Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-term forecasting of wind speed in Brazil using GAS modelling

It is well known that the world is experiencing a rapid growth in the implementation of renewable energy sources (RES). In particular, the wind power generation is one that is growing very fast. This is a consequence of the reduction of the use of fossil fuels to generate electricity which is the main responsible of the so-called global warming. To assess the wind potential for a region or even to analyze the behavior of the wind regimes and how these could affect the energy system operation it is required accurate forecasts of wind speed. Such forecasts provide essential information to project a wind farm and it might be applied as complementary tool to investigate the relationship with other renewable sources. In this paper, two models are employed for this purpose: a model with time-varying parameters, named GAS model and the well-known SARIMA model. They are applied to two wind speed hourly average datasets from the states of Bahia and Ceará in Brazil. The models were fitted and their 3-month-ahead forecasting performance evaluated. The obtained results revealed that the GAS model outperformed the SARIMA counterpart in both: in-sample and out-sample forecasts.
Achieving Stable and Optimal Passenger-Driver Matching in Ride-Sharing System

Ride-sharing systems enable individual car owners with idle time to provide commercial taxi-like services via an online platform. By crowdsourcing a large population of individual car owners, it can provide more flexible services with a lower serving cost, comparing with the traditional taxi system. Due to the autonomous nature of car owners (drivers), a decentralized driver dispatching algorithm that can achieve a stable (self-motivated) and optimal passenger-driver matching is highly desired for a ride-sharing system. In this paper, we will study such a driver dispatching algorithm systematically. We first show that the optimal passenger-driver matching achieved by the centralized driver dispatching algorithm is often not stable, in the sense that some drivers and passengers may break with their matched partners and form new matching pairs. To this end, we introduce a virtual order fee on each passenger (which the platform will charge the drives who want to serve the passenger) to motivate the behaviors of drivers. Specifically, we propose a novel auction-based decentralized driver dispatching algorithm, where each driver proposes the most profitable passenger that he wants to serve, considering the potential profit that he can achieve and the order fee that he needs to pay from/to serving each passenger. The virtual order fee on a passenger will be gradually increased when multiple drivers want to serve the passenger, until there exists only one driver who is willing to serve. We analytically show that such a decentralized driver dispatching algorithm will converge to an equilibrium (stable) outcome, which achieves the optimal passenger-driver matching (i.e., that maximizes the social income of the whole system). Simulation results further show how the converging speed and the achieved social income change with the system parameters such as the step size of order fee increasement. Moreover, it is easy to implement the proposed distributed algorithm in a practical system.
Multi-User Visible Light Communications: State-of-the-Art and Future Directions

Visible light communications (VLC) builds upon the dual use of existing lighting infrastructure for wireless data transmission. VLC has recently gained interest as cost-effective, secure, and energy-efficient wireless access technology particularly for indoor user-dense environments. While initial studies in this area are mainly limited to single-user point-to-point links, more recent efforts have focused on multi-user VLC systems in an effort to transform VLC into a scalable and fully networked wireless technology. In this paper, we provide a comprehensive overview of multi-user VLC systems discussing the recent advances on multi-user precoding, multiple access, resource allocation, and mobility management. We further provide possible directions for future research in this emerging topic.
LPPCO: A Novel Multimodal Medical Image Registration Using New Feature Descriptor Based on the Local Phase and Phase Congruency of Different Orientations

At present, many feature descriptors-based registration methods have been proven to be robust in case of complex intensity distortions. However, most of these feature descriptors are only related to intensity information in a patch of neighboring pixels and ignoring the neighbor orientation information, which make the registration performance for medical images with low-resolution appear to be weak robustness and low accuracy. To improve the robustness and accuracy, a novel feature descriptor, named Local-Phase mean and Phase-Congruency values of different Orientations (LPPCO), is developed using filter-bank of Log-Gabor filters at different orientations and frequencies. Next, a similarity measure named LPPCOncc is developed using the normalized cross correlation (NCC) of the LPPCO descriptors, followed by a fast template matching techniques for detecting correspondences between the different images. Additionally, a more sensitivity of phase deviation function is presented for the calculation of phase congruency. The main steps of constructing the similarity measure LPPCOncc include: firstly, we extract the local phase mean and phase congruency values for each pixel in each orientation; secondly, local phase mean orientation histograms and phase congruency values orientation histograms over all the pixels are computed respectively, where the maximum response values are chosen to vote for the corresponding bin; thirdly, combining the two resulting histograms obtains the feature descriptor LPPCO; finally, the LPPCO descriptor is compared across images using NCC. Experimental results show that LPPCOncc is robust to complex intensity distortions between multimodal medical images and outperforms other similarity metrics such as NCC and DLSC. Furthermore, LPPCOncc-based registration algorithm preformed on various types of multimodal medical image pairs shows that it outperforms the NMI-based and DLSC-based registration methods in the registration robustness and accuracy.
Analysis of a Speech-Based Intersection Assistant in Real Urban Traffic

