Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting the Potential of Flexible Processing Units

In order to meet the increased computational demands and stricter power constraints of modern applications, architectures have evolved to include domain-specific accelerators. In order to design efficient accelerators, three main challenges need to be addressed: compute, memory, and control. Moreover, since SoCs usually contain multiple accelerators, selecting the right one for each task also become crucial. This becomes specially relevant in Flexible Processing Units (xPUs), processing units that provide multiple functionalities with the same hardware. While it is possible to use shared support components for all functionalities, this will lead to sub-optimal performance. In this work, we take one example of such xPU, and analyze the aspects which have not yet been fully addressed, showing that there is more potential to be exploited. By understanding the required memory patterns, we can achieve up to 72% speedup gains compared to using the memory support optimized for a different functionality. Furthermore, we propose an in-depth analysis of the different functionalities provided by the xPU. We then leverage the insights obtained from this analysis by providing a mechanism that selects the right functionality, maximizing hardware utilization.
The utilisation of reed (Phragmites australis): a review

Reed (Phragmites) is a wetland plant genus that has been utilised by man since ancient times. It is a tall, thin, highly productive grass (Poaceae) with an above-ground biomass of up to 30 t ha y. Due to its world-wide dominance, it is often cheap and readily available as a raw material. Reed has been used for centuries as a fodder plant in summer, and the stems have traditionally been harvested in winter as a raw material for crafts and for construction materials including roofing. It became popular for pulp and paper production in the middle of the twentieth century and, in recent years, has been used in sewage water treatment and as a source of renewable energy that is unlikely to compete with food production. This article explores the global extent of reedbeds and potential yields; and catalogues historical uses of reed, forgotten applications and new opportunities for its utilisation. Quality requirements, products and related stand management (such as harvesting time) are also reviewed.
Forecasting File Lifecycles for Intelligent Data Placement in Hierarchical Storage

The increasing disparity between computing speed and data access latency poses significant challenges in managing data storage, particularly for massively parallel supercomputers. To address this issue, storage systems have evolved into hierarchical architectures with different tiers offering varying performance, cost, and capacity based on specific hardware technologies. This heterogeneous and hierarchical nature of storage comes with the need for an optimal data placement strategy. Existing strategies have primarily approached this problem from a block perspective, focusing on analyzing application I/O behavior. However, such approaches fail to capture the contextual usage of information. To overcome this limitation, considering file-level usage patterns and adopting a file-centric perspective for data placement can leverage the context and semantics of files, leading to more efficient data placement strategies. This study proposes a novel file-based approach for data placement, focusing on the concept of file re-use and representing files through their life cycles (FLCs). The FLC of a file captures the sequence of operations it undergoes during its lifetime, referred to as FLCevents. By analyzing the time series data associated with the FLCs of actively used files, our algorithm detects repetitive patterns of FLCevents to predict future events using pattern matching and facilitate the anticipation of file movement. This allows for optimal data placement that aligns with the expected near-future usage. To validate our approach, we conducted experiments using traces extracted from representative high-performance computing (HPC) applications, namely NEMO, NAMD, LQCD, and an IO-Benchmark. The results we obtained are highly promising, demonstrating that in term of file prediction accuracy our proposed F-LRU (File-based Least Recently Used) achieves from 77% to 55% precision in most difficult scenarios to 100% for the others. Also, it is at least as effective as traditional LRU and LFU and can increase the hit rate by a factor of 1.94 against LFU and of 1.06 against LRU for LQCD and IO-Bench respectively. These findings highlight the potential of our approach to significantly enhance hierarchical storage performance, partlcularly in HPC environments.
A Solidly Mounted 55 GHz Overmoded Bulk Acoustic Resonator

In this work we present a solidly mounted overmoded bulk acoustic resonator (OBAR) designed to operate at millimeter wave (mmWave) frequencies. This device uses a combination of overmoded operation, a layer transfer based fabrication process, all metal Bragg mirrors, and series arrays to allow acoustic resonator frequency scaling through V band. The solidly mounted OBAR can have up to 2/3 the $k_t^2$ of a fundamental mode (~4% for AlN, >10% for ScAlN or LN), reasonable piezoelectric film thicknesses (>100 nm at 50 GHz for AlN), arbitrarily thick electrodes to minimize ohmic loss, and be made in series arrays to allow 50 Ω matched devices with reasonable area to perimeter ratios. We demonstrate a proof of concept device using a 110 nm AlN piezoelectric layer operating at 55 GHz with an electromechanical-coupling coefficient $\left( {k_t^2} \right)$ of 2.2%, and a series resonance quality factor (Qs) of 90.
A Decision Support System to Provide an Ongoing Prediction of Robot-Assisted Rehabilitation Outcome in Stroke Survivors

