Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-based Place Recognition Using ConvNet Features and Temporal Correlation Between Consecutive Frames

The most challenging part of vision-based place recognition is the wide variety in appearance of places. However temporal information between consecutive frames can be used to infer the next locations of a vehicle and obtain information about its ego-motion. Effective use of temporal information is useful to narrow the search ranges of the next locations, hence an efficient place recognition system can be accomplished. This paper presents a robust vision-based place recognition method, using the recent discriminative ConvNet features and proposes a flexible tubing strategy which groups consecutive frames based on their similarities. With the tubing strategy, effective pair searching can be achieved. We also suggest to add additional variations in the appearance of places to further enhance the variety of the training data and fine-tune an off-the-shelf, CALC, network model to obtain better generalization about its extracted features. Experimental results show that our proposed temporal correlation based recognition strategy with the fine-tuned model achieves the best (0.572) F1 score improvement over the original CALC model. The proposed place recognition method is also faster than the linear full search method by a factor of 2.15.
High-speed UAV Delivery System with Non-stop Parcel Handover Using High-speed Visual Control

Although physical distribution research using unmanned aerial vehicles (UAVs) has garnered increasing interest, the task of automatically loading a parcel onto a UAV remains inadequately researched. In this study, after evaluating previous research related to UAV loading, we propose a system that can achieve non-stop handover of a parcel to an airborne UAV and aid in the design of an automatic UAV delivery system. To evaluate the efficiency of our proposed system for introduction to an actual delivery system, we construct a mathematical model for UAV delivery. The model simulation results show that the proposed system can achieve a maximum improvement of 65% in the number of parcels delivered per day, and that non-stop handover has a greater effect on reducing the cost of the system when the average delivery distance is short.
Whole brain volume and cortical thickness based automatic classification of Wilson’s disease

Wilson’s disease (WD) is a progressive autosomal-recessive genetic disorder of copper metabolism that can induce cognitive, physical and psychiatric symptoms. Despite the wide use of machine learning methods in neuroimaging analysis, WD-related research has been very rare. In this work, we proposed and validated an efficient pipeline for an automated WD classification based on whole brain segmentation volumes and cortical thicknesses obtained from T1-weighted magnetic resonance images (MRIs). Three well-known supervised machine learning algorithms, including support vector machine (SVM), linear discriminant analysis (LDA), and logistic regression (LR), were evaluated and compared in the setting of WD classification. A total of 51 images, including 27 acquired from WD patients and 24 from age-matched healthy controls, were used in the validation analysis. Univariate feature selection was conducted to eliminate non-relevant features and retain features contributing to the classification performance. Two nested leave-one-out cross validations were adopted, with the inner folder used for optimal parameter estimation and the outer folder for classification performance evaluation. Experimental results showed that when employing volume features, SVM significantly outperformed both LDA and LR, yielding an overall accuracy of 96.1%, a sensitivity of 92.6% and a specificity of 100%. LR could also reach such best classification performance when using a combination of volumes and thicknesses as input features. This study provides a new non-invasive tool (MRI-based) for an automated detection of WD.s
Fast Triangle Counting on GPU

Triangle counting is one of the most basic graph applications to solve many real-world problems in a wide variety of domains. Exploring the massive parallelism of the Graphics Processing Unit (GPU) to accelerate the triangle counting is prevail. We identify that the stat-of-the-art GPU-based studies that focus on improving the load balancing still exhibit inherently a large number of random accesses in degrading the performance. In this paper, we design a prefetching scheme that buffers the neighbor list of the processed vertex in advance in the fast shared memory to avoid high latency of random global memory access. Also, we adopt the degree-based graph reordering technique and design a simple heuristic to evenly distribute the workload. Compared to the state-of-the-art HEPC Graph Challenge Champion in the last year, we advance to improve the performance of triangle counting by up to $5.9 \times $ speedup with $\gt 10^{9}$ TEPS on a single GPU for many large real graphs from graph challenge datasets.
Longitudinal Position Control for Highway On-Ramp Merging: A Multi-Agent Approach to Automated Driving

Highly automated driving requires effective handling of many complex scenarios. Here we study a specific important task of highly automated driving: merging into traffic from a highway ramp. We take a multi-agent view of this problem. We propose a simplified mathematical formulation that captures the most fundamental interactions involved in this complex scenario and show that collisions may not be universally avoidable. We then propose a multi-agent simulator based on a slightly more sophisticated version of the problem to study the interaction of a single vehicle pair: an on-ramp merger and an in-lane traffic vehicle. While simple, the simulator captures the fundamental interaction of the general complex on-ramp merge scenario. We apply single- and multi-agent Q-learning within the simulator as a way to try to infer and analyze the individually optimal behavior that each vehicle may reach for the merging task under different informational conditions governing vehicles’ interaction. Our results suggest that a multi-agent approach can produce controllers for highway-ramp merging with lower collision rates than those produced via a single-agent approach, but only if the individual behavior of the in-lane traffic vehicle remains optimal. We also discuss implications of our results for future work on this problem.
An Optimal Control Based Motion Planning Method in the Environment of Positioning Errors

