Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a High Gain Tri-band Antenna Based on the Polygonal Outer Frame Nested Thunderbolt Branch and AMC Reflector

A polygonal outer frame nested thunderbolt branch tri-band microstrip antenna is presented in this paper, which consists of a polygonal ring, a thunder pattern (a kind of Bronze Pattern) branch, a rectangular branch and an elliptic-shaped defected ground structure (DGS). In order to improve the radiation performance of the antenna, a new type of artificial magnetic conductor (AMC) reflector is proposed. Its reflection coefficient has three zero-phases at 2.5, 3.5 and 5.5 GHz and it is mainly composed of two circular rings and one square ring. With the help of the tri-band AMC, the three impedance bandwidths of the AMC-based antenna are in the range of 2.44-2.67, 3.38-3.58, and 4.48-7.18 GHz with the relative bandwidths of 9.4%, 4.7%, and 46.3%, respectively. It can effectively cover all 2.45 / 5.2 / 5.8 GHz WLAN and 2.5 / 3.5 / 5.5 GHz WiMAX systems. Furthermore, compared with the antenna without using AMC, the AMC-based antenna’s gains at 2.5, 3.5, 5.0, and 5.8 GHz have increased by 4.8, 5.8, 4.2, and 4.4 dB, respectively.
Study and Simulation of the Interference from IMT-2020 Systems (5G) to Satellite Systems in the Candidate Millimeter-Wave Bands

With the rapid development of land public mobile services, the existing frequency bands for IMT systems can no longer meet the needs for the future development of IMT systems, therefore more spectrum are required. At the 2015 World Radiocommunication Conference (WRC-15), an agenda item was identified to study the possibility of identifying new spectrum for IMT systems in millimeter wave frequency bands globally. Among the candidate frequency bands for this agenda item, the 37.5-42.5 GHz band attracted many attention of the research. Because the 37.5-42.5 GHz band has been allocated to the fixed-satellite service (FSS) from space to Earth, the co-existence of the two systems in this band has also become an urgent issue for further study. Based on the parameters of the two systems given by the International Telecommunication Union (ITU), this paper analyzes the possible coexistence of the two systems in this frequency band by means of interference simulation and gives the conditions for coexistence.
Velocity trajectory tracking control: an Adaptive Ohnishi’s Disturbance Observer approach

The online estimation of the input gain in servo systems through an adaptive algorithm is carried out to design a Proportional controller equipped with a Disturbance Observer proposed by K. Ohnishi, to counteract constant perturbations affecting a servo system while executing velocity trajectory tracking tasks. This work relaxes the condition of knowing exactly the value of the input gain trough a Gradient update law plus projection, the latter precluding singularities in the control law. A stability analysis allows concluding that all the closed loop signals remain bounded and that the velocity tracking error converges to zero. The effectiveness of the proposed controller is assessed by performing real-time experiments on a laboratory prototype.
Creating Kinematics-dependent Pedestrian Crossing Willingness Model When Interacting with Approaching Vehicle

The interaction between automated vehicles (AVs) and vulnerable road users is increasingly important since the adoption of AVs is closer to reality. Particularly, the pedestrians’ crossing behaviour are extremely complex, and it is difficult for AVs to predict pedestrians’ decisions and motion behaviour. One of the important problems is how to characterize pedestrians crossing willingness (PCW), which is important for AV systems. Currently, few models have been proposed to characterize PCW. The most relevant models, pedestrian gap acceptance models, are mostly pure statistical approaches which are difficult to apply to a wide range of scenarios. In this paper, to avoid these drawbacks, we developed a novel PCW model by employing a continuously changing psychophysical stimulus, looming, which characterizes the visual information of approaching vehicles through the kinematics model of crossing scenario. In addition, a perception threshold is introduced to constrain the model. Results in this study showed that the PCW model can accurately capture the effects of the vehicle speed, distance and size on pedestrians’ behaviour pattern. It was also found that pedestrians have maximum willingness to cross the street when this stimulus is beyond the perception threshold. We found that the model fit well with data collected from previous gap acceptance studies.
Scheduling Algorithms for Wireless Downlink with Deadline and Retransmission Constraints

