Top PDF Think DSP: Digital Signal Processing in Python

Think DSP: Digital Signal Processing in Python

Think DSP: Digital Signal Processing in Python

//en.wikipedia.org/wiki/Nyquist-Shannon_sampling_theorem). This example does not prove the Sampling Theorem, but I hope it helps you understand what it says and why it works. Notice that the argument I made does not depend on the original sampling rate, 44.1 kHz. The result would be the same if the original had been sam- pled at a higher frequency, or even if the original had been a continuous analog signal: if we sample at framerate f , we can recover the original sig- nal exactly, as long as it contains no energy at frequencies above f /2.

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Python for audio signal processing

Python for audio signal processing

There are many problems that are common to a wide variety of applications in the field of audio signal processing. Examples include procedures such as loading sound files or communicating between audio processes and sound cards, as well as digital signal processing (DSP) tasks such as filtering and Fourier analysis [Allen and Rabiner, 1977]. It often makes sense to rely on existing code libraries and frameworks to per- form these tasks. This is particularly true in the case of building prototypes, a practise com- mon to both consumer application developers and scientific researchers, as these code libraries allows the developer to focus on the novel as- pects of their work.
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Various Applications of Digital Signal Processing DSP

Various Applications of Digital Signal Processing DSP

Radar system comprises trigger source, modulator, output tube, video amplifier, if amplifier, indicator and duplexer. A duplexer is a must to use a single antenna both for transmission and reception of signals. It operates in transmit mode during the transmission of the signals and then in the receiver mode to receive the signals. The transmitter should produce enough power to obtain the desired radar range. The transmitted power depends on the fourth power of the radar range. The purpose of heterodyne receiver is to separate the wanted echo signal from the combination of noise, clutter and interference. After separating, we amplify the desired before further processing. The main aim of any receiver is to maximize the SNR of the received echo signal.
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pdf DSP - Real Time Digital Signal Processing

pdf DSP - Real Time Digital Signal Processing

It is essential to document programs thoroughly with titles and comment statements because this greatly simplifies the task of software maintenance. As discussed earlier, good programming technique plays an essential part in success- ful DSP application. A structured and well-documented approach to programming should be initiated from the beginning. It is important to develop an overall specifica- tion for signal processing tasks prior to writing any program. The specification includes the basic algorithm/task description, memory requirements, constraints on the program size, execution time, etc. Specification review is an important component of the software development process. A thoroughly reviewed specification can catch mistakes before code is written and reduce potential code rework risk at system integration stage. The potential use of subroutines for repetitive processes should also be noted. A flow diagram will be a very helpful design tool to adopt at this stage. Program and data blocks should be allocated to specific tasks that optimize data access time and address- ing functions.
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Digital Signal Processing (DSP) in Java, Periodic Motion and Sinusoids

Digital Signal Processing (DSP) in Java, Periodic Motion and Sinusoids

By now, you are may be saying "So what?" What in the world does DSP have to do with bags of sand with holes in the bottom? The answer is everything. Almost everything that we will discuss in the area of DSP is based on the premise that every time series, whether generated by sand leaking from a bag onto a moving carpet, or acoustic waves generated by your favorite rock band, can be decomposed into a large (possibly infinite) number of sine and cosine waves, each having its own amplitude and frequency.

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Assessment of Graphic Processing Units (GPUs) for Department of Defense (DoD) Digital Signal Processing (DSP) Applications

Assessment of Graphic Processing Units (GPUs) for Department of Defense (DoD) Digital Signal Processing (DSP) Applications

. Previous GPU Implementations of the FFT In our recent GPGPU survey we compiled a recent list of FFT algorithms [OLG + ], which we copy below: Motivated by the high arithmetic capabilities of modern GPUs, several projects have recently developed GPU implementations of the fast Fourier transform (FFT) [BFH + b, JvHK, MA, SL]. (The GPU Gems 2 chapter by Suman- aweera and Liu, in particular, gives a detailed description of the FFT and their GPU implementation [SL].) In general, these implementations operate on d or d input data, use a radix- decimation-in-time approach, and require one fragment-program pass per FFT stage. The real and imaginary components of the FFT can be computed in two components of the -vectors in each fragment processor, so two FFTs can easily be processed in parallel. These implemen- tations are primarily limited by memory bandwidth and the lack of effective caching in today’s GPUs, and only by processing two FFTs simultaneously can match the performance of a highly tuned CPU implementation [FJ]. Daniel Horn maintains an open-source optimized FFT library based on the Brook dis- tribution [Horb].
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Implementation of a digital signal processing (DSP) boost inverter for fuel cell energy generation

Implementation of a digital signal processing (DSP) boost inverter for fuel cell energy generation

iv DECLARATION “I, JOEVIS JULIAN CLAVERIA, declare that the Master by Research thesis entitled, “Implementation of a Digital Signal Processing (DSP) Boost Inverter for Fuel Cell Energy Generation”, is no more than 60,000 words in length including quotes and exclusive of tables, figures, appendices, bibliography, references and footnotes. This thesis contains no material that has been submitted previously, in whole or in part, for the award of any other academic degree or diploma. Except where otherwise indicated, this thesis is my own work”.
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Demystifying digital signal processing (DSP) programming: The ease in realizing implementations with TI DSPs

