Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Data Assessment Approach for Deep Learning-Based Metal Surface Defect-Detection Systems

The current trend in automated optical inspection (AOI) systems employs deep learning models to detect defects on a metal surface. The setback of deep learning models is that they are time-consuming because the images obtained after every lighting adjustment must be used to train the deep learning models again and confirm whether the detection results have improved. To save the time spent using datasets to train deep networks, we proposed a comprehensive assessment score that combines defect visibility, visibility distribution, and overexposure based on the operation principles of convolution neural networks. It can be used to assess whether the training image dataset can improve the defect detection rate of the deep learning model such as You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and Faster Region-based Convolutional Neural Network (Faster R-CNN) without training defect image datasets. We collected all of the weight combinations with the right prediction results and used linear regression to obtain the optimal weight coefficients. We found that visibility and overexposure had a greater impact on the comprehensive assessment score. We compared the proposed approach with existing image quality assessment methods, including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), natural image quality evaluator (NIQE), perception-based quality evaluator (PIQE), and blind/referenceless image spatial quality evaluator (BRISQUE). The experiment results indicated that our proposed comprehensive assessment score is more correlated to the F2-score of the detection models than the IQA methods by the verification methods of Spearman Rank Correlation Coefficient (SRCC), Pearson Correlation, and Kendall Correlation. Thus, referring to this index during the collection of image data and choosing datasets with the highest score to train the model will produce better detection accuracy.
A Bowtie-Slot Dual-Polarized Antenna With High Port Isolation

This paper has proposed one dual-polarized antenna. An open-end slot has been inserted into a bowtie-slot shaped coplanar waveguide center conductor. The simulated working bandwidth is about 10.4%. The isolation between two ports of the proposed antenna is higher than 60 dB over the frequency of 4.68-5.2 GHz. It has bidirectional radiation characteristics and simple structure.
A 26 GHz Partially Air-Filled Substrate Integrated Waveguide Filter Based on LTCC

In this work, a partially air-filled substrate integrated waveguide filter (AFSIW) based on Low Temperature Co-fired Ceramic (LTCC) technology is proposed. In order to improve the performance of the regular substrate integrated waveguide (SIW) filter, two cavities were embedded into each resonator. Two kinds of SIW filter with center frequency at 26.3 GHz and 26.4 GHz were designed and manufactured. The simulation results show that the minimum insertion loss of the AFSIW filter is 0.51 dB and the return loss is more than 20 dB which are better than those of the filter without air-filled cavity. The measured results of the two filters are consistent with the simulation results.
Differentially Fed Package-Substrate Distributed Antenna for 5G Millimeter-Wave Application

A novel integrated antenna with its structure distributed on both package and substrate is presented in this paper. In this way, the low-loss interconnection from the package to the substrate is realized by the antenna’s internal coupling instead of the conductor connection. In addition, the same excitation structure on package design can be reused by being integrated with different radiation structures on the substrate to achieve different radiation performances, according to different application requirements. Two package-substrate distributed antenna working at 5G millimeter wave band with end-fire and broadside radiation sharing the same excitation structure on the package, are designed to prove the concept.
Survey of Distributed Amplifiers with >100 GHz Bandwidth and Link-Budget Estimations for Short-Range Wireless up to 0.5 Tbit/s

In this paper, a survey of leading-edge distributed amplifiers is given. The measurements of our CMOS (Complementary Metal Oxide Semiconductor) and SiGe BiCMOS (Silicon Germanium Bipolar CMOS) amplifiers show bandwidths of 110 GHz and 180 GHz, and gains of 8.5 dB and 18.7 dB, respectively. The link-budget and strategies for short-range wireless communication up to 0.5 Tbit/s are discussed.
CMOS Reconfigurable Dual-Band Low Noise Amplifier for eMTC Receiver

This paper presents a low power dual-band low noise amplifier (LNA) targeting for GSM and LTE standard, designed for eMTC receiver. Selections of parallel cascade amplifiers, switching input inductors and loads are used for reconfigurable wireless bands and gain control in this LNA operating at 0.9/1.9 GHz. Simultaneously, switching source inductors are used because of the different input impedance of dual bands far apart. The maximum power gain is from 18.4 to 19.2 dB between dual bands with controlled range of over 12dB. The noise Figure (NF) is 1.57/1.68 dB at 0.9/1.9 GHz bands, respectively. This LNA achieves the IIP3 of -5.9/-4.3 dBm while consuming an average power of 7mW (without buffer) with the highest gain at two different frequencies. The proposed reconfigurable LNA is designed in 65nm CMOS process from 1.2V supply.
Real time-Employee Emotion Detection system (RtEED) using Machine Learning

