Miracle-dz/UniFAR-FTUs huggingface dataset:
Wide-Angle Frequent Scanning Metamaterial Leaky Wave Antenna Array for Automotive Radars

This paper designs a wide-angle frequency scanning metamaterial (MTM) leaky wave antenna (LWA) array for automotive radars. The gap capacitors and the vialess open stub-loaded resonators are used to construct the frequency scanning MTM LWA array. the propagation constant of the MTM LWA array is able to change from negative to positive values as the frequency is increased, resulting in backward to forward beam scanning. Compared to mechanical beam scanning arrays and phased arrays, the frequency scanning MTM LWA arrays have advantages of reducing the system complexity, light weight and low cost. The presented MTM LWA array has a maximum gain of 13 dB and wide scanning angle from −60°to +80° as the frequency varies from 23.5 to 25.6 GHz which is well suited for automotive radar applications.
Disturbance-Observer Assisted Controller for Stand-Alone Four-Leg Voltage Source Inverter

In this paper, a newly disturbance-observer based control strategy for stand-alone four-leg VSI has been proposed and analyzed. The disturbance-observer operates together with a simple and effective resonant controller in order to regulate the inverter output voltage. In particular, the resonant controller regulates the output voltage fundamental harmonic, whereas the disturbance observer is designed for compensating output voltage harmonics when non-linear loads have to be fed. The new control strategy effectiveness and performance have been validated at first through a full MATLAB/Simulink model and then by experimental results on a dedicated 40 kVA three-phase 4-leg.
A Text Mining Methodology to Discover Syllabi Similarities among Higher Education Institutions

Students' mobility and credit validation has been a concern for several years among higher education institutions in Ecuador, this process involves a huge amount of manual work due to the absence of an automatic system to measure the similarity between different course contents. In order to tackle this problem, we propose an approach to semantically compare the syllabi contents through text similarity methods. Such methods have been widely used in different domains, in this work we take the higher education institutions syllabi to the Text mining world and develop a method to compare their semantic contents. We propose an approach that uses pre-processing techniques, Latent Semantic Analysis for dimensionality reduction, text enrichment through the Wikipedia API and Google Engine, Support Vector Machine as classifier, and cosine similarity as similarity metric. Our results show that our method successfully measures similarity among higher education institutions syllabi and can be generalized to most Ecuadorian institutions.
Object recognition and simultaneous indoor location algorithm with stereo camera

Visual SLAM mainly depends on the measurement data of camera for dead reckoning, it is difficult to maintain the positioning accuracy and stability for a long time. Therefore, visual SLAM can’t be widely used in indoor positioning at present. The algorithm of object recognition and simultaneous indoor location is proposed in this paper, which combines the technology of object recognition based on deep learning, image feature matching and binocular parallax positioning. Using a low cost binocular stereo camera to output the object category and its three-dimensional position in camera coordinate system. Finally, the static and dynamic indoor positioning tests are carried out, the indoor horizontal positioning error for static test is 74 mm, the indoor horizontal positioning error for dynamic test is less than 250 mm. When the distance between object and camera becomes larger, the positioning error becomes larger. When the parallax becomes larger, the positioning error becomes smaller.
A Novel Reduced Voltage Sensor-Count Control of a DC/DC Converter

This paper presents a new voltage sensing approach for a DC/DC boost converter used as a battery charger. The input DC source is the thermoelectric generator (TEG) and can be of any form like the PV as well. The purpose of the proposed strategy is to reduce the voltage sensors count used. Two different voltages are measured using only one voltage sensor. The proposed strategy can be expanded to different switch mode DC/DC converters. The input and output voltage information are collected from an intermediate switching point in the converter. The reconstructed voltages are used in the control loops as well as for monitoring the battery output or load voltages. Simulation results aid the solid mathematical derivation. Hardware prototype was designed and built in the lab to verify the proposed approach.
Draft Genome Sequence of “Candidatus Spirobacillus cienkowskii,” a Pathogen of Freshwater Daphnia Species, Reconstructed from Hemolymph Metagenomic Reads

We report here the near-complete genome sequence of “Candidatus Spirobacillus cienkowskii,” a spiral-shaped, red-pigmented uncultivated bacterial pathogen of Daphnia spp. The genome is 2.74 Mbp in size, has a GC content of 32.1%, and contains genes associated with bacterial motility and the production of carotenoids, which could explain the distinctive red color of hosts infected with this pathogen. ABSTRACT We report here the near-complete genome sequence of “Candidatus Spirobacillus cienkowskii,” a spiral-shaped, red-pigmented uncultivated bacterial pathogen of Daphnia spp. The genome is 2.74 Mbp in size, has a GC content of 32.1%, and contains genes associated with bacterial motility and the production of carotenoids, which could explain the distinctive red color of hosts infected with this pathogen.
Rigorous Approach to Simulate Electromagnetic Interactions in Biological Systems

