Miracle-dz/UniFAR-FTUs huggingface dataset:
Rate Equation Modeling of Interband Cascade Lasers on Modulation and Noise Dynamics

This work presents a rate equation model for interband cascade lasers, which is used for investigating modulation dynamics and optical noise properties. Through standard small-signal analysis of the rate equations, we analytically derive the resonance frequency, the damping factor, the relative intensity noise (RIN), and the frequency noise (FN) of the interband cascade lasers. It is shown that more cascading stages are beneficial for reaching a broad modulation bandwidth at a low pump current. On the other hand, fewer cascading stages are favorable to achieve low-RIN and low-FN (or narrow-linewidth) laser emission.
Combining Residual Neural Networks and Feature Pyramid Networks to Estimate Poverty Using Multisource Remote Sensing Data

Reliable poverty data are critical for regional economic analysis and policy making, especially considering that economic inequality and sustainable development are widespread social concerns. This article proposes a multitask learning model combining deep residual neural networks and feature pyramid networks to estimate poverty level from multiple sources including the night-time light data, Landsat 8 imagery, and spectral index data. We first train the multitask learning model using the multisource data in Chongqing, China and then estimate the representative economic indicators in the study area. The model is evaluated with the Pearson correlation coefficient of the actual and estimated economic indicators. The result shows that the proposed model outperforms other models with the Pearson correlation coefficient up to 0.87 in the annual estimates of economic indicators between 2013 and 2017. As all the data used in this article are publicly available, the proposed model can be used to estimate the economic indicators in other regions as well.
Quadrotor System Design for a 3 DOF platform based on Iterative Learning Control

Research into autonomous control and behaviour of mobile vehicles has become more and more widespread. Unmanned aerial vehicles (UAVs) have seen an upsurge of interest and of the many UAVs available, the quadrotor has shown significant potential in monitoring and surveillance tasks. This paper examines the performance of iterative learning control (ILC) in gradient-based control that enhances a quadrotor’s controllability and stability during attitude control. It describes the development of the learning algorithms which exploit the repeated nature of the fault-finding task. Iterative learning control algorithms are derived and implemented on a quadrotor in a test bench. The proposed ILC algorithms on the quadrotor model are evaluated for system stability, convergence speed, and trajectory tracking error. Finally, the performance of the proposed algorithms is compared against a baseline performance of the PID control schemes.
Secrecy Energy Efficiency Optimization for Multi-User Distributed Massive MIMO Systems

This paper studies the energy-efficient power allocation problem for physical-layer security in multi-user (MU) distributed massive multiple-input multiple-output (MIMO) systems. A new metric called global average secrecy energy efficiency (GASEE) is proposed to measure the MU secrecy energy efficiency (SEE) with a single eavesdropper (Eve). We first derive closed-form expressions for the signal to interference-plus-noise ratios (SINRs) of legitimate users and the Eve with pilot contamination. Under a power consumption model that incorporates transmit power, backhaul power, remote antenna unit (RAU) circuit and signal processing power, and with transmit power constraints as well as SINR constraints for both users and the Eve, the GASEE maximization problem is formulated as a joint optimization of power allocation, RAU clustering, RAU selection and artificial noise (AN) selection. The formulated problem is a mixed integer nonlinear program (MINLP), which is solved by a double-loop procedure. In the outer loop, the denominator of objective is approximated as a linear function. In the inner loop, an efficient algorithm is proposed to find a near-optimal solution to the approximated problem by solving a sequence of sub-problems. Simulation results demonstrate that the proposed algorithm converges fast and achieves a higher GASEE than some heuristics.
ZigFi: Harnessing Channel State Information for Cross-Technology Communication

