This paper presents a high-level synthesis tool for /spl Sigma//spl Delta/ modulators (/spl Sigma//spl Delta/Ms) that combines an accurate SIMULINK-based time-domain behavioral simulator with a statistical optimization core. Three different circuit techniques for the modulator implementation are considered: switched-capacitor, switched-current and continuous-time. The behavioral models of these circuits, that take into account the most critical limiting factors, have been incorporated into the SIMULINK environment by using S-function blocks, which drastically increase the computational efficiency. The precision of these models has been validated by electrical simulations using HSPICE and experimental measurements from several silicon prototypes. The combination of high accuracy, short CPU time and interoperability of different circuit models together with the efficiency of the optimization engine makes the proposed tool an advantageous alternative for /spl Sigma//spl Delta/M synthesis. The implementation on the well-known MATLAB/SIMULINK platform brings numerous advantages in terms of data manipulation, processing capabilities, flexibility and simulation with other electronic subsystems. Moreover, this is the first tool dealing with the synthesis of /spl Sigma//spl Delta/Ms using both discrete-time and continuous-time circuit techniques.

Click on the title to browse this book

A study is presented into the transient response of switched capacitor (SC) integrators considering amplifier finite bandwidth, slew-rate, and parasitic capacitors during, unlike previous models, both the integration and sampling phases. The model is validated by experimental results on a second-order ΣΔ modulator and provides more reliable estimations of the defective settling in high-speed designs than previously reported models.

The capture, processing and distribution of visual information is one of the major challenges for the paradigm of the Internet of Things. Privacy emerges as a fundamental barrier to overcome. The idea of networked image sensors pervasively collecting data generates social rejection in the face of sensitive information being tampered by hackers or misused by legitimate users. Power consumption also constitutes a crucial aspect. Images contain a massive amount of data to be processed under strict timing requirements, demanding high-performance vision systems. In this paper, we describe a hardware-based strategy to concurrently address these two key issues. By conveying processing capabilities to the focal plane in addition to sensing, we can implement privacy protection measures just at the point where sensitive data are generated. Furthermore, such measures can be tailored for efficiently reducing the computational load of subsequent processing stages. As a proof of concept, a full-custom QVGA vision sensor chip is presented. It incorporates a mixed-signal focal-plane sensing-processing array providing programmable pixelation of multiple image regions in parallel. In addition to this functionality, the sensor exploits reconfigurability to implement other processing primitives, namely block-wise dynamic range adaptation, integral image computation and multi-resolution filtering. The proposed circuitry is also suitable to build a granular space, becoming the raw material for subsequent feature extraction and recognition of categorized objects.