Showing papers on "Noise (signal processing) published in 1998"

Continuous probabilistic transform for voice conversion

Abstract: Voice conversion, as considered in this paper, is defined as modifying the speech signal of one speaker (source speaker) so that it sounds as if it had been pronounced by a different speaker (target speaker). Our contribution includes the design of a new methodology for representing the relationship between two sets of spectral envelopes. The proposed method is based on the use of a Gaussian mixture model of the source speaker spectral envelopes. The conversion itself is represented by a continuous parametric function which takes into account the probabilistic classification provided by the mixture model. The parameters of the conversion function are estimated by least squares optimization on the training data. This conversion method is implemented in the context of the HNM (harmonic+noise model) system, which allows high-quality modifications of speech signals. Compared to earlier methods based on vector quantization, the proposed conversion scheme results in a much better match between the converted envelopes and the target envelopes. Evaluation by objective tests and formal listening tests shows that the proposed transform greatly improves the quality and naturalness of the converted speech signals compared with previous proposed conversion methods.

A comparison of select trigger algorithms for automated global seismic phase and event detection

Abstract: Digital algorithms for robust detection of phase arrivals in the presence of stationary and nonstationary noise have a long history in seismology and have been exploited primarily to reduce the amount of data recorded by data logging systems to manageable levels. In the present era of inexpensive digital storage, however, such algorithms are increasingly being used to flag signal segments in continuously recorded digital data streams for subsequent processing by automatic and/or expert interpretation systems. In the course of our development of an automated, near-real-time, waveform correlation event-detection and location system (WCEDS), we have surveyed the abilities of such algorithms to enhance seismic phase arrivals in teleseismic data streams. Specifically, we have considered envelopes generated by energy transient (STA/LTA), Z -statistic, frequency transient, and polarization algorithms. The WCEDS system requires a set of input data streams that have a smooth, low-amplitude response to background noise and seismic coda and that contain peaks at times corresponding to phase arrivals. The algorithm used to generate these input streams from raw seismograms must perform well under a wide range of source, path, receiver, and noise scenarios. Present computational capabilities allow the application of considerably more robust algorithms than have been historically used in real time. However, highly complex calculations can still be computationally prohibitive for current workstations when the number of data streams become large. While no algorithm was clearly optimal under all source, receiver, path, and noise conditions tested, an STA/LTA algorithm incorporating adaptive window lengths controlled by nonstationary seismogram spectral characteristics was found to provide an output that best met the requirements of a global correlation-based event-detection and location system.

Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing

Abstract: This paper presents an algorithm for speckle reduction and contrast enhancement of echocardiographic images. Within a framework of multiscale wavelet analysis, the authors apply wavelet shrinkage techniques to eliminate noise while preserving the sharpness of salient features. In addition, nonlinear processing of feature energy is carried out to enhance contrast within local structures and along object boundaries. The authors show that the algorithm is capable of not only reducing speckle, but also enhancing features of diagnostic importance, such as myocardial walls in two-dimensional echocardiograms obtained from the parasternal short-axis view. Shrinkage of wavelet coefficients via soft thresholding within finer levels of scale is carried out on coefficients of logarithmically transformed echocardiograms. Enhancement of echocardiographic features is accomplished via nonlinear stretching followed by hard thresholding of wavelet coefficients within selected (midrange) spatial-frequency levels of analysis. The authors formulate the denoising and enhancement problem, introduce a class of dyadic wavelets, and describe their implementation of a dyadic wavelet transform. Their approach for speckle reduction and contrast enhancement was shown to be less affected by pseudo-Gibbs phenomena. The authors show experimentally that this technique produced superior results both qualitatively and quantitatively when compared to results obtained from existing denoising methods alone. A study using a database of clinical echocardiographic images suggests that such denoising and enhancement may improve the overall consistency of expert observers to manually defined borders.

List-mode likelihood: EM algorithm and image quality estimation demonstrated on 2-D PET

Abstract: Using a theory of list-mode maximum-likelihood (ML) source reconstruction presented recently by Barrett et al. (1997), this paper formulates a corresponding expectation-maximization (EM) algorithm, as well as a method for estimating noise properties at the ML estimate. List-mode ML is of interest in cases where the dimensionality of the measurement space impedes a binning of the measurement data. It can be advantageous in cases where a better forward model can be obtained by including more measurement coordinates provided by a given detector. Different figures of merit for the detector performance can be computed from the Fisher information matrix (FIM). This paper uses the observed FIM, which requires a single data set, thus, avoiding costly ensemble statistics. The proposed techniques are demonstrated for an idealized two-dimensional (2-D) positron emission tomography (PET) [2-D PET] detector. The authors compute from simulation data the improved image quality obtained by including the time of flight of the coincident quanta.

