Multidimensional signal processing

An application GUI (Graphical User Interface) is designed as a software simulation to compare the output signal of DFT and DTFT. DFT (Discrete Fourier Transform) and DTFT (Discrete Time Fourier Transform) are part of the digital signal processing. Digital signal processing is an analog signal processing method uses a mathematical technique to perform a transformation or retrieving information in digital form. One of the benefits of digital signal processing is to facilitate the representation of the signal, because the signal in digital form will be more visible, easily processed and has high accuracy. This GUI application designed using LabVIEW from National Instruments.  LabVIEW is a software graphical programming or a block diagram. LabVIEW program known as VI or Virtual Instrument. The input signal in this application is a square signal

Signal area estimation is a critical component of spectrum aware systems. It entails determining the subsets of elements of a time-frequency matrix where a signal is present. This study proposes and assesses the potential of a minesweeper algorithm in estimating accurately the signal area. The proposed method can be employed in two ways: as a standalone signal area estimation technique and also as a pre/post-processing technique in combination with other signal area estimation methods in order to correct signal detection errors before applying the other estimation method (pre-processing) and/or errors introduced by the other estimation method itself (post-processing). The performance of the proposed minesweeper algorithm in both application approaches is evaluated by means of software simulations. The obtained results show that, when used as a standalone method, it can provide similar or even better accuracy than other methods at a much lower computational cost. However, the best performance is obtained when used as a pre/post-processing technique in combination with other existing methods, without increasing significantly the total computation time.

Spectrum awareness is an essential aspect of wireless communication technology. Wireless communication systems can obtain spectrum awareness information by monitoring the spectrum usage in the frequency and time domains and representing this information as a time-frequency matrix. In many practical cases it is useful to determine the subsets of elements of such matrix where a signal is present (i.e., the signal area). Several signal area (SA) estimation methods with varying performance have been proposed in the literature. However, there is a lack of comparative research that shows how the configuration of such methods affects their relative performance. In this context, this work investigates the impact of two essential configuration aspects for any SA method, namely the threshold used to decide whether each element of the time/frequency grid contains a signal component or just noise, and the frequency/time resolution of the measurements carried out to obtain such data matrix. Several popular threshold decision criteria and a broad range of measurement resolutions are investigated, showing that these two particular aspects play a key role in the optimum configuration and performance of SA estimation methods. Several useful findings and design guidelines are provided as well.

An application GUI (Graphical User Interface) is designed as a software simulation to compare the output signal of DFT and DTFT. DFT (Discrete Fourier Transform) and DTFT (Discrete Time Fourier Transform) are part of the digital signal processing. Digital signal processing is an analog signal processing method uses a mathematical technique to perform a transformation or retrieving information in digital form. One of the benefits of digital signal processing is to facilitate the representation of the signal, because the signal in digital form will be more visible, easily processed and has high accuracy. This GUI application designed using LabVIEW from National Instruments.  LabVIEW is a software graphical programming or a block diagram. LabVIEW program known as VI or Virtual Instrument. The input signal in this application is a square signal

This article proposes a supervised tensor-based learning framework for classifying volcano-seismic events from signals recorded at the Ubinas volcano, in Peru, during a period of great activity in 2009. The proposed method is fully tensorial, as it integrates the three main steps of the automatic classification system (feature extraction, dimensionality reduction, and classifier) in a general multidimensional framework for tensor data, joining tensor learning techniques such as the multilinear principal component analysis (MPCA) and the support tensor machine (STM). By exploiting the use of multiple multichannel triaxial sensors, operating simultaneously in two seismic stations, the tensor patterns are constructed as stations × channels × features. The multidimensional structure of the data is then preserved, avoiding the tensor vectorization that often leads to a feature vector with a large dimension, which increases the number of parameters and may cause the "curse of dimensionality."Moreover, the array vectorization breaks down the multidimensional structure of the data, which usually leads to performance degradation. The results showed a good performance of the proposed multilinear classification system, significantly outperforming its vectorial counterparts. The best result was obtained with the STuM classifier along with the MPCA.

