Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports

Green parks in urban areas are believed to enhance the well-being of residents. The importance of green spaces to support health and fitness in urban areas has recently regained interest. Reports released in 2010–2016 by the World Health Organization (WHO) on urban planning, environment, and health stated that green spaces can have a positive impact on physical activity, social and mental well-being, enhance air quality and decrease noise exposure. We analyzed the number of check-ins in various parks of Shanghai by utilizing geotagged social media network check-in data. This article presents a descriptive study using social media data by obtaining the three-year comparison of spatial and temporal patterns of park visits to raise public awareness that green parks provide a healthy environment that can be beneficial for the well-being of urban citizens. We investigated the visitor spatiotemporal behavior in more than 115 green parks in 10 districts of Shanghai with approximately 250,000 check-ins. We examined 3 years of geotagged data and our main findings are: (i) the spatial and temporal variations of users in urban green parks (ii) the gender differences in space and time with relation to urban green parks. The main objective of this article is to present evident data for policymakers on the advantages of providing green spaces access to urban citizens and to facilitate cities with systematic approaches to provide green space access to improve the health of urban citizens.
Electroantennography Measurement by Printed Electronics Electrode

In this study, we aim to measure the electrical potentials of an adult male silkworm moth using printed electronics technology. If we can measure the electrical signals of nerve cells that generate brain activity such as movement and decision making of animals including humans, it will greatly help to elucidate the adaptive mechanism of animals and reconstruct them with artificial systems. Hence, measurement data of physiological responses in moving animals have a high value. However, the physiological response measurement method has many technical difficulties, obtaining a high-quality physiological response greatly depends on the skill level of the experimenter. For that reason, we focused on printed electronics that can easily produce circuit patterns on flexible films. It is assumed that it has the possibility to be used to simplify physiological response measurements. In this study, we measured the physiological responses of the silkworm moth using printed electronics. To make an electrode suitable for measurement, we investigated the resistance value of printed electronics and prepare two electrodes based on that value. As a result, when measuring electroantennogram (EAG) of the silkworm moth using the electrode with a low resistance value, we succeeded in measuring EAG response very similar to previous research. Therefore, we showed the possibility of measuring physiological responses using printed electronics that we can design easily.
Student postures and gestures recognition system for adaptive learning improvement

Adaptive learning is an educational method that uses computers to coordinate interaction with student and provide customized learning resources and activities to meet each student’s unique needs that are a trend. In order to adjust the content and teaching methods appropriate for each learner, it is necessary to track the attitude of students with the content and methods being implemented. One of the ways of detecting student’s attitudes is through the gestures and posture of student in the classroom.In this study, we propose to develop a model to monitor and identify students’ postures and gestures during class time to help assess the level of student participation in the content and teaching methods of teachers. From there, it is possible to give advice to trainers and training managers in adjusting the subject content, teaching methods suitable for each specific object.
3D-printed Wideband Rectangular Dielectric Resonator Antenna

A 3D-printed wideband rectangular dielectric resonator antenna (DRA) with omnidirectional radiation patterns is investigated. It is a multilayered design with four concentric square dielectric rings, which have progressively decreased effective dielectric constants in the azimuthal direction. To facilitate the 3D printing, a unit cubic cell that can be easily adjusted to provide different effective dielectric constants is used in our design. Three quasi transverse magnetic (TM) modes (quasi-TM01δ, quasi-TM02δ, and quasi-TM03δ) of the DRA are simultaneously excited by a centrally penetrated coaxial probe. By merging these quasi-TM modes together, the DRA with a wide impedance bandwidth is obtained. For demonstration, a prototype working in C-band is designed and printed. Measured results show that our design has a wide 10 dB impedance bandwidth of 44.8% (5.20-8.20 GHz), fully covering the current 5.8 GHz WLAN band (5.725-5.875 GHz). Also, fairly stable radiation patterns can be observed across the impedance passband (5.20-8.20 GHz).
Modified Phase-Shifted PWM for Cascaded Half-Bridges of Photovoltaic Application

Photovoltaic power plants are generally far away from loads such as cities and industrial zones. Moreover, long-distance power transmission for renewable energy will become an inevitable trend in the future. Cascaded dc–dc converters can reach a high voltage to collect and transport photovoltaic energy, with advantages such as lower costs and larger capacities. However, the input power mismatch may cause a large amount of switching harmonic components, resulting in dc-link voltage fluctuations, which will not only increase the volume of filter components, but also cause unreliable circuit breaker actions. To solve the aforementioned problems, a hierarchical phase-shifted pulse width modulation (HPS-PWM) is proposed. By rationally grouping and layering the modules, low-order harmonics generated by the switching process of the dc–dc converter can be eliminated. The fluctuation of dc-link voltage can be suppressed by HPS-PWM, according to simulation results. When the number of modules is 27 and 81, the weighted total harmonic distortion (WTHD) of the HPS-PWM method can be reduced by more than 60% compared to the phase-shifted tracking pulse width modulation (PST-PWM) and fault tolerant adaptive phase-shifted pulse width modulation (FTA-PS-PWM). Finally, experiments verify the performance of HPS-PWM, agreeing with the simulation results.
An Evolutionary-Based Algorithm for Smart-Living Applications Placement in Fog Networks

