Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Churn Prediction and Customer Segmentation Framework for Telco Business

In the telco industry, attracting new customers is no longer a good strategy since the cost of retaining existing customers is much lower. Churn management becomes instrumental in the telco industry. As there is limited study combining churn prediction and customer segmentation, this paper aims to propose an integrated customer analytics framework for churn management. There are six components in the framework, including data pre-processing, exploratory data analysis (EDA), churn prediction, factor analysis, customer segmentation, and customer behaviour analytics. This framework integrates churn prediction and customer segmentation process to provide telco operators with a complete churn analysis to better manage customer churn. Three datasets are used in the experiments with six machine learning classifiers. First, the churn status of the customers is predicted using multiple machine learning classifiers. Synthetic Minority Oversampling Technique (SMOTE) is applied to the training set to deal with the problems with imbalanced datasets. The 10-fold cross-validation is used to assess the models. Accuracy and F1-score are used for model evaluation. F1-score is considered to be an important metric to measure the models for imbalanced datasets since the premise of churn management is to be able to identify customers who will churn. Experimental analysis indicates that AdaBoost performed the best in Dataset 1, with accuracy of 77.19% and F1-score of 63.11%. Random Forest performed the best in Dataset 2, with accuracy of 93.6% and F1-score of 77.20%. Random Forest performed the best in Dataset 3 in terms of accuracy, at 63.09%, while Multi-layer Perceptron performed the best in terms of F1-score, at 42.84%. After implementing churn prediction, Bayesian Logistic Regression is used to conduct the factor analysis and to figure out some important features for churn customer segmentation. Churn customer segmentation is then carried out using K-means clustering. Customers are segmented into different groups, which allows marketers and decision makers to adopt retention strategies more precisely.
Neural Network based Indirect Estimation of Functional Parameters of Amplifier by extracting features from Wavelet Transform

In this work, a cost-effective methodology has been proposed for estimating functional parameters of analog circuits to ensure faster production testing. The new test algorithm predicts the functional parameters of the circuit under test from the output response, using wavelet transform as a preprocessor and artificial neural network (ANN) as a prediction algorithm. Daubechies wavelet transform is used for analyzing the output response obtained by exciting the test circuits with a pseudorandom analog signal. The dimension of extracted features is reduced by the principal component analysis method. Specification of the test circuits is then predicted with the help of an artificial neural network. The proposed methodology for performance prediction is verified for Two-Stage Operational Amplifier (OPAMP). The proposed algorithm is also validated with a practical IC, i.e., TLV2762 from Texas Instruments. The accuracy of the proposed prediction algorithm confirms that the proposed methodology can be easily implemented into a test board that can be treated as an inexpensive tester.
A Circularly-Polarized Zero Beam-Squinting Leaky-Wave Antenna Using NRI-TL Metamaterials with Increased Gain

A circularly-polarized metamaterial leaky-wave antenna (LWA) at 5.5 GHz is presented, which exhibits zero beam-squinting (ZBS) at broadside with an increased gain. It consists of two oppositely-directed negative-refractive-index transmission-line (NRI-TL) LWAs. The two LWAs radiate their main beams in opposite directions, and the combination of the two beams results in a broadside beam. The two LWAs are fed through a NRI-TL balun that adds a 180° phase difference between the two output signals. The addition of the balun doubles the gain of the metamaterial LWA, and makes it circularly polarized. The gain of the antenna is 11.75 dBi at 5.5 GHz, 3.35 dBi higher compared to the antenna without the balun. The ZBS bandwidth of the antenna is 0.9 GHz, the axial ratio ranges from 1.1 to 1.4, and the average radiation efficiency is 80%.
Metasurface Luneburg Lens Antenna Implemented with Substrate-Integrated-Holes at Ka-Band