Now a days, employee health and well-being is the most important consideration in the work place. Because it will affect the productivity of an individual employee and team contribution. Eventually, the automatic facial expression analysis using machine learning has become an interesting and active research area from past few decades.In this paper, Real time Employee Emotion Detection System (RtEED) has been proposed to automatically detect employee emotions in real time using machine learning. RtEED system helps the employer can check well-being of employees and identified emotion will be intimated to respective employee through messages. Thereby employees can make better decisions, they can improve their concentration level towards work and adopt to the healthier life style and much productive work styles. CMU Multi-PIE Face Data is used to train machine learning model. Each employee will be equipped with a webcam to capture facial expression of an employee in real time. The RtEED system designed to identify six emotions such as happiness, sadness, surprise, fear, disgust and anger through the captured image. Results demonstrate that expected objectives are achieved.
Basic Study on Analysis and Suppression of Inverse Response Caused by Feedforward Friction Compensation of Ball-screw-driven Stage

Ball-screw-driven stages are often used as feed systems of machine tools. A problem of tracking control of the stages is rolling friction, which degrades tracking accuracy around the velocity reversal point and causes a large tracking error called a quadrant glitch. Although the model-based feedforward friction compensation effectively reduces the tracking error, the compensation often results in an inverse response, which is the tracking error in the direction opposite to the quadrant glitch. This study aims to provide an analysis and a suppression approach to the inverse response. First, the inverse response is analyzed through the simulations. Next, a suppression approach of the inverse response is proposed. In the proposed approach, a step input is injected and cancel the inverse response. The proposed approach is evaluated through the simulations and experiments.
Design and Realization of 20GHz Ultra-wideband Excitation Signal Source

In order to provide an ultra-wideband excitation source signal for the portable vector network analyzer, this paper carries out a study of 30kHz~20GHz signal generation technology. In this paper, we give the overall scheme of the full band signal synthesis. The ultra-wideband frequency synthesis scheme based on the phase-locked loop (PLL) technology is introduced as a key point. We also design the impedance matching to reduce the transmission line loss effectively. Finally, the 30kHz~20GHz ultra-wideband excitation source signal synthesis based on the microwave printed circuit board is completed. Compared with the traditional scheme, the design scheme of this paper takes advantages of low cost, high productivity and low power consumption. After testing, the performance indicators of the output signal reach the desired design requirements.
Decoupling of Wideband Dual-Polarized Base Station Antennas for Sub-6 GHz Applications

A wideband dual-polarized base station antenna array with a three-layer decoupling surface is proposed in this paper. The element of the presented antenna array is two crossed bow-tie dipoles operating in the range of 3.5–4.9 GHz (33.3%) with VSWR<1.5 and isolation between two polarization port better than 35 dB. Furthermore, with the three-layer decoupling surface, the port isolation between two adjacent array elements and the realized gain of 1x2 array are increased by 3–20dB and 0.21–2.26 dBi respectively within the operating band. The designed antenna array and decoupling surface are suitable for sub-6GHz of 5G communication.
Effects of Data Augmentation Method Borderline-SMOTE on Emotion Recognition of EEG Signals Based on Convolutional Neural Network

In recent years, with the continuous development of artificial intelligence and brain-computer interface technology, emotion recognition based on physiological signals, especially electroencephalogram signals, has become a popular research topic and attracted wide attention. However, the imbalance of the data sets themselves, affective features’ extraction from electroencephalogram signals, and the design of classifiers with excellent performance, pose a great challenge to the subject. Motivated by the outstanding performance of deep learning approaches in pattern recognition tasks, we propose a method based on convolutional neural network with data augmentation method Borderline-synthetic minority oversampling technique. First, we obtain 32-channel electroencephalogram signals from DEAP data set, which is the standard data set of emotion recognition. Then, after data pre-processing, we extract features in frequency domain and data augmentation based on the data augmentation algorithm above for getting more balanced data. Finally, we train a one dimensional convolutional neural network for three classification on two emotional dimensions valence and arousal. Meanwhile, the proposed method is compared with some traditional machine learning methods and some existing methods by other researchers, which is proved to be effective in emotion recognition, and the average accuracy rate of 32 subjects on valence and arousal are 97.47% and 97.76% respectively. Compared with other existing methods, the performance of the proposed method with data augmentation algorithm Borderline-SMOTE shows its advantage in affective emotional recognition than that without Borderline-SMOTE.
A Survey on Mobile Edge Computing: Efficient Energy Management System