To precisely model the time dependency of features is one of the important issues for speech recognition. Segmental unit input HMM with a dimensionality reduction method has been widely used to address this issue. Linear discriminant analysis (LDA) and heteroscedastic extensions, e.g., heteroscedastic linear discriminant analysis (HLDA) or heteroscedastic discriminant analysis (HDA), are popular approaches to reduce dimensionality. However, it is difficult to find one particular criterion suitable for any kind of data set in carrying out dimensionality reduction while preserving discriminative information. In this paper, we propose a new framework which we call power linear discriminant analysis (PLDA). PLDA can be used to describe various criteria including LDA, HLDA, and HDA with one control parameter. In addition, we provide an efficient selection method using a control parameter without training HMMs nor testing recognition performance on a development data set. Experimental results show that the PLDA is more effective than conventional methods for various data sets.

Wavelets are functions that satisfy certain mathematical requirement and used in representing data or functions. Wavelets allow complex information such as data compression, signal recognition, signal and image processing, music and computer graphics etc. The wavelet decomposition analysis is used most often in wavelet signal processing. It is used in signal compression as well as in signal identification, although in the latter case, reconstruction of the original is not always required. The decomposition separates a signal into components at various scales corresponding to successive octave frequencies. Each component can be processed individually by a different algorithm. In this work we first try to introduce wavelet and then some of its applications and technique in signal and image processing. Here we also approach a new filtering technique.

sequence of numbers or symbols and the processing of these signals. Digital signal processing and analog signal processing are subfields of signal processing. DSP includes subfields like: audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, digital image processing, signal processing for communications, control of systems, biomedical signal processing, seismic data processing, etc. in this paper mainly concentrate on array signal processing. Array processing involves combining all sensor outputs in some optimal manner so that the coherent signals emitted by the source are received and all other inputs are maximally discarded. Beam forming algorithms are the foundation for all array processing techniques since this is an effective technique to estimate direction of arrival (DOA). The areas in which beam formers play an important role include radar, sonar, seismology etc. in this paper presents comparative study of beam forming techniques and its implementation on ADSP TS 201

The Fast Fourier Transform (FFT) is one of the most widely used and important signal processing functions, and probably one of the most used signal processing algorithms in the world. FFT is generally used in digital communication and in digital signal processing as well as it is widely used as a mathematical tool in different areas. The areas related to digital communications and image processing is an example for this. The results of FFT can also be achieved by DFT but the computations are more in DFT. An FFT computation takes approximately   N * log2(N) operations, whereas a DFT takes approximately N2 operations, so the FFT is significantly faster. The platform on which the application/algorithm is going to run also matters a lot as speed is totally dependent on the hardware and base software on which the application/algorithm is working. So it is very important to choose the proper platform to design an application. If we got the comparative result for this work it will be easy ...

High resolution methods such as the ESPRIT algorithm are of major interest for estimating discrete spectra, since they overcome the resolution limit of the Fourier transform and provide very accurate estimates of the signal parameters. In signal processing literature, most contributions focus on the estimation of exponentially modulated sinusoids in a noisy signal. In this paper, we introduce a more general class of signals, involving both amplitude and frequency modulations. We show that this Polynomial Amplitude Complex Exponentials (PACE) model is the most general model tractable by high resolution methods. We develop a generalized ESPRIT algorithm for estimating the signal parameters, and we show that this model can be characterized by means of a geometrical criterion.

Digital Signal Processing (DSP) is about a mathematical equation and mathematical operations. It is described by the significations of discrete period, discrete frequency, or supplementary discrete area signals by a order of numbers or signals and the processing of all the signals that related. Digital Signal Processing applications consist of the signal processing for communication. For example is the array processing for the mobile communications. Signal processing is a extensive area of scrutiny that extends from the easiest form of 1-D signal processing to the convoluted form of M-D and array signal processing. This report presents the

Nowadays, most of the electronic devices used Digital Signal Processing (DSP). This paper discussed the block diagram, radar detection, implementation of a convolution, Doppler processing, scanning, compression and filtering that been used in the applications of DSP in Radar purposes.