In this work, we explore a novel approach to quantify and predict events related to bimolecular recognition. The theoretical framework leverages mathematics from radar signal processing and communication networks in the form of a matched filter to describe the recognition of a ligand by a receptor. Based on first principles this cross correlation between the electrostatic fields of the ligand and receptor is a new approach to predict the details of interaction between the two biomolecules. The output of this matched filter is analogous to Gibbs free energy of classical thermodynamics with the added advantage that it has the potential to identify the path the ligand takes as it moves towards the receptor. The biomolecules used for demonstration purposes in this paper are C12 (ligand) and QscR (receptor). They are important to the understanding of an opportunistic pathogen,Pseudomonas aeruginosa, which strikes cystic fibrosis patients and burn victims, among others. This work could provide an alternative approach to more rigorous common methods, e.g. molecular dynamics simulations, for analyzing and understanding molecular recognition events.
Distribution Sensitive Product Quantization

Product quantization (PQ) seems to have become the most efficient framework of performing approximate nearest neighbor (ANN) search for high-dimensional data. However, almost all existing PQ-based ANN techniques uniformly allocate precious bit budget to each subspace. This is not optimal, because data are often not evenly distributed among different subspaces. A better strategy is to achieve an improved balance between data distribution and bit budget within each subspace. Motivated by this observation, we propose to develop an optimized PQ (OPQ) technique, named distribution sensitive PQ (DSPQ) in this paper. The DSPQ dynamically analyzes and compares the data distribution based on a newly defined aggregate degree for high-dimensional data; whenever further optimization is feasible, resources such as memory and bits can be dynamically rearranged from one subspace to another. Our experimental results have shown that the strategy of bit rearrangement based on aggregate degree achieves modest improvements on most datasets. Moreover, our approach is orthogonal to the existing optimization strategy for PQ; therefore, it has been found that distribution sensitive OPQ can even outperform previous OPQ in the literature.
Task decomposition for multilimbed robots to work in the reachable-but-unorientable space

Multilimbed industrial robots that have at least one arm and two or more legs are suggested for enlarging robot workspace in industrial automation. To plan the motion of a multilimbed robot, the arm-leg motion-coordination problem is raised and task decomposition is proposed to solve the problem; that is, a given task described by the destination position and orientation of the end-effector is decomposed into subtasks for arm manipulation and for leg locomotion, respectively. The former is defined as the end-effector position and orientation with respect to the legged main body, and the latter as the main-body position and orientation in the world coordinates. Three approaches are proposed for the task decomposition. The approaches are further evaluated in terms of energy consumption, from which an optimal approach can be selected. <<ETX>>
Simulation of Defect Photonic Crystals Using Complex-Envelope LOD-FDTD Method

This paper introduces a complex-envelope (CE) scheme to the locally one-dimensional finite-difference time-domain (LOD-FDTD) method for the simulation of the defect plasma photonic crystal (PPC). The auxiliary differential equation (ADE) technique is used to deal with the dispersive media. The proposed method is much more accurate than the traditional ADE-LOD-FDTD with the same CFL number. The results of the transmission coefficient of the PPC are calculated and they verify the effectiveness of the proposed method.
Development of a Dynamic Model for Twin Hull ASVs

This paper presents an overview of a generalized 6 degrees of freedom model for surface vessels and explains how it can be extended for twin hull surface vehicles. The extended model takes into account the hull characteristics (dimensions and location), which are important to improve the accuracy of simulations and the performance of controllers. The method involves the calculation of the submerged volume of each hull, location of each hull's center of buoyancy and restoring forces/torques due to buoyancy contributions. To evaluate the proposed model, some simulations were performed, using an example of allocation of propulsion system and realistic hydrodynamic coefficients (added mass and damping) and inertial tensors.
Target Detection Based on Dual-Domain Sparse Reconstruction Saliency in SAR Images