With a sharp and steady growth of handheld devices, Internet of Things (IoT) and their related sectors, the need for more demanding, computation-intensive at subsequently lower latency has arisen. To curb the growing need while moving along the ever-increasing pace of resource-hungry and intensive mobile and handheld applications like the better quality of graphics and animation in the mobile gaming industry, there is an ardent need for powerful computational hardware. By bringing cloud computing resources to the end-users (mobile devices in this context), otherwise known as MEC (mobile edge computing) and thus it proves to be efficient energy management of resources like lower offloading computation and less time taken for computation. The need for capable hardware is solved by using existing cloud services. MEC holds the potential to give a platform to the numerous content marketers and services towards the handheld and mobile industry. The improvement in networking and newer and efficient technological advancements make MEC a highly potent possibility. A brief overview of MEC, its related research and advances have been discussed in the paper.
A Framework for the Design and Deployment of Large-Scale LPWAN Networks for Smart Cities Applications

The Internet of Things (IoT) enables the connectivity of massive numbers of sensors to facilitate the delivery of immense sets of data. This valuable data promotes higher productivity, effectiveness, and sustainability. With the accelerating global urbanization, many cities and suburbs are expected to face increasing challenges related to urban planning, resources and livability. These challenges stimulate new opportunities for communities and governments to collaboratively incubate new planning and development strategies relying on the insightful IoT data. This concept is commonly referred to as smart cities. Accordingly, a wave of smart cities implementations is currently emerging. However, the underlying IoT network deployments are developed in silos, and thus require more harmonization. In this paper, we present a design and deployment framework for large-scale low power wireless access networks (LPWAN) in order to harmonize and guide the end-to-end process. This framework is derived from the acquired expertise and learned lessons from the largest open-access LPWAN deployment in Australia. The framework includes various aspects related to the state-of-the-art design techniques, smart cities use-case scenarios, project development and implementation, network management, and finally a robust performance testing mechanism for quality of service assurance. All these components were put into rigorous verification and testing under this realistic large-scale IoT network deployment.
Hybrid Data Aggregation Algorithm for Energy Efficient Wireless Sensor Networks

The Wireless sensor networks are pertained in several applications like agriculture, security areas that the node density for the gathered data through the various types of sensor which has the problem of redundancy and it must be avoided through data aggregation technique. This paper demonstrates the Hybrid Data Aggregation Algorithm (HDAA) for providing data processing in real-time. Initially, the network is constructed into cluster members that the cluster head has been elected according to the ranking of the sensors according to the location from the base station, energy level. The aggregation is applied to the real-time application that every sensor node has been prioritized in energy-efficient way. The simulation results prove that the proposed technique reduces the energy consumption and communication delays which automatically enhancing the network lifetime.
Part Relational Mean Model for Group Re-Identification

Most current research on pedestrian re-identification (ReID) is focusing on single-person ReID. However, people are rarely alone and often walk together in groups. Therefore, there is an urgent need to study the problem of group ReID (G-ReID). G-ReID is challenging because of the difficulties related to the differences in group appearance caused by changes in the group layout and membership. In this paper, we have proposed a part-based minus-average relational and arithmetic mean descriptor (PRM) algorithm to obtain a robust representation of groups. Based on local features, we have designed the arithmetic mean descriptor and the minus-average relational descriptor to solve the G-ReID problem caused by changes in the number of group members and their relative positions within the group. Moreover, the minus-average relational descriptor can also be used to describe the differences in the appearance of group members. Considering the rarity of G-ReID datasets and the need to improve the applicability of the G-ReID algorithm in real scenarios, we have collected a new dataset called the Bus Rapid Transit (BRT) G-ReID dataset. Extensive experimental results demonstrate the effectiveness of the PRM algorithm and indicate that it outperforms state-of-the-art algorithms by 7.5% for the cumulative matching feature (CMC-1) on the i-LIDS MCTS group dataset and by 19.4% for the CMC-1 on the Road Group dataset and it outperforms the baseline by 2.4% for the CMC-1 on the BRT dataset.
The effect of microwaves on hippocampal neurons in vitro and its mechanism

Objective To investigate the effects of microwaves on hippocampal neurons(HNs)and changes in the cellular membrane(CM)and the concentration of Ca~(2+)([Ca~(2+)])in vitro,and to explore the mechanisms in- volved.Methods Cultured HNs were exposed to 10 mW/cm~2 microwave irradiation.The survival rates(SRs)of HNs were detected with MTT,and cell apoptosis and necrosis rates(CANRs)were measured using Flow Cytometer, [Ca~(2+)](? )was determined using a Fluo232AM fluorescent probe and laser scanning confocal microscopy,and the CMs were observed by means of atomic force microscopy(AFM). Results Intracellular[Ca~(2+)](? )was increased and CM perforation was observed after microwave exposure(ME). The SR of HNs was significantly decreased at 6 h after ME (P＜0.01),but CANRs were significantly increased at 24 h after ME(P＜0.01). Conclusion The decrease in SRs and the increase in CANRs in HNs cart be caused by ME,and its mechanism may involve CM perforation and the increase of[Ca~(2+)](? )in HNs.
Axial-Ratio Beamwidth and Bandwidth Enhanced Circularly Polarized Dielectric Resonator Antenna

