Signal Processing Community Research Articles

Periodically nonuniform sampling and averaging of signals in multiresolution subspaces associated with the fractional wavelet transform

Periodically nonuniform sampling is one of the most fundamental theories in the signal processing community. Several sampling theorems, which aid in the reconstruction of periodically nonuniform sampled signals in the fractional Fourier transform (FrFT) and the linear canonical transform (LCT) domain, have been proposed in the existing literature. However, we are given only finite number of samples in practice. Therefore, these theorems may not be suitable for the perfect reconstruction of a signal because of the slow decay of synthesizing functions. Focusing on this issue, we first investigate the reconstruction of a periodically nonuniform sampled signal in multiresolution subspaces associated with the recently-proposed fractional wavelet transform (FrWT). By means of perfect reconstruction (PR) filter banks associated with the FrFT, we derive the expressions of the synthesizing functions which can be guaranteed to be compactly supported, under a mild restriction that the number of channels is constrained by the support of scaling function. In order to relax this restriction, we further propose a periodically nonuniform averaging and reconstruction theorem of signals in multiresolution subspaces associated with the FrWT. Simulation results are presented to validate our algorithms.

Hardware Efficient Reciprocal Using Second Order Harmonized Parabolic Synthesis and Squaring Shrunk Method

In applications as in wireless communication, computer graphics and digital signal processing, a massive of complex matrix operations is often performed. Reciprocal is computed in large quantities in these matrix operations. To obtain high performance, efficient algorithm and hardware architecture are important in terms of low cost, low computation time and high precision. Second order first sub-function and squaring shrunk method have been proposed to build efficient hardware architecture for reciprocal using field programmable gate array. Second order first sub-function in harmonized parabolic synthesis is presented to improve the approximating precision and decrease the memory usage at the cost of additional multipliers. To further reduce the complexity, squaring shrunk method is proposed to decrease the expensive cost of multipliers. The combination of these techniques yields good performance trade-off. Precision simulation and hardware implementation result has shown that hardware reciprocal of high precision, low memory and low multiplier usage has been obtained compared to traditional first order first sub-function harmonized parabolic synthesis method.DOI: http://dx.doi.org/10.5755/j01.eie.24.2.20635

Slow-light transmission with high group index and large normalized delay bandwidth product through successive defect rods on intrinsic photonic crystal waveguide

We proposed a strategy with successive cavities as energy reservoirs of electromagnetic energy and light-speed reducers introduced in the first and second rows of rods on the walls of an intrinsic photonic crystal waveguide (PCW) for slow-light transmission in the PCW concerning applications for optical communication, optical computation and optical signal processing. Subsequently, plane-wave expansion method (PWE) is used for studying slow-light properties and finite-difference time-domain (FDTD) method to demonstrate the slow-light propagating property of our proposed structure. We obtained group index as exceedingly large as 6123 with normalized delay bandwidth product (NDBP) as high as 0.48. We designed a facile but more generalized structure that may provide a vital theoretical basis for further enhancing the storage capacity properties of slow light with wideband and high NDBP.

Uncertainty Inequalities for the Linear Canonical Hilbert Transform

The Heisenberg uncertainty principle plays an important role in signal processing, applied mathematical and physics community. The extensions of the traditional uncertainty principle to the novel transforms have received considerable attention recently. In this paper, the uncertainty inequalities under the linear canonical Hilbert transform (LCHT) are considered. First, the uncertainty principle for the complex signals associated with the one-dimensional LCHT is investigated. Subsequently, the uncertainty inequality for the complex signals derived by the two-dimensional LCHT is proposed. It is shown that the uncertainty bounds for the complex signals derived by LCHT are different from the general complex signals in the LCT domain. In addition, the example and potential applications are also presented to show the correctness and useful of the derived results. Therefore, the uncertainty principles presented here not only can enrich the ensemble of the classical uncertainty principle, but can have many real applications too.

A Delay Efficient Vedic Multiplier

Vedic mathematics is the ancient Indian method of mathematics based on 16 Sutras applicable to various branches of mathematics like trigonometry, calculus, geometry, conics etc. Multiplication is effectively used in modern communication and Digital Signal Processing applications. Ordinary multiplication requires propagation of carry from LSB to MSB while adding binary partial products, which limits the overall speed of multiplication. Vedic mathematics helps in generation of partial products and sums in one step, and ensures reduction in overall propagation delay. Urdhva Tiryakbhyam Sutra and Nikhilam Sutra are the two multiplication techniques used in Vedic mathematics. In this paper, an 8 * 8 Nikhilam Sutra multiplier for three different sets of bases is realized. The concepts of Urdhva Tiryakbhyam Sutra multiplication are used for the implementation of the proposed multiplier. The implementation results are compared with that of a Modified Booth’s multiplier in terms of delay, area and power. The design is synthesized in Synopsys Design Compiler using CMOS 90 nm technology, and results show that the proposed multiplier using Nikhilam Sutra with 25 bases is faster than the Modified Booth’s multiplier by 51.28%.