The conventional target detection methods in synthetic aperture radar (SAR) images usually utilize the clutter information via the clutter statistical modeling. However, it is difficult to select an appropriate clutter statistical distribution model for a real SAR image. To make full use of the clutter information while avoid the clutter statistical modeling, in this paper, we propose a novel target detection method based on the dual-domain sparse reconstruction saliency in SAR images. Considering the speckle noise in SAR images, we utilize not only the intensity information in image domain but also the structure information in feature domain. First, the image-domain saliency map is constructed via the sparse reconstruction on clutter templates extracted from pure clutter SAR images in the image domain with the pixel-level intensity information as the descriptor, and the potential target templates are obtained from this saliency map. Then, the feature-domain saliency map is constructed via the sparse reconstruction on both clutter and potential target templates in the feature domain with the superpixel-level locality-constrained linear coding (LLC) of pixel-level scale invariant feature transform features as the descriptor, where the LLC can describe the spatial and structural information in superpixel level. Finally, the aforementioned two saliency maps are integrated to make our method more robust to speckle noise, and then, the winner-take-all approach is applied to detect targets from the integrated saliency map. We evaluate the proposed method on the miniSAR data and the RADARSAT-2 data. On the miniSAR data, the proposed method is at least 5% higher in terms of AUC than the other saliency methods (i.e., the Itti's method, the SR method, the SVD method, and the JDSR method), and at least 20% higher in F1-score, 22% higher in figure of merit than the Gaussian constant false alarm rate (CFAR), the Gamma-CFAR, and the aforementioned saliency methods. On the RADARSAT-2 data, the proposed method also achieves better performance than the previously compared methods.
A Mokov Decision Process Approach to Optimizing Waiting for Taxis

Taxi services play a critical contribution for the public transportation system in large cities. However, we usually find that our intrinsic experience is often mistaken to wait for taxis smoothly, especially for the passengers travelling to one unfamiliar city. This will greatly affect the user experience of taxi services. Therefore, how to recommend a good waiting place for one passenger is a meaningful problem, and the existing large-scale trajectory data just provides us with more opportunity. Current researches focus on one-time recommendation for passengers, such as recommending the waiting location closest to the current passenger, the one with minimum waiting time or hot spots, etc. However, most recommended strategies recommend only one location to the current passenger without considering the recommendation failure that waiting for no taxis. In response to this deficiency, we propose a Mokov Decision Process (MDP) approach for recommend a sequence places for the current waiting passenger with the constraints of time and distance by solving the value functions. This ensures the maximum probability for waiting for a taxi throughout the process and the overall optimization of the waiting process. A case study and several experimental evaluations on a real taxi dataset from a major city in China show that our proposed MDP approach is in line with the actual situation and reduce greatly the waiting time for passengers and improve the user experience of taxi services.
Real Time Eye Gaze Estimation

In this paper we used a set of searching techniques that allow to gain information about eye movement, its location and point of view in real time. The algorithm determines the point on the monitor at which the user is looking at. To obtain such data it is necessary to determine the relative position of the eye and the head. The first step is the initialization during which a head model is created. After initialization, the tracking phase is started using an Active Appearance Models (AAM) and Pose from Orthography and Scaling with ITerations (POSIT) algorithm for head position estimation. The purpose of eye gaze estimation or eye tracking can be used for testing the effectiveness of the text, game, or advertising message. The aim of this work is to develop and implement a system for real time eye gaze estimation using PC’s webcam only without any additional hardware.
Fast Defense System Against Attacks in Software Defined Networks

With the ever-growing data traffic in computer networks nowadays, the management of large-scale networks is a challenge for guaranteeing the quality of the provided services. This is due to the increasingly usage of connected applications, such as Internet of Things and cloud computing environments. Software-defined networking (SDN) is a new paradigm that aims to make this management process easier by centralizing the configuration of all network devices into a single programmable central controller. However, as any centralized service, this architecture is susceptible to security vulnerabilities, such as distributed denial of service (DDoS) and port scan attacks. Thus, security methods are necessary to guarantee the normal operation of SDN’s central controller. Furthermore, networks are transporting an increasingly amount of information day by day, which could mean data loss in case of long network unavailability. For this reason, security mechanisms must operate online, with fast-responding countermeasures to mitigate the impact of the detected attacks over the SDN. In this paper, we present a fast SDN defense system against DDoS and port scan attacks, which runs directly into the central controller and uses a game theoretical approach for attack mitigation. For the detection, we compare three different approaches, particle swarm optimization, multi-layer perceptron neural network, and discrete wavelet transform. We test our approach over IP flow data generated over Mininet network emulator, along with floodlight controller, and the presented defense system achieved good outcomes for both detection and mitigation processes.
Wear-I: A Multi-Wearable Organic System for Smarter Individual Services

