Miracle-dz/UniFAR-FTUs huggingface dataset:
Supporting User Fusion of AI Services through Conversational Explanations

As systems become more complex, they become harder to understand both in terms of their composition (by system developers) and in terms of their operation (by system users). In dynamic systems the services may have been composed in real-time without human involvement, to support a specific user request. It is therefore even more important that the ability to understand the composition, the data sources and the component services is possible. There may also be restrictions relating to what each user is allowed to know about different aspects of the system, services or datasets, and all of the elements of the system may be widely varied and volatile, with the whole system subject to rapid and pervasive change. In this paper we define a conversational mechanism to enable interaction with such a system. Able to handle volatility and variety through the simple conversational interface, this proposed capability enables the explanation of the system and the results at a number of levels. We map our basic architecture to the JDL data fusion model (at all levels) and view this particular conversational explanation capability as belonging in level 5 (user refinement).
A Method for the Analysis of Small Crop Fields in Sentinel-2 Dense Time Series

Satellite image time series (SITS), such as those by Sentinel-2 (S2) satellites, provides a large amount of information due to their combined temporal, spatial, and spectral resolutions. The high revisit frequency and spatial resolution of S2 result in: 1) increase in the probability of acquiring cloud-free images and 2) availability of detailed information for analyzing small objects. These characteristics are of interest in precision agriculture, where temporally dense SITS can benefit the understanding of crop behaviors. In the past, information about agricultural practices has been collected over large regions and focused on mixed/aggregated crops due to the poor tradeoff between the spatial and temporal resolutions. Products have been generated at low spatial resolution and daily basis or at high spatial resolution and weekly/monthly basis. They are meaningful for large agricultural fields, whereas they are limited when fields show a small average size. In this context, S2 characteristics allow for both high spatial and temporal resolution products. However, no existing automatic method effectively separates small fields from each other in an unsupervised way and deals with data irregularly sampled in time. Thus, this article presents a method suitable for the analysis of small crop fields in S2 dense SITS that accounts for S2 characteristics. The method fuses spatio-temporal information, analyzes data spatio-temporal evolution, and extracts relevant spatio-temporal information. The effectiveness of the proposed method was corroborated by experiments carried out on S2-SITS acquired over an area located in Barrax, Spain.
On the Feasibility of Coordinates-Based Resource Allocation through Machine Learning

Over the last decade there has been a large research interest in exploiting terminal positions for various cellular network services and communication aspects. However, the relevance of terminal coordinates for resource allocation is relatively unexplored to date. In this work, we thus take a first step in that direction by studying coordinates-based resource allocation in an arguably favorable, and straightforward set-up. In particular, we consider the usage of supervised machine learning for resource allocation. Our results show that for the studied scenario, coordinates-based resource allocation can achieve a comparable performance to a CSI-based comparison scheme. While the main limiting factors are channel uncertainty as well as the accuracy of the terminal coordinates, in particular more complex machine learning schemes like Random Forests are able to provide some robustness despite the above mentioned noisy features.
Optimal Frequency-Selective Energy Beamforming with Joint Total and Individual Power Constraints

This paper analyzes the optimal energy beamforming solution for a multiple-input single-output wireless power transfer (WPT) system over frequency-selective fading channels with joint total and individual antenna power constraints. To maximize the total harvested energy, we derive the optimal co-phasing power allocation rule which reveals that all K antennas will participate in energy beamforming with T<K antennas transmitting with their maximum individual powers due to the total power constraint. We prove that optimally no more than T+1 subchannels are selected for power allocation. We highlight that the optimal power allocation solution can be efficiently obtained based on the corresponding low- complexity dual problem. Our proposed power allocation algorithm generalizes previous solutions considering only total or individual power constraints. Numerical examples verify our theoretical results and show the impact of the joint total and individual power constraints on the average harvested power.
Day-Ahead Optimal Scheduling for Grid-Connected Microgrid with Energy Storage Systems

Nowadays, the operation of a microgrid (MG) with minimized operating cost and high employment of renewable energy sources, has become a head corner of the MG operation system. This paper proposes an optimized approach for energy scheduling of grid-connected MG that includes a battery energy storage system (BESS) and different distributed generators as micro-turbines, fuel cells, wind turbines, and photovoltaic units, where the optimal hourly generation scheduling is needed to get the minimum MG’s operating cost. The general algebraic modeling system (GAMS) is used in modelling of the studied system and solving the constrained optimization problem. The optimal hourly generation scheduling of the MG is investigated in three different scenarios. In the first scenario, no BESS is connected to the system, while in the second scenario, the BESS is connected to the system, and in the third scenario, the optimal generation scheduling of the MG is investigated with BESS considered while considering increased limits of the power imported from or exported to the main grid. The results obtained validate the effectiveness of the proposed algorithm compared to the results obtained by other algorithms presented in the literature.
Experimental Comparison of Energy Consumption and Proximity Accuracy of BLE Beacons

