Signal Processing Concepts Research Articles

Selected applications of satellite technologies in rail transport

Global Navigation Satellite Systems (GNSS) are increasingly being used in various modes of transport, including rail transport. When this technology is applied to railway traffic control, it is essential to ensure a high level of safety and reliability. The approval of a railway traffic control system requires a safety analysis, which includes hazard analysis and risk analysis. This also includes GNSS-based solutions in terms of their compliance with safety integrity requirements, i.e. THR (Tolerable Hazard Rate) and SIL (Safety Integrity Level) parameters, as defined in normative documents. In the case of railway traffic control systems, the level of dependability of the determined train position, referred to as position integrity, is very important in ensuring safety. Position integrity is affected by many factors, including: errors due to SIS (Signal-In-Space) propagation, multipath errors, signal interference or GNSS receiver errors. In order to improve position integrity, among other things, new data processing methods can be used to improve the accuracy and reliability of measurements. The paper presents the concept of satellite signal processing for precise determination of the position of objects in selected railway systems. The Kalman filter based model of satellite signal filtration and its selected applications, which were tested in the real condition, was presented. The application of Kalman filtration indicated in the paper is a universal method that improves the estimated measurement parameters and can be used in many applications of satellite systems for railway tasks. The applicability of satellite systems to automatic train operation, defect positioning in automatic and manual flaw detection tests, determining track spatial orientation and train integrity control have been considered. The conducted tests confirmed the correctness of the adopted concept and the model of satellite signals filtration developed for this purpose. According to the authors, the described methods can also be used in many other tasks related to rail transport.

Signal Processing in the Digital Era: Educational Strategies for Developing Curriculum for Teacher’s Training at Secondary School A Need Assessment Analysis in District Kot addu South Pakistan

Due to the fast-growing technological expansion, signal processing has become one of the significant elements of learning, especially in a world where technology is rapidly growing. This study, titled “Signal Processing in the Digital Era: The research study titled “Educational Strategies for Developing Curriculum for Teachers’ Training at Secondary School: A Need Assessment Analysis in District Kot Addu, South Pakistan” will focus on the need to include signal processing concepts in the training curriculum of teachers in secondary schools. This study aims to find out existing level of awareness and existing knowledge of teachers so as to find out the gaps / areas where further curriculum development could be attempted based on the district Kot Addu, South, Pakistan. It will analyze the impact of digital tools and technologies in the improvement of teachers’ competencies in signal processing through an evaluation of them extensively. Particular emphasis will be made on the difficulties which teachers encounter when implementing these concepts and technologies, the necessary conditions in schools and the necessary preparation of educators for proper curriculum implementation. The result from this study will be important for its contribution toward informing the process of curriculum development for teacher training, the context of the digital age, in order to promote positive change in educational outcomes of secondary school. Keywords: Signal Processing, The Digital Age, Curriculum Development, Digital Technologies.

Refined composite multivariate multiscale fuzzy dispersion entropy: Theoretical analysis and applications

Multivariate multiscale sample entropy (mvMSE) is a popular method to quantify the irregularity of multi-channel time series. However, mvMSE generates either undefined or unreliable results for short-length signals and has a high computational cost. To address these issues, multivariate multiscale dispersion entropy (mvMDE) was recently developed, but it depends on its parameters and overlooks some data information. To address these limitations, we herein propose an extension of fuzzy dispersion entropy (FDE) by incorporating fuzzy set theory, multidimensional embedding reconstruction theory, and dispersion patterns. This leads to the multivariate multiscale FDE (mvMFDE) and refined composite mvMFDE (RCmvMFDE) measures. (RC)mvMFDE is the first multivariate analysis based on Shannon entropy that uses the fuzzification concept. Some sets of time series have been synthesized and analyzed using several concepts in multivariate signal processing to evaluate the advantages of our introduced mvMFDE and RCmvMFDE. The results indicated that the proposed methods are less sensitive to their parameters, signal length, and noise, providing more stable results compared to the existing approaches mvMSE and mvMDE. On multi-channel noise signals and bivariate autoregressive processes, mvMSE, mvMDE, and mvMFDE produced similar finding although mvMFDE exhibited superior discriminative capabilities. When applied to one physiological dataset and two mechanical datasets, RCmvMFDE distinguished the diverse dynamics of multichannel signals exceptionally well.