A measurement method based on the wavelet transform for power quality analysis

Abstract: The paper presents a measurement method for power quality analysis in electrical power systems. The method is the evolution of an iterative procedure already set up by the authors and allows the most relevant disturbances in electrical power systems to be detected, localized and estimated automatically. The detection of the disturbance and its duration are attained by a proper application, on the sampled signal, of the continuous wavelet transform (CWT). Disturbance amplitude is estimated by decomposing, in an optimized way, the signal in frequency subbands by means of the discrete time wavelet transform (DTWT). The proposed method is characterized by high rejection to noise, introduced by both measurement chain and system under test, and it is designed for an agile disturbance classification. Moreover, it is also conceived for future implementation both in a real-time measurement equipment and in an off-line analysis tool. In the paper firstly the theoretical background is reported and briefly discussed. Then, the proposed method is described in detail. Finally, some case-studies are examined in order to highlight the performance of the method.

Linear frequency-modulated signal detection using Radon-ambiguity transform

Abstract: A novel time-frequency technique for linear frequency modulated (LFM) signal detection is proposed. The design of the proposed detectors is based on the Radon transform of the modulus square or the envelope amplitude of the ambiguity function (AF) of the signal. A practical assumption is made that the chirp rate is the only parameter of interest. Since the AF of LFM signals will pass through the origin of the ambiguity plane, the line integral of the Radon transform is performed over all lines passing through the origin of the ambiguity plane. The proposed detectors yield maxima over chirp rates of the LFM signals. This reduces the two-dimensional (2-D) problem of the conventional Wigner-Ville distribution (WVD) based detection or the Radon-Wigner transform (RWT) based detector to a one-dimensional (1-D) problem and consequently reduces the computation load and keeps the feature of "built-in" filtering. Related issues such as the finite-length effect, the resolution, and the effect of noise are studied. The result is a tool for LFM detection, as well as the time-varying filtering and adaptive kernel design for multicomponent LFM signals.

Thresholding for change detection

Abstract: Image differencing is used for many applications involving change detection. Although it is usually followed by a thresholding operation to isolate regions of change there are few methods available in the literature specific to (and appropriate for) change detection. We describe four different methods for selecting thresholds that work on very different principles. Either the noise or the signal is modelled, and the model covers either the spatial or intensity distribution characteristics. The methods are: 1) a Normal model is used for the noise intensity distribution, 2) signal intensities are tested by making local intensity distribution comparisons' in the two image frames (i.e. the difference map is not used), 3) the spatial properties of the noise are modelled by a Poisson distribution, and 4) the spatial properties of the signal are modelled as a stable number of regions (or stable Euler number).

Time-frequency analysis of electroencephalogram series. III. Wavelet packets and information cost function

Abstract: Signals obtained during tonic-clonic epileptic seizures are usually neglected for analysis by the physicians due to the presence of noise caused by muscle contractions. Although noise obscures completely the recording, some information about the underlying brain activity can be obtained with wavelet transform by filtering those frequencies associated with muscle activity. One great advantage of this method over traditional filtering is that the filtered frequencies do not modify the pattern of the remanent ones. An accurate analysis of the different seizure stages was achieved using the wavelet packet method, and through the information cost function the brain dynamical behavior can be accessed. @S1063-651X~97!09712-2#

Frequency offset and symbol timing recovery in flat-fading channels: a cyclostationary approach

Abstract: Two open-loop algorithms are developed for estimating jointly frequency offset and symbol timing of a linearly modulated waveform transmitted through a frequency-flat fading channel. The methods exploit the received signal's second-order cyclostationarity and, with respect to existing solutions: (1) they take into account the presence of time-selective fading effects; (2) they do not need training data; (3) they do not rely on the Gaussian assumption of the complex equivalent low-pass channel process; and (4) they are tolerant to additive stationary noise of any color or distribution. Performance analysis of the proposed methods using Monte Carlo simulations, unifications, and comparisons with existing approaches are also reported.

