Miracle-dz/UniFAR-FTUs huggingface dataset:
Significance of Linguistic Indicators for Location Prediction

Twitter is one of the most widely and frequently use microblog. Thousands of tweets are being posted daily on twitter. With the tweet, peoples are share their current location using check-ins point of interest that is known as geo-tagged tweet. However, most of the people not share their locations with the tweets. Location prediction is beneficial for various fields such as targeted advertising, rescue operations during a disaster and recommendation, etc. On the other hand, location prediction from the text of the tweet is a difficult task as it encounters many problems such as spelling mistakes, nonstandard English, etc. In this study, we propose a new set of linguistic features to predict locations from the texts of the tweet more effectively. Two twitter datasets, five popular ML models and five evaluation metrics are used in experiments to explore the impact of the proposed features. The experimental results reveal that proposed set of features outperform as compared to baseline features. In addition, AUC also demonstrates the significance of the proposed features in the form of 59.94 % and 69.65% using MSM2013 and Ritter dataset respectively. The findings indicate that proposed set of features are the most significant indicators and has proved their effectiveness and strong relationship with location prediction.
Selection of Logistics Service Provider for e-Commerce Using AHP and TOPSIS: A Case Study of SMEs in Thailand

e-Commerce plays an important role in the modern business model. A key success factor of e-Commerce is product delivery, which is done by third-party logistics service providers (LSPs). Each provider has different business objectives and behavior. Therefore, the selection of LSP is a very important part that helps e-Commerce provide efficient customer service. This work proposes the framework of LSP selection for e-Commerce by applying an Analytic Hierarchy Process (AHP). Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is also utilized for the last process of AHP analysis for comparison with the conventional AHP. AHP analysis of the survey data shows that the major criteria of selection of LSPs are location, cost, and delivery. Comparing results of AHP and TOPSIS methods shows that the result from TOPSIS is very similar and consistent in value with the results from AHP.
Robust Control of Double Star PMSG using Artificial Neural Network

In this paper, we apply artificial neural networks to control double star smooth poles permanent magnet synchronous generator-PWM rectifiers set connected to standalone DC network. First, we present the model of the machine. Then a command based on a Multi-Layer Perceptron (MLP) neural network is applied to the control of the machine. Database generating method for neural network based on variations in machine parameters is also described in the document. The results show the performance of the neural network controller in robustness.
CycleGAN-based speech enhancement for the unpaired training data

Speech enhancement is an important task of improving speech quality in noise scenario. Many speech enhancement methods have achieved remarkable success based on the paired data. However, for many tasks, the paired training data is not available. In this paper, we present a speech enhancement method for the unpaired data based on cycle-consistent generative adversarial network (CycleGAN) that can minimize the reconstruction loss as much as possible. The proposed model employs two discriminators and two generators to preserve speech components and reduce noise so that the network could map features better for the unseen noise. In this method, the generators are used to generate the enhanced speech, and two discriminators are employed to discriminate real inputs and the outputs of the generators. The experimental results showed that the proposed method effectively improved the performance compared to traditional deep neural network (DNN) and the recent GAN-based speech enhancement methods.
Learning-based Distributed Multi-channel Dynamic Access for Cellular Spectrum Sharing of Multiple Operators

Spectrum sharing is one of the ways to overcome the inefficient use of wireless resources. For successful spectrum sharing, dynamic spectrum access based on reinforcement learning has been actively studied. The environment in which mobile network operators (MNOs) share spectrum has not been considered. This paper considers a cellular spectrum sharing scenario where MNOs share the single band in distributive manner. For successful spectrum sharing in the scenario, we propose a learning-based distributed multi-channel dynamic access scheme. The proposed scheme enables multi-channel access dynamically while minimizing interference among MNOs. To evaluate our proposed scheme, we conduct system level simulations in simple network environments. The simulation results indicate that the proposed scheme not only improve the throughput of the network, but also guarantee the fairness among operators.
High-Performance GaAs/AlAs Terahertz Quantum-Cascade Lasers For Spectroscopic Applications

We have developed terahertz (THz) quantum-cascade lasers (QCLs) based on GaAs/AlAs heterostructures for application-defined emission frequencies between 3.4 and 5.0 THz. Due to their narrow line width and rather large intrinsic tuning range, these THz QCLs can be used as local oscillators in airborne or satellite-based astronomical instruments or as radiation sources for high-resolution absorption spectroscopy, which is expected to allow for a quantitative determination of the density of atoms and ions in plasma processes. The GaAs/AlAs THz QCLs can be operated in mechanical cryocoolers and even in miniature cryocoolers due to the comparatively high wall-plug efficiency of around 0.2% and typical current densities below 500 A/cm$^2$. These lasers emit output powers of more than 1 mW at operating temperatures up to about 70 K, which is sufficient for most of the abovementioned applications.
Drift-Free Indoor Pedestrian Dead Reckoning using Empirical Mode Decomposition