Packet scheduling for wireless downlink to multiple nodes with deadline and retransmission constraints plays an important role in supporting real-time applications. In this paper, under independent packet arrival processes for different nodes, we formulate this downlink problem as a Markov Decision Process (MDP) by considering all the packets in the queues for all the nodes and obtain a deterministic stationary -optimal scheduling policy. To reduce the exponential complexity due to possibly multiple packets in each queue, we modify this MDP by only considering the head-of-line (HoL) packets, and obtain a heuristic scheduling policy. Simulation results show that the proposed heuristic policy achieves near-optimal performance and outperforms three baselines.
Automated Portfolio Rebalancing using Q-learning

Fund Managers and retail Do-it-yourself investors constantly seek ideas and techniques to invest their wealth in various financial assets to maximize their wealth over time while trying to minimize the Investment Risk and Transaction costs. This study uses Basic Q-Learning Reinforcement Learning agents who will learn market patterns to trade in financial assets to maximize the fund value, using portfolio returns net of transaction costs as the learning criteria. 15 Indian financial assets covering Equity Sectoral Indices, Government Security Indices and Gold spot prices were chosen and a Reinforcement Learning agent was trained on each of them. The Reinforcement Learning agents were given Simple Moving Averages, 52-Week Stochastic Indicator and Price Change Momentum Indicators for their respective financial assets to train on. Testing was conducted for the Year 2019 and evaluation of the performance was done using the annual returns net of transaction costs, Max Drawdown and Standard Deviation, in comparison to the benchmarks. Most of the agents have been able to reduce the Max Drawdown and Standard Deviation while there is further scope to improve on the Fund Performance. The study has been successful in implementing a basic Q-Learning algorithm for Portfolio optimization to reduce the downside risk and the study would lay the foundation to perform further research on this Subject to maximize wealth creation from Financial Markets. Exploration of further features, tuning of hyper-parameters for each Asset & use of Deep Q-Learning algorithm could be explored further to take forward this research.
Use of Waste Salmon Bones as a Biomaterial

In the presented study; Hydroxyapatite (HA) used in many areas such as filling of cavities, bone tissue treatments, chin-face, orthopedic and dental surgeries, was obtained from waste salmon fish bones. Instead of producing chemically in a laboratory environment, hard tissue waste of natural resources was used. As trace elements such as magnesium, zinc, and strontium in the structure of natural resources support bone formation, waste salmon fish bones, which are a natural source, were preferred as raw materials. Other advantages include being easy to access raw materials, cheap and environmentally friendly. HA was obtained from salmon bones by the thermal calcination method. The obtained pure salmon hydroxyapatites were sintered at different temperatures, and the effect of changing sintering temperature on the density, microhardness, compressive strength, and elasticity module in the material was investigated. Crystal phase analysis of salmon hydroxyapatite powder and thermal analysis up to a certain temperature were made. MTT cytotoxicity test was performed to measure whether the materials were toxic. This study has the potential to contribute to the development of biomaterial studies for bone repair.
Wireless Charging of Smartwear for Health and Safety Monitoring System

Smart wearables for health monitoring have become an emerging technology that allows consumers to monitor and keep track of their health. However, as the number of sensors in a wearable device increases together with continuous monitoring and wireless communications, the battery lifetime and battery duration per charge becomes a major concern. This issue is particularly important for health monitoring of workers operating in difficult environments such as emergency response responders, mining and construction. In this paper, we present a wireless charging system for a health monitoring smart-wear application of workers by considering the power consumption requirements of sensory hardware, communication technology as well as the practical charging scenarios. We also demonstrate a working prototype of a 5-Watt wireless charging device used by our health monitoring wearable. Our paper provides the design, analysis of the requirements and the critical factors of a ‘wireless charging bin’ that we are currently prototyping for our application context.
Botnet Attack Detection using Machine Learning

