Miracle-dz/UniFAR-FTUs huggingface dataset:
Terahertz Wireless Communications: Recent Developments Including a Prototype System for Short-Range Data Downloading

Nowadays, it is hard to imagine daily life without connecting to cellular communications or wireless local area networks. People are not just accessing digital information in real time but also watching movies and enjoying multimedia data streaming services even in vehicles traveling at high speeds. Technically, contemporary wireless communications systems rely primarily on massive signal processing for orthogonal frequency division multiplexing and channel estimation/calibration, which has resulted in dramatically enhanced spectral efficiency and channel reliability. As society evolves toward a more data- and network-oriented world, data consumers and providers are seeking more advanced communications systems that offer larger data capacity. Therefore, a top priority is to find ways to overcome the limits on capacity due to narrow bandwidths. It is not easy to define the target throughput that should be accommodated in the next decade, but considering the new radio 5G cellular system offering up to 10 Gb/s, the huge spectral resources provided by submillimeter or terahertz (THz) wavebands are attracting a great deal of attention from research communities for future wireless communications systems [1], [2].
Optimal Operation of a real Power Hub based on PV/FC/GenSet/BESS and Demand Response under Uncertainty

Due to global concerns about environmental issues, renewable energy sources (RESs), such as photovoltaics (PV), Fuel Cells (FC), and wind turbines, play an outstanding role in energy production. Also, other devices are needed to achieve a smart grid. First of all, a Microgrid (MG), as a smart grid, must be equipped by the Battery Energy Storage System (BESS) due to its astonishing advantages, such as providing power when RESs are not generating power, power quality improvement, RES integration facilitation, and financial benefits. Secondly, distributed generators should be installed to reduce dependency of MG on the main grid. In this paper, a cost-based mathematical optimization is used to evaluate the optimal amount of imported power from the main grid to the LAMBDA lab MG testbed, which is placed at Sapienza University of Rome. In this regard, this study considers four scenarios based on using different source, including PV array, FC and main grid, for load satisfaction. The LAMBDA lab is considered as a power hub with three optional inputs and an electrical demand in output. In addition, this study considers PV production and load demand as indeterministic parameters and evaluates the problem under uncertainties. As a result, a robust optimization problem is defined, and a powerful optimization function is used to reach the optimal power received from main grid.
Identification of Low-Frequency Oscillation Modes Using PMU Based Data-Driven Dynamic Mode Decomposition Algorithm

Power system inter-area oscillations curtail the power transferring capabilities of the transmission lines in a large interconnected power system. Accurate identification of dominant modes and associated contributing generators is important to avoid power system failures by taking appropriate remedial measures. This paper proposes a multi-channel Improved Dynamic Mode Decomposition (IDMD) algorithm-based modal analysis technique using Synchrophasors measurement. First, a reduced-order dynamic power system model is estimated and using this model dominant oscillation modes, corresponding modes shapes, damping ratio, coherent group of generators, participation factors are determined. To improve the accuracy data stacking technique is used to capture detailed information of the system. An optimal hard threshold technique is utilized to select the most optimal model order to avoid uncertainties due to the presence of high level of measurement noise. The study results show that the proposed algorithm gives an accurate and robust solution even in systems having high level of noise in the measurement data. The performance of the proposed technique is tested on simulated data from two-area four-machine system and wNAPS 41-bus 16-generator system with PMU measurements corrupted with different levels of measurement noise. To further strengthen the viewpoint, the proposed method is validated on real-time PMU measurement from ISO New England data to validate the accuracy of the proposed work.
Small-Scale Indoor Channel Measurements at 24 GHz on a University Campus

In this paper, small-scale indoor channel measurements at millimeter wave frequencies are conducted at the American University of Sharjah campus in the United Arab Emirates. The measurements are performed using a wideband correlation-based channel sounder, which uses a 250 MHz bandwidth signal modulating a 24 GHz carrier. Different indoor scenarios are considered to examine the effect of the environment on the channel characteristics. The measurements are analyzed to obtain the channel's power delay profile, from which various parameters are calculated such as the average excess delay, the delay spread, and the number of resolvable multipath components.
Wideband SIW Half-Mode/Quarter-Mode-Fed Microstrip Patch Complementary Antennas With Back Radiation Suppression

