Analyzing Shortwave Propagation with a Remote Accessible Software-Defined Ham Radio System

Contributors

Error free recovery of signals from irregularly spaced samples in terms of completeness of sets of nonharmonic exponentials

A new undergraduate curriculum in electrical engineering has been adopted by the Department of Electrical and Computer Engineering at the University of Illinois. Major changes have been incorporated, including a redistribution of the circuits and signal processing topics within the curriculum. After giving an overview of the new curriculum, this paper focuses on a new, required sophomore-level course on analog signal processing. This course combines material from the traditional course on circuit analysis with material on continuous-time signals and systems. Students completing this course can study digital signal processing as first-semester juniors, which leaves ample time for more advanced signal and image processing courses in future semesters.

Computational methodologies of signal processing and analysis based on 1D-4D data are commonly used in different applications. In particular, image processing and analysis methodologies have enjoyed increasing deployment in automated recognition, human-machine interfaces, computeraided diagnostics, robotic surgery, and many other areas; however, in the last years their application in Biomechanics has gained special attention. This issue of the EURASIP Journal on Advances in Signal Processing constitutes the special issue related with Image Processing and Analysis applied to biomechanical systems, including data compression, data fusion, image segmentation, image registration, objects recognition, objects modeling, tracking and motion analysis, shape reconstruction, 3D vision, and virtual reality. One important feature to retainment of this special issue is the interdisciplinary of works resulting from the collaboration between mechanical engineers, electrical engineers, biomedical engineers, medical doctors, computational engineers, biologists, physicians, mathematicians, among others. The success of this special issue is directed and associated with the high significance on analysis and simulation of biomechanical structures from images and their challenging problems, regarding geometric modeling, numerical modeling, material models and experimental methodologies, as well as their real application and validation. This great interest has been revealed by users, students, researchers, and all who are interested on areas related with signal processing, image processing and analysis, medical imaging, computational and experimental biomechanics, enhanced computation, and software applications. For this special issue, 31 works were submitted from 18 countries: Belgium, Brazil, Canada, China, Croatia, Czech Republic, France, India, Iran, Ireland, Italy, Japan, Morocco, New Zealand, Spain, Taiwan, Tunisia, and USA. After the review done by 55 international experts, 19 works were accepted for publication. The guest editors would like to express their deep gratitude to the Editor-in-Chief and Associate Editors of EURASIP Journal on Advances in Signal Processing for this opportunity, to all authors that shared their excellent works with us and to all members of the Scientific Committee of this special issue that help us in the review process.

Communication system is one of the major areas in which digital signal processing finds direct application. Recent advances in signal processing have helped in enormously reducing the complexity of communication system design and also in improving the performance of the system. Software Defined Radio (SDR) enables in-orbit re-configurability of frequency, modulation scheme, data rate, bandwidth and channel coding in the case of satellite communication systems where component change is not possible after launch. This paper describes the design and hardware implementation of SDR type communication system based on FPGA for a nano satellite. The major functions carried out onboard are FM/FSK demodulation for tele command uplink and BPSK modulation with raised cosine filtering for telemetry downlink. The full system is designed and implemented based on Microsemi Smartfusion2 FPGA. For hardware evaluation of the system, Virtex-6 FPGA with high speed analog daughter card is employed. Test results are also provided at the end of the paper along with implementation of re-configurability.

Software defined radio (SDR) capitalizes advances in signal processing and radio technology and is capable of reconfiguring RF and switching to desired frequency bands. It is a frequency-agile data communication device that is vastly more powerful than recently proposed multi-channel multi-radio (MC-MR) technology. In this paper, we investigate the important problem of multi-hop networking with SDR nodes. For such network, each node has a pool of frequency bands (not necessarily of equal size) that can be used for communication. The uneven size of bands in the radio spectrum prompts the need of further division into sub-bands for optimal spectrum sharing. We characterize behaviors and constraints for such multi-hop SDR network from multiple layers, including modeling of spectrum sharing and sub-band division, scheduling and interference constraints, and flow routing. We give a formal mathematical formulation with the objective of minimizing the required network-wide radio spectrum resource for a set of user sessions. Since such problem formulation falls into mixed integer non-linear programming (MINLP), which is NP-hard in general, we develop a lower bound for the objective by relaxing the integer variables and linearization. Subsequently, we develop a near-optimal algorithm to this MINLP problem. This algorithm is based on a novel sequential fixing procedure, where the integer variables are determined iteratively via a sequence of linear programming. Simulation results show that solutions obtained by this algorithm are very close to lower bounds obtained via relaxation, thus suggesting that the solution produced by the algorithm is near-optimal.