Fog computing is an emerging model, complementing the cloud computing platform, introduced to support the Internet of Things (IoT) processing requests at the edge of the network. Smart-living IoT scenarios require the execution of multiple processing tasks at the edge of the network and leveraging on the Fog Computing approach results to be a worthwhile solution. Genetic Algorithms (GA) are a heuristic search and optimization class of techniques inspired by natural evolution. We propose two GA-based approaches for optimizing the processing task placement in a fog computing edge infrastructure aiming to support the Smart-living IoT nodes requests. The numerical results obtained in Matlab show that both GA-based approaches allow to maximize the covered areas while minimizing the resource wastage through the minimization of the overlapping areas.
Multiple Speaker Tracking Using Coupled HMM in the STFT Domain

We present a multi-microphone multi-speaker direction of arrival (DOA) tracking algorithm. In the proposed algorithm, the DOA values are discretized to a set of candidate DOAs. Accordingly, and following the W-disjoint orthogonality (WDO) property of the speech signal, each time-frequency (TF) bin in the short-time Fourier transform (STFT) domain is associated with a single DOA candidate. The conditional probability of each TF observation given its corresponding DOA association, is modeled as a multivariate complex-Gaussian distribution, with the power spectral density (PSD) of each source an unknown parameter. By applying the Fisher-Neyman factorization, it can be shown that this conditional probability is proportional to the signal-to-noise ratio (SNR) at the outputs of minimum variance distortionless response (MVDR)-beamformers (BFs), directed towards all candidate DOAs. We model these observations as either a frequency-wise parallel Hidden Markov Model (HMM) or as a coupled HMM with coupling between adjacent frequency bins. The posterior probability of these associations is inferred by applying an extended FB (FB) algorithm, and the actual DOAs can be inferred from this posterior. An experimental study demonstrates the benefits of the proposed algorithm using both a simulated dataset and real recordings drawn from the acoustic source localization and tracking (LOCATA) dataset.
Interference and Coverage Analysis for Indoor Terahertz Wireless Local Area Networks

Providing high-bandwidth and fast-speed links, wireless local area networks (WLANs) in the Terahertz (THz) band have huge potential for varieties of bandwidth-intensive indoor applications. However, due to the specific phenomena in the THz band, including severe molecular absorption, human- body blockage, small-scale fading, analysis on the interference, coverage and achievable rate at the downlink is still a challenging problem. In this paper, by using the tool of stochastic geometry, the exact distribution for the received signal power and the approximated distribution for the interference with its analytical moments at an arbitrary user equipment (UE) are derived. More- over, semi-closed-form expressions for the coverage probability and the average achievable rate are investigated. The theoretical derivations are validated with Monte-Carlo simulations. Finally, numerical results demonstrate that narrower beam-width of main lobe at directional antennas and more access points (APs) can enhance the received signal power and the total interference, and effectively influence the coverage and average achievable rate.
Radiation-Pattern Reconfigurable Phased Array With p-i-n Diodes Controlled for 5G Mobile Terminals

A p-i-n diodes-controlled radiation-pattern reconfigurable phased array for fifth-generation (5G) handsets is proposed in this article. The proposed beam scanning and switching hybrid system can significantly reduce the number of arrays and transmit–receive (TR) components when compared with multiarray systems while keeping the same spatial coverage. The array has wide working band from 24 to 27.5 GHz, which can cover the allocated 5G band for Europe. The four-element array is planar with a small clearance of only 4 mm, which is suitable for most of the current mobile phones. Each reconfigurable array element is fed by one shielded stripline and has three switchable radiation patterns: two broadside directions and one endfire direction. The beam switching is realized by applying two reconfigurable directors on both sides of the dipole, which also reduces the antenna profile. The directors are activated when the p-i-n diodes are at OFF mode, which minimizes the energy loss on the p-i-n diodes. The parasitic influence of the p-i-n diodes, the soldering process, and mobile environment are discussed in detail. The measured results show good agreement with the simulations.
Multi-targets Detection in a Non-homogeneous Radar Environment Using Modified CA-CFAR

