Miracle-dz/UniFAR-FTUs huggingface dataset:
Directional Neighborhood Topologies Based Multi-Scale Quinary Pattern for Texture Classification

This paper ideates a new computationally simple and effective local image feature descriptor, referred to as Directional Neighborhood Topologies based Multi-scale Quinary Pattern (DNT-MQP) for texture description and classification. The essence of DNT-MQP is to encode the structure of local neighborhood by analyzing the differential excitation and directional information using various directional neighborhood topologies and new pattern encoding scheme. We first designed four different versions of single scale DNT-QP features based on four directional neighborhood topologies based sampling sets which are then combined together to build the effective multiscale DNT-MQP model. Unlike some existing parametric methods that employ static thresholds to perform thresholding, the construction process of DNT-MQP includes an automatic mechanism for dynamic thresholds estimation. Thanks to a richer local description ensured by exploiting complementary information resulting from single scale DNT-QP operators’ combination, DNT-MQP descriptor has high capability to elicit stable and discriminative feature representation than other local feature descriptors. In addition, DNT-QP has the advantages of computational simplicity in feature extraction and low-dimensionality in feature representation. The effectiveness of DNT-QP is evaluated on sixteen challenging texture datasets and it is found that it maintains a high level of performance stability where the achieved performances are competitive or better than several recent most promising state-of-the-art texture descriptors as well as deep learning-based feature extraction approaches. Impressively, DNT-MQP showed good tolerance to rotation as well as illumination, scale and viewpoint changes against certain descriptors which are originally conceived to deal with these challenges. Furthermore, statistical hypothesis testing through Wilcoxon signed rank test is applied to prove the statistical significance of the accuracy improvement obtained in all the datasets.
Fraud Prevention Within the Brazilian Governmental Public-Key Infrastructure

Digital certificates are massively used in government systems in Brazil. However, in the last few years, cases of fraud were started to be continuously reported. Current Brazil’s issuing process is slow, costly and susceptible to failure. Thus, this present work proposes an automatic fraud detection process using machine learning in a hybrid approach. The method aims to categorize the applicant’s application at the level of dangerousness, that is, the chances of being a fraud. The proposed method was tested with data from real dossiers, whereas the fraudsters’ data were synthetically created. The results were satisfactory, showing that it is possible to reduce the execution time, reduce costs, and increase the process’s safety.
Low-Complexity Decorrelation NLMS Algorithms: Performance Analysis and AEC Application

In the traditional decorrelation normalized least-mean-square (D-NLMS) algorithm, high computational complexity is mainly caused by finding the decorrelated-vector. To address this issue, this article proposes a low-complexity implementation approach, which cleverly utilizes the periodic update of the decorrelation parameters and delay characteristics of the decorrelated-vector. We firstly develop two low-complexity decorrelation algorithms, (i) fast D-NLMS (FD-NLMS) and (ii) approximate FD-NLMS (AFD-NLMS) which is an approximate version of the first algorithm with even smaller computational requirement. Theoretical performance of the FD-NLMS scheme is also derived. To further obtain low steady-state error in the acoustic echo cancellation (AEC) application, separated-decorrelation AEC structure and robust step-size schemes are designed, resulting in two improved algorithms, namely, fast separated-decorrelation NLMS (FSD-NLMS) and approximate FSD-NLMS (AFSD-NLMS). Finally, extensive simulation study on system identification and AEC is undertaken to verify the efficiency of the proposed methods.
Current Sensor Based on a Fiber Bragg Grating Coated by Electroplated Magnetostrictive Material

A current sensor based on a ferromagnetic-coated fiber Bragg grating (FBG) embedded in a small dimension coil has been presented and experimentally validated. A 5 mm-long FBG has been coated with a Ni thin film by means of sputtering and electrodeposition methods. The measurements and results reported show that a simple and cost-effective metal-coated FBG can be successfully applied as DC-current sensor. The designed sensor measures DC current up to 10 A with a sensitivity of 2,5 pm/A and a linearity error of 3%.
Analysis of Pitot tube Airflow Velocity Sensor Behavior in Blockage Situations

Several airplanes crashes throughout the past decades are linked to the failure of the airplane flow velocity sensor known as pitot device or pitot tube. The study of its behavior in blockage, either due to human errors or due to environmental factors, could be valuable in the development of safer, more sustainable, and robust velocity sensors with improved performance. This work aims to look into this widely used but not very widely studied sensor and presents a three-dimensional model of a pitot device along with its analysis when either one of its ports is blocked. Results show that in blockage condition pitot tubes can overestimate the velocity up to ~ %5.6.
An Efficient RGB-D Scene Recognition Method Based on Multi-Information Fusion

