Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of driver’s inattention: a real-time deep learning approach

Real time monitoring of driver’s attention and fatigue typically is approached in controlled conditions, which cannot be valid for real-world applications. This work focuses on the use of deep learning to classify the inattentive states providing a classification model independent from a specific training for each single user. The database for the experiment is collected through a camera mounted on the car dash, recording live video with adverse light conditions. The proposed system proved to have high accuracy at a low computational cost with a fast processing time, viable for real time car applications, and tested in a ground-truth environment, far from the favourable controlled conditions of a simulator. Moreover, this approach offers an improvement in the classification stage, being error-free when pre-training the system in the detection of fatigue and inattention of a specific user and, with minimal error on a random user dataset.
An Integrated Approach to Probabilistic Vehicle Trajectory Prediction via Driver Characteristic and Intention Estimation

Probabilistic trajectory prediction for other vehicles can be an effective way to improve the understanding of dynamic and stochastic traffic environment for automated vehicles. One challenge is how to predict the vehicle trajectory accurately both in the short-term and long-term horizon. In this paper, we propose an integrated approach combining the driver characteristic and intention estimation (DCIE) model with the Gaussian process (GP) model. Our proposed method makes use of both vehicle low-level and high-level information and inquires parameters by learning from public naturalistic driving dataset. Our method is applied and analyzed in the highway lane change scenarios. Compared with other traditional methods, the advantages of this proposed method are demonstrated by more accurate prediction and more reasonable uncertainty description during the whole prediction horizon.
Artificial neural networks modeling photovoltaic power system allocation – Can artificial intelligence beat a fundamental approach?

Social influences and political decisions drive the expansion and allocation of photovoltaic power systems in Germany. A large share is installed on rooftops by private investors. Factors that have an influence on the local installation of photovoltaic power systems are social adoption behavior as well as legal, spatial planning and economic restrictions. Because information about the future development of photovoltaic power systems is needed as input for assessments of energy market and power flow developments, we model the development based on different identified influencing factors. This leads to a complex interplay of inhomogeneous non-linear correlations, which need to be considered. As fundamental models are well established for the task of photovoltaic power systems expansion planning but may reach their limits dealing with those correlations, for which artificial neural networks are well-suited, we compare the model accuracy of both approaches based on a backtesting of the regional distribution of photovoltaic power systems in Germany.
Highly Integrated SIP for Mobile Device

In mobile application, to integrate the required functions in the limited area is one of the key points of the success of the products. The industries have studied the way of integration method by adopting system on chip or system in package approach. With the conventional approach using laminate substrate, there had been the limit of integration due to its limited pattern capability and larger keep out zone for underfill. It's known that fan-out package is one of the effective solutions to make the compact system. One of the most important factors is to achieve fine joint pitch to make the system within Package on Package format. In this study, the system integration by using advanced packaging technology like fan-out PLP will be provided. One of wearable devices had been selected to see the effect of integration. In conventional approach, the devices had been placed on main PCB, respectively. To make the area compact, some of PKG types were selected and compared. Finally, we had found the most effective package format for fine form factor.
What Does a Good Prediction Look Like?

In the developement of safe intelligent vehicles, intentions of other road users need to be estimated in order to plan a safe and convenient trajectory. The present work tries to approach the answer to the question of how a prediction could be assessed holistically using the example of pedestrian prediction. Recently used metrics are compared on a state of the art (SOTA) prediction method. A good prediction does not only have to verify itself on a limited data set, so machine versus human pedestrian prediction are compared. A study with test persons points out the similarity of human predictions compared to SOTA prediction. However, further research is required since we cannot propose a holistic metric that answers the question in the title, yet.
Alteraciones de los procesos de toma de decisiones vinculados al córtex prefrontal ventromedial en pacientes drogodependientes