Complementary sources concept and half-mode/quarter-mode substrate integrated waveguide (HMSIW/QMSIW) technology are introduced in this paper to improve the back-radiation suppression and bandwidth of microstrip patch antennas (MPAs). The HMSIW cavity as the feeding structure brings an additional resonant frequency to the inherent resonant mode of typical MPAs, which efficiently extends the bandwidth of the MPAs. In addition, while stimulating the MPA mode, the HMSIW cavity drives a monopole by slightly breaking the shorting wall of the HMSIW cavity and loading a printed monopole. The loaded monopole and the equivalent magnetic currents of the MPA construct an orthogonal complementary source model that indicates back radiation suppression. A differential feeding scheme is also employed to enhance the radiation performance. The dual-polarized model, achieved by replacing the HMSIW cavity with QMSIW cavities, is also addressed and even fabricated and tested. The measured results show a differential impedance bandwidth of 15.4% from 1.91 GHz to 2.23 GHz with a differential port-to-port isolation of 38 dB. In the operating band, the average gain is 7.5 dBi, and the front-to-back ratio (FBR) is over 18 dB with a maximum value of 34 dB.
Gridless Channel Estimation for Hybrid mmWave MIMO Systems via Tensor-ESPRIT Algorithms in DFT Beamspace

In this paper, we present a gridless channel estimation algorithm for a hybrid millimeter wave (mmWave) MIMO-OFDM system assuming a frequency-selective channel. The proposed algorithm is based on the Tensor-ESPRIT in DFT beamspace algorithm framework. First, we derive the <inline-formula><tex-math notation="LaTeX">$R$</tex-math></inline-formula>-dimensional (<inline-formula><tex-math notation="LaTeX">$R$</tex-math></inline-formula>-D) Standard/Unitary Tensor-ESPRIT in DFT beamspace framework and its analytic performance. We show that ESPRIT-type algorithms in a reduced-dimensional DFT beamspace can provide a significant performance gain over ESPRIT-type algorithms in full DFT beamspace and in element space under mild conditions. Afterwards, we develop a gridless channel estimation algorithm that is based on 3-D Tensor-ESPRIT in DFT beamspace algorithms. Numerical simulation results show that the proposed channel estimation algorithm can provide accurate channel estimates using only a few training resources.
Not Taken for Granted: Configuring Scalable Live Video Streaming Under Throughput Fluctuations in Mobile Edge Networks

We consider end-to-end live video streaming in mobile edge networks with fluctuating but predictable throughput. We employ scalable video coding (SVC) to adapt the bitrate of each frame to the throughput evolution in a timely manner. When streaming SVC-encoded live video, the video source should dynamically derive and configure a set of coding parameters for each upcoming chunk, which consists of a number of consecutive frames. The coding parameters quantify the throughput evolution of an upcoming chunk by providing a few bitrate options for the frames within this chunk to encode with. Unlike the majority of existing works that simply treat the coding parameters as given (i.e., taking them for granted), in this paper, we derive the coding parameters from a comprehensive mathematical modeling and optimization perspective. Our approach consists of an optimization problem that captures the fundamental trade-off in determining the coding parameters and a customized search algorithm based on the analysis of the optimization problem's solution structure. Numerical results produced by the NS-3 network simulator show that our approach outperforms the state-of-the-art benchmarks.
Empowering Knowledge Graph Construction with Hyper-graph for Personalized Recommendation

Personalized Recommendation (PR) aims to generate user-specific items in response to users' preferences. While existing works have developed various personalized recommendation methods which are effective to some extent, they require setting the explicit and implicit feedback between users and items. In contrast to traditional Collaborative Filtering (CF)-based approaches that suffer from sparse data and cold-start issues, our proposal of mining the higher-order auxiliary information between users and items by combining the knowledge graph (KG) construction with hyper-graph, which is more expressive and semantically plausible. To address these issues, we propose employing hyper-graph to empower knowledge graph construction to express and integrate much richer semantic information for improving the performance of recommendation, namely Hyper-graph based Personalized Recommendation (HPR), for the personalized recommendation task. Our model consists of two components. First, we employ the hyper-graph to model the higher-order relations between users, through the calculation and analysis of the user-item interaction matrix, the users with the highest similarity are seemed as hyper-edges to construct the user hyper-graph. Second, we propose to use knowledge graph to capture and express auxiliary information for recommendation. The two components are integrated in a principled way for returning a more accurate result. Our experimental results on a MovieLens-lM and another Book-Crossing datasets suggest that our proposed HPR framework provides a promising improvement for the personalized recommendation task, and significantly outperforms the state-of-the-art methods. Moreover, the component for mining higher-order relations by hyper-graph is so flexible that can help to improve the performance of KG-based personalized recommendation models significantly,
Cooperative Tracking Control of Multi-Agent Systems with General Linear Dynamics

The cooperative tracking control of multi-agent systems with identical, general linear dynamics is investigated. Based on the availability of information about the state of an autonomous exogenous system, the agents are divided into leaders and followers. The distributed protocol is devised such that through combination of absolute and relative measurements, the followers achieve zero tracking error. The role of the leaders is to inject the proper control input into the followers’ network. For achieving stability, a simple rule for choosing the feedback gain is provided. A numerical example with simulations is given to demonstrate the effectiveness of the proposed solution.
A Stochastic Local Search Algorithm for the Partial Max-SAT Problem Based on Adaptive Tuning and Variable Depth Neighborhood Search