Clinicians often deal with complex robotic platform and serious games in stroke patients rehabilitation contexts, and they face two main problems: 1) the interpretation of either the performance in game or measures of a robotic system from the motor recovery point of view, and 2) the duration and complexity of clinical scales administration that makes repetitive assessments during the therapy unpractical. In this paper, a Random Tree Forest based system was trained and tested to provide a prediction of different clinical outcomes (i.e. FMA, ARAT, and MI) along the whole therapy duration, having non-clinical measures only as inputs, acting as a simulated decision support system. The dataset includes 30 post-stroke patients, that underwent a 30-session robot-assisted rehabilitation treatment. Results have shown that the system is able to produce very accurate and reliable predictions about the motor recovery of the patient at the end of the therapy, already in the first phases of the rehabilitation (i40% of therapy execution), just using robotic platform measures. Such a tool would provide a great benefit in terms of rehabilitation objectives planning, as a decision support tool for highly personalized rehabilitation treatments.
An Exploration-Estimation Beamforming Scheme For 5GNR FDD Massive MIMO Communications

In real-life 5G massive multiple-input and multiple-output (MIMO) cellular systems, achieving high-performance beamforming heavily depends on accurate downlink channel state information (CSI) acquisition, which is particularly challenging in frequency division duplexing (FDD) systems due to a lack of channel reciprocity. To tackle this challenge with low communication overhead, current FDD systems adopt the limited feedback scheme to encode the downlink CSI estimated by the user into a few scalars and feed them back. However, this scheme hinders optimal beamforming vector acquisition at the base station due to the difficulty in CSI recovery. In this paper, we propose an online alternating exploration-estimation approach based on a comprehensive analysis of the limited feedback scheme to address this problem. By extracting information hidden in the feedback values, this approach can effectively estimate the optimal beamforming vector. Moreover, the beamforming exploration step in this approach can guide the limited feedback scheme to provide a more accurate CSI. Numerical results demonstrate the outstanding performance of the proposed approach in terms of optimal beamforming vector acquisition.
DeepCEUS: Interleaved Signals Estimation in Checkerboard Imaging for Contrast Media Imaging using Context-aware Deep Learning

The extraction of harmonic signals for contrast imaging is achieved by using filters and N-pulse imaging techniques (e.g., pulse inversion (N≥2) or checkerboard apertures technique (N≥3)). On one hand, the former is limited by spectral leakage, while on the other hand, the latter suffers from an N-fold reduction in frame rate and increased susceptibility to motion artifacts resulting in incomplete removal of the fundamental echoes. In this work, we propose a context-aware signal-based deep learning approach (DeepCEUS) enabling single shot focused harmonic imaging for contrast media. This is accomplished by implementing an asymmetric convolutional autoencoder, to learn the nonlinear mapping between the backscattered echo from the full-aperture transmitted pulse and the combination of the interleaved pulses used in the conventional checkerboard apertures harmonic imaging technique. With the presented approach, the frame rate of focused contrast imaging can be almost tripled, while maintaining the reconstructed image resolution and contrast. Furthermore, an obvious reduction of noise and motion artefacts can be achieved.
Impact of Slowness Shape on Anti-resonance Q of SAW Resonators

This paper investigates impact of the surface acoustic wave (SAW) slowness curvature on anti-resonance quality factor (Qa) of SAW resonators. The variation of SAW resonance characteristics with aperture width W is studied using periodic three dimensional finite-element method for three structures with different slowness curvature. It is shown that the slowness shape affects the frequency (f) dependence of Bode Q, and smaller slowness curvature makes the Bode Q less sensitive to both f and W. This means that when the curvature is small, SAWs propagate like parallel beams on the substrate surface and are scarcely influenced by discontinuities at side boundaries such as electrode tips. Since loss mechanisms in the aperture region mainly contribute to the Bode Q reduction, high Qa and maximum Bode Q are achievable.
EMG-Based Control Strategies of a Supernumerary Robotic Hand for the Rehabilitation of Sub-Acute Stroke Patients: Proof of Concept