In recent years pedestrian dead reckoning (PDR) has been a popular indoor localization technique that takes advantage of inertial navigation sensors (INS) of smartphones due to its availability. Despite the convenience of INS, noise has been a recurring problem in PDR which encourages many researches on noise filtering. This paper proposes a model that utilizes empirical mode decomposition (EMD) as the noise filter. EMD is an unconventional filter being applied in PDR to remove noise by processing the raw signal before implementing step counts, step lengths and heading estimation. The experiment made using the proposed model was able to perform the standard PDR technique without overlaps in heading estimation.
On-Line Estimation of Base Station Location

In the wardriving type method proposed in this letter, the localization of the base stations (BSs) is performed based on unscented Kalman filter with state constraints (UKFC). The state vector is formed by the coordinates and velocities of BSs, and the observations are localization measurements made by a mobile station. The localization measurements show that the BSs are situated on a 3D surface, and this information, fused with the constraint that the base stations form a rigid network, lead to a consistent estimator. The distances between BSs provided by cooperative localization networks, added as constraints, results in a drastic reduction of estimation uncertainty.
Increasing the Share of Wind Power by Sensitivity Analysis based Transient Stability Assessment

The massive integration of wind power plants into the transmission system calls for a careful scrutinization of their dynamic behaviour and the interactions with the power system in the transient stability time-frame of interest. This paper presents a sensitivity analysis based approach to investigate the impact of full converter wind turbine generators on the transient stability of the interconnected power system. A combination of different fault ride through and voltage support mechanisms such as 1) low voltage ride through, 2) voltage-dependent reactive power injection, 3) current limitation strategy during grid faults, and 4) the application of inertia emulation capability are considered for the sensitivity analysis. Critical clearing times are used as the transient stability indicator. The implementation of the wind turbine models are according to IEC 61400-27-1 and the assessment is done through time domain simulations using DIgSILENT PowerFactory. The results on a modified version of the IEEE 9-bus system show that by more effective use of the existing controllers of wind turbines, the share of wind power plants can be increased substantially.
Efficient Technique for the Analysis of Electromagnetic Problems Involving Antenna Trajectories in Complex Scenarios

This letter presents an efficient approach for the electromagnetic analysis of complex scenarios involving transmitting antennas moving along predetermined trajectories. In order to efficiently reduce the size of the problem for each position of the antenna, we propose a technique based on macro basis functions that are dynamically generated using a ray-tracing analysis. The multilevel fast multipole algorithm is also included in order to reduce the memory consumption and speed up the solution process. Some representative test cases serve to validate the efficiency and performance of the proposed approach.
A Clustering Approach Based on Genetic Algorithm for Wireless Sensor Network Localization

Exact localization of randomly deployed sensor nodes is critically important in Wireless Sensor Networks (WSNs) setup especially for tracking and monitoring purposes. With this advantage, the preferred approach can be applied to obtain the location of a particular node based on all existing information known in the WSN. In this paper, the normal Genetic Algorithm (GA) will be extended by a clustering approach which contributes to the increase in localization accuracy. The number of clusters constructed in the network helps to improve the accuracy. The selection of the initial cluster center is done in order to lower the intensity of distant points. Simulation results show the proposed approach can reach better results in comparison with the centroid and Distance Vector-Hop (DV-Hop) methods.
Detection System for Capacitive Plantar Pressure Monitoring

This study presents a detection system with a prototype acquisition module and display interface for capacitive plantar pressure monitoring. The detection system mainly focuses on a system-level design (not a system on chip). The prototype acquisition module consists of commercial ICs and self-designed chips: A capacitance-to-voltage converter (CVC) and a 10-bit fully differential successive approximation register analog-to-digital converter are fabricated by the TSMC $0.18~\mu \text{m}$ 1P6M 3.3 V process. The feature of the CVC is selectable for the detection points from 1 to 64, which is controlled by the digital codes of a field-programmable gate array (FPGA). It also has the embedded functions of crosstalk attenuation (CA) for cross-type capacitive sensors and a self-separated input signal for capacitive plantar pressure monitoring. The CA can effectively reduce parasitic capacitances and influences of the temperature and relative humidity of the environment: An $\text{M}\times \text{N}$ array only requires M + N tracks of I/O ports to reduce detected complexity. The measured results shown on the display interfaces reveal the positions and amplitudes (voltage swings) of the corresponding capacitances. The conversion time for one capacitance is 0.4 ms, and the maximum conversion time for 64 capacitances is 25.6 ms. A handmade cross-type capacitive array and a cross-type capacitive plantar pressure sensor are used to demonstrate the detection system.
Vector Control of Permanent Magnet Synchronous Machine with Reduced Switch Five-Level Voltage Source Inverter