Active noise cancellation aircraft headset system

Abstract: An active noise cancellation aircraft headset system. A speaker is mounted within each earcup of a headset for receiving and acoustically transducing a composite noise cancellation signal. A microphone is also mounted within each earcup for transducing acoustic pressure within the earcup to a corresponding analog error signal. An analog filter receives the analog error signal and inverts it to generate an analog broadband noise cancellation signal. The analog error signal is also provided to an analog to digital converter, which receives the analog microphone error signal and converts it to a digital error signal. A DSP takes the digital error signal and, using an adaptive digital feedback filter, generates a digital tonal noise cancellation signal. A digital to analog converter then converts the digital tonal noise cancellation signal to an analog tonal noise cancellation signal so that it can be combined with the analog broadband noise cancellation signal. The resultant composite cancellation signal is provided to the speakers in the earcups to cancel noise within the earcups. The broadband analog cancellation is effective to reduce overall noise within the earcup, and the DSP not only provides active control of the analog cancellation loop gain to maximize the effectiveness of the broadband analog cancellation but also uses the adaptive feedback filter/algorithm to substantially reduce at least the loudest tonal noises penetrating the earcup, including engine and propeller noises, as well as harmonic vibrations of components of the aircraft's fuselage.

Apparatus and method for automatic sensing threshold determination in cardiac pacemakers

Abstract: An automatic sensing system for an implantable cardiac rhythm management device comprises a variable gain amplifier and associated filters where the gain of the amplifier is adjusted as a function of the peak amplitude of a cardiac depolarization signal (either a P-wave or an R-wave) and especially the relationship of the peak value to a maximum value dictated by the circuit's power supply rail. The trip point comparator has its trip point adjusted as a function of the difference between the detected peak value of the signal of interest and the peak value of noise not eliminated by the filtering employed.

One-dimensional signal adaptive filter for reducing blocking effect and filtering method

Abstract: A one-dimensional signal adaptive filtering method and a one-dimensional signal adaptive filter. The one-dimensional signal adaptive filtering method capable of reducing a blocking effect of image data when a frame is composed of blocks of a predetermined size includes the steps of: (a) applying a one-dimensional window of a predetermined size along the boundaries of the blocks to perform a predetermined gradient operation on each pixel within the one-dimensional window; (b) calculating threshold values (T) for each pixel within the one-dimensional window, which is determined by a predetermined function of a quantization step (Q); (c) comparing the results of the gradient operation on each pixel within the one-dimensional window with the corresponding calculated threshold value to generate a comparison result as a binary edge map; (d) applying a one-dimensional filter window of a predetermined size on the generated binary edge map to generate weighted values using the binary value belonging to the one-dimensional filter window; and (e) performing filtering using the generated weighted values to generate new pixel values. The effect of the method is to eliminate blocking noise from a restored block-based image, thereby enhancing an image restored from compression.

Nonlinear and nonstationary signal processing

Abstract: Presents a brief discussion of the need for and evolution to nonlinear and nonstationary signal processing. Applications where these are useful are mentioned.

Adaptive-neighborhood filtering of images corrupted by signal-dependent noise.

Abstract: In many image-processing applications the noise that corrupts the images is signal dependent, the most widely encountered types being multiplicative, Poisson, film-grain, and speckle noise. Their common feature is that the power of the noise is related to the brightness of the corrupted pixel. This results in brighter areas appearing to be noisier than darker areas. We propose a new adaptive-neighborhood approach to filtering images corrupted by signal-dependent noise. Instead of using fixed-size, fixed-shape neighborhoods, statistics of the noise and the signal are computed within variable-size, variable-shape neighborhoods that are grown for every pixel to contain only pixels that belong to the same object. Results of adaptive-neighborhood filtering are compared with those given by two local-statistics-based filters (the refined Lee filter and the noise-updating repeated Wiener filter), both in terms of subjective and objective measures. The adaptive-neighborhood approach provides better noise suppression as indicated by lower mean-squared errors as well as better retention of edge sharpness than the other approaches considered.

