From the Publisher: 
This is undoubtedly the most accessible book on digital signal processing (DSP) available to the beginner. Using intuitive explanations and well-chosen examples, this book gives you the tools to develop a fundamental understanding of DSP theory. The author covers the essential mathematics by explaining the meaning and significance of the key DSP equations. Comprehensive in scope, and gentle in approach, the book will help you achieve a thorough grasp of the basics and move gradually to more sophisticated DSP concepts and applications.

数字信号处理 = Understanding digital signal processing

An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique

A maximum likelihood (ML) estimator for digital sequences disturbed by Gaussian noise, intersymbol interference (ISI) and interchannel interference (ICI) is derived. It is shown that the sampled outputs of the multiple matched filter (MMF) form a set of sufficient statistics for estimating the input vector sequence. Two ML vector sequence estimation algorithms are presented. One makes use of the sampled output data of the multiple whitened matched filter and is called the vector Viterbi algorithm. The other one is a modification of the vector Viterbi algorithm and uses directly the sampled output of the MMF. It appears that, under a certain condition, the error performance is asymptotically as good as if both ISI and ICI were absent.

Maximum Likelihood Receiver for Multiple Channel Transmission Systems

Orthogonal frequency division multiplexing (OFDM) is an efficient multi-carrier modulation technique that underlies most of the current and probably future high-speed wireless communication systems. However, the OFDM waveform is characterized by a high peak-to-average power ratio (PAPR), especially when a large number of subcarriers are used. A high PAPR is a major waveform defect since it leads to non-linear distortion when passing through the transmitter’s power amplifier. Most of the PAPR reduction techniques found in the literature reduce the PAPR mainly at the cost of either excessive computational complexity or degrading the transmission bit error rate (BER). We propose a low-complexity technique for PAPR reduction based on linear scaling of a portion of signal coefficients by an optimal factor. This paper is backed up by the extensive analysis of various performance metrics, which leads to optimal choices of key parameters and hence maximum achievable gains. The analytic and simulated results show that the proposed technique is capable of reducing the PAPR effectively with negligible effect on BER in return for a slight reduction in data rate. For example, for 1024 subcarriers, the PAPR can be reduced from 13 dB to below 7.4 or 6.9 dB, in return for only 1% or 2% reduction in data rate, respectively. In addition, the achievable PAPR varies very slightly in response to increasing the number of subcarriers. This offers a highly competitive and flexible tradeoff compared with those provided by current techniques found in the literature. Therefore, this technique has a very good potential for practical application in current and future OFDM-based systems, especially those which employ a very large number of subcarriers, such as LTE, DVB-T2, and 5G systems.

/pdf/an-optimal-low-complexity-papr-reduction-technique-for-next-50uejlzj05.pdf

An Optimal Low Complexity PAPR Reduction Technique for Next Generation OFDM Systems

With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

In this study, a new modulation method defined as Ramanujan periodic subspace division multiplexing (RPSDM) is proposed using Ramanujan subspaces. Each subspace contains an integer-valued Ramanujan sum and its circular downshifts as a basis. The proposed RPSDM decomposes the linear time-invariant wireless channels into a Toeplitz stair block diagonal matrices, whereas orthogonal frequency division multiplexing (OFDM) decompose the same into diagonal. Advantages of such structured subspaces representation are studied and compared with an OFDM representation in terms of a peak-average power ratio and bit error rate. Zero-forcing and minimum mean square error detectors are applied to evaluate the performance of OFDM and RPSDM techniques. Finally, the simulation results show that the proposed design (with an additional receiver complexity) outperforms OFDM under both detectors.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-com.2018.5828

Ramanujan periodic subspace division multiplexing

In this letter, we study a few properties of Complex Conjugate Pair Sums (CCPSs) and Complex Conjugate Subspaces (CCSs). Initially, we consider an LTI system whose impulse response is one period data of CCPS. For a given input x(n), we prove that the output of this system is equivalent to computing the first order derivative of x(n). Further, with some constraints on the impulse response, the system output is also equivalent to the second order derivative. With this, we show that a fine edge detection in an image can be achieved using CCPSs as impulse response over Ramanujan Sums (RSs). Later computation of projection for CCS is studied. Here the projection matrix has a circulant structure, which makes the computation of projections easier. Finally, we prove that CCS is shift-invariant and closed under the operation of circular cross-correlation.

On Complex Conjugate Pair Sums and Complex Conjugate Subspaces

In this paper we propose an adaptive channel estimation scheme for Amplify and Forward (AF) two-way relay systems with frequency selective and fast varying channels. The modulation used is Orthogonal Frequency Division Multiplexing (OFDM). This adaptive filter is called Block Fast Array Multichannel 2D Recursive Least Square (BFAM 2D-RLS) filter. The complexity of this adaptive filter is comparable to that of Least Mean Square (LMS) algorithm while maintaining similar convergence rate as that of the standard Recursive Least Mean Square (RLS) algorithm. Computer simulation using Matlab is performed to analyse the performance of BFAM 2D-RLS filter in estimating fast varying Vehicular-A (Veh-A) channel. The results obtained is compared with slow varying channels.

BFAM 2D-RLS channel estimation for frequency selective environment in two-way relay

In signal processing applications the information about the signal such as frequency (or) period is known a prior for most of the practical signals like ECG, EEG, speech, etc. Inspired by this, in this paper, we propose a new signal representation to estimate the period and frequency information of a given signal with low computational complexity. We achieve this by representing a finite-length discrete-time signal as a linear combination of signals belongs to Ramanujan subspaces. Further, we evaluate the performance of the proposed representation with a simulated example and also by addressing the problem of reducing Power Line Interference (PLI) in an ECG signal. Finally, for a given integer-valued signal, we show that the computational complexity of the proposed transform is quite low in comparison with the existing transforms, and it is quite comparable for a given real (or) complex-valued signal.

Signal Representation Using Ramanujan Subspaces Utilizing A Prior Signal Information

MassiveMIMO(MaMI) is one of the fiery topics in 5G wireless communications. It is introduced based on the fact that the channel vectors becomes asymptotically orthogonal (as antenna elements tending to infinity). Convergence of frequency selective channel (quasi static block fading) characteristics in terms of favorable propagation and channel hardening (asymptotically orthogonal) conditions are studied using metrics like Eigenvalue Ratio (EVR), Mean Absolute Deviation (MAD) and Diagonal Dominance (DD). Finally, simulations results for convergence metrics EVR, MAD and DD are compared with its limiting behavior. The effect of time delay spread on the channel convergence is studied based on the condition number of channel matrix.

Vijay Kumar Chakka

Papers

Ramanujan periodic subspace division multiplexing

On Complex Conjugate Pair Sums and Complex Conjugate Subspaces

BFAM 2D-RLS channel estimation for frequency selective environment in two-way relay

Signal Representation Using Ramanujan Subspaces Utilizing A Prior Signal Information

Convergence of MassiveMIMO Frequency Selective Channels