Miracle-dz/UniFAR-FTUs huggingface dataset:
Sound Source Localization Based on GCC-PHAT With Diffuseness Mask in Noisy and Reverberant Environments

Although sound source localization is a desirable technique in many communication systems and intelligence applications, the distortion caused by diffuse noise or reverberation makes the time delay estimation (TDE) between signals acquired by a pair of microphones a complicated and challenging problem. In this paper, we describe a method that can efficiently achieve sound source localization in noisy and reverberant environments. This method is based on the generalized cross-correlation (GCC) function with phase transform (PHAT) weights (GCC-PHAT) to achieve robustness against reverberation. In addition, to estimate the time delay robust to diffuse components and to further improve the robustness of the GCC-PHAT against reverberation, time-frequency(t-f) components of observations directly emitted by a point source are chosen by “inversed” diffuseness. The diffuseness that can be estimated from the coherent-to-diffuse power ratio (CDR) based on spatial coherence between two microphones represents the contribution of diffuse components on a scale of zero to one with direct sounds from a source modeled to be fully coherent. In particular, the “inversed” diffuseness is binarized with a very rigorous threshold to select highly reliable components for accurate TDE even in noisy and reverberant environments. Experimental results for both simulated and real-recorded data consistently demonstrated the robustness of the presented method against diffuse noise and reverberation.
Thermal Conductivity Sensor with Isolating Membrane Holes

In this paper we present a thermal conductivity gas sensor with improved sensitivity by adding holes in the thin-film membrane. A numerical model is created and validated against the reference CMOS MEMS thermal conductivity sensor. The numerical model is used to investigate the advantages of having isolating holes through the membrane, located on either side of the heating resistor. These holes increase robustness and minimise catastrophic failure caused by pressure difference whilst simultaneously increasing sensitivity by enhancing convective interaction. The electro-thermal efficiency is shown to increase by 16% whilst the sensitivity to measuring percentage CO2 increases by 39.2%. It is also shown that increasing the width of the holes does not have significant effect on these sensitivities; thus, small holes can be incorporated, leaving room for multiple resistors across the membrane for different measuring techniques. This paper serves as proof that membrane holes can be used to optimise thermal conductivity sensors and will serve as a reference when these designs are fabricated and tested, leading to a new low-power, high-sensitivity gas sensor.
Health Index Determination of Aged High Voltage Substation in Distribution System

This paper presents condition assessment of high voltage equipment and substation as health index. Major equipment of high voltage substation consists of circuit breaker, disconnecting switch, current transformer, voltage transformer and surge arrester. The condition of equipment is assessed by visual inspection and electrical test. The weighting and scoring method is applied throughout the process of health index calculation. The Analytic Hierarchy Process is used to calculate the weight for each criterion. The conditional score and its weight of each testing are used to calculate the weighting average score. Then, the obtained weighting average score is adjusted by visual inspection, maintenance completeness and failure rate of equipment. The adjusted average score of all equipment in a bay is integrated into bay health index and subsequently adjusted by age and operational stress of bay. Finally, the health index of fifteen substations can be determined.
Dependency Investigation of Sea Surface Temperature on Sea Bottom Temperature and Sea Surface Salinity

Sea Surface Temperature is the most popular parameter under analysis to sense the variations in Sea. Better sensors clubbed with still better recording instruments, over the period of time, have made analysis easier for the researchers to investigate the dependencies amongst various parameters. The proposed work is an attempt in the same direction. Analysis of data collected over almost a century, dating from 1916 to 2014 at the site La Jolla, West California, USA by staff from Scripps Institution of Oceanography reveals significant relationship between Sea Surface Temperature, Sea Surface Salinity and Sea Bottom Temperature. A feed forward neural network using Back Propagation Algorithm is used to predict the future values by optimizing the count of delay and hidden neurons. Using these optimum values, additional exogenous input is added to the network and the error is computed. It is observed that the performance of the system with exogenous inputs gets better and the error reduces. Multiple step prediction for the same data is also performed and the errors are tabulated in terms of MSE, RMSE and NRMSE.
Novel Pulse-Based Analog Divider With Digital Output

A high-accuracy pulse-based analog divider with 12-bit digital output is proposed in this letter. Moreover, a comparator-delay-free voltage-to-pulse generator (VPG) and a pulse divider (PDV) are proposed: the on-time of the VPG-generated pulses is proportional to its input voltage, and is free from comparator delay; the PDV performs the functions of a divider and an analog-to-digital converter, so the output of the proposed divider is 12-bit digital code directly. The proposed chip was fabricated using a 0.35-<inline-formula> <tex-math notation="LaTeX">${{\mu }}\text{m}$ </tex-math></inline-formula> 2P4M 3.3-V mixed-signal polycide process. According to the measured results, the maximal error is only 1.06% and the quiescent power is only 205 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>.
Developing a Questionnaire to Evaluate Customers’ Perception in the Smart City Robotic Challenge