RGB-D scene recognition is a challenging problem due to lack of RGB-D datasets and inefficient RGB-D fusion. In this paper, we exploit several factors that affect the RGB-D scene recognition performance, including the representations of the RGB-D data and objects in the scene. We propose an effective multi-information fusion method composed of two modules: a revised detection-based method, and a multi-feature fusion based classifier. The revised detection-based method leverage the auxiliary RGB data. And the multi-feature fusion based classifier select the optimal feature configuration for RGB-D data description. The proposed method is validated on two publicly available datasets: the SUN RGB-D dataset, and the NYU Depth v2 dataset. The obtained results show that the proposed fusion method is effective and is comparable with the state-of-the-art method. Furthermore, the proposed framework contains much less parameters than the state-of-the-art model and thus requires much less time for training. The code and the fine-tuned model parameters are available at: https://github.com/zhangbin28/MulInfo_RGBDScene.
Low-cost gestural interaction based on motion estimation of a projected dot pattern : Experiments with a working prototype

The concept and realization of an easy-to-use, low-cost handheld IR optical sensor for universal, long-range gesture-controlled operation of technical devices is presented and recognition results with a working prototype are given. The system comprises two elements: a handheld buttonless light emitter which resembles a small laser pointer but emits a wide-angle IR random-dot pattern along with a visible light beam (for device selection) and a small photo-sensor array along with a processing/decoder unit near or in the device to be controlled. The emitted IR light is spatially modulated by a diffractive optical element (DOE) which has a specially designed micro-structured surface to generate a binary pseudo-random array of light beams. This concept allows to come up with a handheld device which contains only a few inexpensive mass-market components (since the IR-optical signal is both the information and transmission channel). User experience and gesture recognition capabilities were examined with a group of 29 elderly volunteers who were asked to repeatedly "draw" quasi-intuitive gestures with the handheld emitter towards typical home appliances like light switch, blinds, heating device, and door from distances of up to 6 meters. Applying a correlation-based reconstruction algorithm and adaptive rule-based pattern recognition with low computational load for real-time processing on low-end processors, an overall recognition rate of 94 % could be achieved so far. As the algorithm is being optimized to enable sequential spotting of meaningful gestures and enhanced class separation, the list of gestures will be extended.
DeepNet: An Efficient Neural Network for Fake News Detection using News-User Engagements

The rise of social media allows every user to share and immediately publish their views. Today, the problem of fake news has obtained significant consideration among researchers due to its harmful nature to deceive the people of the society. It has created an alarming situation in the world. News ecosystem evolved from a small set of trusted and regulated sources to numerous online news sources. Fake news has an adverse impact on society as it may manipulate public opinions. Thus, it is essential to investigate the credibility of news articles shared on social media outlets. In this paper, we have designed an effective deep neural network that is capable of handling not only the content of the news article but also the user-relationships in the social network. We have designed our proposed approach using tensor factorization method. A tensor expresses the social context of news articles formed by a combination of the news, user, and user-group information. Our proposed method (DeepNet) has validated on a real-world fake news dataset: BuzzFeed and Fakeddit. DeepNet outperforms from existing fake news detection methods by employing deep architecture with different kernelsizes convolutional layers combining news content and social context-based features.
Improved PCA-based Fault Isolation using Sparse Group Lasso

In industrial process control, data-driven fault detection and isolation methods have developed rapidly due to the easy availability of large amount of data. In fault isolation, principal component analysis(PCA) based contribution plot is a standard tool. The problem of PCA based contribution plot is that they are affected by the so called smearing effect. In fact, industrial process variables can be classified into groups according to their correlation or process structure, hence it is straightforward to consider the group-wise fault isolation problem. This paper introduces the sparse group Lasso as a regularization method to improve the fault isolation ability of PCA based contribution plot. The sparse group Lasso term considers both group-wise sparsity and within-group sparsity. Hence more accurate diagnosis can be obtained. In order to solve the optimization problem of sparse group Lasso, an efficient algorithm based on ADMM(Alternating Direction of Method of Multipliers) is proposed. Application study to the Tennessee Eastman(TE) process shows that the proposed method can better isolate faulty variables than competitive methods.
Impact of Gamma-Ray Radiation on DC and RF Performance of 10-nm Bulk N-Channel FinFETs

