Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-Polarized, High-Gain, and Low-Profile Magnetic Current Array Antenna

In this communication, a dual-polarized 36-element (<inline-formula> <tex-math notation="LaTeX">$3 \times 6 \times 2$ </tex-math></inline-formula>) magnetic current array antenna is designed without using complex feeding network. Instead, a pair of differential feeds is adopted to excite the overall dual-polarized array within a low profile. Such a simple feeding strategy is achieved by using the TM<sub>90</sub> mode of a microstrip antenna, which is loaded by <inline-formula> <tex-math notation="LaTeX">$3 \times 4$ </tex-math></inline-formula> identical half-wavelength slots for each polarization. In this configuration, an in-phase <inline-formula> <tex-math notation="LaTeX">$3 \times 6$ </tex-math></inline-formula> element single-polarized magnetic current array is achieved for high gain, but with narrow bandwidth due to the high-order mode. By trivially 90° rotating and combining, the proposed dual-polarized, high-gain, and low-profile magnetic current array is designed, constructed, and measured. Within a total size of <inline-formula> <tex-math notation="LaTeX">$2.92\lambda _{0}\times 2.92\lambda _{0}\times 0.085\lambda _{0}(\lambda _{0}$ </tex-math></inline-formula> is the free-space wavelength at the center frequency), the measured maximum gain is 15.5 dBi, also with the merits of low profile, low sidelobes, simple feeding, and high port isolation, exhibiting promising usage in MIMO and diversity applications.
Comments Mining With TF-IDF: The Inherent Bias and Its Removal

Text mining have gained great momentum in recent years, with user-generated content becoming widely available. One key use is comment mining, with much attention being given to sentiment analysis and opinion mining. An essential step in the process of comment mining is text pre-processing; a step in which each linguistic term is assigned with a weight that commonly increases with its appearance in the studied text, yet is offset by the frequency of the term in the domain of interest. A common practice is to use the well-known tf-idf formula to compute these weights. This paper reveals the bias introduced by between-participants’ discourse to the study of comments in social media, and proposes an adjustment. We find that content extracted from discourse is often highly correlated, resulting in dependency structures between observations in the study, thus introducing a statistical bias. Ignoring this bias can manifest in a non-robust analysis at best and can lead to an entirely wrong conclusion at worst. We propose an adjustment to tf-idf that accounts for this bias. We illustrate the effects of both the bias and correction with with seven Facebook fan pages data, covering different domains, including news, finance, politics, sport, shopping, and entertainment.
Energy-Aware Mobile Edge Computation Offloading for IoT Over Heterogenous Networks

The rapid development of the Internet of Things gives rise to the emergence of delay-sensitive and computation-intensive applications. Due to the inherent long delay of cloud computing and the limited resources at end devices, mobile edge computing is considered a promising approach to meet the stringent delay requirement of such demanding applications. To handle the massive connection of the Internet of Things, the 5G network is shifting toward heterogenous architecture, where each end device can access more than one edge server (e.g., base stations and access points). In the presence of multiple edge servers, this paper investigates the interesting problem of how to exploit the heterogenous computation resources at the network edge to achieve the best energy efficiency among multiple end devices while satisfying their delay requirements. We study a computation offloading management problem by jointly considering the heterogeneous computation resources, latency requirements, power consumption at end devices, and channel states. The formulated energy minimization problem falls into the category of mixed-integer and nonlinear program. To solve it efficiently, we decompose the original problem into two subproblems and propose an iterative solution framework to solve for transmission power allocation strategy and computation offloading scheme. Through simulation results, we show that the proposed solution is competitive when compared with the optimal solution. Moreover, we leverage the optimal solutions to analyze the impact of computation resource distribution on energy consumption and computation offloading decision.
A Wideband, Single-Layer Reflectarray Exploiting a Polarization Rotating Unit Cell