One of the most frequent and severe aftermaths of a stroke is the loss of upper limb functionality. Therapy started in the sub-acute phase proved more effective, mainly when the patient participates actively. Recently, a novel set of rehabilitation and support robotic devices, known as supernumerary robotic limbs, have been introduced. This work investigates how a surface electromyography (sEMG) based control strategy would improve their usability in rehabilitation, limited so far by input interfaces requiring to subjects some level of residual mobility. After briefly introducing the phenomena hindering post-stroke sEMG and its use to control robotic hands, we describe a framework to acquire and interpret muscle signals of the forearm extensors. We applied it to drive a supernumerary robotic limb, the SoftHand-X, to provide Task-Specific Training (TST) in patients with sub-acute stroke. We propose and describe two algorithms to control the opening and closing of the robotic hand, with different levels of user agency and therapist control. We experimentally tested the feasibility of the proposed approach on four patients, followed by a therapist, to check their ability to operate the hand. The promising preliminary results indicate sEMG-based control as a viable solution to extend TST to sub-acute post-stroke patients.
Oversample minority classes in Lung Ultrasound using Generative Adversarial Network

The development of a strong methodological strategy focusing on class imbalance issues for Lung Ultrasound (LUS) data is lacking in the state of the art. Traditional oversampling methods such as geometric and affine transformations generate quasi-duplicates of the original data. This problem could be resolved through generative adversarial networks (GANs). However, in classic GANs the generator cannot handle mode collapse issues and the model’s performance remains unstable when images in different classes look similar. To address this problem, we proposed a novel approach based on a supervised autoencoder generative adversarial network (SA-GAN), which could help to oversample the minority classes. At first, we build a latent space with the help of a supervised autoencoder (SA) to handle the mode collaspe issues. Then the trained weights of the SA are used to construct the generator and discriminator models. During the adversarial training of both models, the loss function is calculated for real and generated images through a customized gradient penalty loss function. This also allows classifying each image into real or generated, and its corresponding classes. This approach was tested on a dataset comprising 5500 LUS images, which were collected from 35 COVID-19 patients. As a result, SA-GAN provided the 900 generated LUS samples in minority classes to balance with the majority class. The proposed approach significantly improved the classification accuracy by up to 5% using synthetic augmentation through SA-GAN over the classic data augmentation method.
Precipitation Estimation from Himawari-8 Multiple Spectral Channels Using U-Net

Precipitation profoundly impacts Earth's ecosystems, from soil and plants to animals and humans. Accurate rainfall estimation is vital for various purposes, including weather and flood forecasting, enhancing agriculture, ensuring traffic safety, and minimizing natural disaster damage while safeguarding lives. Currently, rainfall estimation methods are mainly based on three main sources of information: satellite images, radar images and rain gauges data. However, these methods have limitations such as spatial coverage, limited range, capturing precipitation dynamics in complex terrainand, differentiating between precipitation types and accurately estimating intensity. To address these challenges, recent research explores deep learning-based methods that combine these data sources for accurate and comprehensive precipitation estimatation. This paper outlines directions for developing such methods using the U-Net deep learning model, incorporating data from Himawari-8 satellite imagery, rain radar data, meteorological data, and topographic information. This approach yields improved results in predicting rainfall rates and generating radar maps.
Rosetta: the Reusable Organizer for Simulating, Experimenting, Teaching, and Testing in Acoustics

Recent works have proposed several open-source solutions to standardize the format and reproducible communication of new ultrasound imaging techniques. However, these solutions either focus exclusively on computational simulations or signal postprocessing after data acquisition. We propose the Reusable Organizer for Simulating, Experimenting, Teaching, and Testing in Acoustics (Rosetta), an object-oriented conceptual framework to describe ultrasound experimental protocols in an intuitive and platform-independent manner. Rosetta intends to simplify the implementation of novel techniques in ultrasound imaging by facilitating the translation between simulation and experimental domains. This work outlines a workflow that describes ultrasound experiment protocols in a modular manner and enables their translation with a single function call. We demonstrate the process of modifying a data acquisition script using Rosetta, achieving a 94% reduction in the number of code lines needed to describe a B-mode imaging sequence relative to existing example scripts for Field-II and the Verasonics Vantage scanner. Furthermore, we demonstrate implementations of experimental imaging techniques, combine multiple modalities into one acquisition event, and translate a configuration script for simulations into physical imaging.
ConfTainter: Static Taint Analysis For Configuration Options

The prevalence and severity of software configuration-induced issues have driven the design and development of a number of detection and diagnosis techniques. Many of these techniques need to perform static taint analysis on configuration-related variables to analyze the data flow, control flow, and execution paths given by configuration options. However, existing taint analysis or static slicer tools are not suitable for configuration analysis due to the complex effects of configuration on program behaviors. In this experience paper, we conducted an empirical study on the propagation policy of configuration options. We concluded four rules of how configurations affect program behaviors, among which implicit data-flow and control-flow propagation are often ignored by existing tools. We report our experience designing and implementing a taint analysis infrastructure for configurations, ConfTainter. It can support various kinds of configuration analysis, e.g., explicit or implicit analysis for data or control flow. Based on the infrastructure, researchers and developers can easily implement analysis techniques for different configuration-related targets, e.g., misconfiguration detection. We evaluated the effectiveness of ConfTainter on 5 popular open-source systems. The result shows that the accuracy rate of data- and control-flow analysis is 96.1% and 97.7%, and the recall rate is 94.2% and 95.5%, respectively. We also apply ConfTainter to two types of configuration-related tasks: misconfiguration detection and configuration-related bug detection. The result shows that ConfTainter is highly applicable for configuration-related tasks with a few lines of code.
Autonomic Computing Based Respiratory Disorders Assessment Using Speech Parameters: A Systematic Review