The problem of multi targets inside the clutter cloud detection, represented the worst radar environments. In order to detect targets in such environments, it needs to develop a robust constant false alarm cell CFAR algorithm. Therefore, the proposed algorithm could excite the target spikes from CFAR window and deal with clutter edges to give best possible evaluation with the given noise background. The radar which is known as modified cell averaged (CA-CFAR) is then developed. The proposed method analyzed and compared with Two important algorithms which are cell averaged (CA-CFAR) and ordered statistics (OS-CFAR) algorithms in additional to the modified CA-CFAR algorithm. All these algorithms are simulated with Matlab and applied them to Matlab clutter test model that represented different radar environment cases.
Industrial Load Management using Multi-Agent Reinforcement Learning for Rescheduling

Industrial load management plays an important role in the balance of energy consumption and electricity generation, which is increasingly fluctuating due to the growing share of renewable energies. Manufacturing companies are able to adapt their energy consumption by considering energy aspects in their production schedule. It may even be beneficial to temporarily force production resources into idle states and to thus reduce energy demand for a limited period. However, the resulting scheduling problem is very complex and at the same time should be executed in real-time. This paper presents an approach for industrial load management using multi-agent reinforcement learning for energy-oriented rescheduling. A simulation study serves to validate the approach. The results show good solutions and at the same time low computational expense compared to a metaheuristic approach using simulated annealing.
Metallized Blind Via Holes and Surface Etching Incorporation for Miniaturization Enhancement in Slow-wave Substrate Integrated Waveguide

In this paper, a new class of capacitance-inductance dual-enhanced slow-wave substrate integrated waveguide (CIDE-SW-SIW) is proposed and investigated. The topology takes the advantages of both metallized blind via holes for capacitance enhancement and surface etching for inductance enhancement, enables a further size reduction without increasing the processing cost. This structure firstly discussed and realized the combination of two slow-wave loading techniques, further improving the slow-wave effect by about 20%, and achieved the best miniaturization capability to date, which demonstrates the promising potentials for the implementation of microwave and millimeter-wave components in 5G and IoT circuits and systems.
A Location and Optimal Coverage Based Filtering Scheme in Wireless Sensor Networks

Injected false reports by attackers bring fateful consequences in wireless sensor networks, i.e., wasting the limited batteries of nodes and misleading the decision making of users. Existing security designs mainly attach some extra fields by a group of sensing nodes after the pure data, and check the correctness of attached MACs (Message Authentication Codes) in the process of report forwarding, each of which represents the agreement of sensors on the report, thus to drop the ones which failed on MAC checking. They cannot recognize the reports forged by <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> arbitrary compromised sensors collaboratively; the variable <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> is a security parameter. Furthermore, failures of reporting usually occur in sparse regions for lacking of enough detecting sensors, which is incurred by the complicated deploying region and the adopted random deploying strategy. This paper proposes a <italic>Location and Optimal Coverage based Filtering scheme</italic> (LOCF). It first derived the optimal coverage degree <inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> by considering both the network size and covering efficiency, and then employed covering algorithm to deploy sensors accordingly. Each deployed sensor has to dispense its location to downstream sensors, through which sensors are bounded with locations. A report for the observed event must attach <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> endorsements along with locations of detecting sensors. In the forwarding process, intermediate sensors evaluate the correctness and rationality of both MACs and locations. Simulation results demonstrate that LOCF outperforms existing works in terms of covering effectiveness, filtering efficiency and compromise robustness.
Non-contact Measurement of Heart Rate Based on Facial Video

Heart rate refers to the number of heartbeats per minute, which is one of the most important indicators to estimate human health. Traditionally, the heart rate can be measured by using a stethoscope or electrocardiogram, or even by feeling the pulse. In addition, a growing number of wearable devices are invented to detect human heart rate. However, these methods rely on physical contact, which makes the measurement inconvenient to some extent. This paper discusses the method of non-contact heart rate measurement based on Photoplethysmography (PPG) by capturing human face from the video under ordinary light. With this method, the change of light is utilized to perceive the volume pulse wave.In this paper, a webcam embedded in laptop is used to shoot facial videos under ordinary light conditions. With the Region of Interest (ROI) selected, we can first extract the initial data of three channels of RGB and then convert the color space of the image from RGB to YCbCr. Eulerian Video Magnification (EVM) is used to magnify the motion. In EVM, the converted results are decomposed in the spatial domain and filtered in temporal domain. By collapsing the amplified signals to the original ones, we can obtain the final signal waveform to estimate the heart rate. After the consistency analysis with the traditional method, it is found out that the error of the results obtained by our method is relatively small. All the experimental results indicated that the method to measure heart rate proposed in this paper is effective.
A 28 GHz Dual Slant Polarized Phased Array using Silicon Beamforming Chipsets