Introduccion. La hipotesis del marcador somatico propone que senales corporales guian los procesos de toma de decisiones en un sentido adaptativo para el organismo. El procesamiento de estas senales se asocia al funcionamiento del cortex prefrontal ventromedial. La influencia de estos marcadores somaticos en la TD se ha medido mediante la Gambling Task (GT). Diversos estudios realizados con el empleo de la GT en drogodependientes han mostrado un deterioro de los procesos de toma de decisiones de estos pacientes. Objetivo. Examinar el rendimiento de una muestra espanola de drogodependientes en la GT y analizar la influencia de variables de interes clinico: tipo de sustancia, tiempo de consumo y abstinencia en el rendimiento en la tarea. Pacientes y metodos. Participaron 106 pacientes drogodependientes (PDD) en fase de deshabituacion. Se administro una version computarizada de la GT en una unica sesion individual. Se realizaron analisis descriptivos para examinar la ejecucion de los pacientes en la tarea y analisis de regresion multiple para estudiar la influencia de las variables clinicas sobre el rendimiento en la GT. Resultados. El 76,4% de los pacientes de la muestra presentaban deterioro en los procesos de toma de decisiones medidos con la GT. Ninguna de las variables examinadas se relacionaba significativamente con el rendimiento en la GT. Conclusiones. Estos resultados sugieren la existencia de alteraciones en los procesos de toma de decisiones de PDD, que pueden relacionarse con alteraciones premorbidas, o bien con el efecto neurotoxico de las drogas de abuso, en el funcionamiento del cortex ventromedial.
Predicting QoS for Cloud Services through Prefilling-Based Matrix Factorization

Quality of service (QoS) is an important indicator that users need to focus on when choosing services from the cloud center to build a service-based system. However, it is often difficult for users to collect the QoS records of all services under consideration in real circumstances. In this paper, we proposed an adjusted matrix factorization (MF) model named PFMF to predict the missing QoS values so as to make a scientific decision on service selection. Different from the existing MF models, in our PFMF method, the training is performed on the prefilled QoS matrix rather than the original sparse matrix. The prefilling of matrix is implemented by improving the PCC-based CF method. The key improvement is to consider the fluctuation degree of QoS records when finding the neighbor users or services. To validate the prediction performance of the proposed method, the comparison experiments are conducted on a widely-used large-scale dataset. The experimental results show that our PFMF method outperforms both the MF-based methods and PCC-based CF methods. In addition, it also shows good stability.
RRAM Device Models: A Comparative Analysis With Experimental Validation

Resistive Random Access Memories (RRAM) have recently shown outstanding characteristics such as high-scalability, high-speed, high-density, and low-energy operation. A simple and accurate model is crucial for rapid design and verification when using RRAM devices at the circuit level. The appropriate model selection gives insight into the behavior of RRAM as well as the efficient use of its unique properties. This work intends to guide the circuit designers in selecting the most appropriate RRAM model for their applications. We introduce a complete set of evaluation criteria for memristor models: type of model, type of switching, genericity, complexity, compatibility with actual physical switching mechanisms, linearity, symmetry, voltage/current control, hard set/soft reset, support electroforming, support for high programming signal frequencies, existence of a threshold, voltage level, timing dependence, temperature dependence and variability. This study compares the main existing RRAM models and summarizes the results in a table showing the main features and limitations of each model. Through extensive simulations and comparisons with experimental data, we provide an analysis and a validation of the reviewed models within the same simulation environment, ranging from individual elementary cells to large memory arrays. Furthermore, we provide a single and unique Verilog-A code integrating all the compared models.
A low-cost, high-power-density DC-DC converter for hybrid and electric vehicle applications

A 400 V to 12 V soft-switching DC-DC converter is designed to supply power to the low-voltage system of the hybrid and electric vehicle from the high-voltage battery. The use of soft-switching topology, SiC MOSFET and an optimised design of the transformer and filter inductors enables a low profile layout for the converter. Optimised layouts of the components increased its volumetric and specific power density over the commercial state-of-the-arts and ensured a simple construction process. The 3.5 kW converter achieved a volumetric power density of 2 kW / litre and a specific power density of 1.2 kW / kg. The prototype converter operated at 3.6 kW, 150 kHz, 425 – 13 V and achieved 87% efficiency. Experimental results from 100 W to 3.6 kW operations verified the superiority of the soft-switching topology over hard-switching operations.
Markerless Multi-View-based Multi-User Head Tracking System for Virtual Reality Applications

In this paper, we propose a markerless multi-view-based multi-user head tracking system for VR/AR/MR applications. In the Virtual Reality domain, the user typically interacts with the virtual world through his movements. The point-of-view of the user’s head mounted display (HMD) is based on the user’s head pose. Most systems track the head of the user based on the tracking of the HMD itself and are typically constrained to a single user. Aiming to improve the user’s experience in complex dynamic environments, we propose a head tracking method based on a multi-camera scenario, able to deal with the interaction of multiple users while coping with the existence of dynamic objects. The method relies on the voxelization of dense point cloud data, which is segmented to detect elements in the scene. Each element is holistically tracked along time and each detected user has his head’s position tracked using a dedicated Particle Filter. The system was evaluated using an annotated ground-truth, according to several metrics, and demonstrated a mean 3D positioning error below 18 mm. Furthermore, tests with multiple users were performed that successfully validate the proposed head tracking approach. The aforementioned tests also showed that the proposed approach is computationally efficient (running time below 20 ms for 4 users).
Differential Power Processing Architecture with Virtual Port in Series and MPPT in Submodule Level

