Digital Signal Processing Concepts Research Articles

Reconstruction of Time-Varying Graph Signals via Sobolev Smoothness

Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampling and reconstruction of static graph signals have played a central role in GSP. However, many real-world graph signals are inherently time-varying and the smoothness of the temporal differences of such graph signals may be used as a prior assumption. In the current work, we assume that the temporal differences of graph signals are smooth, and we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples. We explore some theoretical aspects of the convergence rate of our Time-varying Graph signal Reconstruction via Sobolev Smoothness (GraphTRSS) algorithm by studying the condition number of the Hessian associated with our optimization problem. Our algorithm has the advantage of converging faster than other methods that are based on Laplacian operators without requiring expensive eigenvalue decomposition or matrix inversions. The proposed GraphTRSS is evaluated on several datasets including two COVID-19 datasets and it has outperformed many existing state-of-the-art methods for time-varying graph signal reconstruction. GraphTRSS has also shown excellent performance on two environmental datasets for the recovery of particulate matter and sea surface temperature signals.

Forward and backward difference equations in digital signal processing

The relationship between difference equations with constant coefficients, obtained by establishing forward and backward differences in the filter design concepts of digital signal processing is explored in this paper. Moreover, thecorrelation is established between the input-output difference equations along with definite time invariant linear systemand the state spacedifference equationsrelated to filter design andthisleads to identify the efficient adaptive filterdesign and validate its function. Finally, the state space illustration of a system is provided, that is acceptable to construct and validate a new developed system between the difference equation and the digital signal processor.

Deep Learning Shared Bandpass Filters for Resource-Constrained Human Activity Recognition

Classification of human activities from wearable sensor data is challenged by inter-subject variance and resource-constrained platforms. We address these issues with SincEMG, a deep neural network that exploits digital signal processing concepts and transfer learning to reduce model size for activity recognition on raw sensor data. The model’s first layer decomposes signals into frequency bands using finite impulse response filters optimized directly from the data. The subsequent convolutional layers downsample across time and aggregate the first layer’s band data. Batch normalization and dropout help to regularize intermediate layer outputs. This approach reduces compute requirements by decreasing the number of learned parameters and eliminating any significant data pre-processing. In addition to these improvements, the model’s first layer learns a set of bandpass filters, which provide insight into predictive regions of the source spectrum. We evaluate SincEMG using two publicly available surface electromyography datasets. Our model uses far fewer parameters and achieves state-of-the-art results with 98.53% accuracy for 7-classes and 68.45% accuracy for 18-classes.

CUBATURE FORMULA FOR APPROXIMATE CALCULATION INTEGRAL OF HIGHLY OSCILLATING FUNCTION OF TREE VARIABLES (IRREGULAR CASE)

Context. The integrals of highly oscillating functions of many variables are one of the central concepts of digital signal and image processing. The object of research is a digital processing of signals and images using new information operators. Objective. The work aims to construct a cubature formula for the approximate calculation of the triple integral of a rapidly oscillating function of a general form.Method. Modern methods of digital signal processing are characterized by new approaches to obtaining, processing and analyzing information. There is a need to build mathematical models in which information can be given not only by the values of the function at points, but also as a set of traces of the function on the planes and as a set of traces of the function on the lines. There are algorithms which are optimal by accuracy for calculating the integrals of highly oscillating functions of many variables (regular case), which involve different types of information in their construction. As a solution of a broader problem for the irregular case, the work presents the cubature formula for the approximate calculation of the triple integral of the highly oscillating function in a general case. The presented algorithm for approximate calculation of the integral is based on the application of operators that restore the function of three variables using a set of traces of functions on the mutually perpendicular planes. Operators use piece-wise splines as auxiliary functions. The cubature formula correlates with a formula of the Filon type. An error estimation of the approximation of the integral from the highly oscillating function by the cubature formula on the class of differential functions is obtained.Results. The cubature formula of the approximate calculation of the triple integral from the highly oscillating function of a general form is researched.Conclusions. The experiments confirm the obtained theoretical results on the error estimation of the approximation triple integral from the highly oscillating function in a general form by the cubature formula. The prospect of further research is to obtain an estimation of the approximation error on wider classes of functions and to prove that the proposed cubature formula is optimal by the order of accuracy.

