Basic Digital Signal Processing Research Articles

LUAEMA: A Loop Unrolling Approach Extending Memory Accessing for Vector Very-Long-Instruction-Word Digital Signal Processor with Multiple Register Files

Loop unrolling can provide more instruction-level parallelism opportunities for code and enables a greater range of instruction pipeline scheduling. In high-performance very-long-instruction-word (VLIW) digital signal processors (DSPs), there are special registers to address. To further improve the instruction-level parallelism of code for such DSPs by making full use of these registers, in this paper, we propose a more effective loop unrolling approach through extending memory accessing (LUAEMA). In this approach, the final unrolling factor is computed by a model in which every register kind and every memory accessing operation are considered. For basic digital signal processing algorithms, the unrolling factor under the LUAEMA is larger than that under the conventional loop unrolling approach. We also provide the opportunity to reduce the number of instructions in a loop during the code transformation of loop unrolling. The experimental results show that the loop unrolling approach proposed in this paper can achieve an average speedup ratio ranging from 1.14 to 1.81 compared with the conventional loop unrolling approach. For some algorithms, the peak speedup ratio is up to 2.11.

Unfolding-synthesis technique for digital pulse processing, Part 2: Synthesis

Pulse shaping is an important step in the unfolding-synthesis technique. In this paper we present efficient digital pulse-shaping algorithms that utilize repeated sum polynomials. These algorithms address the most common constraint in pulse shape synthesis — the finite duration of the pulses. The presented digital methods for efficient real-time synthesis use only basic digital signal processing functions (addition, constant multiplication, and shift), thereby minimizing required signal processing resources. A differentiation technique to decompose pulse shapes defined by polynomials is presented and used to synthesize arbitrary trapezoidal/triangular pulse shapes. The synthesis of rational, exponential, trigonometric and other non-polynomial defined pulse shapes can be approximated in real time. A methodology to approximate non-polynomial defined pulse shapes is described, and Gaussian and sinusoidal pulse shapes are synthesized via polynomial approximation and linear interpolation. The pulse shape synthesis algorithms are presented in recursive form and are suitable for efficient implementation by using integer only arithmetic.

Arithmetic and Logic Circuits Based on ITO-Stabilized ZnO TFT for Transparent Electronics

In this paper, general logic cell and module designs in basic digital signal processing (DSP), for transparent, flexible chips and wearable electronics are presented. Modified Circuits from ratioed logic and pass transistor logic styles are modified and proposed, based on n-type-only indium tin oxide (ITO) stabilized ZnO thin-film transistors (TFTs) process. Elaborations on logic circuits with purely n-type TFT transistors were carried out to extend the logic swing of circuit outputs and accelerate the signal propagation in complex logic functions with simplified pull-up/pull-down or passive networks. To implement logic complexes on multiple OR-of-ANDs functions with better performance, tailored active controlled ratioed logic style is adopted with faster speed and smaller area by simplifying the transistor networks properly. All the circuits were fabricated on transparent glass plate. The featured 2-input/3-input XOR gate, 4-1 MUX and D flip flop (DFF) can operate with maximum featured delays of 2- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6~\mu \text{s}$ </tex-math></inline-formula> at 5 V power supply, with comparatively over 50% less delays and areas than other state-of-art works. The featured 4-bit adder performs maximum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16.41~\mu \text{s}$ </tex-math></inline-formula> delay at 5 V in measurement. A 4-bit multiplier is also presented based on the adder and DFF. These proposed TFT circuits exhibited smaller area with relatively moderate-high performance in comparison, which were promising building blocks for transparent flexible DSP electronics with low-speed requirements.

Digital Convolution with Digital Signal Processing (DSP)

Fourier theory says that any periodic signal can be created by adding together different sinusoids (of varying frequency, amplitude and phase). In many applications, an unknown analog signal is sampled with an A/D converter and a Fast Fourier Transform (FFT) is performed on the sampled data to determine the underlying sinusoids. In this steps a visual approach based on convolution is used to explain basic Digital Signal Processing (DSP) up to the Discrete Fourier Transform (DFT). The DFT is explained instead of the more commonly used FFT because the DFT is much easier to understand. (The DFT is equivalent to the FFT except the DFT is far less computationally efficient.) In particular, convolution is shown to be the key to understanding basic DSP. Also, some of the concepts are far more intuitive in the frequency-domain vs. the more familiar time-domain.