In general, the cooperative driving problems for Connected and Automated Vehicles (CAVs) consist of two parts: determine the desired arrival times and motion planning. However, most of the existing works don’t take positioning errors into account. To improve the ability of handling the positioning errors, this paper formulates motion planning as an optimization problem and adds collision avoidance constraints into the optimization problem. Then, an analytical solution of the constructed optimization problem is derived so that it can be solved quickly in practice. A series of simulation tests show that this method is superior to a common-used method in terms of safety and energy consumption. As long as the appropriate safety distance is chosen, the proposed method can make CAVs operate safely against a certain degree of positioning errors.
Evaluating Risk Attitudes Across Cultures

Decision-making under risk or uncertainty is quite common in various environments. According to decision-maker’s preferences, there are three types of attitudes toward risk: risk averse, risk prone or risk neutral. The aim of this work is to perform a cross-cultural analyzes of the risk preferences of subjects who belong to two different cultures. A questionnaire is applied with Brazilians and Indians to capture their preferences in order to observe the attitudes towards risk and check if they act in different way. Using the concept of Certainty Equivalent, it was found they present the same attitude when facing risk situations, being risk averse. At the end of this step, the information serves as an input to consider when negotiating with a counterpart from different cultures.
DF4CRAN: Dataflow Framework for Cloud-RAN Signal Processing

Nowadays mobile operators face several challenges because of huge growth of internet traffic usage. Cloud Radio Access Network(C-RAN) was proposed as a solution for this problem. There are already many implementations of C-RAN but they are not easily configurable and are designed for specific hardware architecture. Our motivation for this work was to provide an implementation of C-RAN as an open-architecture, which will provide solutions for mapping the C-RAN applications to any architecture.
Accelerated Inverse Reinforcement Learning with Randomly Pre-sampled Policies for Autonomous Driving Reward Design

To learn a reward function that a driver adheres to is of importance to the human-like design of autonomous driving systems. Inverse reinforcement learning (IRL) is one of the recent advances that can achieve this objective, but it often suffers from the low efficiency of generating optimal policy by reinforcement learning (RL) each time when updating reward weights. This paper presents an accelerated IRL method by approaching the optimal policy among randomly pre-sampled policies in designed sub-space instead of finding it through RL in the whole policy space. The corresponding trajectories are targeted via an optimal trajectory selector in the candidate trajectory library generated by pre-sampled policies. The weights then are updated by comparing the selected trajectories and the expert ones. The proposed method is very suitable for improving learning efficiency for low-dimensional problems like autonomous driving, whose expert policies are nearly tractable. Results with simulated driving data show that it only took 11 iterations to converge, while the average longitudinal RMS error of the recovered trajectories based on the learned reward function was only 2.14m.
Real-time Stochastic Optimization of Energy Storage Management using Rolling Horizon Forecasts for Residential PV Applications

In this paper, an energy management method for a residential PV-storage hybrid system composed of a solar photovoltaic (PV) generation and a battery energy storage (BES) is formulated as an offline optimization model concurrent with a real-time rule-based controller. Existing offline energy management approaches for day ahead scheduling of BES generally suffers from energy loss in real-time due to the stochastic nature of load and solar generation. On the other hand, typical online algorithms do not offer optimal solutions for minimizing electricity purchase costs to the owners. To overcome such limitations, we propose an integrated framework where optimization is performed in receding horizon utilizing the forecasted load and solar generation profiles from long short term memory (LSTM) in rolling horizon to reduce daily electricity purchase costs. The optimization model is formulated as a multi-stage stochastic program where we use the stochastic dual dynamic programming (SDDP) algorithm in the receding horizon to update the optimal set-point for BES dispatch at a fixed interval. To prevent loss of energy during optimal solution update instants, we propose a rule-based controller beneath the optimization layer in finer time resolution at the power electronics converter control level. The proposed framework is evaluated using a realtime controller-hardware-in-the-Ioop (CHIL) test platform in an Opal-RT simulator. The proposed real-time method reduces the net electricity purchase cost relative to other existing energy management methods.
A Non-Regulated Full-Bridge Resonant Converter for Implementing CC and CV Charging strategies of Electric Vehicles

