Representation In Signal Processing Research Articles

Wave2Graph: Integrating spectral features and correlations for graph-based learning in sound waves

This paper investigates a novel graph-based representation of sound waves inspired by the physical phenomenon of correlated vibrations. We propose a Wave2Graph framework for integrating multiple acoustic representations, including the spectrum of frequencies and correlations, into various neural computing architectures to achieve new state-of-the-art performances in sound classification. The capability and reliability of our end-to-end framework are evidently demonstrated in voice pathology for low-cost and non-invasive mass-screening of medical conditions, including respiratory illnesses and Alzheimer’s Dementia. We conduct extensive experiments on multiple public benchmark datasets (ICBHI and ADReSSo) and our real-world dataset (IJSound: Respiratory disease detection using coughs and breaths). Wave2Graph framework consistently outperforms previous state-of-the-art methods with a large magnitude, up to 7.65% improvement, promising the usefulness of graph-based representation in signal processing and machine learning.

Fast and efficient recursive algorithm of Meixner polynomials

Meixner polynomials (MNPs) and their moments are considered significant feature extraction tools because of their salient representation in signal processing and computer vision. However, the existing recurrence algorithm of MNPs exhibits numerical instabilities of coefficients for high-order polynomials. This paper proposed a new recurrence algorithm to compute the coefficients of MNPs for high-order polynomials. The proposed algorithm is based on a derived identity for MNPs that reduces the number of the utilized recurrence times and the computed number of MNPs coefficients. To minimize the numerical errors, a new form of the recurrence algorithm is presented. The proposed algorithm computes $$\sim $$ 50% of the MNP coefficients. A comparison with different state-of-the-art algorithms is performed to evaluate the performance of the proposed recurrence algorithm in terms of computational cost and reconstruction error. In addition, an investigation is performed to find the maximum generated size. The results show that the proposed algorithm remarkably reduces the computational cost and increases the generated size of the MNPs. The proposed algorithm shows an average improvement of $$\sim $$ 77% in terms of computation cost. In addition, the proposed algorithm exhibits an improvement of $$\sim $$ 1269% in terms of generated size.

A new steganography algorithm based on video sparse representation

Steganography has been a great interest since long time ago. There are a lot of methods that have been widely used since long past. Recently, there has been a growing interest in the use of sparse representation in signal processing. Sparse representation can efficiently model signals in different applications to facilitate processing. Much of the previous work was focused on image and audio sparse representation for steganography. In this paper, a new steganography scheme based on video sparse representation (VSR) is proposed. To exploit proper dictionary, KSVD algorithm is applied to DCT coefficients of Y component related to video (cover) frames. Both I and Q components of video frames are used for secure message insertion. The aim is to hide secret messages into non-zero coefficients of sparse representation of DCT called, I and Q video frames. Several experiments are performed to evaluate the performance of the proposed algorithm, in case of some metrics such as pick signal to noise ratio (PSNR), the hiding ratio (HR), bit error rate (BER) and similarity (Sim) of secret message, and also runtime. The simulation results show that the proposed method exhibits appropriate invisibility and robustness.

The Parallel Research of MP Sparse Decomposition in Grid Environment

Signal sparse representation in signal processing has many important applications, but the calculation amount of sparse decomposition is difficult to spread to realize industrialization because of its enormous calculation amount. At present, most of us make efforts to shorten the time of sparse decomposition calculation and improve the algorithm in order to make sparse decomposition feasible in its actual application. Although we make great achievements, the time cost by these improved algorithms is still difficult to be accepted. With the rapid development of internet in recent years, cloud computing and grid computing improve the computational ability so greatly that it can make a large amount of data calculation possible in reality. We introduce the grid computing into the sparse calculation so as to make its applicability possible in the practice. The thesis is to build a grid frame work. The performance of sparse decomposition is greatly improved by its calculating allocation to each node of grid computing, which makes it possible in its practical application.

Sparse representation and position prior based face hallucination upon classified over-complete dictionaries

In compressed sensing theory, decomposing a signal based upon redundant dictionaries is of considerable interest for data representation in signal processing. The signal is approximated by an over-complete dictionary instead of an orthonormal basis for adaptive sparse image decompositions. Existing sparsity-based super-resolution methods commonly train all atoms to construct only a single dictionary for super-resolution. However, this approach results in low precision of reconstruction. Furthermore, the process of generating such dictionary usually involves a huge computational cost. This paper proposes a sparse representation and position prior based face hallucination method for single face image super-resolution. The high- and low-resolution atoms for the first time are classified to form local dictionaries according to the different regions of human face, instead of generating a single global dictionary. Different local dictionaries are used to hallucinate the corresponding regions of face. The patches of the low-resolution face inputs are approximated respectively by a sparse linear combination of the atoms in the corresponding over-complete dictionaries. The sparse coefficients are then obtained to generate high-resolution data under the constraint of the position prior of face. Experimental results illustrate that the proposed method can hallucinate face images of higher quality with a lower computational cost compared to other existing methods.

Adaptive information processing in microtubule networks

Microtubule networks provide a wide range of microskeletal and micromuscular functionalities. Evidence from a number of directions suggests that they can also serve as a medium for intracellular signaling processing. The model presented here comprises an empirically motivated representation of microtubule growth dynamics, an abstract representation of signal processing, and a feedback learning mechanism that we refer to as adaptive self-stabilization. The growth model mimics the dynamic instability picture of microtubule formation and decomposition, but as modulated by the binding activity of microtubule associated proteins (or MAPs). The signal processing submodel treats each microtubule as a string of linked discrete oscillators capable of propagating signals that are introduced, manipulated, and extracted by bound MAP activity. Adaptive self-stabilization is essentially feedback acting on signal processing capabilities via the growth dynamics. The network is presented with a training set of patterns. If the input–output behavior is satisfactory MAP binding affinity increases, thereby stabilizing the network structure; otherwise the binding affinity decreases, allowing for more structural variation. The results obtained suggest that adaptive capabilities are practically inevitable in microtubule networks, a conclusion strengthened by the fact that the signal processing and growth dynamics mechanisms available in nature are undoubtedly much richer than those represented in the model.