Demystifying digital signal processing (DSP) programming: The ease in realizing implementations with TI DSPs

Applications executed on DSPs commonly spend a lot of time executing loops, and as such, loop performance is critical to overall DSP processing performance. The TI DSP compiler is able to create instruction-level parallelism by overlapping iterations of a loop, thereby software pipelining them, as shown in Figure 3, which optimizes the use of CPU functional units and thus improves performance. The example in Figure 3 shows that, without software pipelining, loops are scheduled so that loop iteration i completes before iteration i+1 begins. Thus with software pipelining, as long as correctness can be preserved, iteration i+1 can start before iteration i finishes. This generally permits a much higher utilization of the machine’s resources than might be achieved from non-software-pipelined scheduling techniques. In a software-pipelined loop, even though a single-loop iteration might take s cycles to complete, a new iteration is initiated every ii cycles.
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Digital Closed loop Fiber Optic Gyroscopes Signal Processing System Based on DSP

Digital Closed loop Fiber Optic Gyroscopes Signal Processing System Based on DSP

At present, there are two main methods to achieve digital processing system of closed-loop FOGs. One is to use high-capacity logic gate arrays to achieve digital demodulation, integration, rate register output, ladder wave generation, square wave modulation and other functions. This method are used in all digital closed-loop FOGs in early time. However, this method has great difficulties in achieving digital filtering, temperature compensation, which limits the improvement of FOG’s accuracy. For that reason, DSP technology is proposed to realize digital processing of all digital closed-loop FOGs. This method uses a high-speed DSP chip to achieve digital demodulation, filtering, angular velocities generation, ladder wave generation, square wave modulation signal generation and other functions. It is flexible and convenient. And the accuracy of FOGs are increased by digital filtering. In addition, temperature compensations and reduction of zero drift can be achieved by measuring temperatures. Modern DSP technology has been highly developed. With the improved performance of DSP chips and decline in the price, DSP has been widely used in various fields. Therefore, it is feasible to use advanced DSP technology in all digital closed-loop FOGs.
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Digital Signal Processing

Digital Signal Processing

FIR filters are one of two primary types of digital filters used in Digital Signal Processing (DSP) applications, the other type being IIR. 1.2 What does "FIR" mean? "FIR" means "Finite Impulse Response". If you put in an impulse, that is, a single "1" sample followed by many "0" samples, zeroes will come out after the "1" sample has made its way through the delay line of the filter.

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Digital Signal Processing

Digital Signal Processing

In the early 1980s, DSP was taught as a graduate level course in electrical engineering. A decade later, DSP had become a standard part of the undergraduate curriculum. Today, DSP is a basic skill needed by scientists and engineers in many fields. Unfortunately, DSP education has been slow to adapt to this change. Nearly all DSP textbooks are still written in the traditional electrical engineering style of detailed and rigorous mathematics. DSP is incredibly powerful, but if you can't understand it, you can't use it!

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Digital Signal Processing

Digital Signal Processing

makes use of the calorimeter information with reduced granularity (through trigger tower signals) and fast muon detectors[1]. Both the second and the third levels reduce further the acceptance rate to ≈ 100 Hz[12]. In order to reduce the rate of fake triggers, LVL1 is considering to use an additional muon trigger, which will be provided by the scintillating tile calorimeter (Tilecal). As a case study, we propose the development of a muon detection system based on Tilecal information, which would be ready to satisfy LVL 1 stringent requirements, in terms of speed and detection efficiency. Two main aspects concerning system development will be addressed: filter design and full detection system implementation. In the following, we describe the main characteristics of the hadronic calorimeter detector and the muon signal it produces. After, we cover some methods for signal detection and detail the main results of the proposed matched filter design approach. Finally, we discuss the implementation of the system on a DSP platform. It should be mentioned that the actual design may go analog, in order to profit from the analog signal provided by Tilecal, so that processing speed can be optimized.
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Digital Signal Processing

Digital Signal Processing

This course introduces the fundamentals of applied digital signal processing by implementing a wide range of digital signal processing (DSP) applications on the Com3lab boards (70073). Applications covered include digitization of analog signals, FIR filtering, IIR filtering, FFT, FFT filtering, data, and wireless communication. Real-time implementation issues as well as performance tradeoffs and processor/algorithm limitations are stressed with the intent that upon completing this lab course, you will be able to apply DSP processing to your work, projects, experiments or any real-world application that you may encounter.
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Digital Signal Processing Foundations

Digital Signal Processing Foundations

David Dorran Page 4 ‘Cleaning’ a ‘noisy’ ECG signal The signals that you see on a heart monitor are taken from a patient who has electrode pads attached to his/her skin and electrical leads carry the signal from the patient to the monitor. Unfortunately any electrical lead can suffer from interference as a result of being in the vicinity of other electrical equipment - and there are lots of interesting electrical devices in hospitals! This interference, referred to as noise, would make the ECG (electrocardiogram) signal very difficult for a healthcare professional to interpret as it effects what the signal would look like on the monitor. DSP filtering techniques clean up the noisy signal so that the noise is removed making it easier for doctors to read the signal.
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Foundation of Digital Signal Processing