This paper introduces a new converter for implementing CC and CV charging strategies for battery electric vehicles by using an isolated full-bridge converter with adding a variable-resonant tank. Thanks to the variable-resonant tank, the proposed converter is able to implement CC and CV control strategies with a fixed-switching frequency and not using any regulation for the primary switches. This capability of nonregulation is achievable due to inherent characteristics (constant-current frequency and constant-voltage frequency) of the variable-resonant tank. Since the proposed converter only operates at these resonant frequencies, it can achieve the ZVS and ZCS for all semiconductor devices. A design optimization framework is used to design the resonant tanks to properly achieve all advantages. To verify the improved performances, a software simulation of 3.3 kW was conducted. The results have shown that the proposed converter can charge the battery with a constant current of 7.8 A, a constant voltage of 420 V at a fixed frequency of 50 kHz.
Spectral EEG-based classification for operator dyads’ workload and cooperation level estimation

There is a growing momentum to design online tools to measure mental workload for neuroergonomic purposes. Most of the research focuses on the monitoring of a single human operator. However, in real-life situations, human operators work in cooperation to optimize safety and performance. This is particularly the case in aviation whereby crews are composed of a pilot flying and a pilot monitoring. The motivation of this study is to evaluate the possibility to apply an hyperscanning approach to estimate the mental workload of crews composed of two operators. We designed an experimental protocol in which ten crews (i.e. 20 subjects) had to perform a modified version of the NASA MATBII during 8 five-minute blocks (i.e. 4 mental workload level configurations * 2 cooperation v. non cooperation conditions). Mental workload and cooperation level were classified using a traditional passive brain-computer interface pipeline that includes a spatial filtering step on frequency features. Our results disclosed that all mental states’ estimations were significantly above chance level. Intra-subject classification accuracy for mental workload (2 classes) was 63% for the pilot flying and 58% for the pilot monitoring. As for cooperation level, the binary classification reached 57% for the pilot flying and 60% for the pilot monitoring. Regarding the team, intra-team classification accuracy of the workload configuration of the team (4-class) reached 35%. As for the team cooperation level, the binary classifier reached 60% of accuracy. The results are discussed in terms of hyperscanning applications.
Analysis of Multi-Operator Resource Sharing

In this paper, we propose a Multi-Operators resource allocation scheme, where the available bandwidth of the Heterogenous Cloud RAN (H-CRAN) is shared among different Virtual Mobile Network Operators (VMNOs), according to static, as well as adaptive sharing. We evaluate the performance of the two schemes, in terms of average spectrum utilization efficiency, user satisfaction metrics and average throughput. Results show that adaptive sharing brings a considerable gain of performance, with respect to these performance evaluation metrics compared to static sharing. Particularly with adaptive sharing, the tradeoff and the interest of keeping spectrum utilization and user satisfactions metrics jointly high can be achieved for a wider interval of user's density variations, and the gain of average throughput would be achieved if the network presents some low load VMNOs.
Influence of the operating frequency on DC-DC converters for HVDC grids

While several DC-DC converters for HVDC have been proposed in literature, comparison studies are needed to identify the best circuit for a particular case of application. This paper proposes an analytical methodology that allows to assess rapidly the comparison of DC-DC converters. It was applied to evaluate two modular DC-DC structures, one isolated circuit and one non isolated circuit, focusing in the variation of the operating frequency for different DC voltage transformation ratios. The results show that the non-isolated structure presents better indicators compared to the isolated circuit for all the considered cases.
Uni-Antenna Spatial Energy Compression Based on Unsteady Coherent Interference

In this work, the unique spatial energy compression phenomenon of uni-antenna frequency diverse system based on the unsteady coherent interference is explored comprehensively. By compressing the element spacing of linear frequency diverse array (FDA) to near-zero, the decoupling of angle dimension is significantly achieved from the conventional range-angle-dependent beampattern. Inspired by the ever-shrinking element spacing, the concept of uni-antenna frequency diverse system is firstly proposed. The relations between a series of parameters (i.e. the number of diversity, frequency offset values) and spatial energy distributions is investigated in-depth. The uni-antenna frequency diverse synthesizing method allows the focusing intensity and focusing period of transmitted energy to be flexibly adjustable. The electric intensity of the proposed uni-antenna frequency diverse system has at least 4dB more compared with that in conventional phased array at each special focusing ribbon, which brings a great improvement of SNR in radar detection. This system provides a very promising and potential research and development direction on collaborative detection scheme while integrating phased array technology. With energy compression and focusing in both angle and range dimensions achieved, illustrating that such interference fringes phenomenon of frequency diverse antenna system is more promising and suitable for high performance detection and ranging applications.
Case Study on an Adaptive Traffic Controlling Method Using Real-time Traffic Streaming

