Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a Tunable UHF RFID Tag Based on Rectangular Loop Antennas for Metallic Objects

In this paper, a modified rectangular loop UHF RFID tag antenna mountable on metallic objects is proposed. We first investigate the impedance and gain of an electrically large loop antenna with and without a metal plate attached to its bottom, and give a typical loop tag antenna mountable on metal with a good performance. Based on the above analysis, a scheme of folding is presented to miniaturize the tag. In addition, utilizing the shorted ends formed by folding can tune the bandwidth to satisfy the global use. The total size is 70 mm × 34 mm × 1.6 mm. The measured maximum reading range is 3.5 m and 2.0 m among 902-928 MHz on metallic objects and in free space, respectively.
LOS/NLOS Channel Identification Technology Based on CNN

In the field of indoor positioning, ultra-wideband (UWB) communication technology is the key technology. But the errors caused by non-line of sight (NLOS) communication channels could greatly affect the positioning accuracy. Therefore, identifying the channel type can effectively avoid the above-mentioned errors, thereby helping to reduce the loss of positioning accuracy. This paper proposes a LOS/NLOS channel identification method by using a convolutional neural network (CNN) to identifying the impulse response figures of the channels. This method is capable of identifying four different types of channel impulse response figures and providing excellent identification accuracy. The results show that the accuracy of identifying LOS (0-4m), NLOS (0-4m), NLOS (4-10m) and extreme NLOS channel impulse response figures can reach 98.24%.
A Fast and Accurate Geometric Programming Technique for Analog Circuits Sizing

In this paper, a fast optimization technique for CMOS analog circuits sizing is presented. The technique is based on geometric programming (GP) optimization. Although GP sizing is a very fast method, it produces inaccurate optimal solution due to the lack of accurate MOSFET equations. The proposed technique generates accurate geometric programming (GP) compatible MOSFET equations. Generation of device equations has two phases; look-up table (LUT) generation phase and monomial data fitting phase. The first phase is completed once per technology model. The second phase is performed during the circuit optimization process. A two-stage op-amp is optimized to test the performance of the proposed technique. The simulation results show that the circuit performance achieved using the proposed technique is better than the circuit performance reported in literature, and that the optimal solution is found in less computation time.
Forest Biomass Inversion in Jilin Province of China Based on Machine Learning and Multi-source Remote Sensing Data

Forest is an important part of natural ecosystems and the environment in which living things depend. Forest is constantly changing under the influence of natural and man-made forces. Therefore, monitoring and estimating forest parameters play a key role in forest resource management, while forest growth situation is most directly reflected by the forest biomass and stock. The study area is located in Jilin Province of China, belonging to a national forest park. Combining the spectral data of Sentinel-2 with the radar data of GF-3, an inversion method of forest biomass and stock was proposed in this paper. The actual data of the field experiment in 2018 was used in this paper to validate the proposed method. Twelve vegetation indexes of spectral features were extracted, for example, ratio vegetation index (RVI), normalized difference vegetation index (NDVI) and soil adjustment vegetation index (SAVI). For the radar data of GF-3, the backscattering coefficients were extracted. Taking sixteen extracted eigenvalues as the independent variables, these variables with high importance to the model were further selected to participate in the construction of the inversion model of forest biomass based on random forest. Finally, the biomass of the research area was effectively obtained.
An Attention-Based User Preference Matching Network for Recommender System

Click-through rate prediction (CTR) is an essential task in recommender system. The existing methods of CTR prediction are generally divided into two classes. The first class is focused on modeling feature interactions, the second class is focused on solving time-series problems. However, the existing models of the second class are not able to handle time-series problems with user feedback information, so we propose PMN to solve this kind of problem. To be able to take full advantage of historical user behavior along with the user feedback, PMN uses the attention mechanism to get the user historical behavior representation and the user preference representation from the original input. Specially, user preference representation is derived from the user feedback information and it explicitly shows the user’s attitude towards the candidate, which greatly improve the model performance. Finally, we introduced user preference baselines to solve the problem of inconsistent scoring standards for different users. In this paper, we focus on the CTR prediction modeling in the scenario of video recommendation in Video On Demand (VOD) service. Experimental results on multiple data sets have shown that our PMN model is effective.
LSRN: A Recurrent Residual Learning Framework for Continuous Wireless Channel Estimation Using Super-Resolution Concept