Cross-technology communication (CTC) is a technique that enables direct communication among different wireless technologies. Recent works in this area have made substantial progress, but CTC from ZigBee to WiFi remains an open problem. In this paper, we propose ZigFi, a novel CTC framework that enables communication from ZigBee to WiFi. ZigFi carefully overlaps ZigBee packets with WiFi packets. Through experiments we show that Channel State Information (CSI) of the overlapped packets can be used to convey data from ZigBee to WiFi. Based on this finding, we propose a receiver-initiated protocol and translate the decoding problem into a problem of CSI classification with Support Vector Machine. We further build a generic model through experiments, which describes the relationship between the Signal to Interference and Noise Ratio (SINR) and the symbol error rate (SER). Moreover, we extend ZigFi to multiple-to-one concurrent transmissions. We implement ZigFi on commercial-off-the-shelf WiFi and ZigBee devices. We evaluate the performance of ZigFi under different experimental settings. The results demonstrate that ZigFi achieves a throughput of 215.9bps, which is 18X faster than the state of the arts.
Generic Linear ESO for the State Observation of Unknown Nonlinear SISO Systems

This paper introduces a generic procedure for the state estimation of unknown nonlinear SISO systems, i.e. when no information is available on their structure, possibly time-varying parameters and potential disturbances. Such systems are met for instances for systems based on complex micro and nano mechatronic designs that are interacting in an unknown way with their environment at nano scales. This procedure relies on the choice of an arbitrary linear model and the use of a Generic Linear Extended State Observer, whose principle is also introduced in the paper. The proposed approach overcomes well-known model-based nonlinear techniques in the sense that it is easy to implement, all the while avoiding any identification step and mathematical complexity. Simulation results involving nonlinear systems, subject to external disturbances, compare the performance of the proposed approach to the one of some model-free nonlinear observers described in the literature.
Detailed Study of Breakdown Voltage and Critical Field in Wide Bandgap Semiconductors

A study comparing the breakdown voltage and critical field of various wide bandgap semiconductors (β-Ga2O3, GaN, 4H-SiC) relevant for power electronics, as well as Si, is presented. We used Gauss's law to solve the electric-field profile within p-i-n structures, where the i-layer is doped n−, and we employed field-dependent impact-ionization coefficients from the literature to compute the breakdown voltage of those p-i-n structures as a function of doping and thickness of the blocking i-layer. Instead of treating the critical field as a constant material property, it is shown that the critical field varies not only as a function of doping, but also with device geometry. Furthermore, we have found that even though GaN has a higher critical field, it does not necessarily lead to a larger maximum blocking voltage than that of SiC.
Dual Side Molding SiP Drop Reliability Analysis

The package structural System-in-Package (SiP) has been widely applied for many years. A substrate-based platform integrates heterogeneous components such as RF front-end module, WiFi module and passives into one package. The main strategy and advantages of SiP are small form factor, flexible design, low cost and time to market. To reduce module area and increase mounted components, the advanced technology of dual side molding module is developed to integrate effectively both sides of the substrate. However, dual side molding module face the challenge of the board level reliability, especially drop performance. In this work, both experiment and numerical model were investigated to analyze board level drop reliability of the package. The board level drop experiment was followed JEDEC JESD22-B111 with profile 1500G & 0.5ms and analyzed by Weibull method to calculate the solder fatigue life of dual side molding module. Solder joint performance between module and PCB was focused with different dimensions and materials. Moreover, a three-dimension finite element method (FEM) was used to find the stress level comparison and comes out design guideline for solder joints shape study of the dual side molding module under drop test. The preliminary result shows that without laser ablation gets better characteristic life performance. The numerical analysis will correlate with the actual results to the further investigation of dual side module package.
Fabrication of 3D Multi-Edges Cube Porous SnO2/ZnO Composites as High-Performance NOx Gas Sensor

