Miracle-dz/UniFAR-FTUs huggingface dataset:
A Learning-Based Expected Best Offloading Strategy in Wireless Edge Networks

Recently, Mobile-Edge Computing (MEC) has been considered as a powerful supplement to a wireless network by processing computationally intensive tasks for resource-limited mobile devices. However, despite saving computational energy at User Equipment (UE), there is additional transmission energy consumption. As a result, the joint offloading strategy should be carefully selected to save energy and computational time. In this work, we investigated a sum cost minimization problem in a multi-UE multi-computing access point (CAP) system with time-varying channels. Our approach combines the optimization-based resource allocation algorithm with a Q-learning-based strategy selection mechanism. Without the need for communication overhead for CSI and inter- neighborhood cost value exchange, our algorithm shows prominent performance over the benchmark schemes with moderate assumptions.
Efficient Steering Mechanism for Mobile Network-Enabled UAVs

The consideration of mobile networks as a communication infrastructure for unmanned aerial vehicles (UAVs) creates a new plethora of emerging services and opportunities. In particular, the availability of different mobile network operators (MNOs) can be exploited by the UAVs to steer connection to the MNO ensuring the best quality of experience (QoE). While the concept of traffic steering is more known at the network side, extending it to the device level would allow meeting the emerging requirements of today's applications. In this vein, an efficient steering solutions that take into account the nature and the characteristics of this new type of communication is highly needed. The authors introduce, in this paper, a mechanism for steering the connection in mobile network-enabled UAVs. The proposed solution considers a realistic communication model that accounts for most of the propagation phenomena experienced by wireless signals. Moreover, given the complexity of the related optimization problem, which is inherent from this realistic model, the authors propose a solution based on coalitional game. The goal is to form UAVs in coalitions around the MNOs, in a way to enhance their QoE. The conducted performance evaluations show the potential of using several MNOs to enhance the QoE for mobile network-enabled UAVs and prove the effectiveness of the proposed solution.
Nucleotide composition bias in high throughput gene expression measurement methods

Studies of gene expression levels are known to be affected by biases related to the experimental procedure. Nucleotide composition of the studied genes significantly affects the efficiency of biochemical reactions used to prepare the material for study which translates to the measured expression levels. Data pre-processing methods aim at reducing such differences and the use of replicate samples is expected to increase the data quality, however all commonly used methods suffer from the reproducibility problems. In this work we utilize information provided by over two thousand experiments in order to explain the origin of nucleotide composition bias, highlight the magnitude of the problem and show its consequences for the interpretation of gene expression studies. We show that despite data standardization and the use of sample replicates the effect can have significant impact on the detection of differentially expressed genes between analyzed conditions, affecting interpretation of results from such studies.
Modeling and Simulation of PMSG-Based Wind Power Generation System

Wind energy is one of the best technologies and widely used source of renewable energy for supplying the electric power to the world due to its environmental and economic advantages. An application of permanent magnet synchronous generator (PMSG) into the wind energy system is continuously increasing. In this paper, the modeling and simulation of a PMSG-based wind power generation system under power system dynamic conditions are presented. The dynamic behavior of the wind power generation system is analyzed during the start-up process and the gust, ramp and noisy variation of wind conditions using PSCAD/EMTDC simulation.
Text Mining of Open-Ended Questions in Self-Assessment of University Teachers: An LDA Topic Modeling Approach

The large amount of text that is generated daily on the web through comments on social networks, blog posts and open-ended question surveys, among others, demonstrates that text data is used frequently, and therefore; its processing becomes a challenge for researchers. The topic modeling is one of the emerging techniques in text mining; it is based on the discovery of latent data and the search for relationships among text documents. In this paper, the objective of the research is to evaluate a generic methodology based on topic modeling and text network modeling, that allows researchers to gather valuable information from surveys that use open-ended questions. To achieve this, this methodology has been evaluated through the use of a case study in which the responses to a teacher self-assessment survey in an Ecuadorian university have been studied. The main contribution of the article is the inclusion of clustering algorithms in order to complement the results obtained when executing topic modeling. The proposed methodology is based on four phases: (a) Construction of a text database, (b) Text mining and topic modeling, (c) Topic network modeling and (d) The relevance of the identified topics. In previous works, it has been observed that the human interpretative contribution plays an important role in the process, especially in phases (a) and (d). For this reason, the visualization interfaces, such as graphs and dendograms, are of critical importance for researchers in order allow topic to efficiently analyze the results of the topic modeling. As a result of this case study, a compendium of the main strategies that teachers carry out in their classes with the aim of improving student retention is presented. In addition, the proposed methodology can be extended to the analysis of the unstructured textual information found in blogs, social networks, forums, etc.
Planar Dual-Band Branch-Line Coupler With Large Frequency Ratio

