A Prototype Laboratory Environment for Digital Signal Processing Using Simulink and a Texas Instrument DSP Device
Normally, when a model is designed from building blocks in Simulink, the simulation is performed within the Simulink environment. A test of the design in a real-time environment requires that source code is generated, compiled and downloaded to the target hardware. As a first attempt to bridge this software gap, this thesis describes and evaluates a prototype laboratory environment, which directly links Simulink to a Texas Instrument DSP device. The prototype system converts graphical models and makes available various real-time signal processing algorithms, such as adders, delays, FFTs, IIR filters and multipliers. Future work is to consider modification of the prototype to allow for feedback in the graphical models and to find an efficient way of handling signal processing algorithms where variable buffer lengths are required.
A DGPS/Radiobeacon Receiver for Minimum Shift Keying with Soft Decision Capabilities
The Global Positioning System (GPS) is now in operation, and many improvements to its performance are being sought. One such improvement is Differential GPS (DGPS), where known errors in the GPS broadcast are identified and the corrections broadcast to the end user. One implementation of DGPS being considered is the use of coastal marine radio direction finding (RDF) radiobeacons in the 285-325kHz band as transmitters for the DGPS broadcast. The normal RDF beacon signal consists of a continuous carrier on a one kilohertz boundary plus a Morse-code identification signal 1025Hz above the carrier. In the DGPS/radiobeacon implementation proposed for the US coastal regions, the differential data link signal uses minimum shift keying (MSK) at a data rate of 25, 50, 100, 200 or 400 baud (the exact baud rat has not yet been decided). This MSK signal is centered between the RDF beacon carrier and identification signal. At the frequencies that these radiobeacons are operated, the prevailing atmospheric noise is both non-Gaussian and very strong. This noise characteristic makes the design of a long-range data link difficult. One solution that has been proposed is the use of forward error correction (FEC) coding of the data. The performance of FEC decoders can be improved by the used of a soft decision receiver, which delivers both bit decisions and information about the validity of the bit decisions. This work describes the design of a radio receiver for DGPS/Radiobeacon servics which is capable of reception of 400 baud MSK in the DGPS/Radiobeacon band. The receiver is designed to be easily augmented to provide soft decisions and easily modified to recieve MSK at data rates of 25 to 400 baud. The radio is a microprocessor controlled dual conversion superheterodyne with an audio frequency of 1kHz. The demodulator runs on the same microprocessor that controls the radio. The weak-signal performance of the demodulator is very good: the Eb/No vs. bit error rate performance of the demodulator is only a couple of dB worse than the theoretical performance of differential phase-shift keying. The radio has a noise floor of -114dBm referenced to it's 500Hz wide audio bandwidth and a 3rd order intermodulation intercept of +7dBm for a dynamic range of 83dB. This work concludes with a thumbnail analysis of the operations needed to implement a soft bit decision estimator, and some suggestions for the implementation of said soft bit decision estimator.
IMPLEMENTATION OF PERIODOGRAM SMOOTHING OF NOISYIMPLEMENTATION OF PERIODOGRAM SMOOTHING OF NOISY SIGNALS USING TMS320C6713 DSK
Periodogram Smoothing is a technique of power spectrum estimation. The discrete Fourier transform of a digital signal simply resolves the frequency components. The algorithm is implemented on Texas Instruments’ TMS320C6713 DSP Starter Kit (DSK). This is a 32-bit floating-point digital signal processor running at 225 MHz. The programs are basically written in the C programming language. However, those sections of code which are time-critical and memory-critical are written in assembly language of C6713. A MATLAB™ graphical user interface is also provided. The MATLAB™ program calls C programs loaded in Code Composer Studio (CCS). The C programs in turn call the assembly programs when required.
