A formula is derived and demonstrated that is capable of directly generating digital filter coefficients from an analog filter prototype using the bilinear transformation. This formula obviates the need for any algebraic manipulation of the analog prototype filter and is ideal for use in embedded systems that must take in any general analog filter specification and dynamically generate digital filter coefficients directly usable in difference equations.
Efficient convolutional coding and decoding algorithms are most crucial to successful operation of wireless communication systems in order to achieve high quality of service by reducing the overall bit error rate performance. A widely applied and well evaluated scheme for error correction purposes is well known as Viterbi algorithm . Although the Viterbi algorithm has very good error correcting characteristics, computational effort required remains high. In this paper a novel approach is discussed introducing a convolutional decoder design based on fuzzy logic. A simplified version of this fuzzy based decoder is examined with respect to bit error rate (BER) performance. It can be shown that the fuzzy based convolutional decoder here proposed considerably reduces computational effort with only minor BER performance degradation when compared to the classical Viterbi approach.
This paper describes a simple method to calculate the invers of a complex matrix. The key element of the method is to use a matrix inversion, which is available and optimised for real numbers. Some actual libraries used for digital signal processing only provide highly optimised methods to calculate the inverse of a real matrix, whereas no solution for complex matrices are available, like in . The presented algorithm is very easy to implement, while still much more efficient than for example the method presented in .  Visual DSP++ 4.0 C/C++ Compiler and Library Manual for TigerSHARC Processors; Analog Devices; 2005.  W. Press, S.A. Teukolsky, W.T. Vetterling, B.R. Flannery; Numerical Recipes in C++, The art of scientific computing, Second Edition; p52 : “Complex Systems of Equations”;Cambridge University Press 2002.
In recent years, the amount of digital data which is stored and transmitted for private and public usage has increased considerably. To allow a save transmission and storage of data despite of error-prone transmission media, error correcting codes are used. A large variety of codes has been developed, and in the past decade low-density parity-check (LDPC) codes which have an excellent error correction performance became more and more popular. Today, low-density parity-check codes have been adopted for several standards, and eﬃcient decoder hardware architectures are known for the chosen structured codes. However, the existing decoder designs lack ﬂexibility as only few structured codes can be decoded with one decoder chip. In consequence, diﬀerent codes require a redesign of the decoder, and few solutions exist for decoding of codes which are not quasi-cyclic or which are unstructured. In this thesis, three diﬀerent approaches are presented for the implementation of fully programmable LDPC decoders which can decode arbitrary LDPC codes. As a design study, the ﬁrst programmable decoder which uses a heuristic mapping algorithm is realized on an ﬁeld-programmable gate array (FPGA), and error correction curves are measured to verify the correct functionality. The main contribution of this thesis lies in the development of the second and the third architecture and an appropriate mapping algorithm. The proposed fully programmable decoder architectures use one-phase message passing and layered decoding and can decode arbitrary LDPC codes using an optimum mapping and scheduling algorithm. The presented programmable architectures are in fact generalized decoder architectures from which the known decoders architectures for structured LDPC codes can be derived.
In this thesis, the design of a music synthesizer implementing the Scalable Polyphony-MIDI soundset on a low cost DSP system is presented. First, the SP-MIDI standard and the target DSP platform are presented followed by review of commonly used synthesis techniques and their applicability to systems with limited computational and memory resources. Next, various oscillator and ﬁlter algorithms used in digital subtractive synthesis are reviewed in detail. Special attention is given to the aliasing problem caused by discontinuities in classical waveforms, such as sawtooth and pulse waves and existing methods for bandlimited waveform synthesis are presented. This is followed by review of established structures for computationally efﬁcient time-varying ﬁlters. A novel digital structure is presented that decouples the cutoff and resonance controls. The new structure is based on the analog Korg MS-20 lowpass ﬁlter and is computationally very efﬁcient and well suited for implementation on low bitdepth architectures. Finally, implementation issues are discussed with emphasis on the Differentiated Parabole Wave oscillator and MS-20 ﬁlter structures and the effects of limited computational capability and low bitdepth. This is followed by designs for several example instruments.