The Partial Max-SAT (PMSAT) problem is an optimization variant of the well-known Propositional Boolean Satisfiability (SAT) problem. It holds an important place in theory and practice, because a huge number of real-world problems, such as timetabling, planning, routing, bioinformatics, fault diagnosis, etc., could be encoded into it. Stochastic local search (SLS) methods can solve many real-world problems that often involve large-scale instances at reasonable computation costs while delivering good-quality solutions. In this work, we propose a novel SLS algorithm called adaptive variable depth SLS for PMSAT problem solving based on a dynamic local search framework. Our algorithm exploits two algorithmic components of an SLS method: parameter tuning and neighborhood search. Our first contribution is the design of an adaptive parameter tuner that searches for the best parameter setting for each instance by considering its features. The second contribution is a variable depth neighborhood search (VDS) algorithm adopted for PMSAT problem, which our empirical evaluation proves is a more efficient w.r. t. single neighborhood search. We conducted our experiments on the PMSAT benchmarks from MaxSAT Evaluation 2014 to 2019, including more than 3600 instances which have been encoded from a broad range of domains such as verification, optimization, graph theory, automated-reasoning, pseudo Boolean, etc. Our experimental evaluation results show that AVD-SLS solver, which is implemented based on our algorithm, outperforms state-of-the-art PMSAT SLS solvers in most benchmark classes, including random, crafted, and industrial instances. Furthermore, AVD-SLS reports remarkably better results on weighted benchmark, and shows competitive results with several well-known hybrid PMSAT solvers.
A modified framework based on LSTM-FC for wind turbine health status prediction

The wind turbine is a complex system which may shut down and result in huge economic lost due to component failures. The health status prediction of wind turbines can provide an important guidance and basis for the preventive maintenance of wind turbines, reduce maintenance costs and unplanned downtime, and improve the productivity and economic benefits of wind turbines. Aiming at the problem of wind turbines health status predication, this paper uses Long Short-Term Memory (LSTM) combined with Full Connected Networks (FC) to construct a network model. The LSTM is used to effectively extract the information from time-series data, and then the FC integrates the extracted information and completes the state classification to predict the wind turbines health status at the next moment. The proposed model was tested using the data of a wind power station in the north of China in 2019. The results show that the proposed method has a high classification accuracy compared to other state-of-the-art algorithms, which proves the effectiveness of the proposed model. Moreover, the proposed method can effectively predict the wind turbine health status in the next two hours, play a role in predictive maintenance, and has strong practical significance.
Optimal Dispatch Model of Active Distribution Network Based on Particle Swarm optimization Algorithm with Random Weight

A multi-objective optimal dispatching model of active distribution network is established. The objective function of the model considers both economic cost and environmental cost in this paper. The constraint conditions are constituted by the operation conditions of each distributed generation and power balance conditions. The particle swarm optimization algorithm with random weight is used. Compared with the conventional PSOA, the particle swarm optimization algorithm with random weight can effectively improve the global optimization ability and the convergence performance. It is verified by simulation that the proposed model and the adopted algorithm has achieved good results.
A SiGe Power Amplifier With Double Gain Peaks Based on the Control of Stationary Points of Impedance Transformation

A broadband SiGe power amplifier (PA) with double gain peaks for 5G applications is presented. The differential structure is widely adopted in millimeter-wave circuit design to decrease the impact of the source parasitic inductance that is induced by the bonding wires. Since differential PA results in a higher impedance transformation ratio of the matching network, differential PAs generally have narrower bandwidth than single-ended PAs. Through the bandwidth analysis of the matching network, the transformer used for interstage matching is elaborately designed to ease gain fluctuations. By controlling the stationary points of impedance transformation, the maximum available gain characteristic of a transistor, which decreases with increasing frequency, can be compensated. The gain curve of PA, thus, has double gain peaks within operational frequencies to make its frequency responses as flat as possible. The proposed PA for the frequency band from 27 to 37 GHz is fabricated in a standard 130-nm SiGe BiCMOS process, which occupies 0.36 mm2. Under the 2.5-V voltage supply, the SiGe PA exhibited a power gain of 33.5 dB and output power of 21.3 dBm with 24% power-added efficiency at 31 GHz. It achieves a 34% 3-dB small-signal $S_{21}$ bandwidth from 27.3 to 38.5 GHz.
Readiness, Challenges, and Satisfaction in the New Paradigm of Teaching and Learning: An E-Learning Evaluation