Performance analysis of wavelength converters based on cross-gain modulation in semiconductor-optical amplifiers

Abstract: An analytical theory describing all-optical wavelength converters based on cross-gain modulation (XGM) in semiconductor-optical amplifiers is derived. Our theory consists of two parts: a large-signal analysis yielding the transmission function for the signal, and a small-signal analysis in order to describe the transformation of the signal and probe intensity noise. Both the large-signal as well as the small-signal theory reveal similar performance for the co- and the counterpropagating injection scheme for bit rates up to 2.5 Gb/s. This is confirmed by computer simulations. Consequently, the counterpropagating configuration is preferable because the implementation is simpler and conversion to the same wavelength is possible. In order to increase the conversion efficiency it is better to reduce the average signal power than to increase the probe power, which additionally reduces the output power range. However, there is a tradeoff between conversion efficiency and output extinction ratio. According to the small-signal analysis, the relative-intensity noise (RIN) due to the probe and due to the amplified spontaneous emission is negligible. Moreover, the converted signal has a lower RIN than the input signal.

An iterative algorithm for decomposition of speech signals into periodic and aperiodic components

Abstract: The speech signal may be considered as the output of a time-varying vocal tract system excited with quasiperiodic and/or random sequences of pulses. The quasiperiodic part may be considered as the deterministic or periodic component and the random part as the stochastic or aperiodic component of the excitation. We discuss issues involved in identifying and separating the periodic and aperiodic components of the source. The decomposition is performed on an approximation to the excitation signal, instead of decomposing the speech signal directly. The linear prediction residual signal is used as an approximation to the excitation signal of the vocal tract system. Speech is first analyzed to determine the voiced and unvoiced parts of the signal. Decomposition of the voiced part into periodic and aperiodic components is then accomplished by first identifying the frequency regions of harmonic and noise components in the spectral domain. The signal corresponding to the noise regions is used as a first approximation to the aperiodic component. An iterative algorithm is proposed which reconstructs the aperiodic component in the harmonic regions. The periodic component is obtained by subtracting the reconstructed aperiodic component signal from the residual signal. The individual components of the residual are then used to excite the derived all-pole model of the vocal tract system to obtain the corresponding components of the speech signal. Experiments were conducted using synthetic speech. They demonstrated the ability of the algorithm for decomposition of a synthetic speech signal made of a mixture of periodic and aperiodic components. Application to natural speech is also discussed.

Signal Analysis and Prediction

Abstract: This book surveys the latest information concerning methods of time-frequency and time-scale signal analysis, higher order statistics in signal processing, selected methods of signal identification, nonlinear modeling by neural networks, fuzzy-rule based systems, and methods of signal prediction and noise rejection. Many chapters include Matlab examples and visualizations.

Adaptive weighted myriad filter algorithms for robust signal processing in /spl alpha/-stable noise environments

Abstract: Stochastic gradient-based adaptive algorithms are developed for the optimization of weighted myriad filters (WMyFs). WMyFs form a class of nonlinear filters, motivated by the properties of /spl alpha/-stable distributions, that have been proposed for robust non-Gaussian signal processing in impulsive noise environments. The weighted myriad for an N-long data window is described by a set of nonnegative weights {w/sub i/}/sub i=l//sup N/ and the so-called linearity parameter K>0. In the limit, as K/spl rarr//spl infin/, the filter reduces to the familiar weighted mean filter (which is a constrained linear FIR filter). Necessary conditions are obtained for optimality of the filter weights under the mean absolute error criterion. An implicit formulation of the filter output is used to find an expression for the gradient of the cost function. Using instantaneous gradient estimates, an adaptive steepest-descent algorithm is then derived to optimize the weights. This algorithm involves a very simple update term that is computationally comparable to the update in the classical LMS algorithm. The robust performance of this adaptive algorithm is demonstrated through a computer simulation example involving lowpass filtering of a one-dimensional chirp-type signal in impulsive noise.