Previously, we have presented a speech-based intersection assistant prototype. The system is activated on-demand by the driver and gives afterwards, via speech, information on suitable gaps between the traffic vehicles approaching from the right. It is comparable to a front seat passenger which helps in the maneuver decision for an intended turn left. This system has assumed a more or less constant flow of the traffic. To also handle situations of more dynamic urban traffic, including vehicles that may be slowing down or stopping, we have now extended our previous approach by a dynamic vehicle model. This model predicts the future traffic vehicle state based on second-order vehicle dynamics. We perform an in depth analysis of our system on a set of recordings under various traffic conditions. In this analysis we compare in particular the previous and the novel vehicle model. Both approaches lead to a correct recommendation in approximately 90% of the cases. Unexpectedly, the dynamic model does not lead to significant improvements in the system behavior, despite its increased accuracy.
OpenSky Report 2018: Assessing the Integrity of Crowdsourced Mode S and ADS-B Data

Air traffic tracking platforms such as the OpenSky Network use crowdsourcing to track air traffic world wide. While crowdsourcing allows for unprecedented coverage, it comes with challenges concerning the integrity of the data. More specifically, by delegating the data collection task to a mostly unknown group of individuals, the network becomes vulnerable to data integrity breaches by inexperienced or malicious actors. Users might - intentionally or not - send faulty or fake data to the tracker and in this way threaten the integrity of the information provided by the network. In this paper, we provide unique insights into the data integrity challenges we faced during 6 years of operating the OpenSky Network. We analyze the different types of integrity breaches and discuss methods to detect and filter faulty data.
Characterizing User Reactions Towards Twitter's 280 Character Limit

Twitter is one of the most popular forums for information sharing and interactions among individuals. The continuous rise in the demand and usage of Twitter in various applications have led to the increase in the character limit recently. However, this decision to increase the character limit evokes a variety of research questions, regarding the actual necessity of this decision for users, utilization of the increased characters by the users, reactions of the users and whether the users across the world reacts to this change. In this paper, we intend to answer the queries and characterize the initial reactions, measure the actual usage of new character limit. The initial user reactions depict emotion diversity as well as dependence on geography. Additionally, the usage of character count post this change is highly skewed with newer character count usage present in tail of the distribution which highlights that a very small number of subsequent tweets are of high character count(ranging from 150--240 characters).
Design and Analysis of a Novel MOEMS Gyroscope Using an Electrostatic Comb-Drive Actuator and an Optical Sensing System

In this paper, a novel vibratory optical MEMS gyroscope is proposed, which is composed of an optical sensing system, an electrostatic comb-drive actuator, and a mass-spring system with 2 degrees of freedom and a decoupling frame. The optical sensing system consists of a laser diode light source, a tunable photonic crystal, a photodetector, and integrated waveguides. The sensitive element of the proposed gyroscope has been designed with separated primary and secondary resonance frequencies to increase the operating bandwidth. Simulation results show that the functional characteristics of the proposed gyroscope are as follows: an operating frequency of 1830 Hz, a sense mode resonance frequency of 2330 Hz, an operating bandwidth of 200 Hz, a mechanical sensitivity of 0.14 nm/(°/s), an optical sensitivity of 0.051 nm/(°/s), and a measurement range of ±500 °/s.
In-Vivo Quantitative Mapping of the Perforasomes of Deep Inferior Epigastric Artery Perforators

Background: There is limited understanding of anatomy of perforator angiosomes, or “perforasomes,” of the deep inferior epigastric artery (DIEA). A perforasome is defined as the territory perfused by a single perforator vessel of a named artery, such as the DIEA. Given the clinical significance of this anatomical concept in microsurgical breast reconstruction, this study is a quantitative investigation of DIEA perforasome characteristics and patterns associated with perforasome size, perforator caliber, location and branching, using computed tomographic (CT) angiography. Methods: Twenty abdominal arterial-phase CT angiograms were analyzed in 3 dimensions using software (Horos). DIEA perforasomes were mapped, yielding data on 40 medial-row and 40 lateral-row perforasomes. Perforator branch extents and number were measured using 3-dimensional multi-planar reconstruction, and perforator caliber on axial slices. Results: Perforasomes exhibited eccentric branching distributions in horizontal and vertical axes, that is, a majority of perforators were not centrally located within their perforasomes. Lateral-row perforasomes displayed greater horizontal eccentricity than medial-row. There was a positive correlation between perforator caliber and perforasome size. Medial-row perforators had more branches and larger caliber than lateral-row. Conclusions: This is the first article to quantify relationships between perforators and their territories of supply in vivo, augmenting current understanding of perforasome theory. DIEA perforasomes can be readily visualized and mapped with CT angiography, which may enable effective preoperative flap planning in DIEA perforator flap breast reconstruction. Future investigation may highlight the importance of this information in improving surgical outcomes, including flap survival and fat necrosis reduction, through careful, perforasome-based flap design.
Paradigm Shifts in Gifted Education: An Examination Vis-à-Vis Its Historical Situatedness and Pedagogical Sensibilities

