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.

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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

One of the main issues of orthogonal frequency division multiplexing (OFDM) is the high peak-to-average power ratio (PAPR) of the transmitted signal which adversely affects the complexity of power amplifiers. The selected mapping (SLM) technique is one of the promising PAPR reduction techniques for OFDM. In this reported work, rows of normalised Riemann matrices are selected as phase sequence vectors for the SLM technique. MATLAB simulations show PAPR reduction of around 2.3 dB using the proposed method compared with methods reported in the literature.

SLM based PAPR reduction of OFDM signal using new phase sequence

Selected Mapping (SLM) technique is one of the promising PAPR reduction techniques for Orthogonal Frequency Division Multiplexing (OFDM). But this reduction technique results in a huge amount of computation complexity which is costly and time consuming. In this paper, Riemann sequences are used as a phase vectors along with the thresholding of the power amplifier(PA). The default index of the phase sequence is used to reduce the computational complexity of SLM technique. The default index of a phase sequence refers to the starting index for the phase vectors in the SLM scheme. This index is derived from the experimentation results. The Matlab simulations show that the complexity has been reduced by 98 percent if the threshold value of PA is around 3.0dB. Index Terms—OFDM, PAPR, Complexity, Riemann Sequence, Error function, Default Index.

A Low Complexity SLM Technique for PAPR Reduction in OFDM Using Riemann Sequence and Thresholding of Power Amplifier

Epileptic Seizure is a chronic nervous system disorder which is analyzed using Electroencephalogram (EEG) signals. This paper proposes a Graph Signal Processing technique called Graph Discrete Fourier Transform (GDFT) for the detection of epilepsy. EEG data points are projected on the Eigen space of Laplacian matrix of graph to produce GDFT coefficients. The Laplacian matrix is generated from weighted visibility graph constructed from EEG signals. It proposes Gaussian kernel based edge weights between the nodes. The proposed GDFT based feature vectors are then used to detect the seizure class from the given EEG signal using a crisp rule based classification. Simulation results show that the proposed GDFT based features from Gaussian Weighted Visibility Graph (VG) can detect epileptic seizure with 100 % accuracy.

Graph Signal Processing of EEG signals for Detection of Epilepsy

Suppression of interference from narrowband frequency signals play vital role in many signal processing and communication applications. A transform based method for suppression of narrow band interference in a biomedical signal is proposed. As a specific example Electrocardiogram (ECG) is considered for the analysis. ECG is one of the widely used biomedical signal. ECG signal is often contaminated with baseline wander noise, powerline interference (PLI) and artifacts (bioelectric signals), which complicates the processing of raw ECG signal. This work proposes an approach using Ramanujan periodic transform for reducing PLI and is tested on a subject data from MIT-BIH Arrhythmia database. A sum (E) of Euclidean error per block (e i ) is used as measure to quantify the suppression capability of RPT and notch filter based methods. The transformation is performed for different lengths (N), namely 36, 72, 108, 144, 180. Every doubling of N-points results in 50% reduction in error (E).

Removal of narrowband interference (PLI in ECG signal) using Ramanujan periodic transform (RPT)

This paper presents a MIMO two-way relaying scheme where the transceiver filter at the Relay Station (RS) processes the data using Least Mean Square Algorithm (LMS) and Normalized Least Mean Square (NLMS) algorithm. The relaying technique used is Amplify and Forward scheme (AF). Channel State Information (CSI) is assumed to be available only at the RS. So the transmit and receive processing are both done at the RS. The data at the two nodes S 1 and S 2 is Quadrature Phase Shift Keying (QPSK) modulated. The metrics considered for performance comparison of the LMS/NLMS transceiver filter is Mean Square Error (MSE) and Bit Error Rate (BER). The computer simulations are performed using MATLAB.

Vijay Kumar Chakka

Papers

SLM based PAPR reduction of OFDM signal using new phase sequence

A Low Complexity SLM Technique for PAPR Reduction in OFDM Using Riemann Sequence and Thresholding of Power Amplifier

Graph Signal Processing of EEG signals for Detection of Epilepsy

Removal of narrowband interference (PLI in ECG signal) using Ramanujan periodic transform (RPT)

Performance comparison of LMS/NLMS based transceiver filters for MIMO two-way relaying scheme