Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring the Total Radiated Power of a LoRa Device in a Reverberation Chamber

This paper builds a LoRa-based IoT (Internet of Things) system, including a custom-made LoRa (Long Range) node, a LoRa gateway, and an app display page. We proposed a prototype of a low-cost, full-duplex LoRa device. The system forms a seamless coverage scene with LoRa nodes, while the LoRa master node can act as data relay to the LoRa gateway. To measure the overall performance of the LoRa device, we evaluated the Total Radiated Power (TRP) of the LoRa node in a reverberation chamber (RC). This work may provide an opportunity to compare the overall performance of the LoRa communication systems. The proposed system can be well applied to smart city, industrial and agricultural application scenarios.
Compact Wideband Antennas Based on Substrate Integrated Waveguide Using for sub-5G

Two compact wideband antennas based on Half Mode Substrate Integrated Waveguide (HMSIW) with different substrates of magneto-dielectric material (MDM) and dielectric material (DM) are presented. Both antennas have a similar structure, consisting of an HMSIW cavity, a microstrip to CPW transition feedline, as well as a leaf-type radiation slot. The metal via is employed as a perturbation to extend the bandwidth. The simulated and measured results show that the fractional bandwidths of both antennas are about 10%. Compared to the DM antenna, the bandwidth of the MDM antenna almost keeps unchanged, but the dimension is reduced about 38.5% and the peak gain is also increased from 4.1dBi to 6.14dBi.
Representation Learning for Wafer Pattern Recognition in Semiconductor Manufacturing Process

The Demand for low-power and high-performance semiconductors has rapidly increased. To improve the competitiveness of system semiconductor companies, an efficient defect management process through Automatic Defect Classification (ADC) is essential. However, as semiconductor patterns are miniaturized, defects are becoming very small. It is becoming increasingly difficult for scanning electron microscope (SEM) equipment to accurately find defects. In addition, a lot of costs is required for image collection, learning, and classification to increase defect detection accuracy. In this study, we proposed a novel volumetric representation learning to perform 3D wafer handling in the Wafer Pattern Recognition (WPR) framework. The proposed method generates a 3D representation using a structure generator and pseudo-rendering and then performs appropriate pattern recognition. The experimental results demonstrate the 3D representation shows competitive performance compared to the existing model with a performance difference of 2% to 22% based on the F-score compared to the 2D image. Also, in terms of the same cost, it is expected that it will lead to better performance if we can overcome the limitations of 2D and consider the increase in cost for 3D models. Consequently, it gives feedback to the ADC, and higher productivity in semiconductor products is expected.
Convergence rank and its applications

In this paper we explore an algorithm to determine the relevance of each item in a finite set of items in reference to each other, where in order to address an item you have to first go through a convergence or proxy item. If we imagine a media streaming company (convergence item) and all its available genres for playback (items in a finite set), how relevant is each music genre at different moments in time? Or with a sports broadcasting company and the covered sports, how does the relevance of each sport changes throughout the year as sports seasons begin and end? As the algorithm gets developed in the paper, we introduce an artificial node to the relationship graph which brings a disproportional weight in importance. Later, we show how to remove the artificial node from the final rank vector in order obtain a ranking of items in the set without any distortions. In addition to the ranking of a single point in time, the algorithm expands to analyze a sequence of consecutive convergence rankings, infers the behavior of trends and allows for the forecasting of near-future or cyclical ranks. The applications of this algorithm are immediate and plentiful in possibilities. In its essence this algorithm can help understand the usage behavior of services by its users based on non-invasive simple data collection. From its results, it is possible to better plan the allocation of resources.
The Profile Reduction of an Electronically-steerable Parasitic Array Radiator Based on Inductance Loaded Quarter Wavelength Dipole