Design and construction of an in-air sonar for signal processing education

Sonar is an acoustic sensing method commonly used in underwater imaging and navigation. Students are often introduced to sonar theory, but few gain hands-on experience due to the cost and logistical difficulties associated with in-water experimentation. Alternatively, a sonar system can be designed for in-air use to mitigate some of the practical and operational challenges of acquiring sonar field data. Inspired by previous in-air sonar projects, the Pennsylvania State University student chapter of the Acoustical Society of America acquired $5,000 to construct an in-air sonar for exploring signal processing and mechanical design concepts. For a cost-effective solution, consumer audio equipment and additive manufacturing were leveraged. This presentation will walk through the sonar design and build process, discuss the project's current status, and present initial results. Plans for project development and potential inclusion into the curriculum will be discussed. Advice will be offered to those who may want to pursue similar projects.

Interactive interface for spatio-temporal mapping of epileptic human brain using characteristics of high frequency oscillations (HFOs)

Spontaneous High Frequency Oscillations (HFOs) have been considered as emerging specific biomarkers of the epileptogenic region. As a first issue, a significant difference in the implementation of automatic HFOs detection methods can sometimes occur between researchers. In addition, clinicians are not even particularly familiar with the concept of signal and image processing, and programming skills. To overcome these limitations, we propose a plug-and-play interactive Graphical User Interface (GUI) that incorporates an amalgamation of six validated methods used for detecting and quantifying of HFOs events. As a second issue, the most automated HFOs detection methods to date have a high false detection rate and low specificity, ranging, in some cases up to 80% and below 37% respectively. Therefore, the eventual utilization of HFOs detection algorithms in clinical settings requires a checking step to save clinically relevant HFOs and remove spurious oscillations from the detection results. As a last issue addressed in the present study, the major previous HFOs studies have been limited only to the detection and classification of HFOs, but only a few studies have been conducted to efficiently follow the neural dynamics of epileptic focus by studying HFOs characteristics through different brain regions and clinical stages. Therefore, in our software, the brain mapping of HFOs characteristics is done based on the duration, the inter-duration, the average frequency, and the power of HFOs. The present developed software may be considered helpful for understanding the functional significance of HFOs and also to reduce the interaction gap between fundamental research and applied clinical practice related to HFOs.

All‐Optical Parametric‐Assisted Oversampling and Decimation for Signal Denoising Amplification

AbstractDecimation is a common process in digital signal processing that involves reducing the sampling rate of an oversampled signal by linearly combining consecutive samples. Among other applications, this process represents a simple means to mitigate noise content in the digital signal. In this work, a novel optical signal processing concept inspired by these operations is proposed, which is called Parametric‐assisted Oversampling and Decimation (POD). By using a simple all‐fiber setup, the POD processor first realizes an ultra‐fast parametric oversampling of the incoming temporal signal (at >100 Gigasamples per second), a process that is followed by a decimation that reduces the sampling rate by any user‐defined factor in a lossless manner. In this way, the POD delivers an amplified sampled copy of the optical signal, where the peak‐to‐peak gain results from the combination of parametric amplification and a “passive” amplification equal to the decimation factor. In this report, joint parametric and passive amplification by a factor ≈50 on GHz‐bandwidth signals is demonstrated. Furthermore, it is shown that the decimation process can effectively mitigate effects of narrowband noise, outperforming traditional optical and digital filtering techniques. By experimentally achieving ultra‐high decimation factors (>750), narrowband (MHz‐bandwidth) optical waveformsthat are lost in a much stronger noise background are recovered.

Apply Graph Signal Processing on NILM: An Unsupervised Approach Featuring Power Sequences.