In this work, we study the substrate-integrated-holes (SIHs) metasurface. This metasurface consists of dielectric-filled cavities in the two conductors of a parallel plate waveguide (PPW). The accurate control of the effective refractive index in the SIH structure is demonstrated. Additionally, we design a metasurface Luneburg lens operating at 28 GHz based on the studied structure. The lens is fed with 11 waveguides with an angular spacing of 10 degrees, and thus, 100 degrees beam steering is obtained. A flare is used to match PPW to free-space. Since the wave propagates mainly in the air in the studied metasurface, low losses are obtained in the designed lens.
Broadband D-Band Antenna Array Based on 64 Stepped Horns for 5G Backhauling Applications

This work proposes an 8×8 D-band antenna array constructed from 64 stepped horns. The structure is designed to be machined from single metallic block adhered with an additional bottom metallic cover. The proposed design can be realized using low-cost commercially accessible milling tools. The configuration at hand is composed of three building blocks: transition, power divider and horn radiator. The array footprint has a volume of 30×30×8 mm3 while a 29.5% fractional bandwidth is achieved from 130 till 175 GHz where reflection coefficient is lower than 7.8 dB. Radiation characteristics are investigated for both single horn radiator and 8×8 antenna arrays where the obtained average gain values are 15.25 and 32 dBi, respectively. Directive pencil beam, required for 5G backhauling communication, has been confirmed for 8×8 antenna array where the half power beam width is 1.5° for both E and H-planes while grating lobes appear at 28°.
A dual-band deployable Cassegrain reflector antenna for Space applications

This paper presents a dual-band deployable Cassegrain reflector antenna, that operates in K-band for reception and Ka-band for transmission. The antenna achieves gain of approximately 38 dBi in K-band and 42 dBi in Ka-band. The antenna fits into a stowed volume of 2.15U CubeSat and weigh approximately 2.6 kg. The paper also demonstrates different design stages that were involved in achieving the proposed antenna system. Furthermore, the simulated system level performance calculated in CST as well as in HFSS are compared and presented in this paper.
Ghost Image Removal Using Physical Layer Spatial Asymmetry in Frequency-Diverse Computational Imaging

Ghost images are one of the undesired artifacts in an imaging system besides a real image. It also happens in frequency-diverse computational imaging used to reduce the complexity of traditional imaging systems using a mechanical scan or active circuits. We present that by breaking the symmetric structure of frequency-diverse wave-chaotic metasurface antennas, the ghost image artifacts in reconstructions retrieved using frequency-diverse computational imaging can be eliminated.
Travel Time Prediction in Missing Data Areas: Feature-based Transfer Learning Approach

Travel time prediction based on deep learning techniques has received much attention in recent years. It is of great importance to advanced traffic management systems but remains a challenging problem as historical data are available in some but not all areas. Our preliminary experiments on New York City road network demonstrated that GPS pings recorded by in-vehicle GPS devices providing speed, position and time, do not cover the whole road network, thus, travel time in some regions could not be predicted accurately. However, predicting accurate travel time in each and every region in a city is fundamental to intelligent transportation systems. To predict travel time of target areas where little historical traffic data are available, we propose a transfer learning framework that exploits historical data of some source areas which are data-abundant regions. To accurately predict travel time in source areas, we propose a hybrid architecture that models the spatial and temporal closeness as well as the road network typology. Experimental results on New York City road network show that our proposed model can achieve competitive performance comparing to baseline methods and classic regression models.
Design of a Planar Wideband WideScan Phased Antenna Array

The design of a novel planar wideband phased antenna array operating over 7.5-19.5 GHz has been presented. The array radiator consists of a feeding patch and a couple of shorting patches which are connected to the ground plane with shorting vias and isolating vias. Good impedance matching can be obtained due to the shorting vias while the common-mode resonance is eliminated by the isolating vias. Both the infinite unit-cell model and the finite simulation model have been implemented to investigate the performance of the proposed antenna array. The obtained results have shown that the active VSWR is less than 2 for wide scan in the E-plane while the active VSWR is less than 3 for 45° scan in the H-plane. The whole thickness of the radiator is 3.4mm, indicating the possibility for low-profile applications.
Analyzing the Effects of Road Type and Rainy Weather on Fuel Consumption and Emissions: A Mesoscopic Model Based on Big Traffic Data

