Top PDF Think DSP: Digital Signal Processing in Python

Think DSP: Digital Signal Processing in Python

Think DSP: Digital Signal Processing in Python

With mode='same', the result has the same length as close, corresponding to lags from − N/2 to N/2 − 1. The gray line in Figure 8.8 shows the result. Except at lag=0, there are no peaks, so there is no apparent periodic behav- ior in this signal. However, the autocorrelation function drops off slowly, suggesting that this signal resembled pink noise, as we saw in Section 5.3. To compute autocorrelation using convolution, have to zero-pad the signal to double the length. This trick is necessary because the FFT is based on the assumption that the signal is periodic; that is, that it wraps around from the end to the beginning. With time-series data like this, that assumption is invalid. Adding zeros, and then trimming the results, removes the bogus values.
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A Logic-Based Framework for Digital Signal Processing

A Logic-Based Framework for Digital Signal Processing

So far we have described how SWI-DSP provides a vocabulary of logic predicates and datatypes for performing audio analysis, but this aproach is still inappropi- ate for some tasks. We have organised the modules in communication layers and task-oriented modules, but there is a trade-off between understandability and rich functionality. Use of the computational facilities provided by the various modules requires quite detailed knowledge of their capabilities as well as famil- iarity with Prolog. We do not expect this type of expertise from most of our potential users. Hence, we require a front-end that is closer to the application domain, providing an interactive “laboratory” at a level of abstraction closer to that expected by the signal processing practitioner.
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Improvement of Digital Signal Processing for Multichannel TDEMI Measurements

Improvement of Digital Signal Processing for Multichannel TDEMI Measurements

second, measuring for only half a second will not assure that the interference is measured. This fact of an interference repeating over time is also not incorporated in the standards, which are specified as a threshold over frequency [4]. To still notice the effect of the time domain variation these receivers have to measure every frequency bin for a certain amount of time, with two parallel detectors. A well founded description of analyzing time variant disturbances can be found in [5], where a simulation model is developed to mimic these types of detectors. The measurement time per frequency bin, the dwell time, is dependent on the time variation at that specific frequency. A lot of measurement steps and a minimum amount of measurement time per frequency bin result in a very long and detrimental measurement time, as has been shown in [6]. Additionally, having to do these measurements at many different positions around a large equipment under test (EUT) increases the total measurement time even more, for some systems this can even be as long as one week, resulting in very high costs. To reduce these long and therefore expensive measurement times time-domain electromagnetic interference (TDEMI) analyzers became very popular, but remain expensive. Advantages [6], [7] and challenges [8], [9] of TDEMI analyzers have been discussed previously. In this paper, a low cost digitizer known as a PicoScope is used in conjunction with Digital Signal Processing (DSP). With this DSP, it is proposed to quickly determine the minimum dwell times needed at every frequency bin. This can be used to make an estimation of the total measurement time needed for a traditional EMI receiver. The EMI receiver used in this paper is the Rohde & Schwarz ESS [10]. To accomplish the comparison, a short-time Fourier transform (STFFT) is used to create a spectrogram that holds information in both the frequency and the time domain. From this, the time repetitive behaviour of frequencies is retrieved and analyzed by taking a fast Fourier transform (FFT) of the time slice and studying the lowest significant frequency.
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Digital Signal Processing Leveraged for Intrusion Detection

Digital Signal Processing Leveraged for Intrusion Detection

The attacks used to validate the use of DSP techniques for intrusion detection were extracted from the DARPA data set [Lab99]. Generally the attacks that demon- strated the highest level of frequency content were those that fell into the Denial of Service (DoS) category of attacks. This type of attack lends itself to frequency de- tection because it typically involves a larger number of packets and at some level is trying to overload the abilities of the target computer. This large number of packets offers greater amounts of data over which to run the algorithm, offering the benefit of higher confidence in the results. The DoS attacks targeted are the Processtable attack and the Teardrop attack.
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EC2302-Digital-Signal-Processing-Lecture-Notes

EC2302-Digital-Signal-Processing-Lecture-Notes

(usually -1.0 to +1.0). Other processors use floating-point arithmetic, where values are represented by a mantissa and an exponent as mantissa x 2 exponent. The mantissa is generally a fraction in the range -1.0 to +1.0, while the exponent is an integer that represents the number of places that the binary point (analogous to the decimal point in a base 10 number) must be shifted left or right in order to obtain the value represented. Floating-point arithmetic is a more flexible and general mechanism than fixed-point. With floating-point, system designers have access to wider dynamic range (the ratio between the largest and smallest numbers that can be represented). As a result, floating- point DSP processors are generally easier to program than their fixedpoint cousins, but usually are also more expensive and have higher power consumption. The increased cost and power consumption result from the more complex circuitry required within the floating-point processor, which implies a larger silicon die. The ease-of-use advantage of floating-point processors is due to the fact that in many cases the programmer doesn’t have to be concerned about dynamic range and precision.
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Digital signal processing for optical communications and networks