This paper presents a six switch five-level (0, ±Vdc/2, ±Vdc) VSI (voltage source inverter) fed PMSM (permanent magnet synchronous motor) drive. The circuit topology and number of switching devices are same as that of basic three phase two-level voltage source inverter. The implementation of reduced switch 5-level inverter improves the pulsating torque and dynamic speed response under various operating conditions. The concept of vector control is applied to obtain good dynamic performance and the machine can also be control like separately excited DC motor. Three PI controllers is employed as speed, torque, and flux controller and PWM technique is used to trigger switches of six switch 5-level inverter. The circuit configuration and PMSM model are developed in MATLAB/SIMULINK environment and the performance is investigated with change in speed and load torque. Based upon simulation result the performance of proposed technique is recommended for significant reduction in THD (total harmonics distortion) of stator current and ripple in electromagnetic torque without increased in additional cost of overall system.
Emergency Message Dissemination in Vehicular Networks: A Review

The number of vehicles on the roads has increased proportionally over the last couple of years and this number is likely to rise due to the increase in population growth and the number of vehicles that are manufacturing every day. This high traffic density leads to several problems, from which effectively disseminating the emergency messages is a major concern. Keeping in view the dynamic characteristics of VANETs, significant challenges are faced in disseminating the message across the network. The major challenges are the broadcast storm problem, hidden node problem and the packet collision. Many studies have been performed to devise an effective and reliable mechanism for disseminating emergency messages in a Vehicular ad-Hoc Network (VANET). Researchers have proposed different models to tackle various types of scenarios for emergency message dissemination. This paper not only reviews some recent contributions to emergency message dissemination in vehicular networks but also discusses various proposed methods based on Intelligent Transportation System (ITS), Internet of Things (IoT), Priority messaging, Clustering approach, Software Defined Network (SDN) and Fog Computing. We have also tried to explore the latest developments in emergency message dissemination using 5G networks.
Compliant Control of Single Tendon-Sheath Actuators Applied to a Robotic Manipulator

Tendon-sheath actuators (TSAs), similar to Bowden cable-based series elastic actuators (SEAs), have a wide range of applications in robots. However, when applying them to manipulators, force sensors cannot be mounted due to limited space. Therefore, this paper proposes a new method for compliant control based on a dual encoder, and the existing transmission model was improved by considering the joint torque. To validate the proposed method, experimental setups composed of a motor, a tendon-sheath component, a robotic joint, force sensors, and position sensors are established. The transmission processes are interpreted clearly through a position control mode. Parameter identifications without external disturbance force are conducted to acquire the ideal transmission model, and the error value between the experiment data and the fitted curve is measured. The availability of the presented scheme is verified by compliant control experiments, including collision detection and hand guiding, and the frequency characteristics of the actuator are analyzed. The results show that the function can be realized without force and torque sensors during the whole process.
Design and Analysis of 15.8 GHz LC-VCO Using PMOS Cross Coupled

This paper presents a design of low phase noise LC-VCO. A circuit using LC tank oscillator with PMOS cross coupled along with varactor in PMOS type is proposed. The VCO uses 5 bits for coarse and fine-tuning frequency. The post-layout simulation shows the frequency tuning range from 14.2 to 15.8 GHz has phase noise of −95 dBc/Hz, −114.5 dBc/Hz at 100 kHz and 1 MHz offset, respectively. The circuit is designed based on 28nm process under supply voltage of 0.9 V and current consumption of 10 rnA.
Subtraction-Positive Similarity Learning

Many methods evaluate the similarity between two vectors $x$ and $y$ by norm or metric learning. They need to get a subtraction vector $x$ - $y$ and then evaluate its length. However, only considering the length of subtraction vector and ignoring its position may lost a lot of information. In this paper, we propose to utilize the position information of subtraction vector to evaluate the similarity. As the subtraction vector between $x$ and $y$ can be expressed either by x-y or by y-x, its distribution is centrosymmetric and redundancy. Thus, only half of the subtraction vectors are chosen and named as subtraction positive vectors. The subtraction positive vectors from different classes or from the same class are then modeled by Gaussian mixture models or deep neural network. Experiments were carried out on speaker verification databases including NIST SRE08, SRE10 and NIST i-vector challenge 2014. Results demonstrate the effectiveness of the proposed method.
Finding summaries to obtain event phrases from streaming Microblogs using Word Co-occurrence Network