Wear-I is aimed at a novel wearable system with multi wearable devices that are worn in different body parts of an individual or a user in daily life. These wearables and their wearing states may change dynamically in a day, and thus should be monitored and controlled in an organic manner similar to the human sense and action organs do. Different from other systems using fixed wearables for specific applications, the proposed Wear-I is a general-purposed system enabling various smarter personal services by sharing and coordinating these multi wearables. Organic controls of changing wearables and their coordination are the most challenging in this research. The Wear-I system has been designed based on a wearable-gateway-cloud architecture for adaptivity and scalability. Its partial system functions such as device management, data collection scheduling, synchronization between wearables and cloud-based data storage have been developed. This research is to provide a new paradigm in wearable computing.
Robot manipulator control using decentralized linear time-invariant time-delayed joint controllers

It is shown that robot manipulator control can be accomplished using simple decentralized linear time invariant time-delayed joint controllers instead of the complicated computed torque control scheme. This means that all the online computational problems associated with computing robot inverse dynamics can be avoided, and the robot control problem is essentially reduced to that of computing n linear proportional-derivation controls for n joint subsystems. The proposed control technique employs a time-delayed control with a specially designed constant diagonal gain matrix to decouple and linearize the robot joint dynamics so that linear centralized joint control can be achieved. It is shown that the proposed controller is stable, and the value of the special gain matrix can be selected based on a sufficient condition of stability presently developed. However, the establishment of this sufficient condition requires knowledge of the inertial matrix of the robot. It is also shown that the controller is robust in the presence of payload uncertainty. A two-link planar robot is presented to illustrate the controller design procedures and the performance of the controller. <<ETX>>
Design of an IPT Battery Charger with Double-sided LCC Compensation

A high-performance IPT charger should provide the battery required constant charging current and constant charging voltage efficiently in the whole charging process. With a wide load range from the battery charging profile, it is hard to realize the first load-independent constant current (CC) and the late load-independent constant voltage (CV) in one converter, whilst the nearly zero reactive power and soft switching of power switches should be realized simultaneously. To solve it, this paper systematically analyzed the characteristics of an double-sided LCC compensated IPT converter and proposed a design method to realize the battery required load-independent CC and CV outputs at two zero-phase angle (ZPA) frequencies. The design combats the constrains of IPT transformer and input voltage, which facilitates to use the simple duty cycle control running at two fixed operating frequencies for both CC and CV. The sensitivities of compensation parameters to input impedance, output current and voltage are also discussed. Finally, an IPT charger prototype will be built to verify the analysis in the final version.
Depth-based Object Detection using Hierarchical Fragment Matching Method

Identifying a workpiece in industrial processes using depth sensors has received increasing attention over the past few years. However, this is a challenging task particularly when the object is large or cluttered. In these scenarios, captured point clouds do not provide sufficient information to detect the object. To address this issue, we present a hierarchical fragment matching method for 3D object detection and pose estimation. We build a library of object fragments by scanning the object from different viewpoints. A descriptor, named Clustered Centerpoint Feature Histogram (CCFH), is proposed to compute the features for each fragment. The proposed method aims to enhance the robustness of the existing Clustered Viewpoint Feature Histogram (CVFH) descriptor. Subsequently, an Extreme Learning Machine (ELM) classifier is applied to identify the matched segments between the scene and the library of fragments. Finally, the pose of the object in the scene is estimated using the matched segments. Unlike existing approaches that require the CAD model of the object or pre-registration process, the proposed method directly use the scanned point clouds of the object. The experimental results are presented to illustrate the performance of the proposed method.
Dynamic Patient-Regulated Access Control Framework for Electronic Health Information

The easiest and convenient approach to exchange and share medical-based information from one domain to another is through the use of Electronic Health Record (EHR). In advanced countries of the world, there are standard legal framework for ensuring security, confidentiality and privacy of medical information. Specifically, absolute privilege should be given to patient to direct, control and provide permission to anyone authorized to handle his medical details. As important as the issue of security and privacy is in healthcare delivery system, many researchers have suggested and even implemented various techniques. Existing solutions to handle this challenge are however suffering from one problem or the other. For instance, encryption technique that requires the use of smartcard for authentication and authorization strictly demands the physical presence of a patient. If however, a patient fails to show up for authorization, a physician will not be able to access basic information regarding the EHR of a patient. Against this backdrop, this paper presents a privacy and security architecture for EHR that gives room for dynamism and flexibility with absolute assurance for patient-regulated authorization. The security framework of this work relies heavily on applied cryptographic techniques (digital signatures) with unbridled integration into Electronic Health Record infrastructure. A patient is required to receive authorization from a medical institution through the provision of his identity number via mobile phone or proxy. The choice of a doctor to access patient’s EHR details depends solely on his role hierarchy. Finally, the framework provides a feedback mechanism for the patient in form of a report through the Doctor’s Feedback Server (DFS).