Showing papers by "Timo Hämäläinen published in 2023"

Leveraging Modern C++ in High-Level Synthesis

Abstract: High-level synthesis (HLS) enables the automated conversion of high-level language algorithms into synthesizable register-transfer level code, allowing computation-intensive algorithms to be accelerated on FPGAs. Most HLS tools have C++ as their input language, as it is widely known in both software and hardware industry. However, even though C++ receives a new standard every three years, the HLS tool vendors have mostly provided support and examples using C++98/03. Limiting to early C++ standards imposes a productivity penalty, since the newer standards provide both compilation time reductions and more concise, expressive, and maintainable way of writing code. In this study, we make the case for adopting modern C++ in HLS. We inspect the language features of C++11 and forward, and consider their benefits for HLS. We also test the present support for the modern language features with two state-of-the-art commercial HLS tools. Finally, we provide an extended example, demonstrating the increased clarity of code achieved using the newer standards. We note that the investigated HLS tools already have good support for modern C++ features, and urge their adoption to increase designer productivity.

Cybersecurity of COSPAS-SARSAT and EPIRB: threat and attacker models, exploits, future research

Abstract: COSPAS-SARSAT is an International programme for"Search and Rescue"(SAR) missions based on the"Satellite Aided Tracking"system (SARSAT). It is designed to provide accurate, timely, and reliable distress alert and location data to help SAR authorities of participating countries to assist persons and vessels in distress. Two types of satellite constellations serve COSPAS-SARSAT, low earth orbit search and rescue (LEOSAR) and geostationary orbiting search and rescue (GEOSAR). Despite its nearly-global deployment and critical importance, unfortunately enough, we found that COSPAS-SARSAT protocols and standard 406 MHz transmissions lack essential means of cybersecurity. In this paper, we investigate the cybersecurity aspects of COSPAS-SARSAT space-/satellite-based systems. In particular, we practically and successfully implement and demonstrate the first (to our knowledge) attacks on COSPAS-SARSAT 406 MHz protocols, namely replay, spoofing, and protocol fuzzing on EPIRB protocols. We also identify a set of core research challenges preventing more effective cybersecurity research in the field and outline the main cybersecurity weaknesses and possible mitigations to increase the system's cybersecurity level.

Towards a Unified Cybersecurity Testing Lab for Satellite, Aerospace, Avionics, Maritime, Drone (SAAMD) technologies and communications

Abstract: Aviation, maritime, and aerospace traffic control, radar, communication, and software technologies received increasing attention in the research literature over the past decade, as software-defined radios have enabled practical wireless attacks on communication links previously thought to be unreachable by unskilled or low-budget attackers. Moreover, recently it became apparent that both offensive and defensive cybersecurity has become a strategically differentiating factor for such technologies on the war fields (e.g., Ukraine), affecting both civilian and military missions regardless of their involvement. However, attacks and countermeasures are usually studied in simulated settings, thus introducing the lack of realism or non-systematic and highly customized practical setups, thus introducing high costs, overheads, and less reproducibility. Our"Unified Cybersecurity Testing Lab"seeks to close this gap by building a laboratory that can provide a systematic, affordable, highly-flexible, and extensible setup. In this paper, we introduce and motivate our"Unified Cybersecurity Testing Lab for Satellite, Aerospace, Avionics, Maritime, Drone (SAAMD)"technologies and communications, as well as some peer-reviewed results and evaluation of the targeted threat vectors. We show via referenced peer-reviewed works that the current modules of the lab were successfully used to realistically attack and analyze air-traffic control, radar, communication, and software technologies such as ADS-B, AIS, ACARS, EFB, EPIRB and COSPAS-SARSAT. We are currently developing and integrating support for additional technologies (e.g., CCSDS, FLARM), and we plan future extensions on our own as well as in collaboration with research and industry. Our"Unified Cybersecurity Testing Lab"is open for use, experimentation, and collaboration with other researchers, contributors and interested parties.

Linearity-based Sensor Data Online Compression Methods for Environmental Applications

Abstract: Environmental monitoring is a typical Internet of Things (IoT) application. Environmental monitoring plays a significant role, for example, in smart farming and smart city applications. Environmental magnitudes are usually measured using wireless sensor nodes, which are often battery-powered, and the number of sensing nodes can be large. One effective method for reducing the energy consumption of a sensor node is to use data compression to reduce the amount of data required for transmission via a wireless connection. Compressing the sensor data means fewer transmission periods, and thus, lower energy consumption. Compression methods should be effective for compressing environmental magnitudes and be computationally light to be suitable for constrained sensor nodes. A compression algorithm should be able to compress an online data stream. In this paper, we review some compression algorithms suitable for environmental monitoring and present two new versions of those algorithms. The algorithms were evaluated, tested, and compared. The main parameters used for the comparisons were compression ratio, root mean square error, and inherent latency. The simulation results obtained using real datasets demonstrate that simple linearity-based compression algorithms are effective and suitable for compressing environmental data. Two new compression algorithm versions proved to be effective for compressing sensor data with reasonable compression quality and predictable inherent latency.