Digital signal processing for optical communications and networks

The performance of high-capacity optical communication systems can be significantly degraded by fiber attenuation, chromatic dispersion (CD), polarization mode dispersion (PMD), laser phase noise (PN), and Kerr nonlinearities [1–10]. Using coherent detection, the powerful com- pensation of transmission impairments can be implemented in electrical domain. With the full information of the received signals, the chromatic dispersion, the polarization mode dispersion, the carrier phase noise, and the fiber Kerr nonlinearities can be equalized and mitigated using digital signal processing (DSP) [11–22].
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Adaptive digital signal processing Java teaching tool

Adaptive digital signal processing Java teaching tool

communicate at rates of 56k bps and above as a result of the integration of adaptive echo cancellers and adaptive equalization algorithms. Similarly, the new generation of mobile multimedia systems and set-top boxes will also require the use of adaptive DSP as will adaptive acoustic echo cancelation, arguably the next key ``plug-in card'' for PCs. Adaptive active noise cancellation is another hi-tech and mature technology found in the cabins of some airliners to reduce the level of noise. More generally, adaptive DSP can be found in biomedical systems, telecommunications systems, industrial control and so on. In this section we briefly review the key adaptive architectures, the generic adaptive signal processor, and also present a few applications.
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Digital Signal Processing Of Coherent and Generation Detection of QPSK Signal

Digital Signal Processing Of Coherent and Generation Detection of QPSK Signal

We demonstrate an optical quadrature phase-shift keying (QPSK) signal transmitter and an optical receiver for demodulating optical QPSK signal with homodyne detection and digital signal processing (DSP). DSP on the homodyne detection scheme is employed without locking the phase of the local oscillator (LO). In this paper, we present an extracting one-dimensional array of down-sampling method for reducing unwanted samples of constellation diagram measurement. Such a novel scheme embodies the following major advantages over the other conventional optical QPSK signal detection methods. First, this homodyne detection scheme does not need strict requirement on LO in comparison with linear optical sampling, such as having at spectral density and phase over the spectral support of the source under test. Second, the LabVIEW software is directly used for recovering the QPSK signal constellation without employing complex DSP circuit. Third, this scheme is applicable to multilevel modulation formats such as M-ary PSK and quadrature amplitude modulation (QAM) or higher speed signals by making minor changes.
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Vedic Mathematics for Digital Signal Processing Operations: A Review

Vedic Mathematics for Digital Signal Processing Operations: A Review

A MAC unit based on „Urdhva tiryakbhyam' algoriothm has been implemented on FPGA in [16]. In this the multiply accumulate unit computes product of two numbers and adds the product to accumulator. MAC unit consists of multiplier, adder and accumulator register to store the result. According to authors the 16X16 and 32X32 bit MAC modules show improved speed which may be used in DSP applications. From ref. [1]-[16] it can be understood that FFT is an algorithm which calculates N point DFT, FFT implementation needs large number of multiplications which are very complex and time consuming. Such issues can be solved by implementing multipliers by using Vedic mathematical operations. From ref.[1]-[16] Urdhva tiryakbhyam„ algoriothm is considered to be the best approach for speedy multiplication. According to ref.[17] Vedic FFT is superior in aspects like speed, simplicity, delay, area, power consumption etc. But for large numbers it suffers from high carry propagation delay. Ref. [18] presents a reconfigurable FFT design using Vedic multiplier with high speed and small area.
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Design of Vedic Multiplier for Digital Signal Processing Applications

Design of Vedic Multiplier for Digital Signal Processing Applications

Fixed point number representation is a good option to implement at silicon level.Hence our focus in this work is to develop optimized hardware modules for multiplication operation which is one of the most frequently used operation in signal processing applications like Fourier transforms, FIR and IIR filters, image processing systems, seismic signal processing, optical signal processing etc. Any attempt to come out with an optimized architecture for this basic block is advantageous during the product development stages. Considering fixed point representation, 16 bit Q15 format and 32 bit Q31 format provide required precision for most of the digital signal processing applications and it is best suited for implementation on processors. The advantage it provides over floating point multipliers is in the fact that Q format fraction multiplications can be carried out using integer multipliers which are faster and consume less die area. DSP Processors like TMS320 series from Texas Instruments work on 16 bit Q15 format. In this paper we propose the implementation of fixed point Q-format [6] high speed multiplier using Urdhava Tiryakbhyam method of Vedic mathematics. Further we have also implemented multipliers using normal booth algorithm and presented a comparative study on maximum frequency or speed of these multipliers.
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Digital Signal Processing Solutions for the Switched Reluctance Motor

