Signal Processing, General

Diffraction and holography are fertile areas for application of signal theory and processing. Recent work on 3DTV displays has posed particularly challenging signal processing problems. Various procedures to compute Rayleigh-Sommerfeld, Fresnel and Fraunhofer diffraction exist in the literature. Diffraction between parallel planes and tilted planes can be efficiently computed. Discretization and quantization of diffraction fields yield interesting theoretical and practical results, and allow efficient schemes compared to commonly used Nyquist sampling. The literature on computer-generated holography provides a good resource for holographic 3DTV related issues. Fast algorithms to compute Fourier, Walsh-Hadamard, fractional Fourier, linear canonical, Fresnel, and wavelet transforms, as well as optimization-based techniques such as best orthogonal basis, matching pursuit, basis pursuit etc., are especially relevant signal processing techniques for wave propagation, diffraction, holography, and related problems. Atomic decompositions, multiresolution techniques, Gabor functions, and Wigner distributions are among the signal processing techniques which have or may be applied to problems in optics. Research aimed at solving such problems at the intersection of wave optics and signal processing promises not only to facilitate the development of 3DTV systems, but also to contribute to fundamental advances in optics and signal processing theory.

PrefaceThis issue of Proceedings gathers the papers presented at 2019 4th International Conference on Intelligent Computing and Signal Processing (ICSP 2019) held in Xi’an, China during March 29-31, 2019. ICSP 2019 is an international conference covering research and development in the field of intelligent computing and signal processing and participation from all over the world.More than 400 papers were finally accepted after a double blinded peer review process by international reviewers and academic committee members. Divided into 4 chapters, the papers provide a wide spectrum of researches on wide range of intelligent computing and signal processing. The chapters are devoted to Algorithm and Data Mining, Signal and Image Processing, Automation Engineering and Intelligent Application, Computer Modeling and Performance Structure. Specific research results by conference participants were presented and examined in the light of the frameworks outlined above, which is of interest to academics, researchers and professionals in this field.Two keynote speeches were presented from Prof. Weihua Zhuang, University of Waterloo, Canada, whose topic was about Service Provisioning in 5G Communication Networks; Prof. Nagula Sangary, University of Waterloo & Prudential Technology Ltd., whose topic was about Trends and Challenges in Terrestrial Satellite Wireless Communication Systems in mm-Wave range. All the talks were very impressive for the high level of professionalism, and in many cases original ideas and activities have been accomplished or proposed.List of Committees are availble in this PDF.

In the course of signal and information processing, the linear processing method is easier to master than the nonlinear process. However, in practical application, the nonlinear problem is often encountered, which is too complex to be limited by the existing signal and information processing resources. Therefore, it is necessary to transform the complex nonlinear problem into a simple linear problem. According to teaching practice of signal processing course, the linear correlation characteristics of signal in high dimensional space analysis are discussed in this paper. From the perspective of information theory, the advantages of high dimensional spatial analysis and processing of signal and information are also discussed. The results of this paper can provide a new perspective for students to understand why we analyze and deal with signal and information processing in high dimensional space.

Rotor position sensor systems based on resolvers are state-of-the-art in electric drive train applications, e.g. for permanent magnet synchronous motors, due to their robustness and accuracy. However, the effort for signal processing in terms of angle determination has a great complexity. Electric powertrains in automotive applications make high demands on the quality of control, which requires a precise detection of the current motor shaft position and speed. Different types of influences, like sensor signal cables, interfaces, sensor signal quantization, conditioning (e.g. filtering) and processing, cause error accumulation that interacts with the general mechanical-based disturbing factors, e.g. displacements, shock, vibrations and temperature influences. This work introduces a holistic view of the sensor signal measurement chain in terms of analysis of signal processing and error characteristics, exemplary shown on a state-of-the-art resolver system of an electric powertrain architecture. The second emphasis of this work investigates the mechanisms of signal processing and the signal processing - based effects that provoke additional errors to the measured rotor position information. The methods developed in the scope of this work are based on both, simulations of the entire electric drive train sensor system and verifications under realistic conditions by use of a highly accurate reference system on a specifically developed position sensor system test bench. The developed methods support measures of optimization to reduce the angular rotor position error of the entire sensor system based on an integration of computational and experimental investigations. As a result, causes and consequences of signal processing characteristics are pointed, which provides fundamental knowledge about the different contributing factors in terms of rotor position distortion. In this way, an approach is introduced that enables improvement and optimization of the resolver-based rotor position sensor system and thus the overall quality of control of electric traction motors even in the early design phase of electric drivetrain systems.