As only a few parts of wireless resources can be utilized for pilot transmission, channel estimation, especially the interpolation process, has often been recognized as a challenging ill-posed reconstruction problem. To deal with this task, we formulate it as a typical image super resolution problem, and propose a recurrent residual learning framework named LSRN. Our proposed scheme jointly utilizes the advantages of recurrent and residual structure in the machine learning area to approximate the non-linear interpolation relations between the reference signal and surrounding resource elements. In addition, we propose a low complexity implementation scheme called LSRN-L to address the stringent processing delay requirement in the channel estimation tasks. Through numerical examples as well as prototype verification, the proposed LSRN/LSRN-L can easily outperform the convolutional GI plus DFT based interpolation scheme by 10dB in terms of normalized mean square error. Meanwhile, the low complexity LSRN-L can maintain the processing delay within one millisecond.
Layer Structured Multiple-UAV System Mixing Centralized and Distributed Agents

In the control of multiple unmanned aerial vehicles (UAVs), the distributed control method has been gathered a lot of attention because of the advantage that a load of communication and calculation can be reduced. In the distributed control, the controlled object (agent) is operates depending on the local information which is acquired by communication or measurement. In general, its performance is low compared with the centralized control method. Therefore, in order to solve this problem, we propose a layered structure comprised of centralized and distributed control methods that complements the advantages of each method. The proposed structure is applied to a disaster site survey with multiple UAVs. In the investigation by UAVs, the resolution of the map information depends on the flight altitude. Based on this feature, the agents that fly at a high altitude are controlled by a centralized way and capture wide-area information with low resolution. The other group of agents fly at a low altitude, move distributed way and obtain complemented information. By designing the collaborative motion among centralized and distributed controlled agents, the proposed structure possesses both advantages of centralized and distributed control methods. The efficacy of the proposed method is demonstrated via numerical simulations.
Review and Planning on Surface-based Observing System in Western China by the WMO Rolling Review of Requirements Process

To meet specific international observations needs in 22 different application areas, the world meteorological organization has established several recommended standards for environment measurement, which are currently about 200 physical variables in different observing platform. The requirements are stated quantitatively in terms of five criteria(horizontal and vertical resolution, frequency, timeliness, and uncertainty), and three levels are determined by experts in these criteria (the maximum requirement “goal”, the minimum requirement “threshold“, and the between “breakthrough“). Western China, as being the upper position of most synoptic system and having the most complex terrain in china, observation there is most important. Unfortunately, it has the minimum meteorological observing stations and platforms in China for the economically less developed. In this article, we did a review of the technology-free Members’ requirements for observations, especially on six principle variables as temperature, humidity, pressure, wind direction, wind speed, and precipitation. Taking into account the uneven distribution of surface-based observing systems, the average horizontal resolution is calculated by area and national station numbers in each province in Western China, then values of threshold and breakthrough are analyzed separately. Results show that, there is a big gap in surface precipitation that cannot meet demands of application areas, besides, temperature and humidity observation in shanxi and xinjiang are short. In the future, more precipitation observation should be planned in Western China, as well as temperature and humidity in shanxi and xinjiang. In the end, the study points out that it is not the efficient and economical way to build more surface-based rain gauge or thermometer screen to meet the application command, other useful skills such as collaborative observation, data assimilation, remote sensing and space-based observing should be taken into account and be fully used ntal results show that the full link active calibration method plays a positive role in promoting the development of dual-polarization weather radar calibration.
Reducing Dimensionality of Variables for a Classification Problem: Fraud Detection

Fraud detection can be considered as a classification task since we can use datasets with labelled instances as fraud cases and legal cases. Although, many classifiers were applied to this problem, the data pre-processing related to the reduction of values of each variable is an uncommon approach. We explore a method to reduce the cardinality of the variables in a dataset of fraud transaction to identify improvement in this classification problem. Our best result indicated an improvement of $+$ 31.8% in terms of F1-measure when we reduce the cardinality to detect fraud cases.
Fault Estimations and Non-Fragile Control Design for Fractional-Order Multi-Weighted Complex Dynamical Networks

This paper deals with the robust fault estimation based synchronization problem for a class of fractional-order multi-weighted complex dynamic networks subject to external disturbances. Specifically, the synchronization problem is converted to an equivalent uniformly ultimately boundedness problem of the corresponding error system by use of spectral decomposition of the Laplacian and proper scalings of the faults. First, by introducing an intermediate estimator technique and using monochromatic Lyapunov function method, the sufficient condition for uniformly ultimately boundedness of the resulting error system via fault estimation based non-fragile controller is presented. Next, by using the linear matrix inequality (LMI) technique and optimization approach, the existence condition in the form of LMIs for designing the fault estimation based robust non-fragile controller is derived. Finally, two numerical examples including financial model are provided to demonstrate the validity and feasibility of the developed theoretical results.
Lateral Swing Support System for Parkinsonism patients with Freezing of Gait

