Miracle-dz/UniFAR-FTUs huggingface dataset:
A Wideband Triple-mode Substrate Integrated Waveguide Cavity-backed Slot Antenna

In this paper, A wideband substrate integrated waveguide (SIW) cavity-backed slot antenna with triple resonances is presented. The SIW cavity used for the antenna is fed by an offset coaxial probe to excite the TE210 and TE410 modes, and two radiation slots are etched on the left and right sides of the cavity. The TE410 mode is split into two resonances by the perturbation of the two slots. Besides, several pairs of shorting vias are loaded inside the cavity for moving up the TE210 mode so as to merge it with the two split TE410 modes. At the center of the cavity, a shorter slot and two shorting vias are introduced for the impedance matching. Thus, these three modes can be allocated together to achieve a wideband characteristic. Simulated results show that the proposed antenna has a bandwidth of 20.2% (from 4.7 GHz to 5.75 GHz) and a peak gain of 9.88 dBi, while keeping a low profile of $0.038 \lambda_{0}$ (wavelength in free space).
Coordinated optimal path planning of multiple substation inspection robots based on conflict detection

In recent years, there is an increasing interest in adopting intelligent robots to carry out the substation inspection tasks. With the recognition that a single substation robot generally takes a long time to complete the inspection task in most the high voltage electric substations due to limited sensing capability. To address the challenge of multiple inspection robots operation in electric substations, the path planning problem in the presence of complex substation environment needs to be studied. This paper exploited the path planning problem for multiple substation inspection robots and a time window based solution is presented. The proposed solution is implemented and assessed through simulation experiments and the numerical results demonstrated that the proposed solution is viable and efficient to avoid different types of conflicts in the path planning process. As a result, the proposed solution enables multiple substation robots to carry out inspection tasks simultaneously in the substations that significantly promote the efficiency of the substation maintenance.
Analyzing Gate-Driven Circuit Parameters for Adding ESD Performances

In this paper, five device parameters (resistor, capacitor, inverter PMOS transistor, inverter NMOS transistor and ESD NMOS transistor) are discussed as design parameters for acquiring good ESD performances. For the resistor parameter, ESD (Electrostatic Discharge) or I/O (Input/ Output) circuit designers often adopt PMOS transistor as the resistor (PMOS resistor) for reducing IC size. H-spice tool is used for designing the suitable PMOS resistor. However, that the architectures with PMOS resistors can obtain good ESD results, but bad gate-driven circuit characteristics (large triggering-on voltages) is observed. Hence, ESD damaging IC risks with PMOS resistor designs are proposed in this study.xvxcx
Robust Repetitive Learning-Based Trajectory Tracking Control for a Leg Exoskeleton Driven by Hybrid Hydraulic System

For the purpose of reducing power consumption of a leg exoskeleton for augmenting human performance, a novel hybrid hydraulic system (HHS) which includes a unidirectional servo valve and a solenoid on-off valve is excogitated, and its energy saving control is studied in this paper. Inspired by the varieties of contact force between human leg and ground during walking, the unidirectional servo valve and the solenoid on-off valve are only activated in the stance phase and the swing phase, respectively. In the stance phase, a robust repetitive learning scheme is presented by using the backstepping technique for the unidirectional servo valve, aiming to track the periodic human leg movement, and in the swing phase, an on-off control is proposed for the solenoid valve to release the pressure in the hydraulic cylinder so that the exoskeleton leg is bent by the human leg passively. The proposed control strategy is implemented in an ARM-based embedded microprocessor and the control performance is verified via experiment on the developed exoskeleton robot. The experimental results show that the power consumption of the proposed system is almost 30% less than that of systems with bidirectional hydraulic system.
A Review on The Battery State of Charge Estimation Methods For Electric Vehicle Battery Management Systems

Battery technology plays an important role in the development of battery-powered devices. Especially, it is regarded as a key role in electric vehicles. To contribute to this important topic, researchers have focused on battery technologies and battery management systems. The battery management system performs valuable tasks such as increasing the efficiency of multi-cell battery packs, ensuring safe operation and providing information to the user about the battery. In order to achieve the most efficient management system, several methods have been proposed to estimate the battery state of charge (SoC). In this study, the methods used for battery SoC estimation are analyzed and their positive and negative aspects are discussed. As a result, advanced and efficient methods are highlighted and recommendations are expressed.
Facial Sentiment Classification Based on Resnet-18 Model