Biomedical signal and image processing is the study of the dynamic behavior of various bio-signals, which benefits academics and research. Signal processing is used to assess the behavior of analogue and digital signals for the assessment, reconfiguration, improved efficiency, extraction of features, and reorganization of patterns. This paper unveils hidden characteristic information about input signals using feature extraction methods. The main feature extraction methods used in signal processing are based on studying time, frequency, and frequency domain. Feature exaction methods are used for data reduction, comparison, and reducing dimensions, producing the original signal with sufficient accuracy with a structure of an efficient and robust pattern for the classifier system. Therefore, an attempt has been made to study the various feature extraction methods, feature transformation methods, classifiers, and datasets for biomedical signals.

The high performance embedded computing software initiative (HPEC-SI) program is developing a unified computation and communication application programming interface (API) and framework for high performance signal processing tasks on parallel computers. The goal of the program is to address the high cost of software in Department of Defense (DoD) systems by improving the portability and productivity of signal processing application development threefold, while improving performance by one half compared to current practices. This paper describes the motivation for the HPEC-SI program, its goals and approaches, and progress of the HPEC-SI Working Groups in extending the vector, signal, and image processing library (VSIPL) standard to C++ and transparent operation in parallel computing systems. The current draft version of the C++ extensions to VSIPL is described, and highlights of its advantages are considered. This paper also examines results from the Demonstration Working Group, and describes requirements and plans developed by the Applied Research Working Group for data parallel extensions to VSIPL and describes Development Working Group progress so far in developing parallel VSIPL.

Even in this digital age, SAW and Optical devices remain indispensable analogue signal processing technologies whose operation and success derive from common wave propagation phenomena. In this review I hope to explain why this is so, and will remain so for the foreseeable future. Historically, the development and exploitation of these technologies arose during the Cold War primarily from the military need for an unsurpassed signal processing capability, although both are now widely exploited in civil applications as well. Following the Introduction, this paper briefly reviews the construction, operation, attractions, and selected applications of the SAW devices developed since the invention of the Interdigital Transducer (IDT) in 1965. These devices include delay lines, bandpass filters, and the first SAW device used in anger, the dispersive delay line which was vital to the realisation of pulse compression radar. I shall also review the attractions of SAW oscillators, which I believe still enjoy an opportunity to contribute to the insatiable military need for improved overall stability. Following this I introduce some components and techniques devised by the optics community for signal and image processing since the invention of the laser in 1960, beginning with techniques for pattern recognition and the ingenious technique devised to realise real-time Synthetic Aperture Radar, (SAR) image formation. More recently, the invention and development of laser diodes and fibre optics by the telecommunications industry has led to the realisation of optical signal processing techniques whose functionality is in many respects complementary to that of SAW devices. In particular, optical techniques are now useful for the generation, delay, sampling, and distribution of signals at higher rf frequencies (and bandwidths) than SAW devices, ie at microwave and mm- wave frequencies and above, leading to the modern field of activity known as Microwave Photonics. Some current research topics in this field are described, including stable microwave generation, phased array antenna beamforming, and A/D conversion, each being relevant to future military and civil systems.

Abstract. Software infrastructure is a growing part of modern radio science systems. As part of developing a generic infrastructure for implementing Software Radar systems, we have developed a set of reusable signal processing components. These components are generic software-based implementations for use on general purpose computing systems. The components allow for the implementation of signal processing chains for radio frequency signal reception, correlation-based data processing, and cross-correlation-based interferometry. The components have been used to implement the signal processing necessary for incoherent scatter radar signal reception and processing as part of the latest version of the Millstone Hill Data Acquisition System (MIDAS-W). Several hardware realizations with varying capabilities have been created, and these have been used successfully with different radars. We discuss the signal processing components in detail, describe the software patterns in which they are used, and show example data from the Millstone Hill, EISCAT Svalbard, and SOUSY Svalbard radars.