Freezing of gait (FOG) is one of the typical locomotion disorders associated with Parkinsonism, and is described as the inability to move the feet despite an attempt to walk. The fear of falling over and other symptoms of FOG can be distressing to patients. Considering conventional external cues and physical therapy, it is necessary for Parkinsonism patients suffering from FOG to swing the body laterally and rhythmically. The purpose of this study is to develop a wearable system that can assist rhythmical and lateral swing, and to validate the performance of the system in preventing FOG by conducting a gait experiment on a Parkinsonism patient. To develop this system, we simulated lateral swing in the standing position based on features of Parkinsonism and defined a mechanism and appropriate force configurations for the system. A power unit at the back of the waist transmits a cyclic force to the chest through a link in the frontal plane. A gait experiment was performed on a Parkinsonism patient with FOG to confirm the reduction in duration time and number of steps of turning. The result showed that the cyclic assistance of the system and gait cycle were harmonized, and the developed system decreased duration time and number of steps during turning by half. In addition, Pre-FOG, i.e., transient small stepping gait, occurred when turning without using the system, while it did not occur during and immediately after the use of the system. These results suggest that the developed system is effective in preventing occurrences of FOG by providing lateral and cyclic force to the upper body.
Power Transfer by Volume Conduction: In Vitro Validated Analytical Models Predict DC Powers Above 1 mW in Injectable Implants

Galvanic coupling, or more precisely volume conduction, has been recently studied by different research groups as a method for intrabody communications. However, only in a very few occasions its use for powering implants has been proposed and proper analyses of such capability are still lacking. We present the development and the in vitro validation of a set of analytical expressions able to estimate the maximum ac and dc powers attainable in elongated implants powered by volume conduction. In particular, the expressions do not describe the complete power transfer channel but the behavior of the implants when the presence of an electric field is assumed. The expressions and the in vitro models indicate that time-averaged powers above 1 mW can be readily obtained in very thin (diameter < 1 mm) and short (length < 15 mm) implants when ac fields that comply with safety standards are present in the tissues where the implants are located. The expressions and the in vitro models also indicate that the obtained dc power is maximized by delivering the ac field in the form of short bursts rather than continuously. The study results support the use of volume conduction as a safe option to power implants.
Comprehensive Design and Analysis of Fan-Out Wafer Level Package

This paper presents an advanced modeling technology on wafer warpage after post mold curing (PMC) for the 12 inch mold-1st Fan-Out Wafer Level Packaging (FOWLP) with consideration of effects of viscoelastic model and chemical cure shrinkage, and layer design of the mold-1st packages. Results show that warpage of mold-1st FOWLP wafer predicted by the advanced modeling technology agrees well with the experimental result. A new guideline for FOWLP process flow is provided as well, which will be useful to the packaging industry. The advantages of the new guideline for FOWLP process flow are: (1) to eliminate wafer warpage correction process and save a lot of cost and time; (2) to serve as a basis for process selection to meet the trends and needs of a reliable fan-out wafer level package with lower wafer warpage during FOWLP wafer process; and (3) to be important to enhance survivability during wafer process and thin-wafer handling.
Analysis and Design of Mutual Inductance Stability Using Hexagonal Coil

A novel type of planar spiral hexagonal coil with uniform axial magnetic field distribution for wireless power transfer is presented in this paper. Compared to the conventional planar spiral hexagonal coil, the improved coil remains the mutual inductance between the two coils stable when there is a certain horizontal offset between the transmitting coil and the receiving coil. The mutual inductance calculation method of parallel non-coaxial hexagonal coils is introduced as well. Compared with the result calculated by MAXWELL simulation software, the correctness and efficiency of this method are verified. The proposed method overcomes the influence of horizontal offset on transmission efficiency and transmission power and is thus a great promising method to design efficient wireless power transmission system.
Improved Recurrent Neural Networks (RNN) Based Intelligent Fund Transaction Model

Fund correlation analysis can guide investors' investment and wealth management, avoiding the selection of highly relevant funds in the investment process, which can make the risk sharing among funds. There is a strong dependence between the features of the fund data and a long-term dependence between the output of different time steps, which makes it difficult to obtain good performance in the fund data in the data analysis model used in the traditional intelligent investment system. This has brought difficulties to fund correlation analysis. In order to solve the above problems, this paper uses an encoder-decoder model combined with the attention mechanism--Improved RNN model. The Encoder-decoder model has made great strides in the application of financial time series analysis. And the attention mechanism can select specific feature inputs and previous time step outputs, both of which are highly correlated with the current output, making system predictions more efficient. This paper applies this model to the historical data set containing multiple public funds. The results show that the fund intelligent investment system proposed in this paper performs best.
Modeling of Mobile Antenna optimization based on Artificial Neural Network

