On the use of the Discrete-Time Convolution Model for the compensation of non-idealities within digital acquisition channels

This work studied the influence of MPEG-4 HE-AAC v.2 and MPEG-4 xHE-AAC compression algorithms on the reproduction performance of high-quality audio signals. The first part of the article is based on the analysis of earlier publications, studied quality of the compression algorithms for MPEG-4 ISO/IEC 14493-3 (AAC, AAC+SBR, PS, AAC+SBR+PS) and MPEG D Surround standards applied in MPEG-4 HE-AAC v.2 and MPEG-4 xHE-AAC codecs of DAB and DRM systems for digital audio broadcasting, respectively. It has been demonstrated that their use for processing of high-quality audio signals in the frequency range of 40…15000 Hz, allowed to reduce the digital-data rate at the output to 24…30 кbit/s, without observing noticeable phenomena. This conclusion was confirmed in the second part of the article, which described the results of relative comparison of the quality of reproduction of audio signals broadcasted via analogues FM or digital DRM channels. The equipment employed for their realization was described, as well as the methodology to obtain test recordings in order to perform the statistical data analysis. Further, the specific data scales were shown to evaluate the signal pairs being compared. Results of the experiments and their analysis confirmed that, for a digital data rate of 30 кbit/s obtained at the output of the MPEG-4 xHE-AAC coder, the difference in quality was rather weak for audio signals ranging from 40 to 15000 Hz, broadcasted via both analogues and digital channels. In addition, only a marginal difference was noticed for the signal pairs received from the digital output channels of the transmitter processor and DRM receiver. It has been demonstrated that the MPEG-4 xHE-AAC codec, compared to the previous MPEG-4 HE-AAC v.2 version, exhibited clear advantages for practical applications in terms of sound quality. Based on the gained experience, common criteria of deterioration of the data quality were formulated and generalized for all audio compression MPEG standards.

Since digital images are able to be encoded through the luminance-bandwidth-chrominance (YUV) mode, and the contribution of luminance information is greater than that of chrominance information for human visual perception, it can be inferred that the appropriate reduction of chrominance information in convolutional neural network does not disturb image object recognition. In this paper, we propose a new multi-scale non-uniform convolution called YuvConv, wherein the output feature map of the convolutional layer is regarded as an image. First, the output channels in the new convolution are divided into three kinds of components: Y, U, and V tensors. Then, the tensor Y is used to process luminance information, which is high-resolution and occupies more output channels. Next, the tensors U and V are low-resolution and use fewer channels to process chrominance information. Finally, the adjacent tensors (Y-U, Y-U-V, and U-V) are fused as the output of YuvConv. Experimental results indicate that the use of the YuvConv instead of the standard convolution can improve the performance of deep learning tasks, and it can also reduce memory consumption and computation cost.

In this paper we present a study of some tools needed for the identification of nonlinear digital channels and we provide some known identification algorithms. The study is presented in a pedagogical way so that allows a good comprehension of: (a) the nonlinear Volterra modelization of digital transmission channels. (b) Usual communication inputs and their properties. (c) The problem of identification of digital transmission channels. We discuss the two identification approaches: blind and non blind identification. (d) Some nonlinear identification algorithms known by their simplicity and efficiency. The study is also strengthened by some educational examples to enable the reader to see closely the different proposed concepts. This work is addressed to different researcher categories. For young researchers, they can find in this work many definitions and tools that help them in the comprehension of the nonlinear identification problems etc., and for the experimented researchers, they can find moreover different identification algorithms presented in a lucid manner

A recursive algorithm is developed which is both computationally compact and enables the nth-order frequency response functions of a large class of non-linear difference equation models to be found without restriction on the order n.

Recursive estimation of kernels of nonlinear rainfall-runoff models

The customer acquisition process consists of defining target markets and recruiting new customers. This chapter describes how marketers can use owned and earned Digital Marketing communication channels to connect with customers and lead them to digital sales channels. It discusses the two main forms of search engine marketing. Then, the chapter explains the concepts of link-building and link profiles. It also looks at the factors that determine the quality of the individual links, as well as what is necessary in terms of preparation for link-building. After explaining what a healthy link profile contains, the layers of the link profile are explained, using some visual examples. Then, the chapter explains the role of owned and earned social media channels during the customer acquisition process, the steps towards a digital customer acquisition campaign and methods for stimulating sales on social media. Finally, the chapter also discusses on email marketing and its role in the Digital Marketing communication funnel.

