Miracle-dz/UniFAR-FTUs huggingface dataset:
Oxygen reduction reaction on active sites of heteroatom-doped graphene

With first-principle DFT calculations, the catalytic activity of heteroatom-doped carbon nanostructures in oxygen reduction reaction is investigated by exploring the active site of B-doped, N-doped and (B, N)-codoped and analyzing the kinetic pathways of oxygen reduction with the participation of protons. It is found that the heteroatom-doped graphene can become the effective catalysis materials for ORR with four-electron pathway. Especially, the formation of epoxide groups may be important for the four-electron processes on B-doped and (B, N)-codoped graphene. By the analysis of charge redistribution, the formation of active catalytic sites is attributed to the localized positive charge and electronic dipole induced by the dopant.
A Compact Network Using Attention Mechanism for Image Super Resolution

With the emergence of the 2K / 4K Ultra High-Definition (UHD) videos super-resolution, single image super resolution based on convolutional neural networks (CNNs) is a critical method to transfer Standard Definition (SD) videos to High Definition (HD) videos. However, the state-of-the-arts methods rely on a deeper or wider network, which poses a challenge in computational complexity and memory consumption. To address this problem, we proposed a compact super-resolution framework using refined features. In addition, an attention channel sorted split (ACSS) module was proposed to significantly improve the performance of super-resolution (SR). Especially, the ACSS module can make the network pay more attention to transfer features from coarse to fine. The experimental results show that our proposed method is efficient and effective.
An Intelligent 3D Placement Methodology for Drone Networks

Today, it is hard to assume the difficulties of providing network communication over environments that include changeable and mobile nodes. In that kind of situation, traditional applications of cellular networks do not keep up with this unpredictable and mobile environment. Drones are the most preferred technologies that are useful with their characteristic features for that kind of environment. Our goal is to provide network connectivity by placing drones without including any terrestrial base stations in this paper. To set and optimize the number of used drones in related environments, we propose a heuristic method. Finding 3-D placement with the optimum number of used drones, we aim to serve and cover the target percentage of users. And to verify our optimization algorithm, which provides the 3-D placements of drones, we use a simulation tool ns-3 to create network environments that include mobile users and aerial base stations. Our simulation results are based on users' network requirements. We verify that our optimization approach with the heuristic method satisfies the users according to their QoS needs.
Effect of Millimeter Waves on Genome Architecture and the Transcriptome of Primary Human Fibroblasts

Millimeter waves (MMWs) are known to induce a range of biological effects, although the causes remain largely unknown. We exposed primary human fibroblasts to MMWs and observed changes in the genome architecture, with modified DNA secondary structure formation effecting gene expression and subsequent protein production. This work establishes the potential for MMWs to induce targeted modifications to the chromatin landscape for therapeutic applications.
A Distributed Storage Method for High Speed Data Transmission System

In some high-speed transmission communication systems, a great quantity of data require to be stored because of their computing power and post analysis evaluation. This makes the reliable storage of large amounts of data become an important issue. In this paper, author generalizes a coding construction method for distributed storage systems through considering a lot of protection-example. At the same time, the theoretical basis, principle and architecture of the coding method are introduced. The analysis shows that this distributed storage method can save 50% repair bandwidth of system nodes.
Open-Source MQTT Evaluation

MQTT is a lightweight publish-subscribe protocol used in the Internet of Things. Its popularity leads to several implementations in different languages. In this paper, we evaluate the most popular open-source MQTT implementations, Mosquitto, HiveMQ, EMQX, VerneMQ, MQTT.js and Paho. Our evaluation includes interoperability, resource consumption and latency. We create a generic test framework independent of any MQTT implementation or language. According to our interoperability results, major client and server implementations conform to the base requirements of the standard and thus can interoperate. The language of implementation is a crucial factor for resource consumption and causes considerable differences in scalability. Our results show that latencies between implementations in lossy networks differ. Overall, there is a trade-off between resource consumption and network latency.
Inter-Datacenter Multicast with Store-and-Forward in Software-Defined Optical Networks