Small-size and low-profile of the antenna play a key role in wireless mobile communication equipment. This paper designs a small-size and low-profile quarter-wave electronically-steerable parasitic array radiator (ESPAR) monopole antenna working in the WLAN band. The antenna is miniaturized by series inductors on the antenna, and capacitors are loaded on top of the antenna to adjust the impedance matching of the antenna. The simulation results show that the length of the proposed ESPR antenna is reduced to 16.7 mm, which is 46.6% lower than the length of the traditional quarter-wavelength monopole antenna. It is clear that this way can successfully reduce the size of ESPR antenna while ensuring a good impendence matching.
On the properties of $r$-excessive mappings for a class of diffusions

We consider the convexity and comparative static properties of a class of r-harmonic mappings for a given linear, time-homogeneous and regular diffusion process. We present a set of weak conditions under which the minimal r-excessive mappings for the considered diffusion are convex and under which an arbitrary nontrivial r-excessive mapping is convex on the regions where it is r-harmonic. Consequently, we are able to present a set of usually satisfied conditions under which increased volatility increases the value of r-harmonic mappings. We apply our results to a class of optimal stopping problems arising frequently in studies considering the pricing of perpetual American contingent claims and state a set of conditions under which the value function is convex on the continuation region and, consequently, under which increased volatility unambiguously increases the value function and expands the continuation region, thus postponing the rational exercise of the claim.
Dynamic characteristics of tweeting and tweet topics

Twitter, having more than 200 million world users and more than 4 million Korean users, is still growing fast. Because Twitter users can ‘tweet’ about any topic within the 140-character limit, and other users who follow the users and see the tweets can ‘retweet’ them, Twitter is regarded as a new medium of transferring and sharing information. Nevertheless, the propensities of Twitter users to tweet or to retweet still remain unclear. In order to investigate these propensities, we propose a simple model for the dynamics of the total number of tweets about specific topics. We then observe that the topics can be categorized into three kinds according to predictability and sustainability: predictable events, unpredictable events, and sustainable events. Comparing model results with real data, we infer the tweet propensities motivated by external causes as well as retweet propensities.
Compact Wideband Three-Port Metasurface Antenna Using Characteristic Mode Analysis

A compact wideband three-port metasurface (MTS) antenna is proposed by exciting three characteristic modes (CMs) of a MTS resonator. Characteristic mode analysis (CMA) is used for guiding the port placement with high isolation. With a planar size of $34.3 \times 34.3\ \text{mm}^{2}$ for the MTS, the proposed antenna achieves a measured bandwidth of 20.4%, 27.7% and 31.4% for each port, all with a least return loss and port isolation of 10 dB and 27 dB, respectively. Compared with existing multi-port MTS antennas, the proposed multi-port MTS antennas achieves the highest port densities without sacrificing port isolation, paving the way for designing extremely high-density m-MIMO antennas for the next generation of wireless networks.
Modification of Precipitation from Warm Clouds—A Review

Abstract This review is begun with a brief summary of the current status of our understanding of the physics of precipitation in warm clouds. The impact of warm-cloud precipitation processes on the evolution of the ice phase in supercooled clouds also is discussed. This is followed by a review of experimental attempts to modify the microstructure of warm clouds. Modeling studies of warm cloud modification and observational studies of inadvertent warm cloud modification also are drawn upon to further elucidate the physics of warm cloud modification. The hypotheses, and evidence, for dynamic modification of warm clouds are then discussed. A few brief comments an modeling of warm cloud processes also are given. These comments are intended to serve as a warning to the non-modeler to be very cautious in taking the results of the modeling studies at face value. Finally, the review is concluded with specific recommendations regarding the current status of warm cloud modification, and future directions for the sc.
Multi-Granularity Shadow Paging with NVM Write Optimization for Crash-Consistent Memory-Mapped I/O