With the advancement of computers and technology, security threats are also evolving at a fast pace. Botnets are one such security threat which requires a high level of research and focus in order to be eliminated. In this paper, we use machine learning to detect Botnet attacks. Using the Bot-IoT and University of New South Wales (UNSW) datasets, four machine learning models based on four classifiers are built: Naive Bayes, K-Nearest Neighbor, Support Vector Machine, and Decision Trees. Using 82,000 records from UNSW-NB15 dataset, the decision trees model has yielded the best overall results with 99.89% testing accuracy, 100% precision, 100% recall, and 100% $\Gamma-$score in detecting botnet attacks.
A Train Arrival Delay Prediction model using XGBoost and Bayesian Optimization*

This paper proposes a data-driven method that combines eXtreme Gradient Boosting (XGBoost) and Bayesian optimization algorithm to predict train arrival delays. First, 11 characteristics that may affect train arrival delay at the next station are identified as independent variables. Second, an XGBoost prediction model that capture the relation between train arrival delays and various characteristics of a railway system is established. Third, the Bayesian optimization algorithm is applied to the hyper-parameter optimization of XGBoost model to improve the prediction accuracy. Finally, a case study is illustrated to show the prediction accuracy of the proposed method. The results demonstrate that the proposed method has a higher prediction precision and outperforms other benchmark methods (i.e., Random Forest, Deep Extreme Learning machine and Gradient Boosting Regression Tress).
Web-Based Information System for City Transport Management

This paper describes the development of a WebBased Information System for City Transport Management. For its development, the used technologies include – JDK, MySQL, JDBC and Java programming languages. Since its official introduction almost a quarter-century ago, Java is now one of the most popular programming languages in the world, one of the main reasons contributing to this fact is its platform independence – the code is compiled to byte code and the program is executed via Java Virtual Machine.
A New Multilevel Inductive Power Transfer System

Conventional on-line inductive power transfer (IPT) systems use a 60- Hz transformer and a rectifier at their front-ends to step down a medium-level input voltage (22.9 kV or 25 kV) to a low voltage of 0.1 - 3 kVrms, so it is compatible with voltage ratings of commercial power switches. To achieve high output power level, multiple inverters and high-frequency transformers can be tied in parallel to the rectified DC bus. However, the 60-Hz transformer and the output high-frequency transformers are the main impediments that make the system bulky and expensive. This paper proposes a new on-line IPT system that utilizes multilevel inverters and excitation coils instead of the input and output transformers. Instead of the line frequency step-down transformer, series-connected active rectifiers and DC/AC inverters are used to convert the medium-level input voltage to multiple low-level voltage outputs. The excitation coils have strong magnetic coupling with the transmitter coil. Its magnetic field induces voltage and eddy-current in the transmitter coil and then, the transmitter coil induces a load current to a receiver coil via a loose coupling between the transmitter and the receiver. A theoretical analysis using an equivalent circuit model is presented. The frequency response of the input impedance and efficiency of the proposed IPT system are investigated. Also, an example design for the excitation coil is demonstrated. The feasibility of the proposed system is evaluated using a circuit analysis of a 4-level IPT system. The simulated efficiency of the proposed system was 88 % over a 7-cm air-gap.
Characterization of Ammonium Sorption by Beef Cattle Feedyard Manure

