Miracle-dz/UniFAR-FTUs huggingface dataset:
RSS-Based DoA Estimation with ESPAR Antennas Using Reduced Number of Radiation Patterns

In this letter, we investigate how direction-of-arrival (DoA) estimation algorithms, which are designed for electronically steerable parasitic array radiator (ESPAR) antennas and rely solely on received signal strength (RSS) values recorded at antenna’s output port, will perform when limited number of radiation patterns will be used. To this end we have inspected two algorithms, which are easily applicable in WSN nodes having simple and inexpensive transceivers and provide accurate results. Measurements conducted using a fabricated ESPAR antenna indicate that it is possible to limit the number of radiation patterns in DoA estimation procedure and still keep the original accuracy. Hence, depending on application, the time required for DoA estimation in WSN nodes using ESPAR antennas can be reduced from 50% up to 75%.
Wideband Dual-polarized Array Antenna on Dielectric-based Inverted Microstrip Gap Waveguide

A wideband dual-polarized planar array antenna is presented in this paper. The proposed array antenna consists of 8 × 8 bowtie antennas and dielectric-based inverted microstrip gap waveguide network. It is composed of multilayer substrate which can be fabricated by low cost PCB process. The simulated results show that the proposed array achieves a relative impedance bandwidth of 57% from 18.92GHz to 34 GHz for both polarizations with VSWR of 2.
A new Multiple Input Multiple Output V2V automotive antenna for Long Term Evolution band applications

The aim of this paper is to evaluate a dual-band Multiple-Input–Multiple-Output (MIMO) Long Term Evolution (LTE) antenna for automotive applications. Such an antenna has an overall volume relatively small and can be fit in different locations on the vehicle. The compact planar structure facilitates that.The antenna is composed of two radiant symmetrical elements connected through a neutralizing line in order to cancel the reactive coupling. The radiating element consisting of a folded monopole and a rectangular metal patch can produce multiple resonant modes at 800 MHz (790–862 MHz) and 2600 MHz (2575–2635 MHz) for the geographical region of Europe. The proposed antenna is fed by using 50 Ω coplanar waveguide (CPW) transmission lines.
Radiation Limitations for Small Implanted Antennas

Medical implants with communication capability are becoming increasingly popular with today’s trends to continuously monitor patient’s condition. This is a major challenge for antenna designers since the implants are inherently small and placed in a communication-wise very lossy environment. Our goal is to determine the fundamental limitations of such antennas when placed inside human bodies and to develop guidelines for most efficient design. We base our findings on in-house analysis tools based on spherical and cylindrical wave expansion applied to simplified spherical and cylindrical body models respectively. These give us insight into wave propagation and show the maximum power density levels that can be reached just outside the body. Based on the obtained limits we can propose a useful upper bound for more complex scenarios.
Compressed Projection Bases for Model-Order Reduction of Multiport Microwave Components Using FEM

This paper presents a projection basis compression technique for generating compact reduced-order models (ROM) in the FE analysis of microwave devices. In this approach redundancy is removed from the projection basis by means of the proper orthogonal decomposition technique applied to the projected system of linear equations. Compression allows for keeping the size of a reduced-order model as small as possible without compromising ROM’s accuracy. Effectiveness of the basis compression technique, including memory and time consumption as well as the size of the resultant ROM, are discussed for both global and local model-order reduction schemes.
A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining

The amount of biomedical literature is vast and growing quickly, and accurate text mining techniques could help researchers to efficiently extract useful information from the literature. However, existing named entity recognition models used by text mining tools such as tmTool and ezTag are not effective enough, and cannot accurately discover new entities. Also, the traditional text mining tools do not consider overlapping entities, which are frequently observed in multi-type named entity recognition results. We propose a neural biomedical named entity recognition and multi-type normalization tool called BERN. The BERN uses high-performance BioBERT named entity recognition models which recognize known entities and discover new entities. Also, probability-based decision rules are developed to identify the types of overlapping entities. Furthermore, various named entity normalization models are integrated into BERN for assigning a distinct identifier to each recognized entity. The BERN provides a Web service for tagging entities in PubMed articles or raw text. Researchers can use the BERN Web service for their text mining tasks, such as new named entity discovery, information retrieval, question answering, and relation extraction. The application programming interfaces and demonstrations of BERN are publicly available at https://bern.korea. ac.kr.
Wavelet Decomposition based Channel Estimation and Digital Domain Self-Interference Cancellation in In-band Full-Duplex OFDM Systems