Emerging online services have fueled unprecedented demands for multicast transfers across geo-distributed datacen-terse However, the time- and space-varying nature of background traffic in inter-datacenter networks (inter-DCNs) makes conventional multicast trees difficult to fully utilize the residual bandwidth in the inter-DCNs. In this paper, we introduce datacenter storage into multicast transfers, and propose Store-and-Forward-Tree (SnFTree) scheduling method for multicast transfers in the software-defined optical network. Compared to the conventional multicast scheduling methods, the advantages of SnFTree are as follows: (i) by enabling temporary storage at intermediate sites, SnFTree can segment the conventional multicast tree into independent subtrees, which maximizes the flexibility of provisioning; (ii) by introducing load-balancing routing into the tree computation, SnFTree can balance the traffic load across network efficiently; (iii) benefiting from optical multicast, storing, forwarding and reception can be simultaneously implemented on a site with negligible processing delay; (iv) by formulating the multicast scheduling problem into a routing problem, SnFTree greatly simplifies the problem for dynamic traffic. Simulations demonstrate that compared to conventional scheduling methods, SnFTree obtains lower blocking probability and requires fewer OpenFlow Agent Updates.
Resonant Frequency Modeling of Microstrip Antenna Based on Deep Kernel Learning

When modeling and optimizing electromagnetic components, it is the most time consuming for obtaining the training samples with labels from full-wave electromagnetic simulation software. The traditional machine learning (ML) model is usually effective in the training process, unfortunately, its generalization ability is limited for practical application. Inspired by the artificial neural network (ANN) and the Gaussian process (GP) kernel learning model, a deep kernel learning (DKL) model with multiple-nonlinear-mapping layers is proposed based on particle swarm optimization (PSO) algorithm. In this work, correlation characteristics of training samples are transformed by multiple layers, and then they act as inputs of the GP model. Simultaneously, we use PSO to optimize the weights, biases of each mapping layer, and GP hyperparameters, aiming to determine and optimize the DKL model structure. As a result, the proposed DKL is suitable for processing small samples, as well as has the generalization ability of a deep network, which avoids the phenomenon of gradient disappearance to some extent caused by error back propagation (BP) in the deep network and improves the modeling accuracy while effectively ensuring the modeling efficiency. In this study, the performance of the DKL model is evaluated by using the resonant frequencies of two microstrip antennas (MSAs), and the predicted results of the DKL model are compared with those via different modeling methods. The results show that prediction accuracy of the DKL is 18.906% higher than that of the GP, 38.926% higher than the ANN, and 46.660% higher than the neural network ensemble (NNE).
Experimental Demonstration for Indoor Localization Based on AoA of Bluetooth 5.1 Using Software Defined Radio

The new direction-finding feature incorporated in version 5.1 of the Bluetooth Core Specification accelerates the development of applications in the context of the Internet of Things (IoT) for indoor localization and asset tracking. We evaluate the position of the Bluetooth Low Energy (BLE) transmitter based on the angle of arrival (AoA) mechanism proposed by the Bluetooth standard using Software Defined Radio (SDR). The system is comprised of a phased array antenna, an analog section for the conversion of Radio Frequency (RF) signals to the baseband, followed by a digital section for AoA estimation. The experimental results validate the proposal with precise estimates and a high resolution for the AoA. The proposed approach can be used for digital beamforming to increase the operating range or decrease the level of power consumed by BLE devices in IoT applications.
Ultra-high performance millimeter-wave oscillator and metrology

We report a new class of millimeter-wave oscillators spanning 250 GHz to 400 GHz. Based on photomixing of two optical lines generated by a 75 m long fiber-ring cavity, record low power spectral density of phase noise is achieved reaching − 118 dBc/Hz at 1 MHz Fourier frequency for a 300 GHz millimeter-wave carrier. Together with a state-of-the-art oscillator we have implemented experimental tools to characterize the noise performance of ultra-low noise millimeter-wave oscillator to unprecedented levels, without the need for a microwave local oscillator.
CMOS Camera-Type THz Compact Antenna Test Range for Far-Field Radiation Pattern Analysis

This paper presents a CMOS camera-type THz compact antenna test range (CATR) to facilitate a convenient means to measure far-field radiation patterns of transmitters (TXs). For concept validation, we measured the far-field radiation pattern of a 26-dBi 0.852-THz standard gain horn antenna-type TX with known far-field characteristics with the THz CATR. With the help of geometrical multi-frame super-resolution imaging, the directivity determination accuracy achieved at a peak signal-to-noise ratio (PSNR) of 34.8 dB is 0.2 dB. Further, we investigated a THz continuous-wave (CW) photomixer TX with unknown far-field characteristics at 0.852 THz. It emits a higher-order mode far-field radiation pattern, and its directivity is 25.4 dBi, determined at a PSNR of 31.8 dB.
The Interactive Ward Round Table: A Cognitive User Interface for Multi-Touch Tables to Support Clinical Diagnosis