In this study, a kind of novel porous 3D multi-edges cube SnO<sub>2</sub>/ZnO composite with n-n heterojunctions was prepared through a facile hydrothermal route. A kind of ordered mesoporous ZnO must be used as precursor in the hydrothermal reaction which was fabricated by soft-template method. The surfaces of the 3D multi-edges cube SnO<sub>2</sub>/ZnO composite were distributed with ordered mesoporous of 10.2 nm. The grain refinement effect was happen by SnO<sub>2</sub>, it led to the specific surface area of the material reached to 135.641 <inline-formula> <tex-math notation="LaTeX">$\text{m}^{\textsf {2}}\cdot \text{g}^{\textsf {-1}}$ </tex-math></inline-formula>, which was nearly 3.9 times of ZnO matrix. At room temperature (25°), the 3D multi-edges cube SnO<sub>2</sub>/ZnO nanomaterial showed excellent gas response to NO<sub>x</sub>, whose response to 100 ppm NO<sub>x</sub> reached 60.41±0.5%, the response time was only 2 s, and the detection limit was 0.2 ppm. It broke the tradition that SnO<sub>2</sub>/ZnO composite showed gas response to NO<sub>x</sub> only in relatively high temperatures.
Controlling an Exoskeleton with EMG Signal to Assist Load Carrying: A Personalized Calibration

Implementing an intuitive control law for an upper-limb exoskeleton to perform force augmentation is a challenging issue in the field of human-robot collaboration. The aim of this study is to design an innovative approach to calibrate electromyography (EMG) data in order to detect the intention to lift or put down a charge while wearing an upper-limb exoskeleton. Based on a low-cost EMG sensor bracelet placed around the arm (Myo armband, Thalmics Lab, Ontario), a subject-specific mapping procedure is implemented to discriminate motion intentions during lifting tasks with a 1-DoF upper-limb exoskeleton. The processing is divided into two main parts: (i) Direction estimation with an artificial neural network, and (ii) A model-based intensity prediction. The mapping procedure has been tested on 7 healthy participants with a precision of 96.9 ± 3.1% for the classification and a RMS Error of 3.8 ± 0.8 $N$ at the end effector. This study opens up the way for fast-deployment applications involving exoskeletons or cobots.
A Passive Gamma Radiation Dosimeter Using Graphene Field Effect Transistor

In this work, Graphene-based field-effect transistors (GFETs) are demonstrated as a highly sensitive dosimeter for gamma radiation. Graphene-based field-effect transistors exhibit p-type doping with the Dirac point shifting in the positive direction upon exposure to gamma radiation. Concurrently, an asymmetric degradation in the electron and hole mobility was observed with the former degrading more rapidly. It is shown that change in the Dirac voltage and carrier mobility is strongly dependent on the dose of gamma radiation. A sensitivity of ~1 V/kGy is reported. Gamma radiation causes partial aerial oxidation of graphene-channel which leads to p-doping as confirmed by the emergence of a higher binding energy peak (286.8 eV) in X-ray photoelectron spectra (XPS). The decrease in contact potential difference estimated through Kelvin probe force microscopy (KPFM) confirms this finding. The radiated devices showed a stable response for ~70 days. Our work demonstrates that gamma irradiation can also be used to induce large and stable hole concentrations in graphene. Such highly sensitive GFET can serve as real-time dosimeter operating in ambient conditions.
English and Chinese Neural Metonymy Recognition Based on Semantic Priority Interruption Theory

Metonymy is one of the types of common figurative languages and often used in human conversation without any difficulties. However, metonymy recognition in NLP requires a deep semantic/contextual processing to interpretation because it is highly related to the discourse of the contexts. Moreover, the fact that few available datasets of figurative languages make it more problematic. Motivated by the shortcomings of metonymy recognition, we develop several new data sets, including the Chinese version of the data, and design an end-to-end neural network metonymy recognizer. Our framework is based on the semantic priority interrupt theory and additional knowledge is introduced which makes to learn contexts effectively. Through a series of experiments, we show that our method is comparable to the state-of-the-art metonymy recognition method, especially we verified that metonymy trigger words information contributes to performance improvement in our model.
Improving the Performance of Convolutional Neural Network for the Segmentation of Optic Disc in Fundus Images Using Attention Gates and Conditional Random Fields