Road transportation accounts for significant percentages of urban energy consumption and carbon emissions. Therefore, it is important to predict and analyze the fuel consumption and emissions for on-road vehicles, which are varied under different conditions. Previous studies have shown that some traffic elements such as road type and weather condition have considerable influence on transportation fuel consumption and emissions. However, limited to the data availability, most of the existing studies focus on specific routes or scenarios, and few of them consider the effects of road type and weather condition systematically at large scale. In this research, a data-driven mesoscopic model was developed to investigate the effects of road type and weather condition on the link-level fuel consumption and emission factors based on big traffic data. This built model utilized the neural network for the prediction algorithm with inputs including road type, weather condition, and link-level aggregated operation data obtained through link-based segregation over trajectory snippets. The investigation was carried out with real-world big traffic data collected from 10,944 taxis over a 2-month period of operation in Shenzhen, and produced reliable predictions for four road types with clear and rainy weather conditions. Both statistical analysis and model prediction results showed that fuel consumption and emission factors are lower in low-speed range for freeway and expressway, and are lower in middle-speed range for main road and secondary road. In addition, rainy weather condition tends to have lower fuel consumption and emission factors than clear weather condition.
Broadband and Wide-angle Microwave Metamaterial Absorber with Effective EM Wave Absorption in the S-, C-, X- and Ku-band

A broadband microwave metamaterial absorber with wide-angle property is introduced in this paper. The bandwidth of effective absorption (over 90% absorptivity) of the proposed absorber is from 2.2 GHz to 18.4 GHz, covering almost the whole S-, C-, X-, and Ku-band, with relative absorption bandwidth of 157%. The total thickness of the proposed absorber is 15 mm, which corresponds to 0. 11λ at the lowest operating frequency. To provide wide-angle and broadband frequency matching, a wide-angle impedance matching layer is used in the unit cell design. The proposed absorber can realize more than 80% absorptivity for TE polarization and more than 90% absorptivity for TM polarization from S-band to Ku-band when the incident angle is less than 50°. Equivalent circuit method is used to analyze the proposed absorber and the circuit model analysis results agree quite well with the numerical full-wave simulation.
Image Defect Detection and Segmentation Algorithm of Solar Cell Based on Convolutional Neural Network

The use of infrared or electroluminescence(EL) images of solar cell modules for defect detection is a very important method in non-destructive testing. Traditionally, this work is done by skilled technicians, which is time-consuming and susceptible to subjective factors. The surface defect detection method of solar cells based on machine learning has become one of the main research directions because of its high efficiency and convenience. For this reason, this paper proposes an improved fusion model based on VGGNet and U-Net++, which is used for defect detection and segmentation of EL images of solar cells. In the defect detection stage, the input image is processed pertinently, and by modifying the convolutional layer and the fully connected layer of the network, while improving the performance of the algorithm, it accelerates the convergence and avoids the phenomenon of over-fitting. In the defect segmentation stage, the defect location is marked based on the public data set, which is used for the training of each segmentation model, and the effect of different segmentation networks is compared to select a reasonable model. The experimental results show that the defect detection accuracy of the improved VGG16 network on the elpv-dataset is 95.2%, and the U-Net++ defect segmentation model has an average MIoU value of 0.955, which is better than other existing methods.
Algorithms for Preemptive Co-scheduling of Kernels on GPUs