With the Interactive Ward Round Table, we developed an advanced prototype of a patient record system for multi-touch tables that is aimed to support clinicians in making diagnoses. This is a complex task that requires clinicians to integrate critical information across multiple medical documents. The incorporated features of our user interface (UI) are thus based on research findings on how readers construct an integrated understanding from multiple documents. Specifically, previous psychological and human-computer interaction research suggests that readers’ integrated understanding can be supported by facilitating comparison and corroboration of information across documents. Because the features of the UI are aimed to support these cognitive processes, we refer to it as a cognitive UI. In this paper, we motivate the core features of the UI, namely (1) a simultaneous presentation of multiple documents on screen, (2) relevance marking of full documents or text within a document (i.e., highlighting), (3) an interactive timeline enabling temporal filtering of documents, (4) a keyword search, (5) categorical filtering of documents, and (6) notifications of newly added documents. Further, we present a user study with N = 11 clinicians, which indicates a greater satisfaction with and better usability of the Ward Round Table compared to their system currently in use. Finally, we discuss additional qualitative results of the user study and possible implications for UIs used in hospitals. (Abstract)
A Low-Profile Broadband High-Gain Mechanically Pattern Reconfigurable Antenna

This paper presents a low-profile, broadband and high-gain mechanically pattern reconfigurable antenna, which is composed of two dielectric substrates stacked up and down. A periodic square patch is arranged on the top surface of the first dielectric substrate which is to broaden the antenna bandwidth without affecting its low profile characteristics. The rectangular microstrip feedline arranged on the bottom surface of the second dielectric substrate is fed through the rectangular excitation gaps printed on the metal ground plate on the top surface of the second dielectric substrate. The antenna with the overall size of 1. 90λ0x1. 50λ0x. 04λ0 (λ0is the free-space wave-length at 3.8 GHz). By moving the position of the dielectric substrate, and then changing the position of the antenna feed point, thereby changing the current distribution, the radiation pattern of the antenna can be reconfigured, which solves the problems of fixed antenna radiation pattern performance. Meanwhile, the profile of the antenna is extremely low, which makes it suitable for wireless communication.
Knowledge-Driven Framework for Designing Visual Analytics Applications

Machine learning and data analysis are becoming an essential part of the decision-making process in modern organizations. Even though new and improved analytics algorithms are developed frequently, organizations are struggling to develop analytics applications that can stay up-to-date with changing business requirements and technology innovations. The rapid development of ad-hoc programs to conduct machine learning tasks at hand has resulted in creating more expenses and efforts in the long term, a phenomenon referred to as technical debt in literature. This paper addresses the technical debt associated with data analytics applications by proposing a knowledge repository that captures analytics-related knowledge, which can be developed and maintained separately from the organization’s IT infrastructure and used to design analytics applications with visual interfaces. This way, organizations can develop dynamic and adaptable analytics applications with easy-to-follow front-ends and can accommodate new data sources or machine learning models. We evaluate the proposed approach by conducting a case study that develops an application for the acquisition and management of high-frequency financial market data.
All-Silicon Terahertz Components Towards Efficient Integrated Systems

Efficient integrated systems can be considered as a next milestone for practical applications of terahertz technology. Various types of integrated platforms have been directly scaled from either the microwave or optical domain, with benefits and drawbacks. Here, we discuss a concept of all-silicon components based on our recently proposed effective-medium-clad dielectric waveguides. The concept prioritizes bandwidth and efficiency, much needed in the terahertz domain. Auxiliary factors include cost and structural simplicity, which are also accounted for in this context. We demonstrate two generic components, namely filters and polarization splitters, that are fundamental to integrated systems.
Inverse Optimal Robust Adaptive Controller for Upper Limb Rehabilitation Exoskeletons With Inertia and Load Uncertainties

We propose a robust adaptive controller for the safe and accurate trajectory tracking control of upper limb rehabilitation exoskeletons. The proposed controller can adapt to the inertia and load uncertainties, and compensate for their effects. The <inline-formula><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> robustness of the controller in <inline-formula><tex-math notation="LaTeX">$l_2$</tex-math></inline-formula> perturbation/disturbance attenuation is realized by nonlinear robust control theory via inverse optimality. We mathematically prove the asymptotic stability and optimality of the controller by stabilizing a Lyapunov function and minimizing a meaningful cost function, respectively. We then demonstrate the performance of the controller with the simulations of two different exoskeleton control systems. The results show that the controller can identify and compensate for model uncertainties, and realize good tracking performance in the presence of perturbations and disturbances. These qualities are crucial to the reliability and safety of exoskeleton operations. In addition to rehabilitation exoskeletons, the proposed framework can also be applied to the control of other multibody robotic systems.
A Novel Data Calibration Method for MIMO Near-Field Millimeterwave Imaging in Human Security

