Miracle-dz/UniFAR-FTUs huggingface dataset:
Snomed2Vec: Representation of SNOMED CT Terms with Word2Vec

Hospital Information Systems (H.I.S) use Electronic Health Record to store heterogeneous data from the patients. One important goal in this kind of systems is that the information must be, normalized and codify with a clinical terminology to represent exactly the healthcare meaning. Usually this process need human experts to identify and map the correct concept, this is a slow and tedious task. One of the most widespread clinical terminologies with more projection is Snomed-CT. This is an ontology multilingual clinical terminology that represent the clinical concepts with a unique code. We introduce in this paper Snomed2Vec, new approach of semantic search tool to find the most similar concepts using Snomed-CT. This is an ontology based named entity recognition system using word embedding, that suggest what is the most similar concept, that appear in a text. To evaluate the tool we suggest two kind of validations, one against a corpus gold with diagnostic from clinical reports, and a social validation, with a public free web access. We publish an access web to the academic world to use, test and validate the tool. The results of validation shows that this process help to the specialist to the election of choose the correct concepts from Snomed-CT. The paper illustrates 1) how create the initial big corpus of texts, to train the word2vec models, 2) how we use this vector space model to create our final Snomed2Vec vector space model, 3) The use of the cosine similarity distance, to obtain the most similar concepts, grouping by the hierarchies from Snomed-CT. We publish to the academic world: https://github.com/NachusS/Snomed2Vec access to the public web tool, and the notebook, for develop and test this paper.
Sensor Single and Multiple Anomaly Detection in Wireless Sensor Networks for Healthcare

Wireless Body Area Network is used in healthcare applications for collecting information from remotely monitored patients. As they work in medical Applications, these kinds of networks must be robust and flexible to the sensors failure. This means that it must differentiate between patient's emergency alarms, and ill-behaved sensor's false alarms. In this paper, we propose an approach for faulty measurements detection in order to make alarming of emergency situations more precisely. The proposed approach is based on decision tree, threshold biasing and linear regression. Our objective is to detect single and multiple faults in order to reduce unnecessary healthcare intervention. The proposed approach has been applied to real healthcare dataset. Experimental results demonstrate the effectiveness of the proposed approach in achieving high Detection Rate and low False Positive Rate. The ability of this algorithm to detect single and multiple anomalies make it more reliable for medical emergency use.
New Star Detector and Descriptor for Deep-sky Image Live Registration

Recently, a new image registration application named live stacking appears in deep-sky imaging fields. This kind of live registration mainly handles translation and rotation caused by vibration of imaging devices and the Earth’s rotation. The existing star-based registration methods mainly have two defects: highly parameter related and very complicated matching mechanism to cope with image rotation instead of using rotation invariant descriptor. In order to make up these defects, in this work, a live registration algorithm via star detection and description is proposed. Firstly, an automatic detection method based on Otsu, centralization and contour tracing is proposed to implement star detection. Then, a translation and rotation invariant binary descriptor is constructed to serve feature matching. Extensive experiments illustrate that our proposed method is feasible in deep-sky image live registration compared with these state-of-the-art feature-based methods used in computer vision fields.
Research on Information Retrieval Algorithm Based on TextRank

Compared with people's urgent desire for information, the current information retrieval still has problems such as slow speed and low precision. In response to this problem, this paper proposes an information retrieval method based on NLP. Firstly, the semi-supervised learning algorithm is used to describe the natural language under the manifold condition, and the undirected graph is constructed for all the data. For the undirected graph after construction, we use the label propagation algorithm to simplify the undirected graph to reduce the computational complexity in the information retrieval process. Then the keywords will be extracted by TextRank algorithm to achieve information retrieval. Finally the experimental results show that the retrieval efficiency can be improved by using this algorithm.
A holistic DC link architecture design method for multiphase integrated modular motor drives

This article describes a holistic DC link architecture design method that considers the end-application of the drive and its corresponding constraints e.g. the maximum battery ripple current for a battery-supplied inverter. Also, the levers that are available to comply with the design criteria are presented e.g. the use of interleaved carrier waves. This holistic approach will result in a feasible and performant Integrated Modular Motor Drive from an application point of view. Finally, a platform is presented that was developed for feasibility and performance assessment of various DC link architectures obtained from the holistic design approach. The platform comprises a fifteen phase integrable modular motor drive for an Axial Flux Permanent Magnet Synchronous Machine. It allows non-intrusive reconfiguration of the DC link architecture and implementation of various control strategies and interleaved PWM schemes.
Cryptographic Coprocessor with Modular Architecture for Research and Development of Countermeasures Against Power-Based Side-Channel Attacks

