Adaptive Pre- and Post-Filtering for Block-Based Communication Systems

基于块的通信系统的自适应预过滤和后过滤

• 批准号: 0728893
• 负责人: Trac Tran
• 金额: $ 20万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0728893&HistoricalAwards=false
• 关键词: Adaptive; Pre; Post; Filtering; Block

This research involves the theory, design, and efficient implementation of locally adaptive time-domain pre-/post-processing operators for efficient signal representation and robust communications. The time-domain interpretation leads to a powerful signal decomposition and signal reconstruction approach with an unprecedented level of adapting capability. This framework retains all flexible features of block-based approaches and adds on top a high level of adaptivity: data samples are processed with small pre-/post-processing block operators and each can be adapted on-the-fly if necessary. Furthermore, small local operators lend themselves nicely to parallel computing, and fast, VLSI-friendly, possibly multiplierless, implementations. Finally, the fact that time-domain pre-filtering is the closest link to the sensor and post-filtering to the display/renderer allows the highest level of integration flexibility as well as the most economical implementation with minimum software or hardware upgrade. This research is particularly geared toward low-complexity signal coding and communication algorithms or systems in resource-constrained, speed-critical, and real-time applications for wireless hand-held devices. In particular, the investigators study local adaptivity via adaptive Wiener filtering technique, providing the critical bridge between ad-hoc, but effective, pre-/post-filtering approaches and fundamental information-theoretic signal decomposition strategies. Target applications of such time-domain local operators under investigation include: (i) under-sampled pre-filtering and over-sampled post-filtering for low bit-rate coding and fast local compressed sensing;(ii) transform-based multiple description coding; (iii) error-resilient pre-/post-filters in error concealment for packet-switched erasure channels; (iv) optimal transform for distributed source coding in a dense sensor networks; and (v) design and application of adaptive two-dimensional (2-D) non-separable local decomposition for visual data representation and processing.

本研究涉及局部自适应时域预处理/后处理算子的理论、设计和有效实现，以实现有效的信号表示和鲁棒通信。时域解释提供了一种强大的信号分解和重构方法，具有前所未有的自适应能力。该框架保留了基于块的方法的所有灵活特性，并在此基础上增加了高水平的适应性：数据样本使用小的预处理/后处理块操作符进行处理，并且每个操作符都可以在必要时进行动态调整。此外，小型本地运算符很适合并行计算和快速的、对vlsi友好的、可能没有乘数的实现。最后，时域预滤波是与传感器和显示器/渲染器的后滤波最密切的联系，这一事实允许最高水平的集成灵活性以及最经济的实现，最小的软件或硬件升级。这项研究特别针对低复杂性的信号编码和通信算法或系统，在资源受限，速度关键，以及无线手持设备的实时应用。特别是，研究人员通过自适应维纳滤波技术研究了局部自适应性，为自适应的、有效的预/后滤波方法和基本的信息论信号分解策略提供了关键的桥梁。研究中的时域局部算子的目标应用包括：(1)低比特率编码和快速局部压缩感知的欠采样预滤波和过采样后滤波；（ii）基于变换的多重描述编码；（iii）包交换擦除通道错误隐藏中的容错前/后滤波器；（iv）密集传感器网络中分布式源编码的最优变换；(v)设计和应用自适应二维（2-D）不可分局部分解，用于可视化数据表示和处理。