In this paper, we present an approach to develop a new type of questionnaire for evaluating customers’ perceptions in the upcoming Smart CIty RObotic Challenge (SciRoc). The approach consists of two steps. First, it relies on interviewing experts on Human-Robot Interaction (HRI) to understand which robot’s behaviours can potentially affect the users’ perceptions during a HRI task. Then, it leverages a user survey to filter out those robot’s behaviours that are not significantly relevant from the end user perspective. We concretely enacted our approach over a specific scenario developed in the context of SciRoc, which instructs a robot to take an elevator of a shopping mall asking support to the customers of the mall. The results of the survey have allowed us to derive a final list of 17 behaviours to be captured in the questionnaire, which has been finally developed relying on a 5-point Likert-scale.
Machine Learning Based Early Detection System of Cardiac Arrest

Target—Most of the Cardiac Arrest (CA) cases are preventable because the CA patients usually had abnormal clinical signs or symptoms before their suffering from CA. In general, the appropriate steps of Cardiopulmonary resuscitation (CPR) for CA patients will increase the patients’ survival rate and reduce the consequent medical expenses. Accordingly, we propose a system and the related methods for detecting CA before the CPR event occurred earlier and it is not only assisting physicians to early diagnose of CA and immediately warning but also improving the medical quality.Methods—In this study, the raw dataset is collected from the electronic health records (EHRs) of the adult patients (age ≧20 years) who visited emergency department (ED) and stayed in the emergency detention area for more than 6 hours during January 2014 to December 2015, and it is provided by National Taiwan University Hospital (NTUH). We perform the data preprocessing and cleaning for the dataset using a resampling technique to balance the data amount of CPR and Non-CPR patients of the dataset, and then we construct a sliding window and apply several classifiers for model training and reducing the possible overfitting problem. Additionally, we use the measures such as the Area Under the Receiver Operating Characteristic Curve (AUROC) and the Area Under the Precision-Recall Curve (AUPRC) to comparative evaluate the performance of our models built.Results—Our approach avoids the problems of dataset imbalance and possible overfitting effectively. The performance among classifiers selected show that the best one is random forest (RF) when CPR event happened, but the better ones are Logistic Regression and LSTM than the remaining classifiers and close to that of RF during 1 to 4 hours before the CPR time.Conclusion—We have the contribution in predicting CA and CPR event before it occurred around 3 to 3.5 hours in advance, and it is similar to that of the state-of-the-art such Early Warning Score (EWS) with around 3.5 hours. In addition, avoiding the problem of dataset imbalanced may effectively improve the accuracy of predicting CA as well. Accordingly, it helps to assist the emergency clinical physicians to achieve the hospital’s quality management including the clinical or medical resources allocation.
Low-Cost Weed Identification System Using Drones

Weeds compete with crops for resources such as light, nutrients, water and space. When mature, weeds can produce thousands to hundreds of thousands of seeds that can survive for a long time and posing a great threat to crops. The best way to avoid weed threats is to remove weeds before they bloom such that the chances of weed seeds falling into the soil can be reduced. Most existing drone-based weeds identification methods use additional equipments to enhance the identification ability. In addition to increasing the cost, such solutions also increase power consumption and load burden of drones. In this paper, we propose a low-cost Weed Identification System (WIS) using RGB images taken by drones as training data and applying Convolutional Neural Networks (CNN) to build the identification model. The result of the WIS can be used as a reference for agriculture researchers and can also be used to inform farmers to take necessary reactions. The WIS identifies weeds with an accuracy of up to 98.8%. Compared to other high-cost methods, the WIS does achieve similar identification accuracy at low cost.
Propagation of UWB pulse in two turns of meander microstrip line connected in cascade

The processing of a pulse signal by a passive device is considered. A detailed analysis of the ultra-wideband (UWB) pulse propagation in two turns of meander microstrip line (MSL) connected in cascade is performed. The possibility of the UWB pulse decomposition in a meander MSL into a sequence of nine main pulses with 5.2 times attenuation is demonstrated. The conditions for such decomposition are determined and presented. A detailed description of each of the decomposition pulses at the end of the line under investigation is performed.
ALEAP: Attention-based LSTM with Event Embedding for Attack Projection