This work is a continuation of our preliminary tests on NIRCA - the Near Infrared Readout and Controller ASIC [1]. The primary application for NIRCA is future astronomical science and Earth observation missions where NIRCA will be used with mercury cadmium telluride image sensors (HgCdTe, or MCT) [2], [3]. Recently we have completed the ASIC tests in the cryogenic environment down to 77 K. We have verified that NIRCA provides to the readout integrated circuit (ROIC) regulated power, bias voltages, and fully programmable digital sequences with sample control of the analogue to digital converters (ADC). Both analog and digital output from the ROIC can be acquired and image data is 8b/10bencoded and delivered via serial interface. The NIRCA also provides temperature measurement, and monitors several analog and digital input channels. The preliminary work confirms that NIRCA is latch-up immune and able to operate down to 77 K. We have tested the performance of the 12-bit ADC with pre-amplifier to have 10.8 equivalent number of bits (ENOB) at 1.4 Msps and maximum sampling speed at 2 Msps. The 1.8-V and 3.3-V output regulators and the 10-bit DACs show good linearity and work as expected. A programmable sequencer is implemented as a micro-controller with a custom instruction set. Here we describe the special operations of the sequencer with regards to the applications and a novel approach to parallel real-time hardware outputs. The test results of the working prototype ASIC show good functionality and performance from room temperature down to 77 K. The versatility of the chip makes the architecture a possible candidate for other research areas, defense or industrial applications that require analog and digital acquisition, voltage regulation, and digital signal generation.

A versatile low-cost automation system for thermal ionization mass spectrometers

The problem of reliably transmitting a real-valued random vector through a digital noisy channel is relevant for the design of distributed estimation and control techniques over networked systems. One important example consists in the remote state estimation under communication constraints. In this case, an anytime transmission scheme consists of an encoder—which maps the real vector into a sequence of channel inputs—and a decoder—which sequentially updates its estimate of the vector as more and more channel outputs are observed. The encoder performs both source and channel coding of the data. Assuming that no channel feedback is available at the transmitter, this paper studies the rates of convergence to zero of the mean squared error. Two coding strategies are analyzed: the first one has exponential convergence rate but is expensive in terms of its encoder/decoder computational complexity, while the second one has a convenient computational complexity but subexponential convergence rate. General bounds are obtained describing the convergence properties of these classes of methods.

This study aims to develop a linear nonintrusive reduced-order model (NIROM) for facilitating combined fast parametric analysis and reconstruction of unsteady aerodynamic flow variables by using a novel signal interpolation approach, which is combined with a subspace-identification-based method. The NIROM is then applied to airfoil flutter and gust interaction problems where the assumption of linearized dynamics gives reasonably accurate estimates. In the traditional subspace-identification method, the training datasets of force coefficient signals corresponding to each flow parameter (like the Mach number and angle of attack) are required to generate the linear time-invariant system. The generation of the training signal for each flow parameter is the most computationally expensive process in any parametric reduced-order model (ROM). In the present work, the discrete empirical interpolation method (DEIM) combined with the radial basis function (RBF) method is used for the interpolation of training signals at a specific flow parameter to reduce the overall computational cost. The conventional nonintrusive approaches are generally constrained by the number of output channels, and hence not suitable for the reconstruction of unsteady surface force distribution or flowfield variables. A few suitable sensor locations using the DEIM are selected as output channels of the subspace-identification method to realize the full datasets of surface force distribution over the airfoil. Finally, a suitable algorithmic structure is proposed to achieve unsteady distributive flow-variable reconstruction and parametric variation simultaneously. The current signal interpolation-based NIROM is applied to a wing section with two- and four-degree-of-freedom structural motions.

