Almost all biological phenomena, physiological functions and psycho-physiological conditions can be more completely ana- lyzed, in details, by contemporary technologies and methods. Foremost among these is digital signalprocessing (DSP). DSP is one of the most powerful technologies that will shape sci- ence and engineering in the twenty-first century. It refers to methods of filtering, processing and analyzing signals based on the assumption that signal amplitudes can be represented by a finite set of integers corresponding to the amplitude of the
In the past three decades, a lot of various applications of Ground Penetrating Radar (GPR) took place in real life. There are important challenges of this radar in civil applications and also in military applications. In this paper, the fundamentals of GPR systems will be covered and three important signalprocessing methods (Wavelet Transform, Matched Filter and Hilbert Huang) will be compared to each other in order to get most accurate information about objects which are in subsurface or behind the wall.
grouped by phase. It is also consistent with previously reported simulation studies  that pointed to the absence of an identiﬁable nonoscillating fraction of genes in our data. We believe that the source of the discrepancy results from the intuitive, but unfounded, assumption made in the formulation of the null hypothesis applied to tests of gene expression proﬁles for periodicity. It is natural to suppose that gene expression proﬁles that fail a statistical test for periodicity are not periodic (i.e., expressed in a steady-line manner). However, there are other reasons for the test to fail: the time series acquired in a microarray or RT-PCR experiment is too short, sampled at too low a level, and/or includes a high degree of stochastic variation. Thus, failure to pass a test for periodicity does not necessarily imply a steady-line expression proﬁle. This could simply reﬂect an oscillating pattern obstructed by noise. The use of RT-PCR as a standard for validation does not represent an improve- ment, because the number of points is no higher than that of the microarray experiment. Indeed, the low number of time points and thus low sampling rate is the main problem for identiﬁcation of periodicity in gene expression proﬁles. Consequently, all algorithms for analysis of periodicity are at a disadvantage when applied to one gene at a time. Although our same-phase continuum approach does not eliminate the problem of low sampling rate, it increases the statistical power by grouping genes oscillating in the same phase and scaled to the same amplitude. These genes are also similar in the likelihood of their oscillating pattern because they are next to each other in the window W, ranked by circadian autocorrelation. As a group, these genes provide a sufﬁcient number of time points to identify the pattern of circadian oscillation with conﬁdence. Conversion of single gene proﬁles into a long continuous stream allows application of digital signalprocessing to reduce the noise and enhance the signal.
conversion, equalization, and speech coding and recognition . The d-step prediction (d positive integer) is useful in Geophysical SignalProcessing and Economy. Here we generalise the concept for fractional steps. The basic idea underlying the proposed algorithm is to develop a system capable of linear predicting the signal over time instants, between the current ones, without converting the signal to the continuous-time domain. The new samples fit in between
The hybrid technique of ultrasonic signalprocessing im- plemented in this study, which is comprised for compu- tational procedures of detection, characterization and clas- sification showed a good performance to identify during mechanical agitation, the continuous and dispersed phase of a liquid mixture of sugar cane juice and yeast particles (species saccharomyces cerevisiae) during the alcoholic fermentation process. The identification of ultrasonic signals associated to insoluble solid particles allowed to obtain in the RoI an approximation of the variation of the solids percentage in the continuous phase. This can be very useful to analyze the dynamic behavior of the fluid inside the fermentation system such as growth of solid particles during the tumultuous phase and homogenization in the stationary phase (post- fermentation) described by the dilution and sedimentation of particles. This study also showed that a classical parameter as the integrated backscattering coefficient (IBC) was effective to describe quantitatively the main phases of the fermentation process. This parameter describes growth and depletion of biomass which can be correlated with the efficiency of the fermentative activity. Also, the correlation between estimated parameters and experimental measurements of pH, density and percentage of solid mass provided a good description of the fermentation time evolution such as exponential and deceleration phases. We expect to improve the classification process by robust implementation of a non linear classifier based in logic fuzzy and mining of input variables.
The Wigner Ville distribution offers a visual display of quantitative in formation about the way a signal’s energy is distributed in both, time and frequency. Through that, this distribution embodies the fundamen tally concepts of the Fourier and time domain analysis. The energy of the signal is distributed so that specific frequencies are localized in time by the group delay time and at specifics instants in time the frequency is given by the instantaneous frequency. The net positive volum of the Wigner distribution is numerically equal to the signal’s total energy. The paper shows the application of the Wigner Ville distribution, in the field of signalprocessing, using Scilab environment.