This paper presents a novel digital domain self-interference (SI) cancellation scheme for full-duplex (FD) OFDM systems employing noise robust SI channel estimation technique. First using the least-square (LS) technique, both the desired and the SI channels are estimated jointly assuming that the desired as well as SI signals are perfectly known. Since, LS estimator does not consider any noise effect in obtaining its solution, it may suffers from noise. Next, to suppress the noise effect, we propose wavelet decomposition (WD) of SI channel estimate to perform denoising in time domain. In the proposed algorithm, a standard deviation of noise obtained by WD is used as a threshold value which is a requisite to achieve an optimal performance. The simulation results show that the proposed algorithm outperforms the joint LS algorithm as well as the previous works using conventional LS where the desired signal is assumed as noise.
Quasi-analytical Near-to-Far Field Transformation Based on Field Matching Method for Scattering Problems

A new quasi-analytical near-to-far field transformation based on field matching method (field expansion in a base of Hankel functions) is presented. This approach uses finite element method to obtain near field, then the field is expressed in a base of Hankel functions. The evaluated coefficients allow to calculate the field outside the numerical domain, also in a far distance. The main advantage of the proposed technique is avoiding of the Green’s function integration. The method can be applied for obstacles of an arbitrary cross section and homogeneous in one direction. In order to confirm the validity of the presented technique three different structures were analyzed and the results were verified with the other methods.
Inverse Synthetic Aperture Radar Imaging Using Fourier Transform Technique

Inverse Synthetic Aperture Radar (ISAR) imaging techniques are used to estimate the target spatial image using target backscatter data. In the ISAR measurement environment, the target is often modeled as a collection of point scatterers to take advantage of the Fourier relationship between scatterer location and measured backscatter data. The technique is utilized for imaging a target based on employing scattering mechanism and Fourier Transform (FT). With Processing the backscattered data ISAR image, using the Inverse Fourier Transform (IFT), the target’s range and cross range are formed and imaging is formed accordingly.
F(Vi)DE: A Fusion Approach for Deep Web Data Extraction

Web mining is the application of data mining techniques to discover patterns from the Web. Many web applications such as information retrieval, information extraction and automatic page adaptation can benefit from web structure mining. This paper presents an automatic top-down, tag-tree independent approach to detect web content structure. Comparing to other existing techniques, our approach is independent to underlying documentation representation such as HTML and works well even when the HTML structure is far different from layout structure. Web2DB is a web data extraction service. It takes unstructured data from web html pages and converting it into structured records. They analyze several types of visual features which exist in all response pages, including position features, layout features, appearance features and content features. Based on these visual features, in this paper, we propose a system that can extract informative or useful content from Web pages across different sites. We implemented F(Vi)DE which can performs the extraction using only the visual information of the response pages when they are rendered on web browsers. Our approach is independent to underlying documentation representation such as HTML.
Hardware-Accelerated Index Construction for Semantic Web

In this paper, an optimized data structure for managing triples used in a Semantic Web Database and a hardwareengine for index construction are presented. We propose anFPGA-centric design, which we call Hardware-Triplestore. Aspart of the design, a scalable and parallel architecture forTriplestore construction is introduced. We propose a hybrid datastructure consisting of three layers, one for every element ofthe semantic triple. The data structure is optimized for ourhardware-centric design and is stored on an external DDR4-Memory. The Hardware-Triplestore is evaluated separately fromthe rest of the database system and achieves an insertion rateof 1.24 million triples per second, which is 17 times faster thanone of the fastest software Triplestore—RDF-3X—.
Network Flow Dynamics Modeling and Analysis of Arrival Traffic in Terminal Airspace

This paper reveals and explores the flow dynamics of arrival traffic in a terminal airspace network on both mesoscopic and macroscopic levels in a systematic way from empirical data to dynamics modeling. The existence of link-based typical fundamental diagram (FD) that expresses the fundamental relations of newly defined flow-density-speed and network-based arrival macroscopic fundamental diagram (MFD-A) that represents aggregate demand-supply dynamics is demonstrated using empirical data collected in Guangzhou Terminal Airspace. After establishing heterogeneous FDs for flows along a different route segments using piecewise approximation, a density-speed-based modified cell transmission model (MCTM) is developed to simulate the spatio-temporal evolution of flow and congestion in arrival network for the enhancement of the adaption to non-uniform cell length and unique speed profile in terminal airspace. To further improve the simulation accuracy without compromising computational time, hybrid simulation is designed by deploying a queuing inspector (QI) to MCTM. The proposed network flow model is shown to be an efficient and accurate method capable of capturing the flow evolution at mesoscopic and macroscopic levels and supporting air traffic flow prediction and control. In the end, characteristics of arrival flow dynamics, including hysteresis, critical steady state (CSS), critical unsteady state (CUS), and its acceptable duration are discussed in depth as crucial theoretical parameters for air traffic flow management. This study provides a novel perspective method to model and understand the evolution of air traffic flow, and underpins advanced technical potentials for future air traffic management.
CRISPR/CAS9 Target Prediction with Deep Learning