Modern GPUs allow concurrent kernel execution and preemption to improve hardware utilization and responsiveness. Currently, the decision on the simultaneous execution of kernels is performed by the hardware, which can lead to unreasonable use of resources. In this work, we tackle the problem of co-scheduling for GPUs in high competition scenarios. We propose a novel graph-based preemptive co-scheduling algorithm, with the focus on reducing the number of preemptions. We show that the optimal preemptive makespan can be computed by solving a Linear Program in polynomial time. Based on this solution we propose graph theoretical model and an algorithm to build preemptive schedules which minimize the number of preemptions. We show, however, that finding the minimum amount of preemptions among all preemptive solutions of optimal makespan is a NP-hard problem. We performed experiments on real-world GPU applications and our approach can achieve optimal makespan by preempting 6 to 9% of the tasks.
Characteristic Mode Analysis of Split-Dipole for Dual-Layer Metasurface Lens Design

A split-dipole-based unit cell is modeled with characteristic mode analysis for the dual-layer metasurface lens at Ka-band. The split-dipole is first analyzed to show the modal behaviors over the operating band. Then the first two modes are selected for reflectionless incidence by rotating the split-dipole with a certain angle. After that, a planar unit cell is developed for substrate implementation with a magnitude of transmission coefficient better than 0.72 and a phase shift range of 360°. As an example, a dual-layer metasurface lens with an aperture size of 162 mm × 162 mm and a focal length of 135 mm is designed at 30 GHz. The achieved results show the peak gain of 31.6 dBi at 30 GHz with an aperture efficiency of 45% which well verified the proposed method. The metasurface lens can be realized on a single-layer substrate which shows great potential for low-cost communication and radar applications.
An Antenna Array for Photonic Beam Switching in mm-Wave Wireless Communications

We present a photonic transmitter at E-band for beam switching in mm-wave wireless links. First, we discuss the trade-offs between beam switching and beam steering with arrays of photonic transmitters. Then, the designed transmitter is presented; it includes two 2×2 sub-arrays of stacked patches fed independently by two photodiodes. The transmitter is placed at the focal plane of a PTFE lens to increase the overall gain. Excitation of either sub-array allows 1D beam witching between ±2. 7º for accurate beam alignment or reconfigurable links.
TDDB Reliability in Gate-All-Around Nanosheet

Time dependent dielectric breakdown (TDDB) reliability is studied on interfacial layer (IL)/high-K gate stack of Gate-All-Around Nanosheet (GAA-NS) N- and P-type Field Effect Transistors (FETs) with volume-less multiple threshold voltage (multi-Vt) integration scheme enabled by the dual dipoles (n-dipole and p-dipole). We report for the first time Key TDDB Modeling parameters: voltage acceleration exponent (VAE), Weibull slope ($\beta$), and activation energy (Ea) and show robust TDDB reliability in multi-Vt NS transistors enabled by different dipoles.
Charge pumping source-drain current for gate oxide interface trap density in MOSFETs and LDMOS

In this paper, we obtained interface trap density through source-drain CP (charge pumping) current, instead of conventional substrate CP current in MOSFETs and LDMOS. We observed that interface density is same for both source drain CP current and substrate CP current when LOCOS (local oxidation of Silicon) field oxide is used to separate substrate (body) and source in different channel MOSFETs. However, source-drain CP current is higher than substrate CP current when modern STI (shallow trench isolation) is used to separate the substrate and source in different channel MOSFETs. The difference between Ids and Isub is independent on Ibase voltage. Furthermore, we showed that the source-drain CP current is a useful method for interface traps in LDMOS. We observed that interface traps density is higher in drift region than in channel region in LDMOS.
Technological Solution for Enabling 5G NR and TVRO Peaceful Coexistence in C-band

This work proposes a technological solution for allowing a peaceful coexistence between 5G New Radio (5G NR) and satellite Television Receive Only (TVRO) services in C-band. A detailed analysis and an efficient redesign of a typical low-noise block converter feeder (LNBF), used by TVRO home users, is conducted aiming to reduce the 5G NR interference, which is going to operate at TVRO adjacent channels in some countries. Basically, we exploit two enhancements on the LNBF topology, namely: increasing the 1 dB compress point (P1dB) (input) level of its low noise amplifiers (LNAs), in both first and second stages; incorporating a low-cost and high-performance passband RF filter. As the main contribution, it is demonstrated the protection distance - calculated in accordance to the International Telecommunication Union (ITU) recommendations- might be significantly reduced from 20.85 km to only 22 m, by using the proposed high-performance LNBF.
Sulfate and Hydroxide Supported on Zirconium Oxide Catalysts for Biodiesel Production