Recently, human-computer interaction is become increasingly common in daily life, in which facial expression recognition has a wide application potential. At present, the state-of-the-art accuracy of facial expression classification based on the Kaggle database is only 71.20%, which still indicates some room for improvement. Based on the resnet-18 model, we find that a large number of parameters in the model are prone to over-fitting, which hinders the face recognition accuracy. In this paper, we propose a modified network model based on the resnet18-network, in which we change the original average pooling layer to a global average pooling layer with a double convolution layer. Our experiment result suggests that our model outperformed the state-of-the-art results showing a 1.49% increase in accuracy on the Kaggle database.
Applying the stimulus-organism-response model to consumer purchasing behavior in social commerce

More and more companies are relying on social media sites to interact with their consumers for building social word-of-mouth and then enhancing consumer purchase intention. As companies continue to invest substantial resources and efforts on social media sites, understanding the influencing factors of consumer purchase behavior in order to increase their performance becomes critical. Thus, this study used the stimulus-organism-response (S-O-R) as a research framework to identify the key factors influencing consumer purchasing behavior in social commerce. Research data were collected from Facebook fan page users who had shopping experience in social commerce. Collected data was analyzed with partial least squares using SmartPLS 3.0 statistical software. Research results show that online word-of-month (S) has significant positive effects on information adoption and trust (O), which, in turn, significantly affect consumer purchasing intention (R).
Scheme Recommendation of Regional Wind Power Access System Based on Analytic Hierarchy Process

in recent years, with the rapid growth of installed capacity of regional wind power, the impact of wind power Grid-connected has become increasingly prominent. It is more and more important to recommend the optimal access system scheme for wind power enterprises and power grid companies, taking into account all aspects of the impact. Firstly, this paper puts forward the relevant evaluation index of wind power access system, and makes a comprehensive comparative analysis of the influence factors such as economy, reliability, power quality, local absorption, power grid and power supply development adaptability; secondly, according to actual work experience and multi-level verification results, the analytic hierarchy process (ahp) is adopted to assign the weight of each influencing factor. Finally, the recommended method for regional wind power access system based on analytic hierarchy process (AHP) is proposed. The validity and feasibility of the evaluation method are verified by engineering cases.
Verification Device for Temperature and Relative Humidity Inside the Infant Incubator via IoT

This research aims to design a prototype for verification of the temperature and relative humidity inside the infant incubator and to improve the accuracy of the sensor, as well as allowing the prototype to monitor temperature and relative humidity via IoT for teaching in medical instrumentation calibration topic. The designed device composed of 3 main parts: 1) Input consists of 5 DS18B20 temperature sensors and 1 DHT22 humidity sensor, 2) The processor part used NodeMCU (ESP8266) board which programming with Arduino IDE and 3) The display was divided into two parts: IoT and data logger to save data into external memory before published data to thingspeak.com. The designed device was set in the water bath together with OM-CP Data Logger tested on 5 to 40° C to find temperature error for each sensor at each temperature value. The prototype was reprogrammed with compensation the error equations of each sensor in the program and then retest with the infant incubator temperature 32- 40°C. The result found that the error values were highly decreased. Performance testing of the designed device in the infant incubator was compared to the Incubator Analyzer Brand Fluke Biomedical INCU II based on IEC 60601-2-19 test on 32° C and 36° C. The pattern of the temperature changes inside infant incubator of the prototype device compared with the standard device was closely same for both temperatures. The average temperatures and relative humidity measured from the prototype device on Steady Temperature Condition (STC) were had approximate acceptable values compared to mean values obtained from the standard instrument. All temperatures reading from the prototype were different from the standard device temperature less than 0.5°C, and the relative humidity reading from the prototype was different from relative humidity reading from the standard device of less than 5 %.
Joint Access and Resource Allocation in Ultradense mmWave NOMA Networks With Mobile Edge Computing

This article considers a two-tier heterogeneous network consisting of conventional sub-6-GHz macrocells along with millimeter-wave (mmWave) small cells, where mobile devices (MDs) can connect to either macrocell or small cells opportunistically via the nonorthogonal multiple access (NOMA) protocol. We employ the queuing theory in our network model to conduct an assessment on the execution delay, energy consumption and the total cost of offloading tasks in a mobile-edge computation offloading (MECO) system. The main goal is to design an energy-efficient MECO decision algorithm in an ultradense Internet of Thing (UD-IoT) network to analyze the tradeoff between execution delay and energy consumption. The proposed scheme jointly optimizes the communication and computation resource management, subject to the energy and delay constraints. Due to the mixed-integer nonlinear problem (MINLP) for resource allocation and computation offloading, an iterative algorithm along with the successive convex approximation (SCA) is proposed to achieve the optimum local frequency scheduling, power allocation, and computation offloading. The superior performance of the proposed MECO algorithm in our UD-IoT network is verified by the extensive numerical results.
A Wideband Substrate Integrated Waveguide Filtering Power Divider