Digital Signal Processing Solutions for the Switched Reluctance Motor

This document deals with solutions to control a switched reluctance motor using the TMS320C24x. This new DSP family enables cost-effective design of intelligent controllers for switched reluctance motors. Two speed control algorithms are presented, they allow the Switched Reluctance motor drive to reach high efficiency, smooth operation, very good dynamic behavior, very high speed and low acoustical noise. This article also presents some mechanical position sensorless algorithms in order to reduce the overall system cost and to enhance the drive reliability. All these solutions use only TMS320C24x resources, thus providing a single chip cost efficient control structure.
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New Digital Signal Processing Method for Spectrum Interference Monitoring

New Digital Signal Processing Method for Spectrum Interference Monitoring

Frequency spectrum is a limited shared resource, nowadays interested by an ever growing number of different applications. Generally, the companies providing such services pay to the governments the right of using a limited portion of the spectrum, consequently they would be assured that the licensed radio spectrum resource is not interested by significant external interferences. At the same time, they have to guarantee that their devices make an efficient use of the spectrum and meet the electromagnetic compatibility regulations. Therefore the competent authorities are called to control the access to the spectrum adopting suitable management and monitoring policies, as well as the manufacturers have to periodically verify the correct working of their apparatuses. Several measurement solutions are present on the market. They generally refer to real-time spectrum analyzers and measurement receivers. Both of them are characterized by good metrological accuracies but show costs, dimensions and weights that make no possible a use “on the field”. The paper presents a first step in realizing a digital signal processing based measurement instrument able to suitably accomplish for the above mentioned needs. In particular the attention has been given to the DSP based measurement section of the instrument. To these aims an innovative measurement method for spectrum monitoring and management is proposed in this paper. It performs an efficient sequential analysis based on a sample by sample digital processing. Three main issues are in particular
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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

The improvement of DSP eliminates the external source circuit in the system and became the sole heart of a controller system. The influence of DSP has much more to offer in the synchronization of computer and electronic hardware devices. It is an exciting development on electronic technology. The involvement of DSP in power electronics flourished the unimaginable progress in the power industry. The applications are found in the range of power systems, uninterruptible power supplies, controller for variable speed drives and interfacing other devices of several types.
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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

The data feedback output circuit mainly completes the output of the FOG’s closed-loop control signal. The output signal acts on the phase modulator to modulate phase, thus to complete closed- loop control. In order to meet the requirements of high-speed and real-time signal processing, a high-speed D/A convertor chip AD768 is used. The maximum set-up time of AD768 is 25ns maximum meeting the system requirements.TheAD768 clock signal is also given directly by a DSP.

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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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Various Applications of Digital Signal Processing DSP

Various Applications of Digital Signal Processing DSP

and storage of speed.Synthesis of Speechis the process of generating a speech signal using computational means for effective human-machine interaction. Speech Recognition is the process of extracting usable linguisticinformationfrom a speech signal in support of human-machine communication by voice which means translation of spoken words into text. Speech Verification is used for access to premises or information. Speech Enhancement is used in noisy environment. Language translation is conversion of one language into another to facilitate natural language dialogues between people speaking different languages, as in case of tourists and business people.
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The Essential Digital Signal Processing In HF Radar Target Detection

The Essential Digital Signal Processing In HF Radar Target Detection

processing, azimuth processing and subsequent processing. Pre-processing is done by the low-pass filtering and signal sampling. Range processing is used to produce the initial distance information, mainly consist of the low-pass filter processing and fast Fourier transform (FFT).

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Digital signal processing: IIR Digital Filter Design Research and Simulation on MATLAB

Digital signal processing: IIR Digital Filter Design Research and Simulation on MATLAB

In modern communication systems, filtering is the most common and extremely important signal processing technology, it is an effective method of interference suppression, and the design of filter has become the core issues of the signal processing. Generally speaking, filter can be divided into analog filter and digital filter. Today, the development of analog filter has been more mature. However, digital filter has many advantages, such as higher stability, higher precision. With the development of digital technology, using digital technology to realize filter function is widely used. Then this paper analyzes the design principle of digital filter, and introduced the impulse invariance method and window function method. This paper presents a new design of a effective IIR filter, which realized via two methods.
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LOW POWER AND AREA EFFICIENT MULTIPLIERS FOR DIGITAL SIGNAL PROCESSING

LOW POWER AND AREA EFFICIENT MULTIPLIERS FOR DIGITAL SIGNAL PROCESSING

All digital devices which deal with audio, speech, image, signal processing, multimedia content consists of one or more multiplier circuits. The multiplier is the basic and the most crucial unit of any Digital Signal Processor (DSPs). There are numerous algorithms and techniques used to perform multiplication of two numbers in any processor. In this paper we present a detailed study on three multipliers i.e. Booths Multiplier, Floating point Multiplier and Q Point Multiplier. Speed, area and precision play a major role in any multiplier. We have studied various multipliers in order to design the most efficient multiplier in terms of power consumption, speed, area and precision which can be used in several Signal processing applications.
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A digital signal processing solution for multichannel base stations

A digital signal processing solution for multichannel base stations

This relationship was used to develop a methodology that related the number of channels to the oversampling ratio required to comply with a communication system ACI specification when[r]

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