The aim of this project consists in the FPGA design and implementation of a transmitter and receiver (Tx/Rx) multicarrier system such the Orthogonal Frequency Division Multiplexing (OFDM). This Tx/Rx OFDM subsystem is capable to deal with with different M-QAM modulations and is implemented in a digital signal processor (DSP-FPGA). The implementation of the Tx/Rx subsystem has been carried out in a FPGA using both System Generator visual programming running over Matlab/Simulink, and the Xilinx ISE program which uses VHDL language. This project is divided into four chapters, each one with a concrete objective. The first chapter is a brief introduction to the digital signal processor used, a field-programmable gate array (FPGA), and to the VHDL programming language. The second chapter is an overview on OFDM, its main advantages and disadvantages in front of previous systems, and a brief description of the different blocks composing the OFDM system. Chapter three provides the implementation details for each of these blocks, and also there is a brief explanation on the theory behind each of the OFDM blocks to provide a better comprehension on its implementation. The fourth chapter is focused, on the one hand, in showing the results of the Matlab/Simulink simulations for the different simulation schemes used and, on the other hand, to show the experimental results obtained using the FPGA to generate the OFDM signal at baseband and then upconverted at the frequency of 3,5 GHz. Finally the conclusions regarding the whole Tx/Rx design and implementation of the OFDM subsystem are given.
Discrete Wavelet Transform (DWT) is an efﬁcient tool for signal and image processing applications which has been utilized for perfect signal reconstruction. In this paper, twenty seven optimum combinations of three different wavelet ﬁlter types, three different ﬁlter reconstruction levels and three different kinds of signal for multi-level perfect reconstruction ﬁlter bank were implemented in MATLAB/Simulink. All the ﬁlters for different wavelet types were designed using Filter Design Analysis (FDA) and Wavelet toolbox. Signal to Noise Ratio (SNR) was calculated for each combination. Combination with best SNR was then implemented on TMS320C6713 DSP kit. Real time testing of perfect reconstruction on DSP kit was then carried out by two different methods. Experimental results accede with theory and simulations.
This Master Thesis presents the design of the core of a fixed point general purpose multimedia DSP processor (MDSP) and its instruction set. This processor employs parallel processing techniques and specialized addressing models to speed up the processing of multimedia applications. The MDSP has a dual MAC structure with one enhanced MAC that provides a SIMD, Single Instruction Multiple Data, unit consisting of four parallel data paths that are optimized for accelerating multimedia applications. The SIMD unit performs four multimedia-oriented 16-bit operations every clock cycle. This accelerates computationally intensive procedures such as video and audio decoding. The MDSP uses a memory bank of four memories to provide multiple accesses of source data each clock cycle.
One of the major threats to wireless communications is jamming. Many anti-jamming techniques have been presented in the past. However most of them are based on the precondition that the communicating devices have a pre-shared secret that can be used to synchronize the anti-jamming scheme. E.g. for frequency hopping the secret could be used to derive the hopping sequence and for direct sequence spread spectrum the secret is used to derive the spreading codes. But how can the devices bootstrap a jamming-resistant communication without having a pre-shared secret? Christina Popper and Mario Strasser propose as scheme for Uncoordinated Frequency Hopping (UFH) and Uncoordinated Direct Sequence Spread Spectrum (UDSSS) in their papers  and  respectively. The goal of my project was an implementation of Uncoordinated Direct Sequence Spread Spectrum (UDSSS) using Software Dened Radios. The First version should serve as an easy to use and extendable proof of conceptfor the proposed scheme.
The increased complexity of Digital Signal Processing (DSP) algorithms demands for the development of more complex and more eﬃcient hardware structures. The work presented herein describes the core components for the development of a tool capable of automatic generation of eﬃcient hardware structures, therefore facilitating developers work. It comprises algorithms and techniques for i) balancing the paths in a graph, ii) scheduling of operations to functional units, iii) allocating registers and iv) generating the VHDL code. Results show that the developed techniques are capable of generating the hardware structure of typical DSP algorithms represented in data-ﬂow graphs with over 2,000 nodes in around 200 ms, scaling to 80,000 nodes in about 214 s. Within the developed techniques, solving the scheduling problem is one of the most complex tasks: it is a NP-complete problem and directly inﬂuences the number of functional units and registers required. Therefore, experimental analysis was made on scheduling algorithms for time-constrained problems. Results show that simple list-based algorithms are more eﬃcient in large problems than more complex algorithms: they run faster and tend to require less functional units.