We present a new technique for designing wideband, single-layer reflectarray antennas. The proposed reflectarray exploits a wideband, polarization rotating unit cell that rotates the polarization of the reflected wave with respect to that of the incident wave by 90°. This unit cell and its mirror image were used as 1 bit spatial phase shifters capable of providing a 0°/180° phase shift over a wide bandwidth of 5.4–12.7 GHz. Using this, a reflectarray antenna with 1 bit phase quantization was designed to operate at <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>-band with aperture dimensions of <inline-formula> <tex-math notation="LaTeX">$28.8\,\,\text {cm}\times 28.8$ </tex-math></inline-formula> cm and a focal length of 30 cm. A prototype of the proposed reflectarray was fabricated and characterized using a spherical near-field measurement system. We determined the antenna’s 3 dB gain bandwidth to be approximately 37.4% with a maximum realized gain of 24.2 dBi. Over its entire band of operation, the polarization purity of the antenna remains better than 13 dB and sidelobe levels of better than 12 dB were achieved.
Polarimetric SAR Image Classification by Multitask Sparse Representation Learning

Classification is an important and difficult problem in Polarimetric SAR (POLSAR) image processing. Most existing classification methods combine multiple features (scattering parameters or statistical distribution) to improve the performance. However, based on the observation that various regions have different characteristics due to the different scattering mechanism, which implies that different features should be used for certain pixels rather than using the combination of various features for the whole image, so that simple combinations will result in numerous error classifications. In this paper, a novel POLSAR classification method based on multitask learning with multiple features is proposed. Firstly, different types of features are extracted, and then POLSAR classification problem is formulated as a multitask joint sparse representation learning problem. The strength of different features are employed by using of a joint sparse norm. Finally, experimental results on real POLSAR data show that our method outperforms several state-of-the-art algorithms.
Exploration of the Relationship Between Inertia Enhancement and DC-Link Capacitance for Grid-Connected Converters

Grid-connected converters (GCCs) are showing potential in providing virtual inertia and has attracted wide attention recently. For the virtual inertia emulated by GCCs, it is proportional with the DC-link capacitance, and thus, the DC-link capacitance can directly affect the dynamic performance for the GCC emulating inertia through modifying the inertia constant. However, the impacts of the DC-link capacitance have never been discussed in any literature before. Considering this issue, the influence of the DC-link capacitance on the dynamic performance for the GCC providing virtual inertia is analyzed in this paper. In addition to that, the selection approach of the DC-Iink capacitance is presented by tuning the system damping ratio within its optimal range. Simulations verify the correctness.
Directional Modulation Technique for Linear Sparse Arrays

As millimeter-wave technology becomes more and more mature, directional modulation (DM) based on large uniform linear arrays, where the adjacent antennas spaced half-wavelength of the frequency of interest, can be realized to ensure the security of millimeter-wave systems. However, for small inter-element spacings, the mutual coupling effects are severe. Therefore, a DM technique using linear sparse arrays (LSAs) is presented to improve spatial resolution and mitigate the effects of mutual coupling. The main idea for DM signal synthesis is to modulate the radiation pattern at the symbol rate by randomly selecting a sparse rate, i.e., a filling fraction, from the given interval <inline-formula> <tex-math notation="LaTeX">$[{\alpha _{\min }},{\alpha _{\max }}]$ </tex-math></inline-formula> and an array formation, i.e., an antenna subset, from the codebook <inline-formula> <tex-math notation="LaTeX">$\mathcal {P}$ </tex-math></inline-formula>. This results in a directional radiation pattern that projects a standard constellation in the desired direction and distorted randomized constellations in other directions, which offers security for wireless communication. Numerical comparisons of the symbol error rate performance of the proposed DM technique against conventional array transmission and antenna subset modulation are presented to highlight the advantages of the DM technique for LSAs.
A Novel Approach for Attaining Energy-Accuracy Trade-Off in WSN Based Underwater Target Tracking