Biodiesel is currently produced by homogeneous catalysis. More recently however, heterogeneous catalysis is being considered as a cheaper alternative to the homogeneous process. In this research project, heterogeneous catalysts of zirconium oxide were produced by impregnation. Zirconium oxide impregnation with sulfuric acid produced acidic solid catalysts. It was determined that impregnation and calcination at 550C (SO4/ZrO2-550C) produced the best catalyst for palmitic acid esterification with 10 wt % as the optimum concentration in esterification of palmitic acid. SO4/ZrO2-550C was successfully recycled for eight consecutive runs before permanent deactivation. Its sulfur content was 1.04 wt % using SEM-EDS and 2.05 wt % using XPS for characterization. BET surface area was 90.89 m/g. The reaction mechanism over Bronsted acid (SO4/ZrO2-550C) and Lewis acid (Al2O3) catalysts obeyed Eley-Rideal kinetics with palmitic acid and methanol adsorbed on the active site respectively. Zirconium oxide was also impregnated with sodium hydroxide to produce basic catalysts. The best catalyst was produced when zirconium oxide was impregnated with 1.5 M NaOH and calcined at 600C. Soybean oil was completely converted to biodiesel with 10 wt % catalyst and 1:6 oil to methanol. A mixture of the base catalyst with 30 wt % SO4/ZrO2-550C effectively converted soybean oil containing 5% oleic acid indicating iii that this mixture could be used for waste oils. The reaction was first order with respect to triglyceride and second order with respect to methanol. The activation energy was 49.35 kJ/mol and the reaction mechanism obeyed Langmuir-Hinshelwood kinetics.
Value-based Algorithms Optimization with Discounted Multiple-step Learning Method in Deep Reinforcement Learning

Value-based algorithms have been demonstrated on a range of deep reinforcement learning tasks. However, value-based algorithms suffer from challenges in the aspect of stability, overestimate and convergence, which limit severely the application of such algorithms in real-word environment. In n-step learning method, truncated N-step return was used as a part of multiple-step targets to make faster learning, and improve the defect of such algorithms, but it is still far away to practical application. In this paper, we proposed a straightforward optimal method — Discount Multiple-steps Learning Method (DMLM) to improve the performance of value-based algorithms by giving a discount factor to truncated N-step return which shows better results in our experiments. In this method, regard the discounted truncated N-step return rather than accumulated discount reward as the important part of target network when computing the TD-error as a loss function of evaluate network. In the experiment part, we perform experiments compare to value-based algorithms without this method, and prove this method can make more accurate predict of value function, thereby outperform other optimal methods in terms of stability, overestimate and convergence.
Development of a Robotic Hand Using Bioinspired Optimization for Mechanical and Control Design: UnB-Hand

For the last four decades, the development of robotic hands has been the focus of several works. However, a small part of those approaches consider the exploitation of parallelism of FPGA-based (Field Programmable Gate Arrays) systems or discuss how using bioinspired optimization algorithms could improve the mechanical and controller components. This work considers developing a bioinspired robotic hand that achieves motion and force control with a logic hardware architecture implemented in FPGA intended to be replicated and executed with suitable parallelism, fitting a single device. The developed robotic hand prototype has five fingers and seven DoF (Degrees of Freedom). Using bioinspired optimization, such as PSO (Particle Swarm Optimization), both the rigid finger mechanism and the impedance controller were optimized and incorporated the results in several practical grasping experiments. The validation of this work is done with the Cutkosky grasping taxonomy and some grasping experiments with interference. The tests proved the proficiency of this works for a wide range of power and some precision grasp. The reader can see the experiments in the attached videos.