The complex software stack has become the performance bottleneck of the system with high-speed Non-Volatile Memory (NVM). Memory-mapped I/O (MMIO) could avoid the long-stack overhead by bypassing the kernel, but the performance is limited by existing crash-resilient mechanisms. We propose a Multi-Granularity Shadow Paging (MGSP) strategy, which smartly utilizes the redo and undo logs as shadow logs to provide a light-weight crash-resilient mechanism for MMIO. In addition, a multi-granularity strategy is designed to provide high-performance updating and locking for reducing runtime overhead, where strong consistency is preserved with a lockfree metadata log. Experimental results show that the proposed MGSP achieves 1.1 ~ 4.21× performance improvement with write and 2.56 ~ 3.76× improvement with multi-threads write compared with the underlying file system. For SQLite, MGSP can improve the database performance by 29.4% for Mobibench and 36.5% for TPCC, on average.
Wide-Angle TM-Wave Polarization Passive Huygens' Metasurface for Gain Enhancement of Millimeter-Wave Multibeam Array Antennas

Scanning antenna array is widely used in wireless communication, satellite communication, radar detection and so on. As a research hotspot in recent years, electromagnetic metasurface has also been used in the design of scanning antenna arrays. However, there is a disadvantage in using traditional passive metasurface to improve the beam gain of the scanning array antenna, which is that the scanning range of the array antenna is narrow. To solve this key issue, in this paper, a novel passive metasurface is proposed. Its core technology includes a wide-angle TM-wave polarization Huygens' unit cell and the point-to-point phase compensation (PPC) method, which can be used for gain enhancement of the millimeter-wave multibeam array antenna.
Evaluation of Deterministic Routing on 100-cores Mesh Wireless NoC

Due to its unique capability to communicate with long-distance communication processing cores in a single hop, on-chip wireless channels are utilized to reduce the network latency between the distant processing cores. Thus, due to its CMOS compatibility and architectural adaptability, wireless network-on-chip (NoC) is envisaged as a complement to the traditional NoC, which is attractive as wireless transmission will not require a wiring infrastructure. This paper evaluates three different deterministic routing algorithms (XY, west-first, and north-last) on a 100-core mesh WiNoC architecture. There are four wireless hubs equally located for each subnet on the mesh WiNoC architecture to examine its global transmission latency, throughput, and energy characteristics. In addition, the cycle-accurate Noxim simulator is employed to carry out the simulation for the WiNoC infrastructure under test using random and transpose traffic workload distribution. Experimental results show that, under a random traffic scenario, the XY routing algorithm provides the best packet injection rate (PIR) performance at 0.013 flits/cycle/tile. However, the investigated deterministic routing algorithms show no significant performance differences under the transpose traffic, as all of the routing approaches saturated at the same PIR point of 0.007 flit/cycle/tile.
Impact of blue light irradiation on the viability of four types of human cells

Blue light therapies can be applied in treating various diseases, best represented by but limited to microbial infections. Since blue light can induce reactive oxygen species (ROS) in cells via various cellular pigments, it may have negative impact on normal human cells, while killing the target pathogens. This work aims at investigating this impact on the viability of four types of human cells, including those in lungs (represented by the A549 cell line), kidneys (represented by the 293T cell line), brain (represented by the SH-SY5Y cell line), and the immune system (represented by the THP-1 cell line). These cells were irradiated respectively by 450nm LED light sources, which is widely applied in both blue light therapies and optogenetics research. After the irradiation, the cell viability was tested by CCK-8 assay kits. The results showed significant reduction in the viability of 293T cells and SH-SY5Y cells. what’s more, with the increase of exposure time, the damage to the cells became more severe. We conclude that blue light may cause cell dysfunction through the action of ROS on DNA, and may contribute to cell death. Therefore, the dosage of 450nm blue light must be strictly controlled when used to treat diseases in kidney and brain.
18.1 A W-Band Transceiver Array with 2.4GHz LO Synchronization Enabling Full Scalability for FMCW Radar