Underwater surveillance systems demand accurate detection, classification and efficient tracking of moving submersibles. For sensing, processing and communicating data in these applications, Underwater Acoustic Wireless Sensor Networks (UAWSN) are extensively used. In this paper, a new target tracking method which minimises the energy expenditure in a UAWSN is proposed and referred to as 3DUT-UKF(n) where, $n$ refers to the number of nodes used in the ranging process. The Three Dimensional Underwater target Tracking (3DUT) algorithm and the Unscented Kalman Filter (UKF) based estimation are investigated, and the possibility of using the least number of nodes is analysed. By varying $n$ as per demand, a trade-off between the tracking accuracy and network energy consumption can be attained. Energy and data consumption of the proposed algorithm are analysed and found to be significantly reduced without compromising the tracking efficiency.
Congestion Management in Power System Based on Optimal Load Shedding Using Grey Wolf Optimizer

As a result of rising the load demand, outage of any component of power system (generator, transmission line, transformer), unexpected failure in any equipment of the network all these situations lead to power system became more congest and overloaded and reach to emergency state thereby, should be there are proper management or control action for that. One of the emergency control actions is congestion management which aim to mitigate or eliminate overloading and violation in transmission power system. This paper presents proper congestion management based on optimal load using grey wolf optimizer (GWO) for minimizing load shedding, active power losses and improves voltage profile, voltage stability. The obtained results have shown that optimal load shedding is a robust control action for congestion management. The developed algorithm has applied to IEEE 30 bus system considering (N - 1) contingency. Single and multi-objectives is implemented under normal and contingency state.
Thermal Transport in Armchair Graphene Nanoribbon Using Equilibrium Molecular Dynamics Simulation

The thermal transport property of armchair graphene nanoribbons (GNRs) is studied employing equilibrium molecular dynamics (EMD) simulation based on Green-Kubo (GK) method with optimized Tersoff interatomic potential and original Tersoff potential. The calculated room temperature thermal conductivity of 10 nmx3 nm armchair graphene nanoribbon is ~1300 W/m-K. Our study comprises of the impact of temperature and width on thermal conductivity of armchair GNRs. The investigation shows a decreasing trend of the thermal conductivity with the increase of temperature. Corresponding phonon density of states (PDOS) curves show red shift at elevated temperatures. Thermal conductivity is found to increase with the increasing width of GNRs. Total acoustic PDOS at different sample width showed presence of higher energy phonons at wider GNRs. A comparative study shows that original Tersoff Potential underestimates thermal conductivity than those with optimized Tersoff interatomic potential. Such study would provide a detail insight of thermal transport in graphene nanoribbons.
On the Virtual Cell Power Allocation in Ultra Dense Networks

Ultra-dense network (UDN) can meet ultra-high peak rate, traffic density and density by densely deploying low-power base stations (BSs). The traditional iterative search water filling (ISWF) algorithm requires many iterations to obtain the optimal power allocation. We adopt a low-complexity iterative water filling (LAWF) algorithm to reduce iterations. We propose sorting water filling (SWF) algorithm. The simulation results show that the sum rate achieved by the LAWF and SWF are better than ISWF. With deployment of UDN, speed and presentation of SWFs are increasing.
Synthesizable Memory Arrays Based on Logic Gates for Subthreshold Operation in IoT

This paper identifies novel directions of standard-cell-based synthesizable memory design. A compact 18T-bitcell of OR-AND-Invert (OAI) and AND-OR-Invert (AOI) logic gates is presented with bit-selective write and multiplexed read accesses. It reduces the bitcell area by 11%–40% compared to the state-of-the-art clock-gating-based D-latch schemes while avoiding custom-cell design. The improved storage density comes from the tight integration on two levels–coupling the read multiplexing within each bitcell and taking advantage of structured datapath placement in bitcell arrays by using commercial CAD tools. As measured on a 40 nm SRAM of 90 kb, the OAI/AOI-gate-based synthesizable memory features 0.4 V minimum access voltage, 30 fJ minimum read energy per bit, and a leakage power of 1.4 pW per bit at 0.3 V data retention voltage. It is ideally suited for IoT-node design operating in sub- $V_{\mathrm {t}}$ for joint dynamic energy and leakage power reduction. Further, it enables fast turn-around times by IP reuse over technology nodes with minimal (re)design effort.
Flexible Fractional Repetition Codes for Distributed Storage Networks