Inculcation of Life Philosophy through DSP Concepts

Engineering education is a training that encompass learning the theoretical knowledge of concepts, and real time application domains. The role of engineering is also bound by ethical values and the understanding of social and cultural values. In this context a new approach has been designed to facilitate learning of life philosophies through Digital Signal Processing (DSP) concepts. To facilitate learning process, skits based on core DSP concepts were developed using simple day to day tools and performed with learners. The concept was introduced with simple analogies. Then students were made to participate in skits corresponding to key concepts. After the skits, learners were educated about the correlation with Life philosophies. Three case studies were taken up for this study. We found that the inculcation of life philosophies through simple skits, helped learners better to understand the concepts as well the moral values of life.

Revisiting signal processing with spectrogram analysis on EEG, ECG and speech signals

Abstract Biomedical signal processing is the utilization of digital signal processing techniques, such as Fourier transform, filtering, spectral estimation and wavelet transform to biomedical complications, such as the analysis of breathing cycle, cardiac signals, brain signals, etc. Digital filters are used to preserve the in-band signals and to block out-of-band noise. Low-pass, high-pass, band-pass, and band-stop filters are commonly used for filtering applications. The digital signal processing (DSP) concepts can be used for other biomedical applications such as biomedical imaging (MRI, CT, X-ray, PET, ultrasound) and genomic signal processing too. The experiments such as denoising corrupted ECG signals, respiratory artifacts removal, and an identification of rhythmic patterns from EEG signals with spectrogram analysis are performed. Matlab R2016b tool is used for this study. Finally, the assessment, survey and feedback results from the student are tabulated for the better improvement of the course study.

Design of a Remote Signal Processing Student Lab

We describe our experience of introducing digital signal processing (DSP) concepts via a software-defined radio project using a very inexpensive TV USB capture dongle. Through a series of weekly lab exercises, the students learned and applied DSP concepts to design a completely digital FM receiver. The proposed lab experience introduced concepts, such as sampling, IQ signal representation, sample rate conversion, filter design, filter delays, and more, all with an attractive learn-by-doing approach. The first offering of this course initially took place in Fall 2014 and has been successfully offered and repeated with growing success ever since. Our experience can serve as a proof of concept of the possibility of carrying out, in a massive open online course-like fashion, certain engineering labs that require inexpensive and readily available hardware components.

The Magnitude Response Learning Tool for DSP Education: A Case Study

Many concepts in digital signal processing are intuitive, despite being mathematically challenging. The lecturer not only has to teach the complicated math but should also help students develop intuition about the concept. To aid the lecturer in this task, the Magnitude Response Learning Tool has been introduced, a computer-based learning game addressing the connection between the pole/zero chart of a discrete-time linear time-invariant (LTI) system and its magnitude response. In each game round, the student has to draw a magnitude response given only the poles and zeros of the LTI system. The accuracy of the student's estimate acts as a game score, allowing leaderboards and player-versus-player comparisons on the tool's webpage. This paper analyzes how the tool affects student performance. While the qualitative results show that the students are satisfied with the tool and consider it a useful supplement to the available teaching material, the quantitative part of the study suggests a strong positive correlation between the number of rounds played and the student performance in related tasks.

Computer‐based experiments for learning seismic signal processing concepts

ABSTRACTA good understanding of digital signal processing concepts is essential for students of geophysical engineering, electrical engineering, and image processing. This paper presents a computer‐based training software, which uses signal processing libraries to perform a number of interesting seismic experiments. Each experiment is focused towards a specific concept and presents a parameters input interface. The results are instantly generated in the form of a graphical display. This facilitates the student to play with parameters and observe their effects on the output. Some selected experiments have been discussed along with their parameters input interfaces and sample outputs. Such experiments provide a better understand and visualization of various seismic concepts. Studies on student groups indicate that these experiments motivate students and expedite self‐learning, thus reducing the teaching load. © 2015 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:959–966, 2015; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21669

Low Power Systolic Array Based Digital Filter for DSP Applications.

