Miracle-dz/UniFAR-FTUs huggingface dataset:
Power losses comparison between Silicon Carbide and Silicon devices for an isolated DC-DC converter

In recent years, new efficient power devices have been implemented. Silicon Carbide has replaced silicon as regards the production and the utilization of many devices, such as MOSFETs, diodes, IGBTs and many others. SiC devices are characterized by a low reverse recovery charge, high carrier saturation velocity, by which it is possible to work at high frequency, and high breakdown voltage. Thanks to the great thermal conductivity and the wide bandgap, these devices can operate at high temperature and reach high voltages and currents. What is important to stress is the fact that power losses in SiC devices are lower than the silicon ones. These are the reasons why these devices are utilized in a wide range of technological applications, including power electronics. In this paper, power losses in Si and SiC devices are evaluated in simulation and compared to each other. In order to demonstrate the remarkable advantages of the SiC devices over the silicon ones, a study which makes use of an isolated DC-DC converter has been conducted. As regards the proposed full-bridge converter, SiC and silicon MOSFETs and diodes, of whom some static and dynamic parameters are defined, are used in order to transfer power from a DC voltage supply to a load.
A Bayesian Approach for Estimating Heart Rate using FMCW Radar

This paper illustrates a Markov model of peak-finding and gives a Bayesian method for estimating heart rate based on an FMCW radar. The Bayesian method provides a framework for estimation and inference and noise peaks can be filtered out. Conjugate priors are used making the algorithm computation-ef-ficient. A real-time algorithm is given and several experiments have been carried out. It is shown that the accuracy of the heart rate is close to reference and much more stable than the traditional FFT method and promising in overcoming noise.
Learning Human Activity From Visual Data Using Deep Learning

Advances in wearable technologies have the ability to revolutionize and improve people’s lives. The gains go beyond the personal sphere, encompassing business and, by extension, the global economy. The technologies are incorporated in electronic devices that collect data from consumers’ bodies and their immediate environment. Human activities recognition, which involves the use of various body sensors and modalities either separately or simultaneously, is one of the most important areas of wearable technology development. In real-life scenarios, the number of sensors deployed is dictated by practical and financial considerations. In the research for this article, we reviewed our earlier efforts and have accordingly reduced the number of required sensors, limiting ourselves to first-person vision data for activities recognition. Nonetheless, our results beat state of the art by more than 4% of F1 score.
Disentangling the association between genetics and functional connectivity in Mild Cognitive Impairment

Despite the increasing effort being devoted to the investigation of the link between imaging endophenotypes (IDPs) and genetic determinants (GDs) in Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD), many issues remain open and deserve investigation. Among these, the role of functional connectivity (FC) is still blurred. This paper aims at shading some light on the topic relying on the ADNI repository (177 patients, out of which 82 MCI and 95 controls). The within/between-network connectivities were derived from individual FC matrices and used as IDPs. Conversely, the GDs consisted of two Polygenic Risk Scores (PRS) that have recently been proven to play a role in AD. A Partial Least Squares (PLS) model equipped with LASSO regularization was finally fitted to the data for associating IDPs and GDs. In the first component, all FC coefficients had the same sign, and were correlated with PRS2. Connectivities involving the dorsal attention (DAN) and frontoparietal control (CON) networks reached the highest weights, while within/between-network FC for the limbic (LIM) were less represented. Overall, the within-network FC values were less pronounced compared to the between-network ones. In the second component, most of the FC features had zero weights. Visual (VIS) and somatomotory (SMN) showed a correlated trend, while being anti-correlated with LIM, CON and default mode network as well as with PRS1. Our findings suggest that the two PRSs correlated with a possible pattern of aberrant within/between-network FC changes occurring in RSNs devoted to higher cognitive functions and more vulnerable in this pathology.
The Effect of Titanium Dental Implants Surface Modification on the Macrophage Mediated Cell Response

Titanium dental implants are currently one of the best treatment alternatives for replacing missing teeth. The direct bond that forms between the jawbone and titanium in a process defined as osseointegration has made titanium the material of choice for such devices. Whilst the underlying biological mechanisms responsible for the osseointegration process have not been fully elucidated, high demand by both patients and clinicians has driven the clinical application of titanium implants. Modification of the surface properties of titanium has been proven to be effective in promoting osseointegration, and thus understanding the fundamental cellular and molecular mechanisms responsible for the clinically observed outcomes as a result of titanium surface modification is the focus of considerable research. Previous research in this field has, as expected, focused on osteoblasts as they are the bone forming cells. This however, ignores the possible fundamental role of early mediators of the healing process, such as platelets and macrophages, which may interact with the implant surface well ahead of osteoblasts. As platelets and macrophages have well documented roles in modulating the function of other cells including osteoblasts, it is reasonable to postulate that the events that occur during the very early stages of the healing process may modulate repair in later stages, and ultimately influence the final osseointegration outcome.
Technology Impact on Neutron-Induced Effects in SDRAMs: A Comparative Study