As a low-cost demand-side management application, non-intrusive load monitoring (NILM) offers feedback on appliance-level electricity usage without extra sensors. NILM is defined as disaggregating loads only from aggregate power measurements through analytical tools. Although low-rate NILM tasks have been conducted by unsupervised approaches based on graph signal processing (GSP) concepts, enhancing feature selection can still contribute to performance improvement. Therefore, a novel unsupervised GSP-based NILM approach with power sequence feature (STS-UGSP) is proposed in this paper. First, state transition sequences (STS) are extracted from power readings and featured in clustering and matching, instead of power changes and steady-state power sequences featured in other GSP-based NILM works. When generating graph in clustering, dynamic time warping distances between STSs are calculated for similarity quantification. After clustering, a forward-backward power STS matching algorithm is proposed for searching each STS pair of an operational cycle, utilizing both power and time information. Finally, load disaggregation results are obtained based on STS clustering and matching results. STS-UGSP is validated on three publicly accessible datasets from various regions, generally outperforming four benchmarks in two evaluation metrics. Besides, STS-UGSP estimates closer energy consumption of appliances to the ground truth than benchmarks.

Translation of surface electromyography to clinical and motor rehabilitation applications: The need for new clinical figures.

Advanced sensors/electrodes and signal processing techniques provide powerful tools to analyze surface electromyographic signals (sEMG) and their features, to decompose sEMG into the constituent motor unit action potential trains, and to identify synergies, neural muscle drive, and EEG-sEMG coherence. However, despite thousands of articles, dozens of textbooks, tutorials, consensus papers, and European and International efforts, the translation of this knowledge into clinical activities and assessment procedures has been very slow, likely because of lack of clinical studies and competent operators in the field. Understanding and using sEMG-based hardware and software tools requires a level of knowledge of signal processing and interpretation concepts that is multidisciplinary and is not provided by most academic curricula in physiotherapy, movement sciences, neurophysiology, rehabilitation, sport, and occupational medicine. The chasm existing between the available knowledge and its clinical applications in this field is discussed as well as the need for new clinical figures. The need for updating the training of physiotherapists, neurophysiology technicians, and clinical technologists is discussed as well as the required competences of trainers and trainees. Indications and examples are suggested and provide a basis for addressing the problem. Two teaching examples are provided in the Supplementary Material.

Using acoustic signals for demonstrating key signal processing and communication systems concepts

In this talk, I will highlight the importance of reinforcing mathematical concepts in signal processing and communication systems theory with Matlab-based data analysis. In particular, I will provide examples and demos of homework and project problems I assigned in my linear systems and communication systems courses, and discuss how it helped the students learn in an agile hybrid virtual setting during the pandemic, and whether repeating the same (or similar) problems in an in-person or hybrid setting made a difference. I will also discuss the importance of playing with real-world acoustic signals, and lessons learned, including changes I made in the problem assignments based on student feedback, student performance, and classroom discussions.

Model and data-driven homework problems for learning signal processing concepts

The Electrical and Computer Engineering Department at Portland State University offers a course in Sensor Array Processing. The content is designed to be introductory and approachable for undergraduate, first year graduate and students from other disciplines such as Mechanical Engineering or Physics. The signal processing techniques explored use two or more sensors and can be applied to acoustics or electromagnetic waves. I have two favorite homework problems: one is based on simulated data and the other based on measured data and both help solidify important concepts. For the simulations, a model is developed based on the method of images and can be applied to signals from a sonar in the ocean or to voices in a room. These multipath environments are used to illustrate how a signal (such as an audio clip the student creates) would change in different environments or source/receiver geometry. The channel impulse response is modeled and together with convolution allows different waveform responses to be easily computed. My favorite problem using measured data is based on recordings of a broadband source on two sensors. Cross-correlation is used to determine the source direction. This problem explores the concepts of pulse compression and pre-whitening for time-delay and direction estimation.