After nearly a century of development, gifted education has evolved into a complex educational discipline with well thought out pedagogy and research agendas. However, while the number of studies escalates, the field as a whole has been criticized for producing fragmented and piecemeal results. One of the reasons for these shortfalls is that the field has invested little in meta-theoretical aspects, such as historical perspectives and philosophical foundations. This article is a comprehensive review and analysis of the conceptual changes and paradigm shifts in gifted education. Three major paradigm shifts in gifted education were identified—demystification (i.e., giftedness as manifested wonders), identification (i.e., giftedness as measurable predictions), and transaction (i.e., effectuation of human possibilities). Presently, there is still an implicit focus on the identification paradigm despite considerable efforts to shift the focus to creating and sustaining appropriate developmental niches for all individuals. Debates in the field are highlighted to provoke discussion of future directions.
Virtual Hug Induces Modulated Impression on Hearsay Information

Although it is perceivable that interpersonal touch affects recipient's impression of touch provider as well as the information relating to the provider alike, its utility in touch mediated through communication devices (virtual interpersonal touch) remains unclear to date. In this article, we report the alleviating effect of virtual interpersonal touch on social judgment. In particular, we show that virtual hug with a remote person modulates the impression of the hearsay information about an absentee. In our experiment, participants rate their impressions as well as note down their recall of information about a third person. We communicate this information through either a speaker or a huggable medium. Our results show that virtual hug reduces the negative inferences in the recalls of information about a target person. Furthermore, they suggest the potential that the mediated communication offers in moderating the spread of negative information in human community via virtual hug.
A Rotating Machinery Fault Diagnosis Method for High-Speed Trains Based on Improved Deep Learning Network

Rotating machinery is an important part of highspeed train system. Any failure of such components will have a serious impact on the service itself and even jeopardize the safe and reliable operation of the train. However, the limited diagnostic accuracy has become the bottleneck of fault diagnosis. In this paper, an adaptive fault diagnosis method based on deep sparse autoencoder (SAE) network is proposed. Firstly, the original time domain signals collected by sensor are preprocessed and used as input to the diagnostic network. Secondly, the SAE fault diagnosis network model is built. Thirdly, the loss function of SAE is modified to improve the diagnostic accuracy and efficiency of network. Finally, experiments verify the feasibility and advantages of the proposed method.
Towards a Standardized Mapping from Automotive Security Levels to Security Mechanisms

Modern vehicles are becoming targets and need to be secured throughout their lifetime. There exist several risk assessment models which can be used to derive security levels that describe to what extent components, functions and messages (signals), need to be protected. These models provide methods to gather application specific security requirements based on identified threat and item combinations that need to be coped with. However, a standardized mapping between security levels and required mandatory security mechanisms and design rules is currently missing. We address this problem first by suggesting that the risk assessment process should result in five security levels, similar to the functional safety standard ISO 26262. Second, we identify suitable security mechanisms and design rules for automotive system design and associate them with appropriate security levels. Our proposed methodology is as much as possible aligned with ISO 26262 and we believe that it should therefore be realistic to deploy in existing organizations.
A Collision-Free Surveillance System Using Smart UAVs in Multi Domain IoT

In this letter, we introduce a surveillance model for multi domain IoT environment, which is supported by reinforced barriers with collision-avoidance using heterogeneous smart UAVs. Formally, we define a problem whose goal is minimizing the maximum movement of smart UAVs on condition that collision-avoidance among UAVs is assured when they fly between initial positions and specific spots in order to build reinforced barrier in multi domain. After ILP formulation of the defined problem, a novel approach is proposed to solve the problem. Also, we analyze the performance of the developed scheme through extensive simulations with various scenarios.
A Belief Propagation Algorithm for Multipath-Based SLAM