MIMO array is a very effective millimeter wave imaging system which can be used for human security inspection. Due to the inconsistency between the equivalent phase center and the actual transmit-receive antenna positions, we need to calibrate the original receiving data so that we can use FFT imaging method for fast reconstruction. In this paper, a data calibration method is proposed. According to the actual MIMO array, multiple calibration reference centers are selected to calibrate the near-field data, and then the FFT method is used for fast image reconstruction. Simulation results show the effectiveness of the method.
Metamaterial-Based Antenna Performance Enhancement for MIMO System Applications

This paper presents a method for designing a new two-element ultra-wideband (UWB) multiple-input-multiple-output (MIMO) antenna. We propose the design of an ultra-wideband antenna has been proposed with a study of its properties. An isolation technique using well-designed metamaterials is presented to achieve the miniaturization and performance of the two antennas and ensure proper operation of the MIMO system with a very small spacing between radiating elements (<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>/12) where <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> is the length waves in a vacuum. The metamaterials used considerably reduce the coupling of multi-antennas. The application of these materials called split-ring resonator (SRR) on the antenna patch plane has contributed to the increase in the performance of the antenna studied in terms of S parameters, multiplexing efficiency, diversity gain (DG), radiation properties and envelope correlation coefficient (ECC). This increases the transmission rate. This antenna system offers advantages such as reduction in weight and bulk, which facilitates its integration into a receiver, dedicated to 5G and connected objects.
SIW Dual-Mode Dual-Band Balanced Filter With Closely Spaced Passbands

In this paper a substrate integrated waveguide (SIW) dual-mode dual-band (DMDB) balanced filter (BF) with closely spaced passbands has been proposed. The first differential-mode (DM) passband can be achieved by the TE401 mode in SIW rectangular cavities (RCs) and the second can be obtained by the TE202 mode. The two bandwidths could be realized over a wide range of ratio by applying the design parameters ratio technique, the desired coupling coefficients of both passbands are achieved by properly setting positions and size of the coupling windows which are opened on the adjacent RCs with common post-wall. A third-order direct-coupled DMDB BF operates at 15.97GHz and 18.31GHz with respective fractional bandwidth (FBW) of 1.7% and 2.5%, and the center frequency ratio is approximately equal to 1.147. The simulation results show good performance of the DMDB BF.
AI-Driven Partial Topology Discovery Algorithm for Broadband Networks

Complete topology discovery is the most important MAC service in broadband networks but it holds spatial and temporal complexities for the network-wide, i.e in the data link layer it requires considerable time amount to update all link status management information. Moreover, many service providers are complaining about high operational time and resource usage in the complete topology discovery process. Additionaly, consuming high resources leads to a huge amount of management traffic on the links. At this point, partial topology discovery arises as an alternative solution as a more efficient MAC service for the next generation broadband networks to reduce complexities and maintain smooth functioning. However, manual execution of partial topology discovery leads to risky situations for the network arising from human intervention. Therefore, in this paper, we propose an AI-driven partial topology discovery approach to maintain a global MAC service which serves both physical and virtual connections in a broadband network. Besides, with this approach, we not only preserve the network resources but also have the ability of forecasting the device-based network statistics. For this aim, we use Hidden Markov Model in order to estimate the path to be discovered regarding the arrived log patterns of the devices. Thanks to the partial path estimation, we eliminate the usage of every node in the discovery and achieve up-to-date topology information more rapidly. Consequently, according to our simulations, we succeed in a significant reduction in the number of nodes used by 60%, required time to have up-to-date topology by 35%. And finally, as a consequence of using less amount of nodes, we reduce the management traffic on the links on average 50%.
Research Progress on Benign and Malignant Lung Nodule Classification Based on Deep Learning

Lung cancer is a malignant tumor with extremely high morbidity and mortality. The early classification of benign and malignant lung nodules is of great significance for the lung cancer treatment. In recent years, the application of deep learning on the classification of benign and malignant lung nodules has become a new development trend. This article reviewed the latest research in this field in two-dimensional and three-dimensional deep learning, compared the model performances and analyzed the research trend in the next sten.