In engineering applications, either the unknown is an integer or a real-number, and errors may exist or not. In the latter, we have deterministic applications, related for instance to cryptography  , Digital Signature Standard (DSS) , image processing and security  , secret sharing schemes   and E-Voting systems . However, not only the remainders have errors, but also the unknown value is a real-valued number. Hence, beyond undersampling systems, CRT is also employed to estimate unknown numbers in the presence of errors such as in cognitive radio networks (CRN) , polynomial reconstruction - , electric encoders (EE) for motion control , and radio interferometric positioning system . Another signalprocessing application of CRT is related to phase unwrapping based systems for distance estimation  , where the remainders stand for the phase of arrival in terms of wavelength, and the moduli represent the wavelengths of each component. The state-of-the-art approaches for CRT estimation include the traditional CRT  , the robust CRT    , the closed-form robust CRT   and the maximum likelihood based robust CRT  . In the latter, an optimization of the search routine for a real-valued number is proposed assuming Gaussian distributed errors with dierent variances, whereas in the closed-form robust CRT the variances are presumed constant. There is still the Multi-Stage Robust CRT, which is proposed in  and consists in splitting the moduli of a CRT system over dierent moduli groups in accordance with the GCD of each set. The number of resulting groups is the number of stages. In , a generalization of the two-stage robust CRT algorithm to a multi-stage system is also presented. Splitting the moduli in groups with dierent GCD by group can improve the remainder error bound for a given set of moduli in terms of the remainder error bound of the entire CRT system. However, such a split is based on adopting for each moduli set the same concepts of the CFR-CRT. When all moduli share the same pairwise GCD as presumed in (2.1), applying the Multi-Stage Robust CRT does not improve the estimation of N in terms of the maximum tolerable error.
Furthermore, on the final stage of signalprocessing, a thresholding is applied to the raw data to generate point mea- surements that represent the existence of an existing target and its corresponding distance from the sensor. Since the detector is expected to achieve a high detection rate of the target with a low false detection rate, the threshold is re- quired to be adjusted at a proper level considering the state of the background environment. The constant false alarm rate (CFAR) detector is a well-known effective approach for such a problem, and has been applied to automotive radar ap- plications (Rohling and Mende, 1996). It is a method of adap- tive thresholding by which the background uncertainty is dy- namically calculated, so that it improves detection perfor- mance on a lower SNR target by applying the proper thresh- olding.
Abstract: In this paper, a new approach of signalprocessing for breath prediction pattern recogni- tion is proposed and further analyses are presented. In order to extract key values from raw data, a shift from time domain to phase space has been utilized. It helped to achieve clearer peak-to-peak measurements which are crucial for breath prediction pattern recognition. Based on a special software tool for breath prediction pattern recognition several different algorithms have been compared. As a result, a reduction in error rate can be achieved when applying a new signalprocessing approach in comparison to the previous designs.
The results management system is a series of cascading hierarchical folders created in Window XP system. The main folder consists of four subfolders ‘maximum’, ‘averaged’, ‘EFDD’ and ‘SSI’ for saving the information stemming from the corresponding signalprocessing operations. They have similar structure though they integrate different subfolders for saving different results. All specified directories for saving the processed results are conveniently implemented in the automated signalprocessing toolkit. When this toolkit operates, the results are saved to local disk automatically. It is convenient to create new folders, as well as the corresponding save directories used by the automated signalprocessing toolkit for application of the monitoring system to other structures. The size of each PNG file is no more than 15Kb and the TEXT file is less than 10Kb.