In this article, the impact of gamma-ray radiation on DC and RF response of 10-nm bulk n-channel FinFETs is investigated. Firstly, the radiation tolerance of these devices under DC measurement conditions is reported as various layout level device parameters, such as the gate length (<inline-formula> <tex-math notation="LaTeX">$L_{G}$ </tex-math></inline-formula>), number of fins (<inline-formula> <tex-math notation="LaTeX">$N_{FIN}$ </tex-math></inline-formula>), and number of fingers (<inline-formula> <tex-math notation="LaTeX">$N_{FINGER}$ </tex-math></inline-formula>) are scaled. Then for the first time, the impact of gamma-ray radiation on small-signal RF response of the 10-nm bulk FinFETs is reported, which includes some of the RF figures-of-merit (FOM), such as, the input transconductance, intrinsic current gain, intrinsic voltage gain, and unilateral power gain. Furthermore, the effect of device geometry scaling on maximum oscillation frequency, <inline-formula> <tex-math notation="LaTeX">$f_{MAX}$ </tex-math></inline-formula>, degradation due to gamma-ray radiation is reported. DC results show that long channel devices with small <inline-formula> <tex-math notation="LaTeX">$N_{FIN}$ </tex-math></inline-formula> and large <inline-formula> <tex-math notation="LaTeX">$N_{FINGER}$ </tex-math></inline-formula> are least impacted by gamma-ray radiation as opposed to short channel devices with large <inline-formula> <tex-math notation="LaTeX">$N_{FIN}$ </tex-math></inline-formula> and small <inline-formula> <tex-math notation="LaTeX">$N_{FINGER}$ </tex-math></inline-formula>. The RF results show that gamma-ray radiation causes major degradation (~ 40%) in <inline-formula> <tex-math notation="LaTeX">$f_{MAX}$ </tex-math></inline-formula>, while the cut-off frequency, <inline-formula> <tex-math notation="LaTeX">$f_{T}$ </tex-math></inline-formula> remains mostly unchanged. Finally, an empirical model is developed to predict the degradation in <inline-formula> <tex-math notation="LaTeX">$f_{MAX}$ </tex-math></inline-formula> with changing device geometries and gamma-ray radiation dose.
vMobiDesk: Desktop Virtualization for Mobile Operating Systems

Smart mobile devices have significantly increased the popularity of Bring-Your-Own-Device (BYOD) at work, as they benefit people’s daily lives. However, BYOD comes with several challenging issues such as limited hardware capacity, frequent upgrades of applications, and security and privacy concerns. Virtual Mobile Infrastructure (VMI), a general framework that provides more reliable and secure solution for BYOD, has therefore been proposed. The key of VMI is to host a mobile Operating System (OS) on a remote cloud data center, and run mobile applications on it. However, VMI faces performance challenge as it needs to display the entire virtualized desktop on a mobile device while the real content of the desktop is on a remote server. To address the performance challenge, we design and implement a VMI named vMobiDesk on top of Android with optimized network transfer mechanisms and display virtualization. In particular, vMobiDesk focuses on virtualizing the display of Android desktops, redirecting users’ input events, providing audio support and remote camera. The experimental results show that vMobiDesk has low virtualization overhead, as well as enables mobile users to obtain good experiences with BYOD applications.
Forecasting COVID-19 cases in India Using Machine Learning Models

COVID-19 pandemic has affected the economy and changed the human way of life, disrupting everyone’s mental, physical, and financial well-being. Many of the fastest-growing economies are strained owing to the severity and communicability of the epidemic. Because of the increasing diversity of cases and the resulting burden on healthcare practitioners and the government, therefore, predicting the number of infected COVID-19 cases which could be useful in planning the required hospital resources in the future. In this paper, we focussed on information-led methods of estimating the numbers of COVID-19 confirmed cases in the country and their implications in the future, using different learning models such as Sigmoid modelling, ARIMA, SEIR model and LSTM, for protective measures, such as social isolation or the lockout of COVID-19. Use of raw data by separating an event from the previous event in order to set the time series. The computation of number of positive incidents, number of re-referred incidents are reliable within a limited range. A data-driven forecasting method has been used to approximate the total confirmed cases in coming months. These LSTM model gave very promising results than other models. Hence, this work would help the decision makers to understand the upcoming of the pandemic trajectory in the country and take necessary actions for the effect of interventions.
Range-Doppler Spectrum Estimation Based on Matrix Completion for Frequency Agile Radar

Coherent frequency agile (FA) radar has attracted considerable attention. Compared with the stepped frequency radar, FA radar not only has the high range-resolution characteristic obtained by synthesizing large bandwidth, but also has the ability of the range-Doppler decoupling and better electronic countermeasures performance. Typically, traditional matched filter (MF) is often used to estimate the range-Doppler spectrum for FA radar. However, the processing results of the MF method are affected by the raised sidelobe pedestal, which leads to the degradation of radar performance in target detection and parameter estimation. In this paper, we propose a matrix completion (MC)-based method for range-Doppler spectrum estimation. Based on the concept of the FA pattern, the echo is modeled as a time-frequency observation matrix, and we use the MC technique to jointly recover the unobserved data in the matrix to estimate the range-Doppler spectrum with sidelobe pedestal suppression. Moreover, compared with the FA radar with one-dimensional compressed sensing recovery, the MC-based method overcomes the grid mismatch problem, further enhancing the range-Doppler spectrum estimation performance. The simulation results verify the effectiveness of the proposed method.
Real-time Replanning and Control of Quadruped Robots for Blind Locomotion on Uneven Terrain