We present in this paper a flat panel dual slant linear polarized $(\pm 45^{\circ})$ transmit and receive (TIR) phased array antenna (PAA), that operates in the Ka-Band, which covers the millimeter-wave 5G band (27.5 - 28.35 GHz). The developed beamforming array utilizes commercially available silicon radio frequency integrated circuit (RFIC) beamforming chipsets. The PAA operates from 26 - 30 GHz and features both 6-bit phase and amplitude control. Simulation is compared to measurement results. We demonstrate beam scanning up to $\pm 55^{\circ}$ in both polarizations. We also show ability to form sum and difference beam patterns utilizing amplitude weighting for monpulse tracking. The size of the array board is 102 mm $\mathbf{x}86\ \mathbf{mm}\ \mathbf{x}4$ mm.
Allpass Modeling of Phase Spectrum of Speech Signals for Formant Tracking

Formant tracking is a very important task in speech applications. Most of the current formant tracking methods bank on peak picking from linear prediction (LP) spectrum of speech, which suffers from merged/spurious peaks in LP spectra, resulting in unreliable formant candidates. In this paper, we present the significance of phase spectrum of speech in refining the formant candidates from LP analysis. The short-time phase spectrum of speech is modeled as phase response of an allpass (AP) system, where the coefficients of AP system are initialized with LP coefficients and estimated with an iterative procedure. This technique refines the initial formants from LP analysis using phase spectrum of speech through an AP analysis, thereby accomplishing fusion of information from magnitude and phase spectra. The group delay of the resultant AP system exhibits unambiguous peaks at formants and, delivers reliable formant candidates. The formant trajectories obtained by selection of formants from these candidates are reported to be more accurate than those obtained from LP analysis. The fused information from magnitude and phase spectra has rendered relative improvements of 25%, 15% and 18% in tracking accuracy of first, second and third formants, respectively, over those from magnitude spectrum alone.
Feedback Linearization using High Gain Observer for Nonlinear Electromechanical Actuator

Naval applications need refined regulation, to meet modern era standards, which eventually leads to the requirement of more accurate mathematical modeling of all subsystems. All naval autonomous systems are equipped with electromechanical actuators, which are often controlled for linear operating range and nonlinearities which may lead to chaotic behavior, are ignored. Additionally, the necessary use of observers for the linear model limits the level of autonomy. In this paper, a typical solenoid actuator is studied with the inclusion of third-order nonlinear dynamic properties. A nonlinear output feedback control is designed for tracking the position of the plunger in solenoid actuator. High gain observer (HGO) is realized to estimate the unknown states. The proposed topology for solenoid actuator uses feedback linearization along with the HGO to achieve the performance of state feedback control which solidifies the effectiveness of HGO. The results are obtained using MATLAB/ SIMULINK.
Vessel Silhouette Identification Based on Edge Detection

The purpose of this paper is to demonstrate how AI identify vessels with dusky images. In this study, we use the calculation of tangent-angles along the boundary of dusky image and reduced the data dimensions by using a piecewise aggregate approximation (PAA). Finally, we used well-known distance function such Dynamic Time Warping (DTW) for well-known classifier such k-Nearest Neighbor (k-NN). We applied this method to vessels silhouette images and found that it provides a good result for this approach.
Shaping Electromagnetic Waves by Using Bianisotropic Huygens’ Metasurface

In this article, a method of shaping electromagnetic waves using low profile and high transmittance antenna array is proposed. The transmitarray is designed by the Huygens’ metasurface [1], in which each unit cell is comprised of bianisotropic pairs. We design transmitar-ray with bianisotropic pairs which are easier to process on PCB board [3]. By altering the electric surface impedance and magnetic surface admittance of unit cells, 18 unit cells which transmission coefficients more than 0.8 are obtained. And the Huygens’ transmitarray composed of those unit cells is utilaized to shape the beam in the case of plane wave incident. Subsequently, a transmi-tarray which can generate Bessel beam is designed by using Huygens’ metasurfaces. The array is excited by the microwave antenna, which can transform the incoming electromagnetic wave of the pyramidal horn antenna into Bessel beam. The full wave simulation of the antenna is carried out. The Huygens’ metasurfaces achieve beam shaping with low profile and high transmittance. In the future, other more complex beamforming could also be realized by this method.
Regularized Sparse Band Selection via Learned Pairwise Agreement

Desired by sparse subset learning, in this paper, a hyperspectral band selection method via pairwise band agreement with spatial-spectral graph regularier, referred as Regularized Band Selection via Learned Pairwise Agreement (RBS-LPA), was proposed. The process was formulated as a graph-regularized row-sparse constrained optimization problem, which select a few representative bands to code the all bands based on the learned pairwise band agreement. In RBS-LPA, a spatial-spectral informative graph, constructed by spatial-spectral neighbor relationship, is incorporated to encode both the spatial and spectral geometrical structure. By combining the learning procedure with graph regularizer jointly, the graph regularizer can adaptively change with the sparse representative bands, which ensures that the selected bands can well preserve the local manifold structure. Experimental results on three real urban hyperspectral data demonstrate the efficiency of the proposed RBS-LPA and achieve convincing performance.