The CRISPR/CAS9 system is a powerful tool for regulating damaged genome sequences. Nucleases that are damaged in their sequence are called miRNAs (micro RNAs). The miRNAs targeted by multiple promoter sgRNA (single guide RNA) are cut or regulated from RNA by the CRISPR/CAS9 method. The sgRNAs targeted to the wrong miRNAs may cause unwanted genome distortions. In order to minimize these genome distortions, sgRNA target estimation was performed for CRISPR/CAS9 with deep learning in this study. In this article, convolutional neural networks (Convolutional Neural Networks- CNN) and multilayer perceptron (Multi Layer Perceptron-MLP) algorithms are used. A performance comparison of the CRISPR/CAS9 system for both algorithms was performed.
Creating Green Industry through the Implementation of an Energy Management System : Case Study at PT. X

The green industry is an industry that in its production process prioritizes the efforts to efficient and effective using of resources in a sustainable manner so as to be able to harmonize the industrial development with the environmental sustainability and can provide benefits to the community. One of the assessment criteria for the green industry is the energy aspect, namely industrial efforts in implementing energy efficiency, the usage of renewable energy, and conducting energy management activities. This research was conducted based on a case study of the application of Energy Management System at PT. X, which is one of the largest pulp and paper industry in Indonesia. The industry has an impact on reducing CO2 emissions through the implementation of Energy Management System. PT. X can save energy consumption by 47,700 GJ and reduce CO2 emissions by 11,805.75 tons CO2e, in 2017.
Trajectory Control of a Swashplate-less Coaxial Helicopter Using Nonlinear Techniques

This paper addresses the modeling and controller design of a swashplate-less coaxial helicopter with a center-of-gravity variation mechanism. The moving-mass system is used to decouple the control moments from the propulsion system. The mathematical model is thoroughly derived to capture the interaction between the dynamics of the vehicle and of the moving-mass mechanism. Furthermore, based on this model, the nonlinear controller that guarantees the exponential tracking between the system and any arbitrary trajectory is developed. This controller is designed using the geometric approach, which allows the system to be considered separately as a few subsystems. The coupling effects between these subsystems are shown to be bounded, which preserve the stability of the overall system. Two simulation trials, in which this controller is implemented, demonstrate the stability.
Diagnosis of Pneumonia from Chest X-Ray Images Using Deep Learning

Pneumonia is a disease which occurs in the lungs caused by a bacterial infection. Early diagnosis is an important factor in terms of the successful treatment process. Generally, the disease can be diagnosed from chest X-ray images by an expert radiologist. The diagnoses can be subjective for some reasons such as the appearance of disease which can be unclear in chest X-ray images or can be confused with other diseases. Therefore, computer-aided diagnosis systems are needed to guide the clinicians. In this study, we used two well-known convolutional neural network models Xception and Vgg16 for diagnosing of pneumonia. We used transfer learning and fine-tuning in our training stage. The test results showed that Vgg16 network exceed Xception network at the accuracy with 0.87%, 0.82% respectively. However, the Xception network achieved a more successful result in detecting pneumonia cases. As a result, we realized that every network has own special capabilities on the same dataset.
3D Mechatronic Structures via Hybrid Additive Manufacturing Technology

For rapid manufacturing of customized 3D mechatronic structures, this work proposes a hybrid additive manufacturing technology combining dual-material fused deposition modeling (FDM) and selective electroless plating. High-conductive metallic film can be selectively deposited on 3D printed plastic structures made of ABS and PETG plastics. With current used 0.4 mm nozzle, FDM printer could produce the finest conductor of about 0.5 mm wide. Through four-point probe measurement, EDS inspection and SEM cross-section analysis, it was found that the minimal resistivity of Ni-P film was identified to be approximate 8.4 × 10−5 Ω cm, when the electroless plating was conducted at 50 °C and pH 8.5. A demonstrator of 3D LED blinking circuit was developed to testify the feasibility and potential of this technology in industry applications.
Harmonic Source Detection Methods: A Systematic Literature Review

