver (ROD). The ROD system is placed between the first and the second level trigger and it is the responsible for processing the data gathered by the detector. The principal devices of the RODs are the Digital Signal Processors (DSPs) mounted in the Processing Units (PUs) daughterboards. The architecture and functionality of the RODs are briefly explained. Then, it is presented the ROD system installation in the ATLAS electronics cavern. Currently, the RODs are being used for the detector commissioning. It is detailed the Detector and Verific…

Introduction to Digital Signal Processing

2018

Signal processing deals with the representation, transformation, and manipulation of signals and the information they contain. Typical examples include extracting the pure signals from a mixture observation (a field commonly known as deconvolution) or particular signal (frequency) components from noisy observations (generally known as filtering). This chapter outlines the basics of signal processing and then introduces the more advanced concepts of time‐frequency and time‐scale representations, as well as emerging fields of compressed sensing and multidimensional signal processing. When moving to multidimensional signal processing, a modern approach is taken from the point of view of statis…

A Novel Approach to Introducing Adaptive Filters Based on the LMS Algorithm and Its Variants

2004

This paper presents a new approach to introducing adaptive filters based on the least-mean-square (LMS) algorithm and its variants in an undergraduate course on digital signal processing. Unlike other filters currently taught to undergraduate students, these filters are nonlinear and time variant. This proposal introduces adaptive filtering in the context of a linear time-invariant system using a real problem. In this way, introducing adaptive filters using concepts already familiar to the students motivates their interest through practical application. The key point for this simplification is that the input to the filter is constant so that the adaptive filter becomes linear. Therefore, a …

From Signal Processing to Machine Learning

2018

This chapter reviews the main landmarks of signal processing in the 20th century from the perspective of algorithmic developments. It focuses on cross‐fertilization with the field of statistical (machine) learning in the last decades. In the 21st century, model and data assumptions as well as algorithmic constraints are no longer valid, and the field of machine‐learning signal processing has erupted, with many successful stories to tell. The chapter also focuses on digital signal processing (DSP), which deals with the analysis of digitized and discrete sampled signals. Machine learning is a branch of computer science and artificial intelligence that enables computers to learn from data. Mac…

Optimized FPGA-implementation of quadrature DDS

2003

This paper presents the optimized implementation of high performance quadrature direct digital synthesizers (DDS). Although VLSI designs and optimizations have already been discussed in the literature they may not be successfully translated into an FPGA-based technology. This work examines each phase-to-amplitude mapping technique, such as ROM compression and partitioning techniques and the CORDIC algorithm, and it proposes the most suitable structure for Virtex FPGAs in order to obtain the most efficient implementation in terms of area and throughput.