Machine Learning Models for Predicting Adverse Pregnancy Outcomes in Pregnant Women with Systemic Lupus Erythematosus

Abstract: Predicting adverse outcomes is essential for pregnant women with systemic lupus erythematosus (SLE) to minimize risks. Applying statistical analysis may be limited for the small sample size of childbearing patients, while the informative medical records could be provided. This study aimed to develop predictive models applying machine learning (ML) techniques to explore more information. We performed a retrospective analysis of 51 pregnant women exhibiting SLE, including 288 variables. After correlation analysis and feature selection, six ML models were applied to the filtered dataset. The efficiency of these overall models was evaluated by the Receiver Operating Characteristic Curve. Meanwhile, real-time models with different timespans based on gestation were also explored. Eighteen variables demonstrated statistical differences between the two groups; more than forty variables were screened out by ML variable selection strategies as contributing predictors, while the overlap of variables were the influential indicators testified by the two selection strategies. The Random Forest (RF) algorithm demonstrated the best discrimination ability under the current dataset for overall predictive models regardless of the data missing rate, while Multi-Layer Perceptron models ranked second. Meanwhile, RF achieved best performance when assessing the real-time predictive accuracy of models. ML models could compensate the limitation of statistical methods when the small sample size problem happens along with numerous variables acquired, while RF classifier performed relatively best when applied to such structured medical records.

Does SoC Hardware Development Become Agile by Saying So: A Literature Review and Mapping Study

Abstract: The success of agile development methods in software development has raised interest in System-on-Chip (SoC) design, which involves high architectural and development process complexity under time and project management pressure. This article discovers the current state of agile hardware development with the questions (1) how well literature covers the SoC development process, (2) what agile methods and practices are applied or (3) what proposals are made to increase the agility, and (4) what is the impact for the SoC community. To answer the questions, a mapping study and literature review were performed. Seven hundred thirty papers were first studied, and eventually, after a rigorous filtering process, 25 papers were thoroughly analyzed. The results show that the popular agile SW development methods are applied in 5 cases, ideas adapted from the agile Hardware manifesto in 9 cases, and 11 cases do not define the Agile HW development method. Most of the papers address shorter development time by better methodologies and tools that indirectly shape the SoC development toward agility. The focus of agile hardware development is mostly on the SoC artifacts and methodological improvements have not been quantified. However, the literature indicates a significant impact on many academic chip prototypes. The challenges are better understood and the interest in agile methods is clearly increasing. The methodological gaps in the prevalent situation encourage further research and more accurate reporting of the development in addition to the SoC artifacts.

Robust Beamforming Design for RIS-Aided Integrated Sensing and Communication System

Abstract: It is expected that the future intelligent transportation system will be endowed with the sensing ability to cope with the complex road environment. Therefore, the integrated sensing and communications (ISAC) system can complement the development of intelligent transportation. In this work, a novel reconfigurable intelligent surface (RIS)-aided ISAC system is investigated, in which an RIS reflects signals to the vehicle target and user by creating a directional path to enhance sensing and communication performance. We are interested in the joint robust design of transmitted beamformer at the dual-functional radar-communication (DFRC) base station and phase-shift at the RIS to maximize the radar mutual information subject to user achievable rate constraint under imperfect angles knowledge and channel state information (CSI). Specifically, two CSI error models, namely, the bounded and the mixed bounded-moment error models, are considered. Then, a worst-case robust (WCR) beamforming problem, as well as a mixed chance-constrained and worst-case robust (MCWR) beamforming problem, are separately formulated. Furthermore, we develop two efficient methods to convert the formulated semi-infinite constraint problems into feasibility ones, and an alternate optimization framework is proposed to obtain stationary points of the original problems. Simulation results are provided to validate the effectiveness of the proposed transformation methods and solution.

Satellite-Assisted Multi-Connectivity in Beyond 5G

Abstract: Due to the ongoing standardization and deployment activities, satellite networks will be supplementing the 5G and beyond Terrestrial Networks (TNs). For the satellite communications involved to be as efficient as possible, techniques to achieve that should be used. Multi-Connectivity (MC), in which a user can be connected to multiple Next Generation Node Bs simultaneously, is one such technique. However, the technique is not well-researched in the satellite environment. In this paper, an algorithm to activate MC for users in the weakest radio conditions is introduced. The algorithm operates dynamically, considering deactivation of MC to prioritize users in weaker conditions when necessary. The algorithm is evaluated with a packet-level 5G non-terrestrial network system simulator in a scenario that consists of a TN and transparent payload low earth orbit satellite. The algorithm outperforms the benchmark algorithms. The usage of MC with the algorithm increases the mean throughput of the users by 20.3% and the 5th percentile throughput by 83.5% compared to when MC is turned off.

A Competency Framework for Participatory Modeling

Abstract: Abstract Participatory modeling (PM) is a craft that is often learned by training ‘on the job’ and mastered through years of practice. There is little explicit knowledge available on identifying and documenting the skills needed to perform PM. In the modeling literature, existing attempts to identify relevant competencies have focused on the specific technical skills required for specific technical model development. The other skills required to organize and conduct the stakeholder process seem to be more vaguely and poorly defined in this context. The situation is complicated by PM being an essentially transdisciplinary craft, with no single discipline or skill set to borrow ideas and recommendations from. In this paper, we aim to set the foundation for both the practice and capacity-building efforts for PM by identifying the relevant core competencies. Our inquiry into this topic starts with reviewing and compiling literature on competencies in problem-solving research areas related to PM (e.g., systems thinking, facilitated model building, operations research, and so forth). We augment our inquiry with results from a PM practitioners’ survey to learn how they perceive the importance of different competencies and how the scope of these competencies may vary across the various roles that participatory modellers play. As a result, we identified five core competency areas essential for PM: systems thinking, modeling, group facilitation, project management and leadership, and, more recently, designing and running virtual workshops and events.