Ammonia (NH3) emissions from beef cattle feedyards represent a loss of agronomically important N and can potentially affect the environment. Sorption of ammonium (NH4+) and NH3 by mineral and organic solids decreases the proportion of free, aqueous ammoniacal N, which reduces volatilization potential. In the U.S., the High Plains region of Texas is subject to arid conditions with widely fluctuating temperatures. Furthermore, feedyard manure contains little soil and has high dry matter (DM) content; therefore, it is unclear if sorption parameters determined for soils or liquid manure systems are valid for feedyards. Our objectives were to use batch equilibration experiments to characterize NH4+ sorption by two feedyard manures. Kinetic and isotherm studies with (NH4)2SO4/0.01 M CaCl2 solutions revealed that feedyard manures could sorb up to 227 cmol kg-1 NH4+-N and that sorption was rapid and linearly related to NH4+ concentration. The Freundlich partitioning coefficient (Kp) for NH4+ sorption averaged 13.3 L kg-1. From 58% to 96% of the sorbed NH4+ was readily desorbable with 0.01 M CaCl2, and up to 81% was volatilized as NH3 when manures were air-dried. Temperature influenced sorption, with 112% more NH4+ sorption at 4°C than at 22°C. These results indicate that NH4+ sorption by manure on feedyard surfaces may temporarily reduce NH3 volatilization, particularly during winter.
On the Comparison of Social Distancing Violation Detectors with Graphical Processing Unit Support

Social distancing or sometimes referred as physical distancing is claimed as the best spread stopper in the present COVID-19 pandemic. Social distancing monitoring by using computer vision becomes an important technological aspect in the current pandemic. This type of technology ensures automatic human object detection followed by physical distance measurement. The actual distances are measured as the number of pixels separating two centroids. The social distancing violations are known based on the measured distances. In this works, we compare three deep learning methods used for social distancing monitoring i.e YOLOv3, YOLOv3-Tiny, and MobileNetSSD. Those methods are executed with and without GPU support, and we assess the their performances in terms of speed and detection accuracies. The results show that the use of GPU significantly increases the speed of both YOLOv3 and YOLOv3-Tiny, but not for MobilenetSSD. GPU support increases about 300 % the Frame-per-Second (FPS) rate of YOLOv3 and the highest FPS rate is achieved for YOLOv3-Tiny. The results also indicate that YOLOv3 offers the best detection accuracies compared to YOLOv3-Tiny and MobilenetSSD, but in the exchange of heavy computational process.
Link Speed Estimation for Traffic Flow Modelling Based on Video Feeds from Monocular Cameras

In this paper, we present a reliable and scalable approach for real-time estimation of link speeds (i.e., traffic speeds on specific road segments) based on video feeds coming from monocular cameras. We detect and track vehicles of specific types, identify anchor points (or keypoints) on them, compute their poses, and use this information to estimate their speeds. We use deep learning methods for vehicle detection, tracking, keypoint detection and localization, and traditional 3D pose estimation techniques for which precise mathematical solutions are available. Thus, our approach exploits the best of both worlds. The proposed approach does not require any physical measurements (extrinsics) in the road scene, making it scalable and easy to install. Our results on video feeds from Bangalore, India, show that the method is able to generalize well for cameras mounted on street light poles, congested traffic situations, and various lighting conditions. Thus, the solution is suitable for emerging market scenarios where traffic tends to be chaotic and dense, and mounting speed sensors or strategically located downward-facing cameras is not feasible. The code and dataset for this work are being made available2.2https://github.com/ShantamShorewala/vehicle-speed-keypoint-data
A Semi-blind Greedy Support Recovery Algorithm with Adjustable Stop Threshold

Greedy algorithm is an important class of sparse recovery algorithm of compressive sensing. Most existing greedy recovery algorithms need the prior knowledge of the signal’s sparsity, which is unknown or even time-varying in actual applications, to determine the stop threshold of the iterative recovery process. Recently, the power of the residual signal is used to compare with the noise power to determine whether there is some sparse signal remaining. In this paper, the statistical property of the residual signal power is proved to follow the chi square distribution with parameter L(M-k), where L and M and the size of the observation matrix, and k is the recovery iteration times. Then, a semi-blind greedy algorithm is proposed, in which the stop threshold is adjust according to the recovery times. Simulation results illustrate that the proposed algorithm is more effective than the existing energy-based support recovery algorithm.
Optimized Application Driven Scheduling for Clustered WSN