Event detection from social media data has gained much momentum in research community. Existing event detection techniques are not scalable over less frequently discussed events. To handle this, BArank was proposed to identify keyphrases from topic specific Twitter data. Another concern for event detection from Microblogs is handling streaming data. Real-time Tweets are handled using Hadoop platform, or by referring old and new corpus which are complex and computationally expensive. However, handling streaming data depends upon the event detection technique. In this research work, different parameters are proposed to obtain discrete set of Tweets. The resulting phrases from BArank are ranked using Multi-Criteria Decision Making approach of Analytical Hierarchical Process (AHP) to identify events. Experimental results have proved high effectiveness of the proposed approach for FA Cup dataset with improved keyword-precision and keyword-recall without affecting topic-recall
Parameter identification of Anand constitutive models for SAC305 using the intelligent optimization algorithm

Through finite element simulation to obtain the reliability of electronic products is an effective way to reduce experiment cost and shorten the development period for electronic industry. The accurate identification of the parameters of the constitutive model is a prerequisite for finite element simulation. One of the most widely employed constitutive model for describing the mechanical properties of solder alloys is the Anand model, which are determined by nine material parameters. The identification method of the nine material parameters of the Anand model is by gradient descent method, which is sensitive to the value of the initial values. An effective approach to solve this problem is employing intelligent algorithm to obtain the nine parameters of Anand model. The Particle Swarm Optimization (PSO), which is a parallel intelligent algorithm based on evolving, has the advantages of fast search speed, high efficiency and easy to implement. In this work, through gradient descent method and PSO, we obtain the Anand constitutive model parameters of SAC305 from a set of uniaxial tensile tests of SAC305 performed under different constant strain rates and different constant test temperatures. Compared with the gradient descent method, the PSO algorithm is an effective method to obtain the Anand parameters of SAC 305. The PSO algorithm shows a great application in determining the constitutive model parameters.
Joint Optimization of Access and Backhaul Links for UAVs Based on Reinforcement Learning

In this paper, we study the application of unmanned aerial vehicle (UAV) base stations (BSs) in order to improve the cellular network capacity. We consider flying BSs where BS equipments are mounted on UAVs, making it possible to move BSs freely in space. We study the optimization of UAVs' trajectory in a network with mobile users to improve the system throughput. We consider practical two-hop communications, i.e., the access link between a user and the UAV BS, and the backhaul link between the UAV BS and a macrocell BS plugged into the core network. We propose a reinforcement learning based algorithm to control the UAVs' mobility. Additionally, the proposed algorithm is subject to physical constraints of UAV mobility. Simulation results show that considering both the backhaul and access links in the UAV mobility optimization is highly effective in improving the system performance than only focusing on the access link.
Characterization of Coating on Fine Interconnect Materials

Interfacial materials, such as intentionally deposited coatings enhance the behavior of interconnect materials by providing unique properties, then the existing interconnects available in the market. For example, an organic coating on ultrafine solder powder particles prevent formation of native oxide, thus reducing the propensity of agglomeration. The powder particles are in the range between 2 and $8\mu \mathrm{m}$ and organic coating is in a few nanometer thicknesses, which is a challenge to observe, measure its thickness and identify its chemical formulae. The paper discusses characterization of the nano-coating using Transmission Electron Microscope (TEM). Thickness of coating is in the range of 2 to 4.5nm, and it is measured by depth profiling using Auger Electron Spectroscopy (AES). Chemical formulae of the coating is identified using Time of Flight Secondary Ion Mass Spectrometry (ToF-SIMS). The observation resolved ultrafine powder process optimization to manufacture un-agglomerated ultrafine solder powders with high yield. In addition, the powder with clean surface showed good solderability and reliability. Similarly, electrodeposited gold over silver wires are analyzed using ToF-SIMS. Depth profiling of wires with good and inferior bondability differentiated the surface of the wires corresponding to their composition. Hence, their differences were later on resolved by process modification. On the other hand, nano-indentation on coating decipher the mechanical behavior of the wire surface, exhibiting good correlation on wire bond performance. The findings enabled to develop high quality bonding wires through chemical and mechanical analysis. An observation of gold distribution over silver free air ball is also discussed.
MFCC coefficient and ANN classifier applied to roadway classification

Currently, Intelligent driver assistance systems provide more satisfactory solutions to the field of road safety. These are based on the use of powerful image or signal processing algorithms in terms of speed, accuracy of calculation and decision making. This document describes an intelligent roadway classification system in real-time in 4 classes: asphalt, gravel, snow and stone road. This system relies on the use and combination of two algorithms; the Mel Frequency Cepstrum Coefficient algorithm that extracts the characteristics of each type of road from the sound due to friction of the vehicle wheels with the road, and Multilayer Perceptron algorithm, which receives its characteristics to its inputs and classifies them. This document also describes the stages of the process of learning and testing. The robustness of this system is shown by the obtained satisfactory experimental results that combine between the respective advantages of the MFCC descriptor and MLP classifier.