Fernando A. Mujica

Digital predistortion using direct learning with reduced bandwidth feedback

Digital predistortion (DPD) is a popular approach for linearizing power amplifiers (PAs) in wireless base stations and improving system efficiency. The feedback path in a DPD system typically requires 5x signal bandwidth to accommodate the bandwidth expansion caused by the DPD and nonlinear PA. In this paper, we propose a new approach for adapting DPD parameters using direct learning and reduced bandwidth feedback. The new approach is capable of achieving near full-rate DPD performance and linearization bandwidth with significantly reduced feedback bandwidth. Measurement results on a Doherty PA achieved more than 20 dB corrections over 200 MHz bandwidth for a 2-carrier WCDMA signal spanning 40 MHz with only 81.92 MHz feedback bandwidth.

A new motion parameter estimation algorithm based on the continuous wavelet transform

This paper presents a novel motion parameter estimation (ME) algorithm based on the spatio-temporal continuous wavelet transform (CWT). The multidimensional nature of the CWT allows for the definition of a multitude of energy densities by integrating over a subset of the CWT parameter space. Three energy densities are used to estimate motion parameters by sequentially optimizing a state vector composed of velocity, position, and size parameters. This optimization is performed on a frame-by-frame basis allowing the algorithm to track moving objects. The ME algorithm is designed to address real world challenges encountered in the defense industry and traffic monitoring scenarios, such as attaining robust performance in noise and handling obscuration and crossing object trajectories.

Spatio-temporal wavelet transforms for motion tracking

This paper addresses the problem of detecting and tracking moving objects in digital image sequences. The main goal is to detect and select mobile objects in a scene, construct the trajectories, and eventually reconstruct the target objects or their signatures. It is assumed that the image sequences are acquired from imaging sensors. The method is based on spatio-temporal continuous wavelet transforms, discretized for digital signal analysis. It turns out that the wavelet transform can be used efficiently in a Kalman filtering framework to perform detection and tracking. Several families of wavelets are considered for motion analysis according to the specific spatio-temporal transformation. Their construction is based on mechanical parameters describing uniform motion, translation, rotation, acceleration, and deformation. The main idea is that each kind of motion generates a specific signal transformation, which is analyzed by a suitable family of continuous wavelets. The analysis is therefore associated with a set of operators that describe the signal transformations at hand. These operators are then associated with a set of selectivity criteria. This leads to a set of filters that are tuned to the moving objects of interest.

A DSL customer-premise equipment modem SoC with extended reach/rate for broadband bridging and routing

A highly-integrated DSL modem for residential gateway applications is described. The chip integrates a C62x based DSL PHY, AFE, line driver and receiver, power management, and broadband controller subsystem. A 0.13/spl mu/m 5M CMOS process is used to implement the 2.3W chip. Supplies are 1.5V for digital and 3.3V for analog subsystems.

SpatioTemporal Wavelets: A Group-Theoretic Construction for Motion Estimation and Tracking

This paper presents a new group-theoretic perspective for signal and image analysis. It addresses the problem of motion analysis and trajectory determination. The construction exploits the properties of motion parameters to be structured in Lie algebras and Lie groups. The motion models are provided by the group structure which carries an entire theoretical build-up. This theoretical construction is based on a natural association of Lie group representations, minimum-mean-squared-error estimations, and variational principles of optimality. These concepts naturally provide a corresponding association of tools based on continuous wavelet transforms, Kalman filters, and Lagrangians. These tools result in highly parallelizable algorithms based on FFTs, gradients, and dynamic programming. The core of the construction is made of spatiotemporal continuous wavelets that are tuned to the motion parameters to perform motion estimation. The motion parameters consist of scale, orientation, location, velocity, acceler...