Various educational institutions around the world have undergone a paradigm shift from onsite to online teaching-learning due to the COVID-19 pandemic. The shift has propelled both educators and learners to adapt to technology in a new learning environment. Educational institutions have likewise partnered with different tech companies for instructional delivery. This paper intends to investigate the teachers' readiness in delivering e-learning; the depth of learners' e-learning experience; the challenges encountered in e-learning, and the satisfaction of teachers and learners on the practices implemented using various e-learning dimensions. The study reveals that online teaching experience, readiness in implementing e-learning, practices on various e-learning dimensions and challenges encountered in e-learning have a positive impact on the evaluation of e-learning effectiveness. Further, online learning experience and implementation of e-learning satisfaction have positive relationships to the evaluation of e-learning effectiveness. In both teachers' and learners' perspectives, the challenges encountered in e-learning bear a negative impact. It was also found that there is no significant difference between the teachers' and learners' evaluation of e-learning effectiveness. Through the findings, e-learning implementation will be enhanced along the areas of policy development, staff training, infrastructure upgrade, and learner support.
Effect of Misaligned Relay on Output Power and Efficiency in Wireless Power Transfer

In magnetic resonance-based wireless power transfer (WPT), any misalignment between resonators degrades the strength of the magnetic coupling, and the use of a relay does not always improve performance if it is misaligned with a transmitter or a receiver. It is therefore necessary to determine the proper activation of a relay depending on the degree of misalignment. In this paper, we analyze the effect of a misaligned relay on two important performance metrics in WPT, namely output power and efficiency. In particular, we derive the achievable output power and efficiency with optimal load resistance based on an equivalent circuit model, and determine whether it is beneficial to utilize the relay for a given lateral and angular misalignment. By simulation and experimental verifications under various environments, we validate the exactness of our analysis, and we also confirm two interesting observations, namely 1) with regard to performance improvement in terms of output power and efficiency, it is better to deactivate the relay if it is seriously misaligned with the other resonators and 2) output power is more vulnerable to the misalignment of a relay than efficiency.
NDGCN: Network in Network, Dilate Convolution and Graph Convolutional Networks Based Transportation Mode Recognition

Transportation mode recognition is a crucial task of Intelligent Transportation Systems (ITS) in smart city. Though many works have been investigated on transportation mode recognition in recent years, the accuracy and generality are still not able to meet the application requirements. In this paper, we propose a novel fusion framework for fine-grained transportation mode recognition, which consists of the Network in Network (NIN), Dilate Convolution and the Graph Convolutional Networks (GCN). In this framework, we first use NIN and Dilate Convolution to capture local and global features, respectively, and then introduce the graph convolutional network to learn the correlation of features. We construct a topological structure of the features based on the maximal information coefficient (MIC) criteria which is used to measure the similarity between two variables, and then obtain the adjacency matrix used for graph convolution. Extensive experimental results on the public Sussex-Huawei Locomotion-Transportation (SHL) dataset demonstrate the superiority of our proposed NDGCN to other state-of-the-art baselines with more than 22.3% higher accuracy.
Understanding Challenges Faced in Online Teaching and Learning Among Malaysian Universities' Instructors During COVID-19 Pandemic

This study aims to identify the challenges faced by instructors when conducting online teaching and learning (OTL) entirely using the university's learning management system (LMS) during a global crisis - the Coronavirus disease outbreak. Additionally, the present study proposes strategies for mitigating the challenges. In-depth interviews were conducted with instructors from selected public and private universities in central Malaysia. The fields of teaching involved in this study are Information System, Information Technology, Logistics Management, Accounting and Finance, Business Administration as well as Marketing. Apart from the interviews, the use of LMS and instructor-student communication within the system were observed. This study found several challenges faced by the instructors, which include ineffective real-time communications, technology incompetence, low rate of interactivity and lack of understanding of learner's characteristics and attitude. The possible strategies that are likely could help to mitigate these challenges are adding relevant functions and features in current LMS such as real-time communication, collaborative tools and social function. Additionally, university should also provide resources and professional training on developing material that is in a digital format for the instructors. The findings of this study contribute to the body of knowledge relevant to online teaching and learning. Practically, the findings from this study may increase the understanding and awareness about the challenges among ministries, universities, instructors, students, and system developers. Thus, relevant assistance and solutions can be provided to further improve and ensure the effectiveness of conducting OTL in similar circumstances.
Cervical Pulse Wave Extraction Based On Video Motion Amplification

In the development of computer aided diagnosis (CAD), there are many instruments for pulse signal acquisition. Traditional contact pulse wave collector is inconvenient and depends on complex electronic equipment. Based on Euler video magnification algorithm (EVM), a non-contact pulse wave lifting method based on video action amplification is proposed. The method uses only one digital camera to photograph the pulse in the neck position for pulse wave acquisition. Compared with the method the acquisition rate is higher in other non-contact ways.