Implementing IS-95, the CDMA Standard, on TMS320C6201 DSP
IS-95 is the present U.S. 2nd generation CDMA standard. Currently, the 2nd generation CDMA phones are produced by Qualcomm. Texas Instruments (TI) has ASIC design for Viterbi Decoder on C54x. Several of the components in the forward link process are also implemented in hardware. However, having to design a specific hardware for a particular application is expensive and time consuming. Thus, the possibility of the alternative implementations is of great interest to both customers and TI itself. This research has achieved in successful implementation of IS-95 entirely in software on TI fixed-point DSP TMS320C6201, and met the real time constraint. IS-95 system, the industrial standard for CDMA, is a very complicated system and extremely computationally demanding. The transmission rate for an IS-95 system is 1.2288 Mcps. This research project includes all the major components of the demodulation process for the forward link system: PN Descrambling, Walsh Despreading, Phase Correction & Maximal Ratio Combining, Deinterleaver, Digital Automatic Gain Control, and Viterbi Deccc:r. The entire demodulation process is done completely in C. That makes it a very attractive alternative implementation in the future applications. It is well known that ASIC design is not only expensive and but also time consuming, programming in assembly is easier and cheaper, but programming in C is a much easier and efficient way out, in particular, for general computer engineers. During the whole process, efforts have been devoted on developing various specific techniques to optimize the design for all the components involved. These developments are successfully achieved by making the best use of the following techniques: to simplify the algorithms first before programming, to look for regularity in the problem, to work toward the Compiler's full efficiency, and to use C intrinsics whenever possible. All these attributes together make the implementation scheme great for DSP applications. The benchmark results compare very well to the TI-internal hand scheduled assembly performance of the same type of decoders. The estimated percentage usage of all the components (excluding PN) is only 21.18% of the total CPU cycles available (4,000 K), which is very efficient and impressive.
Towards a Real-Time Implementation of Loudness Enhancement Algorithms on a Motorola DSP 56600
Most of the cellular phone companies with audio speaker capabilities focus on reducing the current drain to extend battery life. None of these companies concentrate on modifying the speech signal itself to make it sound louder in noisy listener environments without adding additional energy. Such algorithms have been described in literature by Boillot and form the backbone of this thesis. The current project focusses on taking a step towards running these algorithms in real-time on a 16-bit fixed point Motorola DSP 56600. Implementation of the autocorrelation, Levinson- Durbin, FIR, and IIR filters in assembly for the Motorola DSP 56600 has been investigated in the thesis. The challenges and alternate solutions to circumvent the challenges have been described, and experimental results have been presented. Results indicate that the modified signed LMS algorithm, which can be considered to be a blend between the LMS and signed LMS algorithms, turns out to be an elegant solution to circumvent the challenges in implementing the Levinson-Durbin recursion.
An Advanced Signal Processing Toolkit for Java applications
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.
A Two-Level Reconfigurable Cell Array for Digital Signal Processing
Reconfigurable hardware has become an attractive option for implementing digital signal processing, especially in systems that require both high performance and flexibility. This thesis presents a novel two-level reconfigurable architecture targeted toward systems with these requirements. The architecture supports a large orthogonal design space whereby designers can customize the word length, amount of parallelism, number of functional units, and functional unit connectivity to meet the needs of the application. On the upper level, algorithms are mapped onto an array of 4-bit cells and a hierarchical interconnection fabric. The interconnection structure contains a mesh of 4-bit busses for local data transfer, as well as an H-tree for communicating results between functional units. On the lower level, each cell contains a small matrix of elements that collectively implement all necessary operations. The matrix of elements has only two configurations: one optimized for mathematical functions such as multiply-accumulates, and the other optimized for memory operations. The system also contains pipeline latches to maximize clock rate and throughput. Circuit simulations indicate that the architecture achieves a clock frequency of 200 MHz in a modest 0.25-μm CMOS technology. An initial prototype of the reconfigurable cell has been fabricated in 0.5-μm CMOS and tested for functionality. The estimated execution time for a 16-bit, 256-point Fast Fourier Transform shows a speedup ranging from 1.6 to 14 compared to contemporary digital signal processors.
Automated Accident Detection in Intersections Via Digital Audio Signal Processing
The aim of this thesis is to design a system for automated accident detection in intersections. The input to the system is a three-second audio signal. The system can be operated in two modes: two-class and multi-class. The output of the two-class system is a label of “crash” or “non-crash”. In the multi-class system, the output is the label of “crash” or various non-crash incidents including “pile drive”, “brake”, and “normal-traffic” sounds. The system designed has three main steps in processing the input audio signal. They are: feature extraction, feature optimization and classification. Five different methods of feature extraction are investigated and compared; they are based on the discrete wavelet transform, fast Fourier transform, discrete cosine transform, real cepstrum transform and Mel frequency cepstral transform. Linear discriminant analysis (LDA) is used to optimize the features obtained in the feature extraction stage by linearly combining the features using different weights. Three types of statistical classifiers are investigated and compared: the nearest neighbor, nearest mean, and maximum likelihood methods. Data collected from Jackson, MS and Starkville, MS and the crash signals obtained from Texas Transportation Institute crash test facility are used to train and test the designed system. The results showed that the wavelet based feature extraction method with LDA and maximum likelihood classifier is the optimum design. This wavelet-based system is computationally inexpensive compared to other methods. The system produced classification accuracies of 95% to 100% when the input signal has a signal-to-noise-ratio of at least 0 decibels. These results show that the system is capable of effectively classifying “crash” or “non-crash” on a given input audio signal.