Fast 2D phantom dosimetry for scanning proton beams

Abstract: A quality control system especially designed for dosimetry in scanning proton beams has been designed and tested. The system consists of a scintillating screen (Gd2O2S:Tb), mounted at the beam-exit side of a phantom, and observed by a low noise CCD camera with a long integration time. The purpose of the instrument is to make a fast and accurate two-dimensional image of the dose distribution at the screen position in the phantom. The linearity of the signal with the dose, the noise in the signal, the influence of the ionization density on the signal, and the influence of the field size on the signal have been investigated. The spatial resolution is 1.3 mm (1 s.d.), which is sufficiently smaller than typical penumbras in dose distributions, The measured yield depends linearly on the dose and agrees within 5% with the calculations. In the images a signal to noise ration (signal/l s.d.) of 10(2) has been found, which is in the same order of magnitude as expected from the calculations. At locations in the dose distribution possessing a strong contribution of high ionization densities (i.e., in the Bragg peak), we found some quenching of the light output, which can be described well by existing models if the beam characteristics are known. For clinically used beam characteristics such as a Spread Out Bragg peak, there is at most 8% deviation from the NACP ionization chamber measurements. The conclusion is that this instrument is a useful tool for quick and reliable quality control of proton beams. The long integration-time capabilities of the system make it worthwhile to investigate its applicability in scanning proton beams and other dynamic treatment modalities. (C) 1998 American Association of Physicists in Medicine. [S0094-2405(98)02104-X].

Multivariate optical computation for predictive spectroscopy.

Abstract: A novel optical approach to predicting chemical and physical properties based on principal component analysis (PCA) is proposed and evaluated using a data set from earlier work. In our approach, a regression vector produced by PCA is designed into the structure of a set of paired optical filters. Light passing through the paired filters produces an analog detector signal that is directly proportional to the chemical/physical property for which the regression vector was designed. This simple optical computational method for predictive spectroscopy is evaluated in several ways, using the example data for numeric simulation. First, we evaluate the sensitivity of the method to various types of spectroscopic errors commonly encountered and find the method to have the same susceptibilities toward error as standard methods. Second, we use propagation of errors to determine the effects of detector noise on the predictive power of the method, finding the optical computation approach to have a large multiplex advantage over conventional methods. Third, we use two different design approaches to the construction of the paired filter set for the example measurement to evaluate manufacturability, finding that adequate methods exist to design appropriate optical devices. Fourth, we numerically simulate the predictive errors introduced by design errors in the paired filters, finding that predictive errors are not increased over conventional methods. Fifth, we consider how the performance of the method is affected by light intensities that are not linearly related to chemical composition (as in transmission spectroscopy) and find that the method is only marginally affected. In summary, we conclude that many types of predictive measurements based on use of regression (or other) vectors and linear mathematics can be performed more rapidly, more effectly, and at considerably lower cost by the proposed optical computation method than by traditional dispersive or interferometric instrumentation. Although our simulations have used Raman experimental data, the method is equally applicable to Near-IR, UV-vis, IR, fluorescence, and other spectroscopies.

A general theory of environmental noise in ecological food webs.

Abstract: We examine the effects of environmental noise on populations that are parts of simple two-species food webs. We assume that the species are strongly interacting and that one or the other population is affected by the noise signal. Further assuming that a stable equilibrium with positive population densities exists, we are able to perform a complete frequency analysis of the system. If only one of the populations is subject to noise, the relative noise response by both populations is fully determined by the sign of a single element of the Jacobian matrix. The analysis is readily extended to cases when both species are affected by noise or when the food web has more than two species. The general conclusion about relative responses to noise is then less unambiguous, but the power spectra describing the frequency composition of the population variabilities are nevertheless completely determined. These results are entirely independent on the exact nature of the interaction (i.e., predation, competition, mutualism) between the populations. The results show that the interpretation of the "color" of ecological time series (i.e., the frequency composition of population variability over time) may be complicated by species interactions. The propagation of noise signals through food webs and the importance of web structure for the expected response of all parts of the web to such signals is a challenging field for future studies.

Full-duplex speakerphone circuit including a supplementary echo suppressor

Abstract: A supplemental echo suppressor controls the attenuators in a transmit path and receive path to selectively set the attenuations based on the signal received from the far-end and the signal transmitted from the near-end. The attenuation levels are set to allow full-duplex communication. If both ends supply speech signals simultaneously, the attenuation is reduced, increasing the clarity of signals at both ends. Attenuation levels are also set when both ends are idle. If, however, the far-end user is talking and the near-end user is silent, and the echo at the near-end is low, then the attenuation is set high. Signal parameters are generated and monitored to control echo suppression. Suppression attenuation decisions are determined on the basis of normalized power estimates that are normalized to a background power estimate that is indicative of background noise. The power and noise parameters are used to determine engagement and disengagement of the suppressors. A slow noise signal is used as a control parameter for controlling suppression. The slow noise is a secondary, lower variance background power estimate that is derived from a background power estimate. The background power estimate, in turn, is derived from a power estimate of an input signal. An Echo Return-Loss Enhancement (ERLE) expectation value is used as a control parameter to discriminate between speech signals and echo signals which, in turn, is used to make suppression gain decisions.