In modern information processing systems, ensuring confidentiality and secure data transmission is an extremely important issue. However, even the most secure cryptographic algorithm may be insufficient, if its implementation does not take into account the threat of information leak through side-channels. Performing an attack on an unsecured device can be very simple and may not require expensive hardware. For this reason research and development of countermeasures against such attacks is very important.This paper presents an environment designed to facilitate research, development and evaluation of countermeasures protecting hardware implementation of cryptographic primitives against side-channel attacks based on power consumption analysis.The environment is composed of hardware cryptographic coprocessor with modular architecture, data transfer and configuration layers and software testbench for design verification. Different crypto primitives (block ciphers, hash functions, checksum) were implemented along with basic countermeasures. Utilized methods of power consumption approximation are also described.Evaluation of capabilities of the entire environment prove that both, hardware architecture and testbench provide easily extensible and versatile framework for research and development of countermeasures against power-based side-channel attacks.
Analysis of Optimum Copper Oxide Hole Transporting Layer for Perovskite Solar Cells

In this paper, numerical simulation of a lead halide perovskite-based solar cell with ultrathin Cu2o (Copper oxide) as inorganic hole transporting material is investigated. The proposed thickness and properties of Cu2o layer must be precisely tuned in order to achieve the optimal cell characteristics. The influences of effective density of states, hole mobility of hole transporting layer, thickness of perovskite absorber and P-type Cu2o layer are considered in details. Improvements in efficiency are achieved by changing the Cu2o and absorber layer thickness. Based on the best possible optimization, power conversion efficiency exceeding 13% is obtained. Simulated values after optimization are described in detail, lead to have a better insight of design, referred to experimental works for achieving high-efficiency solar cells.
Adaptive Sliding Mode Attitude Control of Satellites in the Presence of Gyro Bias

This paper presents an adaptive sliding mode controller for attitude stabilization of fully actuated satellites. Specifically, we assume the attitude presented by unit quaternion is available via attitude sensors and the angular velocity is measured by rate gyro where its output is contaminated by an unknown and constant bias. Despite the inherent robustness of sliding mode technique to parameter uncertainties, the gyro bias has a performance-degrading effect if neglected. Hence, in this paper the unknown bias is compensated by designing an adaptation law. The asymptotic stability of the closed loop system is proved by Lyapunov analysis. Finally to illustrate the performance of the proposed controller, we present simulation of a satellite system and in comparison with another existing attitude sliding mode controller as well.
Towards Generating Multiple-Choice Tests for Supporting Extensive Reading

We propose a method for generating multiplechoice test for an English text selected by a learner and its answer, that are used to make a self-assessment whether the learner comprehends the text after reading it. In our method, the system extracts several important sentences from the text, and replaces one word in each of these sentences with its synonym (if possible). One of these sentences is then selected as a correct optional sentence, while further changes to the polarities or nouns in the remaining sentences are carried out to generate distractor optional sentences for the multiplechoice test. Our method has potential to make extensive reading in English more effective. Keywords; Aided Learning; Important Sentence; Paraphrase; Documents on Web; Extensive Reading.
Supervised-Topic-Model-Based Hybrid Filtering for Recommender Systems

Recommender systems are beneficial to both service providers and users, as they offer item recommendations to individual users based on their preferences. In this paper, we propose a hybrid recommender system based on a widely used topic modeling method: supervised latent Dirichlet allocation (sLDA). This supervised topic model provides a simple clustering method for analyzing large volumes of unlabeled data from potential items. We use matrix factorization based on sLDA for topic finding in item-feature spaces, enabling hybrid recommendations using topic distributions and a user rating matrix. The proposed model can learn latent factors for predicting unknown ratings and recommending new items, thus addressing the cold-start problem, as any user can be recommended items that have not yet been provided with ratings. Our results show that the proposed method improves upon several baselines for the Jester dataset.
Low-dose CT Image Reconstruction via Total Variation and Dictionary Learning

Nowadays, low-dose computed tomography (CT) has also become increasingly important due to concerns about radiation risks. For low-dose CT imaging, ensuring image quality while reducing radiation dose is a major challenge. To facilitate low-dose CT imaging, we propose an improved reconstruction scheme based on the Penalized Weighted Least Squares (PWLS) standard combined with total variation (TV) minimization and dictionary learning (DL) to improve the results of reconstruction. We call this method “PWLS-TV-DL”. To evaluate the PWLS-TV-DL method, we performed experiments on digital phantoms and discussed the results in terms of image quality and calculation. The efficiency is superior to other methods, which confirms the potential of its low-dose CT imaging.
SVNet: A Single View Network for 3D Shape Recognition