In order to deal with mismatch issues caused by partial shading in small-scale photovoltaic systems, distributed MPPT (dMPPT) is applied in panel level or submodule level by means of DC-DC converters including full power processing (FPP) architectures and differential power processing (DPP) architectures. Compared with FPP architectures, DPP architectures can achieve higher efficiency and lower cost, because only portion of power is processed through the converter. This paper proposes a modified DPP architecture with a virtual port in series with the submodule string, which can greatly simplify the installation compared with that of conventional submodule level DPP and FPP converters. Meanwhile, it also retains the high efficiency as DPP architectures usually do. MPPT strategy is implemented by utilizing only one current sensor to reduce the hardware cost. Finally, simulations and experiments are presented to verify the proposed structure.
Application of Fractional Order $PI^{\lambda}D^{\mu}$ Controller for Power Quality Improvement Under Non-Stationary Load Conditions

A non-stationary load has a huge impact on power quality in electrical power distribution system. To maintain the level of power quality, the control algorithm should have capability of rejecting the disturbances. The controller plays a vital role in minimizing the error for the generation of reference current. This paper presents the application of fractional order $PI^{\lambda}D^{\mu}$ controller for power quality improvement in electric power distribution system. Genetic algorithm is used for tuning the control parameters of fractional order $PI^{\lambda}D^{\mu}$ controller. The performance of controller is tested using non-stationary load conditions. To validate the effectiveness of fractional $PI^{\lambda}D^{\mu}$ controller, its dynamic response, Total Harmonic Distortion (THD), complexity and design parameters are compared with integer based controller and fuzzy logic controller. The study has been done in real time simulation environment.
Impedance-Based Small-Signal Modeling and Stability Analysis of Type-3 Wind Turbines in Weak Grid

The small-signal instability problem of type-3 wind turbine (WT) in weak grid has recently attracting more and more attention. However, the grid-side converter (GSC) system is usually neglected in the existing works. In this paper, the small-signal model of type-3 WT considering GSC system is established firstly. Based on it, the system small-signal stability is analyzed by using the well-known impedance analysis method. The analysis indicates that GSC system cannot be ignored, and otherwise, it may cause the stability misjudgment. Moreover, the effects of many factors (e.g., the grid strength, PI current controller, PLL bandwidth, and control delay.) on the system stability are revealed. Finally, the analysis is validated by the simulation.
A calibrated-RSSI/PDR/Map intergrated system based on a novel particle filter for indoor navigation

A novel positioning system integrating Fingerprinting/PDR/map is proposed for indoor location-based service (LBS). The proposed system is used to achieve a superior indoor localization accuracy. The particle filter with multi-directions and adaptive weights, also named MDAW-PF, is proposed to fuse the fingerprinting, PDR and map. In the MDAW-PF method, the particle weights fully consider the noise of fingerprinting, the direction of the particles is to uniformly sample the direction of PDR estimation into multiple directions to ensure particle diversity, and the map information provides constrains for the particles. It is well known that the positioning accuracy of fingerprinting using RSSI is rather susceptible to the changing environment. Thus, the improved fingerprinting, also named MCRT, which calibrates online-RSSI by the matching model between offline-RSSI and online-TDOA, is proposed to mitigate the effect of RSSI variation. All measurements are conducted in indoor public areas, and the results show that the proposed system is featured with better localization performance, with the average error 0.68 m.
Dynamic Origin-Destination Estimation Framework with Iterative Traffic Signal Tuning for Microscopic Traffic Simulation