Novel Uncertainty Principles for Two-Sided Quaternion Linear Canonical Transform

The uncertainty principle, which offers information about a function and its Fourier transform in the time-frequency plane, is particularly powerful in mathematics, physics and signal processing community. In this paper, based on the fundamental relationship between the quaternion linear canonical transform (QLCT) and quaternion Fourier transform (QFT), we propose two different uncertainty principles for the two-sided QLCT. It is shown that the lower bounds can be obtained on the product of spreads of a quaternion-valued function and its two-sided QLCT from newly derived results. Furthermore, an example is given to verify the consequences. Finally, some possible applications are provided to demonstrate the usefulness of new uncertainty relations in the QLCT domain.

Digital Signal Processing for Short-Reach Optical Communications: A Review of Current Technologies and Future Trends

Driven primarily by cloud service and data-center applications, short-reach optical communication has become a key market segment and growing research area in recent years. Short-reach systems are characterized by direct detection-based receiver configurations and other low-cost and small form factor components that induce transmission impairments unforeseen in their coherent counterparts. Innovative signaling and digital signal processing (DSP) play a pivotal role in enabling these components to realize their ultimate potentials and meet data rate requirements in cost-effective manners. This paper presents an overview of recent DSP developments for short-reach communications systems and discusses future trends.

Perspectives in Signal Processing for Communications and Networking: Spotlight on the IEEE Signal Processing Society Technical Committees [In the Spotlight

Presents the mission and work of the Signal Processing for Communications and Networking Technical Committee (SPCOM-TC). Also reports on major areas of research, key events and meetings, and future areas of research and development.

Orthogonal Ramanujan Sums, its properties and Applications in Multiresolution Analysis

Signal processing community has recently shown interest in Ramanujan sums, which were defined by S. Ramanujan in 1918. In this paper, we propose a family of sequences, termed here as orthogonal Ramanujan sums, $c^{k}_q(n)$ and also derive their various properties. We also prove two new properties of the Ramanujan Sums, namely, shifted multiplication property and interpolation property. These properties are used to define higher order orthogonal Ramanujan sums. These sums are periodic in nature and are orthogonal for different values of $k$ and $q$ . Using orthogonal Ramanujan sums, a new representation of finite length signal is given, termed here as orthogonal Ramanujan periodic transform. The coefficients obtained using this transform can be divided into two parts, first coefficient gives smoothing component and the rest give the detail components of the signal. Further, application of the orthogonal Ramanujan sums in discrete wavelet transform is developed. Construction of Ramanujan wavelet analysis matrix for applying discrete wavelet transform on a signal is described. The application of this matrix on an image of size $N\times N$ , where $N$ is divisible by integer $q$ will result in $q^2-1$ detail components and one average component in transform domain. Some of the applications of this transform are also demonstrated, especially in the field of image processing.

A Comprehensive Survey on Blind Source Separation for Wireless Adaptive Processing: Principles, Perspectives, Challenges and New Research Directions

With the rapid proliferation of wireless services, the frequency spectrum has become increasingly crowded, and the interferences and composite signals will be ubiquitous in the wireless receiver. For deeply dissecting and detecting the expected signals, the research community has to investigate the smart signal processing technology to resisting the influence of detrimental signals. For this purpose, blind source separation has been shown to be a promising method for achieving simultaneous spectrum utilization and wireless adaptive interference cancellation. The attractive features and appealing advantages of blind source separation make it an attractive theory for source extraction or recovery, which plays a crucial role in helping realize intelligent signal processing for wireless communication. It can recover the unobserved sources only from the wireless received mixed signals based on the features of the source signal exempted from channel estimation and synchronization manipulation. Wireless communication systems can benefit the high spectrum efficiency, strong anti-interference, and adaptive signal processing through the blind separation mechanism. So far, numerous researchers have made tremendous efforts to investigate this field for enhancing spectrum efficiency, anti-interference ability, and signal detection performance through employing the philosophy of blind separation. These meaningful and appealing research works motivate us to make a comprehensive survey with regard to this area. In this paper, the fundamental principle of the blind separation mechanism, involving independent component analysis, sparse component analysis, non-negative matrix factorization, and bounded component analysis will be reviewed briefly, and then, the critical technologies applied in various wireless communication systems will be overviewed, such as in direct-sequence code division multiplexing access, frequency hopping, orthogonal frequency-division multiplexing, multiple input multiple output, wireless sensor networks, cognitive radio networks, radio frequency identification devices, and communication security. In addition, the important research challenges and meaningful research directions pertaining to the area of blind separation applied in wireless communications systems are also discussed.