Intelligent detection of IT assets in power grid enterprises based on signal processing and cyberspace

Signal processing can complete the processing of various types of signals to obtain key data. Signal processing can also filter out redundant noise signals and ensure the quality of source signals. Due to the various functions of signal processing technology, it is widely used in network security, especially in IT asset detection and other aspects. Cyberspace is a general concept, which represents all devices associated with network connections. Detecting network assets in cyberspace means finding all networked devices. The detection of IT assets for power grid enterprises can help power grid enterprise IT administrators understand enterprise IT assets. At the same time, it can also discover the security loopholes existing in the operation of the current enterprise IT assets, and use the detection results to fix the loopholes as soon as possible to prevent the occurrence of network security incidents. This paper firstly sorts out the concepts of signal processing, cyberspace and power grid IT assets, then combines CNN and signal processing technology to design an intelligent detection strategy for power grid IT assets, and compares the designed strategy with the efficiency and other indicators of traditional detection methods. In contrast, finally, a cyberspace-based IT asset intelligent detection strategy was designed, and a power grid enterprise IT asset intelligent detection system was designed. Through the deployment strategy test environment, a test case was written in Python language, and the cyberspace-based IT asset intelligent detection was found. Compared with the traditional power grid enterprise IT asset detection scheme, the strategy has better performance in all aspects. The research adopts the method of modeling and analysis to calculate the signal values of the IT assets involved as much as possible, thereby greatly reducing the possible errors in the evaluation of enterprise IT assets. The test of the results obtained by the formula shows that the IT asset detection combined with the cyberspace search method shows superiority in different indicators.

Erroneous detection of desensitization doses in the prevention of hypersensitivity reactions

BackgroundDesensitization protocols have empirically established their efficacy and safety in eliminating most of the hypersensitivity reactions to drugs and other allergens. Without such procedures, the offending drugs can otherwise be lethal, for some patients, when singularly administered at therapeutic doses. These binding events and the subsequent signaling cascades have been extensively modulated by different desensitization methods, without any clear explanation as to why it is necessary to use increasing allergen doses.PurposeTo use a novel theoretical approach in order to model the desensitization algorithms currently in practice, that seeks to shed light on the mechanism behind their clinical efficacy.MethodAn approach using signal processing concepts is applied in this work to introduce aliasing as the erroneous detection of higher drug doses responsible for the efficacy of desensitization procedures.ResultsAvailable experimental data is modeled and correct predictions as to the efficacy of the drug treatment procedures are produced.ConclusionsDesensitization algorithms may benefit from using concepts from signal processing theory in order to avoid hypersensitivity reactions.

TiN/Ti3C2 heterojunction-based photonic device for optical Kerr switch.

As one of the new nanomaterials, TiN/Ti3C2 shows excellent optoelectronic characteristics, thus it has been widely used in many applications, such as biomedicine, optical sensors, image processing, and optical switching. With the advancement of communication capabilities and communication networks, optical fiber communication has put a higher demand on signal processing. In order to overcome the limitations of the electronic transfer rate bottleneck, the concept of all-optical signal processing has been proposed. Utilizing the excellent optical nonlinear effect of the TiN/Ti3C2 heterojunction-coated microfiber (THM), a novel THM-based optical Kerr switch has been proposed. Injecting a strong control light and a signal light into the device simultaneously, and controlling the state of turn on or off of the control light, can adjust the intensity of the signal light. Based on this, the amplitude modulation of the signal light can be achieved. With a control light power of 200 mW, the maximum extinction ratio of the signal light reaches 27 dB. We believe that this type of compact device can demonstrate great potential for integration with current high-speed fiber communication networks, providing a possible method for all-optical signal processing through nonlinear effects, and has broad prospects in the field of all-optical signal processing, robots, and high-speed communication.