The widespread use of applications through Wireless Sensor Network (WSN) demands an adaptive management system. One of the issues in autonomous WSN is finding the optimal solution for minimizing energy consumption while taking into account the application data rate requirements. This trade-off is done by modifying the size and composition of the MAC superframe. In this paper, an optimized application-aware superframe scheduling for clustered WSN is presented that optimizes the trade-off between power consumption and sensor data acquisition rate, by utilizing a differential evolution algorithm to find the optimal size and composition of the MAC superframe. The approach presented also leverages a cluster network topology in the design of the network. It also considers multi-superframe configurations that have not been previously leveraged in the literature. Simulation comparisons leveraging LEACH-C show that our approach can optimize the 802.15. 4 superframe size and composition for particular sensor data rates and improve energy consumption in the network compared to using a standard single superframe.
Abnormal state recognition method for power-heatgas integrated system based on unified power flow model

Tight coupling between different energy systems makes state analysis of multi-energy integrated systems difficult. From the perspective of data-driven, this paper proposes an anomaly state identification method for power-heat-gas integrated system based on unified power flow model. First, establish a unified power flow model of the power-heat-gas integrated system. Then, using the random matrix theory in big data technology, the historical data and real-time data of the state quantity in the unified power flow model form a random matrix. Finally, the M-P law and the ring law in the random matrix theory are used to qualitatively analyze the system operation. The simulation results verify that the method does not need to identify the physical structure of the system, which provides a new idea for the abnormal state recognition of the power-heat-gas integrated system.
A Mode Switching Extended Kalman Filter for Real-Time Traffic State and Parameter Estimation

Traffic state estimators (TSEs) based on the cell transmission model (CTM) are vulnerable to biased initial estimates of traffic flow parameters, in particular the critical density. For example, an overestimated (underestimated) initial estimate of critical density can cause a delayed (premature) switching of a TSE from free-flow working mode to congestion working mode, hence distorting estimates of traffic states. Only augmenting the traffic flow parameters into the state vector cannot resolve the issue of biased initial estimate of the critical density, because the critical density is unobservable under free-flow mode. To overcome this issue, this paper proposes an innovative supervisory observer to inform the TSE of correct instants for mode switching. In particular, the proposed supervisory observer requires no a priori knowledge of any traffic flow parameter. The idea is to make decisions for mode switching through capturing anomalies in the residuals of the extended Kalman filter (EKF) of the TSE. This paper also proposes, for the first time in relevant literature, to augment capacity drop proportion into state vector so that its value can be calibrated online. Simulation experiments show that the proposed method can correctly capture mode switching times and generate satisfactory estimates of traffic states and track time-varying traffic flow parameters.
Ensuring Service Fairness in Taxi Fleet Management

On-demand mobility services form a major section of the transport system, especially in metropolitan cities. Contemporary on-demand systems work in a partially coordinated manner where the drivers follow their own passenger finding strategies. This leads to sub-optimal performance at the company level due to a spatial-temporal supply-demand mismatch. A coordinated fleet of taxis that also employs the knowledge of predicted future demand is being seen as a promising upgrade to contemporary systems. However, for this system to work, the drivers must see the clear benefits of switching from the contemporary system to the coordinated system. In other terms, the drivers must feel that the system is fair. In this paper, we have built a framework to simulate an on-demand taxi system working in a coordinated manner. Our objective is to ensure company profit maximization while improving service fairness in terms of driver profit disparity reduction as well as increasing the scheduling guarantee for drivers who are following the recommendation of the system. We show that the proposed model can serve 49.8% more demand with 71% higher mean driver profit, 83% lower profit disparity and a 39.8% higher scheduling guarantee.