Guest Editorial

Non-destructive testing (NDT) is a broad group of testing and analysis techniques used in science and industry to evaluate the properties of a material, structure, or system for characteristic defects and discontinuities without causing damage. Recently, infrared thermography is one of the most promising technologies as it can inspect a large area quickly using a non-contact and non-destructive method. Moreover, thermography testing has proved to be a valuable approach for non-destructive testing and evaluation of structural stability of materials. Pulsed thermography is one of the active thermography technologies that utilizes external energy heating. However, due to the non-uniform heating, lateral heat diffusion, environmental noise, and limited parameters of the thermal imaging system, there are some difficulties in detecting and characterizing defects. In order to improve this limitation, various signal processing techniques have been developed through many previous studies. This review presents the latest advances and exhaustive summary of representative signal processing techniques used in pulsed thermography according to physical principles and thermal excitation sources. First, the basic concept of infrared thermography non-destructive testing is introduced. Next, the principle of conventional pulsed thermography and signal processing technologies for non-destructive testing are reviewed. Then, we review advances and recent advances in each signal processing. Finally, the latest research trends are reviewed.

The object of research: The research object is brain-computer interfaces (BCI) and issues related to the acquisition, processing, and analysis of neural signals. Investigated problem: The research focuses on the challenges related to the acquisition and processing of neural signals in brain-computer interface (BCI) systems and the solutions required to improve the system's efficiency. Specifically, issues such as signal weakness, data loss, artifacts, difficulties in real-time operation, and individual adaptation requirements are emphasized. Additionally, the comparison of invasive and non-invasive BCIs, the advantages and limitations of both approaches, and cybersecurity risks are central topics of this study. The goal is to overcome these challenges and develop new signal processing techniques and artificial intelligence algorithms to make BCI technologies more accurate, faster, and reliable. The main scientific results: The factors determining the effectiveness of BCI systems: actors such as the acquisition and processing of neural signals, the algorithms used for signal analysis, hardware, and user feedback are identified as key elements affecting the performance of BCI systems. Comparison of invasive and non-invasive BCIs: Both approaches' advantages and limitations have been reviewed. Invasive BCIs allow for more accurate signal acquisition but require surgical intervention. Non-invasive BCIs, on the other hand, are more comfortable and safer but are prone to artifacts and data loss. Advancements in signal processing methods: The application of new signal processing techniques and artificial intelligence algorithms is emphasized as crucial to improving the efficiency of BCI systems. Individual adaptation and real-time operation challenges: BCI systems' need for individual adaptation and the challenges of real-time operation are highlighted as significant problems that negatively affect system efficiency. Cybersecurity risks: There are cybersecurity risks associated with the remote control of BCIs, which pose a serious threat, particularly for medical implants and neurological devices. Improved signal analysis algorithms: The importance of algorithms, particularly approaches like SVM and LDA, for the classification of motor imagery signals and the correct analysis of signals is emphasized. The area of practical use of the research results: BCI systems can be used in the rehabilitation process for individuals suffering from neurological diseases or physical disabilities. These systems can help restore patients' physical and neural functions. BCI technologies can be applied in controlling robots, especially robotic prosthetics and interactions with the environment for individuals with disabilities. BCIs could allow users to control computers and other technological devices through thought, enabling more natural and comfortable interactions with technology. The use of EEG signals in biometrics could provide a novel approach for identifying individuals and ensuring data security. Innovative technological product: The innovative technological product is BCI systems. This technology connects brain activity directly with computers or other devices, enabling various applications. Specifically, BCIs open new possibilities in medical rehabilitation, robotics, human-computer interaction, security, and biometric identification. The article highlights the importance of applying artificial intelligence algorithms and new signal processing techniques to improve the efficiency of BCI systems. This aims to ensure that BCI systems operate more accurately, quickly, and reliably. Such technologies can lead to significant advancements, particularly in the fields of medical devices and robotics. Scope of the innovative technological product: BCI systems are widely used across various fields. In the medical sector, particularly in neurorehabilitation, they are extensively applied. Brain signals are utilized in the treatment of several neurological disorders. Additionally, in robotics, the development of brain-controlled robotic arms, exoskeletons, and autonomous systems that enhance human capabilities – especially for individuals with limited mobility – is closely linked to the integration of this technology into automation. BCI systems are also successfully implemented in Human-Computer Interaction (HCI), the education sector, and security fields. The uniqueness of brainwave patterns makes BCIs a promising tool for biometric authentication. Unlike traditional security methods, brain-based authentication systems offer a higher level of security. In the modern era, advancements in artificial intelligence, machine learning, and signal processing are making BCIs more efficient and accessible, enabling their broad integration into various aspects of daily life