The Lab-In-A-Box project: An Arduino compatible signals and electronics teaching system

The Stanford “Lab-In-A-Box” project comprises an open source hardware and software tool chain for teaching signal processing and analog electronics. It is intended to improve the teaching of these concepts by providing a platform that is more open and understandable and by lowering the economic barriers to students interested in the field. To do this, the Lab-In-A-Box brings a full powered Digital Signal Processor (DSP) core to the popular Arduino microcontroller environment and marries it with a simple to use analog front end (AFE). The software platform provided with the Lab-In-A-Box includes an Arduino-like development environment that facilitates learning and quick development of signal processing applications without abstracting away the intricacies of a practical implementation. This system has been used to create several teaching examples and has been tested in courses at Stanford University.

Robust object tracking in compressed image sequences

Accurate object tracking is important in defense applications where an interceptor missile must hone into a target and track it through the pursuit until the strike occurs. The expense associated with an interceptor missile can be reduced through a distributed processing arrangement where the computing platform on which the tracking algorithm is run resides on the ground, and the interceptor need only carry the sensor and communications equipment as part of its electronics complement. In this arrangement, the sensor images are compressed, transmitted to the ground, and decompressed to facilitate real-time downloading of the data over available bandlimited channels. The tracking algorithm is run on a ground-based computer while tracking results are transmitted back to the interceptor as soon as they become available. Compression and transmission in this scenario introduce distortion. If severe, these distortions can lead to erroneous tracking results. As a consequence, tracking algorithms employed for this purpose must be robust to compression distortions. In this paper we introduced a robust object tracking algorithm based on the continuous wavelet transform. The algorithm processes image sequence data on a frame-by-frame basis, implicitly taking advantage of temporal history and spatial frame filtering to reduce the impact of compression artifacts. Test results show that tracking performance can be maintained at low transmission bit rates and can be used reliably in conjunction with many well-known image compression algorithms.

Spatiotemporal continuous wavelets applied to missile warhead detection and tracking

This paper addresses the problem of tracking a ballistic missile warhead. In this scenario, the ballistic missile is assumed to be fragmented into many pieces. The goal of the algorithm presented here is to track the warhead that is among the fragments. It is assumed that images are acquired from an optical sensor located in the interceptor nose cone. This imagery is used by the algorithm to steer the course of interception. The algorithm proposed in this paper is based on continuous spatio-temporal wavelet transforms (CWTs). Two different energy densities of the CWT are used to perform velocity detection and filtering. Additional post-processing is applied to discriminate among objects traveling at similar velocities. Particular attention is given to achieving robust performance on noisy sensor data and under conditions of temporary occlusions. First we introduce the spatio-temporal CWT and stress the relationships with classical orientation filters. Then we describe the CWT- based algorithm for target tracking, and present results on synthetically generated sequences.

Teaching digital signal processing with Stanford's Lab-in-a-Box

This paper describes our efforts to include a hands-on component in the teaching of core concepts of digital signal processing. The basis of our approach was the low-cost and open-source “Stanford Lab in a Box.” This system, with its easy to use Arduino-like programming interface allowed students to see how fundamental DSP concepts such as digital filters, FFT, and multi-rate processing can be implemented in real time on a fixed-point processor. The paper describes how the Lab in a Box was used to provide a new dimension to the teaching of DSP.

Digital timing recovery for communication systems

The paper presents a novel digital timing recovery mechanism for communication systems. The proposed approach jitters the ADC clock based on phase information obtained directly or indirectly from the received signal. The paper studies the effect of jitter resolution and its distribution on the timing SNR. It is demonstrated through simulations that the best SNR is achieved when the jitters are equally spaced. The proposed timing recovery scheme can be implemented by means of a numerically controlled oscillator (NCO), a tapped delay line, or a combination of the two. Practical aspects of all these schemes are presented. The resulting mechanism is able to achieve voltage controlled crystal oscillator (VCXO)-like performance at a much lower implementation cost. Although the proposed methodology can be applied to most communication systems, we focus on its application to asymmetric digital subscriber line (ADSL) modems.