Method for encoding and decoding a speech signal including background noise

Abstract: A method for encoding speech wherein an input speech signal is separated by a component separator into a first component mainly constituted by speech and a second component mainly constituted by a background noise at each predetermined unit of time, a bit allocation selector selects bit allocation for each component based on the first and second components from among a plurality of predetermined candidates for bit allocation, a speech encoder and a noise encoder encode the first and second components from the component separator based on the bit allocation according to predetermined different methods for encoding, and a multiplexer multiplexes encoded data of the first and second components and information on the bit allocation and outputs them as transmitted encoded data.

A novel psychoacoustically motivated audio enhancement algorithm preserving background noise characteristics

Abstract: In this paper we propose an algorithm for reduction of noise in audio signals. In contrast to several previous approaches we do not try to achieve a complete removal of the noise, but instead our goal is to preserve a pre-defined amount of the original noise in the processed signal. This is accomplished by exploiting the masking properties of the human auditory system. The speech and noise distortions are considered separately. The spectral weighting rule, adapted by utilizing only estimates of the masking threshold and the noise power spectral density, has been designed to guarantee complete masking of distortions of the residual noise. Simulation results confirm that no audible artifacts are left in the processed signal, while speech distortions are comparable to those caused by conventional noise reduction techniques.

Apparatuses and methods of suppressing a narrow-band interference with a compensator and adjustment loops

Abstract: Method of suppression of narrow-band interferences attending at the receiver input added to the useful broadband signal and noise. There is a disclosed compensator rejecting narrow-band interferences by means of adjustment loops. Two general methods of construction of such loops are considered. The first general method is based on filtration of the in-phase and quadrature components of the error vector—difference of the interference vector and compensating vector. The second method is based on filtration of the amplitude and full phase of the interference signal. Automatic tuning of the compensator to the mean frequency and effective interference band is ensured.

Masking patterns for sinusoidal and narrow-band noise maskers.

Abstract: The masking patterns produced by narrow-band maskers can show distinct irregularities. These experiments attempted to clarify the relative importance of factors contributing to these irregularities. A three-alternative adaptive forced-choice method with feedback was used, to promote use of the optimal detection cues. The masker and signal were either a sinusoid or a band of noise that was 80 Hz wide, giving four possible combinations of masker and signal type. In experiment 1, masking patterns were measured for maskers centered at 1 kHz, for all combinations of masker and signal type (tone or noise). The masking patterns showed irregularities (dips or “shoulders”) above the masker frequency, and the irregularities were larger for the sinusoidal than for the noise masker. Experiment 2 was similar to experiment 1, except that low-pass noise was added to mask combination products. For the noise masker, the low-pass noise slightly increased thresholds, and largely eliminated the irregularities in the patterns, but for the tone masker, the irregularities persisted. Experiment 3 used a noise signal with tone and noise maskers centered at 250, 1000, and 4000 Hz. The tone masker produced less masking than the noise masker for masker-signal frequency separations of 150–250 Hz, regardless of masker frequency. Experiment 4 used an additional masker tone to introduce beats similar to those produced by the interaction of the signal and (main) masker, and to mask combination products. This largely eliminated the dips in the masking patterns for both the noise and tone maskers. Experiment 5 used an additional pair of high-frequency tones to introduce beats, with similar results. We conclude that temporal fluctuations (beats) have a strong influence on the masking patterns for sinusoidal maskers, for masker-signal frequency separations up to a few hundred Hz. Beats may also have some influence on the masking patterns for noise maskers. The detection of combination products also plays a role.

Optical interface between receiver and tag response signal analyzer in RFID system for detecting low power resonant tags

Abstract: Noise reduction schemes are provided in a radio frequency identification (RFID) system for use with RFID intelligent tags. Fiber optics are used to communicate analog tag response signals from the output of the receiver circuit to the input of a tag response signal analyzing module, which includes a digital signal processor (DSP). The fiber optics creates electrical isolation between these circuit elements breaking ground loops, stopping internal switching noise from the DSP from entering the receiver circuitry, and preventing common mode signals from interfering with the desired RFID tag signal.