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

In resource allocation for secure cooperative communication systems, along with power allocation, relay placement also has equal importance in improving the system performance. In this paper, we study the joint optimization of relay placement and power allocation to minimize the secrecy outage probability (SOP) for a four- node cooperative system with a trusted randomize- and-forward relay in the presence of an external eavesdropper. Initially, we derive the expression of SOP, and then formulate an optimization problem to minimize SOP under given power budget and relay placement (RP) constraints. By providing analytical insights on power allocation (PA) between source and relay, we obtain closed form expressions of optimal PA (OPA) for a given RP that minimizes the SOP. Next, we propose a low complexity algorithm to obtain the near-optimal RP (ORP) for a given PA. Finally, we obtain optimal SOP with joint RP and PA. Numerical results present validation of analytical SOP through Monte Carlo simulations, optimal PA for a given RP, optimal RP for a given PA, and joint PA and RP for a given secrecy threshold rate. Finally, we highlight the significant performance gains achieved by the joint design over the conventional scheme.

Optimizing Secrecy Performance of Trusted RF Relay against External Eavesdropping

This paper proposes a method for gesture classification based on Graph Fourier transform (GFT) coefficients. GFT coefficients are the projection of image pixel block onto the eigenvectors of a Laplacian matrix. This Laplacian matrix is generated from undirected graph, representing a spatial connectedness between each pixel within an image block. This work proposes a method for generating an undirected graph by using edge information of the image. Edge information of the image is obtained by average sum of absolute difference between the current pixel and its neighboring pixels by using an appropriate threshold. The resulting GFT based feature vector is formed by concatenating GFT coefficients of each block. The resultant feature vector is applied to linear Support Vector Machine (SVM) classifier to predict the gesture class. For NTU and Massey hand gesture datasets, threshold value 30 gives maximum prediction accuracy. We compare the results of the proposed GFT based descriptor approach with Karhunen-Loeve transform (K-LT) and Discrete Cosine transform (DCT) based descriptors on three different gesture datasets: NTU, Cambridge and Massey. Simulation results show that the proposed GFT based descriptor gives a comparable results with Karhunen-Loeve transform (K-LT) and Discrete Cosine transform (DCT) based descriptors for gesture classification.

Graph fourier transform based descriptor for gesture classification

In this paper we introduce the concept of Adaptive Orthogonal Frequency Division Multiplexing (AOFDM) for two-way relay systems. In order to implement AOFDM at the nodes, the Channel State Information (CSI) is required. Usually CSI is estimated and hence the effect of estimation error should be considered while implementing AOFDM. In the case of two-way relay, channel estimation at the nodes usually result in the overall channel between the source and relay. But for implementing AOFDM, we require the individual channel between node and relay. We propose a simple method to obtain the individual channel assuming that the estimate of combined channel is available. The loading algorithm used is the Levin-Campello loading algorithm. But the general frame work provided by us can be used to implement other loading algorithms for two-way relay systems.

AOFDM based two-way relay systems

In this paper, we propose a learning based approach for alias minimization of 1-D signals. Given an under-sampled test speech signal and a training set consisting of several speech signals each of which are under-sampled as well as sampled at greater than Nyquist rate, we estimate the non-aliased frequencies for the test signal using the training set. The learning of non-aliased frequencies corresponds to estimating them using a training set. The test signal and each of the under-sampled training set signal are first interpolated to the size of The non-aliased signals. They are then divided into a number of segments and discrete cosine transform (DCT) is computed for each segment. Assuming that the lower frequencies are non-aliased and minimally distorted, we replace the aliased DCT coefficients of the test signal with the best search from the training set. The non-aliased test signal is then re-constructed by taking the inverse DCT. The comparison with the standard interpolation technique in terms of both subjective and quantitative analysis indicates better performance.

Alias minimization of 1-D signals using DCT based learning

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.

Vijay Kumar Chakka

Papers

Optimizing Secrecy Performance of Trusted RF Relay against External Eavesdropping

Graph fourier transform based descriptor for gesture classification

AOFDM based two-way relay systems

Alias minimization of 1-D signals using DCT based learning

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