Ignal Enhancement Using Time-Frequency Based Denoising
This thesis investigates and compares time and wavelet-domain denoising techniques where received signals contain broadband noise. We consider how time and wavelet-domain denoising schemes and their combinations compare in the mean squared error sense. This work applies Wiener prediction and Median filtering as they do not require any prior signal knowledge. In the wavelet-domain we use soft or hard thresholding on the detail coefficients. In addition, we explore the effect of these wavelet-domain thresholding techniques on the coefficients associated with cycle-spinning and the newly proposed recursive cycle-spinning scheme. Finally, we note that thresholding does not make an attempt to de-noise coefficients that remain after thresholding; therefore we apply time domain techniques to the remaining detail coefficients from the first level of decomposition in an attempt to de-noise them further prior to reconstruction. This thesis applies and compares these techniques using a mean squared error criterion to identify the best performing in a robust test signal environment. We find that soft thresholding with Stein’s Unbiased Risk Estimate (SURE) thresholding produces the best mean squared error results in each test case and that the addition of Wiener prediction to the first level of decomposition coefficients leads to a slightly enhanced performance. Finally, we illustrate the effects of denoising algorithms on longer data segments.
Active control of automobile cabin noise with conventional and advanced speakers
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.
Automatic Parallel Memory Address Generation for Parallel DSP Computing
The concept of Parallel Vector (scratch pad) Memories (PVM) was introduced as one solution for Parallel Computing in DSP, which can provides parallel memory addressing efficiently with minimum latency. The parallel programming more efficient by using the parallel addressing generator for parallel vector memory (PVM) proposed in this thesis. However, without hiding complexities by cache, the cost of programming is high. To minimize the programming cost, automatic parallel memory address generation is needed to hide the complexities of memory access. This thesis investigates methods for implementing conflict-free vector addressing algorithms on a parallel hardware structure. In particular, match vector addressing requirements extracted from the behaviour model to a prepared parallel memory addressing template, in order to supply data in parallel from the main memory to the on-chip vector memory. According to the template and usage of the main and on-chip parallel vector memory, models for data pre-allocation and permutation in scratch pad memories of ASIP can be decided and configured. By exposing the parallel memory access of source code, the memory access flow graph (MFG) will be generated. Then MFG will be used combined with hardware information to match templates in the template library. When it is matched with one template, suited permutation equation will be gained, and the permutation table that include target addresses for data pre-allocation and permutation is created. Thus it is possible to automatically generate memory address for parallel memory accesses. A tool for achieving the goal mentioned above is created, Permutator, which is implemented in C++ combined with XML. Memory access coding template is selected, as a result that permutation formulas are specified. And then PVM address table could be generated to make the data pre-allocation, so that efficient parallel memory access is possible. The result shows that the memory access complexities is hiden by using Permutator, so that the programming cost is reduced.It works well in the context that each algorithm with its related hardware information is corresponding to a template case, so that extra memory cost is eliminated.
Implementation of Elementary Functions for a Fixed Point SIMD DSP Coprocessor
This thesis is about implementing the functions for reciprocal, square root, inverse square root and logarithms on a DSP platform. A multi-core DSP platform that consists of one master processor core and several SIMD coprocessor cores is currently being designed by a team at the Computer Engineering Department of Linköping University. The SIMD coprocessors’ arithmetic logic unit (ALU) has 16 multipliers to support vector multiplication instructions. By efficiently using the 16 multipliers, it is possible to evaluate polynomials very fast. The ALU does not have (hardware) support for floating point arithmetic, so the challenge is to get good precision by using fixed point arithmetic. Precise and fast solutions to implement the mathematical functions are found by converting the fixed point input to a soft floating point format before polynomial approximation, choosing a polynomial based on an error analysis of the polynomial approximation, and using Newton-Raphson or Goldschmidt iterations to improve the precision of the polynomial approximations. Finally, suggestions are made of changes and additions to the instruction set architecture, in order to make the implementations faster, by efficiently using the currently existing hardware.