Main concepts in DSP include filtering, averaging, modulating, and correlating the signals in digital form to estimate characteristic parameter of a signal into a desirable form. This paper presents a brief concept of low power datapath impact for Digital Signal Processing (DSP) based biomedical application. Systolic array based digital filter used in signal processing of electrocardiogram analysis is presented with datapath architectural innovations in low power consumption perspective. Implementation was done with ASIC design methodology using TSMC 65 nm technological library node. The proposed systolic array filter has reduced leakage power up to 8.5% than the existing filter architectures.

Folded Architecture for Digital Gammatone Filter Used in Speech Processor of Cochlear Implant

Emerging trends in the area of digital very large scale integration (VLSI) signal processing can lead to a reduction in the cost of the cochlear implant. Digital signal processing algorithms are repetitively used in speech processors for filtering and encoding operations. The critical paths in these algorithms limit the performance of the speech processors. These algorithms must be transformed to accommodate processors designed to be high speed and have less area and low power. This can be realized by basing the design of the auditory filter banks for the processors on digital VLSI signal processing concepts. By applying a folding algorithm to the second-order digital gammatone filter (GTF), the number of multipliers is reduced from five to one and the number of adders is reduced from three to one, without changing the characteristics of the filter. Folded second-order filter sections are cascaded with three similar structures to realize the eighth-order digital GTF whose response is a close match to the human cochlea response. The silicon area is reduced from twenty to four multipliers and from twelve to four adders by using the folding architecture.

MASTERS: A Virtual Lab on Multimedia Systems for Telecommunications, Medical, and Remote Sensing Applications

Digital signal processing (DSP) has been an integral part of most electrical, electronic, and computer engineering curricula. The applications of DSP in multimedia (audio, image, video) storage, transmission, and analysis are also widely taught at both the undergraduate and post-graduate levels, as digital multimedia can be encountered in most human daily activities. In this paper, a virtual lab on multimedia systems for telecommunications, medical, and remote sensing applications is presented; consisting of 20 graphical user interfaces, it can support teaching activities for various DSP concepts. The virtual lab developed was used as a teaching companion for a post-graduate course on multimedia content management, to enhancing student comprehension of the material taught; it has been made freely available for similar educational or research purposes.

Enhancing the Teaching of Digital Signal Processing through Project-Based Learning

Engineering education is constantly challenged to bridge the gap between classroom and real world problems. This paper reports on the experience at PSUT to complement the teaching of digital signal processing through project-based learning. A low cost digital stethoscope is utilized by students to record heartbeat sounds and apply DSP concepts to extract vital information from the signal. The ability to apply concepts learned in the classroom to a real world problem was an important element that motivated students. The feedback from students reinforced the effectiveness of these techniques in teaching concepts of digital signal processing

Study of DNA Sequence Analysis Using DSP Techniques

Recently there are greater advances in bioinformatics and genomic signal processing. Digital Signal Processing (DSP) applications in genomic sequence analysis have received great attention in recent years. New methods are being developed to analyze Deoxyribonucleic acid (DNA) sequences. In order to use DSP principles to analyze DNA sequences, the DNA sequences should be converted into numeric sequences. Then the DSP algorithms are used in DNA analysis. Discrete Fourier Transform (DFT), digital filtering, Discrete wavelet transform (DWT), Parametric modeling and entreopy are some of the important DSP concepts used for DNA analysis. In this paper, we present a review of the DSP techniques used for DNA analysis. We have explained the required basics behind molecular biology. We have discussed about the applications of DSP in sequence analysis. The DSP techniques used in the literature are studied and their results are compared. 

The Foundations of Digital Signal Processing Using Signal Wizard Systems®

Signal Wizard Systems® is a digital signal processing (DSP) research venture within the School of EEE at the University of Manchester, UK. It specialises in the development and supply of real-time DSP products for audio signal analysis and processing. The unique and underpinning philosophy of these products is their ease of use. The systems require minimal knowledge of DSP theory on the part of the user and none of the mathematics associated with digital filter design. Filters and other algorithms can be designed in seconds, downloaded and executed in real time with just a few mouse clicks. Since 2004 Signal Wizard products have been sold all over the world for applications ranging from noise suppression, adaptive filtering and system modelling to musical instrument research. In particular, their ease of use ensures that they are ideally suited for teaching simple and more advanced concepts in DSP both at undergraduate and postgraduate level. For this purpose, a DSP laboratory teaching package has been developed using the Signal Wizard range of devices, and has proven an invaluable tool for training our student cohort in the practical aspects of DSP engineering design and programming.