Towards Efﬁcient and Robust Automatic Speech Recognition: Decoding Techniques and Discriminative Training●1 comment
Automatic speech recognition has been widely studied and is already being applied in everyday use. Nevertheless, the recognition performance is still a bottleneck in many practical applications of large vocabulary continuous speech recognition. Either the recognition speed is not sufﬁcient, or the errors in the recognition result limit the applications. This thesis studies two aspects of speech recognition, decoding and training of acoustic models, to improve speech recognition performance in different conditions.
This report covers a master thesis in embedded systems, the goal of which was to investigate the high speed data collection capabilities with a Blackfin DSP. Basic theory about sampling and noise is covered briefly from a practical point of view. The theory is intended to be useful for those diving into a ADC datasheet for the first time. After an investigation of the delimiting factors, suitable components were selected and a prototype ADC PCB was designed from scratch. The goal is to design a general low noise data collecting unit compatible with the Blackfin DSP. Finally simple DSP software is designed to prove that DSP can handle such a high datastream.Testing the ADC card with the target Blackfin platform indicates thatthe analog parts indeed works. An analog bandwidth of over 10MHz ismeasured at a resolution exceeding 10 bits with respect to noise. The digital parts intended to interleave the two channels digital streams into one Blackfin unit did not work as intended. Only one channel is supported as of now. The report contains suggestions for future work in this area.
In the signals and systems course and in the first course in digital signal processing, a signal is, most often, characterized by its amplitude spectrum in the frequency-domain and its amplitude profile in the time-domain. So much a student gets used to this type of characterization, that the student finds it difficult to appreciate, when encountered in the ensuing statistical signal processing course, the fact that a signal can also be characterized by its autocorrelation function in the time-domain and the corresponding power spectrum in the frequency-domain and that the amplitude characterization is not available. In this article, the characterization of a signal by its autocorrelation function in the time-domain and the corresponding power spectrum in the frequency-domain is described. Cross-correlation of two signals is also presented.
The aim of this study is to examine the capability, performance, and relevance of a signal processing toolkit in Java, a programming language for Web-based applications. Due to the simplicity, ease and application use of the toolkit and with the advanced Internet technologies such as Remote Method Invocation (RMI), a spectral estimation applet has been created in the Java environment. This toolkit also provides an interactive and visual approach in understanding the various theoretical concepts of spectral estimation and shows the need to create more application applets to better understand the various concepts of signal and image processing. This study also focuses on creating a Java toolkit for embedded systems, such as Personal Digital Assistants (PDAs), embedded Java board, and supporting integer precision, and utilizing COordinate Rotation DIgital Computer (CORDIC) algorithm, both aimed to provide good performance in resource-limited environments. The results show a feasibility and necessity of developing a standardized Application Programming Interface (API) for the fixed-point signal processing library.
Recently much research has focused on the control of enclosed sound fields, particularly in automobiles. Both Active Noise Control (ANC) and Active Structural Acoustic Control (ASAC) techniques are being applied to problems stemming from power train noise and road noise (noise due to the interaction of the tires with the surface of the road). Due to the low frequency characteristics of these noise problems, large acoustic sources are required to obtain efficient control of the sound field. This creates demand in the automobile industry for compact lightweight sources. This work is concerned with the application of active control to power train noise, as well as road noise in the interior cabin of a sport utility vehicle using advanced, compact lightweight piezoelectric acoustic sources. First, a test structure approximately the same size as the automobile was built to study the principles of active noise control in a cavity. A finite element model of the cavity was created in order to optimize the positions of the error sensors and the control sources. Experimental work was performed with the optimized actuator and sensor locations in order to validate the model, and draw conclusions regarding the conditions to obtain global control of the sound field. Second, a broad-band feedforward filtered-X LMS algorithm was used to control power train noise. Preliminary power train noise tests were conducted using arrangements of four microphones and up to four commercially available speakers for control. Attenuation of seven decibel (dB) at the error sensors was measured in the 40-500 Hz frequency band. The dimensions of the zone of quiet generated by the control were measured, and show that noise reductions were obtained for a large volume surrounding the error sensors. Next, advanced speakers were implemented for active control of power train noise. The results obtained with different arrangements of these speakers were very similar to those obtained with the commercially-available speakers. These advanced speakers use piezoelectric devices to induce the displacement of a speaker membrane, which radiates sound. Their lighter weight and compact dimensions are a significant advantage over conventional speakers, for their application in automobile. Third, preliminary results were obtained for active control of road noise. The controller used an optimized set of four reference signals to control the noise at one error sensor using one control source. Two sets of tests were conducted. The first set of tests was performed on a dynamometer, which simulates the effects of the road on the tires. The second set of tests was performed on a rough road. Reduction of two to four decibel of the sound pressure level at the error sensor was obtained between 100 and 200 Hz.
