Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectory Planning of Multiple Dronecells in Vehicular Networks: A Reinforcement Learning Approach

The agility of unmanned aerial vehicles (UAVs) have been recently harnessed in developing potential solutions that provide seamless coverage for vehicles in areas with poor cellular infrastructure. In this letter, multiple UAVs are deployed to provide the needed cellular coverage to vehicles traveling with random speeds over a given highway segment. This letter minimizes the number of deployed UAVs and optimizes their trajectories to offer prevalent communication coverage to all vehicles crossing the highway segment while saving energy consumption of the UAVs. Due to varying traffic conditions on the highway, a reinforcement learning approach is utilized to govern the number of needed UAVs and their trajectories to serve the existing and newly arriving vehicles. Numerical results demonstrate the effectiveness of the proposed design and show that during the mission time, a minimum number of UAVs adapt their velocities in order to cover the vehicles.
Low-Energy Dynamic Clustering Scheme for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are mainly used to collaboratively sense and process information in the monitoring area. Sensor nodes have limited energy and are inconvenient to be replaced when the monitoring area is remote or dangerous. That makes the energy consumption an important problem for WSNs. For heterogeneous network, where sensor nodes have different energy, existing clustering methods only consider single factor, heterogeneity of node energy, for head selection. This paper applies fuzzy logic to consider multiple factors for clustering with the purpose of prolonging the lifetime of the network. Important factors, such as relative density of nodes in the network and the relative distance from the nodes to the base station, are considered together with the initial energy to select the cluster head dynamically. Simulation results demonstrated that proposed clustering algorithm can balance the energy consumption of the nodes in network and effectively prolong the survival time of the network, thus ensuring the accuracy of data aggregation.
Stochastic Zero-Sum Differential Games for Forward-Backward SDEs

We consider stochastic zero-sum differential games (SZSDGs) described by fully-coupled forward-backward stochastic differential equations (FBSDEs). The fully-coupled FBSDE means that the drift and diffusion terms of forward SDEs depend on the solution of the backward SDE (BSDE). The objective functional is modeled by the BSDE part of the FBSDE. For the lower and upper value functions of the SZSDG, we establish the dynamic programming principle via the generalized stochastic backward semigroup associated with the BSDE. We then show that the (lower and upper) value functions are viscosity solutions to the associated HamiltonJacobi-Isaacs partial differential equations together with an algebraic equation. This additional algebraic equation emerges due to dependence of the diffusion term on the solution of the BSDE. The problem formulation and the results of this paper generalize those in the existing literature on SZSDGs to the controlled FBSDE framework.
State Observer Embedded in a Digital Processor for DC Motor Velocity Estimation

With the increase in processing speed and memory of digital systems the implementation of digital controllers has become more frequent. One of the problems that are most frequently encountered in the implementation of digital control systems is the limitation of observing all the dynamics involved in the processes. This fact is evidenced in the control of DC motors at the moment of sensing the angular velocity. For this reason, state observers are an important part of control theory due to their ability to filter noise and estimate unmeasured states. Complex dynamics systems frequently use DC motors as actuators. Because of this, the proposal presented in this paper describes the development, implementation and validation of a full-order state observer for angular velocity estimation of a DC motor using a digital processor in order to improve the accuracy in the control process. The results obtained demonstrate the effectiveness of the observer embedded in a low-cost development board in the estimation of DC motor velocity.
Post Disaster Management Using Satellite Imagery and Social Media Data

Efficient post disaster management is imperative to deal with the havoc a disaster creates. Providing the disaster response agencies with the necessary tools and advanced technologies could accelerate the process of rescuing people in affected areas and providing them with the necessary assistance. Social media provides NGOs, individuals, etc., a platform to reach out to people in other parts, expressing the needs of the affected people. Using social media data, the disaster response agencies could gather all the immediate needs of the people. The solution proposed makes use of satellite imagery and social media data to assist the disaster response agencies. The system is capable of tracing usable roads in flooded areas and distinguishes areas based on the extent of damage using satellite imagery, and analyzing areas where people could be stranded and identifying items of need using social media data. Tracing of usable roads has been carried out using maximum number of connected pixels concept. Distinguishing areas based on extent of damage is carried by comparing pre and post disaster satellite images. For analyzing items of need and identifying locations of stranded people, Twitter data was filtered based on certain keywords. It's an all-in-one solution, which could be used by response agencies to gather real time information about the on-ground situation.
Mobility-Aware Traffic Offloading via Cooperative Coded Edge Caching