Respiratory disorders (RD) are common health problems, and they represent a huge health burden to people around the world. Nowadays, speech analysis is a very important research area in the biomedical field. Therefore, speech analysis can be a useful tool for pre-diagnosing of RD. This review paper aimed to select the features of speech affected by RD and to define autonomic computing in terms of self-care and self-management in disease assessment using the machine learning (ML) paradigm. Selected articles in this work have been published in the last decade and are written in the English language.722 articles in total were obtained, of which 15 were chosen based on the meta-analyses (PRISMA) approach, and for systematic literature reviews (SLR), the reporting items were preferred. Articles were compiled by searching different databases such as Google Scholar, Crossref, and PubMed. This SLR mainly contributed to identifying the role of autonomic computing in the application of self-management for the assessment of RD in ML classifiers. It was observed that most of the research work on respiratory disorders with self-management applying speech analysis was done during the year 2012. Then gradually less work was done in this area until 2018 and in 2019, it increased again and stayed finally stable until 2022. It was concluded that the important information provided by this SLR is efficient model to detect and characterize RDs by applying speech parameters using autonomic computing.
On the Performance of RIS-aided OFDM-Based LEO Satellite Networks with Doppler Effects

In this paper, we analyze the outage behavior of a reconfigurable intelligent surface (RIS)-aided low earth orbit (LEO) satellite network based on orthogonal frequency division multiplexing (OFDM). In the satellite network, a RIS node with multiple reflecting elements is deployed between the satellite and ground user since there are strong blockages in the line-of-sight link. Due to the very high velocity of the LEO satellite, the satellite-RIS link suffers from a severe Doppler shift. We propose deploying the RIS node between the satellite and ground user to overcome the performance degradation caused by the Doppler shift in the satellite link. To evaluate the system performance, we obtain an approximated closed-form outage probability (OP) expression corresponding to any sub-carrier. Also, an asymptotic expression for the obtained OP is provided to present further insights. The theoretical results validated by the Monte Carlo simulations demonstrate that the effects of the Doppler shift can be significantly decreased as the number of RIS elements and sub-carrier spacing are increased.
Backscattering Interference Channel Characteristics in Full-Duplex Underwater Optical Wireless Communication

The performance of an underwater optical wireless communication (UOWC) system, which is capable of long distance full-duplex transmission, is limited due to the strong attenuation of water, causing the incoming optical signals at the high sensitive photomultiplier tube (PMT) based receiver to become weak. Moreover, the backscattering photons emitted from a co-side light source can cause non-negligible interference for symbol recovery. Using the single-scattering model, this paper provides closed form expressions to approximate the path loss and channel impulse response of the backscattering link. Numerical results demonstrate a good agreement between the derived analytical results and those obtained through Monte Carlo simulation under various conditions.
An IMPLY-based Semi-Serial Approximate In-Memristor Adder

To alleviate the Von Neumann bottleneck, new technologies and computing paradigms have been a hot topic in research and development in recent years. Memristors offer new innovative possibilities from technological and computational points of view. They can store data well and are suitable for in In-Memory Computation (IMC) since they are able to perform logical operations in memory. Another emerging computing paradigm to reduce computing time and area consumption is approximate computing, which is used in error-resistant applications. Here, we propose a novel approximated full adder hat uses the stateful logic Material Implication (IMPLY) in a semi-serial structure. We embedd this full adder in a Ripple Carry Adder (RCA) that we then evaluate on the circuit-level. The error metrics were evaluated and compared to State-of-the-Art (SoA) IMPLY-based adders. At 8-bit our approach requires up to 29% fewer steps and up to 34% less energy compared to the exact algorithm, while the Normalized Median Error Distance (NMED) is less than 0.01 for most scenarios. The proposed adder is applied in image processing and the respective quality metrics are calculated. All of the tested approximation degrees create a satisfactory result since the Peak Signal-to-Noise Ratio (PSNR) is over 30 dB. Thanks to the proposed approach, we save more than 13.5mJ of energy in gray-scale filtering of a 684×912 8-bit image compared to the exact calculations.