Analog to Digital Conversion and Data Compression

为将模拟数据转换为数字表示的系统开发了基本理论和设计算法。主要关注的例子是过采样模数转换器（ADC）和矢量量化系统。后者强调低速率通信和数据存储系统，PI在过去做出了重大贡献。前者强调了最近开发的高速率ADC架构，通过使用采样率远高于奈奎斯特率的标量反馈二进制量化器。另一个研究课题是这些系统之间的相互作用，例如使用过采样ADC前端到使用矢量量化的数据压缩系统。本工作的主要应用领域是语音传输与识别。信号主要以模拟形式存在，因此在进行数字处理之前，必须对信号进行采样，并通过称为量化的过程将其转换为有限长度的计算机字。该任务由所谓的模数转换器（ADC）执行。PI为过采样ADC的分析带来了新的数学方法，从实现的角度来看，过采样ADC是一类特别有吸引力的量化器。

Both fundamental theory and design algorithms are developed for systems which convert analog data into digital representation. The examples of principal interest are oversampled analog-to-digital converters (ADC) and vector quantization systems. The latter emphasizes low-rate communications and data storage systems where the PI has made significant contributions in the past. The former emphasizes recently developed architectures for high rate ADC by using scalar feedback binary quantizers at a sampling rate much higher than the Nyquist rate. An additional research topic is the interplay between these systems, e. g. the use of an oversampled ADC front end to a data compression system using vector quantization. The main application area for this work is speech transmission and recognition. Signals exist mainly in analog form, thus prior to digital processing they must be sampled and converted to finite-length computer words by a process called quantization. This task is performed by the so-called analog-to-digital converter (ADC). The PI brings new mathematical methods to bear on the analysis of oversampled ADC's, a particularly attractive class of quantizers from the implementation point of view.