A wideband substrate integrated waveguide (SIW) filtering power divider (FPD) is presented in this paper. It works in the C band. The FPD used two SIW rectangular cavities and one half-mode substrate integrated waveguide (HMSIW) triangular cavity cascade and centered at 7.5GHz with fractional bandwidth (FBW) of 10.6%. The design parameters of the presented FPD are given and the simulated results are proposed with good expectations. This FPD meets the requirements for miniaturization and integration with other microwave circuits.
A New Percentage of Sales Method for Forecasting Additional Funds Needed

A popular financial forecasting approach for forecasting Additional Funds Needed (AFN) is the percentage of sales method (PSM). This method is based on the premise that most pro forma financial statement accounts vary with sales. A company's future sales level and its AFN are derived based on the sales growth rate of the company's product portfolio. However, there is a lack of collective evaluation method for forecasting subjective and fuzzy sales growth rates and AFN. Thus this study proposes a new PSM to forecast sales growth rate of company's product portfolio and further calculating sales revenue and AFN. An illustrative case study was also performed to demonstrate the contributions of the new PSM: (1) providing the financial forecasting team a collective evaluation method for forecasting company's pro-forma financial statement, (2) deriving company's individual product sales growth rate, (3) deriving compound sales growth rate of company's product portfolio, and (4) calculating sales revenue and other amounts in the pro-forma financial statement accounts.
High-Efficiency Dual-Band Power Amplifier Based on High-Selectivity Suspended Substrate Stripline Dual-Band Filter

This letter presents a high-efficiency dual-band power amplifier (PA) based on high-selectivity suspended substrate stripline (SSS) filter. Output match network can realize dual-band impedance matching and manipulate the second and third harmonic. The SSS filter is employed not only to realize low insertion loss but also to provide high-selectivity bandpass responses. The filter is made up of microstrip stepped-loaded resonators and wrapped in a metal cavity. Thus, increasing bandwith and reducing the insertion loss. two transmission zero is generated at both sides of the passband, resulting in high selectivity. Both theoretical analysis and simulated results are presented for validation. For demonstration, a filtering dual-band PA operating at 2.4 GHz and 3.5GHz is designed. It exhibits both high-selectivity bandpass responses and good dual-band performance.
A Robust Single-Sensor Face and Iris Biometric Identification System Based on Multimodal Feature Extraction Network

Joint face-iris identification can integrate complementary information from face and iris to fulfill the requirement of performance improvement and security. However, most of the current face-iris multimodal biometric systems acquire face and iris with different sensors which brings about the increase of capturing complexity and device cost. Besides, they are limited by the identification performance degradation under non-ideal scenarios. In order to address these problems, a robust single-sensor face and iris biometric identification system based on multimodal feature extraction (MFE) network is proposed. Only a single sensor is needed to obtain face and iris images in the proposed system, with the goal of improving recognition performance while minimizing sensor cost and acquisition time. The MFE network is designed as a general network module to extract both face and iris features and it is trained with a triplet framework to reduce intra-class variations and enlarge inter-class variations. Our experimental results on CASIA.v4-distance and FRGC v2.0 non-ideal datasets show that the proposed system achieves better identification performance in terms of Equal Error Rate (EER) and False Reject Rate (FFR), etc. compared with other unimodal and multimodal biometric systems.
Analysis of Walking Movement Using EEG for the Lower-Limb Paralysis

The relationship between brain activity and lower-limb movement has been investigated for several decades. Due to brain activity, the motor cortex area has been represented for the movement controlled by the brain including pattern generation and adaptation. One of the techniques, Brain-computer Interface (BCI), has been developed which makes people interacted with computers to command devices or their bodies using their thought. This work aims to analyze brain signals of walking movement imagine and to reduce the number of EEG channels for lower-limb activity thinking. Each participant has participated in this work for 2 parts (idle and walking movement imagine part) on the same date. At first, all participants were started with idle part and the next is walking movement imagine part for every day of the experiment. The participants were asked to report their general information and acquired a brief protocol about the experiment. The significant differences indicated that changes in alpha and beta activity in the central region are varied for imagining each activity, which has been related to dominant middle activity in the center on the front region. Especially, the change of absolute power of both alpha and beta power activity in the left central area during imagine turn right. The variation of alpha and beta EEG wave is affecting to imagine walking movement. This work has also reduced the channels of EEG for the lower-limb activity of human beings to 4 channels.
An SDN-based Approach to Protect Communication Between Virtual Machines

As a result of the increasing virtualization of computer systems, areas arise in corporate networks and cloud environments that are insufficiently supervised by established security mechanisms, such as firewalls or network monitoring. Conventional firewalls cannot protect Virtual Machines (VMs) because the communication between them runs only within the virtualization server/host. Thus, virtualized systems represent blind spots for network monitoring. They are particularly susceptible to attacks on the data link and network layers (L2/L3 attacks). Software-Defined Networking (SDN) provides the opportunity to better control communication relationships. In this paper, we present an SDN-based approach to protect the communication between VMs on a virtualization host which preserves the multi-gigabit throughput of interconnected VMs.
Power Minimization for Cell-Free Massive MIMO