Magnetic-free unidirectional polarization rotation and free-space optical isolators and circulators

Optical nonreciprocity is an essential concept in optical signal processing and communication. The traditional way to achieve nonreciprocity is using magneto-optical nonreciprocal polarization rotation, which is incompatible with existing on-chip integration due to the applied magnetic field. Here, we theoretically and experimentally realize unidirectional polarization rotation with a magnetic-free mechanism using atomic ensembles. Free-space isolators and circulators are further formed based on the nonreciprocity of polarization rotation. As only one direction of the signal changes its polarization, the forward transmission and backward isolation can be adjusted separately. Compared with the existing magnetic-free circulators that rely on high-Q factor cavities or Mach–Zehnder interferometers, we experimentally realized circulators in free space. This dramatically simplifies the magnetic-free circulator structure and provides better stability.

New Methodology in Interpretation of Aeromagnetic Anomalies over Pipe-Like Sources using Analytic Signal Ratio Approach

Concepts of signal processing like Fourier transform, filtering, power spectrum, derivatives etc., are widely used in geophysical models in exploring the Earth structures. For example, quantitative interpretation of aeromagnetic maps is based mainly on choosing an appropriate model for evaluating parameters of the causative source. For circular or elliptical anomalies caused by plug-like intrusive sources, the vertical pipe of infinite depth extent (also known as single or mono pole) is most suited. It is also more common to use analytic signal maps in interpretation. For a vertical pipe, the total magnetic intensity (TMI) anomaly is anti-symmetric at the magnetic equator and progressively becomes symmetric at the pole. Based on this property, a new methodology is developed for identifying location and depth of magnetic structures beneath. It is concluded that the analytic signal is almost symmetric for any magnetic latitude and by using both the properties of the anomaly and its analytic signal the location and depth can be evaluated. This new approach is validated by field examples. HIGHLIGHTS The theory and application of using analytic signal to determine the location and depth to the point pole is described The method is useful in locating pipe-like (vertical cylinder, kimberlite pipes) structures The shape of the magnetic anomaly changes with magnetic inclination whereas the analytic signal map is symmetric and almost circular, with its maximum centred over the top of the pipe The analytic signal is almost symmetric for any magnetic latitude and by using both the properties of the anomaly and its analytic signal, the location and depth to the top can be evaluated GRAPHICAL ABSTRACT

A GNSS meta-signal SAGE-based multipath-mitigating loop design

A major challenge suffered by Global Navigation Satellite System (GNSS) receivers is the multipath interference, which results in serious tracking performance degradation and increased positioning errors. Growing evidence suggests that wide-bandwidth signals and multi-antenna technology are set to become two vital factors to improve the GNSS multipath mitigation, leading to the concept of meta-GNSS signal and multi-dimensional processing. To estimate the time delay of line-of-sight (LOS) ray in a multipath environment, we propose a meta-signal Space Alternating Generalized Expectation (SAGE) based locked loop (SAGELL) design, which exploits the high-resolution of the meta-signal together with the characteristics from spatial and temporal domains. The SAGELL design comprises two main setups: the SAGE-based discriminator and combinational loops. Benefiting from the multi-dimensional processing and meta-signal bandwidth, the SAGE-based discriminator first estimates the LOS ray and the multipath rays in single side-band components and achieves the global estimates of the dual side-band components coherently. Aiming to accurately track each ray of the meta-signal, we further close the SAGELL via angle, code phase, and carrier phase locked loops to filter and predict the estimates. The proposed GNSS meta-signal multipath-mitigating loop design allows for GNSS high-precision discrimination and tracking in multipath scenarios, even in the presence of highly correlated rays, as confirmed by the numerical results.

Robust Analog Beamforming for Periodic Broadcast V2V Communication

We generalize an existing low-cost analog signal processing concept that takes advantage of the periodicity of vehicle-to-vehicle broadcast service to the transmitter side. In particular, we propose to process multiple antennas using either an analog beamforming network (ABN) of phase shifters, or an antenna switching network (ASN) that periodically alternates between the available antennas, to transmit periodic messages to receivers that have an analog combining network (ACN) of phase shifters, which has been proposed in earlier work. To guarantee robustness, we aim to minimize the burst error probability for the worst receiving vehicular user, in a scenario of bad propagation condition that is modeled by a single dominant path between the communicating vehicles. In absence of any form of channel knowledge, we analytically derive the optimal parameters of both ABN and ASN. The ABN beamforming vector is found to be optimal for all users and not only for the worst receiving user. Further, we demonstrate that Alamouti scheme for the special case of two transmit antennas yields similar performance to ABN and ASN. At last, we show that the derived parameters of the two proposed transmission strategies are also optimal when hybrid ACN-maximal ratio combining is used at the receiver.