Adaptability to different terrains is crucial for legged robots to perform diverse tasks. This paper proposes a real-time updated control framework that improves the robustness of quadruped robots walking blindly on rough terrain. The proposed framework that considers both stability and kinematics has several advantages: 1) no visual feedback, 2) self-adaptation to unpredictable touchdown position and 3) self-adjustment for unknown rough terrain. The controller tracks the desired robot’s CoM (centor of mass) postion and posture through optimized distributed ground reaction forces obtained by QP (quadratic programming). By implementation of the proposed method, SIT-Dog robot can achieve stairs climbing and irregular terrrain crossing in the simulation environment.
Reusable acoustic tweezers enable 2D patterning of microparticles in microchamber on a disposable silicon chip superstrate

We report the first demonstration of two-dimensional (2D) patterning of microparticles on a silicon superstrate via a plug-and-play reusable acoustofluidic setup. The proposed integrated device comprises two independent parts: (1) a silicon superstrate enclosed in a PDMS microfluidic chamber (containing the particles) and (2) a reusable lithium niobate (LN) piezoelectric substrate for generating standing surface acoustic waves (SSAW). The proposed method enables patterning of particles on a silicon superstrate with the aim of ultimately enabling 2D patterning of particles or cells on functional silicon micromechanical chips. Such a capability would enable functional applications such as diagnostics and biosensing. This contactless, label-free, low-cost technique would be widely employed in biological application regardless of shape, size, charge or polarity of samples.
Empirical Evaluation of Gated Recurrent Neural Network Architectures in Aviation Delay Prediction

The aviation industry is one of the largest industries in the transportation sector. One of the major problems faced in this industry is the prevalence of delays. These delays not only cause dissatisfaction among the passengers but also huge losses for airlines. In this study, an approach using gated recurrent neural networks is explored for the purpose of predicting delays encountered in the aviation industry. The proposed approach uses Long Short-Term Memory(LSTM) architectures such as vanilla LSTM and Bi-directional LSTM to empirically evaluate its efficiency in predicting the delay for a future flight. While previous works have explored this approach for classifying the delay status as either delay or no-delay, we take this a step further by predicting the amount of delay that will be experienced. The trained models were tested on an unseen test data set and were able to predict the delay with an error value of 33 minutes.
Unsupervised Domain Adaptation for Position-Independent IMU Based Gait Analysis

Inertial measurement units (IMUs) together with advanced machine learning algorithms have enabled pervasive gait analysis. However, the worn positions of IMUs can be varied due to movements, and they are difficult to standardize across different trials, causing signal variations. Such variation contributes to a bias in the underlying distribution of training and testing data, and hinder the generalization ability of a computational gait analysis model. In this paper, we propose a position-independent IMU based gait analysis framework based on unsupervised domain adaptation. It is based on transferring knowledge from the trained data positions to a novel position without labels. Our framework was validated on gait event detection and pathological gait pattern recognition tasks based on different computational models and achieved consistently high performance on both tasks.
Developing a Bluetooth Based Tracking System for Tracking Devices Using Arduino*

Modernization has paved way for many technological developments, leading to increased security dependency across the globe. People are cognizant of how crucial security is and how it must be embedded into every system they use. Therefore, researchers, scientists, and developers have carried out numerous studies to provide better solutions for challenges encountered in this field of study. Our paper discusses and analyses, an Arduino based device, designed to track and monitor in real-time, valuable items that a user prefers to keep within a range of 10meters at all times. A comparison of the physical and simulated design of the device is proposed to measure reliability, cost and speed. Unique to this design is the use of Bluetooth, which enables a connection between the user’s phone Bluetooth and the Arduino device. The experiment results prove that; (1) as soon as connection is lost, a call is made from the device to the user’s phone to signal a disconnection, and an alert message containing the current location is sent via Global System for Mobile Communications (GSM); (2) after every 1000milliseconds updated coordinates are sent to the user, assisting in tracking the device even if its location has changed.
Agile-Based Requirement Challenges of Government Outsourcing Project: A Case Study

In 2019, one of the government organizations in Indonesia conducted several agile-based system development projects outsourced. However, some of the projects claimed to be failed regarding requirement issues. For government projects, this failure became a state loss. This research aims to analyze the practical requirements issue or problem in the failed project from an agile perspective, to avoid the same loss in the future. A systematic literature review conducted and matched with official documents, direct observation and correspondence, results in the identification of 25 factors grouped into 7 categories. The findings show that requirement changing, over-scooping, and communication become the main challenges in the case study. Agile best practice solutions were offered for each factor and challenge, along with a comparison view between the provider and government organization. Although these findings cannot be claimed generally, it can contribute as the requirement issues to beware and mitigated in an agile-based government outsourcing project.