In this paper, we investigate the total power minimization problem for the downlink cell-free massive multiple- input multiple-output (mMIMO) system, subjecting to the users' quality-of-service (QoS) constraint. Specifically, the total power consists of circuit power, data transmission power, and backhaul power. Two precoding schemes are considered in this paper, namely conjugate beamforming (CB) and zero-forcing (ZF) method. It is proved that the power minimization problems for both precoders can be reformulated as the second-order-cone programming (SOCP) and hence, are easily determined. Simulation results indicate that, in the low QoS SE regime, the total power required by ZF precoder is larger than that of CB case. However, the superiority of CB precoder in power saving vanishes in both medium and high QoS SE regimes.
A Comparison of Optical Transport Technologies for Wireless Communications Using Optical Ground Wire in Smart Grid

Realization of smart grid is a big challenge for not only electrical power grid but also communication network, because real time and highly reliable mission critical communication network based on wireless communications will be indispensable. Furthermore, requirements of wideband communication network for sensing and monitoring utilities and maintenance works are also increasing. To realize future communication network cost-effectively and energy-efficiently, centralized radio access network architecture that use optical fiber based fronthaul network has attracted attention from both academia and industry. In this paper, we propose a design of wireless access network using existing optical ground wires for centralized radio access network architecture for the first time. In the proposed configuration, remote radio heads on transmission towers are arranged linearly. We investigate the applicability of optical data transport technologies of physical layer split, analog radio-on-fiber, and common public radio interface. Finally, we comparatively analyze the deployment costs under different transport technologies and our results show that physical layer split and analog radio-on-fiber with subcarrier multiplexing result in lower cost than other technologies.
What Makes a National Football League Team Successful? an Analysis of Play by Play Data

The National Football League (NFL) is one of the most popular sports in North America. The league showcases many strong athletes and winning is very important to all teams; however, for every winning team, there is a losing team, and it is the coaches' responsibility to decide what plays to call in order to help their teams win. Data mining play data can help show trends and areas where the top teams in the NFL excel by looking at questions like how often certain plays are run, how many yards do the plays get and where on the field are touchdown scored. With the help of data mining intelligent tools such as Naive Bayes, decision tree algorithms and association rules, we worked to isolate the areas where the best teams in the league separate themselves and produce winning franchises. By classifying the teams into two categories - the top teams and bottom teams - we were able to compare the two classes for differences which explain what results in more success in the league. Although we found that most teams - both top and bottom teams - use similar plays, there were also factors that distinguished the two types of teams. This research highlights these specific factors and some overall distinctions found between the more successful versus less successful teams to the NFL community.
Robust Indoor SLAM based on Pedestrian Recognition by Using RGB-D Camera

The scene rigidity assumption limits the use of most feature-based visual simultaneous localization and mapping (SLAM) systems in real world. To relax this assumption and improve the robustness and accuracy, we develop a novel RGB-D visual SLAM system based on ORB-SLAM2 and dynamic object detection in this paper. The developed system is able to detect indoor pedestrians in dynamic scenarios and then deletes the corresponding features. Such a manipulation enables the subsequent visual SLAM module to generate a more accurate camera trajectory since dynamic objects are removed. Experimental results on TUM RGB-D dataset and real world scenarios show that the proposed system reduces the trajectory estimation error by one order of magnitude in comparison with the original ORB-SLAM2. In addition, the results tested on NVIDIA Jetson TX2 show that its processing speed surpasses 24 frames per second.
Resource Optimization for Cognitive Radio Based Device to Device Communication Under an Energy Harvesting Scenario

Device to device (D2D) communication has emerged as a potential candidate for next-generation communication networks to support higher data rates and minimize power consumption. The spectral and energy efficiency of D2D communication can further be improved using cognitive radio (CR) and radio frequency energy harvesting (RF-EH) technologies. Therefore, in this paper, a sum throughput maximization problem for a CR assisted D2D network is modeled considering the RF-EH mechanism. A joint optimization problem is formulated for sum-rate maximization of cellular and D2D users by considering power allocation, channel assignment, user pairing and transmission time ratio allocation. Then, the problem is transformed into a standard convex optimization problem subject to power constraints at individual nodes, interference constraint, and the individual rate. Furthermore, the secrecy capacity requirements of cellular and D2D nodes are also considered. The duality theory is used to decompose the problem into multiple sub-problems and Karush-Kuhn-Tucker conditions are exploited to provide the solution of the sub-problems. The simulation results are provided for the validation of our proposed schemes.