This study analyses the response of synchronous dynamic random access memories to neutron irradiation. Three different generations of the same device with different node sizes (63, 72, and 110 nm) were characterized under an atmospheric-like neutron spectrum at the ChipIr beamline in the Rutherford Appleton Laboratories, UK. The memories were tested with a reduced refresh rate to expose more single-event upsets and under similar conditions provided by a board specifically developed for this type of study in test facilities. The board has also been designed to be used as a nanosatellite payload in order to perform similar tests. The neutron-induced failures were studied and characterized, presenting the occurrence of single-bit upsets and stuck bits. The cross sections for each type of event and technology node show that the 110 nm model is more sensitive to neutron-induced single-event effects than the other models.
A Bayesian Deconvolution Application to Calibrate Multi-port RC Network Representation of Electronic Packages

The reliability and thermal issues on electronic components are more and more frequent, complex and crucial for product lifecycle management. Indeed the component temperatures are close to exceed their operating limits. The integration of precise components models at board level simulation is mandatory to analyze their mutual thermal interactions in real concurrent cooling environment. The multi-port RC network approach to model components offers accurate temperature prediction at an affordable simulation time. Nevertheless, establishing a fine representation of the internal structure as well as setting its material properties is required for this approach. This article proposes a way to calibrate the model on a lifelike numerical dataset obtained by different simulations of a perfect numerical model. The study focuses on the deconvolution methods to identify RC ladders network. An iterative Bayesian deconvolution method is preferred to classic Fourier one for this application, thus a fast extraction of a Foster model from discrete time constant spectrum is achieved. Then a Foster-to-Cauer network transformation with many stages of parallel RC ladders is presented with a special attention to performances and limitations of this network conversion. A physical interpretation is possible with this new network model, and its behavior demonstrates a high agreement with the step response impedance of the studied component. This Cauer network finally allows creating the Cumulative Structure Function (CSF) and the Differential Structure Function (DSF) of the component with a great resolution. The spatial superposition of these functions on a numerical dataset of the same component mounted on different configurations of a test board permits to quantify the thermal resistance of vias structures, imperfect report contact and calibrate thermal material properties. This complete calibration procedure permits fixing model discrepancies in order to create a relevant multipath RC network including real manufacturing impact on electronic package and its assembly on PCB. The full process is validated on a usual literature case, and its relevance highlighted on numerical simulations of an electronic component.
Multi-operator Radio Access Network Sharing for 5G SA Network Design and Laboratory test

Due to the massive costs of 5G telecommunications infrastructure, Radio Access Network (RAN) sharing, by means of which investment as well as operational expenditure is reduced and the network deployment period is shortened, attracts much attention from mobile operators recently. Particularly, Multi Operator Core Network (MOCN) is an effective way for RAN sharing, in fact for spectrum sharing. 5G Standalone (SA) network, which is completely newly built network, supports all 5G service application scenarios, and is chosen by many mobile operators instead of non-Standalone (NSA) as the main 5G construction network. In this paper, we focus on MOCN for 5G SA network, solve problems that arise when RAN sharing, design a laboratory test to verify the performance of MOCN, and analyze the test results. The test results show that the scheduling fairness for data service among mobile operators performs well and the 200Mbps shared bandwidth is effectively utilized. Then, MOCN for 5G SA network is suitable for a field trial, and furthermore, for the whole network construction in the near future.
The Impact of Representation on the Optimization of Marker Panels for Single-cell RNA Data