Cyberattacks have developed rapidly in diversity and complexity in recent years. Despite the existence of various defense systems, it cannot provide early warnings and prevent catastrophic consequences in advance. Therefore, the need for prediction becomes more and more urgent, especially for those multiple step attacks in which several steps are required for achieving the attack successfully. In this paper, we focus on attack projection that is aimed to predict the next step of the attack based on historical information and gained knowledge of similar events happened in the past. Previous models on attack projection based on probability graph model or simple RNN models, which may limit their capability of noise tolerance and sequence association analysis. To remedy this, we propose a method called ALEAP which incorporates event embedding and attention mechanism into LSTM models to better predict the future events. We test ALEAP on a dataset of millions of security events collected from the multi-source security devices, and show that our approach is effective in event prediction. ALEAP also provides a useful method for security specialists and all computer environment-related parties to better predict attack projection and defend known attacks.
EdgeEye: A Long-Range Energy-Efficient Vision Node For Long-Term Edge Computing

Recent Internet of Things (IoT) devices start to integrate data-intensive sensors such as cameras or microphones. To reduce the amount of data that needs to be transmitted to the cloud, an IoT device needs to process the data on-board, which is commonly referred to as edge computing. This paper presents a stand-alone, edge computing device called EdgeEye, capable of data-centric processing in the mW range. It employs a GAP8 processor which belongs to the PULP processor family to perform machine learning inference on images that were acquired with an ultra-low- power (ULP) camera. The results of the inference are transmitted using long-range communication (LoRa and LoRaWAN). EdgeEye is evaluated in the application scenario of people counting, using a newly proposed convolutional neural network that runs in the 8-cores of GAP8. The system performs the people recognition task (image acquisition, image processing, and LoRa transmission) consuming only 17.5 mJ. The estimated system battery life is more than 400 days when performing a new prediction once per minute on a 2000 mAh battery (3.7 V).
I Know What You Are Doing With Remote Desktop

Remote desktop enables users to remotely access their computers via the Internet, which is widely used as a basic tool in areas such as remote work, remote assistance and remote administration. However, existing remote desktop is designed to work in the mode of updating user’s real-time command and remote screen’s state interactively for a better user experience, such working mode may cause serious side-channel information leakage problem in spite of encryption of the traffic, as revealed in this paper. We carry out an experimental research to assess the side-channel information leakage of six most popular remote desktop softwares in Windows 10 & 7 platforms: Anydesk, ConnectWise, MicroRDS, RealVNC, Teamviewer, and Zoho Assist. With the help of machine learning techniques including logistic regression, support vector machine, gradient boosting decision tree, random forest as well as statistic features of flow burst, we observe that an adversary can excellently uncover (top at 99.26% TPR, 0.57% FPR, 97.17% F1-score) 5 rough kinds of daily activities covering editing documents, reading documents, surfing webs, watching videos and installing softwares and even worse precisely classify 4 fine activities predefined as editing documents with Microsoft Office Word and the other three edit tools with high true positive rate and low false positive rate. Our results prove the fact for remote desktop traffic encryption mechanism is nothing sufficient to prevent side-channel information leakage and both users and providers of remote desktop should pay more attention to such serious privacy leakage problem.
Community Detection in Attributed Social Networks: A Unified Weight-Based Model and Its Regimes

Community detection is a fundamental problem in social network analysis consisting in dividing users into densely knitted or highly related groups with each group well separated from the others. Classical approaches for community detection usually deal only with the structure of the network, however major real-world networks provide more information about users, namely, their attributes (e.g. age, work, gender, interests, etc.), which can clarify and enrich the knowledge about communities. In this paper, we deal with community detection in node-attributed social networks. Our main purpose is to deepen the knowledge about one of the most popular models that fuse both structural and attribute information — the weight-based model that has already shown its efficiency in the community detection task and has advantages over several other models based on different ideas. More precisely, we first propose a unified weight-based model with proper normalisation that generalises some of weight-based models known so far. Secondly, we study different regimes of the model functioning on synthetic attributed networks, including the transition regime when a tunable tradeoff between structure and attributes is available. In particular, we show that the length of the transition regime is determined by the ratio of inter-and intra-cluster density in the initial network. We further illustrate and analyse the regimes for several real-world networks and discuss the related dynamics of some structural and attribute evaluation measures.
Optimal Linear Quadratic Gaussian Torque Controller (LQG) for Upper Limb Rehabilitation

With the increasing numbers of degrees of freedom (DOF), modeling and control of the upper-limb robotic devices become significantly challenging. Model uncertainties, parameter inaccuracies, and incompletely known frictional effects also become inevitable, leading to the need for robust controller design. This paper presents the design of an optimal Linear Quadratic Gaussian torque controller (LQG) with integral action for upper limb rehabilitation robot under the independent joint control paradigm. The controller is motivated to ensure optimal robust torque control, to avoid modelling uncertainties, and to simplify control design process. The proposed method is demonstrated through a simulation study and implemented experimentally on two active joints of a 5-DOF robot prototype. The LQG closed-loop control system responses to both step and input/output disturbance inputs demonstrated superior performance of the controller to the traditional PID controller. The elbow flexion/extension and shoulder abduction/adduction experiments involving healthy subjects verified that the controller is able to deliver better performance within 0.0047Nm and 0.0068 Nm RMS torque tracking errors for shoulder and elbow respectively.
Towards Comprehensible Representation of Controllers using Machine Learning