The localization and segmentation of optic disc (OD) in fundus images is a crucial step in the pipeline for detecting the early onset of retinal diseases, such as macular degeneration, diabetic retinopathy, glaucoma, etc. In this paper, we are proposing a novel convolutional neural network architecture for the precise segmentation of the OD in fundus images. We modify the basic architectures of DeepLab v3+ and U-Net models by integrating a novel attention module between the encoder and decoder to attain the finest accuracy. We also use fully-connected conditional random fields to further boost the performance of these architectures. We compare the results of our best proposed architecture against other established architectures for optic disc segmentation on our private dataset, as well as on publicly available datasets, namely, DRIONS-DB, RIM-ONE v.3, and DRISHTI-GS. The results obtained with the proposed method outperforms the existing methods in the literature.
AudioForesight: A Process Model for Audio Predictive Maintenance in Industrial Environments

Nowadays, the digitalization and automation of industrial processes is already a standard in many companies. Es-pecially maintenance is an essential process with huge impact in business trying to minimize downtimes of industrial equipment. Maintenance can be separated in several different categories including predictive maintenance which we will focus on. In this paper we present AudioForesight, an audio-based approach for industrial equipment predictive maintenance. The approach is based on a combination of two machine learning techniques: Anomaly detection and classification. The anomaly detection is used to predict abnormal behavior in comparison to the normal behavior observed after setting up the system. After detecting an anomaly, a classifier tries to identify the system error based on predefined failure classes. In the case study, we present an example implementation of our approach for an industrial metrology machine provided by our partner from industry. The case study shows the feasibility of the approach and allowed the prediction of machine failures before they occurred using an autoregression model based on mean-squard-errors from the anomaly detection and classification. We present the results, challenges we uncovered and an outlook of future work.
Investigating the Effect of Load Modeling on Network Reconfiguration of a Distribution System

In order to minimize the losses of the distribution network, solving the reconfiguration problem is a vitally established issue. Modification in the bus connection reduces the system loss and improves the bus voltages as well. Major intricacy for reconfiguring distribution system lies in the development of a convex optimization model constituting voltage-dependent loads. This paper models mixed integer second order conic program for Network Reconfiguration of a Distribution System (NRDS), where binary variables depict the branch connections between the buses. A ZIP load model composed of constant impedance, constant current, and constant power type loads, has been deployed for voltage-dependent load study. Proposed ZIP load model based NRDS has been tested on 33-bus, and 69-bus distribution system. Results are compared with the existing model given in literature incorporating only constant power loads that establishes the case for adaptation of the proposed model. Simulated results of constant power and voltage-dependent load model are also validated by performing load flow for evaluated branch connections.
Recurrent Model for Wireless Indoor Tracking and Positioning Recovering Using Generative Networks

Indoor person tracking attracts a considerable effort from the research community as it allows to perform Human Behaviour Analysis tasks, where wireless technologies play a key role. However, complex signal propagation effects in indoor environments are the main issue to face when performing accurate indoor positioning and tracking. The advances in machine and deep learning models, applied to improve the estimation of the position captured by wireless sensors, can provide a more precise tracking and positioning, an open field for research which has been used to improve the prior art. In this paper, a novel framework for Adaptive Indoor Tracking using Recurrent models, in combination with Generative networks for new data generation (recovery), is presented (RecTrack-GAN). Firstly, a Received Signal Strength Indicator RSSI Fingerprinting database is collected. Secondly, a Recurrent Neural Network (RNN) takes as input the RSSI parameters collected by a Wireless Sensor Network (WSN) and estimates of both orientation and velocity using devices equipped with Inertial Measurement Unit (IMU) sensors, and learns to model the human movement based on these parameters. Thirdly, a Conditional Generative Adversarial Network (CGAN) is used to perform data recovering when no measurements are received and to update the Fingerprinting database taking into account the day time. The experiments performed showed that RecTrack-GAN improves accuracy performance and reduces error deviation for tracking up to 15% compared to the prior art in the literature.
Wireless Indoor Positioning Using Online Machine Learning