As technology gets cheaper and wireless networks grow, more efforts have been put into developing smart buildings and homes. A key characteristic of these smart buildings is to provide indoor navigation and localization services to its occupants. As this demand increases, simple, scalable solutions are sought after. Current localization technologies, such as GPS, do not work in indoor environments, nor does it provide the level of accuracy required for an effective indoor navigation solution. This paper explores the feasibility of a wireless indoor location system based on Bluetooth Low Energy beacons (BLE). Five popular BLE beacon devices are compared in terms of energy consumption and proximity accuracy. Experiments are conducted in three different rooms and three filtering techniques are also examined, to improve the performance of each beacon. According to experimental results, a proper selection of the filtering technique can help to improve their proximity estimation.
Implementation and Comparison of a Fast Shape Recognition Algorithm using Different FPGA Platforms

In the present work, we implement and compare a fast shape recognition algorithm in two FPGA platforms: ZYBO and VIRTEX-5. We successfully implement our algorithm for several image resolutions in both hardware. We have found that VIRTEX-5 provides 14–19% faster processing than ZYBO, making it ideal for real time execution and higher resolution algorithms for computer vision, however, this comes at a cost of 8–10% more power consumption with respect to ZYBO platform. These results show that both platforms are good candidates for implementing this kind of algorithms depending on the focus of the hardware: performance or power saving.
Beam Squint Exploitation for Linear Phased Arrays in a mmWave Multi-Carrier System

To support millimeter-wave (mmWave) communications successfully, a large number of antennas (in the order of hundreds or thousands) must be implemented to mitigate significant propagation and scattering losses. Designing phased arrays for carrier frequency is a great method for narrowband systems, but the performance degrades significantly with larger bandwidth. We show that as the system bandwidth increases, the beams steer away from the focus direction, which is an effect known as beam squint in wideband systems. In this paper, at first, analysis of capacity is performed for an increasing number of antennas, to show the significance of beam squint. Then, a solution in digital domain is proposed. Conventionally, a single beam would be allocated to a user. By exploiting beam squint effect, in this solution more than one beam can carry a single-userâ€™s data, which improves the systemâ€™s performance significantly, especially when the number of antennas in an array is large and there are multiple users.
Probabilistic 3D Point Cloud Fusion on Graphics Processors for Automotive (Poster)

Nowadays, Advanced Driver Assistance Systems (ADAS) support the driver in common driving situations by visually or auditory alerting the driver, or to some extent by intervening the driving. These systems fuse object lists and occupancy grids, generated within each separate sensor system. However, safe autonomous driving in adverse weather conditions or under (partial) sensor defects requires the fusion of sensor information at a lower abstraction level, as several accidents in the near past have shown. Multimodal, probabilistic sensor fusion can be achieved, e.g. by fusing the confidence of each sensor detection in a 3D occupancy grid. The free space between the sensor and detection is modelled using ray casting and inverse sensor models. The resulting, robust 3D representation of the vehicle environment can be used by subsequent algorithms for driving situation analysis and maneuver planning. Fusing the sensor data on a lower abstraction level, e.g. on 3D point level, exposes new challenges regarding data transfer and computation, due to the massively increased amount of data. As Graphics Processors exhibit a high degree of parallelism and are optimized for 3D graphics, this contribution presents a GPGPU implementation of the previously described 3D sensor fusion on point level. Three different data structures (3D memory voxel grid and two octree structures) are implemented and evaluated regarding memory consumption and runtime for the sensor fusion algorithm. As an evaluation platform, the Nvidia Jetson Xavier and Nvidia Tesla V100 are chosen for energy-efficiency and highest performance, respectively. Using the optimizations for parallel execution, the algorithm can be executed in realtime within less than 100 ms on the high-performance GPU.
Towards Studying Service Function Chain Migration Patterns in 5G Networks and Beyond