The increasing number of single-cell transcriptomic and single-cell RNA sequencing studies are allowing for a deeper understanding of the molecular processes underlying the normal development of an organism as well as the onset of pathologies. These studies continuously refine the functional roles of known cell populations, and provide their characterization as soon as putatively novel cell populations are detected. In order to isolate the cell populations for further tailored analysis, succinct marker panels—composed of a few cell surface proteins and clusters of differentiation molecules—must be identified. The identification of these marker panels is a challenging computational problem due to its intrinsic combinatorial nature, which makes it an NP-hard problem. Genetic Algorithms (GAs) have been successfully used in Bioinformatics and other biomedical applications to tackle combinatorial problems. We present here a GA-based approach to solve the problem of the identification of succinct marker panels. Since the performance of a GA is strictly related to the representation of the candidate solutions, we propose and compare three alternative representations, able to implicitly introduce different constraints on the search space. For each representation, we perform a fine-tuning of the parameter settings to calibrate the GA, and we show that different representations yield different performance, where the most relaxed representations— in which the GA can also evolve the number of genes in the panel—turn out to be the more effective, especially in the case of 0-knowledge problems. Our results also show that the marker panels identified by GAs can outperform manually curated solutions.
Ensembled Crossover based Evolutionary Algorithm for Single and Multi-objective Optimization

A unique way evolutionary algorithms (EAs) are different from other search and optimization methods is their recombination operator. For real-parameter problems, it takes two or more high-performing population members and blends them to create one or more new solutions. Many real-parameter recombination operators have been proposed in the literature. Each operator involves at least a parameter that controls the extent of exploration (diversity) of the generated offspring population. It has been observed that different recombination operators and specific parameters produce the best performance for different problems. This fact imposes the user to use different operator and parameter combinations for every new problem. While an automated algorithm configuration method can be applied to find the best combination, in this paper, we propose an Ensembled Crossover based Evolutionary Algorithm (EnXEA), which considers a number of recombination operators simultaneously. Their parameter values and applies them with a probability updated adaptively in proportion to their success in creating better offspring solutions. Results on single-objective and multi-objective, constrained, and unconstrained problems indicate that EnXEA’s performance is close to the best individual recombination operation for each problem. This alleviates the use of expensive parameter tuning either adaptively or manually for solving a new problem.
Degradation Mechanisms of Epoxy Molding Compound Subjected to High Temperature Long Term Aging

FCBGAs are finding applications in automotive underhood environments where they may be subjected to sustained temperatures of 125-200°C for sustained periods during operation. While, FCGBAs have been previously used in consumer applications where operating temperatures typically range in 55-85°C, relatively little is known on methods to design damage-tolerant packages in automotive underhood environments. There is insufficient information on plastic-encapsulated FCBGAs capable of surviving high temperatures for long periods (>100,000 hours). In this paper, the degradation mechanisms of epoxy-molding compound under sustained high temperature aging have been studied. Two popular molding compounds, EMC-1 and EMC-2 are studied under three aging temperatures of 100°C, below glass transition temperature, 125°C, around glass transition temperature and 150°C, above glass transition temperature. Thermo-gravimetric analysis has been conducted to study the weight loss change as the function of aging temperature and aging time. Differential scanning calorimetry has been conducted to study the curing behavior. The evolution of the storage modulus, loss modulus and the tangent-delta has been quantified for aging short-term aging up to 40-days and long-term aging up to 1-year at temperatures of 100°C and 150°C.
Adaptive Dynamic Surface Control for a Class of Dead-Zone Nonlinear Systems via Output Feedback

The paper aims to address output feedback problem for a class of nonlinear systems subjected to unknown dead-zone. High-gain K-filters is firstly designed such that unavailable states of system can be reconstructed. Then, with the help of dynamic surface control (DSC) method, an adaptive output feedback controller design is established, which can alleviate undesired influence of the dead-zone and guarantee the semi-global stability of the system. Furthermore, the $\mathcal {L_{\infty }}$ performance of tracking error is achieved by means of initialization technique. Ultimately, numerical and practical examples clarify the efficiency of the proposed solution.
Multi-Agent Deep Reinforcement Learning for Resource Allocation in the Multi-Objective HetNet

Resource allocation in a heterogeneous network is an NP-hard problem, especially in 5G network scenarios. Multiobjective optimization in resource allocation is a challenging task that cannot be solved by the conventional optimization algorithm. In this paper, we propose a distributed multi-agent deep reinforcement learning (MADRL) for joint resource allocation to maximize spectrum efficiency (SE) and energy efficiency (EE). We employ the distributed learning in a stochastic geometry-based realistic heterogeneous network, where multiple femto base stations, a fixed number of macro and pico base stations in addition to randomly distributed mobile users are deployed. We propose a distributed MADRL multi-objective optimization problem (MADRL-MOP) framework to validate the performance. The simulation results demonstrate that the DDPG-based MADRL-MOP framework can not only handle the joint resource allocation problem effectively but also achieve better spectrum efficiency as well as convergence performance.
A Dynamic Predictor Selection Method Based on Recent Temporal Windows for Time Series Forecasting