Near-real-time observations of Earth's ionosphere electron density are an important input for a variety of technologies and services that depend on HF radio or space-based communication. An ionosonde is a radar that performs ionospheric sounding and provides measurements of the current state of the ionosphere. The critical parameters (foF2, foF1, foE) derived from ionosonde data define the maximum frequencies of the ionospheric E- and F-layers for estimates of their electron densities. Users of space weather services, e.g. in aviation, use these parameters to search for optimal frequencies in over-the-horizon communication. Ray-tracing codes as combined with statistical ionospheric electron density models are often used in more accurate radio wave propagation assessments.Finnish Meteorological Institute (FMI) started ionospheric measurements in Southern Finland with a new ionosonde in February 2025. The ionosonde, manufactured by RF-shamans Ltd, is based on a software-defined radio (SDR) implementation, which enables its small, both transmitter and receiver antenna size (≈ 1m^3), quick and flexible operation, and low transmitting power level (< 0.5W). In addition, the ionosonde antennas are easy to install and relocate when needed. The ionosonde currently produces vertical ionograms using a 10 second sweep five times per minute. The measurements are filtered, and the critical parameters are detected in real-time. The more detailed real-height analysis is currently operated manually and an automated process is under development.In addition, FMI analyzes ionograms with a numerical ray-tracing software, AU-Ray [1]. The polynomial analysis software (POLAN) is also used in real-height analysis to verify the scaling results from the AU-Ray method. AU-Ray calculates the radio wave propagation path step-by-step based on Appleton-Hartree (AH) or Booker Quartic (BQ) Hamiltonians. AU-Ray allows ray-tracing not just with statistical ionospheric models but also with background conditions customized by the user. The code is based on freely available programming languages and open-source models. The software package includes also a mapping tool, which allows simulations with large quantities of rays, for propagation maps in user-defined ionospheric conditions. In the presentation, we describe the main specifications of the FMI ionosonde and the AU-ray code, analyze ionosonde performance during a recent space weather storm, and demonstrate the usage of AU-Ray in the real-height analysis, while comparing the simulation results to the ionosonde measurements.[1]: E. A. O. Hirvonen, K. Kauristie and E. Kallio. “AU-Ray Program for Modelling Radio Wave Propagation in the Ionosphere“. Radio Science. Manuscript submitted May 2025.

Advanced Signal Processing Methods for Biomedical Imaging

This discussion paper explores the potential of Software Defined Radio (SDR) technology to provide flexible and low-cost subsurface radar prototypes for the GPR community. Unlike traditional fixed hardware implementations, SDR uses software configurable RF modules which can be used to implement customized signal encoding, decoding and processing. However, the full potential of SDR has not yet been fully understood or exploited for radar-based applications, and so is of special interest for GPR development. This paper introduces the fundamental concepts behind SDRs and describes the underlying hardware and software architectures that are used to implement them. It also provides a simple reference design using open source GNU Radio software and the Universal Software Radio Peripheral (USRP) hardware to indicate how low-cost radar configurations can be designed and evaluated. The benefits and challenges of SDR-based radar architectures are highlighted, and opportunities discussed for new and enhanced subsurface sensing capabilities. Overall, SDR technology is seen to provide new opportunities to boost future radar research and development to provide enhanced GPR system capabilities.