This work presents a novel planar branch-line coupler topology developed for dual-band operation using an E-shaped impedance transformer network to supplant the conventional microstrip line. Explicit closed-form design equations for dual-band operation are derived using the ABCD matrices. The studied coupler features a large dual-band frequency ratio with a compact size. A prototype coupler centered at 1 and 8 GHz is first presented and experimentally examined to demonstrate the large frequency ratio. Further, another prototype coupler centered at 2.4 and 5.2 GHz for potential WLAN applications is also developed and experimentally characterized. Measurements and simulations for both prototype couplers show good performance within the studied dual-band frequencies.
Impedance Control without Environment Model by Reinforcement Learning

In this paper, to balance the learning accuracy and time. We propose hybrid reinforcement learning, which is in both discrete and continuous domains. The action-state space of the is divided into two domains: discrete-time learning has less precision but is fast, continuous-time learning is slow but has better learning precision. This hybrid reinforcement learning can learn the optimal contact force, meanwhile it minimizes positional error in an unknown environment. Convergence of the learning is proven. Real-time experiments are carried out using the two degree-of-freedom (DOF) spin and tilt robot and the 6-DOF force/torque sensor to verify our methods.
Directional Statistics for 3D Model-Based UAV Tracking

We present a vision system based on a monocular camera to track the 3D position and orientation of an Unmanned Aerial Vehicle (UAV) during the landing process aboard a ship. The proposed method uses a 3D model-based approach based on a Particle Filter (PF) with proposal distributions given by an Unscented Kalman Filter (UKF) for the translational motion and filters based on directional statistics for the rotational motion. Our main contributions are (i) the development of a novel 3D model-based tracking architecture based on directional statistics that can be easily adapted to other tracking problems, and (ii) the development of the Unscented Bingham-Gauss Filter (UBiGaF) for rotation estimation. We show the advantages of using directional statistics based filters on 3D model-based tracking in a series of quantitative tests in a challenging simulation scenario with real video data. The obtained position and angular error are compatible with the automatic landing system requirements when using directional statistics. We obtain lower error when using the UBiGaF scheme for the vast majority of the tested combinations.
Identification of Ferroresonance on Transformer using Wavelet Transformatio

Ferroresonance is a transient phenomenon characterized by overvoltage and waveform mismatches affected by a combination of nonlinear inductances and capacitors. Even though ferroressonance is a transient phenomenon however the impact of the ferroresonance continue for a steady state that causes excess temperature and finaly damage the impacted apparatus . Therefore, this study concern to understand and identify ferroresonance phenomena and at the end to distinguish them from other disturbances such as overvoltage and harmonics. Ferroresonance initiation is carried out on a laboratory scale using a basic circuit of ferroresonance consisting of capacitors and transformers. Two kind of transformer core topologies, namely H type and M type, was used in the experiments. Moreover other disturbances such as harmonics was also simulated in laboratory. The ferroresonance and harmonic waveform then be processed using wavelet transformations and its cumulative energy was also determined. The results shows that for H type transformer the threshold value that distinguishes ferroresonance from other disturbances is about 9.24. On the other hand for M type transformer was about 7.97. Based on these values, the type of transformer core affect the energy level of ferroresonance. Moreover wavelet transformation could identify between ferroresonance and other disturbance under study very well, however further study should be done to investigate the effectivity of wavelet to discriminate all possible mode of ferroresonance.
Large-Scale Fabrication of Submicrometer-Gate-Length MOSFETs With a Trilayer PtSe2 Channel Grown by Molecular Beam Epitaxy

This article is the first report of MOSFETs fabricated on PtSe2 grown by molecular beam epitaxy. Both material synthesis and device fabrication are done below 450 °C—the thermal budget of CMOS back-end-of-line processes. The MOSFETs are batch-fabricated by a CMOS-compatible process on 200-mm-diameter Si substrates prepared by a state-of-the-art BiCMOS foundry. With three monolayers of PtSe2, an n-type MOSFET exhibits a current ON/OFF ratio of 43 at room temperature, which increases to 1600 at 80 K. These results are among the best of transistors based on synthesized PtSe2. Despite the thin PtSe2 layer, doping by contact bias lowers the contact resistance significantly and boosts the current capacity and the ON/OFF ratio. Temperature-dependent current-voltage characteristics imply a bandgap of approximately 0.2 eV, which confirms that the semiconductor-semimetal transition of PtSe2 is not as abrupt as originally predicted. Better MOSFET performance can be expected by growing even thinner PtSe2 uniformly and by thickening the PtSe2 in the contact regions.
Design of High-Speed Links via a Machine Learning Surrogate Model for the Inverse Problem