Consider the following fundamental question of distributed storage networks: Given any arbitrary (n, k, d) values, whether there exists an intelligent helper selection scheme (assuming unlimited memory and computing power) that can strictly improve the storage-bandwidth (S-B) tradeoff. Ahmad et at. 18' answered this question by proving that for a subset of (n, k, d) values, no helper selection scheme can ever improve the S-B tradeoff, and for the (n, k, d) not in that subset, a new scheme called family helper selection (FHS) can strictly improve the S-B tradeoff over a blind helper selection scheme. Nonetheless, the analysis of FHS is done by a min-cut analysis with no actual code construction.This work fills this gap between pure min-cut analysis and actual code construction by pairing FHS with a new, generalized version of the existing fractional repetition (FR) codes. Specifically, existing FR codes are exact-repair codes that admit the highly-desirable repair-by-transfer property, but its unique construction limits the application to a restricted set of (n, k, d) values. In contrast, our new construction, termed flexible fractional repetition codes, can be applied to arbitrary (n, k, d) while retaining most of the practical benefits of FR codes, i.e., admitting small repair bandwidth, being exact-repair, and being almost repairable-by-transfer.
Smart Grids and Stability A Whole Year Evaluation of an Inhabited DC microgrid with Energy Exchange

We investigate the grid stability with respect to sustainability of an inhabited PV based DC microgrid, which allows for energy exchange among the peers. Located on the subtropical island of Okinawa, Japan, the microgrid is subject to yearly rainy season and typhoons. This offers a difficult, but at the same time ideal environment to study grid behaviour under strongly varying conditions. We present results from data obtained over a whole year, which gives insight into weather related energy production and consumption patterns and thus into affected self sufficiency. We introduce the Grid Independency Index (GII) and the Off Grid Time (OGT), which reflect independency of the community from the commercial grid and maximum time the whole community sustains itself on battery charge only, respectively. We compare these parameters to the amount of produced energy in comparison to the amount of used energy, the Off Grid Potential (OGP). Together, these three parameters give a good characterization of the grid sustainability. In their role as stability indicators they also point to elements of seasonal trend and necessary improvement in hardware as well as energy exchange algorithm in the system. We for example find the longest OGT during the moderately hot months, despite increasing OGP during summer. Similarly, GII increases in the hotter months, but hardly exceeds 55%. The cause of these phenomena lies in the temperature related increase in consumption, which also results in an increase of energy exchange and thus amplified system loss.
Data Quality Impact in Business Inteligence

In today's digital world enormous amount of data is collected, categorized, stored for further analysis. The high volume of data is challenging for companies to deal with when the quality of data is in a low level of adequacy. Besides, Data Quality (DQ) solutions getting more time-consuming which is a challenge in the case of high volume of data and can cost losses within a company. The DQ has a massive impact on Data Exploration Quality (DEQ) in decisional process. The article aims to describe a model allowing users to deal with data quality and make decisions considering the quality of the data. Depending on the operational context a user can choose to improve the particular aspect of DQ or not, thus by eliminating the need to fully integrate poor data so that not to frustrate the decisions that are made based on that data.
Inrush Current Limiting of a Bridgeless AC- DC Converter

A simple inrush current limiting technique is presented here for hybrid resonant Pulse-Width Modulation bridgeless AC - DC power factor correction converter used in power supplies and battery charging units. The DC bus capacitor voltage is gradually increased to limit the charging current. As boost converter is not operated at high duty cycle, the PWM switch for gradual increase in voltage is incorporated with other PWM switches. The method used here is designed for the boost type converter but can be implemented on other type of converters as well. The technique also reduces spikes in current wave forms of traditional bridgeless AC-DC converters. The simulation is performed in MATLAB/SIMULINK. A comparison is also provided to show the effectiveness of the system.
Low-Profile Circular Patch Array Fed by Slotted Substrate Integrated Waveguide