As the great success of deep learning in the 2D image recognition, applying it to 3D shape recognition has recently received much more attention. In this paper, we propose a Convolutional Neural Network (CNN) framework named Single-view Network (SVNet) for 3D recognition. Unlike multiple-view-based methods that aggregate multiple views into one, SVNet extracts and retains the feature of each view separately. Concretely, there are M fully connected layers in the last layer of SVNet, where M denotes the number of views. SVNet integrates the prediction of each fully connected layer to get the final result, which is similar to voting. In addition, our method does data augment by aligning the 3D shape and using normalized normals as the color of 3D shapes. Compared to the state-of-the-art methods, SVNet achieves better performance in 3D shape classification and retrieval on the benchmark dataset. The implementation of SVNet is available at https://github.com/paopaoer/experiment.git.
Dual band/ Dual Mode Branch-Line/Rat-Race Coupler Using Artificial Transmission Line

This paper presents a novel dual band/dual mode compact hybrid coupler which acts as dual-functional, a dual band branch line coupler at lower band but a rat-race coupler at higher band. The outputs of the proposed coupler at the each mode of operation are the same side. This unique design is implemented by using artificial transmission lines (TLs) based on open split ring resonators (OSRR). The proposed low cost miniaturized coupler could be operated as a dual band 90° branch line coupler at 3.3 and 3.85 GHz and 180° rat-race coupler at 5.3 GHz.
New Design of High-Gain Beam-Steerable Dipole Antenna Array for 5G Smartphone Applications

This paper proposes a new design of high-gain beam-steerable dipole antenna array for fifth generation (5G) smartphone applications. This 5G antenna was designed used RT Duroid 5880 substrate with dimension size 60×130×0.787 mm3. It consists of 10 dipole antenna elements to a linear array form that smartphone can be configured at the top portion of a smartphone PCB. Simulated show results the proposed 5G antenna is working in the frequency range of 34.875–42.000 GHz, providing 7.125 GHz or 18.75 % of impedance bandwidth. The proposed of 5G antenna reaches gain 13 dBi, and produces a 3 dimensional beam steerable characteristics in the entire operational frequency. The proposed antenna also has mutual coupling characteristic between its elements of less than -10 dB.
Autonomic Ranking of Resources in IoT Exploring Fuzzy Logic and Machine Learning

Currently, billions of resources are connected to the Internet, many providing services. There is forecast to be a significant increase in the number of resources in the coming years. The adequate selection of resources that best meet the demands of users among a large number of options has been a relevant and current research challenge in the autonomic IoT management. This paper specifies and evaluates the pre-classification of new resources of the EXEHDA middleware based on the non-functional parameters of QoS. Fuzzy logic is used in the treatment of uncertainties in defining the importance weights of QoS attributes. The results obtained in the evaluation of the accuracy of the pre-classification through fuzzy logic and machine learning are presented.
An Infrared Ultra-Narrowband Intensity-based Plasmonic Sensor/Filter Based on Coupling Through Plasmonic Nano-Cavities

In this paper, an ultra-narrow band integrated refractive index sensor with high sensitivity is proposed that is based on a novel plasmonic nano-cavity made of gold which is located on top of a photonic waveguide. The nano-cavity consists of two metallic slots and two metallic nano-disks. Light is propagating through the photonic waveguide which is silicon nitride (Si3N4). The strong coupling from the photonic waveguide to the plasmonic nano-cavity leads to detection of small refractive index changes which is appropriate for medical applications. The structure presents a full width at half maximum (FWHM) of only 6 nm which is a great improvement in comparison with recent researches. There are a few resent papers that are intensity-based plasmonic sensor. In this article, we use the intensity changes at the output to detect small refractive index changes. This ultra-narrow band structure can be used as an ultra-sharp plasmonic filter as well. The ultra-high selectivity of this infrared plasmonic filter can be used in appropriate applications.
An Ultra Wideband Low Power Detector Logarithmic Video Amplifier in 0.15µm GaAs

In this paper, a Detector Logarithmic Video Amplifier (DLVA) is designed with 50dBm dynamic range and Logarithmic error less than ±3dB using 0.15 µm GaAs which covers bandwidth of 2–24 GHz. The DLVA structure is including 5 distributed amplifier (DA) stages. The bandwidth of these stages is increased around 5 GHz by applying capacitor splitting technique to the conventional cascode distributed amplifier. The proposed DA achieves a flat gain of 10 dB with less than ±0.2dB variation which is much less than other DAs. Input and output matching are below −15 dB over the entire bandwidth, the average noise figure is 4.66 dB, the average IIP3 is 7dBm and gain compression point is −2.5 dBm. The DA area is 1.64 mm2and consumes 75 mW power from a 2.5V supply voltage.
Adaptive Controller with Output Feedback for Dielectric Electro-Active Polymer Actuator