We aim to provide a critical appraisal of basic concepts underlying signal recording and processing technologies applied for (i) atrial fibrillation (AF) mapping to unravel AF mechanisms and/or identifying target sites for AF therapy and (ii) AF detection, to optimize usage of technologies, stimulate research aimed at closing knowledge gaps, and developing ideal AF recording and processing technologies. Recording and processing techniques for assessment of electrical activity during AF essential for diagnosis and guiding ablative therapy including body surface electrocardiograms (ECG) and endo- or epicardial electrograms (EGM) are evaluated. Discussion of (i) differences in uni-, bi-, and multi-polar (omnipolar/Laplacian) recording modes, (ii) impact of recording technologies on EGM morphology, (iii) global or local mapping using various types of EGM involving signal processing techniques including isochronal-, voltage- fractionation-, dipole density-, and rotor mapping, enabling derivation of parameters like atrial rate, entropy, conduction velocity/direction, (iv) value of epicardial and optical mapping, (v) AF detection by cardiac implantable electronic devices containing various detection algorithms applicable to stored EGMs, (vi) contribution of machine learning (ML) to further improvement of signals processing technologies. Recording and processing of EGM (or ECG) are the cornerstones of (body surface) mapping of AF. Currently available AF recording and processing technologies are mainly restricted to specific applications or have technological limitations. Improvements in AF mapping by obtaining highest fidelity source signals (e.g. catheter-electrode combinations) for signal processing (e.g. filtering, digitization, and noise elimination) is of utmost importance. Novel acquisition instruments (multi-polar catheters combined with improved physical modelling and ML techniques) will enable enhanced and automated interpretation of EGM recordings in the near future.

This article describes technical aspects involved in the programming of a system of acquisition, processing and transmission of biomedical signals by using mobile devices. This task is aligned with the permanent development of new technologies for the diagnosis and sickness treatment, based on the feasibility of measuring continuously different variables as electrocardiographic signals, blood pressure, oxygen concentration, pulse or simply temperature. The contribution of this technology is settled on its portability and low cost, which allows its massive use. Specifically this work analyzes the feasibility of acquisition and the processing of signals from a standard smartphone. Work results allow to state that nowadays these equipments have enough processing capacity to execute signals acquisition systems. These systems along with external servers make it possible to imagine a near future where the possibility of making continuous measures of biomedical variables will not be restricted only to hospitals but will also begin to be more frequently used in the daily life and at home.

Subjective tinnitus is an auditory phantom perception that may arise from any aberrant signal within the auditory system. Further processing of this signal and the conscious perception of tinnitus takes place in the cerebral cortex. A few functional brain-imaging studies have been performed to elucidate the underlying cerebral mechanisms of this perception. These studies mostly concern rare types of tinnitus (e.g. tinnitus changeable by oral-facial movements), or compared tinnitus patients with healthy volunteers. These studies attributed variable activation of the primary auditory cortices, associative auditory cortices and the left hippocampus to the perception of tinnitus. Based on these heterogeneous results, no consensus on the underlying mechanisms has been reached. The aim of the present study was to obtain further details of the central perception and processing of the tinnitus signal. Positron emission tomography (PET) was used to map the tinnitus-specific central activity. By contrasting PET-images of suppressed tinnitus with PET-images of the habitual tinnitus sensation, we were able to identify a right prefrontal-temporal network associated with the perception of tinnitus. Besides the evidence of activation of associative auditory sensory regions, the results indicated that activation of cortical centres subserving attention and emotion may underlie the continuous irritability associated with severe tinnitus.

Summary form only given. Signal processing has now had many benefits to our daily life though we may not be aware of. For examples: microprocessors (a kind of signal processor) are used to control gas consumption to make our cars more fuel efficient and signal processing is used to predict the major weather changes. Signal processing has played important roles in our environment including land water and air, through monitoring, control and prediction functions. In this presentation, the recent progress of signal processing, including hardware, software and algorithms, the neural networks, and the roles of remote sensing, will be reviewed first. This is followed by presenting several examples of using signal processing to preserve our environment. Some notable examples are: monitoring of landmines, pollutants in waterways, the salinity in the coastal zones, and the changes in environment; control of oil spills, flood mapping and control; inspection of damages of forest fires; prediction of major storms; etc. It is noted that signal processing is closely linked to pattern recognition in many of these examples. Signal processing in conjunction with pattern recognition will play an increasingly important role in all of these activities that aim to preserve our precious environmental resources.