Closing the angular resolution gap between CMOS radar and optical imaging systems can enable an entirely new cost-effective radar-centric perception solution, but requires extremely large transceiver (TRX) arrays to achieve LiDAR-like angular resolution. Multi-chip cascading of mm-wave radars [1–3] has become the norm to enable these large TRX arrays, but the size of these arrays is still limited due to challenges in achieving low-cost signal distribution across a large aperture. Today, multi-chip radar cascading solutions use mm-wave LO frequencies (20GHz [1], [2], 40GHz [3]) along with on-chip frequency multipliers with modest multiplication factors (×4 [1], [2], ×2 [3]). However, operating at these frequencies is cost-prohibitive and severely limits the size of the array [1]. For example, for a 64-TX and 64-RX array, the calculated path loss on a Rogers 3003 substrate in an H-tree distribution network reaches 87dB at 40GHz, which requires more than 70 amplifiers with 15dBm output power and 25dB gain alongside the distribution network to compensate the loss. Moreover, maintaining phase coherency required for FMCW systems is also infeasible for such amplifier implementations. Thus, enabling truly scalable TRX arrays requires signal distribution at much lower LO frequencies, which introduces fundamental performance challenges.
Evaluating Generative Adversarial Networks: A Topological Approach

Generative adversarial networks (GANs) are an approach to generative modelling using deep learning methods, such as convolution neural networks. Evaluating the performance of GANs has been a challenging task. In this paper, we will show how concepts from algebraic topology, and in particular persistent homology can be used for comparing the geometric and topological features of the latent manifold of real data with those of generated ones. We built a Vietoris-Rips complex to present persistence diagrams. As an evaluating metric between two diagrams of manifolds, we apply a framework which is a reformulation of the Wasserstein distance as an Optimal transport problem, called the WOT Distance. We compare the WOT Distance with the other topological structure metrics, Geometric score (GS) and Topological Distance (TD) on various data sets. Evaluation results demonstrate that our method achieves superior performance in GANs learning.
CMRCV: Causal Modeling to Localize Failed Equipment by Representative Nodes and Contribution Values

Researchers have proposed causal model-based methods to localize failed equipment in an information and communication technology (ICT) system using various kinds of data. However, expert knowledge is needed to use these methods for constructing the causal model and determining the threshold for inputting data into the existing methods. In this paper, we propose CMRCV, an Causal Modeling method, which constructs the causal model only from the ICT system topology without expert knowledge. We introduce Representative nodes, which aggregate the status of all data collected from the corresponding causal model and use Contribution Values of an anomaly as input, which indicates the degree of the contributions of each data to the failed equipment. The experimental results show that CMRCV improves the F1 score by approximately 25.8% at most and 13.1% on average compared with the existing method.
High Gain and Low RCS Huygens' Metasurface Lens Antenna

In this paper, we propose a high gain and low RCS Huygens' metasurface lens(meta-lens) antenna, which is composed of a combination of a horn antenna and a Huygens metasurface. The Huygens' metasurface consists of two same metallic structures that are antisymmetrically etched on the upper and lower sides of the F4B dielectric substrate. Compared with the multilayer frequency selective surface, the proposed Huygens' metasurface can provide a larger transmission phase-shift range which can be used to realize thin and high-performance focusing meta-lens with low RCS in broadband.
Two-Dimensional Scattering Center Extraction Algorithm based on Continuous Dictionary

In this paper, Alternating Direction Method of Multipliers (ADMM) is used to speed up the SDP problem in the scattering center extraction algorithm based on continuous dictionary. the continuous compressive sensing technique is applied to the estimation of the scattering center parameters of the Two-Dimensional metal GTD model. On the basis of the One-Dimensional scattering center extraction, Atomic Norm Minimization and Genetic Algorithm are combined (ANM-GA), which can accurately extract the scattering center parameters. For large size targets, ADMM is proposed, which uses partial eigenvalue decompositions to solve positive eigenvalues and eigenvectors. The complex convex optimization problems can be transformed into iterative solutions of several small convex optimization problems. Thus, the efficiency of solving the problem is accelerated.
Simulation Investigation of Trenched Silicon Carbide Super-junction Schottky Diodes