This paper examines theories of signal processing as applied to peak magnitude estimation in absorption and emission spectroscopy. Signals obtained from Fourier transform, fixed wavelength, and scanning dispersive instruments are modeled in terms of the time required to obtain a spectrum. The differences between these techniques and the signal processing procedures that should be used for each technique are characterized for a Lorentzian spectral feature. Including the time required to scan over a range of optical frequencies results in optimal signal processing procedures that are different from those previously supposed. In particular, it is found that the optimal matched filter is less efficient than repetitive measurements at a single frequency. The theory developed for the Lorentzian line model is extended to include an arbitrary shaped peak.

Spectral warping is a digital signal processing transform which shifts the frequencies contained within a signal along the frequency axis. The Fourier transform coefficients of a warped signal correspond to frequency-domain 'samples' of the original signal which are unevenly spaced along the frequency axis. This property allows the technique to be efficiently used for DSP-based analog and mixed-signal testing. The analysis and application of spectral warping for test signal generation, response analysis, filter design, frequency response evaluation, etc. are discussed in this paper along with examples of the software and hardware implementation.

The technology of synthesis of the nonparametric algorithms for processing of correlated random processes is proposed. The use of a Markov model of correlated signals allows to build Markov copula and synthesize the nonparametric rank algorithms on a single Ncube that use nonparametric estimation of a onedimensional cumulative distribution function using a training sample containing interference only. The theory of synthesis of nonparametric rank Markov decision rules is constructed, the problem of synthesis of rank nonparametric algorithm for detection of correlated signal against the background of uncorrelated noise on the output of amplitude demodulator is solved. Properties of this algorithm are investigated.

This article proposes a supervised tensor-based learning framework for classifying volcano-seismic events from signals recorded at the Ubinas volcano, in Peru, during a period of great activity in 2009. The proposed method is fully tensorial, as it integrates the three main steps of the automatic classification system (feature extraction, dimensionality reduction, and classifier) in a general multidimensional framework for tensor data, joining tensor learning techniques such as the multilinear principal component analysis (MPCA) and the support tensor machine (STM). By exploiting the use of multiple multichannel triaxial sensors, operating simultaneously in two seismic stations, the tensor patterns are constructed as stations × channels × features. The multidimensional structure of the data is then preserved, avoiding the tensor vectorization that often leads to a feature vector with a large dimension, which increases the number of parameters and may cause the "curse of dimensionality."Moreover, the array vectorization breaks down the multidimensional structure of the data, which usually leads to performance degradation. The results showed a good performance of the proposed multilinear classification system, significantly outperforming its vectorial counterparts. The best result was obtained with the STuM classifier along with the MPCA.

In this paper, a new Fourier technique for digital signal processing is developed. This approach to Fourier analysis is based on the number-theoretic method of the Miihius inversion of series. The Fourier transform method developed in this paper is shown also to yield the convolution of two signals. A computer simulation shows that this method to find Fourier coefficients is quite suitable for digital signal processing. Also, it competes with the classical FFT approach in terms of accuracy, complexity, and speed.

The instantaneous area illuminated by a single-aperture synthetic aperture radar (SAR) is fundamentally limited by the minimum SAR antenna area constraint. This limitation is due to the fact that the number of illuminated resolution cells cannot exceed the number of collected data samples. However, if spatial sampling is added through the use of multiple-receiver arrays, then the maximum unambiguous illumination area is increased because multiple beams can be formed to reject range-Doppler ambiguities. Furthermore, the maximum unambiguous illumination area increases with the number of receivers in the array. One spaceborne implementation of multiple-aperture SAR that has been proposed is a constellation of formation-flying satellites. In this implementation, several satellites fly in a cluster and work together as a single coherent system. There are many advantages to the constellation implementation including cost benefits, graceful performance degradation, and the possibility of performing in multiple modes. The disadvantage is that the spatial samples provided by such a constellation will be sparse and irregularly spaced; consequently, traditional matched filtering produces unsatisfactory results. We investigate SAR performance and processing of sparse, multiple-aperture arrays. Three filters are evaluated: the matched filter, maximum-likelihood filter, and minimum mean-squared error filter. It is shown that the maximum-likelihood and minimum mean-squared error filters can provide quality SAR images when operating on data obtained from sparse satellite constellations. We also investigate the performance of the three filters versus system parameters such as SNR, the number of receivers in the constellation, and satellite positioning error.