A DSP and FPGA Testbed for Teaching and Research of Wireless Communications

This paper describes the teaching and research of signal processing and communications systems that took place during the development of a real-time transceiver and radio channel testbed at Hokkaido University, Japan. Digital signal processing (DSP) concepts were taught and learnt during both the testbed system development and also the results gathering and analysis stages. The performance of a modern multiple antenna communications system is dependent on a number of key parameters, and the student interaction with such a real-time system can assist in the understanding of key but often abstract theoretical concepts. The communications algorithm and architecture overview on a signal processing board containing a Xilinx field programmable gate array (FPGA) and Analog Devices TigerSharc DSP is detailed. The lessons learned and potential uses of the testbed in both teaching and research are also described.

Hands-On DSP Education through Experiments

A new graduate from electrical engineering education must know about digital signal processing (DSP) to find a secure place in the competitive jobs market. Although this topic can be taught theoretically, its importance comes from practical applications. Therefore, students must be equipped with appropriate tools. Fortunately, DSP platforms serve this purpose. At Yeditepe University, we established a laboratory to guide students in real-time digital signal processing applications. We selected a Texas Instruments TMS320C6713 DSK platform for this purpose. In this study, we provide several laboratory applications on this platform. We also provide more advanced projects developed by our students which emerged from these applications. We observed that this laboratory improved the understanding of theoretical DSP concepts.

Fundamentals of Radar Signal Processing (Richards, M.A.; 2005) [Book review

This book introduces the fundamental concepts of radar by building upon digital signal processing concepts. It provides a thorough covering of all of the basic radar topics, in addition to more advanced topics, such as CFAR detection, synthetic aperture radar (SAR), and space-time adaptive processing (STAP). The book is clear, concise, and informative, and represents a valuable resource for both the classroom and the professional world.

Teach-Ware: DSP education resources

This paper describes a variety of TeachWare, i.e., digital signal processing (DSP) education resources that are useful for introducing DSP concepts, operations, and algorithms. The paper provides such information as the type of resource, its status, and format.

Charge-Domain Signal Processing of Direct RF Sampling Mixer with Discrete-Time Filters in Bluetooth and GSM Receivers

RF circuits for multi-GHz frequencies have recently migrated to low-cost digital deep-submicron CMOS processes. Unfortunately, this process environment, which is optimized only for digital logic and SRAM memory, is extremely unfriendly for conventional analog and RF designs. We present fundamental techniques recently developed that transform the RF and analog circuit design complexity to digitally intensive domain for a wireless RF transceiver, so that it enjoys benefits of digital and switched-capacitor approaches. Direct RF sampling techniques allow great flexibility in reconfigurable radio design. Digital signal processing concepts are used to help relieve analog design complexity, allowing one to reduce cost and power consumption in a reconfigurable design environment. The ideas presented have been used in Texas Instruments to develop two generations of commercial digital RF processors: a single-chip Bluetooth radio and a single-chip GSM radio. We further present details of the RF receiver front end for a GSM radio realized in a 90-nm digital CMOS technology. The circuit consisting of low-noise amplifier, transconductance amplifier, and switching mixer offers 32.5 dB dynamic range with digitally configurable voltage gain of 40 dB down to 7.5dB. A series of decimation and discrete-time filtering follows the mixer and performs a highly linear second-order lowpass filtering to reject close-in interferers. The front-end gains can be configured with an automatic gain control to select an optimal setting to form a trade-off between noise figure and linearity and to compensate the process and temperature variations. Even under the digital switching activity, noise figure at the 40 dB maximum gain is 1.8 dB and +50 dBm IIP2 at the 34 dB gain. The variation of the input matching versus multiple gains is less than 1 dB. The circuit in total occupies 3.1 mm2 . The LNA, TA, and mixer consume less than 15.3 mA at a supply voltage of 1.4 V.