With caching popular contents at the small-cell base stations (SBSs), cooperative edge caching has emerged as an effective approach to offload explosively increasing network traffic from a massive number of users in mobile edge networks (MENs). However, most previous works ignored impact of user mobility on caching strategy, thus having limited practical applications. How to improve the caching strategy by exploiting user mobility is still a challenging problem. Accordingly, in this paper, we propose a mobility-aware and cooperative coded caching (MoCoCoCa) for achieving traffic offloading in MENs, by considering user mobility and hard service deadline constraints. Specifically, we first develop the MoCoCoCa framework to satisfy users’ requests locally and conduct the construction of access trajectory set via random walks on a Markov chain, which takes the randomness of contact into account. Then, based on user mobility predictions, we formulate the caching problem into a mixed integer nonlinear programming (MINLP) problem to minimize the load of core network, subject to finiteness of network resources (e.g., storage capacity, transmission rate) and non-uniformity of content popularity constraints. The objective function’s submodular property is proved and a cooperative placement algorithm is given, which can achieve $\left({1-\frac {1}{e}}\right)$ -optimality. Furthermore, to adapt the given content placement configuration to the spatio-temporal dynamics of realtime request flow, the original placement problem is decomposed into some independent subproblems of content placement at individual SBSs. Each subproblem is converted into a submodular optimization problem, and a request-aware distributed replacement algorithm with linear computational complexity is proposed. Trace-based simulations and numerical results demonstrate that our proposed caching strategy can offload up to 69% network traffic than existing caching strategies.
Calibration-free SSVEP-based BCI Switch

Steady state visually evoked potentials (SSVEP) are periodic responses elicited when a participant looks at a visual stimulus flickering at a particular frequency. Brain computer interfaces (BCI) based on SSVEP have been shown to have consistent responses across most human subjects. We use this property for designing a two-state BCI switch. Canonical correlation analysis (CCA) is used to distinguish between the intentional control and the no control states of the switch. The traditional approach of using CCA for SSVEP detection uses the target frequencies only in the reference templates. This cannot be directly used to realize a BCI switch, since the templates do not incorporate the background EEG activity. To overcome this, we propose a template frequency selection method. The study is carried out on seven healthy participants. The proposed method achieves an average F1-Score of 0.95 (offline) and 0.85 (online). The mean activation time is found to be of 2.09 s (offline) and 3.01 s (online) across participants. These results indicate that the proposed system can be effectively used as a two-state BCI switch to enable control of external applications.
Deep Reinforcement Learning-Based Optimal Decoupling Capacitor Design Method for Silicon Interposer-Based 2.5-D/3-D ICs

In this article, we first propose a deep reinforcement learning (RL)-based optimal decoupling capacitor (decap) design method for silicon interposer-based 2.5-D/3-D integrated circuits (ICs). The proposed method provides an optimal decap design that satisfies target impedance with a minimum area. Using deep RL algorithms based on reward feedback mechanisms, an optimal decap design guideline can be derived. For verification, the proposed method was applied to test power distribution networks (PDNs) and self-PDN impedance was compared with full search simulation results. We successfully verified by the full search simulation that the proposed method provides one of the solution sets. Conventional approaches are based on complex analytical models from power integrity (PI) domain expertise. However, the proposed method requires only specifications of the PDN structure and decap, along with a simple reward model, achieving fast and accurate data-driven results. Computing time of the proposed method was a few minutes, significantly reduced than that of the full search simulation, which took more than a month. Furthermore, the proposed deep RL method covered up to <inline-formula> <tex-math notation="LaTeX">$10^{17}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$10^{18}$ </tex-math></inline-formula> cases, an approximately <inline-formula> <tex-math notation="LaTeX">$10^{12}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$10^{13}$ </tex-math></inline-formula> order increase compared to the previous RL-based methods that did not utilize deep-learning techniques.
Output Voltage Control of Inverters Using SDRE Tracking and LQT Controllers

This paper presents novel nonlinear and linear control methods for output voltage control of a three-phase inverter. For implementation of these optimal controllers, the nonlinear and linear models of the system including LC filter have been derived. Moreover, analysis of the system under the State-Dependent Riccati Equation (SDRE) and Linear Quadratic Tracking (LQT) as the nonlinear and linear optimal controllers has been done and comparison between performance of them is investigated in MATLAB environment.
Scheduling Generation and Reserve by Lagrangian Relaxation Unit Commitment Considering Demand Response Provider

In a power system, balance between the supply side and the demand side is important. This paper proposes Lagrangian relaxation method to implement on demand response programs with the help of a market participant known as Demand Response Provider (DRP). The DRP takes an interest in power market as a medium between the independent system operators (ISOs) and retail clients. It totals and deals with the customer's reactions to ISO offered programs. The presented algorithm cares about various security constraints which are practically handled by the system. After modelling these constraints, the cost of generation is minimized for the given load profile in the given time interval. The reserve is also scheduled along with the generation scheduling, thus the cost function of DRP is also added to the objective function.
Precision Actuator Control for on/off Type Solenoid Valves using Pid Control Loop