The development of accurate forecasting systems for real-world time series modeling is a challenging task. Due to the presence of temporal patterns that change over time, the adoption of a single model can lead to underperformed forecasts. In this scenario, Multiple Predictor Systems (MPS) emerge as an alternative to adopting single models since they struggle to learn in the presence of temporal patterns that change over time. Dynamic prediction/ensemble selection is a special case of MPS where each model is an expert in the time series’s specific patterns. In dynamic selection, instead of combining all models, the most competent models per test pattern are selected. A criterion commonly used is to evaluate the models’ performance in the region of competence, formed by the patterns present in the in-sample set (training or validation sets) more similar to the test pattern. Thus, the region of competence’s quality is a key factor in the precision of the MPS. However, adequately defining the similarity criterion and the size of the region of competence is challenging and problem-dependent. Furthermore, there is no guarantee that similar data exist in the in-sample set. This paper proposes a dynamic selection approach entitled Dynamic Selection based on the Nearest Windows (DSNAW) that selects one or more competent models according to their performance in the region of competence composed of the nearest antecedent windows to the new target time window. This strategy assumes that the temporal windows closer to a test pattern have a behavior more similar to the target than in-sample data. The experimental study using ten well-known time series showed that the DSNAW outperforms the literature approaches.
Edge Computing Assisted Autonomous Driving Using Artificial Intelligence

The emergence of new vehicles generation such as connected and autonomous vehicles led to new challenges in the vehicular networking and computing managements to provide efficient services and guarantee the quality of service. The edge computing facility allows the decentralization of processing from the cloud to the edge of the network. In this paper, we design and propose an end-to-end, reliable and low latency communication architecture that allows the allocation of compute-intensive autonomous driving services, in particular autopilot, to shared resources on edge computing servers and improve the level of performance for autonomous vehicles. The reference architecture is used to design an Advanced Autonomous Driving (AAD) communication protocol between autonomous vehicles, edge computing servers, and the centralized cloud. Then, a mathematical programming approach using Integer Linear Programming (ILP) is formulated to model the autopilot chain resources Offloading at the network edge. Further, a deep reinforcement learning (DRL) approach is proposed to deal with dense Internet of Autonomous Vehicle (IoAV) networks. Moreover, several scenarios are considered to quantify the behavior of the optimization approaches. We compare their efficiency in terms of Total Edge Servers Utilization, Total Edge Servers Allocation Time, and Successfully Allocated Edge Autopilots.
Trust-aware truth discovery with Long-term Vehicle Reputation for Internet of Vehicles Crowdsensing

In Internet of Vehicles (IoV), vehicle-based crowd-sensing provides various significant data, such as weather condition and GPS data, etc. However, due to different source quality, the sensed data of vehicles may vary from the ground truth. Truth discovery is usually used to analyze conflicting data, and it traditionally estimates source quality only from the current task. Aiming to take the long-term reputation into consideration, in this paper, we propose a trust-aware model. Specifically, we first propose a new model to define vehicle reliability. Instead of using weight as reliability, we define source reliability by its contribution to the loss function. Then, we propose a novel trust-aware model to accumulate long-term reputation of vehicle from its history tasks. Finally, based on vehicle reputation, initial weights are assigned for the future task. Experiments conducted on weather conditions and GPS datasets demonstrate that our trust-aware model can save execution time and achieve higher accuracy.
MACRO: Multi-Attention Convolutional Recurrent Model for Subject-Independent ERP Detection

Due to the low signal-to-noise ratio, limited training samples, and large inter-subject variabilities in electroencephalogram (EEG) signals, developing a subject-independent brain-computer interface (BCI) system used for new users without any calibration is still challenging. In this letter, we propose a novel Multi-Attention Convolutional Recurrent mOdel (MACRO) for EEG-based event-related potential (ERP) detection in the subject-independent scenario. Specifically, the convolutional recurrent network is designed to capture the spatial-temporal features, while the multi-attention mechanism is integrated to focus on the most discriminative channels and temporal periods of EEG signals. Comprehensive experiments conducted on a benchmark dataset for RSVP-based BCIs show that our method achieves the best performance compared with the five state-of-the-art baseline methods. This result indicates that our method is able to extract the underlying subject-invariant EEG features and generalize to unseen subjects. Finally, the ablation studies verify the effectiveness of the designed multi-attention mechanism in MACRO for EEG-based ERP detection.