Artificial Neural Network (ANN) has been realized as a robust technique that successfully applied in many Electromagnetic problems. Based on the learning process from prior knowledge, ANN can be considered as a non-linear mapping of the complex relationship of the inputs and the outputs. This reliable replacement model (referred to surrogate model) allows reducing the computational cost of high-EM simulation in antenna designs. In this article, we propose a novel approach where ANN is trained with the data which is collected from mobile antenna simulation to retrieve a surrogate model. This work contributes a new model for researchers to save time and efforts in mobile antenna design because results will be predicted quickly and accurately by the model instead of by the computational expensive cost of simulation. To be more specific, engineers can tune the antenna in stand-alone to get new data and input them into the predicted model to observe |S11| of the antenna in mobile.
Deep Reinforcement Learning-Based Mobility-Aware Robust Proactive Resource Allocation in Heterogeneous Networks

Proactive resource allocation (PRA) is an essential technology boosting intelligent communication, as it can make full use of prediction and significantly improve network performance. However, most promising gains base on perfect prediction which is unrealistic. How to make PRA robust against prediction uncertainty and maximize benefits brought by prediction becomes an important issue. In this paper, we tackle this problem and propose a mobility-aware robust PRA approach (MRPRA) in heterogeneous networks. MRPRA pre-allocates resources in both time and frequency domains among mobile users with users’ trajectories predicted by hidden Markov model. The objective is to minimize service delay under constraints of different levels of quality-of-service (QoS) requirement and mobility intensity. MRPRA is robust against prediction uncertainty by exploiting probabilistic constraint programming to model QoS requirements in a probabilistic sense. To this end, the probabilistic distribution of achievable rate is derived. To flexibly coordinate resource allocation among multiple mobile users over time horizon, a deep reinforcement learning based multi-actor deep deterministic policy gradient algorithm is designed. It learns robust PRA policies by distributed acting and centralized criticizing. Extensive numerical simulations are performed to analyze performances of the proposed approach.
Polite Centralized Random Backoff for Seamless Transition to Collision Free Wi-Fi Networks

Wireless Local Area Networks (WLANs) using the standard Distributed Coordination Function (DCF) have widely been deployed. A large number of wireless multiple access techniques were studied for achieving efficient and fair WLANs; however, little attention has been paid to access methods for improving total network throughput performance while maintaining fairness in the presence of legacy devices using DCF. So, we propose a novel random access technique called polite when unsynchronized (PWU) for seamless transition to efficient and fair WLANs. In PWU, each node has a new parameter, called the politeness level, which is a number between zero and one. A node with a higher politeness level becomes polite more frequently, and it tends to sense the channel for a bit longer compared to nodes using DCF. A politeness control algorithm (PCA) is studied, and it is shown that nodes using PCA maintains a high level of fairness to nodes using DCF in terms of channel access opportunities when they coexist.
Patients’ concerns and perceptions of anesthesia-associated risks at University Hospital: A cross-sectional study

Background/Aim: The expectation of undergoing general anesthesia triggers fear in many individuals, and such anxiety can even exceed anxiety about surgery. The only opportunity patients usually have to express their concerns and ask questions is during a preoperative visits to their anesthesiologist. Therefore, a good anesthesiologist-patient relationship is important to reduce patients' anxiety. Achieving this end requires information on patients' attitudes and concerns regarding anesthesia. This study aimed to assess patients' knowledge, attitudes, and concerns about preoperative assessment and fear associated with anesthesia at University Hospital, Jeddah, Saudi Arabia. Methods: This cross-sectional study used a self-administered questionnaire distributed to 399 outpatients. Data were collected on patient's characteristics, perceptions about anesthesiologists, preferences for anesthetic management, and preoperative concerns regarding anesthesia. Results: Most patients thought that anesthesiologists spent only 3 years in medical school and 2 years in a residency program. Survey participants had several misconceptions about anesthesiologists' role, but it did not affect ratings of their importance. Although, the confidence of patients in anesthesiologists was high, it was significantly lower than their confidence in surgeons. The most common concern expressed by the patients was based on whether anesthesiologists had sufficient experience and qualifications. Conclusions: Discussing anesthetic forms preoperatively can help decrease patients' anxiety. More efforts should be made preoperatively to address patients' high level of fear about rare side effects and discuss common side effects they tend to ignore. Preoperative preparation must allow the anesthesiologists enough time to reassure patients about their concerns, as they obtain patients' informed consent.