Implementation of digital signal processing algorithms in biomedical electronics devices on the XILINX FPGA

The process of creating fully digital biomedical diagnostic devices involves deep knowledge in both digital electronics and digital biomedical signals processing [1-3]. The purpose of this paper is development of the engineering approach of hardware implementation of basic digital signal processing algorithms (DSP) applied in biomedical electronic devices based on programmable logic integrated circuits. The processes of hardware implementation of the basic algorithms for digital signal processing (DSP) can be divided into three main steps. At the first step, the amplitude values of the samples of discrete pulse characteristics of the low-pass filter and the Hilbert filter are calculated in the FDATool application of the Matlab. At the next step, the Simulink application performs simulation modeling based on the results of the first step, and at the hardware implementation step, the field programmable gate array Xilinx Spartan 6 is used to simulate DSP circuits in the Xilinx ISE design system, as well as directly measured measurements. The amplitude-modulated oscillations demodulator was chosen as an example of the full-scale implementation of basic DSP algorithms in biomedical devices. Experimental measurements were made of the nonlinear distortion coefficient of the detected (informational) signal for three variants of amplitude modulation demodulators: full-wave, using the square root function and synchronous with phase locked loop . During the experimental study, an optimal modulation factor of 60% was chosen from the point of view of energy efficiency. As the results of simulation and experimental studies show, the synchronous demodulator has turned out to be the best option.The obtained results of the experiment and simulation have confirmed the practical applicability of the proposed embodiment in DSP for use in biomedical electronic devices.

Application of microphone arrays for distant speech capture

Application of microphone arrays and beamforming techniques for speech information collection has significant advantages compared to systems operating with a single microphone. This paper presents a brief overview of microphone array systems for collecting distant speech information. The paper is based on an analysis of publications on the use of microphone arrays for speech information collection tasks, as well as on the author’s experience in the development and practical application of planar microphone arrays. The paper describes the main stages of the development of systems for remote capture of audio information. It provides a review of the main applications of microphone arrays, the basic types of microphone arrays and their features. The bulk of the paper deals with planar microphone arrays. We analyze the work of microphone arrays in different acoustic environments. The paper contains the basic equations for calculating the parameters of equidistant planar microphone arrays. Some methods of designing non-equidistant arrays are also mentioned (a list of references is included). We also provide a list of basic digital signal processing algorithms for planar microphone arrays, as well as a list of references on processing algorithms in the frequency domain. The paper includes a list of foreign companies offering systems based on microphone arrays for a wide range of tasks associated with the processing of speech and audio signals. We describe some state-of-the-art speech information collection systems based on microphone arrays. Some promising directions for the development of speech information collection systems using microphone arrays are described in conclusion. The material of the review is usable in designing of microphone arrays for specific practical applications.

Is complex signal processing for bone conduction hearing aids useful?

ObjectivesTo establish whether complex signal processing is beneficial for users of bone anchored hearing aids.MethodsReview and analysis of two studies from our own group, each comparing a speech processor with basic digital signal processing (either Baha Divino or Baha Intenso) and a processor with complex digital signal processing (either Baha BP100 or Baha BP110 power). The main differences between basic and complex signal processing are the number of audiologist accessible frequency channels and the availability and complexity of the directional multi-microphone noise reduction and loudness compression systems.ResultsBoth studies show a small, statistically non-significant improvement of speech understanding in quiet with the complex digital signal processing. The average improvement for speech in noise is +0.9 dB, if speech and noise are emitted both from the front of the listener. If noise is emitted from the rear and speech from the front of the listener, the advantage of the devices with complex digital signal processing as opposed to those with basic signal processing increases, on average, to +3.2 dB (range +2.3 … +5.1 dB, p ≤ 0.0032).DiscussionComplex digital signal processing does indeed improve speech understanding, especially in noise coming from the rear. This finding has been supported by another study, which has been published recently by a different research group.ConclusionsWhen compared to basic digital signal processing, complex digital signal processing can increase speech understanding of users of bone anchored hearing aids. The benefit is most significant for speech understanding in noise.

DSP for MATLAB™ and LabVIEW™ IV: LMS Adaptive Filtering

Abstract This book is Volume IV of the series DSP for MATLAB™ and LabVIEW™. Volume IV is an introductory treatment of LMS Adaptive Filtering and applications, and covers cost functions, performance surfaces, coefficient perturbation to estimate the gradient, the LMS algorithm, response of the LMS algorithm to narrow-band signals, and various topologies such as ANC (Active Noise Cancelling) or system modeling, Noise Cancellation, Interference Cancellation, Echo Cancellation (with single- and dual-H topologies), and Inverse Filtering/Deconvolution. The entire series consists of four volumes that collectively cover basic digital signal processing in a practical and accessible manner, but which nonetheless include all essential foundation mathematics. As the series title implies, the scripts here will run on both MATLAB™ and LabVIEW™. The text for all volumes contains many examples, and many useful computational scripts, augmented by demonstration scripts and LabVIEW™ Virtual Instruments (VIs) that can be run...

