Abstract
The C/C++ compilation stack (Intermediate Representations (IRs), compilation passes and backends) is encumbered by a steep learning curve, which we believe can be lowered by complementing it with approaches such as source-to-source compilation. Source-to-source compilation is a technology that is widely used and quite mature in certain programming environments, such as JavaScript, but that faces a low adoption rate in others. In the particular case of C and C++ some of the identified factors include the high complexity of the languages, increased difficulty in building and maintaining C/C++ parsers, or limitations on using source code as an intermediate representation. Additionally, new technologies such as Multi-Level Intermediate Representation (MLIR) have appeared as potential competitors to source-to-source compilers at this level. In this paper, we present what we have identified as current challenges of source-to-source compilation of C and C++, as well as what we consider to be opportunities and possible directions forward. We also present several examples, implemented on top of the Clava source-to-source compiler, that use some of these ideas and techniques to raise the abstraction level of compiler research on complex compiled languages such as C or C++. The examples include automatic parallelization of for loops, high-level synthesis optimisation, hardware/software partitioning with run-time decisions, and automatic insertion of inline assembly for fast prototyping of custom instructions. © João Bispo, Nuno Paulino, and Luís Miguel Sousa.

Abstract
Advancements in semiconductor technology no longer occur at the pace the industry had been accustomed to. We have entered what is considered by many to be the post-Moore era. In order to continue scaling performance, increasingly heterogeneous architectures are being developed and the use of special purpose accelerators is on the rise. One notable example are Field-Programmable-Gate-Arrays (FPGAs), both in the data-center and embedded spaces. Advances in FPGA features and tools is allowing for critical kernels to be accelerated on specialized hardware without fabrication costs. However, re-targeting code to such heterogeneous platforms still requires significant refactoring of the compute intensive kernels, as well as knowledge of parallel compute and hardware design concepts for maximization of performance. We present Tribble, a source-to-source framework under active development, capable of transforming regular C/C++ programs for execution on heterogeneous architectures. This includes transforming the target kernel source code so that it is amenable for circuit generation while keeping the original version for software execution, inserting code for task and memory management and injecting a scheduler algorithm.

Abstract
This paper introduces two machine learning optimization algorithms to significantly enhance position estimation in Reconfigurable Intelligent Surface (RIS) aided localization for mobile user equipment in Non-Line-of-Sight conditions. Leveraging the strengths of these algorithms, we present two methods capable of achieving extremely high accuracy, reaching sub-centimeter or even sub-millimeter levels at 3.5 GHz. The simulation results highlight the potential of these approaches, showing significant improvements in indoor mobile localization. The demonstrated precision and reliability of the proposed methods offer new opportunities for practical applications in real-world scenarios, particularly in Non-Line-of-Sight indoor localization. By evaluating four optimization techniques, we determine that a combination of a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) results in localization errors under 30 cm in 90 % of the cases, and under 5 mm for close to 85 % of cases when considering a simulated room of 10 m by 10m where two of the walls are equipped with RIS tiles. © 2023 IEEE.

Abstract
Semantic segmentation consists of classifying each pixel according to a set of classes. This process is particularly slow for high-resolution images, which are present in many applications, ranging from biomedicine to the automotive industry. In this work, we propose an algorithm targeted to segment high-resolution images based on two stages. During stage 1, a lower-resolution interpolation of the image is the input of a first neural network, whose low-resolution output is resized to the original resolution. Then, in stage 2, the probabilities resulting from stage 1 are divided into contiguous patches, with less confident ones being collected and refined by a second neural network. The main novelty of this algorithm is the aggregation of the low-resolution result from stage 1 with the high-resolution patches from stage 2. We propose the U-Net architecture segmentation, evaluated in six databases. Our method shows similar results to the baseline regarding the Dice coefficient, with fewer arithmetic operations.

Abstract

Abstract
Stress-ribbon footbridges are often prone to excessive vibrations induced by environmental phenomena (e.g., wind) and human actions (e.g., walking). This paper studies a stress-ribbon footbridge at the Faculty of Engineering of the University of Porto (FEUP) in Portugal, where different degrees of vertical vibrations are perceptible in response to human actions. We adopt sonification techniques to create a sonic manifestation that shows the footbridge’s dynamic response to human interaction. Two distinct sonification techniques – audification and parameter mapping – are adopted to provide intuitive access to the footbridge dynamics from low-level acceleration data and higher-level spectral analysis. In order to evaluate the proposed sonification techniques in exposing relevant information about human actions on the footbridge, an online perceptual test was conducted to assess the understanding of the three following dimensions: 1) the number of people interacting with the footbridge, 2) their walking speed, and 3) the steadiness of their pace. The online perceptual test was conducted with and without a short training phase. Results of n= 23 participants show that parameter mapping sonification is more effective in promoting an intuitive understating of the footbridge dynamics compared to audification. Furthermore, when exposed to a short training phase, the participants’ perception improved in identifying the correct dimensions. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.