Given the indispensable need for a reliable network architecture to cope with 5G networks, 3GPP introduced a covet technology dubbed 5G Service Based Architecture (5G-SBA). Meanwhile, Multi-access Edge Computing (MEC) combined with SBA conveys a better experience to end- users by bringing application hosting from centralized data centers down to the network edge, closer to consumers and the data generated by applications. Both the 3GPP and the ETSI proposals offered numerous benefits, particularly the ability to deliver highly customizable services. Nevertheless, compared to large data- centers that tolerate the hosting of standard virtualization technologies (Virtual Machines (VMs) and servers), MEC nodes are characterized by lower computational resources, thus the debut of lightweight micro-service based applications. Motivated by the deficiency of current micro-services-based applications to support usersâ€™ mobility and assuming that all these issues are under the umbrella of Service Function Chain (SFC) migrations, we aim to introduce, explain and evaluate diverse SFC migration patterns. The obtained results demonstrate that there is no clear vanquisher, but selecting the right SFC migration pattern depends on usersâ€™ motion, applicationsâ€™ requirements, and MEC nodesâ€™ resources.
Fast charging a 12V battery using wireless power transfer concept

The main idea for this paper is to focus on two different concepts and try to abridge them. Firstly, we have shown how power can be transferred without wires using the concept of Electromagnetic Induction. In this method of power transfer, a high voltage of 230V AC is brought down to 12V AC. Then AC is converted to DC and then it is used to charge the battery. The distance between the two coils could be extended up to 3 cm. The second concept used in this paper is of fast charging. The AC output after transmission is passed through a fastcharging circuitry, which would focus on charging a load, say a 12V, 1.3 AH battery. Combining both these concepts, we reach a conclusion of fast charging through wireless power transfer. This could be a big jump in technology as it has various applications, starting from Electric Vehicles to biological implants such as pacemakers.
High-Precision Adaptive Slope Compensation Circuit for DC-DC Converter in Wearable Devices

This paper presents a high precision adaptive slope compensation circuit for for DC-DC converter in wearable devices. Compared with the traditional adaptive slope compensation circuit, the comparator is used to sample the output voltage and input voltage, which greatly improves the accuracy.In this paper, the circuit is designed in UMC 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS Technology and verified by Virtuoso Spectre Circuit Simulator. The simulation results show that the accuracy of the adaptive slope compensation circuit in this paper can reach more than 96%.
Electrical Tracking and Erosion of Silicone Rubber Filled with Alumina Tri-Hydrate

This paper investigates the electrical tracking and erosion of silicone rubber samples using the standard Inclined-Plane Test (IPT). This test shall be performed on the insulating material before using it in the production of insulators. The silicone rubber is loaded with 45 wt% Alumina Tri-Hydrate (ATH) and was provided by two different suppliers. Samples were subjected to test voltages of 4.5 and 6 kV with the liquid contaminant flowing at rates of 0.6 and 0.9 ml/min, respectively, for 6 hours as per the IEC 60587. The voltage and flow conditions are chosen such that the two testing protocols are equivalent and lead to similar results. However, the experimental results show that the material manufactured by one supplier did not pass the 6 kV test whereas the other one did; both materials passed the 4.5 kV test. This comparative analysis indicates that the resistance of housing materials to tracking and erosion is dependent upon the applied test voltage; it is therefore suggested that testing them at a variety of test voltages may develop a better understanding to the passing criterion.
Local-View-Assisted Discriminative Band Selection With Hypergraph Autolearning for Hyperspectral Image Classification

For hyperspectral images (HSIs), it is a challenging task to select discriminative bands due to the lack of labeled samples and complex noise. In this article, we present a novel local-view-assisted discriminative band selection method with hypergraph autolearning (LvaHAl) to solve these problems from both local and global perspectives. Specifically, the whole band space is first randomly divided into several subspaces (LVs) of different dimensions, where each LV denotes a set of low-dimensional representations of training samples consisting of bands associated with it. Then, for different LVs, a robust hinge loss function for isolated pixels regularized by the row-sparsity is adopted to measure the importance of the corresponding bands. In order to simultaneously reduce the bias of LVs and encode the complementary information between them, samples from all LVs are further projected into the label space. Subsequently, a hypergraph model that automatically learns the hyperedge weights is presented. In this way, the local manifold structure of these projections can be preserved, ensuring that samples of the same class have a small distance. Finally, a consensus matrix is used to integrate the importance of bands corresponding to different LVs, resulting in the optimal selection of expected bands from a global perspective. The classification experiments on three HSI data sets show that our method is competitive with other comparison methods.
Respiration Monitoring of Multiple Car Passengers by using One Doppler Sensor with ARS

