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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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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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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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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(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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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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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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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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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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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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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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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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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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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**.

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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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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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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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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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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