We have recently witnessed a rapidly increasing demand for, and hence, a shortage of, wireless network bandwidth due to rapidly growing wireless services and applications. It is, therefore, important to develop an efficient way of utilizing this limited bandwidth resource. Fortunately, recent technological advances have enabled software-defined radios (SDRs) to switch from one frequency band to another at minimum cost, thereby making dynamic multiband access and sharing possible. On the other hand, recent advances in signal processing combined with those in antenna technology provide multiple-input multiple-output (MIMO) capabilities, thereby creating opportunities for enhancing the throughput of wireless networks. Both SDRs and MIMO together enable next-generation wireless networks, such as mesh networks, to support dynamic and adaptive bandwidth sharing along time, frequency, and space. In this paper, we develop a new framework that 1) identifies the limits and potential of SDRs and MIMO in terms of achievable network throughput and 2) provides guidelines for designers to determine the optimal parameters of wireless mesh networks equipped with multiband and multiantenna capabilities.

The field of wireless communications over the years has been constantly making significant advancements in the domain of Software Defined Radios (SDRs). These reconfigurable radios are also capable to learn from their environment like the spectrum and then undertake rapid decisions based on its selfdefined policies leading to the development of wider range of new applications. Thus, new topics and concepts are not only been investigated in the Digital Signal Processing domain but also in the Cognitive Radio domain for an effective and efficient use of the spectrum. Using such technologies with widely accepted international standards like the Software Communications Architecture (SCA) in military software defined radios also requires that they go through the testing and certification processes. Thus, in this Special Issue of the Springer Journal of Signal Processing Systems, all these topics leading towards the evolution of SDRs are discussed. This Special Issue comprises of three sets containing a total of nine articles. The first set of articles put focus on the various cognitive radio technologies like Dynamic Spectrum Access and Spectrum Sensing. The still existing inefficient use of the spectrum is discussed in various SDR research communities. New and innovative ideas are continuously investigated to use the spectrum in the most efficient way in order to meet the ever growing demands of high data throughput. Use of Cognitive Radios is one such way to achieve it. These radios are able to sense the radio environment in order to identify portions of the spectrum that are unused at a specific time or location and are able to reconfigure themselves. Various algorithms and techniques are being investigated to achieve such results in a dynamic environment. The second set of articles discusses several advanced Digital Signal Processing techniques. All these techniques aim at optimizing operationally relevant criteria. These criteria vary from achieving higher robustness, lower bit error rates, longer communication ranges, and minimal energy consumption to faster deployments. The proofs of the achievements are given either by simulation or by prototype implementation on off-the-shelf low cost hardware. The third set of articles illustrates the importance test and certification of Software Defined Radios in the military domain using internationally accepted standard like the SCA. Standards like the SCA allow waveforms to be ported easily across heterogeneous platforms. It also provides neatly defined interfaces and Application Program Interfaces (APIs) aimed to increase portability and interoperability of radios. In the final phase of the development of SDRs, efficient ways of testing and agencies providing the certification of standards play a significant role. * Marc Adrat marc.adrat@fkie.fraunhofer.de

Software-Defined Radio (SDR) is a rapidly evolving technology that is receiving enormous recognition and generating widespread interest in the telecommunication industry. Over the last few years, analog radio systems are being replaced by digital radio systems for various radio applications in military, civilian and commercial spaces. In addition to this, programmable hardware modules are increasingly being used in digital radio systems at different functional levels. Commercial wireless communication industry is currently facing problems due to constant evolution of link-layer protocol standards (2.5G, 3G, and 4G), existence of incompatible wireless network technologies in different countries inhibiting deployment of global roaming facilities and problems in rolling-out new services/features due to wide-spread presence of legacy subscriber handsets. SDR technology aims to take advantage of these programmable hardware modules to build an open-architecture based radio system software. The digital signal processing chains can be implemented in software (software-defined radio [SDR] application) running on general-purpose hardware (SDR platform). Ongoing advances in radio engineering and digital signal processing necessitate employing processor array and automatic resource allocation tools that provide computing resources on demand. This paper explains how software-defined radio (SDR) clouds combine SDR concepts with cloud computing technology for designing and managing future base stations.