The fields of neurobiology and electrical engineering have come together to pursue an integrated Brain-Machine Interface (BMI). Signal processing methods are used to find mapping algorithms between motor cortex neural firing rate and hand position. This cognitive extension could help patients with quadriplegia regain some independence using a thought-controlled robot arm. Current signal processing methods to achieve realtime neural-to-motor translation involve large, multi-processor systems to produce motor control parameters. Eventually, software running in a portable signal processing system is needed to allow for the patient to have the BMI in a backpack or attached to a wheelchair. This thesis presents a DSP-Based Computational Engine for a Brain-Machine Interface. The development of a DSP Board based on the Texas Instruments TMS320VC33 DSP will be presented, along with implementations of two digital filters and their training methods: 1) FIR trained with Normalized Least Mean Square Adaptive Filter (NLMS) and 2) Recurrent Multi-Layer Perceptron (RMLP) trained with Real-Time Recurrent Learning (RTRL). The requirements of the DSP Board, component selection and integration, and control software are discussed. The DSP implementations of the digital filters are presented, along with performance and timing analysis in real data collected from an Owl Monkey at Duke University. The weights of the FIR-NLMS filter converged similarly on the DSP as they did in MATLAB. Likewise, the weights of the RMLP-RTRL filter converged similarly on the DSP as they did using the Backpropagation Through Time method in NeuroSolutions. The custom DSP Board and two digital algorithms implemented in this thesis create a starting point for an integrated, portable, real-time signal processing solution for a Brain-Machine Interface.
The success of multicarrier modulation in the form of OFDM in radio channels illuminates a path one could take towards high-rate underwater acoustic communications, and recently there are intensive investigations on underwater OFDM. In this paper, we implement the acoustic OFDM transmitter and receiver design of [4, 5] on a TMS320C6713 DSP board. We analyze the workload and identify the most time-consuming operations. Based on the workload analysis, we tune the algorithms and optimize the code to substantially reduce the synchronization time to 0.2 seconds and the processing time of one OFDM block to 1.7 seconds on a DSP processor at 225 MHz. This experimentation provides guidelines on our future work to reduce the per-block processing time to be less than the block duration of 0.23 seconds for real time operations.
This article presents a way to compute and plot the image response of a decimator. I'm defining the image response as the unwanted spectrum of the impulse response after downsampling, relative to the desired passband response.
Field programmable gate arrays (FPGAs) have long been an attractive alternative to microprocessors for computing tasks — as long as floating-point arithmetic is not required. Fueled by the advance of Moore’s Law, FPGAs are rapidly reaching sufficient densities to enhance peak floating-point performance as well. The question, however, is how much of this peak performance can be sustained. This paper examines three of the basic linear algebra subroutine (BLAS) functions: vector dot product, matrix-vector multiply, and matrix multiply. A comparison of microprocessors, FPGAs, and Reconfigurable Computing platforms is performed for each operation. The analysis highlights the amount of memory bandwidth and internal storage needed to sustain peak performance with FPGAs. This analysis considers the historical context of the last six years and is extrapolated for the next six years.
High Performance Computing (HPC) or scientific codes are being executed across a wide variety of computing platforms from embedded processors to massively parallel GPUs. We present a comparison of the Basic Linear Algebra Subroutines (BLAS) using double-precision floating point on an FPGA, CPU and GPU. On the CPU and GPU, we utilize standard libraries on state-of-the-art devices. On the FPGA, we have developed parameterized modular implementations for the dot product and Gaxpy or matrix-vector multiplication. In order to obtain optimal performance for any aspect ratio of the matrices, we have designed a high-throughput accumulator to perform an efficient reduction of floating point values. To support scalability to large data-sets, we target the BEE3 FPGA platform. We use performance and energy efficiency as metrics to compare the different platforms. Results show that FPGAs offer comparable performance as well as 2.7 to 293 times better energy efficiency for the test cases that we implemented on all three platforms.