In this article, a two-dimensional finite element simulation tool Silvaco TCAD was used to simulate the structure of trenched silicon carbide (SiC) super-junction Schottky diodes. The effects of key structural parameters, including mesa width, trench sidewall angle and trench depth on the electrical performance of the device were studied. On this basis, the forward conduction and reverse blocking performance of the device structures with different parameter combinations were simulated. The simulation results show that larger mesa width and smaller trench sidewall angle are more beneficial to obtain lower specific on-resistance. Larger trench sidewall angle and larger trench depth are more beneficial to obtain high blocking voltage.
Transferring Indoor Corrosion Image Assessment Models to Outdoor Images via Domain Adaptation

Corrosion of materials impacts critical economic sectors from infrastructure, transportation, defense, health, to the environment. The development of safe anti-corrosive materials is thus an important area of study in materials science. Corrosion science of preparing materials and then monitoring their corrosion under adverse conditions is labor intensive, time consuming, and extremely costly. While deep learning has become popular in automating various engineering tasks, the development of deep models for corrosion assessment is lacking. We are the first to study deep domain adaptation (DA) models for the automated assessment of the corrosion status of anti-corrosive materials. Corrosion data, i.e., photographic images of treated corroding materials, is abundant when produced in artificially controlled laboratory settings, while corrosion image data sets from rich natural outdoor environments are more challenging to produce and thus much smaller. We leverage the more readily available indoor corrosion data to train a classifier and then transfer it via deep domain adaptation to also perform well on the small yet more realistic outdoor corrosion image data set – without requiring target labels. We empirically compare 5 popular domain adaptation models on real-world corrosion image data sets. Our study finds that DA achieves 27% improvement in test accuracy compared to the performance of the no-DA baseline for classifying real-world outdoor corrosion data.
A High Isolation Dual-Linearly Polarized Ultra Thin Transparent Patch Antenna with Metal Mesh for Wearable Glasses

In this paper, a high isolation dual-linearly polarized transparent patch antenna with metal mesh is designed at 60 GHz. A single meshed patch element is excited by direct feeding for vertical polarization, and slot-coupling feeding for horizontal polarization. The simulation results demonstrate that the proposed antenna has a good TX/RX isolation, which reaches 43 dB @ 60.1 GHz, and the realized gains for both TX and RX ports are up to 7.3/7 dBi @ 60 GHz. Owing to the merits of high transparency and ultra thin characteristics of the applied printed metal grid transparent conductive film, the proposed transparent antenna could be integrated on wearable glasses without blocking sight, and provides hardware equipment foundation for the application of drivers' fatigue detection in radar sensing.
Investigating Community Detection in Arabic Scholarly Network Using Ontology-based Semantic Expansion

Clustering researchers in communities is an important task to support a range of techniques for analyzing and making sense of the research environment and helps re-searchers find people in the same field of interest to collaborate. In computer science, ontology is commonly used to capture knowledge about a particular area using relevant concepts and relations. This study investigates the use of overlapping community detection algorithms on a multilayered Arabic scholarly network to detect communities of researchers who share their research interests. Two researchers can share an interest if they co-authored a publication or share some keywords in their publications. The set of keywords is expanded via semantic search within a cross-domain ontology, e.g. DBpedia, allowing more researchers with indirect relationships to be connected. A 2-layer scholarly network was constructed by retrieving the scholarly data of faculty members from three colleges at Umm AlQura University (UQU) with rich Arabic publications. Four versions of this network were tested: unweighted, weighted, semantically expanded, and reduced semantically expanded. It was found that weights have an insignificant role in community detection within this study. In addition, a semantically expanded network does have better clustering potentials but only if was performed selectively. Otherwise, the expanded network might suffer from generic and non-discriminative keywords, making the community detection task more challenging. To our knowledge, this is the first investigation into detecting communities within an Arabic scholarly network.