Fractional calculus is an area of mathematics that deals with derivatives and integrals of non integer order (i.e., real or, even, complex) that are joined under the name of differintegration. In the last decade, fractional calculus has been rediscovered by physicists and engineers and applied in an increasing number of fields [1-3], namely in the areas of signalprocessing, control engineering and electromagnetism [4-10, 18-20]. Despite the progress that has been made, several topics remain without a clear and concise formulation. Surprisingly, one of them is the definition of Fractional Differintegration (FD). In fact, there are several definitions that lead to different results [11-13], making the establishment of a systematic theory of fractional linear systems difficult. In facing this problem, we can adopt one of the following strategies:
2.1 Obtaining the differintegration operator In the previous section, we made a brief introduction to the fractional calculus. We were not exhaustive in the sense that there are several other definitions of fractional differintegration we did not consider here. However, it seems to be clear to exist an inherent difficulty in obtaining a definition with enough generality and compatibility with the usual SignalProcessing practice. In fact, in SignalProcessing, we frequently assume that the signals have R as domain and use the Bilateral Laplace (LT) and Fourier (FT) Transforms. With these tools, the remarkably important Transfer Function and Frequency Response concepts are defined, with properties we want to preserve in the fractional case. These considerations led us to start from the transform point of view in order to generalise to the fractional case well-known properties of the Laplace Transform. For example, if α is a real number we expect to obtain:
Low rank tensor decomposition has been playing for the last years an important role in many appli- cations such as blind source separation, telecommunications, sensor array processing, neuroscience, chemometrics, and data mining. The Canonical Polyadic tensor decomposition is very attractive when compared to standard matrix-based tools, manly on system identification. In this thesis, we propose: (i) several algorithms to compute specific low rank-approximations: finite/iterative rank-1 approxima- tions, iterative deflation approximations, and orthogonal tensor decompositions. (ii) A new strategy to solve multivariate quadratic systems, where this problem is reduced to a best rank-1 tensor approx- imation problem. (iii) Theoretical results to study and proof the performance or the convergence of some algorithms. All performances are supported by numerical experiments.
Popular image processing systems utilize digital signalprocessing (DSP) techniques . Aliasing and filtering are considered to be problematic processes in DSP based image processing systems , , . To overcome some of these shortcomings we propose an intelligent ANN system that is robust and immune to noise. Intelligent systems are widely applied in pattern recognition, security surveillance and biomedical imaging , . Furthermore changes in input data, such as illumination and varying noise levels, are handled more robustly by ANN systems , . This paper is arranged as follows: section 2 discusses DSP for motion detection and image
Continuous wavelet transform (CWT) was proposed for the simultaneous determination and dissolution proiles of valsartan (VAL) and hydrochlorothiazide (HCT) in tablets, without the use of a chemical separation procedure. The CWT approach was applied to the original UV spectra and their ratio spectra in the optimal wavelength ranges. After testing several wavelet families, Mexican hat function-CWT and Daubechies7-CWT (mexh-CWT and db7-CWT, respectively) were found to be suitable for the transformation of the original UV spectra. In the following procedure, mexh-CWT and Coilets3-CWT (coif3-CWT) were found to be appropriate for the signal analysis of ratio spectra (RS) of VAL/HCT and HCT/VAL. Calibration graphs for VAL and HCT were obtained by measuring db7-CWT and mexh-CWT amplitudes in the transformation of the original absorption spectra and RS-coif-CWT and RS-mexh- CWT amplitudes in the transformation of the ratio spectra. The validity and applicability of the proposed CWT methods were evaluated through the analysis of an independent set of synthetic binary mixtures consisting of VAL and HCT. The proposed signalprocessing methods were then successfully applied to the simultaneous quantitative evaluation and simultaneous dissolution proiles of the related drugs in commercial tablets, with good agreement reported for the experimental results.
After the de-spreading operation, the data estimates for each user ^a m;p are obtained by submitting ~a m;p to an hard-decision device and used to form an estimate of the chip samples ^s n . By submitting these chip estimates to a replica of the nonlinear signalprocessing chain at the transmitter we can obtain an estimate of the self-interference component f ^ D k g. For the next iteration, this self-interference estimate is removed form the frequency-domain samples at the output of the FDE. This procedure is repeated in an iterative way. For the first iteration
With the ever increasing number of subscribers and their seemingly “greedy” demands for high-data-rate services, the next generation networks will have to provide global connectivity to ensure success. So the combination of multiple-input multiple-output (MIMO) signalprocessing with orthogonal frequency division multiplexing (OFDM) is regarded as a promising solution for enhancing the data rates of next-generation wireless communication systems operating in frequency-selective fading environments.
Physical medicine and rehabilitation (PM&R), also known as physiatry or rehabilitation medicine, aims to enhance and restore the functional ability and quality of life to those with physical impairments or disabilities affecting the brain, spinal cord, nerves, bones, joints, ligaments, muscles, and tendons . Subjective and objective evaluations that are current used by physiotherapist provide information about rehabilitation process. The usage of scale physical rehabilitation outcome is a current method to extract information about motor capability of the patient under physical rehabilitation, however is highly affected by subjective elements that conduct to less accurate evaluation results. Nowadays, to increase the accuracy of the motor condition progress of the patients under physical rehabilitation, the smart sensors and advanced signalprocessing are used [1-2], however, there are still a lack of implementation in the field of cerebral palsy rehabilitation monitoring and physical rehabilitation outcome.