Time-frequency representations (TFRs) such as the spectrogram are important two-dimensional tools for processing time-varying signals. In this paper, we present the Java software module we developed for the spectrogram implementation together with the associated programming environment. Our aim is to introduce to students the advanced concepts of TFRs at an early stage in their education without requiring a rigorous theoretical background. We developed two sets of exercises using the spectrogram based on signal analysis and speech processing together with on-line evaluation forms to assess student learning experiences. In the paper, we also provide the positive statistical and qualitative feedback we obtained when the Java software and corresponding exercises were used in a signal processing course.

The construction of Gabor's complex signal-which is also known as the analytic signal-provides direct access to a real one-dimensional (1-D) signal's local amplitude and phase. The complex signal is built from a real signal by adding its Hilbert transform-which is a phase-shifted version of the signal-as an imaginary part to the signal. Since its introduction, the complex signal has become an important tool in signal processing, with applications, for example, in narrowband communication. Different approaches to anD analytic or complex signal have been proposed in the past. We review these approaches and propose the hypercomplex signal as a novel extension of the complex signal to-D. This extension leads to a new definition of local phase, which reveals information on the intrinsic dimensionality of the signal. The different approaches are unified by expressing all of them as combinations of the signal and its partial and total Hilbert transforms. Examples that clarify how the approaches differ in their definitions of local phase and amplitude are shown. An example is provided for the two-dimensional (2-D) hypercomplex signal, which shows how the novel phase concept can be used in texture segmentation.

Phase spaces are different ways to represent signals. Owing to their properties, they are often used for signal compression and recognition with high discrimination abilities. We present several recently introduced Wigner-related sets of representations that have improved signal processing performance, and we introduce an optical implementation. This study deals with the generalized Wigner spaces, the fractional Fourier transform, and the xp and the rp representations. The optical implementations are demonstrated and discussed.

A review of the discrete Fourier transform, emphasizing the use of DFT in direct and indirect methods of time domain signal processing. T HE discrete Fourier transform (DFT), implemented as a computationally efficient algorithm called the fast Fourier transform (FFT), has found application to all aspects of signal processing. These applications include time domain processing as well as frequency domain processing. The proper noun "Fourier" may elicit images of frequency domain data and, by these images, restrict the vista of applications of this important tool.. W e will review the DFT with emphasis on perspectives which facilitate time domain processing. In particular, we will review a number of applications to communications.

Sc. for attention, support, and versatile help that contributed greatly to expediting the writing of this book and improving its content. The author would like to express his frank acknowledgment to Allerton Press, Inc. and also to its Senior Vice President Ruben de Semprun for granted permissions that allow using the material published by the author in Radioelectronics and Communications System. The author would like to acknowledge understanding, patience, and support provided within Taylor & Francis Group by its staff and the assistance from Nora Konopka, Robin Lloyd-Starkes, and Prachi Mishra. The author would like also to thank Meeta Singh and her team from KnowledgeWorks Global Ltd. for providing help with the final text preparation. The author would like to express his thanks to all L A T E X developers whose tremendous efforts greatly lighten STEM book author's burden. Finally, seizing the opportunity, the author thanks all the readers that devoted their precious time to the first book of this series, and also express his deep appreciation to those of them, who formulated their critical remarks and gave valuable suggestions that will be taken into account when republishing the book.