Benchmarking a DSP processor
This Master thesis describes the benchmarking of a DSP processor. Benchmarking means measuring the performance in some way. In this report, we have focused on the number of instruction cycles needed to execute certain algorithms. The algorithms we have used in the benchmark are all very common in signal processing today. The results we have reached in this thesis have been compared to benchmarks for other processors, performed by Berkeley Design Technology, Inc. The algorithms were programmed in assembly code and then executed on the instruction set simulator. After that, we proposed changes to the instruction set, with the aim to reduce the execution time for the algorithms. The results from the benchmark show that our processor is at the same level as the ones tested by BDTI. Probably would a more experienced programmer be able to reduce the cycle count even more, especially for some of the more complex benchmarks.
DSP Memory Management in a Third Generation High Performance Base Station
Most of the tasks in a mobile cellular network base station are performed with programmable digital signal processors. Their memory spaces and management features are very limited. The buffering requirements in the base station can have large instantaneous variations during the simultaneous transmission of burst' data on multiple channels to multiple users. In particular the high bit-rates of the Wideband Code Division Multiple Access data transfer evolution High Speed Downlink Packet Access create very high demands for buffering. The fragmentation of the buffer memory is a threat. It causes a gradual decrease in performance, which is critical in a long running process like the base station. The amount of fragmentation is different with different memory management methods. In this work the features and applicability of different memory management methods for signal processors used in the base stations of third generation cellular networks have been studied. Software based memory management includes a high amount of conditional branches. The signal processor, which is optimized for highly parallel sequential computing, executes conditional branches very badly when compared to microcontrollers and general-purpose processors. The memory management methods are first studied in theory and then experimentally. In the experiments two different memory management methods were analyzed. The memory managers were loaded with a synthetic workload program that simulates multi-user high bit-rate data transmissions in the base station. The performances of the memory managers were measured in terms of fragmentation, execution time and memory utilization. The experiments confirmed the information gained from the theoretical studies that different memory management methods are usually optimized for a certain feature. The experiments showed that a simple method is fast to execute and works well with small and intermediate loads. When the load is increased the performance decreases. The second, more complex, measured method was found to require more computing, but to be capable of using the memory space assigned to it more effectively.
IMPLEMENTATION OF PERIODOGRAM SMOOTHING OF NOISYIMPLEMENTATION OF PERIODOGRAM SMOOTHING OF NOISY SIGNALS USING TMS320C6713 DSK
Periodogram Smoothing is a technique of power spectrum estimation. The discrete Fourier transform of a digital signal simply resolves the frequency components. The algorithm is implemented on Texas Instruments’ TMS320C6713 DSP Starter Kit (DSK). This is a 32-bit floating-point digital signal processor running at 225 MHz. The programs are basically written in the C programming language. However, those sections of code which are time-critical and memory-critical are written in assembly language of C6713. A MATLAB™ graphical user interface is also provided. The MATLAB™ program calls C programs loaded in Code Composer Studio (CCS). The C programs in turn call the assembly programs when required.
Towards a Real-Time Implementation of Loudness Enhancement Algorithms on a Motorola DSP 56600
Most of the cellular phone companies with audio speaker capabilities focus on reducing the current drain to extend battery life. None of these companies concentrate on modifying the speech signal itself to make it sound louder in noisy listener environments without adding additional energy. Such algorithms have been described in literature by Boillot and form the backbone of this thesis. The current project focusses on taking a step towards running these algorithms in real-time on a 16-bit fixed point Motorola DSP 56600. Implementation of the autocorrelation, Levinson- Durbin, FIR, and IIR filters in assembly for the Motorola DSP 56600 has been investigated in the thesis. The challenges and alternate solutions to circumvent the challenges have been described, and experimental results have been presented. Results indicate that the modified signed LMS algorithm, which can be considered to be a blend between the LMS and signed LMS algorithms, turns out to be an elegant solution to circumvent the challenges in implementing the Levinson-Durbin recursion.
A Two-Level Reconfigurable Cell Array for Digital Signal Processing
Reconfigurable hardware has become an attractive option for implementing digital signal processing, especially in systems that require both high performance and flexibility. This thesis presents a novel two-level reconfigurable architecture targeted toward systems with these requirements. The architecture supports a large orthogonal design space whereby designers can customize the word length, amount of parallelism, number of functional units, and functional unit connectivity to meet the needs of the application. On the upper level, algorithms are mapped onto an array of 4-bit cells and a hierarchical interconnection fabric. The interconnection structure contains a mesh of 4-bit busses for local data transfer, as well as an H-tree for communicating results between functional units. On the lower level, each cell contains a small matrix of elements that collectively implement all necessary operations. The matrix of elements has only two configurations: one optimized for mathematical functions such as multiply-accumulates, and the other optimized for memory operations. The system also contains pipeline latches to maximize clock rate and throughput. Circuit simulations indicate that the architecture achieves a clock frequency of 200 MHz in a modest 0.25-μm CMOS technology. An initial prototype of the reconfigurable cell has been fabricated in 0.5-μm CMOS and tested for functionality. The estimated execution time for a 16-bit, 256-point Fast Fourier Transform shows a speedup ranging from 1.6 to 14 compared to contemporary digital signal processors.