As a new approach for the period estimation of vital signs, the accumulation for real-time serial-to-parallel converter (ARS) has been proposed. This paper reports experimental results of non-contact respiration monitoring for two persons using a Doppler sensor conducted in a car. It will be shown that respirations of two persons sitting on a rear seat in the car are monitored simultaneously by one Doppler sensor. Through experiments, we confirmed that ARS can successfully detect the two respirations by using one Doppler sensor even though the computational load is drastically reduced compared with the fast Fourier transform (FFT).
Joint Bandwidth, Caching, and Computing Resource Allocation for Mobile VR Delivery in F-RANs

The emerging demands of the immersive virtual reality (VR) experience require current and future wireless networks to provide ultra-low end-to-end latency. Against this backdrop, fog radio access networks (F- RANs), which take full advantages of both fog computing and caching technologies, are anticipated as a promising solution for meeting the stringent latency requirement of mobile VR delivery. In this paper, we propose a mobile VR delivery framework, in which certain VR videos and computing tasks are cached at and offloaded to the edge of F-RANs, respectively. In the considered framework, we jointly optimize the bandwidth, caching, and computing resource allocation in order to minimize the average latency. To this end, we first derive the closed-form expression of the average latency. Based on which, we then analytically examine the optimal resource allocation decision. Moreover, through the numerical results, we reveal the non-trivial trade-offs among communication, caching, and computation, showing how the bandwidth, caching, and computing capabilities can have significant impacts on the average latency.
A Queueing Game Approach for Fog Computing with Strategic Computing Speed Control

In this paper, a novel queueing game framework for computational workload assignment and strategic computing speed control in fog computing is proposed. Unlike most existing studies in the literature, our work jointly addresses two practical issues related to fog computing, i.e., i) computation tasks offloaded by mobile users are generated dynamically over the time; and ii) each third-party fog node can strategically allocate its computing resource (or control its computing speed) for balancing the tradeoff between the reward gained from executing offloaded computation tasks and the cost incurred by the dissatisfied service to its own subscribed tasks. To describe the long-term performance of the computation task distribution/assignment and inherent strategic interactions among fog nodes, a noncooperative game upon a queueing model is formulated. Based on this, an adaptive algorithm is designed to jointly determine the optimal task distribution and the equilibrium computing speed of each fog node. Theoretical analyses and simulation results examine the performance of the proposed approach and demonstrate its superiority over counterparts.
Space Charge in Electron-Beam Irradiated Cross-Linked Polyethylene (XLPE): Charge Implantation and Release from the Bulk

The aim of the present work is to study the dynamics of the space charge in crosslinked polyethylene (XLPE) induced by electron beam irradiation. For this purpose three energies have been chosen so as to implant charges in a controlled way, in the volume, at different depths not too close to the electrodes. Pulsed Electro Acoustic (PEA) method was used to measure space charge in each sample, at the end of the irradiation process, in ambient conditions. The space charge measurements show the existence of two electron implantation sites after irradiation, one in the bulk at a depth depending on the irradiation energy and the other near the irradiated surface and a fixed position (25 µm). The application of the electric field shows that the charge implanted in the volume tends to be evacuated during the polarization. The charge accumulated persists during the relaxation and is stable after 1 hour. The current measurements reveal the impact of the irradiation in XLPE sample: the steady current measured on the irradiated sample is substantially higher compared to the one recorded on the non-irradiated specimens. Also a change in conduction mechanism is observed. The current recorded on XLPE irradiated weakly diverges from linearity with the electric field in the field range up to 40 kV/mm.
Pain management in knee arthroplasty: an overview

Perioperative pain management after knee arthroplasty has undergone a conceptual revolution in the last decade. Along with other exciting innovations, including minimally invasive techniques, computer-assisted procedures and a significant stride in tribology, understanding pain modulation and drug action at a molecular level is recognized as the game changer in arthroplasty surgeries. While most patients usually recover and experience pain relief within 3 mo after TKA, about 20% (10–34%) of the patients are left with an unfavorable long-term pain outcome. Fifty-two percent of patients report moderate pain and 16% report severe pain at rest 30 days after TKA, while pain at movement affects as much as 78% of the patients. Inability to adequately control postoperative pain causes undue suffering, inability to participate in fast-track rehabilitation programs, sleep disturbance (44% patients first 3 nights), delayed discharge, and the development of persistent postsurgical pain. The goal of this review article is to give an overview of the fundamental concept of surgical pain, the molecular mechanism of action of different drugs, evolution of the concept of preventive analgesia, and state of the art for current pain management. When combined and standardized, these factors allow arthroplasty surgeons to offer outpatient arthroplasty procedures.