DSP for MATLAB™ and LabVIEW™ III: Digital Filter Design

This book is Volume III of the series DSP for MATLAB™ and LabVIEW™. Volume III covers digital filter design, including the specific topics of FIR design via windowed-ideal-lowpass filter, FIR highpass, bandpass, and bandstop filter design from windowed-ideal lowpass filters, FIR design using the transition-band-optimized Frequency Sampling technique (implemented by Inverse-DFT or Cosine/Sine Summation Formulas), design of equiripple FIRs of all standard types including Hilbert Transformers and Differentiators via the Remez Exchange Algorithm, design of Butterworth, Chebyshev (Types I and II), and Elliptic analog prototype lowpass filters, conversion of analog lowpass prototype filters to highpass, bandpass, and bandstop filters, and conversion of analog filters to digital filters using the Impulse Invariance and Bilinear Transform techniques. Certain filter topologies specific to FIRs are also discussed, as are two simple FIR types, the Comb and Moving Average filters. The entire series consists of four volumes that collectively cover basic digital signal processing in a practical and accessible manner, but which nonetheless include all essential foundation mathematics. As the series title implies, the scripts (of which there are more than 200) described in the text and supplied in code form here will run on both MATLAB™ and LabVIEW™. The text for all volumes contains many examples, and many useful computational scripts, augmented by demonstration scripts and LabVIEW™ Virtual Instruments (VIs) that can be run to illustrate various signal processing concepts graphically on the user's computer screen. Volume I consists of four chapters that collectively set forth a brief overview of the field of digital signal processing, useful signals and concepts (including convolution, recursion, difference equations, LTI systems, etc), conversion from the continuous to discrete domain and back (i.e., analog-to-digital and digital-to-analog conversion), aliasing, the Nyquist rate, normalized frequency, sample rate conversion and Mu-law compression, and signal processing principles including correlation, the correlation sequence, the Real DFT, correlation by convolution, matched filtering, simple FIR filters, and simple IIR filters. Chapter four of Volume I, in particular, provides an intuitive or first principle understanding of how digital filtering and frequency transforms work. Volume II provides detailed coverage of discrete frequency transforms, including a brief overview of common frequency transforms, both discrete and continuous, followed by detailed treatments of the Discrete Time Fourier Transform (DTFT), the z-Transform (including definition and properties, the inverse z-transform, frequency response via z-transform, and alternate filter realization topologies including Direct Form, Direct Form Transposed, Cascade Form, Parallel Form, and Lattice Form), and the Discrete Fourier Transform (DFT) (including Discrete Fourier Series, the DFT-IDFT pair, DFT of common signals, bin width, sampling duration, and sample rate, the FFT, the Goertzel Algorithm, Linear, Periodic, and Circular convolution, DFT Leakage, and computation of the Inverse DFT). Volume IV, the culmination of the series, is an introductory treatment of LMS Adaptive Filtering and applications, and covers cost functions, performance surfaces, coefficient perturbation to estimate the gradient, the LMS algorithm, response of the LMS algorithm to narrow-band signals, and various topologies such as ANC (Active Noise Cancelling) or system modeling, Periodic Signal Removal/Prediction/Adaptive Line Enhancement (ALE), Interference Cancellation, Echo Cancellation (with single- and dual-H topologies), and Inverse Filtering/Deconvolution/Equalization. Table of Contents: Principles of FIR Design / FIR Design Techniques / Classical IIR Design