Interpolated and warped digital waveguide mesh algorithms have been developed to overcome the problem caused by direction and frequency-dependence of wave travel speed in digital waveguide mesh simulations. This paper reviews the interpolation methods applicable in the two-dimensional case. The bilinear interpolation technique and two other approaches are briefly recapitulated. The use of 2-D quadratic interpolation results in a mesh structure that is efficiently implemented using only additions and binary shifts. The optimized interpolated 2-D mesh is obtained by choosing the free parameters of the interpolation method so that the difference in wave travel speed in the axial and diagonal directions is minimized. The output signal of any digital waveguide mesh can be processed with a warped FIR filter to reduce the dispersion error at all frequencies. The frequency-warping factor must be optimized individually for each mesh structure. We introduce an extended method called multiwarping, which can be used to further decrease the dispersion error. The frequency range of digital waveguide mesh simulations is also discussed.

This paper revisits an existing method of constructing high-dimensional probability density functions (PDFs) based on the PDF at the output of a dimension-reducing feature transformation. We show how to modify the method so that it can provide the PDF with the highest entropy among all PDFs that generate the given low-dimensional PDF. The method is completely general and applies to arbitrary feature transformations. The chain-rule is described for multi-stage feature calculations typically used in signal processing. Examples are given including MFCC and auto-regressive features. Experimental verification of the results using simulated data is provided including a comparison with competing generative methods.

A multidimensional signal processing method is described for detection of bleeding stroke based on microwave measurements from an antenna array placed around the head of the patient. The method is data driven and the algorithm uses samples from a healthy control group to calculate the feature used for classification. The feature is derived using a tensor approach and the higher order singular value decomposition is a key component. A leave-one-out validation method is used to evaluate the properties of the method using clinical data.

Digital signal processing (DSP) is concerned with the representation of discrete time signals by a sequence of numbers or symbols and the processing of these signals. Digital signal processing and analog signal processing are subfields of signal processing. DSP includes subfields like: audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, digital image processing, signal processing for communications, control of systems, biomedical signal processing, seismic data processing, etc. in this paper mainly concentrate on array signal processing. Array processing involves combining all sensor outputs in some optimal manner so that the coherent signals emitted by the source are received and all other inputs are maximally discarded. Beam forming algorithms are the foundation for all array processing techniques since this is an effective technique to estimate direction of arrival (DOA). The areas in which beam formers play an important role include radar, sonar, seismology etc. in this paper presents comparative study of beam forming techniques and its implementation on ADSP TS 201 processor

The hydrocarbon industry in Colombia is one of the principal pillars for the Colombian economy, representing around 5% of its gross domestic product. Since petroleum reserves have decreased, gas becomes one main alternative for economical growth. However, current gas pipelines have been in service for over 30 years and some of them are buried and phenomena, such as metal losses, corrosion, mechanical stress, strikes by excavation machinery, and another type of damages, are presented. The maintenance program of these structures is typically corrective type and is very expensive. To overcome this situation, the native research institute “Research Institute of Corrosion—Corporación para la Investigación de la Corrosión” recently developed an in-line inspection tool to be operated in Colombian gas pipelines to get valuable information of their current state along thousands of kilometers. A huge quantity of data is recorded (including tool movement, magnet, magnetic flow leakage, and cal...

Many concepts that are used in multi{dimensional signal processing are derived from one{dimensional signal processing. As a consequence, they are only suited to multi{dimensional signals which are intrinsically one{dimensional. We claim that this restriction is due to the restricted algebraic frame used in signal processing, especially to the use of the complex numbers in the frequency domain. We propose a generalization of the two{dimensional Fourier transform which yields a quaternionic signal representation. We call this transform quaternionic Fourier transform (QFT). Based on the QFT, we generalize the conceptions of the analytic signal, Gabor lters, instantaneous and local phase to two dimensions in a novel way which is intrinsically two{dimensional. Experimental results are presented.