Ignal Enhancement Using Time-Frequency Based Denoising
This thesis investigates and compares time and wavelet-domain denoising techniques where received signals contain broadband noise. We consider how time and wavelet-domain denoising schemes and their combinations compare in the mean squared error sense. This work applies Wiener prediction and Median filtering as they do not require any prior signal knowledge. In the wavelet-domain we use soft or hard thresholding on the detail coefficients. In addition, we explore the effect of these wavelet-domain thresholding techniques on the coefficients associated with cycle-spinning and the newly proposed recursive cycle-spinning scheme. Finally, we note that thresholding does not make an attempt to de-noise coefficients that remain after thresholding; therefore we apply time domain techniques to the remaining detail coefficients from the first level of decomposition in an attempt to de-noise them further prior to reconstruction. This thesis applies and compares these techniques using a mean squared error criterion to identify the best performing in a robust test signal environment. We find that soft thresholding with Stein’s Unbiased Risk Estimate (SURE) thresholding produces the best mean squared error results in each test case and that the addition of Wiener prediction to the first level of decomposition coefficients leads to a slightly enhanced performance. Finally, we illustrate the effects of denoising algorithms on longer data segments.
Evaluation of a Floating Point Acoustic Echo Canceller Implementation
This master thesis consists of implementation and evaluation of an AEC, Acoustic Echo Canceller, algorithm in a floating-point architecture. The most important question this thesis will try to answer is to determine benefits or drawbacks of using a floating-point architecture, relative a fixed-point architecture, to do AEC. In a telephony system there is two common forms of echo, line echo and acoustic echo. Acoustic echo is introduced by sound emanating from a loudspeaker, e.g. in a handsfree or speakerphone, being picked up by a microphone and then sent back to the source. The problem with this feedback is that the far-end speaker will hear one, or multiple, time-delayed version(s) of her own speech. This time-delayed version of speech is usually perceived as both confusing and annoying unless removed by the use of AEC. In this master thesis the performance of a floating-point version of a normalized least-mean-square AEC algorithm was evaluated in an environment designed and implemented to approximate live telephony calls. An instruction-set simulator and assembler available at the initiation of this master thesis were extended to enable; zero-overhead loops, modular addressing, post-increment of registers and register-write forwarding. With these improvements a bit-true assembly version was implemented capable of real-time AEC requiring 15 million instructions per second. A solution using as few as eight mantissa bits, in an external format used when storing data in memory, was found to have an insignificant effect on the selected AEC implementation’s performance. Due to the relatively low memory requirement of the selected AEC algorithm, the use of a small external format has a minor effect on the required memory size. In total this indicates that the possible reduction of the memory requirement and related energy consumption, does not justify the added complexity and energy consumption of using a floating-point architecture for the selected algorithm. Use of a floating-point format can still be advantageous in speech-related signal processing when the introduced time delay by a subband, or a similar frequency domain, solution is unacceptable. Speech algorithms that have high memory use and small introduced delay requirements are a good candidate for a floating-point digital signal processor architecture.
Development of a real time test platform for motor drive algorithms
In this thesis a real time test platform for a permanent magnet synchronous motor is developed. The implemented algorithm is Field Oriented Control (FOC) and it is implemented on a Texas Instruments TMS320F2808 Digital Signal Processor (DSP). The platform is developed in a rapid prototyping approach using Matlab/Simulink and the Real Time Workshop (RTW) packages.With this software the control algorithm and its interface to different DSP modules, such as A/D converter and PWM module, is constructed as a Simulink block scheme. The blocks used come from ordinary Simulink libraries and libraries provided by the RTW packages. From the Simulink block scheme Matlab can auto generate embedded C code adapted for different embedded targets, in this case the 2808 DSP.The developed real time test platform is also a Simulink model, though different from the algorithm model. When the start simulation command is given in the platform model a Graphical User Interface is loaded which lets the user specify motor parameters and certain algorithm parameters. Once the parameters are chosen RTW generates code from the algorithm model, loads it into the DSP and runs the generated program. From the platform model it is possible to set the reference speed of the motor in real time and monitor/log motor parameters such as actual speed and stator currents.