The traffic congestion in urban areas has emerged as one of the most notable issues in the past few decades due to the rapid rise of the number of vehicles in the country. The existing solutions available for countering this issue do not possess the capability to handle the excessive traffic congestion situations successfully. Presently, traffic light systems are widely used as a method to control the traffic flow in major junctions. However, the traffic lights operate according to a predefined set of light patterns and time cycles. The lack of ability in traffic lights to dynamically handle the traffic flows based on the real time traffic is an issue which should not be neglected. In this case study, an adaptive traffic controlling system for 4-way junctions, has been proposed where the aim is to minimize the traffic congestions that occur commonly in urban areas. The system was developed based on image processing techniques and fuzzy logic. Moreover, the image processing was used for recognizing the current traffic situation and the fuzzy logic based adaptive decision-making model was proposed for dynamically controlling the traffic. The test results of the system proved that the segmentation of image processing component had a sensitivity of 83.20% and a fall-out rate of 0.20%. The test results of adaptive decision-making component suggested that the adaptive system was better than existing system in 6 different phases out of the 12 tested phases. Thus, it can be concluded that the proposed system is capable of minimizing the traffic congestions considerably to meet the goals of reducing traffic related issues.
Modeling forecasting errors of fluctuating renewables and electrical loads

The integration of large amounts of fluctuating renewable energy into the power system, for instance in central western Europe raises a need for accurate handling of forecasting error in power system modeling. Especially, fundamental power system models should incorporate these forecasting errors to supply robust results. This paper presents an approach to generate scenarios of day-ahead wind, photovoltaic and load forecasting errors for a stochastic unit commitment approach. The stochastic character is sampled using an ARMA-GARCH-approach, after deterministic components of underlying time series have been filtered. Subsequently, the clustering of the simulated samples is performed to account for parsimonious model selection. We show, that a scenario set clustered to 20 time-series has the best possible trade-off between accuracy and parsimony.
Control of Single Phase Solar Photovoltaic Supply System

This work deals with the modified multilayer fifth order generalized integrator (MLFOGI) based grid tied single phase solar photovoltaic (PV) system employing fuzzy logic control (FLC) based maximum power point tracking (MPPT) algorithms. The MLFOGI is used to extract the fundamental component of grid voltage under polluted grid due to voltage sag, swell, harmonics, frequency variations and dc offsets. The FLC based MPPT is capable to extract optimum energy under change in weather conditions. It offers fast dynamic response under reduced PV voltage oscillation. The proposed system is developed on MATLAB 2018a and obtained results shows the feasibility of system control and Total Harmonics Distortions (THDs) of grid current are in acceptable limits as per IEEE-519 standard.
Design of Current Regulator for Induction Machines at Low Sampling-to-Fundamental Frequency Ratios with Improved Current Observer

Induction machines for high-power and high-speed applications often operate at low sampling-to-fundamental frequency (S2F) ratios. The current regulator performance degrades at low S2F ratios due to the inherent delay caused by the digital implementation. This paper introduces the design of a current regulator with an improved current observer which estimates the current at the next sample instant. The digital implementation delay is compensated using the estimated current as feedback. The analysis and experimental results show that a better current command tracking is achieved at very low S2F ratios. The decoupling control of d-axis and q-axis current is maintained at very low S2F ratios with fast dynamics.
Exploring the Effect of Virtual Depth on Pupil Diameter

Virtual and Augmented reality (VR/AR) are being extensively used in many applications that extends from entertainment, training to rehabilitation and treatment of disorders. Studies on the effects of extended use of VR immersion has been performed. However, the change of pupil diameter with the change of VR simulated depth has not been investigated. Pupil dilation is an indicative measure of cognitive overload. In this paper, we investigate the relationship between VR simulated depth and the pupil diameter change. Results showed a significant difference in pupil diameter change with simulated depth and also a strong negative correlation. This indicates that as the depth of the VR object increase (distance from the VR user increase), the VR user’s pupil diameter decreases. These results show that change in pupil diameter can be an indicative of change in the depth of the observed virtual object. This can be an effective VR/AR scene scanning and understanding tool.
Stationary batteries in the EU countries, Norway and Switzerland: Market shares and system benefits in a decentralized world

In terms of the economic performance of residential PV systems, the rate of self-consumption is becoming ever more important. Battery systems are available in order to increase onsite-consumed electricity. We calculate a scenario for the market diffusion of decentralized battery systems for residential self-consumption in Europe until 2050. The main drivers of market uptake, future electricity prices, and technology costs are assessed in detail via comprehensive model coupling of electricity market and energy demand models and the inclusion of experience curves that reflect cost reduction through technological learning with increasing battery installations. In our decentralized scenario, these considerations result in 467 GWh of stationary batteries for residential self-consumption.