The focus of Part I of this monograph has been on both the fundamental properties, graph topologies, and spectral representations of graphs. Part II embarks on these concepts to address the algorithmic and practical issues centered round data/signal processing on graphs, that is, the focus is on the analysis and estimation of both deterministic and random data on graphs. The fundamental ideas related to graph signals are introduced through a simple and intuitive, yet illustrative and general enough case study of multisensor temperature field estimation. The concept of systems on graph is defined using graph signal shift operators, which generalize the corresponding principles from traditional learning systems. At the core of the spectral domain representation of graph signals and systems is the Graph Discrete Fourier Transform (GDFT). The spectral domain representations are then used as the basis to introduce graph signal filtering concepts and address their design, including Chebys...

The focus of Part I of this monograph has been on both the fundamental properties, graph topologies, and spectral representations of graphs. Part II embarks on these concepts to address the algorithmic and practical issues centered round data/signal processing on graphs, that is, the focus is on the analysis and estimation of both deterministic and random data on graphs. The fundamental ideas related to graph signals are introduced through a simple and intuitive, yet illustrative and general enough case study of multisensor temperature field estimation. The concept of systems on graph is defined using graph signal shift operators, which generalize the corresponding principles from traditional learning systems. At the core of the spectral domain representation of graph signals and systems is the Graph Discrete Fourier Transform (GDFT). The spectral domain representations are then used as the basis to introduce graph signal filtering concepts and address their design, including Chebys...

: In the area of signal processing, considerable attention has been devoted to digital array processing in recent years. This attention is due to the increasingly wide use of array processing for both civilian and military purposes. Digital beamforming, for example, is popular because of its advantages in speed, accuracy, etc., over conventional analog beamforming. Many papers concerning digital beamforming have recently been published. It has been shown that the quality of performance of a beamformer, such as beam pattern, signal- to-noise ratio, etc., depends on a great extent on the number of sensors used, i.e., the more sensors used, the better the beam pattern becomes. In a practical situation, however, the number of sensors may be restricted by economical reasons or physical restrictions. In this situation one may weight the output of each sensor before beamforming. This helps to some extent, although the improvement is rather limited. A totally different issue, signal extrapo...

Recently, ESPRIT-based parameter estimation algorithms have been developed to exploit the structure of signals from strictly secondorder (SO) non-circular (NC) sources. They achieve a higher estimation accuracy and can resolve up to twice as many sources. However, these NC methods assume that all the received signals are strictly non-circular. In this paper, we present the C-NC Standard ESPRIT and the C-NC Unitary ESPRIT algorithms designed for the more practical scenario of a received mixture of circular and strictly non-circular signals. Assuming that the number of circular and strictly non-circular signals is known, the two proposed methods yield closed-form estimates and C-NC Unitary ESPRIT also enables an entirely real-valued implementation. As a main result, it is shown that the estimation accuracy of the presented algorithms improves with an increasing number of strictly non-circular signals among a fixed number of sources. Thereby, not only the estimation accuracy of the strictly non-circular signals themselves is improved, but also the estimation accuracy of the circular signals. These results are validated by simulations.

The Canonical Polyadic (CP) decomposition of Rway arrays is a powerful tool in multilinear algebra. Algorithms to compute an approximate CP decomposition from noisy observations are often based on Alternating Least Squares (ALS) which may require a large number of iterations to converge. To avoid this drawback we investigate semi-algebraic approaches that algebraically reformulate the CP decomposition into a set of simultaneous matrix diagonalization (SMD) problems. In particular, we propose a SEmi-algebraic framework for approximate CP decompositions via SImultaneous matrix diagonalization (SMD) and generalized unfoldings (SECSI-GU). SECSI-GU combines the benefits of two existing semi-algebraic approaches based on SMDs: the SECSI framework which selects the model estimate from multiple candidates obtained by solving multiple SMDs and the "Semi-Algebraic Tensor Decomposition" (SALT) algorithm which considers a "generalized" unfolding of the tensor in order to enhance the identifiability for tensors with R > 3 dimensions. The resulting SECSI-GU framework offers a large number of degrees of freedom to flexibly adapt the performance-complexity trade-off. As we show in numerical simulations, it outperforms SECSI and SALT for tensors with R > 3 dimensions.