Meta-Programming and Policy-Based Design as a Technique of Architecting Modular and Efficient DSP Algorithm Implementations

Meta-programming paradigm and policy-based design are less known programming techniques in Digital Signal Processing (DSP) community, used to coding in pure C or assembly language. Major software components, like C++ STL, have proven usefulness of such paradigms in providing top performance of highly optimised native code, along with abstraction and modularity necessary in complex software projects. This paper describes composition of DSP code using these techniques, bringing as an example implementation of Feedback Delay Network (FDN) artificial reverberation algorithm. The proposed approach was proven to be practical, especially in case of prototyping computationally intense algorithms. To provide further performance insight, we discuss the techniques in context of other optimisation methods, like Single Instruction Multiple Data (SIMD) instruction sets usage and exploitation of superscalar architecture capabilities.

2017 2nd International Conference on Communication, Image and Signal Processing (CCISP 2017)

PREFACECCISP 2017 was held in held in Shenzhen, China during 17–19 November 2017. CCISP 2017 was organized by Asia Pacific Institute of Science and Engineering (APISE). The conference provides a useful and wide platform both for displaying the latest research and for exchange of research results and thoughts in Communication, Image and Signal Processing. The participants of the conference were from almost every part of the world, with backgrounds in either academia or industry and even well-known enterprise. The success and prosperity of the conference is reflected high level of the papers received.The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. This book covers three chapters: Communication Engineering, Image Processing and Signal Processing.

A Software Defined Radio Based Airplane Communication Navigation Simulation System

Radio communication and navigation system plays important role in ensuring the safety of civil airplane in flight. Function and performance should be tested before these systems are installed on-board. Conventionally, a set of transmitter and receiver are needed for each system, thus all the equipment occupy a lot of space and are high cost. In this paper, software defined radio technology is applied to design a common hardware communication and navigation ground simulation system, which can host multiple airplane systems with different operating frequency, such as HF, VHF, VOR, ILS, ADF, etc. We use a broadband analog frontend hardware platform, universal software radio peripheral (USRP), to transmit/receive signal of different frequency band. Software is compiled by LabVIEW on computer, which interfaces with USRP through Ethernet, and is responsible for communication and navigation signal processing and system control. An integrated testing system is established to perform functional test and performance verification of the simulation signal, which demonstrate the feasibility of our design. The system is a low-cost and common hardware platform for multiple airplane systems, which provide helpful reference for integrated avionics design.

A new robust adaptive algorithm based adaptive filtering for noise cancellation

Signal de-noising has been sparked and given a great attention by signal processing community since its applications are found in a diverse range of digital signal processing and computer vision problems. To improve signal quality, phase, frequency and power are the important features that should be preserved during the de-noising process. Adaptive filters have been widely used for this purpose due to their ability to cancel out noise signal from the corrupted one precisely. This paper presents a robust adaptive estimator for solving the problem of signal noise cancellation, based on a new adaptive algorithm derived from a new constrained optimization. Simulation results evaluated using MATLAB show that the proposed algorithm is appropriate for several forms of signals contaminated by diverse levels of noise power. The performance of the proposed algorithm is illustrated to be preferable in terms of the power signal to noise ratio, mean square error and time of speed convergence of filter parameters. It is compared to other conventional approaches such as least mean square and normalized least mean square algorithms with various values of white noise power, variance. It exhibits lower steady-state error and faster convergent time than the other implementations. Finally, an efficient performance is achieved comparable with recursive least square and affine projection algorithms.

The Mystery Curve: A Signal Processing Point of View [Lecture Notes

In the first chapter of a recently published book on artful mathematics [1], a linear combination of harmonic signals called mystery curves were introduced. Although their Fourier-based analysis brings interesting results, this lecture note provides a different and important perspective, especially useful for our signal processing community. Based on polar coordinate systems and low-pass filtering approaches, the patterns of the curves can be designed by locally curve tracing instead of trial and error, including not only two-dimensional (2-D) but also three-dimensional (3-D) modeling. Concrete examples and online MATLAB codes are provided so that applications from art and logo design to amplitude modulation (AM)-frequency modulation (FM) signal analysis are realizable.