Ensuring the quality of power supply is a main target of the power utility companies worldwide. Harmonic distortion is one of the power quality problems that can result either from upstream (utility side) through background harmonic, downstream (customer’s side) through non-linear loads, or renewable energy generators. The detection of harmonic sources at the point of common coupling (PCC) is a major concern for both utilities and customers. Various methods have been proposed since the 1990s to be used for harmonic source detection. These methods have been classified into three categories based on the direction of active power flow, reactive power, and voltage–current ratio. In this paper, a systematic literature review is done on the state of the art of current research on the harmonic source detection methods, in order to select the method that gives better practical and commercial results to be used when multiple customers are connected at the PCC. This systematic literature review recognized that most studies concentrated only on harmonic source detection between a customer and utility but the practical power system has multiple customers connected to the PCC with different load conditions. Therefore, the results obtained from this paper review will be useful for researchers and engineers working in the modern grids, who aim to develop a practical and commercial method to quantify the harmonic contribution for different customers and utility.
Fast Inter Mode Decision Algorithms for x265

The latest High-Efficiency Video Coding (HEVC) standard achieves nearly 50% bit rates reduction for similar quality relative to H.264/Advanced Video Coding(AVC) . However, its complexity is enormously increased ,which becomes one of the most challenges for its deployment in real time applications. The only solution to decrease the coding complexity is to set up different settings by adjusting various coding parameters. Among them, low complexity settings are suitable for industrial applications and conducive to the popularization of HEVC. Traditional fast mode decision algorithms mainly aim at decreasing coding complexity for high complexity settings. In this paper, we propose a fast mode decision method for HEVC with low complexity settings according to machine learning. A decision tree is constructed to decide whether to check 2N×2N mode or the SKIP/MERGE mode by exploiting relevant information from spatiotemporal adjacent Coding Units(CUs). Further mode skipping is performed based on the result of the first step. Experiments show that the proposed scheme can only increase by 1.42% Bjotegaard Delta Bit rate(BDBR) with an average time reduction of 22.45% for HEVC with low complexity settings.
UHF-RFID Localization: The Problem of Antenna Phase Center in Phase-based Methods

The correct determination of the antenna phase center is a key point when performing UHF-RFID localization through a phase-based method. In this paper, we investigate the effect of a wrong knowledge on the reader antenna phase center in phase-based methods exploiting the relative motion of the reader antenna with respect to the stationary tags through a Synthetic Aperture Radar approach. An experimental analysis is carried out in a real scenario with the reader antenna attached on a robotic-wheeled walker.
Superior Selective Reporting-Based Spectrum Sensing in Energy Harvesting-Aided HCRNs

In the present contribution we investigate the performance of superior selective reporting (SSR) for cooperative spectrum sensing in an energy harvesting-enabled multi-channel heterogeneous cognitive radio network (HCRN). To this end, we first analyze the throughput of the SSR and the optimal conventional cooperative sensing (CCS). Then, we formulate a nonlinear integer programming problem to find a throughput-optimal set of spectrum sensors scheduled to sense a particular channel, under primary user (PU) interference and energy harvesting constraints. In this context, we derive a solution based on the cross entropy (CE) method, and compare its performance with the exhaustive-search method counterpart. Furthermore, we study the tradeoff between the channel available time and detection accuracy of the SSR and CCS schemes. It is shown that this inherent tradeoff is between the channel available time and the detection accuracy. Furthermore, it is shown that as the number of spectrum sensors increases, the channel available time turns out to be the system's limiting factor in HCRNs.
Fully polarimetric synthetic aperture radar indices for scintillation observation

Low frequencies synthetic aperture radar (SAR) are invariably affected by the dispersive ionospheric layer. Amplitude scintillations arising from ionospheric irregularities damage SAR data in the form of azimuthal stripes, especially at low latitudes. In the absence of good ground-based networks of Global Navigation Satellite System (GNSS) receivers, SAR can act as a supplementary technique with wider coverage to study scintillations. The paper highlights the use to full polarimetric SAR data to differentiate between disturbed and quiet days with respect to scintillations. Two indices are developed from radar measurements, namely total power index (TPI) and depolarization power index (DPI) are formulated. All four polarization channel intensities are utilized for TPI while the depolarization power is computed from the volume scattering power $(P_{\mathrm {v}})$ of model-based decomposition. L-band Advanced Land Observing Satellite (ALOS-2)/Phased Array-type L-band Synthetic Aperture Radar (PALSAR −2) data utilized in the study identify the region of intense scintillation with the peak ofanomaly crest using the indices. The indices are less sensitive to the variation in the ionospheric conditions outside the peak scintillation region. Weakening of the scintillation strength or inappropriate SAR geometry beyond this region can be plausible explanations for it.