Ultrasonic testing (UT) for nondestructive evaluation (NDE) is a critical entity necessary to resolve both the quality and precision questions of complex parts evolving from the classical approaches to the innovative additive manufacturing (AM) process. This modality provides the essential quantitative information for acceptance and potential flaw detectionof apart under investigation. A primary ingredient in UT besides the required precision robotic hardware for theacquisition of high-qualitymeasurement data is the underlying signal processing. It is here that much of the system performance capability resides. In this report,we discuss the basic steps in UT signal processing along with current capabilities that must be achieved in order to satisfy the criticaldemands of the various LLNL programs.In itsbasic foundations, signal processing is essentially the “extraction of critical information (signals) from noisy, uncertain data.” Clearly, in a perfect world the best signal processing is none---just make a reliable, noise-free, uncertainty-free, measurement. Unfortunately, even the best of systems is still straddled within the confines and limitations of physical instrumentation. With this in mind, we discuss the development of signal processing techniques toextract the desired UT information from noisy measurement data. We start with a discussion of a simple homogeneous representation of a “part” under investigation and its insonification by an ultrasonicexcitation from a physics-based perspective. Once developed, we briefly discussthe various choices of excitation signals and their tradeoffs.Next,we discuss a simulation approach employing simple models from the signal processing perspective and then move on to the development of the basic signal processing approach to ultrasonic signal processing all based on estimating the pulse arrival estimation. Starting with simple peak detection techniques, progressing to the processing workhorse so-called “matched-filter” and finally to the more sophisticated “model-based matched filter.”The underlying pre-and post-processing of noisy measurement data along with the application of these techniques are subsequently demonstrated on experimental ultrasonic data.Finally, we present some results for processing “weld” data when the ultrasonic excitation signal is not directly available.

Signal processing is one of the most important and understudied dimensions of contemporary sound cultures and of electronic media more broadly. In the sonic register, it inflects everything from music production, wired or wireless transmission, and radio broadcast to everyday conversation and listening. Because it is embedded in all stages of contemporary sound production, reproduction, and reception, it has remained an elusive subject for critique. This essay considers the poetics of audio signal processing—the figural dimensions of the technical process and the representations of this process in audio-technical discourse. The article focuses on two metaphorical frames commonly applied to signal processing in the everyday language of musicians and audio technologists: cooking and travel. Through a reading of Claude Lévi-Strauss, it suggests that metaphors of rawness and cooking elevate signal processing to a kind of culturing process by which sound is readied for consumption by listeners through specialized technologies and techniques. The argument situates the spatialization of signal flow and the design of circuit topology within long-standing ideas about travel and voyage that inflect Western epistemologies of sound. As metaphors for signal processing, cooking and travel mark cultural locations (e.g., gendered, classed, able-bodied positionalities) much as they do in broader social contexts. Not only is signal processing subject to the critique of representation, it is, more than any other technical register, directly linked to the contemporary cultural politics of perception and reception. Although this article focuses on sound technologies, a full cultural critique of signal processing would consider its central role in every sense register.

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session: 2711 Teaching Hardware Design of Fixed-Point Digital Signal Processing Systems David V. Anderson1 and Tyson S. Hall2 1 Georgia Institute of Technology, Atlanta, GA 30332–0250, dva@ece.gatech.edu 2 Southern Adventist University, Collegedale, TN 37315–0370, tyson@southern.edu Abstract Signal processing theory and practice are enabling and driving forces behind multimedia de- vices, communications systems, and even such diverse fields as automotive and medical sys- tems. Over 90% of the signal processing systems on the market used fixed-point arithmetic because of the cost, power, and area savings that fixed-point systems provide. However, most colleges and universities do not teach or teach only a very little fixed-point signal process- ing. This issue is being addressed slowly around the country but now a new challenge or opportunity presents itself. As reconfigurable logic technology matures, field-programmable gate arrays (FPGAs) are increasingly used for signal processing systems. They have the ad- vantage of tremendous throughput, great flexibility, and system integration. The challenge is that signal processing in FPGAs is a much less constrained problem than signal processing in special purpose microprocessors. The opportunity arises in that it is now possible to explore more options and, more especially, to take a more systems-level approach to signal processing systems. In short, designing a signal processing system using FPGAs provides opportunities to look at many system design issues and trade-offs in a classroom setting. We have developed a course to teach signal processing in FPGAs at Georgia Institute of Technology and in this paper we consider the challenges and methods of teaching fixed- point system design in this course. We discuss the topics chosen and how they differ from traditional microprocessor-based courses. We also discuss how systems engineering concepts are woven into the course. 1 Motivation This paper describes the early development of a system design curriculum that uses field programmable gate arrays (FPGAs) to implement digital signal processing (DSP) systems. The goals of this curriculum are several-fold, including: • to prepare students for system design problems with which they are likely to be pre- sented upon graduation; Proceedings of the 2007 American Society for Engineering Education Annual Conference & Exposition Copyright c 2007, American Society for Engineering Education