From the point of view of a software engineer, having safe and optimal controllers for real life systems like cyber physical systems is a crucial requirement before deployment. Given the mathematical model of these systems along with their specifications, model checkers can be used to synthesize controllers for them. The given work proposes novel approaches for making controller analysis easier by using machine learning to represent the controllers synthesized by model checkers in a succinct manner, while also incorporating the domain knowledge of the system. It also proposes the implementation of a visualization tool which will be integrated into existing model checkers. A lucid controller representation along with a tool to visualize it will help the software engineer debug and monitor the system much more efficiently.
Dual-Polarized Aperture-Coupled Patch Antennas With Application to Retrodirective and Monopulse Arrays

An isolation technique, which does not require conventional circulators, is proposed for the realization of a simple and low-cost aperture-coupled circularly polarized antenna for application to full-duplex devices. The approach is based on the use of slotlines loops to provide surface current cancellation in specific regions of the antenna structure, leading to improved axial ratio and isolation between the ports in excess of 50 dB. Circular polarization is achieved by introducing a double-box hybrid coupler, which is optimized to obtain good matching and isolation of the quadrature signals. On this basis, both right- and left-hand circularly polarized beams are achieved by interchanging the transmitting and receiving antenna ports, enabling full-duplex operation and reconfigurability. While the antenna structure is designed for 2.45 GHz operation, one can take advantage of the proposed approach to tune the frequency of maximum isolation. Both single-element prototypes as well as a $2\times 2$ array are fabricated and measured, showing good agreement with the simulations and validating the proposed isolation approach. The beam steering capabilities as well as the application to a Van Atta retrodirective antenna array and the possibilities of achieving delta and sum patterns for monopulse operation are also reported. The proposed full-duplex antenna can also represent an excellent solution for narrowband wireless power transmission systems.
Land surface temperature analysis of post-mining area using Landsat 8 imagery

The decrease of land surface temperature (LST) in reclaimed mining site can be taken as indication of environmental quality improvement. Remote sensing technology is able to provide information on LST over a large area so that it can be used to assess the effect of land-cover characteristic of mining-affected landscape on LST. This research used Landsat 8 images to study the relationships between land-use and LST in a coal-mined landscape of a mining company in South Kalimantan. Specifically, this study aimed to examine the effect of different planting years of reclaimed forest on LST and to examine the effect of different dominant tree species of reclaimed forest on LST. Data source for LST calculation was band 10 of Landsat 8 thermal infrared sensor (TIRS) from recording years of 2014 and 2018. The images analysis consisted of the following steps: LST data extraction using Avdan and Jovanovska (2016) algorithm, land surface emissivity calculation following Valor and Caselles (1996), and overlay of LST maps with land-cover maps. A field work to measure stand characteristics of reclaimed forests was also conducted. The result shows that reclaimed forests with different planting years tends to have similar temperature-decreasing ability. This is shown by similar average LST values of reclaimed forest stands planted at year 1997, 2014, 2015, and 2016, which were around 27°C at year 2018. Meanwhile, reclaimed forest stands with different dominant trees species and total basal area also tend to similar temperature decreasing ability, being around 4.7-5.7°C after 3-4 years of planting. The results indicates that the tree species currently used by PT Adaro Indonesia for post-mining forest reclamation have similar ability in decreasing LST and the LST decrease could occur in a relatively short time.
Wavelength and Pump-Power Dependent Nonlinear Refraction and Absorption in a Semiconductor Disk Laser

We characterize both nonlinear refraction and absorption in a vertical-external-cavity surface-emitting laser (VECSEL) as a function of pump irradiance over the whole range of lasing wavelengths. We observe an approximately linear decrease in magnitude for both nonlinear refraction and nonlinear absorption with optical pumping when the thermally induced shift of the lasing wavelength is considered. Our results are of particular significance for understanding self-modelocking of VECSELs which might be driven by nonlinear lensing.
Prime Number Theory based Multi-Radio Rendezvous for Cognitive Radio Communication

Effective utilization of radio spectrum by the secondary user has become a tremendous issue for cognitive radio communication. The agreement of having a common channel for both transmitter and receiver is precondition for effectual transmission in the communication system. In this paper, prime-number theory based multi-radio rendezvous is proposed for asymmetric channel conditions. Statistical distribution analysis of rendezvous is illustrated for different scenarios of channel system. Based on analysis, total probability of rendezvous and time to rendezvous with distribution for single and multi-radio are clarified. The performances are demonstrated through montecarlo based simulation in Matlab. The analytical views and proposed method have shown outperformance over conventional methods for guaranteed rendezvous.