An area-efficient 2-D convolution implementation on FPGA for space applications

The 2-D Convolution is an algorithm widely used in image and video processing. Although its computation is simple, its implementation requires a high computational power and an intensive use of memory. Field Programmable Gate Arrays (FPGA) architectures were proposed to accelerate calculations of 2-D Convolution and the use of buffers implemented on FPGAs are used to avoid direct memory access. In this paper we present an implementation of the 2-D Convolution algorithm on a FPGA architecture designed to support this operation in space applications. This proposed solution dramatically decreases the area needed keeping good performance, making it appropriate for embedded systems in critical space application

A 2-digit multidimensional logarithmic number system filterbank processor for a digital hearing aid.

This thesis addresses the design, implementation, and evaluation of a filterbank for digital hearing aids using a Multidimensional Logarithmic Number System (MDLNS). Hearing loss is a function of both frequency and input level. In a typical digital hearing instrument, the hearing loss compensation is performed by separating the incoming sound into several frequency bands which are then compressed to allow the amplification of low level signals while maintaining the amplitude of high level signals. The demands of low power consumption and small size have led to a number of advances in algorithms, semiconductor technologies and system architectures for completely-in-canal (CIC) hearing aid device. Based on research for digital hearing aids that started in the early 1990\u27s, we have developed a new number system (MDLNS) and associated architecture that benefit the digital hearing aid processor in both of these requirements. Although the LNS has been previously considered for digital hearing aid processors, this thesis presents an exploration of the MDLNS for digital hearing aid circuitry. As with the LNS, the MDLNS provides a reduction in the size of the number representation, but the MDLNS promises a lower cost (area.power) implementation of the arithmetic operations required in both the linear and non-linear domains of filtering and compression. In this thesis we discuss an application of the MDLNS on the construction of a finite impulse response FIR filterbank, a major component of digital hearing aid processors. The MDLNS filterbank processor chip was fabricated using a 0.18 micron CMOS technology. After evaluating the MDLNS filterbank and the two state-of-the-art filterbanks using classic binary implementation, we found power, area, and performance of the MDLNS filterbank processor showed competitive results compared to those binary filterbank processors.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .L5. Source: Masters Abstracts International, Volume: 42-02, page: 0647. Adviser: G. A. Jullien. Thesis (M.A.Sc.)--University of Windsor (Canada), 2003

Coherent 100G Nonlinear Compensation with Single-Step Digital Backpropagation

Enhanced-SSFM digital backpropagation (DBP) is experimentally demonstrated and compared to conventional DBP. A 112 Gb/s PM-QPSK signal is transmitted over a 3200 km dispersion-unmanaged link. The intradyne coherent receiver includes single-step digital backpropagation based on the enhanced-SSFM algorithm. In comparison, conventional DBP requires twenty steps to achieve the same performance. An analysis of the computational complexity and structure of the two algorithms reveals that the overall complexity and power consumption of DBP are reduced by a factor of 16 with respect to a conventional implementation, while the computation time is reduced by a factor of 20. As a result, the proposed algorithm enables a practical and effective implementation of DBP in real-time optical receivers, with only a moderate increase of the computational complexity, power consumption, and latency with respect to a simple feed-forward equalizer for dispersion compensation.Comment: This work has been presented at Optical Networks Design & Modeling (ONDM) 2015, Pisa, Italy, May 11-14, 201

Efficient Spectral Power Estimation on an Arbitrary Frequency Scale

The Fast Fourier Transform is a very efficient algorithm for the Fourier spectrum estimation, but has the limitation of a linear frequency scale spectrum, which may not be suitable for every system. For example, audio and speech analysis needs a logarithmic frequency scale due to the characteristic of a human’s ear. The Fast Fourier Transform algorithms are not able to efficiently give the desired results and modified techniques have to be used in this case. In the following text a simple technique using the Goertzel algorithm allowing the evaluation of the power spectra on an arbitrary frequency scale will be introduced. Due to its simplicity the algorithm suffers from imperfections which will be discussed and partially solved in this paper. The implementation into real systems and the impact of quantization errors appeared to be critical and have to be dealt with in special cases. The simple method dealing with the quantization error will also be introduced. Finally, the proposed method will be compared to other methods based on its computational demands and its potential speed

Fast recursive filters for simulating nonlinear dynamic systems

A fast and accurate computational scheme for simulating nonlinear dynamic systems is presented. The scheme assumes that the system can be represented by a combination of components of only two different types: first-order low-pass filters and static nonlinearities. The parameters of these filters and nonlinearities may depend on system variables, and the topology of the system may be complex, including feedback. Several examples taken from neuroscience are given: phototransduction, photopigment bleaching, and spike generation according to the Hodgkin-Huxley equations. The scheme uses two slightly different forms of autoregressive filters, with an implicit delay of zero for feedforward control and an implicit delay of half a sample distance for feedback control. On a fairly complex model of the macaque retinal horizontal cell it computes, for a given level of accuracy, 1-2 orders of magnitude faster than 4th-order Runge-Kutta. The computational scheme has minimal memory requirements, and is also suited for computation on a stream processor, such as a GPU (Graphical Processing Unit).Comment: 20 pages, 8 figures, 1 table. A comparison with 4th-order Runge-Kutta integration shows that the new algorithm is 1-2 orders of magnitude faster. The paper is in press now at Neural Computatio