This paper presents an alternative approach for the design of high-speed link based on a preliminary version of a surrogate model for the inverse problem. Specifically, given the overall structure of the link, our goal is to build an accurate and fast-to-evaluate model for the estimation of the geometrical parameters of its interconnect starting from the desired eye diagram characteristics. The modeling scheme proposed in this paper relies on a powerful machine learning regression technique such as the least-squares support vector machine (LS-SVM) which is used to provide an accurate relationship among the desired eye features and the geometrical parameters of the link interconnect. The proposed model is built from a set of training samples generated by a parametric simulation of the link through the full-computational model. The feasibility and the accuracy of the proposed modeling scheme are then investigated by comparing its predictions with the corresponding results provided by the full-computational model on 250 unseen samples.
Constrained Multibeam Optimization for Joint Communication and Radio Sensing

Multibeam technology has recently been proposed for joint communication and radio sensing (JCAS) in millimeter wave systems using analog antenna arrays. Generation of the multibeam satisfying both communication and sensing requirements is yet to be developed. In this paper, we develop closed-form solutions for optimizing the coefficient that combines communication and sensing subbeams to generate a multibeam. Our solutions maximize the received signal power for communication, in the cases (1) without constraint on sensing beamforming (BF) waveform, (2) with minimum BF gain constraints on discrete sensing directions, and (3) with a minimum total power constraint on a range of sensing directions. Simulation results are provided and validate the effectiveness of the proposed solutions.
Optimal Coil Design for a Quadrotor Wireless Charging System

This paper presents an optimization procedure for the coil design in a quadrotor wireless charging system. Optimization of the coils is an important step for low weight, small size, high efficiency and high power transfer of the charging system. It is difficult to analyze the performance of the coils before testing because of the complexity in evaluating the parasitic effects. Additionally, it is expensive and time-consuming to build and test different design options. This paper provides design equations for the optimization of the coils’ parameters, including inductances, resistances, weight, Litz construction and frequency. MATLAB simulations are used to validate the results for a given frequency and dimension restrictions in the special application of quadrotors.
Single Channel Sleep Staging Based on Unsupervised Feature Learning

Sleep staging based on electroencephalogram (EEG) signal, as one of the vital bases of study on sleep diagnosis, has been under massive attention. With the spring up of deep learning these years, the idea of combining deep learning structure with automatic sleep staging has been an attractive topic. However, the labeling of sleep stages requires professional knowledge as well as plenty of time, which raise the barrier to evaluate this idea. In this study, the method of unsupervised feature learning based on a mass of unlabeled data and a small number of labeled data was proposed to accomplish sleep staging. The unsupervised feature learning structure was built based on a pair of symmetric convolutional neural networks, with the help of a shallow neural network classifier to classify sleep stages. The results showed that under the condition of the very few labeled data, sleep staging based on unsupervised feature learning can achieve similar accuracy to supervised feature learning, which provides a new direction for the application of deep learning method in dealing with data that is difficult to label or lack of prior knowledge.
Edge Caching and Content Delivery with Minimized Delay for Both High-Speed Train and Local Users

In this paper, we investigate the edge caching and content delivery problem for both high-speed train (HST) passengers and low-mobility cellular users. Under multi-dimensional resources constraints, we formulate an optimization problem to minimize the content retrieval delay of HST passengers and meanwhile guarantee the delay requirements of cellular users. As the formulated problem is a mixed-integer nonconvex optimization problem, which is intractable directly, we propose an efficient iterative algorithm that optimizes the three decision variables (i.e., content placement, subchannel allocation, and transmission power allocation) alternately. In specific, Lagrangian multiplier is introduced to convert the constrained optimization, which transforms the content caching problem into a Lagrangian relaxed knapsack problem. Afterwards, the subchannel assignment problem is solved by the Hungarian algorithm with polynomial time complexity, and the power allocation strategy is obtained by the bisection method. Extensive simulations are carried out and results demonstrate that our proposed caching strategy can reduce the content retrieval delay by up to 25% in comparison with the benchmark strategy.
A Review of LoRaWAN Simulators: Design Requirements and Limitations

Several wireless technologies are enabling the growth of the Internet of Things (IoT) industry. One of these, namely Long Range Wide Area Network (LoRaWAN), is very popular in the research community whose focus is on optimising the LoRaWAN protocol for effective use. A key tool in a researcher's arsenal is the use of LoRaWAN simulators. This work describes the considerations that are important when considering choosing a LoRaWAN simulator and presents a comparison of the currently available simulators. Recommendations are made for which simulators would be best for certain IoT use cases as well as a discussion on the reviewed simulators. Recommendations are also made on the focus of future simulation development.
Analysis of Insulin Receiver Operating Characteristic Curve for Impaired Glucose Tolerance and Impaired Fasting Glucose Diagnosis