This paper emphasizes on the control system approach of converting an on/off type solenoid valve into a step-wise proportional solenoid valve which is aimed to be an external approach to control the solenoid actuator positions without any modifications in the architecture of the solenoid which can provide better features to the operation of the solenoid and makes the total system cost effective when compared to the proportional solenoids available. The system makes use of PID with a hall-effect transducer as its feedback device. The experimentation is conducted using 24V DC Solenoid valve and the results are plotted graphically based on the hall sensor outputs.
Measuring the Q-factor of IPT Magnetic Couplers

Accurately predicting the efficiency of an inductive power transfer based electric vehicle charger is essential for an optimal design. Typically, small-signal methods such as an LCR meter, impedance analyser or network analyser are used to determine the quality-factor (Q-factor) of the primary and secondary magnetic couplers. However, these methods fail to emulate the high VA stress and temperature a magnetic coupler is subjected to under normal operation. As such, this paper presents an alternate, frequency-domain based method to measure the large-signal Q-factor of a magnetic coupler. The proposed method eliminates the need for any phase information and merely requires static measurements. Experimental results verify the accuracy of the proposed method and also show a Q-factor variation of almost 17% between a small-signal and large-signal reading.
Real-Time Workflows Oriented Hybrid Scheduling Approach With Balancing Host Weighted Square Frequencies in Clouds

High energy consumption in cloud data centers has become one of the main obstacles to green cities, and an urgent problem to be solved. So far, a large number of scheduling algorithms have been developed to reduce energy consumption for executing workflows. However, most existing algorithms have obvious defects in energy and resource efficiency, because they schedule workflow tasks to hosts directly and ignore that the host is so powerful that a single workflow task cannot make full use of its resources. To resolve the issue, a new scheduling architecture is designed for cloud data centers. Then, two principles are derived: one suggests that hybrid scheduling tasks from different workflows is helpful for improving resource utilization; the other one deduces that balancing host weighted square frequencies can minimize total power consumption of active hosts under a given resource requirement. On the basis of the scheduling architecture and the two principles, an oNline schEduling AlgoriThm, namely NEAT, is proposed to schedule dynamic workflows with deadlines. Furthermore, three strategies for dynamically adjusting the available virtual machines (VMs) and active hosts are proposed and integrated into NEAT to improve the energy and resource efficiency for cloud data centers. Finally, the proposed NEAT is compared with three existing algorithms using real-world workflow traces to demonstrate its superior performance with respect to energy and resource efficiency while guaranteeing the timing requirements of workflows. Compared with baseline algorithms, NEAT is capable of reducing energy consumption for cloud data centers by an average of 40% in the range from 34.0% to 45.67%.
A 1 mm-Thick Miniatured Mobile Soft Robot With Mechanosensation and Multimodal Locomotion

The miniature soft robots have many promising applications, including micro-manipulations, endoscopy, and microsurgery, etc. Nevertheless, it remains challenging to fabricate a miniatured robot device that is thin, flexible, and can perform multimodal locomotor mobility with sensory capacity. In this study, we propose a miniatured, multi-layer (two shape memory polymer layers, a flexible copper heater, a silk particle enhanced actuator layer, and a sensory layer) four-limb soft robot (0.45-gram, 35 mm-long, 12 mm-wide) with a total thickness of 1 mm. A precise flip-assembling technique is utilized to integrate multiple functional layers (fabricated by soft lithography, laser micromachining technologies). The actuator layer's elastic modulus increased ∼100% by mixing with 20% silk particles by weight, which enhanced the mechanical properties of the miniature soft robot. We demonstrate that the soft robot can perform underwater crawling and jumping-gliding locomotion. The sensing data depicts the robot's multiple bending configurations after the sensory data been processed by the microprocessor mounted on the robot torso. The miniatured soft robot can also be reshaped to a soft miniatured gripper. The proposed miniatured soft robots can be helpful for studying soft organisms’ body locomotion as well as medical applications in the future.
A New DOA Estimation Method Based on Sparse Reconstruction of Correlated Signals

In order to improve the DOA estimation accuracy of correlated signals, an ESPRIT-ILSOMP algorithm based on sparse reconstruction is proposed. The algorithm first obtains the signal subspace by eigenvalue decomposition, and then finds the mixed manifold matrix. Based on the mixed manifold matrix, the DOA of the independent signals are obtained by the beamforming algorithm and the DOA of the correlated signal is obtained by the improved ILS-OMP algorithm with the theory of sparse reconstruction. Compared with the previous algorithm, the estimation accuracy of the algorithm is greatly improved under the condition of fewer array elements, low SNR and less sampling points. Simulation results verify the effectiveness of the proposed algorithm.
A Posteriori Strategy to Identify Robust Solutions in the Many-Objective Design Optimization of an Axial-Flux Permanent Magnet Synchronous Generator