A Modulated Wideband Converter Model Based on Linear Algebra and Its Application to Fast Calibration.

In the context of cognitive radio, smart cities and Internet-of-Things, the need for advanced radio spectrum monitoring becomes crucial. However, surveillance of a wide frequency band without using extremely expensive high sampling rate devices is a challenging task. The recent development of compressed sampling approaches offers a promising solution to these problems. In this context, the Modulated Wideband Converter (MWC), a blind sub-Nyquist sampling system, is probably the most realistic approach and was successfully validated in real-world conditions. The MWC can be realized with existing analog components, and there exist calibration methods that are able to integrate the imperfections of the mixers, filters and ADCs, hence allowing its use in the real world. The MWC underlying model is based on signal processing concepts such as filtering, modulation, Fourier series decomposition, oversampling and undersampling, spectrum aliasing, and so on, as well as in-flow data processing. In this paper, we develop an MWC model that is entirely based on linear algebra, matrix theory and block processing. We show that this approach has many interests: straightforward translation of mathematical equations into simple and efficient software programming, suppression of some constraints of the initial model, and providing a basis for the development of an extremely fast system calibration method. With a typical MWC acquisition device, we obtained a speed-up of the calibration computation time by a factor greater than 20 compared with a previous implementation.

Fuzzy Dispersion Entropy: A Nonlinear Measure for Signal Analysis

Entropy is a powerful tool for nonlinear analysis of time series. We have recently introduced dispersion entropy (DispEn), which has widely drawn attention from researchers in a variety of settings in data analysis. However, DispEn is sensitive to its parameters, especially number of classes (quantization level). Additionally, in some cases, a small change in the signal amplitude due to noise changes the quantized series and so entropy value. To address these limitations, we develop fuzzy dispersion entropy (FuzzDispEn) on the basis of fuzzy membership functions for the quantization of a signal and Shannon entropy (ShEn). In FuzzDispEn, the concept of fuzzification in ShEn-based methods is introduced for the first time. Several straightforward concepts in signal processing using a set of time series were utilized to show the advantages of FuzzDispEn over DispEn in terms of the detection of the dynamical variability of signals. The results showed that FuzzDispEn has lower sensitivity to noise and signal length compared to DispEn. FuzzDispEn was also applied to five real world applications: mechanical fault detection in gearbox, fault diagnosis of rolling element bearings, damage level detection of bearings, detection of blood pressure in salt-sensitive rats, and detection of seizure and seizure-free electroencephalographics (EEGs). The results suggest that FuzzDispEn outperforms DispEn as well as popular sample entropy and permutation entropy in distinguishing different kinds of dynamical patterns in synthetic and real data.

Lippmann Photography: A Signal Processing Perspective

Lippmann (or interferential) photography is the first and only analog photography method that can capture the full color spectrum of a scene in a single take. This technique, invented more than a hundred years ago, records the colors by creating interference patterns inside the photosensitive plate. Lippmann photography provides a great opportunity to demonstrate several fundamental concepts in signal processing. Conversely, a signal processing perspective enables us to shed new light on the technique. In our previous work, we analyzed the spectra of historical Lippmann plates using our own mathematical model. In this paper, we provide the derivation of this model and validate it experimentally. We highlight new behaviors whose explanations were ignored by physicists to date. In particular, we show that the spectra generated by Lippmann plates are in fact distorted versions of the original spectra. We also show that these distortions are influenced by the thickness of the plate and the reflection coefficient of the reflective medium used in the capture of the photographs. We verify our model with extensive experiments on our own Lippmann photographs.