In this work, an adaptive controller with output feedback was used in order to solve the position control of a nonlinear actuator built with a Dielectric Electro-Active Polymer (DEAP). The authors present the modeling process of the device taking into account the nonlinear model and its linearization. The identification process was carried out and the results were compared with experiments with good agreement. Due to the fact that it is not possible to obtain full state of DEAP actuator, the authors applied Model Reference Adaptive Controller with output feedback were voltage is an input signal and distance is an output. This approach allows to development a control method resistant to changing the parameters of DEAP device. Presented methods have been presented with illustrative examples.
Application of Asymmetric Networks to Motion Detection and Generating Independent Subspaces

The prominent feature in the visual system is the nonlinear characteristics as the squaring and rectification functions, which are observed in the retinal and visual cortex networks. Conventional model for motion processing in cortex, uses a symmetric quadratic functions with Gabor filters. This paper proposes a new motion processing model in the asymmetric networks. First, the asymmetric network is analyzed using white noise analysis and Wiener kernels. It is shown that the asymmetric network with nonlinearities is effective and general for generating the directional movement compared with the conventional quadratic model. Second, independence maximization of data is an important issue in computational neural networks. To make clear the asymmetric network with Gabor functions, orthogonality is computed, which shows independence characteristics of the asymmetric structured network without conventional maximizing independence in the quadratic model. The orthogonal analyses for the independence of the asymmetric neural networks are applied to the V1 and MT neural networks model to generate independent subspaces.
Design and Implementation of 6kW Boost PFC Topology in Discontinuous Current Mode

the conventional boost PFC converter was applied in continuous current mode due to current would operate continuously to obtain a high efficiency. A boost PFC converter in DCM control approach was investigated in this paper with a 6kW experiment. The operational principles of proposed DCM boost pfc converter was discussed first. And then relationships between inductor current and duty cycle as well as output voltage with duty cycle are evaluated. A modelling control scheme of boost pfc converter are derived through theoretical analysis and relationships of input current and input voltage. At the end, a 6kW prototype is investigated through combination of theoretical design and calculation, the prototype was able to achieve power factor correction which obtains a power factor of 0.99. In addition, the DCM control approach was demonstrated properly during the research.
Comparative Study of Advanced Stator Interior Permanent Magnet Machines

This paper presents a comparative study of novel stator interior permanent magnet (SIPM) machines with different magnet arrangements. The design concept of “alternate U-shaped PM” is introduced to SIPM machine in order to provide a circulating path for low-order field harmonics and hence improve torque capability. The machine topologies and analytical modeling are described first. The multi-objective genetic algorithm is then employed to optimize the design parameters of the proposed SIPM machines. In addition, the electromagnetic characteristics of the SIPM machines with two U-shaped PM arrangements are investigated and compared by finite element (FE) method. The simulation results demonstrate that the larger effective harmonics and higher torque capability can be achieved in the proposed SIPM machine with U-shaped PM-II configuration. Finally, a 6/13-stator/rotor-pole SIPM machine with U-shaped PM-II configuration is manufactured and tested to validate the FE analyses.
A New Rotation-Invariant Deep Network for 3D Object Recognition

When inputs are rotated, most 3D convolutional neural networks (CNNs) will have their performance much dropped, especially for those models with voxelized input of 3D objects. The newly proposed Spherical CNNS, with the concept of the rotation-equivariant spherical correlation, aims to achieve rotation invariance. Inspired by this, we propose a new rotation-invariant deep network to recognize rotated 3D objects. Specifically, we adopt the spherical representation and the spherical correlation S^2 layer of Spherical CNNs, for their capacity of representing 3D objects and rotation equivariance. In the meantime, we improve the computational efficiency and expressiveness of Spherical CNNs, by replacing its time-consuming and depth-limited SO(3) layer with a PointNet-style network architecture. Hence our proposed network can maintain the equivariance as the network grows deeper while substantially reducing its runtime, leading to a much better efficiency and expressiveness of rotation-invariant representation. Experimental results show that our network performs better than or comparable to the state-of-the-art methods in the ModelNet40 classification challenge.
Effects of Annealing on Magnetic Properties of Laminated Stator Cores and Efficiency of Induction Machines

Steel laminations for electric machines are commonly manufactured using punching or laser cutting. The cutting process leads to degraded magnetic properties and therefore reduces the efficiency and the performance of electric machines. This paper presents an experimental approach for quantifying the influence of the manufacturing process as well as the influence of annealing on electrical steel laminations. The magnetic properties of three electrical steel grades are reported for laser cut toroidal cores and for punched stator core laminations. The stator laminations are used to produce three identical induction machine prototypes with a rated power of 4 kilowatt. The machines are measured according to IEC 60404-2-1 and IEC 600404-2-3. We present the influence of material selection on the machine efficiency and we show that the annealing process recovers the magnetic properties of the original material and increases the efficiency of the machine prototype.