A linear array antenna comprising circular-shaped patches fed by slotted substrate integrated waveguide is proposed. The slotted circular-shaped patch operates at the quasi-TM<sub>21</sub> mode, which produces boresight radiation with an improved gain due to the introduction of additional slot loadings in the middle space of the circular-shaped patch. Comparisons among the proposed <inline-formula> <tex-math notation="LaTeX">$1 \times 8$ </tex-math></inline-formula> linear array antenna and two other traditional linear arrays used in automotive radars are presented, highlighting the superior performance of the proposed linear array, i.e., higher gain and wider 1 dB gain bandwidth. Several key geometrical design parameters, which are critical to the performance of the array antenna, are studied and analyzed to provide a better understanding of the proposed array antenna. A <inline-formula> <tex-math notation="LaTeX">$4\times 8$ </tex-math></inline-formula> planar array antenna incorporating four <inline-formula> <tex-math notation="LaTeX">$1\times 8$ </tex-math></inline-formula> linear arrays is further designed to verify the extension capability of the proposed linear array. Both the linear array and the planar array antennas were fabricated and measured, showing a good agreement between simulation and measured results, thus validating the designs. The proposed array antenna can be a good candidate for millimeter-wave automotive radar applications.
Indoor Location Fusion Algorithm Based on Smartphone

In order to improve the accuracy of indoor localization and the convenience of the user, This paper presents a fusion positioning algorithm using the local binary patterns’ feature approach to achieve ubiquitous and accurate positioning for smartphones and tablets. It is based on loosely coupling WiFi-based positioning and image analysis, This algorithm comprised of offline stage and online stage. On the offline stage, WiFi fingerprints were collected, and a fingerprint library sampled in different positions in the coordinate system was constructed. Photos were taken in that stage to extract the image features. On the online stage, the first filter process was to determine the possible area where the user is currently by using WiFi information captured in real-time. Then a distance compensation algorithm was proposed in this paper to extract features of the real-time image taken by the user to determine the exact localized position. Experimental results show that this algorithm can effectively improve localization precision compared with traditional WiFi and image based localization methods in environments with fewer APs (Access Points) and similar layouts, thus is capable for general localization or LBS (Location-Based Service) relevant applications.
Recent Advances in 3D Data Acquisition and Processing by Time-of-Flight Camera

Three-dimensional (3D) data acquisition and real-time processing is a critical issue in an artificial vision system. The developing time-of-flight (TOF) camera as a real-time vision sensor for obtaining depth images has now received wide attention, due to its great potential in many areas, such as 3D perception, computer vision, robot navigation, human–machine interaction, augmented reality, and so on. This paper survey advances in TOF imaging technology mainly from the last decade. We focus only on recent progress of overcoming limitations such as systematic errors, object boundary ambiguity, multipath error, phase wrapping, and motion blur, and address the theoretical principles and future research trends as well.
Spatial and Capacity Optimization of Small and Medium Renewable Energy Grids for Remote Areas with Application

This paper presents an integrated economic-based framework for the spatial and capacity selection of small and medium renewable energy grids of the solar and wind types for remote areas in Egypt. The proposed framework is divided into two levels. The first level handles the analysis and modeling of small and medium micro-grids spatially distributed generation units and the second level provides a HOMER software-based optimization methodology for an assembly of various small and medium distributed generation units to fulfill different demands. Based on information about expected demand pattern and its growth in the future, two renewable small and medium energy grids configurations of different load patters are presented. Each grid is formed of several units of solar and wind generation plants. Application of the proposed framework is carried out for selected small and medium Microgrids case studies at Ain El-Sokhna region, Red Sea, Egypt. Finally, the implementation of the framework demonstrated that the approach could provide a strong multi-phasing decision tool in the energy market for determining the planned additional generation units capacities, their geographical distribution and corresponding justified purchased unit cost for each level of operation demands