The conventional cross-slot fed dielectric resonator antenna can only provide a narrow axial-ratio beamwidth over a limited bandwidth. As a novel approach, the compact ground plane is utilized for the simultaneous enhancement of beamwidth and bandwidth with simplicity in structure. For demonstration, a wideband circularly polarized dielectric resonator antenna was designed, fabricated and measured. Reasonable agreement between simulation and measurement can be observed. A wide 3-dB AR angular beamwidth larger than 175° was obtained across a 8.1% bandwidth.
Torque Ripple Reduction for PMSM based on PWM Pulse Merging Method for High Speed Range

Permanent magnet synchronous motors (PMSMs) are widely used because of their high efficiency and lower energy consumption. However, the motor noise and the vibration caused by the torque ripple is a well-known problem because of an imperfect sinusoidal flux distribution. To reduce the torque ripple, the current controller based on the perfect tracking control (PTC) is reported. Although it has improved the tracking performance of a current reference compared to the proportional-integral (PI) feedback control and the suppression effect, there is still room for improvement of the current control by reference value oversampling. Thus, we have proposed the pulse merging method that samples a reference value twice more than the conventional method to improve the current control and applied it to the torque ripple suppression at the high-speed range. The proposed method could suppress the torque ripple more than the PTC from the simulation results.
Multi-Modal Uterine-Activity Measurements for Prediction of Embryo Implantation by Machine Learning

In-vitro fertilization (IVF) is the most advanced treatment for infertility problems; however, its failure rate is still above 70% and the exact causes are often unknown. There is increasing evidence of the involvement of uterine contractions in IVF failure, especially during and after embryo transfer (ET). In this paper, we propose a new method to predict the success of IVF based on quantitative features extracted from electrohysterography (EHG) and B-mode transvaginal ultrasound (TVUS) recordings. To this end, probabilistic classification of the uterine activity, as either favorable or adverse to embryo implantation, is investigated using machine learning. Prior to machine learning, an additional method for EHG and TVUS feature extraction is here proposed that is based on singular value decomposition of the acquired EHG and TVUS recordings. Sixteen women were measured during three phases of the IVF treatment: follicular stimulation (FS), one hour before embryo transfer (ET1), and five to seven days after ET (ET5-7). After feature space reduction by correlation filtering, three machine-learning models, namely, support vector machine (SVM), K-nearest neighbors (KNN), and Gaussian mixture model (GMM), were optimized and tested by nested leave-one-out cross validation for their ability to predict successful embryo implantation. The highest accuracy (93.8%) was achieved by KNN in all phases and by SVM and in the FS and ET1 phases. Contraction frequency, unnormalized first moment and standard deviation, obtained from EHG and TVUS analysis, were the best features selected by the three classifiers. Our results show a multi-modal, multi-parametric strategy based on quantitative features to represent a novel, promising option for prediction of successful embryo implantation, overcoming the limitations of alternative approaches based on qualitative assessment of clinical variables. Yet, a larger dataset is required for improved training of the classifiers, as well as to assess their clinical value in the context of IVF procedures.
A Gamification Platform to Analyze and Influence Citizens’ Daily Transportation Choices

An effective, efficient, and ecological transport service needs a decision support system to inform the administrations, while intelligently assisting citizens’ daily transportation choices. This work presents a full-fledged Gamification Platform to develop and run sustainable mobility campaigns promoting individual green mobility behaviors. We organize geolocalized and temporal information on users’ tracked trips into a rich dataset that decision-makers can exploit to analyze citizens’ daily transportation choices and balance the demand and the offer of the mobility ecosystem. Through the Gamification Platform, we define three different use case scenarios - i.e., mobility within the city, home-work mobility, and home-school mobility. We also evaluate the platform according to both gameful design guidelines and persuasive technology best practices. Our findings show how long-term retention and engagement can be achieved when the gameful system’s design and maintenance are assisted by the Gamification Platform, merging both administrative requirements and users’ needs towards an iteratively adaptive and personalized experience.
A Tutorial Introduction to Monte Carlo Tree Search

This tutorial provides an introduction to Monte Carlo tree search (MCTS), which is a general approach to solving sequential decision-making problems under uncertainty using stochastic (Monte Carlo) simulation. MCTS is most famous for its role in Google DeepMind’s AlphaZero, the recent successor to AlphaGo, which defeated the (human) world Go champion Lee Sedol in 2016 and world #1 Go player Ke Jie in 2017. Starting from scratch without using any domain-specific knowledge (other than the rules of the game), AlphaZero was able to defeat not only its predecessors in Go but also the best AI computer programs in chess (Stockfish) and shogi (Elmo), using just 24 hours of training based on MCTS and reinforcement learning. We demonstrate the basic mechanics of MCTS via decision trees and the game of tic-tac-toe.