Parametric uncertainties inherently exist in most real-world equipment design. In a generator design optimization process, parametric uncertainties are an important issue given the negative impact they have on generator’s efficiency. In this work, the NSGA-III algorithm is used to obtain a set of robust optimum designs of an Axial-Flux Permanent Magnet Synchronous Generator subjected to parametric uncertainties, and thereafter an a posteriori strategy is applied in the set in order to identify the best robust solution which has low parametric sensitivity and high nominal efficiency to be implemented in practice. This methodology was developed with the aim to select the best robust optimum solution from the robust Pareto-optimal set in a many-objective environment.
A Modified Community-Level Diffusion Extraction in Social Network

Equipped with more convenient facilities and features, online social networks have become the most popular platform for people's communication. It is increasingly important to model information propagation in such networks. Most of the state-of-the-art algorithms of information diffusion model focus on individual-level diffusion and does not consider the impact of social relations on user's expression, making them either unable to uncover diffusion patterns accurately or unable to capture dynamically changing topics of text stream in social networks. To address these issues, we proposed a dynamic community-level diffusion model (DCDM) in this paper to capture diffusion patterns based on coordinated dynamic semantic analysis by multiple topic-word distribution and structure analysis. Comparative experiments are conducted on the real dataset from Tweet. Experimental results show our diffusion model outperforms the state-of-the-art methods.
SURICATE-Nat: Innovative citizen centered platform for Twitter based natural disaster monitoring

It is often difficult to promptly perceive the extent of the consequences of a natural disaster when roads are cut, mobile phone is saturated, and the fragmentary information coming from the stricken area arrive at the drip. It is however on the basis of this diagnosis that must be organized the disaster management, relief, assistance to the victims, and the monitoring of the dynamic of the phenomenon. During natural disaster, social media, especially Twitter, sometimes remain one of the only source of in-situ information, as was the case just after the disastrous earthquake of Haiti in 2010. Some of the messages exchanged make it possible to get a clear idea of the consequences thanks to testimonies, photos or videos. It is with this in mind that we have developed a participatory platform for semi-automatic analysis of Twitter posts related to natural disasters. So-called “SURICATE-Nat”, this online Frenchspeaking platform (www.suricatenat.fr) aims to exploit informative messages posted on Twitter immediately during/after occurrence of natural disasters. This platform aims to structure and enrich the flow of raw data from Twitter into a human based instrumental stream that can then be analyzed as those coming from technological sensors (e.g. seismometers, GPS, piezometers, etc.). In addition, responding to willingness of Internet users to have an active “engagement” in the analysis of their own data, SURICATE-Nat offers participatory functionalities recognizing the value of “citizen expertise” and allowing each user to bring their own testimonies and take part in manual classification of tweets posted by others. This methodology aims to take advantage of the complementarity of artificial intelligence and participative approaches, in order to provide robust indicators for decision support.
To Offload Selective Search: Improving Performance of Fast R-CNN based on A Mobile Cloud Offloading Framework

The Fast R-CNN is an object detection technology based on deep learning. It is utilized to solve the object’s localization and classification problem in a given image. The main bottleneck of Fast R-CNN is to find all region proposals through Selective Search algorithm, which is relatively time-consuming. At present, the state-of-the-art improvement is to combine Region Proposal generation with neural network, such as Region Proposal Network(RPN), or to integrate the detection into a regression problem. Unlike mentioned above, this paper proposes a framework based on Mobile Cloud Computing technology to offload the Selective Search algorithm in Fast R-CNN to the Cloud, which provides another way to shorten the whole detection time. The experimental results demonstrate that the execution time can decrease by more than 5 times by offloading the Selective Search algorithm to Cloud.
Parametric Analysis on Enhanced Facial Emotion Recognition with Weight Optimized Neural Network

This paper intends to design a novel FER model that includes four phases such as (i) Face Detection, (ii) Feature extraction, (iii) Dimension reduction, and (iv) Classification. Here, Viola Jones (VJ) method is deployed for face detection, which is the initial technique to offer better object detection at real-time. Subsequently, feature extraction is carried out by means of Local Binary Pattern (LBP), and Discrete Wavelet Transform (DWT). As the “curse of dimensionality” seems to be a major fact, the dimension reduction of features is done using Principal Component Analysis (PCA). Finally, the classification is carried out by means of Neural Network (NN) with the new training algorithm called Probability based-Bird Swarm Algorithm (P-BSA), by which the weights are optimized. The performance of the proposed algorithm is done by making the algorithmic analysis. More importantly, the positive integer ($U$) of the proposed algorithm is varied to certain values: 0.5, 1, 1.3, 1.5 and 1.8, respectively.