Foundation of Digital Signal Processing

I already had a love of science, and my father was an engineer who encour- aged my interest. Much of my early childhood was spent in my father’s garage, amidst a plethora of tools, car batteries, bits of old televisions, model steam engines, dynamos, valves, tobacco tins crammed with nuts and bolts, bottles of mysterious and wonderful-smelling chemicals, electric motors, relays, bulbs, strange actuators filched from scrapped aircraft, timber, sheet metal, hardboard, paints and varnishes. I would sit on the floor, playing with wires, whilst he constructed miracles of rare device that were usually lethal but almost always wonderful. He taught me to solder when I was nine, and together we built my first short wave radio. These early influ- ences stood me in good stead during secondary school and university, when science got tougher and more and more maths crept in. Somehow I knew that beyond the slog, the essential wonder of science remained, and all the techniques that I found difficult were just so many tools in helping you achieve something really worthwhile. There is a point to all this: DSP is often seen as a bit frightening, since it involves an array of pretty technical subjects. Look beyond this, and imagine what it enables you to do. I once gave a public lecture here at The University of Manchester on real- time DSP, and illuminated the talk with music signals processed using certain DSP algorithms, all of which are described at various stages in this book (the processing involved things like noise cancellation, pitch shifting, reverberation, echo, surround sound and the like). Afterwards a student came up to me in a state of amazement, saying he had never realised it was possible to do such things with DSP. This made me think – I had always assumed, in my narrow way, that everyone must surely be aware of how these things are done. Since this is clearly not the case, it must also be true that many students, scientists and engineers could benefit from using DSP applied to their data if only they were aware of the techniques available.
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Digital Signal Processing Notes

Digital Signal Processing Notes

4. Sound synthesis and manipulation, filtering, distortion, stretching effects are also done by DSP processor. ADC and DAC are used in signal generation and recording. 4. ECHO CANCELLATION In the telephone network, the subscribers are connected to telephone exchange by two wire circuit. The exchanges are connected by four wire circuit. The two wire circuit is bidirectional and carries signal in both the directions. The four wire circuit has separate paths for transmission and reception. The hybrid coil at the exchange provides the

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Digital Signal Processing with the SHARC

Digital Signal Processing with the SHARC

for Windows Block Diagram, a visual design tool which can be used to implement and test DSP algorithms, is available from Hyperception. DSP designs can be executed on a host PC or on the based board using a real-time driver option. Drivers are also available for other processors such as the TMS320C30. Block Diagram contains a wide and extensive range of functional building blocks such as the FFT, correlation, filtering, as well as image processing and communications functions. The user can select the functional block icons, specify their parameters, interconnect them to implement a desired algorithm while monitoring and testing its functionality. A/D and D/A functional blocks for real-time input and output are also supported with the SHARC-based board. Different types of displays allow observation of waveforms at
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Teaching Digital Signal Processing

Teaching Digital Signal Processing

Figure 2 Frequency and phase responses of the filter 4.2 Design Using a DSP Development Kit The kit used in our practical sessions is the dsPICPRO4DSP development kit, manufactured by mikroElektronika, and programmed using the mikroC PRO dsPIC30/33 C language. dsPICPRO4 is a low-cost DSP development kit that can be used in low-cost, low speed DSP applications, and especially in teaching the practical aspects of designing DSP systems. The reason for choosing this development kit was because the kit includes all the hardware needed for developing a DSP project, including A/D and D/A converters. In addition, the included software is fully integrated, where a template program is provided with the required filter coefficients and this program can easily be uploaded to the target DSP kit.
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Digital Filters for Radar Signal Processing

Digital Filters for Radar Signal Processing

Keywords- Signal, Filter, Matched, Noise, Doppler, Windowing. 1. INTRODUCTION Digital filtering is one of the most powerful tools of DSP. Apart from the obvious advantages of virtually eliminating errors in the filter associated with passive component fluctuations over time and temperature, op amp drift (active filters),etc., digital filters are capable of performance specifications that would, at best, be extremely difficult, if not impossible, to achieve with an analog implementation. In addition, the characteristics of a digital filter can be easily changed under software control. Therefore, they are widely used in adaptive filtering applications in communications such as echo cancellation in modems, noise cancellation and speed recognition.
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Using Python for Signal Processing and Visualization

Using Python for Signal Processing and Visualization

In order to process EEG data for interpretation and further analysis, Fourier-based transforms can be used to determine spectral properties of brain activity. Determining how spectral properties change over time is important to the study of working memory. The need for these techniques stems from the assessment of working memory performance through the change in specific spectral properties of the EEG signals mea- sured at the scalp. When working memory is tasked, alpha-band power increases while simultaneously shifting to slightly higher frequencies. Fast Fourier Transforms (FFTs), various filters, and some wavelet implementations are distributed with the Scipy Python package to help accomplish this goal. FFT computa- tion is fast within Scipy as it makes use of the FFTW libraries [2]. However, standard FFTs are not adequate when analyzing the evolution of spectral content over time.
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