Class Of Filtering Algorithms Research Articles

Feature selection for airborne LiDAR data filtering: a mutual information method with Parzon window optimization

ABSTRACT Filtering is one of the key steps for Digital Elevation Model (DEM) generation from airborne Light Detection and Ranging (LiDAR) data. Machine-learning-based filters have emerged as a class of filtering algorithms in recent years. Most existing studies mainly focus on feature generation due to limited available features a point cloud possesses. More than 30 features have been described in the existing literature. But most generated features are based on geometric information of points. Several redundant and irrelevant features may not necessarily improve the filtering accuracy. Hence, this paper proposes a feature-selection method using minimal-Redundancy-Maximal-Relevance (mRMR) combined with Parzen window optimization to deal with both discrete and continuous features. An optimal/suboptimal feature subset is constructed for machine-learning filters in various landscapes. Experimental results based on AdaBoost show that height-related features, particularly height itself, are of the greatest significance in both urban and rural scenes. Moreover, different subsets can be selected from the datasets of the two landscapes by our feature-selection strategy, which increases the data relevance for describing each geographical landscape. This study provides guidelines for the selection of optimal/suboptimal features for point cloud filtering based on machine-learning algorithms.

On the Estimation of Parameters of Takagi–Sugeno Fuzzy Filters

This study derives a class of filtering algorithms for Takagi-Sugeno fuzzy models via solving a nonlinear parameters estimation problem. The considered estimation problem is related to the problem of minimizing the expected value of the exponential of filtering errors energy. Under some stochastic assumptions, the filtering criteria (which involve an expectation operator) are replaced by the deterministic quadratic optimization problems whose solutions provide a class of fuzzy filtering algorithms. From a viewpoint of errors in the estimation of linear parameters of the fuzzy filter, the derived filtering algorithms were analyzed with emphasis on stability, robustness, and steady-state error issues. The stability and robustness analyses have been made deterministically without making any assumption.

A Low-Power Heterogeneous Multiprocessor Architecture for Audio Signal Processing

This paper describes a low-power programmable DSP architecture that targets audio signal processing. The architecture can be characterized as a heterogeneous multiprocessor consisting of small instruction set processors called mini-cores as well as standard DSP and CPU cores that communicate using message passing. The mini-cores are tailored for different classes of filtering algorithms (FIR, IIR, N-LMS etc.), and in a typical system the communication among processors occur at the sampling rate only. The mini-cores are intended as soft-macros to be used in the implementation of system-on-chip solutions using a synthesis-based design flow targeting a standard-cell implementation. They are parameterized in word-size, memory-size, etc. and can be instantiated according to the needs of the application. To give an impression of the size of a mini-core we mention that one of the FIR mini-cores in a prototype design has 16 instructions, a 32-word × 16-bit program memory, a 64-word × 16-bit data memory and a 25-word × 16-bit coefficient memory. Results obtained from the design of a prototype chip containing mini-cores for a hearing aid application, demonstrate a power consumption that is only 1.5–1.6 times larger than a hardwired ASIC and more than 6–21 times lower than current state of the art low-power DSP processors. This is due to: (1) the small size of the processors and (2) a smaller instruction count for a given task.