A topic of interest in financial mathematics is the Black-Scholes model. However, the underlying asset price in the stock market may not be satisfied by this linear model, which was developed under a number of assumptions, including liquidity and the absence of transaction costs. The linear model has restricted its precision in actual market conditions. We study the transaction cost model for modelling illiquid markets from the extended nonlinear Black-Scholes model. Using a semi-discretization finite difference approach, the nonlinear partial differential equation is transformed into a nonlinear ordinary differential equation. Deep Learning (DL) is an advanced technique of machine learning solves the converted ordinary differential equation by fully connected neural network (FCNN). By modelling the complex and nonlinear relationships among market variables, DL models can generate option pricing forecasts that are more dependable and precise, not just for continuous data but also for discontinuous data (at jump point). Introduction: Nonlinear partial differential equation plays a crucial role in financial modelling, especially in the pricing of derivatives like options. The Black- Scholes model, introduced in 1973, continues to be one of the most widely used frameworks for pricing European options. However, this model is a linear one and offers an analytical solution, yet it is not appropriate for the complexities of real market assumptions that exhibit nonlinear effects. We examine the transaction cost model for modelling illiquid markets from the extended nonlinear Black-Scholes model created by Seelama et al. (2021). First using a semi-discretization finite difference approach, the nonlinear partial differential equation is transformed into a nonlinear ordinary differential equation. Solves the converted ordinary differential equation by Deep Learning (DL) based fully connected neural network (FCNN) algorithm. This algorithm is capable of handling the nonlinear behaviour of model and produce more accurate option value for European call. Objectives: Find the solution of more realistic nonlinear model of Black-Scholes equation include transaction cost in illiquid market with deep learning algorithm for a European call option. Methods: From extended nonlinear Black-Scholes model, the nonlinear model of transaction cost in illiquid market is considered for study. The nonlinear partial differential equation is converted into a nonlinear ordinary differential equation by semi-discretization finite difference method. DL is a sophisticated machine learning technique that solves transformed ordinary differential equations using fully connected neural network (FCNN) algorithm. DL algorithm uses a Python program. Results: For European call option, option values are predicted for different number of neurons with different loss functions like MSE and MAE at the time of maturity. Graphical representation shows the accuracy of the algorithm at continuous as well as at jump point (strike price). Conclusions: The method of solving a complex nonlinear partial differential equation by transforming it into a nonlinear ordinary differential equation is valuable. More precise pricing estimates for option values with nonlinear effects in financial data could be improved by deep learning.

MIMO system identification is a fundamental concern in a variety of applications. Various iterative and recursive MIMO system identification algorithms exist in the literature. The iterative algorithms suffer from high computational cost due to large matrix computations and recursive algorithms suffer from slow convergence speed. This paper proposes a fast recursive exact least squares algorithm for MIMO system identification with fast convergence speed and low computational cost. The recursions of the algorithm give rise to a lattice structure. The lattice structure is less sensitive to round-off errors, coefficients variations and can also be used for model order reduction. Although in this work we estimate an FIR (finite impulse response) model of the MIMO system, the framework can also be used for IIR (infinite impulse response) models, and for estimating linear periodically time varying (LPTV) and multivariable systems. The theory proposed is validated using simulation results.

The equalization using the traditional blind estimation is based on the channel outputs and knowledge of the probabilistic property of input signal. But it is difficult for conventional implementation with on-line processing because the traditional algorithms need high-order momentum. We present a new method using the orthogonal projection, in order to enable the on-line processing of blind equalization. First, we estimate the characteristics of the channel using both the skewness and the kurtosis of the output of the channel. Secondly, we will design an equalizer using the orthogonal projection onto the received signal space. The proposed method is based on designing an equalizer parameter so that the matrix P/sub N,N/=W/sub N,N/H/sub N,N/ may be the orthogonal projection matrix onto the received signal space, where W/sub N,N/ and H/sub N,N/ demote the impulse response matrix of an equalizer to be designed and that of the channel respectively. The impulse response matrix of an equalizer is basically expressed with Moore-Penrose inverse matrix. The proposed blind equalization algorithm can be implemented with on-line processing, it is expected that the convergence characteristics of the proposed equaliser are better than that of the traditional device.

Numerical simulation of three-dimensional (3D) seismic wavefields forms the basis of the research on the migration methods of 3D seismic data based on wave equations. Because the simulation precision of wavefield extrapolation determines the imaging accuracy to a certain extent, it is very important to study how to enhance the forward modeling precision of 3D seismic wavefields. Thus, we build on an optimized 3D staggered-grid finite-difference (SFD) method with high simulation precision based on two-dimensional (2D) seismic modeling. Since it generates the corresponding difference coefficients by utilizing the least square (LS) method to minimize the objective function constructed by the time-space domain dispersion relation of the 3D acoustic wave equation, our optimized time-space domain LS-based 3D SFD method can effectively enhance the modeling precision of the 3D seismic wavefields in theory compared with the 3D SFD methods based on the Taylor-series expansion (TE), especially for the large wavenumber range. Examining the numerical dispersion, algorithm stability and computational cost, we compare our optimized time-space domain LS-based 3D SFD method with three conventional TE-based and LS-based 3D SFD methods to illustrate and demonstrate its effectiveness and feasibility. The numerical examples from different 3D models suggest that our optimized time-space domain LS-based 3D SFD method can generate less numerical dispersion and higher simulation accuracy for 3D seismic wavefields than three other conventional 3D SFD methods, but its stability condition is stricter and its computational cost is slightly higher.

The error control of local interpolation for a 2D MLFMA will be discussed. The way to select proper parameters is proposed in terms of both numerical accuracy and computational cost. Satisfying the conditions derived in this paper, error can be controlled at the same level as global interpolation, and the computational cost becomes less expensive than the global one. © 2002 Wiley Periodicals, Inc. Microwave Opt Technol Lett 36: 8–12, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10655