Generative Adversarial Networks: An Overview

Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification. The aim of this review paper is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.

Light Field Image Processing: An Overview

Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data.

A First Public Research Collection of High-Resolution Latent Fingerprint Time Series for Short- and Long-Term Print Age Estimation

The creation of publicly available image databases for the signal processing community is a very time-consuming, yet immensely valuable task, enabling scientific progress by providing the opportunity of an objective comparison and reproduction of results. This paper presents for the first time a public research collection of high-resolution latent fingerprint time series for age estimation, captured from a pool of 116 different test subjects. It comprises ten different sets with a total of 2,618 time series (117,384 scans), varying between capturing devices (CWL and CLSM), data types (intensity versus topography), aging periods (short-term aging: 24 h, long-term aging: 0.5 - 3 years) and resolutions (1,270 - 180,142 ppi). Most series are annotated with donor information (age and gender) and capturing conditions (scan parameters, ambient temperature, and humidity). The data are anonymized (using partial prints only) and an organizational revocation mechanism is included to assure non-identifiability of donors in the future. Baseline results for age estimation on all ten sets are provided in the form of correlation coefficients and machine-learning based age estimation (kappa), using 19 features from prior feature spaces as well as new ones (Tamura contrast, Benford’s law, and improved dust feature). Classification results exhibit kappa values between 0.51 and 0.85, highlighting the progress made in this very challenging area in recent years and also emphasizing the need of future studies on the issue.

On Generalized Stam Inequalities and Fisher–Rényi Complexity Measures

Information-theoretic inequalities play a fundamental role in numerous scientific and technological areas (e.g., estimation and communication theories, signal and information processing, quantum physics, …) as they generally express the impossibility to have a complete description of a system via a finite number of information measures. In particular, they gave rise to the design of various quantifiers (statistical complexity measures) of the internal complexity of a (quantum) system. In this paper, we introduce a three-parametric Fisher–Rényi complexity, named ( p , β , λ ) -Fisher–Rényi complexity, based on both a two-parametic extension of the Fisher information and the Rényi entropies of a probability density function ρ characteristic of the system. This complexity measure quantifies the combined balance of the spreading and the gradient contents of ρ , and has the three main properties of a statistical complexity: the invariance under translation and scaling transformations, and a universal bounding from below. The latter is proved by generalizing the Stam inequality, which lowerbounds the product of the Shannon entropy power and the Fisher information of a probability density function. An extension of this inequality was already proposed by Bercher and Lutwak, a particular case of the general one, where the three parameters are linked, allowing to determine the sharp lower bound and the associated probability density with minimal complexity. Using the notion of differential-escort deformation, we are able to determine the sharp bound of the complexity measure even when the three parameters are decoupled (in a certain range). We determine as well the distribution that saturates the inequality: the ( p , β , λ ) -Gaussian distribution, which involves an inverse incomplete beta function. Finally, the complexity measure is calculated for various quantum-mechanical states of the harmonic and hydrogenic systems, which are the two main prototypes of physical systems subject to a central potential.

Generalized Kalman smoothing: Modeling and algorithms

State-space smoothing has found many applications in science and engineering. Under linear and Gaussian assumptions, smoothed estimates can be obtained using efficient recursions, for example Rauch–Tung–Striebel and Mayne–Fraser algorithms. Such schemes are equivalent to linear algebraic techniques that minimize a convex quadratic objective function with structure induced by the dynamic model.These classical formulations fall short in many important circumstances. For instance, smoothers obtained using quadratic penalties can fail when outliers are present in the data, and cannot track impulsive inputs and abrupt state changes. Motivated by these shortcomings, generalized Kalman smoothing formulations have been proposed in the last few years, replacing quadratic models with more suitable, often nonsmooth, convex functions. In contrast to classical models, these general estimators require use of iterated algorithms, and these have received increased attention from control, signal processing, machine learning, and optimization communities.In this survey we show that the optimization viewpoint provides the control and signal processing community great freedom in the development of novel modeling and inference frameworks for dynamical systems. We discuss general statistical models for dynamic systems, making full use of nonsmooth convex penalties and constraints, and providing links to important models in signal processing and machine learning. We also survey optimization techniques for these formulations, paying close attention to dynamic problem structure. Modeling concepts and algorithms are illustrated with numerical examples.