To validate traffic signal control algorithm’s performance, a setup of microscopic traffic simulation platform with realistic traffic demand is necessary. Traditionally, a bilevel framework of Origin-Destination (OD) calibration and trip assignment, is setup to estimate OD so that realistic traffic demand can be emulated in simulation platform. However, with this approach, we may mislead the calibration process by introducing insufficient green time allocation, as vehicles are likely to be stopped by red signals and thus vehicle throughput will never reach the real traffic demand. While this happens occasionally in unsaturated traffic condition, it is very prevalent in the saturated condition scenario. This paper introduces a trilevel problem formulation with consideration of traffic signal schedules during the OD estimation process. The first level uses an iterative algorithm (LSQR) to generate OD traffic demand with certain constraints based on real loop count data at junctions. Second level applies the traffic demand into a simulation platform to generate the trips between OD points. Dynamic User Equilibrium (DUE) will be satisfied iteratively so that the trip assignment is reasonable. Finally, the third level applies Iterative Tuning (IT) signal controller to tune signal schedules iteratively, such that sufficient green time can be allocated to allow vehicles drive through intersections. Via OD calibrations in corridor and area networks, we show that the trilevel OD estimation approach can achieve better performance as compared to the bi-level approach.
Safety Criteria Analysis for Negotiating Blind Corners in Personal Mobility Vehicles Based on Driver’s Attention Simulation on 3D Map

In this study, we attempt to establish the numerical safety criteria for negotiating blind corners in personal mobility vehicles (PMVs). Safety should be the most important consideration in designing autonomous PMVs. However, determining the suitable trade-off between safety and speed is a weighty concern because speed is significantly compromised when performing overly safe navigation. We analyze the driving behavior of a robotic PMV operated by a human driver. The robotic PMV can measure the driver’s gaze, and allows us to recognize both the pose of the PMV and the driver’s visual attention on a 3D map. As a result, the occluded areas for the driver can be estimated. Then, potential colliding hazard obstacles (PCHOs) are simulated based on the occlusion. PCHOs refer to occluded obstacles that the driver encounters suddenly with which he cannot avoid collision. The participants of our experiments were one skillful and three non-skilled ones. Experimental results demonstrate that similar PCHOs are observed even when the driving styles of the participants are different. Additionally, the existence of a boundary that distinguishes expected and unexpected obstacles is indicated by investigating the parameters of the PCHOs. Finally, we conclude that the boundary could be utilized as a numerical criterion for ensuring safety while negotiating blind corners.
Trade-off analysis using synthetic training data for neural networks in the automotive development process

Applications in the field of Deep Learning are constantly increasing the need for extensive, annotated data sets. Simulation software offers the possibility to create data sets of almost any size and thus the potential to cover this need. In the context of autonomous driving the test effort in the development processes increases to an extent that the use of virtual driving environments comes into focus. Deep Learning offers the opportunity to learn advanced driving strategies, starting from image recognition up to trajectory implementation.In this paper different driving simulation environments are used as data sources for the training of a neural network and examined for applicability on the basis of the working example of vehicle detection in image data.The visualizations include CarMaker, Carla and Grand Theft Auto V with different procedures for exporting ground truth information. For object detection, a pre-trained Convolutional Neural Network (CNN) is used and the effects on the detector quality of the data source and data size are investigated.An important part of the task of generating suitable data is the annotation process. The quality of the generated data is a decisive factor in achieving high performance training results. The test executions show that virtual data sources in training achieve lower detection rates in contrast to real data, but are more cost-effective in the overall context by scales.
Adenosine receptors in vascular sympathetic neurotransmission: The role of endothelium and of nitric oxide-mediated effects and their relation with hypertension

Receptors are the molecular targets through which drugs produce their beneficial effects in a disease state. Receptors location, in the cell membrane or within the cell, contributes for the understanding of the subsequent signalling events triggered by their activation. The subcellular localization and physiological functions of receptors are closely related and thus it is crucial to precisely determine the distribution of different molecules inside the intracellular structures. This is usually studied by fluorescence microscopy and posterior evaluation of the colocalization of the molecule of interest in a definite region in the cell. Laser Scanning Confocal Microscopy (LSCM) imaging is a powerful tool allowing to distinguish a variety of unexpected locations for receptors, manly in subcellular locations such as mitochondria, lysosomes, nuclei[1] turns crucial rethinking the current knowledge of the pharmacological role ascribed to receptors. Moreover, the use of fluorescent ligands in tissues will also be addressed since it allows a multidimensional analysis and provides new insights to receptor dynamics and intracellular traffic. [2] This chapter describes some of the the strengths, limitations and potentialities associated with receptors Laser Scanning Confocal Microscopy (LSCM) imaging, giving some practical examples concerning our current research line. Emphasis is placed on the importance of appropriate image acquisition guiding the reader through the basic and advanced knowledge of major quantitative image analysis approaches. Quantitative evaluation of receptors in tissues provides information about receptor over, subexpression or redistribution contributing for the understanding of the impairment/deregulation associated with receptors putative physiological and/or pathophysiological role.