The indoor positioning system can be applied to smart factories to monitor the location of time-critical items in real-time. It is useful for planning and control in the dynamic manufacturing environment. The challenge of the localization is the non-stationary characteristics of the environment. In this paper, we present a predictive modeling method using online machine learning. The wireless signals from time-critical items can be captured constantly. The online positioning model is built and updated by using the sensor data stream.
Sheet-Based Gripper Featuring Passive Pull-In Functionality for Bin Picking and for Picking Up Thin Flexible Objects

This letter presents a novel versatile parallel gripper equipped with a sheet-embedded belt that enables passive pull-in operations. The belt is wrapped around each fingertip of the gripper, and it passively moves with the opening and closing motions of the gripper. The fingertip is made of plastic, and it is deflected when a grasping force is applied to the fingertip. This deflection enables the belt to roll up with the closing motion of the gripper, thereby passively pulling the grasped workpiece towards the gripper. This pull-in mechanism is effective in performing bin-picking tasks as well as in picking up thin, flexible objects one at a time. The softness of the sheet-embedded belt surface provides high adaptability to the shape of the object; therefore, many kinds of workpieces can be picked up. The effectiveness of the proposed gripper is experimentally demonstrated in this study.
BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset

Intrusion detection can identify unknown attacks from network traffics and has been an effective means of network security. Nowadays, existing methods for network anomaly detection are usually based on traditional machine learning models, such as KNN, SVM, etc. Although these methods can obtain some outstanding features, they get a relatively low accuracy and rely heavily on manual design of traffic features, which has been obsolete in the age of big data. To solve the problems of low accuracy and feature engineering in intrusion detection, a traffic anomaly detection model BAT is proposed. The BAT model combines BLSTM (Bidirectional Long Short-term memory) and attention mechanism. Attention mechanism is used to screen the network flow vector composed of packet vectors generated by the BLSTM model, which can obtain the key features for network traffic classification. In addition, we adopt multiple convolutional layers to capture the local features of traffic data. As multiple convolutional layers are used to process data samples, we refer BAT model as BAT-MC. The softmax classifier is used for network traffic classification. The proposed end-to-end model does not use any feature engineering skills and can automatically learn the key features of the hierarchy. It can well describe the network traffic behavior and improve the ability of anomaly detection effectively. We test our model on a public benchmark dataset, and the experimental results demonstrate our model has better performance than other comparison methods.
High Precision Capacitive Moisture Sensor for Polymers: Modeling and Experiments

In this work a high precision capacitive moisture sensor for polymers was designed and fabricated. The device is part of a compact, fast, and economic moisture analyzer capable of reaching a similar precision than commercial moisture analyzers based on the Karl Fischer method. The design considerations are described in detail and simulation results are presented showing the relationship between the sensor’s geometrical parameters and the sensor’s performance. Numerical and analytical works were also carried out to support the capacitor optimization. The designed capacitive moisture sensor satisfies all constrains on sensitivity, sensing volume and reliability of the measurements due to its inherent conformal features. The experimental results of the prototype obtained during laboratory tests are also reported in this paper.
Convolutional Classification of Pathogenicity in H5 Avian Influenza Strains

Epidemic Avian Influenza (AI) outbreaks pose a considerable threat to poultry and human health. Millions of birds died, or had to be euthanized, during numerous periodic highly pathogenic influenza outbreaks across the globe over the last two decades. This paper presents a study on pathogenicity classification of H5 AI protein sequences using convolutional neural network. We first collected pathogenicity identification labels of 2137 H5 AI sequences and classified pathogenicity of aligned protein sequences with 99.20% mean accuracy using 10-fold cross validation. We also identified the positions in genomic sequences which are supposed to play a role in pathogenicity classification of H5 strains. The positions learned by convolutional network correctly included positions of known pathogenicity markers present at cleavage site of protein sequences. And the network also identified a few other positions present outside of cleavage site which are supposed to play a role in pathogenicity classification of H5 strains.