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

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 discusses a joint adaptive channel estimation and transceiver design scheme for MIMO two-way relay systems. In order to reduce the computational complexity at the transmitting nodes, the transceiver is implemented at the relay station. Adaptive transceiver consists of an adaptive post equalizer known as receive filter and an adaptive pre-equalizer known as transmit filter. The design of adaptive pre-equalizer requires the knowledge of Channel State Information (CSI) between nodes and relay station. So a channel tracking adaptive filter should be implemented at the relay. Due to the structure of a two-way relay system, the training signals used to train the receive filter can be used to train the channel tracking filter. Thus the adaptive channel estimator and receive filter work simultaneously. The channel estimate is then used to design the transmit filter. The adaptive filters are implemented using the NLMS algorithm. The performance of the proposed scheme is verified by making use of MATLAB simulations.

Joint Adaptive Channel Estimation and Transceiver Design for Two-Way Relay Systems

This paper presents an efficient algorithm for disparity map computation with an adaptive window by establishing two frame stereo correspondence. Adaptive window based approach has a clear advantage of producing dense depth maps from stereo images. In recent years there has not been much research on adaptive window based approach due its high complexity and large computation time. Adaptive window based method selects an appropriate rectangular window by evaluating the local variation of the intensity and the disparity. Ideally the window need not be rectangular but to reduce algorithmic complexity and hence computation time, rectangular window is taken. There is a need for correction of errors introduced due to the rectangular window which is not dealt by the existing algorithm. To reduce this error, a method has been proposed which not only improves the disparity maps but also has a lesser computational complexity. To demonstrate the effectiveness of the algorithm the experimental results from synthetic and real image pairs (provided by middlebury research group) including ones with ground-truth values for quantitative comparison with the other methods are presented. The proposed algorithm outperforms most of the existing algorithms evaluated in the taxonomy of dense two frame stereo algorithms. The implementation has been done in C++. The algorithm has been tested with the standard stereo pairs which are used as benchmark for comparison of algorithms in the taxonomy implementation.

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An efficient adaptive window based disparity map computation algorithm by dense two frame stereo correspondence

In this paper, 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 (RS) 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 Peak-Average Power Ratio (PAPR) and Bit-Error-Rate (BER). Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) 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.

Ramanujan Periodic Subspace Division Multiplexing (RPSDM)

Emotion classification plays an important role in the domain of human-computer interaction (HCI). In this paper, a novel framework for learning emotion-specific brain functional connectivity from EEG signals, with blockwise time-varying graph signal processing (GSP), is proposed. Graph corresponding to the last temporal block, which captures the spatio-temporal smoothness from all of the previous blocks is considered for extracting the Laplacian-based graph spectral features. The deviation range of the eigenvalues and their ratio corresponding to low-frequency and high-frequency components are proposed in the form of a convex sum feature. This feature is further utilized to classify cross-subject based positive and negative emotions using the KNN classifier. Simulation results on the benchmark DREAMER database validate the performance of the proposed method with metrics of accuracy, sensitivity, and specificity and are comparable with the existing state-of-the-art techniques.

Vijay Kumar Chakka

Papers

Removal of Narrowband Interference (PLI in ECG Signal) Using Ramanujan Periodic Transform (RPT)

Joint Adaptive Channel Estimation and Transceiver Design for Two-Way Relay Systems

An efficient adaptive window based disparity map computation algorithm by dense two frame stereo correspondence

Ramanujan Periodic Subspace Division Multiplexing (RPSDM)

Time-Varying Graph Signal Processing Based Cross-Subject Emotion Classification from Multi-Electrode EEG Signals