We present a simultaneous localization and mapping (SLAM) algorithm that is based on radio signals and the association of specular multipath components (MPCs) with geometric features. Especially in indoor scenarios, robust localization from radio signals is challenging due to diffuse multipath propagation, unknown MPC-feature association, and limited visibility of features. In our approach, specular reflections at flat surfaces are described in terms of virtual anchors (VAs) that are mirror images of the physical anchors (PAs). The positions of these VAs and possibly also of the PAs are unknown. We develop a Bayesian model of the SLAM problem and represent it by a factor graph, which enables the use of belief propagation (BP) for efficient marginalization of the joint posterior distribution. The resulting BP-based SLAM algorithm detects the VAs associated with the PAs and estimates jointly the time-varying position of the mobile agent and the positions of the VAs and possibly also of the PAs, thereby leveraging the MPCs in the radio signal for improved accuracy and robustness of agent localization. The algorithm has a low computational complexity and scales well in all relevant system parameters. Experimental results using both synthetic measurements and real ultra-wideband radio signals demonstrate the excellent performance of the algorithm in challenging indoor environments.
A Combined Array Design Method Based on Normal Mode Decomposition

For the problem of array matched field processing localization performance of underwater targets in shallow sea environment, this paper proposes a method for analyzing the localization performance of arrays based on the decomposition of normal mode. In the process of establishing the method, by introducing the normal mode decomposition theory of the sound field, a receiving matrix is established by this theory, and the role of the formation in the target localization is characterized according to the matrix singularity. Based on the simulation calculations under the different formations, the effectiveness of the method is verified. The simulation results show that the more the number of the normal mode orders decomposed by the receiving array, the more abundant the sound field information is, and the better the matched field localization processing effect is. At the same time, according to the array effect analysis of this method, if the apertures are not complementary, the array's spatial resolution can hardly be improved, and at most improve the processing gain by increasing the array element. Therefore, for the design of combined arrays, in order to improve the localization performance, it is necessary to focus on increasing the effective aperture of the arrays.
Hybrid Precoding With Partially Connected Structure for Millimeter Wave Massive MIMO OFDM: A Parallel Framework and Feasibility Analysis

Hybrid precoding, a combination of digital and analog precoding, is an alternative to traditional precoding methods in massive MIMO systems with a large number of antenna elements and has shown promising results recently. In this paper, we implement a parallel framework to make hybrid precoding competitive in fast-fading environments. A low-complexity algorithm which exploits the block diagonal phase–only nature of the analog precoder in a partially connected structure is proposed to arrive at a hybrid precoding solution for a multi-carrier single-user system using orthogonal frequency division multiplexing (OFDM). The original problem is broken down into two subproblems of finding the magnitude and the phase components which are solved independently. A per-RF chain power constraint is introduced instead of the sum power constraint over all antennas which are much more practical in real systems. An alternating version of the same algorithm is proposed for increased spectral-efficiency gains. Complexity and run-time analysis demonstrate the advantage of the proposed algorithm over existing hybrid precoding schemes for partially connected structure in an OFDM setting. The simulation results reveal certain insights about the partially connected structure and the tradeoffs that have to be made to make it workable in a real wideband system.
Load forecasting method for Commercial facilities by determination of working time and considering weather information

In recent years, spread of BEMS (Building Energy Management System) is expected for the effective use of renewable energy and saving energy. On the other hand, we need energy management because renewable energy is affected by weather. By forecasting load curves of the next day based on the tendency of past loads, we can manage storage battery operation and demand response. However, in case of load forecasting, two factors should be considered. The first is that the working hours differs in each commercial facility. The second is that the maximum load is different by weather condition and the day of week. In this paper, we propose a load forecasting method focused on the working hours and weather information. Firstly, daily load profiles were categorized into the working day and the non-working day for each commercial facility by using threshold calculated from daily load values of 2270 commercial facilities in Kanto area in Japan. Next, the working time of each facility was decided by analyzing past load values until forecasting date, and clustering was performed with load curves in working time. The cluster which forecasting date belonged to was decided by weather information of forecasting date, load values of the previous day and the day of week. Base load of target facility was calculated by using load curves which belong to correspond cluster. By using parameter with the strongest correlation coefficient between maximum load and weather information, we performed linear regression and calculated the maximum load of forecasting day. As a comparison model to evaluate the forecasting accuracy of the proposed method, a simple persistence model was created. The proposed model was superior to a persistence model.
SIR Analytical Expression for Dispersive Channels in OFDM/OQAM Transmission Systems

In this paper, we derive the analytical expression of the signal to interference ratio (SIR) for orthogonal frequency division multiplexing with offset quadrature amplitude modulation (OFDM/OQAM) systems, in time and frequency dispersive channels. In this derivation, we use the dispersive approach, which is based on using a matched filter and provides optimal reception. Finally, we search optimal transmitter prototype function s that maximize the SIR.