DSP for MATLAB™ and LabVIEW™ II: Discrete Frequency Transforms

This book is Volume II of the series DSP for MATLAB™ and LabVIEW™. This volume provides detailed coverage of discrete frequency transforms, including a brief overview of common frequency transforms, both discrete and continuous, followed by detailed treatments of the Discrete Time Fourier Transform (DTFT), the z -Transform (including definition and properties, the inverse z -transform, frequency response via z-transform, and alternate filter realization topologies (including Direct Form, Direct Form Transposed, Cascade Form, Parallel Form, and Lattice Form), and the Discrete Fourier Transform (DFT) (including Discrete Fourier Series, the DFT-IDFT pair, DFT of common signals, bin width, sampling duration and sample rate, the FFT, the Goertzel Algorithm, Linear, Periodic, and Circular convolution, DFT Leakage, and computation of the Inverse DFT). The entire series consists of four volumes that collectively cover basic digital signal processing in a practical and accessible manner, but which nonetheless include all essential foundation mathematics. As the series title implies, the scripts (of which there are more than 200) described in the text and supplied in code form here will run on both MATLAB™ and LabVIEW™. The text for all volumes contains many examples, and many useful computational scripts, augmented by demonstration scripts and LabVIEW™ Virtual Instruments (VIs) that can be run to illustrate various signal processing concepts graphically on the user's computer. Volume I consists of four chapters that collectively set forth a brief overview of the field of digital signal processing, useful signals and concepts (including convolution, recursion, difference equations, LTI systems, etc), conversion from the continuous to discrete domain and back (i.e., analog-to-digital and digital-to-analog conversion), aliasing, the Nyquist rate, normalized frequency, sample rate conversion and Mu-law compression, and signal processing principles including correlation, the correlation sequence, the Real DFT, correlation by convolution, matched filtering, simple FIR filters, and simple IIR filters. Chapter 4 of Volume I, in particular, provides an intuitive or "first principle" understanding of how digital filtering and frequency transforms work, preparing the reader for the present volume (Volume II). Volume III of the series covers digital filter design (FIR design using Windowing, Frequency Sampling, and Optimum Equiripple techniques, and Classical IIR design) and Volume IV, the culmination of the series, is an introductory treatment of LMS Adaptive Filtering and applications.

Software-defined radio for undergraduate projects

Software-defined radio (SDR) is a self-contained, embedded software system with hardware components and real-time constraints. SDR is the basis for many of today's wireless communications systems. Because SDR combines basic digital signal processing, circuitry, and software elements, it is the perfect project for multi-disciplinary, undergraduate teams. This paper explains both the SDR system created by computer science and engineering undergraduates at Grove City College and how SDR projects can help undergraduates acquire the skills to succeed on multidisciplinary teams.

Hybrid functional- and instruction-level power modeling for embedded and heterogeneous processor architectures

In this contribution the concept of functional- level power analysis (FLPA) for power estimation of programmable processors is extended in order to model embedded as well as heterogeneous processor architectures featuring different embedded processor cores. The basic FLPA approach is based on the separation of the processor architecture into functional blocks like, e.g. processing unit, clock network, internal memory, etc. The power consumption of these blocks is described by parameterized arithmetic models. By application of a parser based automated analysis of assembler codes the input parameters of the arithmetic functions like e.g. the achieved degree of parallelism or the kind and number of memory accesses can be computed. For modeling an embedded general purpose processor (here, an ARM940T) the basic FLPA modeling concept had to be extended to a so-called hybrid functional-level and instruction-level (FLPA/ILPA) model in order to achieve a good modeling accuracy. In order to show the applicability of this approach even a heterogeneous processor architecture (OMAP5912) featuring an ARM926EJ-S core and a C55x DSP core has been modeled using the hybrid FLPA/ILPA technique described before. The approach is exemplarily demonstrated and evaluated applying a variety of basic digital signal processing tasks ranging from basic filters to complete audio decoders or classical benchmark suits. Estimated power figures for the inspected tasks are compared to physically measured values for both inspected processor architectures. A resulting maximum estimation error of 9% for the ARM940T and less than 4% for the OMAP5912 is achieved.

A Digital Signal Processing Teaching Methodology Using Concept-Mapping Techniques

The main goal of this study is to develop a scientific method for designing a teaching methodology used in a basic digital signal processing (DSP) course. The proposed method is based on concept-mapping techniques, which applies multivariate statistic analysis to summarize the experience and knowledge of teachers involved in basic DSP teaching. As a result, a set of teaching methodologies is obtained. This result, as well as other information obtained related to the relative importance of the concepts to be covered, has been used to program the course. Moreover, different teaching tools have been developed to implement the proposed teaching methodology. Finally, the reliability of the method has been compared with similar studies to validate the proposed methodology.

Customization of a DSP integrated circuit for a consumer product

The design of a toy doll's speech recognition and response circuitry is summarized. The digital signal processing (DSP) system used as the core of this audio processing circuitry is discussed. Hardware used and the basic DSP architecture for the audio processing are reviewed. Two systems considered (DSP with codec and custom DSP chip) are briefly outlined. The speech processing, synthesis, and recognition technologies needed for this toy are detailed. Several DSP algorithms for use with this system are reviewed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>