Impaired glucose tolerance (IGT), impaired fasting glucose (IFG) and insulin resistance (IR) are prediabetic conditions diagnosed by glucose and insulin values measured by oral glucose tolerance test (OGTT). In the OGTT, insulin and glucose levels are measured in five different blood samples: a sample in fasting (minute 0) and four others after oral intake of 75 gr. of glucose, at intervals of 30 minutes (minute 30, 60, 90 and 120). The values of glucose at 0 and 120 minutes from OGTT are used for the diagnosis of IFG and IGT, respectively. Several parameters had been used to diagnose the IGT and IFG, such as abdominal circumference, HOMA-IR, area under the curve of glucose during OGTT, among others. However, none of these studies have explored obtaining cut-off points from the area under the curve of insulin (AUCI) during OGTT for IGT and IFG diagnosis. For this propose a database of 172 subjects with values of glucose and insulin during OGTT was used. To assess the predictive capacity of AUCI the receiver operating characteristic (ROC) curves were performed and it was calculated the area under the ROC curve (AUCROC), negative predictive value (NPV) and positive predictive value (PPV). The results show that AUCI can predict the IFG (AUCROC=0.71) and IGT (AUCROC=0.65). The AUCI can discriminate between the subjects that undergo IFG and IGT in concomitance from the subjects that only undergo one of the pathologies. All these findings suggested that AUCI could be used as a diagnostic method for IFG and IGT.
Information Security Analysis as Data Fusion

Engineering of detection systems for Information Security (InfoSec) is still an emerging field, when compared to Multisensor Data Fusion (MSDF). The two fields share important features: diverse data sources, a need for automated assistance in timely situation assessment, and an operational dimension with real consequences. There is an important difference: InfoSec operates mostly in an information space, so much of the MSDF literature on low-level data fusion is not transferable to the InfoSec domain. Application of advanced analytic approaches such as Machine Learning to InfoSec problems in intrusion detection has been uneven at best, with many projects failing to yield clean signals, and deep and widespread (and generally justified) skepticism among subject matter experts. [1] [2] It is vital to construct a useful model of what Security Analysis is, if we hope to automate the process, or even augment human effort with better automated support. The conceptual frameworks in use in InfoSec today focus on the operational dimension without providing a clear roadmap for building automation. In this paper, we present a framework for automation of security analysis based on the Dasarathy model of Data Fusion, as extended by Steinberg and Bowman [3] (DSB). Adapting the DSB framework to Information Security contextualizes much of the existing work in security automation, exposes its limitations and gaps, and provides a roadmap to closing those gaps. It is the goal of this work to guide Data Scientists and Engineers in how to develop semantic and contextual information in an automated data fusion system, to enable effective application of machine learning approaches to problems in detection & response.
Position control of a Stepper Motor using LabView

Stepper motors are used for many applications in industries and robotics where precise control of motor position is essential. The is paper presents the implementation of position control of a Stepper motor using LabView.Half step and Full step mode of stepper motor control is performed and tested for different position angles. The test results show that half step mode makes an angle of 0. 9ºper step (400 steps), whereas full step makes 1.8°per step (200 steps). With LabVIEW, the system has friendly interface, good control effect and easy control in both the directions.
Spectral Efficiency Maximization for Multiuser MISO-NOMA Downlink Systems with SWIPT

In this paper, we study the problem of jointly optimizing user pairing and beamforming design in multiuser multiple-input single-output (MU-MISO) non-orthogonal multiple access (NOMA) downlink systems with simultaneous wireless information and power transfer (SWIPT). Aiming at maximizing the achievable sum throughput subject to energy harvesting (EH) constraints, we propose a hybrid user pairing beamforming scheme (HBS), where two users with distinct channel conditions are optimally selected to perform user pairing. Moreover, we adopt a non-linear EH model for energy users to reveal their practical circuit characteristics. The sum throughput problem is formulated as a class of mixed-integer nonconvex optimization programming which is computationally prohibitive. To solve this challenging problem, we propose a low-complexity iterative algorithm, yet efficient, based on sequential convex approximation method to arrive at least the local optima. Numerical results are provided to demonstrate the performance improvement of the proposed HBS scheme over the multiuser beamforming one without user pairing, revealing to be an effective scheme for MU-MISO-NOMA downlink systems.
Road sludge detection and identification based on improved Yolov3

In order to deal with the challenge of the identification task of road sludge under real scenes, we propose a novel detection for road sludge detection. It combines the road sludge features extracted by the residual network with the feature maps of various scales. The swish activation function is used in the network, and GIoU-loss is used as the loss of position regression. The Improved-YOLOv3 experiment on our self-built road sludge datasets shows superior performance in speed and accuracy and it also realized real-time detection and recognition. Specially, the detection time of each frame is 45ms on the GPU (GTX 1080ti) acceleration. Furthermore, it achieved a 96.7% precise rate and a 63.5% recall rate.