Number Of Filtering Operations Research Articles

Glaucoma laser and surgical procedure rates in Australia

AimTo determine trends in the number of glaucoma laser and surgical procedures performed in Australia between 1994 and 2014.MethodsMedicare claims were analysed to determine the number of glaucoma-related procedures reimbursed...

Multiplierless filter‐bank based multicarrier system by using canonical signed digit representation

ABSTRACTThe filter‐bank based multicarrier (FBMC) system is a candidate for designing the physical layer of a cognitive radio because of its spectral efficiency and the spectral containment. The main drawback of such a system compared with orthogonal frequency division multiplexing systems is its high computation complexity, because each subcarrier is shaped by a non‐rectangular prototype filter. Although poly‐phase decomposition is suggested to decrease the sampling rate of filtering, the number of filtering operations (multiplications) is dramatically increased by the growing number of subcarriers and the amount of the desired spectral containment. Hence, hardware implementation of the FBMC system faces challenges such as high electrical power consumption and large silicon area occupation. In order to reduce computational complexity, a multiplierless filter design based on the canonical signed digit (CSD) representation is proposed. In this technique, at first, a prototype filter is designed. Then a pre‐optimization step is employed to adapt the prototype filter coefficients to system objectives and produce an enriched initial seed for genetic algorithm (GA) optimization. Finally, a customized GA is employed to jointly optimize and synthesize filter coefficients into the finite precision CSD representation so that the system objectives such as intersymbol‐interference, interchannel‐interference, and stop‐band attenuation remain unchanged against the full‐precision representation of coefficients. Copyright © 2014 John Wiley & Sons, Ltd.

Headphone-based Spatial Sound with a GPU Accelerator

Multichannel acoustic signal processing has undergone major development in recent years. The incorporation of spatial information into an immersive audiovisual virtual environment or into video games provides better sense of “presence” to applications. Spatial sound consists in reproducing audio signals with spatial cues (spatial information embedded in the sound) through headphones. This spatial information allows the listener to identify the virtual positions of the sources corresponding to different sounds. Headphone-based spatial sound is obtained by filtering different sound sources through special filters called Head Related Transfer Functions (HRTFs) prior to render them through headphones. Efficient computation plays an important role when the number of sources to be managed is high. This situation increases the number of filtering operations, requiring high computing capacity specially when the virtual sources are moving. Graphics Processing Units (GPUs) are high parallel programmable co-processors that provide massive computation when the needed operations are properly parallelized. This paper discusses the design, the implementation and the performance of a headphone-based spatial audio application whose processing is totally carried out on a GPU. This application is able to interact with the listener who can select and change the location of the sound sources in real-time. This work analyzes also specific computational aspects inside the CUDA environment in order to successfully exploit GPU resources. Results show that the proposed application is able to move up to 2500 sources simultaneously, while leaving free CPU resources for other tasks. This work emphasizes the importance of analyzing all CUDA aspects, since they can influence drastically the performance.

Generalized feature extraction using expansion matching

A novel generalized feature extraction method based on the expansion matching (EXM) method and on the Karhunen-Loeve transform (KLT) is presented. The method provides an efficient way to locate complex features of interest like corners and junctions with reduced number of filtering operations. The EXM method is used to design optimal detectors for a set of model elementary features. The KL representation of these model EXM detectors is used to filter the image and detect candidate interest points from the energy peaks of the eigen coefficients. The KL coefficients at these candidate points are then used to efficiently reconstruct the response and differentiate real junctions and corners from arbitrary features in the image. The method is robust to additive noise and is able to successfully extract, classify, and find the myriad compositions of corner and junction features formed by combinations of two or more edges or lines. This method differs from previous works in several aspects. First, it treats the features not as distinct entities, but as combinations of elementary features. Second, it employs an optimal set of elementary feature detectors based on the EM approach. Third, the method incorporates a significant reduction in computational complexity by representing a large set of EXM filters by a relatively small number of eigen filters derived by the KL transform of the basic EXM filter set. This is a novel application of the KL transform, which is usually employed to represent signals and not impulse responses as in our present work.