The key task in ESPRIT-based parameter estimation is finding the solution to the shift invariance equation (SIE), which is often an overdetermined, linear system of equations. Additional structure is imposed if the two selection matrices, applied to an estimate of the signal subspace, overlap such that the subspace estimation errors on both sides of the SIE are highly correlated. In this paper, we propose a novel SIE solution for Standard ESPRIT and Unitary ESPRIT based on generalized least squares (GLS), assuming a uniform linear array (ULA) and maximum subarray overlap. GLS directly incorporates the statistics of the subspace estimation error via its covariance matrix, which is found analytically by a first-order perturbation expansion. As the subspace error covariance matrix is not invertible, we introduce a regularization with a clever choice of the regularization parameter. The resulting GLS-based Standard ESPRIT and Unitary ESPRIT algorithms achieve a superior performance over existing ESPRIT-type methods and almost attain the Cramér-Rao bound (CRB).

Прва меѓународна научна конференција "Влијанието на научно-технолошкиот развиток во областа на правото, економијата, културата, образованието и безбедноста во Република Македонија" Скопје 20-21 декември 2013 brought to you by CORE View metadata, citation and similar papers at core.ac.uk provided by UGD Academic Repository ЗБОРНИК НА ТРУДОВИ Прва меѓународна научна конференција 20-21.12.2013 Скопје ЗБОРНИК НА ТРУДОВИ: Прва меѓународна научна конференција "Влијанието на научно-технолошкиот развиток во областа на правото, економијата, културата, образованието и безбедноста во Република Македонија" Организатор: Институт за дигитална форензика Универзитет "Евро-Балкан"-Скопје Уредник: Проф.д-р Сашо Гелев Издавач: Универзитет "ЕВРО-БАЛКАН" Скопје Република Македонија www.euba.edu.mk

From 2011-2012, he was the inaugural Signal Processing Education Network (SPEN) Fellow. His research interests include realtime digital signal processing (DSP), the implementation of DSP-based systems, communication systems analysis, IED defeat, efficient simulation of communication systems, spread-spectrum techniques, and ultra-wideband systems.

General single-channel, multirate optimal filtering problem. Note that the estimate and observation signals may be at different rates.. .. .. 4.4 An illustration of ordinary causal FIR Wiener filtering and the relationship between samples of sequencesd[n] and x[n], P = 3.. .. .. . 4.5 An illustration of single-channel, multirate causal FIR Wiener filtering and the relationship between samples of sequencesd[n] and x[m], P = 2.

We first investigate 2D quaternion Fourier transform (QFT) spectrum relationship between an image and its geometrically transformed counterpart from the aspects of gray images and color images respectively, and then propose a 2D QFT-based color image registration approach, which is able to handle large translation, rotation and scaling. Fourier transform (FT) can be utilized in gray image registration but can not process color images naturally. As the extended version of FT in multidimensional signal processing, QFT is capable of processing three color components of color images together as pure quaternions and is able to deal with color image registration.

This paper describes a special-purpose processor for use in performing various operations on sampled signals. The system is fast, flexible, and programmable for performing, in real time, operations such as fast Fourier transformation (FFT) and digital filtering of acoustically derived signals. The main components of interest are a microprogrammed control unit (MCU) and a fully parallel arithmetic unit (AU) implemented with emitter-coupled logic (ECL) microcircuits. A memory unit (MU) and an input-output unit (IOU) are also included.

Nuclear Quadrupole Resonance (NQR) signal detection is a promising explosives detection technology with applications to humanitarian demining. NQR works in the radiofrequency range, and one challenge to using it in such an application is coping with radiofrequency interference. We herein present an algorithm that cancels strong and complex radiofrequency interference (either stationary or nonstationary) improving the utility of NQR-based landmine detection in humanitarian demining. The algorithm has been tested on both simulated and measured data, and demonstrates good performance in NQR detection, in the presence of strong and complex interference.